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data/sampled_jsons/0.077_OR_0.07_MLD_HumanAct12_FID_score_reported.jsonl

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+{"idx": 0, "title": "motion-latent-diffusion/configs/config_ mld _ humanact 12 .yaml at main...", "date": "", "ddg_snippet": "[CVPR 2023] Executing your Commands via Motion Diffusion in Latent Space, a fast and high-quality motion diffusion model - motion-latent-diffusion/configs/config_ mld _ humanact 12 .yaml at main · ChenFengYe/motion-latent-diffusion.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/ChenFengYe/motion-latent-diffusion/blob/main/configs/config_mld_humanact12.yaml", "content": "[CVPR 2023] Executing your Commands via Motion Diffusion in Latent Space, a fast and high-quality motion diffusion model - motion-latent-diffusion/configs/config_ mld _ humanact 12 .yaml at main · ChenFengYe/motion-latent-diffusion."}
+{"idx": 1, "title": "Motion Flow Matching for Human Motion Synthesis and Editing", "date": "", "ddg_snippet": "Our method outperforms the baselines, achieving superior FID scores while maintaining faster sampling times. Please note that some axes in the plots are log-scaled for better comparison. Report issue for preceding element.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2312.08895v1", "content": "Our method outperforms the baselines, achieving superior FID scores while maintaining faster sampling times. Please note that some axes in the plots are log-scaled for better comparison. Report issue for preceding element."}
+{"idx": 2, "title": "LS-GAN: Human Motion Synthesis with Latent-space GANs-Bohrium", "date": "", "ddg_snippet": "We perform experiments on the HumanML3D, HumanAct 12 benchmarks and demonstrate that a remarkably simple GAN in the latent space achieves a FID of 0.482 with more than 91% in FLOPs reduction compared to latent diffusion model.", "subpage_snippet": "", "source": "www.bohrium.com", "link": "https://www.bohrium.com/paper-details/ls-gan-human-motion-synthesis-with-latent-space-gans/1083033613332643939-108597", "content": "We perform experiments on the HumanML3D, HumanAct 12 benchmarks and demonstrate that a remarkably simple GAN in the latent space achieves a FID of 0.482 with more than 91% in FLOPs reduction compared to latent diffusion model."}
+{"idx": 3, "title": "nan value & gt gt2 & KID problems when... - Githubissues", "date": "", "ddg_snippet": "When I ran the eval part, I met a few problems about unconstrained, and hope you can help me. The process is carried out, and no error is reported to stop the program.And why the difference in the FID score can be as much as ten times……", "subpage_snippet": "", "source": "githubissues.com", "link": "https://githubissues.com/GuyTevet/motion-diffusion-model/191", "content": "When I ran the eval part, I met a few problems about unconstrained, and hope you can help me. The process is carried out, and no error is reported to stop the program.And why the difference in the FID score can be as much as ten times……"}
+{"idx": 4, "title": "РЕАЛЬНОЕ СОБЕСЕДОВАНИЕ / Junior ML-engineer (Data Scientist)...", "date": "", "ddg_snippet": "Качество: FID - score (Fréchet Inception Distance) на тесте ~25 (хорошо для CycleGAN; ниже — лучше, идеал 0.8 + rules match → auto-quarantine msg.", "subpage_snippet": "", "source": "careerclue.vercel.app", "link": "https://careerclue.vercel.app/blog/2025/08/30/Nbl4SaO51sA-realnoe-sobesedovanie-junior-ml-engineer-data-scientist-aytiteh", "content": "Качество: FID - score (Fréchet Inception Distance) на тесте ~25 (хорошо для CycleGAN; ниже — лучше, идеал 0.8 + rules match → auto-quarantine msg."}
+{"idx": 5, "title": "", "date": "", "ddg_snippet": "", "subpage_snippet": "", "source": "", "link": "", "content": ""}
+{"idx": 6, "title": "Точная погода в Москве на 14 дней подробно (г. Москва, Россия)...", "date": "", "ddg_snippet": "12:00. день. +7°. пасмурно, возможен небольшой дождь.", "subpage_snippet": "", "source": "pogoda7.ru", "link": "https://pogoda7.ru/prognoz/gorod134242-Russia-g_Moskva-Moskva/14days/full", "content": "12:00. день. +7°. пасмурно, возможен небольшой дождь."}
+{"idx": 7, "title": "AYT • Аэропорт Анталия - табло прилета", "date": "", "ddg_snippet": "Прибыл 07:57. 09:10.Ожидается 12:27.", "subpage_snippet": "", "source": "www.avionio.com", "link": "https://www.avionio.com/ru/airport/ayt/arrivals", "content": "Прибыл 07:57. 09:10.Ожидается 12:27."}
+{"idx": 8, "title": "КРЫМСКИЙ МОСТ: оперативная информация – Telegram", "date": "", "ddg_snippet": "12:00. С обеих сторон Крымского моста затруднений в проезде к пунктам ручного досмотра нет.", "subpage_snippet": "", "source": "t.me", "link": "https://t.me/s/most_official", "content": "12:00. С обеих сторон Крымского моста затруднений в проезде к пунктам ручного досмотра нет."}
+{"idx": 9, "title": "Диета 5-ый стол - что можно и чего нельзя, меню на неделю", "date": "", "ddg_snippet": "питье должно быть не более 200 мл при одноразовом приеме. Продолжительность составляет до 12 месяцев. Далее следует консультация с врачом для определения степени эффективности. При необходимости специалист даст рекомендации по продлению.", "subpage_snippet": "", "source": "wer.ru", "link": "https://wer.ru/articles/dieta-5-yy-stol/", "content": "питье должно быть не более 200 мл при одноразовом приеме. Продолжительность составляет до 12 месяцев. Далее следует консультация с врачом для определения степени эффективности. При необходимости специалист даст рекомендации по продлению."}

data/sampled_jsons/0cEZyhHEks_Taming_Knowledge_Conflicts_in_Language_Models_World_Capital_dataset_Table_3.jsonl

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+{"idx": 0, "title": "Taming Knowledge Conflicts in Language Models", "date": "", "ddg_snippet": "Jun 9, 2025 · For the experiments related to Table 2, we use a small fraction of samples from the filtered World Capital dataset to identify attention heads that achieve the highest parametric probability gains under coherent conflicts when knocked out.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2503.10996v2", "content": "Jun 9, 2025 · For the experiments related to Table 2, we use a small fraction of samples from the filtered World Capital dataset to identify attention heads that achieve the highest parametric probability gains under coherent conflicts when knocked out."}
+{"idx": 1, "title": "Taming Knowledge Conflicts in Language Models - OpenReview", "date": "", "ddg_snippet": "Abstract Language Models (LMs) often encounter knowl-edge conflicts when parametric memory con-tradicts contextual knowledge . Previous works attribute this conflict to the interplay between “memory heads” and “context heads”, attention heads assumed to promote either memory or con-text exclusively. In this study, we go beyond this fundamental assumption by uncovering a criti-cal ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=0cEZyhHEks", "content": "Abstract Language Models (LMs) often encounter knowl-edge conflicts when parametric memory con-tradicts contextual knowledge . Previous works attribute this conflict to the interplay between “memory heads” and “context heads”, attention heads assumed to promote either memory or con-text exclusively. In this study, we go beyond this fundamental assumption by uncovering a criti-cal ..."}
+{"idx": 2, "title": "Taming Knowledge Conflicts in Language Models - GitHub", "date": "", "ddg_snippet": "This repository contains the code and data of the ICML 25 Spotlight Paper Taming Knowledge Conflicts in Language Models . The code is now still being updated.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/GaotangLi/JUICE", "content": "This repository contains the code and data of the ICML 25 Spotlight Paper Taming Knowledge Conflicts in Language Models . The code is now still being updated."}
+{"idx": 3, "title": "ICML Taming Knowledge Conflicts in Language Models", "date": "", "ddg_snippet": "Poster in Workshop: Actionable Interpretability Taming Knowledge Conflicts in Language Models Gaotang Li · Yuzhong Chen · Hanghang Tong [ Abstract ] [ Project Page ] [ OpenReview] Sat 19 Jul 10:40 a.m. PDT — 11:40 a.m. PDT", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/49596", "content": "Poster in Workshop: Actionable Interpretability Taming Knowledge Conflicts in Language Models Gaotang Li · Yuzhong Chen · Hanghang Tong [ Abstract ] [ Project Page ] [ OpenReview] Sat 19 Jul 10:40 a.m. PDT — 11:40 a.m. PDT"}
+{"idx": 4, "title": "[PDF] Taming Knowledge Conflicts in Language Models ...", "date": "", "ddg_snippet": "Mar 14, 2025 · This work proposes Just Run Twice (JuICE), a test-time attention intervention method that steers LMs toward either parametric beliefs or contextual knowledge without requiring fine-tuning, and identifies a set of reliable attention heads and leverages a dual-run approach to mitigate the superposition effects. Language Models (LMs) often encounter knowledge conflicts when parametric memory ...", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/Taming-Knowledge-Conflicts-in-Language-Models-Li-Chen/b7ba9df4eb239708cf48f25be87b5bceeca010e3", "content": "Mar 14, 2025 · This work proposes Just Run Twice (JuICE), a test-time attention intervention method that steers LMs toward either parametric beliefs or contextual knowledge without requiring fine-tuning, and identifies a set of reliable attention heads and leverages a dual-run approach to mitigate the superposition effects. Language Models (LMs) often encounter knowledge conflicts when parametric memory ..."}
+{"idx": 5, "title": "Taming Knowledge Conflicts in Language Models | AI Research ...", "date": "", "ddg_snippet": "Mar 16, 2025 · The researchers acknowledge that their approach primarily addresses binary conflicts (where there are two contradictory answers) but real- world knowledge resolution often involves more complex scenarios with multiple conflicting sources of information. The paper doesn't fully address how models should handle these more nuanced situations.", "subpage_snippet": "", "source": "www.aimodels.fyi", "link": "https://www.aimodels.fyi/papers/arxiv/taming-knowledge-conflicts-language-models", "content": "Mar 16, 2025 · The researchers acknowledge that their approach primarily addresses binary conflicts (where there are two contradictory answers) but real- world knowledge resolution often involves more complex scenarios with multiple conflicting sources of information. The paper doesn't fully address how models should handle these more nuanced situations."}
+{"idx": 6, "title": "[2503.10996] Taming Knowledge Conflicts in Language Models", "date": "", "ddg_snippet": "Mar 14, 2025 · Language Models (LMs) often encounter knowledge conflicts when parametric memory contradicts contextual knowledge . Previous works attribute this conflict to the interplay between \"memory heads\" and \"context heads\", attention heads assumed to promote either memory or context exclusively. In this study, we go beyond this fundamental assumption by uncovering a critical phenomenon we term the ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2503.10996", "content": "Mar 14, 2025 · Language Models (LMs) often encounter knowledge conflicts when parametric memory contradicts contextual knowledge . Previous works attribute this conflict to the interplay between \"memory heads\" and \"context heads\", attention heads assumed to promote either memory or context exclusively. In this study, we go beyond this fundamental assumption by uncovering a critical phenomenon we term the ..."}
+{"idx": 7, "title": "Taming Knowledge Conflicts in Language Models", "date": "", "ddg_snippet": "Language Models (LMs) often encounter knowledge conflicts when parametric memory contradicts contextual knowledge .In this paper, we study how language models respond to varying degrees of knowledge conflict and propose methods to regulate these behaviors.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2503.10996v1", "content": "Language Models (LMs) often encounter knowledge conflicts when parametric memory contradicts contextual knowledge .In this paper, we study how language models respond to varying degrees of knowledge conflict and propose methods to regulate these behaviors."}
+{"idx": 8, "title": "Taming Knowledge Conflicts in Language Models | OpenReview", "date": "", "ddg_snippet": "Language Models (LMs) often encounter knowledge conflicts when parametric memory contradicts contextual knowledge . Previous works attribute this conflict to the interplay between \"memory heads\" and \"context heads\"...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=0cEZyhHEks&referrer=[the+profile+of+Hanghang+Tong](/profile?id=~Hanghang_Tong2)", "content": "Language Models (LMs) often encounter knowledge conflicts when parametric memory contradicts contextual knowledge . Previous works attribute this conflict to the interplay between \"memory heads\" and \"context heads\"..."}
+{"idx": 9, "title": "(PDF) Taming Knowledge Conflicts in Language Models", "date": "", "ddg_snippet": "Taming Knowledge Conflicts in Language Models . Gaotang Li 1Yuzhong Chen 2Hanghang Tong 1. dataset are around 200 for world capital , official language , and company founder, and around 500 for athlete sport, company. headquarters, and book author.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/389894493_Taming_Knowledge_Conflicts_in_Language_Models", "content": "Taming Knowledge Conflicts in Language Models . Gaotang Li 1Yuzhong Chen 2Hanghang Tong 1. dataset are around 200 for world capital , official language , and company founder, and around 500 for athlete sport, company. headquarters, and book author."}

data/sampled_jsons/10l1pGeOcK_SAFE-_Finding_Sparse_and_Flat_Minima_to_Improve_Pruning.jsonl

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+{"idx": 0, "title": "Safe : Finding Sparse and Flat Minima to Improve Pruning", "date": "", "ddg_snippet": "towards sparsity without incurring a sudden change of loss, all while performing flatness induction, yielding a sparse and flat minima . In practice, particularly for image classification, we introduce scheduling to the penalty parameter. λ𝜆\\lambdaitalic_λ.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2506.06866v1", "content": "towards sparsity without incurring a sudden change of loss, all while performing flatness induction, yielding a sparse and flat minima . In practice, particularly for image classification, we introduce scheduling to the penalty parameter. λ𝜆\\lambdaitalic_λ."}
+{"idx": 1, "title": "(PDF) SAFE : Finding Sparse and Flat Minima to Improve Pruning", "date": "", "ddg_snippet": "Specifically, we formulate pruning as a sparsity -constrained optimization problem where flatness is encouraged as an objective.maximum Hessian eigenvalue of minima found by ADMM and SAF E. SAFE yields sparse and flat solutions.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/392531034_SAFE_Finding_Sparse_and_Flat_Minima_to_Improve_Pruning", "content": "Specifically, we formulate pruning as a sparsity -constrained optimization problem where flatness is encouraged as an objective.maximum Hessian eigenvalue of minima found by ADMM and SAF E. SAFE yields sparse and flat solutions."}
+{"idx": 2, "title": "Newly published papers and discussions around them.", "date": "", "ddg_snippet": "SAFE : Finding Sparse and Flat Minima to Improve Pruning .Specifically, we formulate pruning as a sparsity -constrained optimization problem where flatness is encouraged as an objective.", "subpage_snippet": "", "source": "www.eye-on.ai", "link": "https://www.eye-on.ai/ai-articles/e6n7f8m6dc4a3aw-kysfw-p3bpn-gj8zp-p9mmz-b34zn-brxry-mr228-48slx-mew99-4724k-te62h-8ejzm-5lzzt-t3nh2-stb98-5fs7c-l7tsn-p6jbs-szxbt-pf6w4-pyb9z-8sfh4", "content": "SAFE : Finding Sparse and Flat Minima to Improve Pruning .Specifically, we formulate pruning as a sparsity -constrained optimization problem where flatness is encouraged as an objective."}
+{"idx": 3, "title": "GitHub - LOG-postech/ safe -jax: [ICML 2025 Spotlight] Official JAX...", "date": "", "ddg_snippet": "SAFE : Finding Sparse and Flat Minima to Improve Pruning . Authors: Dongyeop Lee, Kwanhee Lee, Jinseok Chung, Namhoon Lee.Our work introduces SAFE , an algorithm designed to find sparse and flat minima , leading to improved model pruning performance.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/LOG-postech/safe-jax", "content": "SAFE : Finding Sparse and Flat Minima to Improve Pruning . Authors: Dongyeop Lee, Kwanhee Lee, Jinseok Chung, Namhoon Lee.Our work introduces SAFE , an algorithm designed to find sparse and flat minima , leading to improved model pruning performance."}
+{"idx": 4, "title": "Kwanhee Lee - Google Scholar", "date": "", "ddg_snippet": "SAFE : Finding Sparse and Flat Minima to Improve Pruning .", "subpage_snippet": "", "source": "scholar.google.com", "link": "https://scholar.google.com/citations?user=QLXgaCMAAAAJ&hl=en", "content": "SAFE : Finding Sparse and Flat Minima to Improve Pruning ."}
+{"idx": 5, "title": "dblp: List of computer science publications by Namhoon Lee", "date": "", "ddg_snippet": "top. '20. '10. '00. coauthors.Dongyeop Lee, Kwanhee Lee, Jinseok Chung, Namhoon Lee: SAFE : Finding Sparse and Flat Minima to Improve Pruning .", "subpage_snippet": "", "source": "dblp.uni-trier.de", "link": "https://dblp.uni-trier.de/pid/63/5359.html", "content": "top. '20. '10. '00. coauthors.Dongyeop Lee, Kwanhee Lee, Jinseok Chung, Namhoon Lee: SAFE : Finding Sparse and Flat Minima to Improve Pruning ."}
+{"idx": 6, "title": "Kwanhee Lee", "date": "", "ddg_snippet": "selected publications. SAFE : Finding Sparse and Flat Minima to Improve Pruning . Dongyeop Lee, Kwanhee Lee, Jinseok Chung, and 1 more author.", "subpage_snippet": "", "source": "kwanhee-lee.github.io", "link": "https://kwanhee-lee.github.io/", "content": "selected publications. SAFE : Finding Sparse and Flat Minima to Improve Pruning . Dongyeop Lee, Kwanhee Lee, Jinseok Chung, and 1 more author."}
+{"idx": 7, "title": "Cles for I terative M agnitude p runing", "date": "", "ddg_snippet": "When it comes to global pruning techniques, Iterative Magnitude Pruning (IMP) still stands as a state-of-the-art algorithm despite its simple nature, particularly in extremely sparse regimes.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=Y9t7MqZtCR", "content": "When it comes to global pruning techniques, Iterative Magnitude Pruning (IMP) still stands as a state-of-the-art algorithm despite its simple nature, particularly in extremely sparse regimes."}
+{"idx": 8, "title": "Youtube to MP3 Converter (Ad-free)", "date": "", "ddg_snippet": "EzConv is the safest platform to download MP3s as it doesn’t contain any third-party scripts or pop-up ads. Yes, it’s completely ad-free and runs on donations from our users. Our converter lets you trim the audio, and you can choose an audio quality from 64 kbps to 320 kbps.", "subpage_snippet": "", "source": "ezconv.com", "link": "https://ezconv.com/", "content": "EzConv is the safest platform to download MP3s as it doesn’t contain any third-party scripts or pop-up ads. Yes, it’s completely ad-free and runs on donations from our users. Our converter lets you trim the audio, and you can choose an audio quality from 64 kbps to 320 kbps."}
+{"idx": 9, "title": "Instant Background Remover - Remove Bg for Free Online | Photoroom", "date": "", "ddg_snippet": "Removing the background from product pictures enhances the focus on the product, maintains consistency and branding, provides versatility for marketing materials, allows for contextual flexibility, facilitates product comparison, and improves the overall aesthetics of your product listings.", "subpage_snippet": "", "source": "www.photoroom.com", "link": "https://www.photoroom.com/tools/background-remover", "content": "Removing the background from product pictures enhances the focus on the product, maintains consistency and branding, provides versatility for marketing materials, allows for contextual flexibility, facilitates product comparison, and improves the overall aesthetics of your product listings."}

data/sampled_jsons/1608.03981_DnCNN_architecture_layers.jsonl

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+{"idx": 0, "title": "[1608.03981] Beyond a Gaussian Denoiser: Residual Learning of ...", "date": "", "ddg_snippet": "Aug 13, 2016 · With the residual learning strategy, DnCNN implicitly removes the latent clean image in the hidden layers . This property motivates us to train a single DnCNN model to tackle with several general image denoising tasks such as Gaussian denoising, single image super-resolution and JPEG image deblocking.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/1608.03981", "content": "Aug 13, 2016 · With the residual learning strategy, DnCNN implicitly removes the latent clean image in the hidden layers . This property motivates us to train a single DnCNN model to tackle with several general image denoising tasks such as Gaussian denoising, single image super-resolution and JPEG image deblocking."}
+{"idx": 1, "title": "GitHub - cszn/DnCNN: Beyond a Gaussian Denoiser: Residual ... (PDF) Beyond a Gaussian Denoiser: Residual Learning of Deep ... Learning Deep CNN Denoiser Prior for Image Restoration Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for ... Beyond a Gaussian denoiser: Residual learning of deep CNN for ... Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Beyond a Gaussian Denoiser : Residual Learning of Deep CNN for Image Beyond a Gaussian denoiser : Residual learning of deep CNN for image Beyond a Gaussian denoiser : Residual learning of deep CNN for image Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image GitHub - yjn870/DnCNN-pytorch: PyTorch implementation of ...", "date": "", "ddg_snippet": "News: DRUNet •State-of-the-art denoising performance •Can be used for plug-and-play image restoration •https://github.com/cszn/DPIR/blob/master/main_dpir_denoising.py See full list on github.com I recommend to use the PyTorch code for training and testing. The model parameters of MatConvnet and PyTorch are same. •main_train_dncnn.py •main_test_dncnn.py •main_test_dncnn3_deblocking.py See full list on github.com •Simplenn version •DnCNN_TrainingCodes_v1.1 •DagNN version •DnCNN_TrainingCodes_DagNN_v1.1 See full list on github.com •[demos] Demo_test_DnCNN-.m. •[models] including the trained models for Gaussian denoising; a single model for Gaussian denoising, single image super-resolution (SISR) and deblocking. See full list on github.com I have trained new Flexible DnCNN (FDnCNN) models based on FFDNet. FDnCNN can handle noise level range of [0, 75] via a single model. Demo_FDnCNN_Gray.m Demo_FDnCNN_Gray_Clip.m Demo_FDnCNN_Color.m Demo_FDnCNN_Color_Clip.m See full list on github.com •Network Architecture •Batch normalization and residual learning are beneficial to Gaussian denoising (especially for a single noise level). The residual of a noisy image corrupted by additive white Gaussian noise (AWGN) follows a constant Gaussian distribution which stablizes batch normalization during training. •Histogram of noisy patches, clean patches, and residual (noise) patches from a batch of training. The noise level is 25, the patch size is 40x40, the batch size is 128. •Histogram of noisy patches, clean patches, and residual (noise) patches from another batch of training. The noise level is 25, the patch size is 40x40, the batch size is 128. •Noise-free image super-resolution does not have this property. •Predicting the residual can be interpreted as performing one gradient descent inference step at starting point (i.e., noisy image). See full list on github.com The average PSNR(dB) results of different methods on the BSD68 dataset. Visual Results See full list on github.com Average PSNR(dB)/SSIM results of different methods for Gaussian denoising with noise level 15, 25 and 50 on BSD68 dataset, single image super-resolution with upscaling factors 2, 3 and 40 on Set5, Set14, BSD100 and Urban100 datasets, JPEG image deblocking with quality factors 10, 20, 30 and 40 on Classic5 and LIVE11 datasets. See full list on github.com •MATLAB R2015b •Cuda-8.0 & cuDNN v-5.1 •MatConvNet or just MATLAB R2015b to test the model. DnCNN /Demo_test_DnCNN.m Lines 64 to 65 in 4a4b5b8 See full list on github.com ==================================================================== See full list on github.com Aug 13, 2016 · The architecture of the proposed DnCNN network. Denoising results of the image \"parrot\" with noise level 50. Color image denoising results of one image from the DSD68 dataset with noise level 35. The architecture of the proposed CNN denoiser is illus-trated in Figure 1. It consists of seven layers with three different blocks, i.e., “Dilated Convolution+ReLU” block in the first layer , five “Dilated Convolution+Batch Normal-ization+ReLU” blocks in the middle layers , and “Dilated Convolution” block in the last layer . Deep Architecture : Given the DnCNN with depth D, there are three types of layers , shown in Fig. 1 with three different colors. (i) Conv+ReLU: for the first layer , 64 filters of size 3 3 c are used to generate 64 feature maps, and rectified linear units (ReLU, max(0; )) are then utilized for nonlinearity. The discriminative model learning for image denoising has been recently attracting considerable attentions due to its favorable denoising performance. In this paper, we take one step forward by investigating the construction of feed-forward denoising convolutional neural networks (DnCNNs) to embrace the progress in very deep architecture , learning algorithm, and regularization method into ... Can a single dncnn model handle a general image denoising task? Moreover, we showed the feasibility to train a single DnCNN model to handle three general image denoising tasks, including Gaussian denoising with unknown noise level, single image super-resolution with multiple up-scaling factors, and JPEG image deblocking with different quality factors. Does dncnn remove a latent clean image? With the residual learning strategy, DnCNN implicitly removes the latent clean image in the hidden layers. This property motivates us to train a single DnCNN model to tackle with several general image denoising tasks such as Gaussian denoising, single image super-resolution and JPEG image deblocking. How does dncnn-3 work? The parameters in DnCNN are mainly representing the image priors (task-independent), thus it is possible to learn a single model for different tasks, such as image denoising, image super-resolution and JPEG image deblocking. The left is the input image corrupted by different degradations, the right is the restored image by DnCNN -3. How effective is dncnn for image denoising? Our extensive experiments demonstrate that our DnCNN model can not only exhibit high effectiveness in several general image denoising tasks, but also be efficiently implemented by benefiting from GPU computing. Dive into the research topics of 'Beyond a Gaussian denoiser: Residual learning of deep CNN for image denoising'. Why should we train a single dncnn model? This property motivates us to train a single DnCNN model to tackle with several general image denoising tasks , such as Gaussian denoising, single image super-resolution, and JPEG image deblocking. Can dncnn-3 produce visually pleasant output result if input image is corrupted? We can see that DnCNN-3 can produce visually pleasant output result even the input image is corrupted by several distortions with different levels in different regions. In this paper, a deep convolutional neural network was proposed for image denoising, where residual learning is adopted to separating noise from noisy observation. The DnCNN -3 is only a single model for three general image denoising tasks, i.e., blind Gaussian denoising, SISR with multiple upscaling factors, and JPEG deblocking with different quality factors.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/cszn/DnCNN", "content": "News: DRUNet •State-of-the-art denoising performance •Can be used for plug-and-play image restoration •https://github.com/cszn/DPIR/blob/master/main_dpir_denoising.py See full list on github.com I recommend to use the PyTorch code for training and testing. The model parameters of MatConvnet and PyTorch are same. •main_train_dncnn.py •main_test_dncnn.py •main_test_dncnn3_deblocking.py See full list on github.com •Simplenn version •DnCNN_TrainingCodes_v1.1 •DagNN version •DnCNN_TrainingCodes_DagNN_v1.1 See full list on github.com •[demos] Demo_test_DnCNN-.m. •[models] including the trained models for Gaussian denoising; a single model for Gaussian denoising, single image super-resolution (SISR) and deblocking. See full list on github.com I have trained new Flexible DnCNN (FDnCNN) models based on FFDNet. FDnCNN can handle noise level range of [0, 75] via a single model. Demo_FDnCNN_Gray.m Demo_FDnCNN_Gray_Clip.m Demo_FDnCNN_Color.m Demo_FDnCNN_Color_Clip.m See full list on github.com •Network Architecture •Batch normalization and residual learning are beneficial to Gaussian denoising (especially for a single noise level). The residual of a noisy image corrupted by additive white Gaussian noise (AWGN) follows a constant Gaussian distribution which stablizes batch normalization during training. •Histogram of noisy patches, clean patches, and residual (noise) patches from a batch of training. The noise level is 25, the patch size is 40x40, the batch size is 128. •Histogram of noisy patches, clean patches, and residual (noise) patches from another batch of training. The noise level is 25, the patch size is 40x40, the batch size is 128. •Noise-free image super-resolution does not have this property. •Predicting the residual can be interpreted as performing one gradient descent inference step at starting point (i.e., noisy image). See full list on github.com The average PSNR(dB) results of different methods on the BSD68 dataset. Visual Results See full list on github.com Average PSNR(dB)/SSIM results of different methods for Gaussian denoising with noise level 15, 25 and 50 on BSD68 dataset, single image super-resolution with upscaling factors 2, 3 and 40 on Set5, Set14, BSD100 and Urban100 datasets, JPEG image deblocking with quality factors 10, 20, 30 and 40 on Classic5 and LIVE11 datasets. See full list on github.com •MATLAB R2015b •Cuda-8.0 & cuDNN v-5.1 •MatConvNet or just MATLAB R2015b to test the model. DnCNN /Demo_test_DnCNN.m Lines 64 to 65 in 4a4b5b8 See full list on github.com ==================================================================== See full list on github.com Aug 13, 2016 · The architecture of the proposed DnCNN network. Denoising results of the image \"parrot\" with noise level 50. Color image denoising results of one image from the DSD68 dataset with noise level 35. The architecture of the proposed CNN denoiser is illus-trated in Figure 1. It consists of seven layers with three different blocks, i.e., “Dilated Convolution+ReLU” block in the first layer , five “Dilated Convolution+Batch Normal-ization+ReLU” blocks in the middle layers , and “Dilated Convolution” block in the last layer . Deep Architecture : Given the DnCNN with depth D, there are three types of layers , shown in Fig. 1 with three different colors. (i) Conv+ReLU: for the first layer , 64 filters of size 3 3 c are used to generate 64 feature maps, and rectified linear units (ReLU, max(0; )) are then utilized for nonlinearity. The discriminative model learning for image denoising has been recently attracting considerable attentions due to its favorable denoising performance. In this paper, we take one step forward by investigating the construction of feed-forward denoising convolutional neural networks (DnCNNs) to embrace the progress in very deep architecture , learning algorithm, and regularization method into ... Can a single dncnn model handle a general image denoising task? Moreover, we showed the feasibility to train a single DnCNN model to handle three general image denoising tasks, including Gaussian denoising with unknown noise level, single image super-resolution with multiple up-scaling factors, and JPEG image deblocking with different quality factors. Does dncnn remove a latent clean image? With the residual learning strategy, DnCNN implicitly removes the latent clean image in the hidden layers. This property motivates us to train a single DnCNN model to tackle with several general image denoising tasks such as Gaussian denoising, single image super-resolution and JPEG image deblocking. How does dncnn-3 work? The parameters in DnCNN are mainly representing the image priors (task-independent), thus it is possible to learn a single model for different tasks, such as image denoising, image super-resolution and JPEG image deblocking. The left is the input image corrupted by different degradations, the right is the restored image by DnCNN -3. How effective is dncnn for image denoising? Our extensive experiments demonstrate that our DnCNN model can not only exhibit high effectiveness in several general image denoising tasks, but also be efficiently implemented by benefiting from GPU computing. Dive into the research topics of 'Beyond a Gaussian denoiser: Residual learning of deep CNN for image denoising'. Why should we train a single dncnn model? This property motivates us to train a single DnCNN model to tackle with several general image denoising tasks , such as Gaussian denoising, single image super-resolution, and JPEG image deblocking. Can dncnn-3 produce visually pleasant output result if input image is corrupted? We can see that DnCNN-3 can produce visually pleasant output result even the input image is corrupted by several distortions with different levels in different regions. In this paper, a deep convolutional neural network was proposed for image denoising, where residual learning is adopted to separating noise from noisy observation. The DnCNN -3 is only a single model for three general image denoising tasks, i.e., blind Gaussian denoising, SISR with multiple upscaling factors, and JPEG deblocking with different quality factors."}
+{"idx": 2, "title": "GitHub - yjn870/DnCNN-pytorch: PyTorch implementation of ...", "date": "", "ddg_snippet": "The DnCNN -3 is only a single model for three general image denoising tasks, i.e., blind Gaussian denoising, SISR with multiple upscaling factors, and JPEG deblocking with different quality factors.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/yjn870/DnCNN-pytorch", "content": "The DnCNN -3 is only a single model for three general image denoising tasks, i.e., blind Gaussian denoising, SISR with multiple upscaling factors, and JPEG deblocking with different quality factors."}
+{"idx": 3, "title": "GitHub - shlpu/ DnCNN : DnCNN implemented purely by Matlab R2018a", "date": "", "ddg_snippet": "1608 . 03981 .pdf.About time: Due to the implementation of Matlab BatchNorm layers , which doesn't support testing before training is finished, I have to make an trade-off (i.e., force finalizing training for each epoch, which is time-consuming) in order to test the performance(PSNR and SSIM)...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/shlpu/DnCNN", "content": "1608 . 03981 .pdf.About time: Due to the implementation of Matlab BatchNorm layers , which doesn't support testing before training is finished, I have to make an trade-off (i.e., force finalizing training for each epoch, which is time-consuming) in order to test the performance(PSNR and SSIM)..."}
+{"idx": 4, "title": "Image and Video Denoising using DnCNN | by Varun Saproo | Medium", "date": "", "ddg_snippet": "DnCNN Architecture . DnCNN Architecture . Conv + ReLU: filter size of 3, no of filters as 64, a stride of 1, using zero paddings to maintain the output shape after convolution, using ReLU as the activation function.", "subpage_snippet": "", "source": "saproovarun.medium.com", "link": "https://saproovarun.medium.com/image-and-video-denoising-using-dncnn-216be1ff8ba1", "content": "DnCNN Architecture . DnCNN Architecture . Conv + ReLU: filter size of 3, no of filters as 64, a stride of 1, using zero paddings to maintain the output shape after convolution, using ReLU as the activation function."}
+{"idx": 5, "title": "deepinv.models. dncnn — deepinverse 0.3 documentation", "date": "", "ddg_snippet": "Source code for deepinv.models. dncnn . import torch.nn as nn import torch from .utils import get_weights_url import math from .base import Denoiser. [docs] class DnCNN (Denoiser): r\"\"\" DnCNN convolutional denoiser. The architecture was introduced in https...", "subpage_snippet": "", "source": "deepinv.github.io", "link": "https://deepinv.github.io/deepinv/_modules/deepinv/models/dncnn.html", "content": "Source code for deepinv.models. dncnn . import torch.nn as nn import torch from .utils import get_weights_url import math from .base import Denoiser. [docs] class DnCNN (Denoiser): r\"\"\" DnCNN convolutional denoiser. The architecture was introduced in https..."}
+{"idx": 6, "title": "DnCNN -Beyond a Gaussian Denoiser: Residual... - Programmer All", "date": "", "ddg_snippet": "DnCNN -Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising.", "subpage_snippet": "", "source": "www.programmerall.com", "link": "https://www.programmerall.com/article/64492156638/", "content": "DnCNN -Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising."}
+{"idx": 7, "title": "DnCNN -pytorch from yjn870 - GithubHelp", "date": "", "ddg_snippet": "The DnCNN -3 is only a single model for three general image denoising tasks, i.e., blind Gaussian denoising, SISR with multiple upscaling factors, and JPEG deblocking with different quality factors.", "subpage_snippet": "", "source": "githubhelp.com", "link": "https://githubhelp.com/yjn870/DnCNN-pytorch", "content": "The DnCNN -3 is only a single model for three general image denoising tasks, i.e., blind Gaussian denoising, SISR with multiple upscaling factors, and JPEG deblocking with different quality factors."}
+{"idx": 8, "title": "【图像去噪】论文复现：新手入门必看！ DnCNN 的Pytorch...", "date": "", "ddg_snippet": "nn.Sequential(* layers ) def forward(self, x): residual = self. dncnn _net(x) return x - residual ``` #### 开始训练过程 一旦完成了上述准备工作之后就可以着手启动正式的学习流程了。", "subpage_snippet": "", "source": "blog.csdn.net", "link": "https://blog.csdn.net/qq_36584673/article/details/139743314", "content": "nn.Sequential(* layers ) def forward(self, x): residual = self. dncnn _net(x) return x - residual ``` #### 开始训练过程 一旦完成了上述准备工作之后就可以着手启动正式的学习流程了。"}
+{"idx": 9, "title": "(PDF) Beyond a Gaussian Denoiser: Residual Learning of Deep ...", "date": "", "ddg_snippet": "Aug 13, 2016 · The architecture of the proposed DnCNN network. Denoising results of the image \"parrot\" with noise level 50. Color image denoising results of one image from the DSD68 dataset with noise level 35.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/306187437_Beyond_a_Gaussian_Denoiser_Residual_Learning_of_Deep_CNN_for_Image_Denoising", "content": "Aug 13, 2016 · The architecture of the proposed DnCNN network. Denoising results of the image \"parrot\" with noise level 50. Color image denoising results of one image from the DSD68 dataset with noise level 35."}

data/sampled_jsons/1IyPRv1A0r_A_Likelihood_Based_Approach_to_Distribution_Regression_Using_Conditional_Deep_Generative_.jsonl

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+{"idx": 0, "title": "(PDF) A Likelihood Based Approach to Distribution Regression ...", "date": "", "ddg_snippet": "distributional regression using a conditional deep generative model , considering full-dimensional noise.A deep generative approach to conditional sampling. Journal of the American Statistical Association, pages 1–12.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/384630603_A_Likelihood_Based_Approach_to_Distribution_Regression_Using_Conditional_Deep_Generative_Models", "content": "distributional regression using a conditional deep generative model , considering full-dimensional noise.A deep generative approach to conditional sampling. Journal of the American Statistical Association, pages 1–12."}
+{"idx": 1, "title": "A Likelihood Based Approach to Distribution Regression Using", "date": "", "ddg_snippet": "Conditional deep generative models for distribution regression . Convergence rates of the Sieve MLE. Neural network class.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2410.02025", "content": "Conditional deep generative models for distribution regression . Convergence rates of the Sieve MLE. Neural network class."}
+{"idx": 2, "title": "A Likelihood Based Approach to Distribution Regression Using ...", "date": "", "ddg_snippet": "A deep neural network models the conditional generator, enabling a flexible approach to distribution regression .", "subpage_snippet": "", "source": "powerdrill.ai", "link": "https://powerdrill.ai/discover/discover-A-Likelihood-Based-cm1v7rba8uvnv013whs4l4bq4", "content": "A deep neural network models the conditional generator, enabling a flexible approach to distribution regression ."}
+{"idx": 3, "title": "Level-Wise Conditional Distribution", "date": "", "ddg_snippet": "Wasserstein- Based Deep Conditional Generative Models : Recent approaches pose conditional generative modeling as learning a mapping from a reference noise distribution (plus covariates. xx. x) to.", "subpage_snippet": "", "source": "www.emergentmind.com", "link": "https://www.emergentmind.com/topics/level-wise-conditional-distribution", "content": "Wasserstein- Based Deep Conditional Generative Models : Recent approaches pose conditional generative modeling as learning a mapping from a reference noise distribution (plus covariates. xx. x) to."}
+{"idx": 4, "title": "A Likelihood Based Approach to Distribution Regression Using ...", "date": "", "ddg_snippet": "This paper presents a likelihood - based approach for distribution regression using conditional deep generative models .", "subpage_snippet": "", "source": "www.aimodels.fyi", "link": "https://www.aimodels.fyi/papers/arxiv/likelihood-based-approach-to-distribution-regression-using", "content": "This paper presents a likelihood - based approach for distribution regression using conditional deep generative models ."}
+{"idx": 5, "title": "Linear Regression -Free Linear Regression Tool", "date": "", "ddg_snippet": "Linear Regression is a statistical method used to model the relationship between a dependent variable and one or more independent variables by fitting a linear equation to observed data.", "subpage_snippet": "", "source": "www.yeschat.ai", "link": "https://www.yeschat.ai/gpts-9t55k5HnQvO-Linear-Regression", "content": "Linear Regression is a statistical method used to model the relationship between a dependent variable and one or more independent variables by fitting a linear equation to observed data."}
+{"idx": 6, "title": "NMA: Network Meta-Analysis Based on Multivariate Meta-Analysis and...", "date": "", "ddg_snippet": "Network meta- regression based on contrast- based approach using the multivariate meta- regression model . Effect modifications by study-level covariates (specified in the setup function) can be assessed.", "subpage_snippet": "", "source": "cran.r-project.org", "link": "https://cran.r-project.org/web/packages/NMA/NMA.pdf", "content": "Network meta- regression based on contrast- based approach using the multivariate meta- regression model . Effect modifications by study-level covariates (specified in the setup function) can be assessed."}
+{"idx": 7, "title": "huggingface/paper-central-data-2 · Datasets at Hugging Face", "date": "", "ddg_snippet": "A Likelihood Based Approach to Distribution Regression Using Conditional Deep Generative Models .", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/datasets/huggingface/paper-central-data-2/viewer/default/train?p=553", "content": "A Likelihood Based Approach to Distribution Regression Using Conditional Deep Generative Models ."}
+{"idx": 8, "title": "Beyond the delta method-Bohrium", "date": "", "ddg_snippet": "[7] A Likelihood Based Approach to Distribution Regression Using Conditional Deep Generative Models .", "subpage_snippet": "", "source": "www.bohrium.com", "link": "https://www.bohrium.com/paper-details/beyond-the-delta-method/867762556003942856-108557", "content": "[7] A Likelihood Based Approach to Distribution Regression Using Conditional Deep Generative Models ."}
+{"idx": 9, "title": "Enhancing brain tumor classification with a diffusion denoising model ...", "date": "", "ddg_snippet": "Brain tumors. Conditional deep convolutional neural network. Denoising diffusion model . Synthetic data augmentation.", "subpage_snippet": "", "source": "accscience.com", "link": "https://accscience.com/journal/AN/articles/online_first/5593", "content": "Brain tumors. Conditional deep convolutional neural network. Denoising diffusion model . Synthetic data augmentation."}

data/sampled_jsons/1rh8iTehBc_Position_Section_3.3_Llama2_Llama3_license_conflict_clause.jsonl

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+{"idx": 0, "title": "LLAMA 3.3 COMMUNITY LICENSE AGREEMENT", "date": "", "ddg_snippet": "Dec 6, 2024 · Llama 3.3 Community License Agreementi. If you distribute or make available the Llama Materials (or any derivative works thereof), or a product or service (including another AI model) that contains any of them, you shall (A) provide a copy of this Agreement with any such Llama Materials; and (B) prominently display “Built with Llama” on a related website, user interface, blogpost, about ...", "subpage_snippet": "", "source": "www.llama.com", "link": "https://www.llama.com/llama3_3/license/", "content": "Dec 6, 2024 · Llama 3.3 Community License Agreementi. If you distribute or make available the Llama Materials (or any derivative works thereof), or a product or service (including another AI model) that contains any of them, you shall (A) provide a copy of this Agreement with any such Llama Materials; and (B) prominently display “Built with Llama” on a related website, user interface, blogpost, about ..."}
+{"idx": 1, "title": "Llama3 License Explained - DEV Community", "date": "", "ddg_snippet": "Apr 19, 2024 · The Meta Llama 3 Community License Agreement seems quite liberal at first glance, offering a breath... Tagged with ai, llama.", "subpage_snippet": "", "source": "dev.to", "link": "https://dev.to/llm_explorer/llama3-license-explained-2915", "content": "Apr 19, 2024 · The Meta Llama 3 Community License Agreement seems quite liberal at first glance, offering a breath... Tagged with ai, llama."}
+{"idx": 2, "title": "llama/LICENSE at main · meta-llama/llama · GitHub", "date": "", "ddg_snippet": "LLAMA 2 COMMUNITY LICENSE AGREEMENT Llama 2 Version Release Date: July 18, 2023 \"Agreement\" means the terms and conditions for use, reproduction, distribution and modification of the Llama Materials set forth herein.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/Meta-Llama/llama/blob/main/LICENSE", "content": "LLAMA 2 COMMUNITY LICENSE AGREEMENT Llama 2 Version Release Date: July 18, 2023 \"Agreement\" means the terms and conditions for use, reproduction, distribution and modification of the Llama Materials set forth herein."}
+{"idx": 3, "title": "Meta Llama 3 License", "date": "", "ddg_snippet": "Apr 18, 2024 · META LLAMA 3 COMMUNITY LICENSE AGREEMENT Meta Llama 3 Version Release Date: April 18, 2024 “ Agreement ” means the terms and conditions for use, reproduction, distribution and modification of the Llama Materials set forth herein.", "subpage_snippet": "", "source": "www.llama.com", "link": "https://www.llama.com/llama3/license/", "content": "Apr 18, 2024 · META LLAMA 3 COMMUNITY LICENSE AGREEMENT Meta Llama 3 Version Release Date: April 18, 2024 “ Agreement ” means the terms and conditions for use, reproduction, distribution and modification of the Llama Materials set forth herein."}
+{"idx": 4, "title": "Llama 3 versus LLama 3.1 License Terms — /dev/lawyer", "date": "", "ddg_snippet": "Jul 24, 2024 · Meta Llama 3 3 .1 Version Release Date: April 18 July 23, 2024 “Agreement” means the terms and conditions for use, reproduction, distribution and modification of the Llama Materials set forth herein.", "subpage_snippet": "", "source": "writing.kemitchell.com", "link": "https://writing.kemitchell.com/2024/07/24/Llama-3-versus-Llama-3-1-License", "content": "Jul 24, 2024 · Meta Llama 3 3 .1 Version Release Date: April 18 July 23, 2024 “Agreement” means the terms and conditions for use, reproduction, distribution and modification of the Llama Materials set forth herein."}
+{"idx": 5, "title": "Meta-Llama-3-8B-Instruct - Hugging Face", "date": "", "ddg_snippet": "Apr 18, 2024 · **Note: Developers may fine-tune Llama 3 models for languages beyond English provided they comply with the Llama 3 Community License and the Acceptable Use Policy. How to use This repository contains two versions of Meta- Llama-3 -8B-Instruct, for use with transformers and with the original llama3 codebase. Use with transformers", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct", "content": "Apr 18, 2024 · **Note: Developers may fine-tune Llama 3 models for languages beyond English provided they comply with the Llama 3 Community License and the Acceptable Use Policy. How to use This repository contains two versions of Meta- Llama-3 -8B-Instruct, for use with transformers and with the original llama3 codebase. Use with transformers"}
+{"idx": 6, "title": "Установка текстовой нейросети \" llama 3 .1\" (3.2) на... | Дзен", "date": "", "ddg_snippet": "Требуемый софт. Скачивание и установка \" llama 3 .1\". Пока скачивается модель, установим \"Docker Desktop\". Если больше ничего не надо, общайтесь прямо так. Установка OpenWebUI. График нагрузки при работе \"llama1.3\".", "subpage_snippet": "", "source": "dzen.ru", "link": "https://dzen.ru/a/ZwOqgGBHnnxRnQQf", "content": "Требуемый софт. Скачивание и установка \" llama 3 .1\". Пока скачивается модель, установим \"Docker Desktop\". Если больше ничего не надо, общайтесь прямо так. Установка OpenWebUI. График нагрузки при работе \"llama1.3\"."}
+{"idx": 7, "title": "Как локально запустить LLama 3 .1? (Бесплатная Нейросеть на...)", "date": "", "ddg_snippet": "Детально разберем, как установить локально себе на ПК бесплатную нейросеть LLama 3 .1 8b на 8 миллиардов параметров, чтобы можно было ей пользоваться без интернета, безлимитно и абсолютно приватно.", "subpage_snippet": "", "source": "rutube.ru", "link": "https://rutube.ru/video/d76af8bc6442199cee97c36834fb23f6/", "content": "Детально разберем, как установить локально себе на ПК бесплатную нейросеть LLama 3 .1 8b на 8 миллиардов параметров, чтобы можно было ей пользоваться без интернета, безлимитно и абсолютно приватно."}
+{"idx": 8, "title": "How to Run Llama 3 .1 Locally on your Computer with... - YouTube", "date": "", "ddg_snippet": "About Press Copyright Contact us Creators Advertise Developers Terms Privacy Policy & Safety How...", "subpage_snippet": "", "source": "www.youtube.com", "link": "https://www.youtube.com/watch?v=2orxyu8c7Ek", "content": "About Press Copyright Contact us Creators Advertise Developers Terms Privacy Policy & Safety How..."}
+{"idx": 9, "title": "Пошаговый гайд по развертыванию LLM на примере Llama 3 .2", "date": "", "ddg_snippet": "Почему модель Llama подходит для быстрого развертывания. Рассмотрим модель Llama 3 .2 в качестве примера LLM. Сейчас это последняя официально выпущенная версия языковой модели с открытым исходным кодом.", "subpage_snippet": "", "source": "mClouds.ru", "link": "https://mClouds.ru/2025/04/how-deploy-llm-on-llama/", "content": "Почему модель Llama подходит для быстрого развертывания. Рассмотрим модель Llama 3 .2 в качестве примера LLM. Сейчас это последняя официально выпущенная версия языковой модели с открытым исходным кодом."}

data/sampled_jsons/2024_arxiv_language_model_reasoning_critical_transition.jsonl

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+{"idx": 0, "title": "Training Large Language Models to Reason in a Continuous Latent", "date": "", "ddg_snippet": "... language models (LLMs) have demonstrated remarkable reasoning abilities, emerging from extensive pretraining on human languages (Dubey et al., 2024 ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2412.06769v2", "content": "... language models (LLMs) have demonstrated remarkable reasoning abilities, emerging from extensive pretraining on human languages (Dubey et al., 2024 ..."}
+{"idx": 1, "title": "Large Language Models and Operations Research: A Structured", "date": "", "ddg_snippet": "... large language models (LLMs) have shown potential to address these limitations through semantic understanding, structured generation, and reasoning ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2509.18180v1", "content": "... large language models (LLMs) have shown potential to address these limitations through semantic understanding, structured generation, and reasoning ..."}
+{"idx": 2, "title": "CogniLoad: A Synthetic Natural Language Reasoning Benchmark", "date": "", "ddg_snippet": "Current benchmarks for long-context reasoning in Large Language Models (LLMs) often blur critical factors like intrinsic task complexity, distractor ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2509.18458v1", "content": "Current benchmarks for long-context reasoning in Large Language Models (LLMs) often blur critical factors like intrinsic task complexity, distractor ..."}
+{"idx": 3, "title": "Context Reasoner: Incentivizing Reasoning Capability for", "date": "", "ddg_snippet": "Large Language Models (LLMs) have demonstrated remarkable capabilities in language understanding, reasoning , and generation Ouyang et al.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2505.14585v2", "content": "Large Language Models (LLMs) have demonstrated remarkable capabilities in language understanding, reasoning , and generation Ouyang et al."}
+{"idx": 4, "title": "Distilling mathematical reasoning capabilities into Small", "date": "", "ddg_snippet": "Large Language Models (LLMs) have limited performance when solving arithmetic reasoning tasks and often provide incorrect answers.", "subpage_snippet": "", "source": "sugaku.net", "link": "https://sugaku.net/oa/W4401263076/distilling-mathematical-reasoning-capabilities-into-small-language-models", "content": "Large Language Models (LLMs) have limited performance when solving arithmetic reasoning tasks and often provide incorrect answers."}
+{"idx": 5, "title": "Vision Language Models Come Rushing In", "date": "", "ddg_snippet": "Only in 2024 did large language models (LLMs) in cars become a “thing” and did designers of automotive silicon begin asking for LLM performance ...", "subpage_snippet": "", "source": "semiengineering.com", "link": "https://semiengineering.com/vision-language-models-come-rushing-in/", "content": "Only in 2024 did large language models (LLMs) in cars become a “thing” and did designers of automotive silicon begin asking for LLM performance ..."}
+{"idx": 6, "title": "AECBench: A Hierarchical Benchmark for Knowledge Evaluation of", "date": "", "ddg_snippet": "Large language models (LLMs), as a novel information technology, are seeing increasing adoption in the Architecture, Engineering, and Construction ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2509.18776v1", "content": "Large language models (LLMs), as a novel information technology, are seeing increasing adoption in the Architecture, Engineering, and Construction ..."}
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+{"idx": 8, "title": "Research on LLM for Vulnerability Detection", "date": "", "ddg_snippet": "As large language models (LLMs) have shown their efficacy in lots of language -related fields, researchers in the field of information security are ...", "subpage_snippet": "", "source": "blog.wohin.me", "link": "https://blog.wohin.me/posts/recent-llm-for-vuln-detection/", "content": "As large language models (LLMs) have shown their efficacy in lots of language -related fields, researchers in the field of information security are ..."}
+{"idx": 9, "title": "Power outage — Wikipedia Republished // WIKI 2", "date": "", "ddg_snippet": "Power failures are particularly critical at sites where the environment and public safety are at risk. ... Other critical systems, such as ...", "subpage_snippet": "", "source": "wiki2.org", "link": "https://wiki2.org/en/Power_outage", "content": "Power failures are particularly critical at sites where the environment and public safety are at risk. ... Other critical systems, such as ..."}

data/sampled_jsons/2024_foundation_segmentation_model_domain_adaptation_promptable_segmentation_year_2024.jsonl

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+{"idx": 0, "title": "Improving the Generalization of Segmentation Foundation Model ...", "date": "", "ddg_snippet": "In particular, as a promptable image segmentation foundation model with strong zero-shot generalization, the Segment-Anything model (SAM) [27] was developed by training on billions of annotated masks.", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2024/papers/Zhang_Improving_the_Generalization_of_Segmentation_Foundation_Model_under_Distribution_Shift_CVPR_2024_paper.pdf", "content": "In particular, as a promptable image segmentation foundation model with strong zero-shot generalization, the Segment-Anything model (SAM) [27] was developed by training on billions of annotated masks."}
+{"idx": 1, "title": "Collaborating Foundation Models for Domain Generalized ...", "date": "", "ddg_snippet": "by Y Benigmim · 2024 · Cited by 150 — SAM, origi- nally trained for promptable segmentation , is adapted to our task through the use of point-based prompting (Fig. 4). Specifically, we adopt the ... 12 pages", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2024/papers/Benigmim_Collaborating_Foundation_Models_for_Domain_Generalized_Semantic_Segmentation_CVPR_2024_paper.pdf", "content": "by Y Benigmim · 2024 · Cited by 150 — SAM, origi- nally trained for promptable segmentation , is adapted to our task through the use of point-based prompting (Fig. 4). Specifically, we adopt the ... 12 pages"}
+{"idx": 2, "title": "Task-Specific Adaptation of Segmentation Foundation Model via ... Improving the Generalization of Segmentation Foundation Model ... Prompting to Adapt Foundational Segmentation Models [CVPR 2024] Open-Set Domain Adaptation for Semantic Segmentation Black-Box Adaptation for Medical Image Segmentation Prompting Foundational Models for Omni-supervised Instance ... Improving the Generalization of Segmentation Foundation Model under Task-Specific Adaptation of Segmentation Foundation Model via Prompt Prompting to Adapt Foundational Segmentation Models Improving the Generalization of Segmentation Foundation Model under Task-Specific Adaptation of Segmentation Foundation Model via Prompt Improving the Generalization of Segmentation Foundation Model under Collaborating Foundation Models for Domain Generalized ...", "date": "", "ddg_snippet": "Mar 14, 2024 · Recently, foundation models trained on massive datasets to adapt to a wide range of tasks have attracted considerable attention and are actively being explored within the computer vision community. Among these, the Segment Anything Model (SAM) stands out for its remarkable progress in generalizability and flexibility for image segmentation tasks, achieved through prompt-based object mask ... In particular, as a promptable image segmentation foundation model with strong zero-shot generalization, the Segment-Anything model (SAM) [27] was developed by training on billions of annotated masks. Oct 28, 2024 · Foundational segmentation models, predominantly trained on scenes typical of natural environments, struggle to generalize across varied image domains. Traditional \"training-to-adapt'' methods rely heavily on extensive data retraining and model architectures modifications. Official PyTorch implementation for CVPR 2024 paper: Open-Set Domain Adaptation for Semantic Segmentation Seun-An Choe*, Ah-Hyung Shin*, Keon-Hee Park, Jinwoo Choi † , and Gyeong-Moon Park † In this work, we proposed one of the first Black-Box adaptation methods, called BAPS, for the adaptation of foundation models for prompted segmentation . BAPS consists of a pretrained image encoder and a trainable IP decoder, that generates a visual prompt as a function of the input image and given prompt. Pixel-level mask annotation costs are a major bottleneck in training deep neural networks for instance segmentation . Recent promptable foundation models like the Segment Anything Model (SAM) and GroundedDINO (GDino) have shown impressive zero-shot performance in segmentation and object detection benchmarks. While these models are not capable of performing inference without prompts, they are ... Which image segmentation Foundation model is able to zero/few-shot generalization? The success of large language models has inspired the computer vision community to explore image segmenta-tion foundation model that is able to zero/few-shot general-ize through prompt engineering. Segment-Anything (SAM) , among others, is the state-of-the-art image segmentation foundation model demonstrating strong zero/few-shot gen-eralization. Can a segmentation Foundation model be customized? To address these challenges, we propose a task-specific adaptation (i.e., customization) of the segmentation foundation model via prompt learning tailored to SAM. What is the difference between a segmentation model and a training-to-adapt model? Foundational segmentation models, predominantly trained on scenes typical of natural environments, struggle to generalize across varied image domains. Traditional \"training-to-adapt'' methods rely heavily on extensive data retraining and model architectures modifications. Is segment-anything a good image segmentation model? Segment-Anything (SAM), among others, is the state-of-the-art image segmentation foundation model demonstrating strong zero/few-shot gen-eralization. Despite the success, recent studies reveal the weakness of SAM under strong distribution shift. What is segment anything model (Sam)? Among these, the Segment Anything Model (SAM) stands out for its remarkable progress in generalizability and flexibility for image segmentation tasks , achieved through prompt-based object mask generation. Does weak supervision improve the generalization of domain adaptive segmen-tation methods? When weak supervision is provided, both state-of-the-art generic source-free domain adaptation methods and weakly supervised domain adaptive segmen-tation method improve the generalization on all three types of weak supervisions. Finally, our proposed weakly super-vised method achieves a remarkable improvement over all competing methods. Segment Anything Model (SAM) [35], a prominent vision foundation model , is trained for promptable segmentation tasks. SAM excels in producing high-quality masks for any segmentation prompt.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2403.09199", "content": "Mar 14, 2024 · Recently, foundation models trained on massive datasets to adapt to a wide range of tasks have attracted considerable attention and are actively being explored within the computer vision community. Among these, the Segment Anything Model (SAM) stands out for its remarkable progress in generalizability and flexibility for image segmentation tasks, achieved through prompt-based object mask ... In particular, as a promptable image segmentation foundation model with strong zero-shot generalization, the Segment-Anything model (SAM) [27] was developed by training on billions of annotated masks. Oct 28, 2024 · Foundational segmentation models, predominantly trained on scenes typical of natural environments, struggle to generalize across varied image domains. Traditional \"training-to-adapt'' methods rely heavily on extensive data retraining and model architectures modifications. Official PyTorch implementation for CVPR 2024 paper: Open-Set Domain Adaptation for Semantic Segmentation Seun-An Choe*, Ah-Hyung Shin*, Keon-Hee Park, Jinwoo Choi † , and Gyeong-Moon Park † In this work, we proposed one of the first Black-Box adaptation methods, called BAPS, for the adaptation of foundation models for prompted segmentation . BAPS consists of a pretrained image encoder and a trainable IP decoder, that generates a visual prompt as a function of the input image and given prompt. Pixel-level mask annotation costs are a major bottleneck in training deep neural networks for instance segmentation . Recent promptable foundation models like the Segment Anything Model (SAM) and GroundedDINO (GDino) have shown impressive zero-shot performance in segmentation and object detection benchmarks. While these models are not capable of performing inference without prompts, they are ... Which image segmentation Foundation model is able to zero/few-shot generalization? The success of large language models has inspired the computer vision community to explore image segmenta-tion foundation model that is able to zero/few-shot general-ize through prompt engineering. Segment-Anything (SAM) , among others, is the state-of-the-art image segmentation foundation model demonstrating strong zero/few-shot gen-eralization. Can a segmentation Foundation model be customized? To address these challenges, we propose a task-specific adaptation (i.e., customization) of the segmentation foundation model via prompt learning tailored to SAM. What is the difference between a segmentation model and a training-to-adapt model? Foundational segmentation models, predominantly trained on scenes typical of natural environments, struggle to generalize across varied image domains. Traditional \"training-to-adapt'' methods rely heavily on extensive data retraining and model architectures modifications. Is segment-anything a good image segmentation model? Segment-Anything (SAM), among others, is the state-of-the-art image segmentation foundation model demonstrating strong zero/few-shot gen-eralization. Despite the success, recent studies reveal the weakness of SAM under strong distribution shift. What is segment anything model (Sam)? Among these, the Segment Anything Model (SAM) stands out for its remarkable progress in generalizability and flexibility for image segmentation tasks , achieved through prompt-based object mask generation. Does weak supervision improve the generalization of domain adaptive segmen-tation methods? When weak supervision is provided, both state-of-the-art generic source-free domain adaptation methods and weakly supervised domain adaptive segmen-tation method improve the generalization on all three types of weak supervisions. Finally, our proposed weakly super-vised method achieves a remarkable improvement over all competing methods. Segment Anything Model (SAM) [35], a prominent vision foundation model , is trained for promptable segmentation tasks. SAM excels in producing high-quality masks for any segmentation prompt."}
+{"idx": 3, "title": "Prompting to Adapt Foundational Segmentation Models", "date": "", "ddg_snippet": "Oct 28, 2024 · Foundational segmentation models, predominantly trained on scenes typical of natural environments, struggle to generalize across varied image domains. Traditional \"training-to-adapt'' methods rely heavily on extensive data retraining and model architectures modifications.", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.1145/3664647.3680884", "content": "Oct 28, 2024 · Foundational segmentation models, predominantly trained on scenes typical of natural environments, struggle to generalize across varied image domains. Traditional \"training-to-adapt'' methods rely heavily on extensive data retraining and model architectures modifications."}
+{"idx": 4, "title": "[CVPR 2024] Open-Set Domain Adaptation for Semantic Segmentation", "date": "", "ddg_snippet": "Official PyTorch implementation for CVPR 2024 paper: Open-Set Domain Adaptation for Semantic Segmentation Seun-An Choe*, Ah-Hyung Shin*, Keon-Hee Park, Jinwoo Choi † , and Gyeong-Moon Park †", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/KU-VGI/BUS", "content": "Official PyTorch implementation for CVPR 2024 paper: Open-Set Domain Adaptation for Semantic Segmentation Seun-An Choe*, Ah-Hyung Shin*, Keon-Hee Park, Jinwoo Choi † , and Gyeong-Moon Park †"}
+{"idx": 5, "title": "Black-Box Adaptation for Medical Image Segmentation", "date": "", "ddg_snippet": "In this work, we proposed one of the first Black-Box adaptation methods, called BAPS, for the adaptation of foundation models for prompted segmentation . BAPS consists of a pretrained image encoder and a trainable IP decoder, that generates a visual prompt as a function of the input image and given prompt.", "subpage_snippet": "", "source": "papers.miccai.org", "link": "https://papers.miccai.org/miccai-2024/paper/0668_paper.pdf", "content": "In this work, we proposed one of the first Black-Box adaptation methods, called BAPS, for the adaptation of foundation models for prompted segmentation . BAPS consists of a pretrained image encoder and a trainable IP decoder, that generates a visual prompt as a function of the input image and given prompt."}
+{"idx": 6, "title": "Prompting Foundational Models for Omni-supervised Instance ...", "date": "", "ddg_snippet": "Pixel-level mask annotation costs are a major bottleneck in training deep neural networks for instance segmentation . Recent promptable foundation models like the Segment Anything Model (SAM) and GroundedDINO (GDino) have shown impressive zero-shot performance in segmentation and object detection benchmarks. While these models are not capable of performing inference without prompts, they are ...", "subpage_snippet": "", "source": "ieeexplore.ieee.org", "link": "https://ieeexplore.ieee.org/document/10678642", "content": "Pixel-level mask annotation costs are a major bottleneck in training deep neural networks for instance segmentation . Recent promptable foundation models like the Segment Anything Model (SAM) and GroundedDINO (GDino) have shown impressive zero-shot performance in segmentation and object detection benchmarks. While these models are not capable of performing inference without prompts, they are ..."}
+{"idx": 7, "title": "Improving the Generalization of Segmentation Foundation ...", "date": "", "ddg_snippet": "10 Apr 2024 — In this work, we aim to adapt SAM to downstream tasks without accessing to the source domain data to avoid the high computation overhead and ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2312.03502v2", "content": "10 Apr 2024 — In this work, we aim to adapt SAM to downstream tasks without accessing to the source domain data to avoid the high computation overhead and ..."}
+{"idx": 8, "title": "CVPR 2024: Foundation Models + Visual Prompting Are ...", "date": "", "ddg_snippet": "In this article, we explore Visual Prompting, a technique that enables the adaptation of large vision models to new tasks.", "subpage_snippet": "", "source": "medium.com", "link": "https://medium.com/@tenyks_blogger/cvpr-2024-foundation-models-visual-prompting-are-about-to-disrupt-computer-vision-026f2c1c3a2f", "content": "In this article, we explore Visual Prompting, a technique that enables the adaptation of large vision models to new tasks."}
+{"idx": 9, "title": "Adapting segment anything model for medical image ...", "date": "", "ddg_snippet": "by J Wu · 2025 · Cited by 797 — We propose the Medical SAM Adapter (Med-SA), which is one of the first methods to integrate SAM into medical image segmentation .", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S1361841525000945", "content": "by J Wu · 2025 · Cited by 797 — We propose the Medical SAM Adapter (Med-SA), which is one of the first methods to integrate SAM into medical image segmentation ."}

data/sampled_jsons/2208.01565_abstract.jsonl

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+{"idx": 0, "title": "[2208.01565] Approximate Bayesian Neural Operators ... Approximate Bayesian Neural Operators: Uncertainty ... Emilia Magnani - Google Scholar Emilia Magnani - dblp arXiv:2208.01565v1 [cs.LG] 2 Aug 2022 Neural Operator induced Gaussian Process framework for ... Learning semilinear neural operators: a unified recursive ...", "date": "", "ddg_snippet": "Aug 2, 2022 · Abstract page for arXiv paper 2208.01565 : Approximate Bayesian Neural Operators: Uncertainty Quantification for Parametric PDEs Aug 2, 2022 · Neural operators are a type of deep architecture that learns to solve (i.e. learns the nonlinear solution operator of) partial differential equations (PDEs). The current state of the art for these models does not provide explicit uncertainty quantification. This is arguably even more of a problem for this kind of tasks than elsewhere in machine learning, because the dynamical systems typically ... University of Tübingen - Cited by 40 - Probabilistic Numerics - Machine Learning Emilia Magnani, Nicholas Krämer, Runa Eschenhagen, Lorenzo Rosasco, Philipp Hennig: Approximate Bayesian Neural Operators: Uncertainty Quantification for Parametric PDEs. CoRR abs/ 2208.01565 (2022) Abstract Neural operators are a type of deep architecture that learns to solve (i.e. learns the nonlinear so-lution operator of) partial differential equations (PDEs). The current state of the art for these mod-els does not provide explicit uncertainty quantifi-cation. This is arguably even more of a problem for this kind of tasks than elsewhere in machine learning, because the dynamical ... Nov 1, 2024 · The study of neural operators has paved the way for the development of efficient approaches for solving partial differential equations (PDEs) compared… Abstract Recent advances in the theory of Neural Operators (NOs) have enabled fast and accurate computation of the solutions to complex systems described by partial differential equations (PDEs). Despite their great success, current NO-based solutions face important challenges when dealing with spatio-temporal PDEs over long time scales. Specifically, the current theory of NOs does not present ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2208.01565", "content": "Aug 2, 2022 · Abstract page for arXiv paper 2208.01565 : Approximate Bayesian Neural Operators: Uncertainty Quantification for Parametric PDEs Aug 2, 2022 · Neural operators are a type of deep architecture that learns to solve (i.e. learns the nonlinear solution operator of) partial differential equations (PDEs). The current state of the art for these models does not provide explicit uncertainty quantification. This is arguably even more of a problem for this kind of tasks than elsewhere in machine learning, because the dynamical systems typically ... University of Tübingen - Cited by 40 - Probabilistic Numerics - Machine Learning Emilia Magnani, Nicholas Krämer, Runa Eschenhagen, Lorenzo Rosasco, Philipp Hennig: Approximate Bayesian Neural Operators: Uncertainty Quantification for Parametric PDEs. CoRR abs/ 2208.01565 (2022) Abstract Neural operators are a type of deep architecture that learns to solve (i.e. learns the nonlinear so-lution operator of) partial differential equations (PDEs). The current state of the art for these mod-els does not provide explicit uncertainty quantifi-cation. This is arguably even more of a problem for this kind of tasks than elsewhere in machine learning, because the dynamical ... Nov 1, 2024 · The study of neural operators has paved the way for the development of efficient approaches for solving partial differential equations (PDEs) compared… Abstract Recent advances in the theory of Neural Operators (NOs) have enabled fast and accurate computation of the solutions to complex systems described by partial differential equations (PDEs). Despite their great success, current NO-based solutions face important challenges when dealing with spatio-temporal PDEs over long time scales. Specifically, the current theory of NOs does not present ..."}
+{"idx": 1, "title": "arXiv:2208.01565v1 [cs.LG] 2 Aug 2022", "date": "", "ddg_snippet": "Abstract Neural operators are a type of deep architecture that learns to solve (i.e. learns the nonlinear so-lution operator of) partial differential equations (PDEs). The current state of the art for these mod-els does not provide explicit uncertainty quantifi-cation. This is arguably even more of a problem for this kind of tasks than elsewhere in machine learning, because the dynamical ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2208.01565.pdf", "content": "Abstract Neural operators are a type of deep architecture that learns to solve (i.e. learns the nonlinear so-lution operator of) partial differential equations (PDEs). The current state of the art for these mod-els does not provide explicit uncertainty quantifi-cation. This is arguably even more of a problem for this kind of tasks than elsewhere in machine learning, because the dynamical ..."}
+{"idx": 2, "title": "Emilia Magnani - Google Scholar", "date": "", "ddg_snippet": "E Magnani, N Krämer, R Eschenhagen, L Rosasco, P Hennig. arXiv preprint arXiv: 2208 . 01565 , 2022.", "subpage_snippet": "", "source": "scholar.google.com", "link": "https://scholar.google.com/citations?user=_zPcNdEAAAAJ&hl=en", "content": "E Magnani, N Krämer, R Eschenhagen, L Rosasco, P Hennig. arXiv preprint arXiv: 2208 . 01565 , 2022."}
+{"idx": 3, "title": "Emilia Magnani - dblp", "date": "", "ddg_snippet": "Emilia Magnani, Nicholas Krämer, Runa Eschenhagen, Lorenzo Rosasco, Philipp Hennig: Approximate Bayesian Neural Operators: Uncertainty Quantification for Parametric PDEs. CoRR abs/ 2208.01565 (2022)", "subpage_snippet": "", "source": "dblp.org", "link": "https://dblp.org/pid/206/6101", "content": "Emilia Magnani, Nicholas Krämer, Runa Eschenhagen, Lorenzo Rosasco, Philipp Hennig: Approximate Bayesian Neural Operators: Uncertainty Quantification for Parametric PDEs. CoRR abs/ 2208.01565 (2022)"}
+{"idx": 4, "title": "Learning semilinear neural operators: a unified recursive ...", "date": "", "ddg_snippet": "Abstract Recent advances in the theory of Neural Operators (NOs) have enabled fast and accurate computation of the solutions to complex systems described by partial differential equations (PDEs). Despite their great success, current NO-based solutions face important challenges when dealing with spatio-temporal PDEs over long time scales. Specifically, the current theory of NOs does not present ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2402.15656v2", "content": "Abstract Recent advances in the theory of Neural Operators (NOs) have enabled fast and accurate computation of the solutions to complex systems described by partial differential equations (PDEs). Despite their great success, current NO-based solutions face important challenges when dealing with spatio-temporal PDEs over long time scales. Specifically, the current theory of NOs does not present ..."}
+{"idx": 5, "title": "Approximate Bayesian Neural Operators: Uncertainty Quantication for", "date": "", "ddg_snippet": "Abstract . Neural operators are a type of deep architecture that learns to solve (i.e. learns the nonlinear so-lution operator of) partial differential equations (PDEs).", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2208.01565", "content": "Abstract . Neural operators are a type of deep architecture that learns to solve (i.e. learns the nonlinear so-lution operator of) partial differential equations (PDEs)."}
+{"idx": 6, "title": "Approximate Bayesian Neural Operators: Uncertainty Quantification for...", "date": "", "ddg_snippet": "Abstract . Neural operators are a type of deep architecture that learns to solve (i.e. learns the nonlinear solution operator of) partial differential equations (PDEs). The current state of the art for...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/362430096_Approximate_Bayesian_Neural_Operators_Uncertainty_Quantification_for_Parametric_PDEs", "content": "Abstract . Neural operators are a type of deep architecture that learns to solve (i.e. learns the nonlinear solution operator of) partial differential equations (PDEs). The current state of the art for..."}
+{"idx": 7, "title": "dblp: List of computer science publications by Emilia Magnani", "date": "", "ddg_snippet": "persistent URL: https://dblp.org/rec/journals/corr/abs- 2208 - 01565 . Emilia Magnani, Nicholas Krämer, Runa Eschenhagen, Lorenzo Rosasco, Philipp Hennig : Approximate Bayesian Neural...", "subpage_snippet": "", "source": "dblp.uni-trier.de", "link": "https://dblp.uni-trier.de/pid/206/6101.html", "content": "persistent URL: https://dblp.org/rec/journals/corr/abs- 2208 - 01565 . Emilia Magnani, Nicholas Krämer, Runa Eschenhagen, Lorenzo Rosasco, Philipp Hennig : Approximate Bayesian Neural..."}
+{"idx": 8, "title": "Calibrated Uncertainty Quantification for Operator Learning", "date": "", "ddg_snippet": "Abstract . Operator learning has been increasingly adopted in scientific and engineering applications, many of which require calibrated uncertainty quantification.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/attachment?id=cGpegxy12T&name=pdf", "content": "Abstract . Operator learning has been increasingly adopted in scientific and engineering applications, many of which require calibrated uncertainty quantification."}
+{"idx": 9, "title": "Learning semilinear neural operators: A unified recursive framework for...", "date": "", "ddg_snippet": "Ap-proximate Bayesian neural operators: Uncertainty quantification for parametric PDEs. arXiv preprint arXiv: 2208 . 01565 , 2022.", "subpage_snippet": "", "source": "hal.science", "link": "https://hal.science/hal-04728344v1/document", "content": "Ap-proximate Bayesian neural operators: Uncertainty quantification for parametric PDEs. arXiv preprint arXiv: 2208 . 01565 , 2022."}

data/sampled_jsons/2403.01698_pdf.jsonl

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+{"idx": 0, "title": "(PDF) How to improve creativity: a study of gamification,", "date": "", "ddg_snippet": "PDF | In today’s world of knowledge-based economies, gig economies, crowdsourcing, and overall ICT-driven creativity, the avenues toward ...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/364435038_How_to_improve_creativity_a_study_of_gamification_money_and_punishment", "content": "PDF | In today’s world of knowledge-based economies, gig economies, crowdsourcing, and overall ICT-driven creativity, the avenues toward ..."}
+{"idx": 1, "title": "[2403.01698] Hypertext Entity Extraction in Webpage", "date": "", "ddg_snippet": "by Y Yang · 2024 · Cited by 1 — This paper introduces a new dataset (HEED) and a MoEEF framework for hypertext entity extraction, outperforming existing models.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2403.01698", "content": "by Y Yang · 2024 · Cited by 1 — This paper introduces a new dataset (HEED) and a MoEEF framework for hypertext entity extraction, outperforming existing models."}
+{"idx": 2, "title": "arXiv:2403.01698v1 [cs.CL] 4 Mar 2024", "date": "", "ddg_snippet": "by Y Yang · 2024 · Cited by 1 — This paper introduces HEED, a dataset with hypertext features, and MoEEF, a framework using Mixture of Experts for webpage entity extraction.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2403.01698", "content": "by Y Yang · 2024 · Cited by 1 — This paper introduces HEED, a dataset with hypertext features, and MoEEF, a framework using Mixture of Experts for webpage entity extraction."}
+{"idx": 3, "title": "Analysis of PDEs May 2024", "date": "", "ddg_snippet": "27] arXiv:2405. 01698 [ pdf , html , other ] ... Comments: arXiv admin note: substantial text overlap with arXiv: 2403 .15057", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/list/math.AP/2024-05", "content": "27] arXiv:2405. 01698 [ pdf , html , other ] ... Comments: arXiv admin note: substantial text overlap with arXiv: 2403 .15057"}
+{"idx": 4, "title": "topological quantum computation in nLab", "date": "", "ddg_snippet": "... and Feed-Forward on a Trapped Ion Quantum Computer , Nature Communications Physics 7 (2024) 205 [ doi:10.1038/s42005-024- 01698 -3 , arXiv:2302.01917 ]", "subpage_snippet": "", "source": "ncatlab.org", "link": "https://ncatlab.org/nlab/show/topological+quantum+computation", "content": "... and Feed-Forward on a Trapped Ion Quantum Computer , Nature Communications Physics 7 (2024) 205 [ doi:10.1038/s42005-024- 01698 -3 , arXiv:2302.01917 ]"}
+{"idx": 5, "title": "Michael Mahoney - Publications", "date": "", "ddg_snippet": "... of the SC23 Workshops of The International Conference on High Performance Computing, Network, Storage, and Analysis , 868–874 (2023) ( pdf ...", "subpage_snippet": "", "source": "www.stat.berkeley.edu", "link": "https://www.stat.berkeley.edu/~mmahoney/pubs.html", "content": "... of the SC23 Workshops of The International Conference on High Performance Computing, Network, Storage, and Analysis , 868–874 (2023) ( pdf ..."}
+{"idx": 6, "title": "Automating XPath Query Generation Using NLP for ...", "date": "", "ddg_snippet": "Available: https://arxiv.org/abs/ 2403.01698 . [19] Y. Zhou et al., ”Simplified DOM Trees for Transferable Attribute. Extraction from the Web,” 2021. [Online] ...", "subpage_snippet": "", "source": "ieeexplore.ieee.org", "link": "https://ieeexplore.ieee.org/iel8/11040147/11042273/11042798.pdf", "content": "Available: https://arxiv.org/abs/ 2403.01698 . [19] Y. Zhou et al., ”Simplified DOM Trees for Transferable Attribute. Extraction from the Web,” 2021. [Online] ..."}
+{"idx": 7, "title": "NOVER: Incentive Training for Language Models via Verifier-Free", "date": "", "ddg_snippet": "Figure 2: Examples of Qwen2.5-7B-NOVER on a range of text-to-text tasks, demonstrating its ability to handle open-ended questions such as “Discuss ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2505.16022v2", "content": "Figure 2: Examples of Qwen2.5-7B-NOVER on a range of text-to-text tasks, demonstrating its ability to handle open-ended questions such as “Discuss ..."}
+{"idx": 8, "title": "NOVER: Incentive Training for Language Models via Verifier-Free", "date": "", "ddg_snippet": "Figure 2: Examples of Qwen2.5-7B-NOVER on a range of text-to-text tasks, demonstrating its ability to handle open-ended questions such as “Discuss ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2505.16022v1", "content": "Figure 2: Examples of Qwen2.5-7B-NOVER on a range of text-to-text tasks, demonstrating its ability to handle open-ended questions such as “Discuss ..."}
+{"idx": 9, "title": "[Literature Review] Hypertext Entity Extraction in Webpage", "date": "", "ddg_snippet": "The paper titled \"Hypertext Entity Extraction in Webpages\" discusses an advancement in the field of natural language processing (NLP), particularly focusing ...", "subpage_snippet": "", "source": "www.themoonlight.io", "link": "https://www.themoonlight.io/en/review/hypertext-entity-extraction-in-webpage", "content": "The paper titled \"Hypertext Entity Extraction in Webpages\" discusses an advancement in the field of natural language processing (NLP), particularly focusing ..."}

data/sampled_jsons/2406.05072_Theorem_3.2_function-valued_Gaussian_processes_are_equivalent.jsonl

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+{"idx": 0, "title": "probabilistic neural operators for functional", "date": "", "ddg_snippet": "by C Bülte · 2025 · Cited by 3 — Linearization Turns Neural Operators into. Function - Valued Gaussian Processes , June 2024. URL http://arxiv.org/abs/ 2406.05072 . arXiv ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2502.12902", "content": "by C Bülte · 2025 · Cited by 3 — Linearization Turns Neural Operators into. Function - Valued Gaussian Processes , June 2024. URL http://arxiv.org/abs/ 2406.05072 . arXiv ..."}
+{"idx": 1, "title": "Probabilistic neural operators for functional uncertainty ...", "date": "", "ddg_snippet": "18 Feb 2025 — Linearization Turns Neural Operators into Function - Valued Gaussian Processes , June 2024. URL http://arxiv.org/abs/ 2406.05072 . arXiv ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2502.12902v1", "content": "18 Feb 2025 — Linearization Turns Neural Operators into Function - Valued Gaussian Processes , June 2024. URL http://arxiv.org/abs/ 2406.05072 . arXiv ..."}
+{"idx": 2, "title": "Approximate Bayesian Neural Operators: Uncertainty ...", "date": "", "ddg_snippet": "Linearization turns neural operators into function - valued Gaussian processes . arXiv preprint arXiv: 2406.05072 , 2024. Radford M. Neal. Bayesian Learning for ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf/70be375e4aec7a205e768c1b81cb4d1e4ba06a2f.pdf", "content": "Linearization turns neural operators into function - valued Gaussian processes . arXiv preprint arXiv: 2406.05072 , 2024. Radford M. Neal. Bayesian Learning for ..."}
+{"idx": 3, "title": "", "date": "", "ddg_snippet": "", "subpage_snippet": "", "source": "", "link": "", "content": ""}

data/sampled_jsons/2408.17052_hyperparameters_beta_gamma.jsonl

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+{"idx": 0, "title": "[ 2408 . 17052 ] Can We Leave Deepfake Data Behind in Training...", "date": "", "ddg_snippet": "Computer Science > Computer Vision and Pattern Recognition. arXiv: 2408 . 17052 (cs).(or arXiv: 2408 . 17052 v1 [cs.CV] for this version).", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2408.17052", "content": "Computer Science > Computer Vision and Pattern Recognition. arXiv: 2408 . 17052 (cs).(or arXiv: 2408 . 17052 v1 [cs.CV] for this version)."}
+{"idx": 1, "title": "Why do we need the hyperparameters beta and alpha in LDA?", "date": "", "ddg_snippet": "The alpha and beta parameters come from the fact that the dirichlet distribution, (a generalization of the beta distribution) takes these as parameters in the prior distribution. So to answer your first question, will the formula above work without the alpha and gamma , yes...", "subpage_snippet": "", "source": "datascience.stackexchange.com", "link": "https://datascience.stackexchange.com/questions/30369/why-do-we-need-the-hyperparameters-beta-and-alpha-in-lda", "content": "The alpha and beta parameters come from the fact that the dirichlet distribution, (a generalization of the beta distribution) takes these as parameters in the prior distribution. So to answer your first question, will the formula above work without the alpha and gamma , yes..."}
+{"idx": 2, "title": "LDA Alpha and Beta Parameters - The Intuition | ThoughtVector", "date": "", "ddg_snippet": "Beta represents topic-word density - with a high beta , topics are made up of most of the words in the corpus, and with a low beta they consist of few words.", "subpage_snippet": "", "source": "www.thoughtvector.io", "link": "https://www.thoughtvector.io/blog/lda-alpha-and-beta-parameters-the-intuition/", "content": "Beta represents topic-word density - with a high beta , topics are made up of most of the words in the corpus, and with a low beta they consist of few words."}
+{"idx": 3, "title": "DLS C2W3 Momentum vs Adam Beta Hyperparameters", "date": "", "ddg_snippet": "In C2W3 video \" Tuning Process\" Professor Ng mentions that the momentum term is second priority for tuning but he goes on to say that he almost never tunes the beta params for ADAM optimization.", "subpage_snippet": "", "source": "community.deeplearning.ai", "link": "https://community.deeplearning.ai/t/dls-c2w3-momentum-vs-adam-beta-hyperparameters/301684", "content": "In C2W3 video \" Tuning Process\" Professor Ng mentions that the momentum term is second priority for tuning but he goes on to say that he almost never tunes the beta params for ADAM optimization."}
+{"idx": 4, "title": "calculate_ beta : Calculate the beta hyperparameter for...", "date": "", "ddg_snippet": "Helper function for calculating the beta parameter of an inverse gamma distribution given absolute deviation from the popReconstruct elicitation statement. Used for quantifying measurement error in popReconstruct components.", "subpage_snippet": "", "source": "rdrr.io", "link": "https://rdrr.io/github/ihmeuw-demographics/popMethods/man/calculate_beta.html", "content": "Helper function for calculating the beta parameter of an inverse gamma distribution given absolute deviation from the popReconstruct elicitation statement. Used for quantifying measurement error in popReconstruct components."}
+{"idx": 5, "title": "A Quality-Centric Framework for Generic Deepfake Detection", "date": "", "ddg_snippet": "8 Nov 2024 — Luo, Z. Wang, and C. Li, “Can We Leave Deepfake Data Behind in Training Deepfake Detector?” arXiv preprint arXiv: 2408.17052 , 2024.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2411.05335v1", "content": "8 Nov 2024 — Luo, Z. Wang, and C. Li, “Can We Leave Deepfake Data Behind in Training Deepfake Detector?” arXiv preprint arXiv: 2408.17052 , 2024."}
+{"idx": 6, "title": "Understanding Optimization Algorithms in AI - Simple Science", "date": "", "ddg_snippet": "Hyperparameters are like the knobs on your favorite radio. They control how the model learns. If you turn the knobs too far, you might end up with a channel full of static. This can lead to unstable learning where the model starts making wild guesses instead of learning effectively.", "subpage_snippet": "", "source": "scisimple.com", "link": "https://scisimple.com/en/articles/2025-05-27-understanding-optimization-algorithms-in-ai--a3d5po5", "content": "Hyperparameters are like the knobs on your favorite radio. They control how the model learns. If you turn the knobs too far, you might end up with a channel full of static. This can lead to unstable learning where the model starts making wild guesses instead of learning effectively."}
+{"idx": 7, "title": "Vinija's Notes • Coursera-DL • Improving Deep Neural Networks...", "date": "", "ddg_snippet": "Sampling the Exponential Weighting Hyperparameter ( Beta ): Beta is used for calculating exponentially weighted averages. If considering beta values between 0.9 and 0.999, it’s inefficient to sample linearly.", "subpage_snippet": "", "source": "vinija.ai", "link": "https://vinija.ai/CourseraDL/improving-deep-neural-networks/", "content": "Sampling the Exponential Weighting Hyperparameter ( Beta ): Beta is used for calculating exponentially weighted averages. If considering beta values between 0.9 and 0.999, it’s inefficient to sample linearly."}
+{"idx": 8, "title": "RLHF — swift 3.9.0.dev0 documentation", "date": "", "ddg_snippet": "Hyperparameters : beta : Coefficient before the implicit reward, default is 2.0. simpo_ gamma : Reward margin term, default is 1.0. cpo_alpha: The mixed CPO NLL loss for improving training stability; defaults to 1.0, set to 0.0 to use the original SimPO algorithm.", "subpage_snippet": "", "source": "swift.readthedocs.io", "link": "https://swift.readthedocs.io/en/latest/Instruction/RLHF.html", "content": "Hyperparameters : beta : Coefficient before the implicit reward, default is 2.0. simpo_ gamma : Reward margin term, default is 1.0. cpo_alpha: The mixed CPO NLL loss for improving training stability; defaults to 1.0, set to 0.0 to use the original SimPO algorithm."}
+{"idx": 9, "title": "Custom guide outperformed by automatic guide in mixture model | Forum", "date": "", "ddg_snippet": "I’ve built a small mixture of betas that I have (approximately) working with both a custom and AutoNormal guides.dist.Dirichlet(jnp.ones(K) * concentration / K) ) #. The hyperparameters for the cluster/component parameters alpha_shape = numpyro.sample(.", "subpage_snippet": "", "source": "forum.pyro.ai", "link": "https://forum.pyro.ai/t/custom-guide-outperformed-by-automatic-guide-in-mixture-model/6289", "content": "I’ve built a small mixture of betas that I have (approximately) working with both a custom and AutoNormal guides.dist.Dirichlet(jnp.ones(K) * concentration / K) ) #. The hyperparameters for the cluster/component parameters alpha_shape = numpyro.sample(."}

data/sampled_jsons/2501.19334_equation_2_Gaussian_policy_value.jsonl

@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Gaussian function - Wikipedia", "date": "", "ddg_snippet": "In mathematics, a Gaussian function, often simply referred to as a Gaussian , is a function of the base form. and with parametric extension. for arbitrary real constants a, b and non-zero c. It is named after the mathematician Carl Friedrich Gauss .", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/Gaussian_function", "content": "In mathematics, a Gaussian function, often simply referred to as a Gaussian , is a function of the base form. and with parametric extension. for arbitrary real constants a, b and non-zero c. It is named after the mathematician Carl Friedrich Gauss ."}
+{"idx": 1, "title": "The Value of Prediction in Identifying the Worst-Off - arXiv.org", "date": "", "ddg_snippet": "We formally define this quantity in Equation 3. While initially developed to specifically study the value of prediction in allocation problems where allocating goods to individuals had hetero-geneous efects, here we extend this concept to analyze the value of prediction in a related, but distinct, setting where we aim to identify the worst-of.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2501.19334", "content": "We formally define this quantity in Equation 3. While initially developed to specifically study the value of prediction in allocation problems where allocating goods to individuals had hetero-geneous efects, here we extend this concept to analyze the value of prediction in a related, but distinct, setting where we aim to identify the worst-of."}
+{"idx": 2, "title": "The Value of Prediction in Identifying the Worst-Off", "date": "", "ddg_snippet": "by U Fischer-Abaigar · 2025 — D. 2 Optimal Policy Value in Gaussian Setting: Proof of Proposition 2 . Following Proposition 4, the value of the optimal screening policy π∗ can then be ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2501.19334?", "content": "by U Fischer-Abaigar · 2025 — D. 2 Optimal Policy Value in Gaussian Setting: Proof of Proposition 2 . Following Proposition 4, the value of the optimal screening policy π∗ can then be ..."}
+{"idx": 3, "title": "The Value of Prediction in Identifying the Worst-Off", "date": "", "ddg_snippet": "This paper examines the welfare impacts of prediction in equity-driven contexts, and how they compare to other policy levers, such as expanding bureaucratic ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2501.19334v3", "content": "This paper examines the welfare impacts of prediction in equity-driven contexts, and how they compare to other policy levers, such as expanding bureaucratic ..."}
+{"idx": 4, "title": "arXiv:2501.19334v1 [cs.CY] 31 Jan 2025", "date": "", "ddg_snippet": "D. 2 Optimal Policy Value in Gaussian Setting: Proof of Proposition 2 Following Proposition 4, the value of the optimal screening policy π∗ can then be expressed as:", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2501.19334v1", "content": "D. 2 Optimal Policy Value in Gaussian Setting: Proof of Proposition 2 Following Proposition 4, the value of the optimal screening policy π∗ can then be expressed as:"}
+{"idx": 5, "title": "Q function and Error functions : demystified - GaussianWaves The Value of Prediction in Identifying the Worst-Off - arXiv.org Book - papers.nips.cc Narayanaswamy Balakrishnan - McMaster Experts JPART - moodle2.units.it Q function and Error functions : demystified - GaussianWaves Q function and Error functions : demystified - GaussianWaves Book - papers.nips.cc Neural Stochastic Differential Equations: Deep Latent ...", "date": "", "ddg_snippet": "Q functions are often encountered in the theoretical equations for Bit Error Rate (BER) involving AWGN channel. A brief discussion on Q function and its relation to erfcfunction is given here. Gaussian process is the underlying model for an AWGN channel.The probability density function of a Gaussian Distribution is given by Generally, in BER deriva... See full list on gaussianwaves.com The complementary error function represents the area under the two tails of zero mean Gaussian probability density function of variance σ 2 =1/ 2 σ 2 =1/ 2 . The error function gives the probability that the parameter lies outside that range. Therefore, the complementary error function is given by Hence, the error function is or equivalently, The erf funct... See full list on gaussianwaves.com From the limits of the integrals in equation (4) and (6) one can conclude that Q function is directly related to complementary error function (erfc). It follows from equation (4) and (6), Q functionis related to complementary error function by the following relation. See full list on gaussianwaves.com Keep a note of the following equations that can come handy when deriving probability of bit errors for various scenarios. These equations are compiled here for easy reference. If we have a normal variable X∼N(μ,σ 2 )X∼N(μ,σ 2 ), the probability that X>xX>xis If we want to know the probability that XX is away from the mean by an amount‘a’ (on the left o... See full list on gaussianwaves.com The Q-function and the error function (erf)are important mathematical functions that arise in many fields, including probability theory, statistics, signal processing, and communications engineering. Here are some reasons why these functions are important: 1. Probability calculations: The Q-function and erf function are used in probability calculat... See full list on gaussianwaves.com We formally define this quantity in Equation 3. While initially developed to specifically study the value of prediction in allocation problems where allocating goods to individuals had hetero-geneous efects, here we extend this concept to analyze the value of prediction in a related, but distinct, setting where we aim to identify the worst-of. The Power of Resets in Online Reinforcement Learning Zak Mhammedi, Dylan J Foster, Alexander Rakhlin Gaussian Approximation and Multiplier Bootstrap for Polyak-Ruppert Averaged Linear Stochastic Approximation with Applications to TD Learning Sergey Samsonov, Eric Moulines, Qi-Man Shao, Zhuo-Song Zhang, Alexey Naumov Accurate approximation of the expected value , standard deviation, and probability density function of extreme order statistics from Gaussian samples. Communications in Statistics Part B: Simulation and Computation. 53:869-878. 2024 Comparison of extreme order statistics from two sets of heterogeneous dependent random variables under random shocks. In this article we describe in detail the Bayesian perspective on statistical inference and demonstrate that it provides a more principled approach to modeling public administration data. Because many datasets in public administration are population-level, one-time unique collections, or descriptive of fluid events, the Bayesian reliance on probability as a descrip-tion of unknown quantities ... Which function is used in probability calculations involving Gaussian distributions? Probability calculations: The Q-function and erf function are used in probability calculations involving Gaussian distributions. The Q-function gives the probability that a random variable from a normal distribution will exceed a certain threshold value. What is Q function in Gaussian probability density function? Thus Q function gives the area of the shaded curve with the transformation \\ (y = \\frac {x-\\mu} {\\sigma}\\) applied to the Gaussian probability density function. Essentially, Q function evaluates the tail probability of normal distribution (area of shaded area in the above figure). Who are the authors of graph diffusion policy optimization? Graph Diffusion Policy OptimizationYijing Liu, Chao Du, Tianyu Pang, Chongxuan LI, Min Lin, Wei Chen UKnow: A Unified Knowledge Protocol with Multimodal Knowledge Graph Datasets for Reasoning and Vision-Language Pre-TrainingBiao Gong, Shuai Tan, Yutong Feng, Xiaoying Xie, Yuyuan Li, Chaochao Chen, Kecheng Zheng, Yujun Shen, Deli Zhao May 23, 2019 · This work develops a variational inference framework for deep latent Gaussian models via stochastic automatic differentiation in Wiener space, where the variational approximations to the posterior are obtained by Girsanov (mean-shift) transformation of the standard Wiener process and the computation of gradients is based on the theory of Stochastic flows. In deep latent Gaussian models, the ...", "subpage_snippet": "", "source": "www.gaussianwaves.com", "link": "https://www.gaussianwaves.com/2012/07/q-function-and-error-functions/", "content": "Q functions are often encountered in the theoretical equations for Bit Error Rate (BER) involving AWGN channel. A brief discussion on Q function and its relation to erfcfunction is given here. Gaussian process is the underlying model for an AWGN channel.The probability density function of a Gaussian Distribution is given by Generally, in BER deriva... See full list on gaussianwaves.com The complementary error function represents the area under the two tails of zero mean Gaussian probability density function of variance σ 2 =1/ 2 σ 2 =1/ 2 . The error function gives the probability that the parameter lies outside that range. Therefore, the complementary error function is given by Hence, the error function is or equivalently, The erf funct... See full list on gaussianwaves.com From the limits of the integrals in equation (4) and (6) one can conclude that Q function is directly related to complementary error function (erfc). It follows from equation (4) and (6), Q functionis related to complementary error function by the following relation. See full list on gaussianwaves.com Keep a note of the following equations that can come handy when deriving probability of bit errors for various scenarios. These equations are compiled here for easy reference. If we have a normal variable X∼N(μ,σ 2 )X∼N(μ,σ 2 ), the probability that X>xX>xis If we want to know the probability that XX is away from the mean by an amount‘a’ (on the left o... See full list on gaussianwaves.com The Q-function and the error function (erf)are important mathematical functions that arise in many fields, including probability theory, statistics, signal processing, and communications engineering. Here are some reasons why these functions are important: 1. Probability calculations: The Q-function and erf function are used in probability calculat... See full list on gaussianwaves.com We formally define this quantity in Equation 3. While initially developed to specifically study the value of prediction in allocation problems where allocating goods to individuals had hetero-geneous efects, here we extend this concept to analyze the value of prediction in a related, but distinct, setting where we aim to identify the worst-of. The Power of Resets in Online Reinforcement Learning Zak Mhammedi, Dylan J Foster, Alexander Rakhlin Gaussian Approximation and Multiplier Bootstrap for Polyak-Ruppert Averaged Linear Stochastic Approximation with Applications to TD Learning Sergey Samsonov, Eric Moulines, Qi-Man Shao, Zhuo-Song Zhang, Alexey Naumov Accurate approximation of the expected value , standard deviation, and probability density function of extreme order statistics from Gaussian samples. Communications in Statistics Part B: Simulation and Computation. 53:869-878. 2024 Comparison of extreme order statistics from two sets of heterogeneous dependent random variables under random shocks. In this article we describe in detail the Bayesian perspective on statistical inference and demonstrate that it provides a more principled approach to modeling public administration data. Because many datasets in public administration are population-level, one-time unique collections, or descriptive of fluid events, the Bayesian reliance on probability as a descrip-tion of unknown quantities ... Which function is used in probability calculations involving Gaussian distributions? Probability calculations: The Q-function and erf function are used in probability calculations involving Gaussian distributions. The Q-function gives the probability that a random variable from a normal distribution will exceed a certain threshold value. What is Q function in Gaussian probability density function? Thus Q function gives the area of the shaded curve with the transformation \\ (y = \\frac {x-\\mu} {\\sigma}\\) applied to the Gaussian probability density function. Essentially, Q function evaluates the tail probability of normal distribution (area of shaded area in the above figure). Who are the authors of graph diffusion policy optimization? Graph Diffusion Policy OptimizationYijing Liu, Chao Du, Tianyu Pang, Chongxuan LI, Min Lin, Wei Chen UKnow: A Unified Knowledge Protocol with Multimodal Knowledge Graph Datasets for Reasoning and Vision-Language Pre-TrainingBiao Gong, Shuai Tan, Yutong Feng, Xiaoying Xie, Yuyuan Li, Chaochao Chen, Kecheng Zheng, Yujun Shen, Deli Zhao May 23, 2019 · This work develops a variational inference framework for deep latent Gaussian models via stochastic automatic differentiation in Wiener space, where the variational approximations to the posterior are obtained by Girsanov (mean-shift) transformation of the standard Wiener process and the computation of gradients is based on the theory of Stochastic flows. In deep latent Gaussian models, the ..."}
+{"idx": 6, "title": "Neural Stochastic Differential Equations: Deep Latent ...", "date": "", "ddg_snippet": "May 23, 2019 · This work develops a variational inference framework for deep latent Gaussian models via stochastic automatic differentiation in Wiener space, where the variational approximations to the posterior are obtained by Girsanov (mean-shift) transformation of the standard Wiener process and the computation of gradients is based on the theory of Stochastic flows. In deep latent Gaussian models, the ...", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/Neural-Stochastic-Differential-Equations:-Deep-in-Tzen-Raginsky/c73211167d621446593f0859f12b6f0679f06b22", "content": "May 23, 2019 · This work develops a variational inference framework for deep latent Gaussian models via stochastic automatic differentiation in Wiener space, where the variational approximations to the posterior are obtained by Girsanov (mean-shift) transformation of the standard Wiener process and the computation of gradients is based on the theory of Stochastic flows. In deep latent Gaussian models, the ..."}
+{"idx": 7, "title": "Learning Gaussian Policies from Smoothed Action Value Functions", "date": "", "ddg_snippet": "We propose a new notion of action value defined by a Gaussian smoothed version of the expected Q- value used in SARSA. We show that such smoothed Q- values still satisfy a Bellman equation , making them naturally learnable from experience sampled from an environment.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=B1nLkl-0Z", "content": "We propose a new notion of action value defined by a Gaussian smoothed version of the expected Q- value used in SARSA. We show that such smoothed Q- values still satisfy a Bellman equation , making them naturally learnable from experience sampled from an environment."}
+{"idx": 8, "title": "Gaussian (Normal) Distribution | Academo.org - Free, interactive...", "date": "", "ddg_snippet": "The Gaussian distribution, (also known as the Normal distribution) is a probability distribution.The peak of the graph is always located at the mean and the area under the curve is always exactly equal to 1. 68% of all the values lie within one standard deviation of the mean.", "subpage_snippet": "", "source": "academo.org", "link": "https://academo.org/demos/gaussian-distribution/", "content": "The Gaussian distribution, (also known as the Normal distribution) is a probability distribution.The peak of the graph is always located at the mean and the area under the curve is always exactly equal to 1. 68% of all the values lie within one standard deviation of the mean."}
+{"idx": 9, "title": "Royale High (RH) Value Calculator | Win Fair Lose WFL | Traderie", "date": "", "ddg_snippet": "Easily calculate fair trade values for Royale High (RH) items with this calculator on Traderie!", "subpage_snippet": "", "source": "traderie.com", "link": "https://traderie.com/royalehigh/calculator", "content": "Easily calculate fair trade values for Royale High (RH) items with this calculator on Traderie!"}

data/sampled_jsons/2502.00921_appendix_table_MATH_dataset_critical_window_frequency_ΔCW.jsonl

@@ -0,0 +1,10 @@
+{"idx": 0, "title": "[ 2502 . 00921 ] Blink of an eye: a simple theory for feature localization in...", "date": "", "ddg_snippet": "arXiv: 2502 . 00921 (cs). [Submitted on 2 Feb 2025 (v1), last revised 5 Jun 2025 (this version, v2)].Finally, we validate our predictions empirically for LLMs and find that critical windows often coincide with failures in problem solving for various math and reasoning benchmarks.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2502.00921", "content": "arXiv: 2502 . 00921 (cs). [Submitted on 2 Feb 2025 (v1), last revised 5 Jun 2025 (this version, v2)].Finally, we validate our predictions empirically for LLMs and find that critical windows often coincide with failures in problem solving for various math and reasoning benchmarks."}
+{"idx": 1, "title": "Can AI Create a Profitable Python Trading Bot for Stocks? An In-Depth...", "date": "", "ddg_snippet": "The process involves several critical steps: Data Sources: Obtain historical and real-time data . For stocks, APIs like Alpha Vantage, IEX Cloud, Polygon.io, or broker-specific APIs (e.g., Alpaca, Interactive Brokers) are common.", "subpage_snippet": "", "source": "trading-strategies.academy", "link": "https://trading-strategies.academy/archives/1648", "content": "The process involves several critical steps: Data Sources: Obtain historical and real-time data . For stocks, APIs like Alpha Vantage, IEX Cloud, Polygon.io, or broker-specific APIs (e.g., Alpaca, Interactive Brokers) are common."}
+{"idx": 2, "title": "How To Find Mean From Dataset In R - Cocafish", "date": "", "ddg_snippet": "Divide the sum by the number of values in the data set . . How do you find the mean in R groups?In order to calculate the mean from a frequency table : Multiply the number values by the frequencies . Find the totals.", "subpage_snippet": "", "source": "www.cocafish.com", "link": "https://www.cocafish.com/wiki/how-to-find-mean-from-dataset-in-r", "content": "Divide the sum by the number of values in the data set . . How do you find the mean in R groups?In order to calculate the mean from a frequency table : Multiply the number values by the frequencies . Find the totals."}
+{"idx": 3, "title": "Тренировочные варианты ОГЭ 2024-2025-2026 по... — math 100.ru", "date": "", "ddg_snippet": "Тренировочный вариант № 181 ОГЭ УСЛОЖНЁННЫЙ Тренировочный вариант № 180 ОГЭ из заданий банка ФИПИ Тренировочный вариант № 179 ОГЭ УСЛОЖНЁННЫЙ Тренировочный вариант...", "subpage_snippet": "", "source": "math100.ru", "link": "https://math100.ru/trenirovochnie-varianti-oge-new/", "content": "Тренировочный вариант № 181 ОГЭ УСЛОЖНЁННЫЙ Тренировочный вариант № 180 ОГЭ из заданий банка ФИПИ Тренировочный вариант № 179 ОГЭ УСЛОЖНЁННЫЙ Тренировочный вариант..."}
+{"idx": 4, "title": "Blink of an eye: a simple theory for feature localization in generative...", "date": "", "ddg_snippet": "Theory of critical windows in diffusion.We defer an example of a critical window for an autoregressive model which expresses the outputs as emissions from a random walk of an underlying concept variable, akin to the model in [5] , to Appendix C.2.3.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2502.00921v1", "content": "Theory of critical windows in diffusion.We defer an example of a critical window for an autoregressive model which expresses the outputs as emissions from a random walk of an underlying concept variable, akin to the model in [5] , to Appendix C.2.3."}
+{"idx": 5, "title": "(PDF) Blink of an eye: a simple theory for feature localization in...", "date": "", "ddg_snippet": "Appendix A. Theory of critical windows in diffusion. Table 1: Differences between Accuracy (Acc) without versus with critical windows and frequency of. critical windows (CW) when the original generation is wrong versus right.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/388658326_Blink_of_an_eye_a_simple_theory_for_feature_localization_in_generative_models", "content": "Appendix A. Theory of critical windows in diffusion. Table 1: Differences between Accuracy (Acc) without versus with critical windows and frequency of. critical windows (CW) when the original generation is wrong versus right."}
+{"idx": 6, "title": "Open LLM Leaderboard - a Hugging Face Space by...", "date": "", "ddg_snippet": "Compare the performance of open-source Large Language Models using multiple benchmarks like IFEval, BBH, MATH , GPQA, MUSR, and MMLU-PRO. Filter results in real-time and see community votes for comp...", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard", "content": "Compare the performance of open-source Large Language Models using multiple benchmarks like IFEval, BBH, MATH , GPQA, MUSR, and MMLU-PRO. Filter results in real-time and see community votes for comp..."}
+{"idx": 7, "title": "Introducing the AI Spreadsheet Benchmark", "date": "", "ddg_snippet": "Data analysis: summaries, pivots, merging tables , classification tasks. Data manipulation: adding or editing columns, formatting, and managing spreadsheet elements.", "subpage_snippet": "", "source": "rows.com", "link": "https://rows.com/blog/post/ai-spreadsheet-benchmark", "content": "Data analysis: summaries, pivots, merging tables , classification tasks. Data manipulation: adding or editing columns, formatting, and managing spreadsheet elements."}
+{"idx": 8, "title": "Pandas rank() Method: Equivalent to ROW_NUMBER(), RANK...", "date": "", "ddg_snippet": "Gradient Used to Highlight Table Outputs. Example 1: Count of New Sellers Per Day. Create Real Estate Transaction Dataset . Find Rank of Home Close Date by Each Seller.", "subpage_snippet": "", "source": "dfrieds.com", "link": "https://dfrieds.com/data-analysis/rank-method-python-pandas.html", "content": "Gradient Used to Highlight Table Outputs. Example 1: Count of New Sellers Per Day. Create Real Estate Transaction Dataset . Find Rank of Home Close Date by Each Seller."}
+{"idx": 9, "title": "Kahoot! stands with Ukraine", "date": "", "ddg_snippet": "Kahoot! is committed to supporting Ukrainian educators and learners affected by the current crisis. To protect the integrity of our platform and our users, we will suspend offering Kahoot!’s services in Russia, with the exception of self-study.", "subpage_snippet": "", "source": "kahoot.com", "link": "https://kahoot.com/blog/2022/03/18/kahoot-stands-with-ukraine/", "content": "Kahoot! is committed to supporting Ukrainian educators and learners affected by the current crisis. To protect the integrity of our platform and our users, we will suspend offering Kahoot!’s services in Russia, with the exception of self-study."}

data/sampled_jsons/2502.10875_FBox_score_equation.jsonl

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+{"idx": 0, "title": "[2502.10875v1] A Geometric Approach to Personalized Recommendation with ...", "date": "", "ddg_snippet": "Abstract page for arXiv paper 2502.10875v1: A Geometric Approach to Personalized Recommendation with Set-Theoretic Constraints Using Box Embeddings", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2502.10875v1", "content": "Abstract page for arXiv paper 2502.10875v1: A Geometric Approach to Personalized Recommendation with Set-Theoretic Constraints Using Box Embeddings"}
+{"idx": 1, "title": "Massive expansion of Ubiquitination-related gene families within the ...", "date": "", "ddg_snippet": "To gain a broader overview of the occurrence of F-box/ Fbox -like and BTB/POZ domains among other prokaryotes and eukaryotes, we extracted domain abundance data from Pfam (Finn et al. 2013) and included the current counts for the Chlamydiae genomes present in this study (fig. 6).", "subpage_snippet": "", "source": "d.docksci.com", "link": "https://d.docksci.com/massive-expansion-of-ubiquitination-related-gene-families-within-the-chlamydiae_5aa1a386d64ab26db17dd1d7.html", "content": "To gain a broader overview of the occurrence of F-box/ Fbox -like and BTB/POZ domains among other prokaryotes and eukaryotes, we extracted domain abundance data from Pfam (Finn et al. 2013) and included the current counts for the Chlamydiae genomes present in this study (fig. 6)."}
+{"idx": 2, "title": "(PDF) Beyond Gender Parity : Actualization of Benefits Verses Fallacy ...", "date": "", "ddg_snippet": "Therefore, while more girls are attending secondary school than ever before, their mean performance has not improved sufficiently enough. 22 fFigure 3.7: Mean Scores by Grade and Gender in 2013 and 2015 Source: Author calculations using LASI (2014 and 2016) data 23 f4 Actualization of Benefits of Gender Parity at Early Grades: Role of Economic ...", "subpage_snippet": "", "source": "www.academia.edu", "link": "https://www.academia.edu/144045815/Beyond_Gender_Parity_Actualization_of_Benefits_Verses_Fallacy_of_Promises", "content": "Therefore, while more girls are attending secondary school than ever before, their mean performance has not improved sufficiently enough. 22 fFigure 3.7: Mean Scores by Grade and Gender in 2013 and 2015 Source: Author calculations using LASI (2014 and 2016) data 23 f4 Actualization of Benefits of Gender Parity at Early Grades: Role of Economic ..."}
+{"idx": 3, "title": "NOTCH1 S2513 is critical for the regulation of NICD levels impacting ...", "date": "", "ddg_snippet": "Δ Cq values were corrected using the pool of housekeeping genes as a reference. Variances from the technical repeats were propagated to allow calculation of the t -statistic for the mean Δ Cq and P -value determination using only the degrees of freedom from the biological repeats to obtain a conservative estimate of significance (Fig. 4 C).", "subpage_snippet": "", "source": "pmc.ncbi.nlm.nih.gov", "link": "https://pmc.ncbi.nlm.nih.gov/articles/PMC12404203/", "content": "Δ Cq values were corrected using the pool of housekeeping genes as a reference. Variances from the technical repeats were propagated to allow calculation of the t -statistic for the mean Δ Cq and P -value determination using only the degrees of freedom from the biological repeats to obtain a conservative estimate of significance (Fig. 4 C)."}
+{"idx": 4, "title": "DoniaGasmii/MNLP_M3_dpo_dataset · Datasets at Hugging Face", "date": "", "ddg_snippet": "We're on a journey to advance and democratize artificial intelligence through open source and open science.", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/datasets/DoniaGasmii/MNLP_M3_dpo_dataset/viewer", "content": "We're on a journey to advance and democratize artificial intelligence through open source and open science."}
+{"idx": 5, "title": "PDF Error-Bounded Graph Anomaly Loss for GNNs", "date": "", "ddg_snippet": "GCN is a transductive model that requires the calculation of whole graph Laplacian during training. Many inductive GNNs [12, 35, 45, 48, 49, 52] that follow a neighborhood aggregation scheme are proposed in recent years.", "subpage_snippet": "", "source": "tzhao.io", "link": "http://tzhao.io/files/papers/CIKM20_GAL.pdf", "content": "GCN is a transductive model that requires the calculation of whole graph Laplacian during training. Many inductive GNNs [12, 35, 45, 48, 49, 52] that follow a neighborhood aggregation scheme are proposed in recent years."}
+{"idx": 6, "title": "w | PDF - Scribd", "date": "", "ddg_snippet": "formula and predicted the future of fanimation. This essay argues that Atlantis: The Lost Empire is a culturally fsignificant artifact: a bridge between traditional hand-drawn animation fand modern genre storytelling, a commentary on colonialism fand cultural preservation , and a philosophica l meditation on the cost of progress. fThrough its ...", "subpage_snippet": "", "source": "www.scribd.com", "link": "https://www.scribd.com/document/918780359/w", "content": "formula and predicted the future of fanimation. This essay argues that Atlantis: The Lost Empire is a culturally fsignificant artifact: a bridge between traditional hand-drawn animation fand modern genre storytelling, a commentary on colonialism fand cultural preservation , and a philosophica l meditation on the cost of progress. fThrough its ..."}
+{"idx": 7, "title": "Music Goes to War: How Britain, Germany and the USA used Jazz as ...", "date": "", "ddg_snippet": "The thesis will demonstrate that the various uses of jazz music as propaganda in World War II were determined by an evolving relationship between Axis and Allied policies and projects. The limited previous scholarship in the area, however, has been restricted to 'single-country studies' which present only national perspectives with little reference to the broader international context ...", "subpage_snippet": "", "source": "www.academia.edu", "link": "https://www.academia.edu/144068116/Music_Goes_to_War_How_Britain_Germany_and_the_USA_used_Jazz_as_Propaganda_in_World_War_II", "content": "The thesis will demonstrate that the various uses of jazz music as propaganda in World War II were determined by an evolving relationship between Axis and Allied policies and projects. The limited previous scholarship in the area, however, has been restricted to 'single-country studies' which present only national perspectives with little reference to the broader international context ..."}
+{"idx": 8, "title": "PDF A Synergistic Approach for Graph Anomaly Detection With Pattern Mining ...", "date": "", "ddg_snippet": "(i.e., the number of hops of the neighborhood). If the graph data have a large scale, people are interested in learning a great number of node features in an unsupervised manner so that they can be used to train simple classifiers very quickly for any type of anomaly detection tasks when some ad hoc labels become available. So, to train GNN model parameters without node labels, random walk (RW ...", "subpage_snippet": "", "source": "tzhao.io", "link": "http://tzhao.io/files/papers/TNNLS21_pamful.pdf", "content": "(i.e., the number of hops of the neighborhood). If the graph data have a large scale, people are interested in learning a great number of node features in an unsupervised manner so that they can be used to train simple classifiers very quickly for any type of anomaly detection tasks when some ad hoc labels become available. So, to train GNN model parameters without node labels, random walk (RW ..."}
+{"idx": 9, "title": "South Africa's 2024 election: What you need to know", "date": "", "ddg_snippet": "2024 Miami Grand Prix 2024 Odds, Picks, and Predictions: Max Loves South Beach Formula 1 2024 Miami Grand Prix odds, picks, and predictions. F1 betting picks and driver odds at Miami International Autodrome on Sunday, May 5. Read more » Edmunds: 2024 Kia Niro versus 2024 Toyota Corolla Cross Many car shoppers simply want a vehicle that's practical, easy to drive and relatively inexpensive ...", "subpage_snippet": "", "source": "ph.headtopics.com", "link": "https://ph.headtopics.com/news/south-africa-s-2024-election-what-you-need-to-know-52061706", "content": "2024 Miami Grand Prix 2024 Odds, Picks, and Predictions: Max Loves South Beach Formula 1 2024 Miami Grand Prix odds, picks, and predictions. F1 betting picks and driver odds at Miami International Autodrome on Sunday, May 5. Read more » Edmunds: 2024 Kia Niro versus 2024 Toyota Corolla Cross Many car shoppers simply want a vehicle that's practical, easy to drive and relatively inexpensive ..."}

data/sampled_jsons/2502.10875_arxiv_Table_1_dataset_statistics_train_interactions_density.jsonl

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+{"idx": 0, "title": "[ 2502 . 10875 ] A Geometric Approach to Personalized Recommendation...", "date": "", "ddg_snippet": "Computer Science > Information Retrieval. arXiv : 2502 . 10875 (cs).Abstract:Personalized item recommendation typically suffers from data sparsity, which is most often addressed by learning vector representations of users and items via low-rank matrix factorization.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2502.10875", "content": "Computer Science > Information Retrieval. arXiv : 2502 . 10875 (cs).Abstract:Personalized item recommendation typically suffers from data sparsity, which is most often addressed by learning vector representations of users and items via low-rank matrix factorization."}
+{"idx": 1, "title": "Find Open Datasets and Machine Learning Projects | Kaggle", "date": "", "ddg_snippet": "Download Open Datasets on 1000s of Projects + Share Projects on One Platform. Explore Popular Topics Like Government, Sports, Medicine, Fintech, Food, More.", "subpage_snippet": "", "source": "www.kaggle.com", "link": "https://www.kaggle.com/datasets", "content": "Download Open Datasets on 1000s of Projects + Share Projects on One Platform. Explore Popular Topics Like Government, Sports, Medicine, Fintech, Food, More."}
+{"idx": 2, "title": "UCI Machine Learning Repository | Discover datasets around the world!", "date": "", "ddg_snippet": "The data set contains 3 classes of 50 instances each, where each class refers to a type of iris plant. One class is linearly separable from the other 2; the latter are not linearly separable from each other.Variables Table .", "subpage_snippet": "", "source": "archive.ics.uci.edu", "link": "https://archive.ics.uci.edu/dataset/53/iris", "content": "The data set contains 3 classes of 50 instances each, where each class refers to a type of iris plant. One class is linearly separable from the other 2; the latter are not linearly separable from each other.Variables Table ."}
+{"idx": 3, "title": "yandex/yambda · Datasets at Hugging Face", "date": "", "ddg_snippet": "About Dataset . Statistics . User History Length Distribution. Item Interaction Count. Data Format.", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/datasets/yandex/yambda", "content": "About Dataset . Statistics . User History Length Distribution. Item Interaction Count. Data Format."}
+{"idx": 4, "title": "Free Public Datasets for Data Science Projects", "date": "", "ddg_snippet": "In this post we can find free public datasets for Data Science projects. There is a big number of datasets which cover different areas - machine learning, presentation, data analysis and visualization.", "subpage_snippet": "", "source": "datascientyst.com", "link": "https://datascientyst.com/datasets/", "content": "In this post we can find free public datasets for Data Science projects. There is a big number of datasets which cover different areas - machine learning, presentation, data analysis and visualization."}
+{"idx": 5, "title": "Обзоры препринтов научных статей «astro-ph/ arxiv .org» за... / Хабр", "date": "", "ddg_snippet": "Выборка интересных публикаций в области астрономии, астрофизики и физики с сайта препринтов arxiv .org. Публикуется с разрешения Сергея Борисовича и указанием ссылок на первоисточники.", "subpage_snippet": "", "source": "habr.com", "link": "https://habr.com/ru/articles/948996/", "content": "Выборка интересных публикаций в области астрономии, астрофизики и физики с сайта препринтов arxiv .org. Публикуется с разрешения Сергея Борисовича и указанием ссылок на первоисточники."}
+{"idx": 6, "title": "World Population Density Interactive Map", "date": "", "ddg_snippet": "World Population Density Map Summary Preview Image. Interactive Statistics . The \" Interactive Stats \" checkbox at the top left of the map turns on density statistics for countries and cities which have been calculated from the GHSL data ( 1 km scale).", "subpage_snippet": "", "source": "luminocity3d.org", "link": "https://luminocity3d.org/WorldPopDen/", "content": "World Population Density Map Summary Preview Image. Interactive Statistics . The \" Interactive Stats \" checkbox at the top left of the map turns on density statistics for countries and cities which have been calculated from the GHSL data ( 1 km scale)."}
+{"idx": 7, "title": "(PDF) A Geometric Approach to Personalized Recommendation with...", "date": "", "ddg_snippet": "Table 1 : Dataset Statistics , the Item-User interaction DU& the Item-Attribute interaction DA. The Train /Test split is created using algorithm 1to test set -theoretic generalization.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/389091382_A_Geometric_Approach_to_Personalized_Recommendation_with_Set-Theoretic_Constraints_Using_Box_Embeddings", "content": "Table 1 : Dataset Statistics , the Item-User interaction DU& the Item-Attribute interaction DA. The Train /Test split is created using algorithm 1to test set -theoretic generalization."}
+{"idx": 8, "title": "Fisch - Secret Cave: How to Unlock the Luminescent and Crimson...", "date": "", "ddg_snippet": "Our complete guide to unlocking the new secret caves in the latest Fisch update. Learn how to use your Keystones, complete the Bestiary, get the Crimson Mutation, and access the new best rod in the game. Table of Contents.", "subpage_snippet": "", "source": "trioner.com", "link": "https://trioner.com/fisch-secret-cave-how-to-unlock-the-luminescent-and-crimson-caverns/", "content": "Our complete guide to unlocking the new secret caves in the latest Fisch update. Learn how to use your Keystones, complete the Bestiary, get the Crimson Mutation, and access the new best rod in the game. Table of Contents."}
+{"idx": 9, "title": "IMTS", "date": "", "ddg_snippet": "View data . Download. The International trade in goods by partner country dataset (formerly Direction of Trade Statistics (DOTS)) includes goods (merchandise) export and import statistics disaggregated according to a country's trading partners.", "subpage_snippet": "", "source": "data.imf.org", "link": "https://data.imf.org/en/datasets/IMF.STA:IMTS", "content": "View data . Download. The International trade in goods by partner country dataset (formerly Direction of Trade Statistics (DOTS)) includes goods (merchandise) export and import statistics disaggregated according to a country's trading partners."}

data/sampled_jsons/26JsumCG0z_The_Value_of_Prediction_in_Identifying_the_Worst-Off.jsonl

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+{"idx": 0, "title": "Positive and negative predictive values - Wikipedia", "date": "", "ddg_snippet": "Positive and negative predictive values . Positive and negative predictive values - 2. The positive and negative predictive values are the proportions of positive and negative results in statistics and diagnostic tests that are true positive and true ...", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/Positive_and_negative_predictive_values", "content": "Positive and negative predictive values . Positive and negative predictive values - 2. The positive and negative predictive values are the proportions of positive and negative results in statistics and diagnostic tests that are true positive and true ..."}
+{"idx": 1, "title": "The Value of Prediction in Identifying the Worst - Off", "date": "", "ddg_snippet": "value as the probability that the worst - off individuals are successfully identified :, i.e. V (α, β) = Pr[Yˆ FYˆ−1(α) | Y FY−1(β)]. In practice, this can be measured using a recall-like metric, capturing the proportion of truly at-risk. individuals ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=26JsumCG0z", "content": "value as the probability that the worst - off individuals are successfully identified :, i.e. V (α, β) = Pr[Yˆ FYˆ−1(α) | Y FY−1(β)]. In practice, this can be measured using a recall-like metric, capturing the proportion of truly at-risk. individuals ..."}
+{"idx": 2, "title": "(PDF) The Value of Prediction in Identifying the Worst - Off", "date": "", "ddg_snippet": "the needs of the worst - off . For instance, in 2012, Wisconsin launched a risk prediction system.Specifically, we identify when improving prediction provides. a higher marginal benefit in helping an institution identify the worst - off . This is compared.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/388634300_The_Value_of_Prediction_in_Identifying_the_Worst-Off", "content": "the needs of the worst - off . For instance, in 2012, Wisconsin launched a risk prediction system.Specifically, we identify when improving prediction provides. a higher marginal benefit in helping an institution identify the worst - off . This is compared."}
+{"idx": 3, "title": "[ICML 2025] The Value of Prediction in Identifying the Worst - Off", "date": "", "ddg_snippet": "The cornerstone of the paper's methodology is the Prediction -Access Ratio (PAR), a metric designed to quantify the trade- off between two key policy levers: Improving Predictions : Enhancing a model's predictive power (measured by R²).", "subpage_snippet": "", "source": "arxiviq.substack.com", "link": "https://arxiviq.substack.com/p/icml-2025-the-value-of-prediction", "content": "The cornerstone of the paper's methodology is the Prediction -Access Ratio (PAR), a metric designed to quantify the trade- off between two key policy levers: Improving Predictions : Enhancing a model's predictive power (measured by R²)."}
+{"idx": 4, "title": "[Paper Note] The Value of Prediction in Identifying the Worst - Off ...", "date": "", "ddg_snippet": "This paper examines the welfare impacts of prediction in equity-driven contexts, and how they compare to other policy levers, such as expanding bureaucratic capacity.", "subpage_snippet": "", "source": "githubissues.com", "link": "https://githubissues.com/AkihikoWatanabe/paper_notes/2220", "content": "This paper examines the welfare impacts of prediction in equity-driven contexts, and how they compare to other policy levers, such as expanding bureaucratic capacity."}
+{"idx": 5, "title": "The Value of Prediction in Identifying the Worst - Off | AI Research...", "date": "", "ddg_snippet": "OverviewMathematical framework for measuring prediction value in welfare programsFocus on identifying and helping society's most disadvantaged groups", "subpage_snippet": "", "source": "www.aimodels.fyi", "link": "https://www.aimodels.fyi/papers/arxiv/value-prediction-identifying-worst-off", "content": "OverviewMathematical framework for measuring prediction value in welfare programsFocus on identifying and helping society's most disadvantaged groups"}
+{"idx": 6, "title": "The value of prediction in identifying the worst - off : Interview with...", "date": "", "ddg_snippet": "The red line marks the 12 month threshold used to classify a jobseeking episode as long-term unemployment in Germany. In the paper, you detail a case study identifying long-term unemployment in Germany. What were some of the main findings from this case study?", "subpage_snippet": "", "source": "aihub.org", "link": "https://aihub.org/2025/08/27/the-value-of-prediction-in-identifying-the-worst-off-interview-with-unai-fischer-abaigar/", "content": "The red line marks the 12 month threshold used to classify a jobseeking episode as long-term unemployment in Germany. In the paper, you detail a case study identifying long-term unemployment in Germany. What were some of the main findings from this case study?"}
+{"idx": 7, "title": "The Value of Prediction in Identifying the Worst - Off Authors: Unai...", "date": "", "ddg_snippet": "Краеугольным камнем методологии статьи является коэффициент «предсказание-доступ» ( Prediction -Access Ratio, PAR) — метрика, предназначенная для количественной оценки компромисса между двумя ключевыми инструментами политики: 1. Улучшение...", "subpage_snippet": "", "source": "vk.com", "link": "https://vk.com/wall49591166_67792", "content": "Краеугольным камнем методологии статьи является коэффициент «предсказание-доступ» ( Prediction -Access Ratio, PAR) — метрика, предназначенная для количественной оценки компромисса между двумя ключевыми инструментами политики: 1. Улучшение..."}
+{"idx": 8, "title": "Helping the Worst - Off : When Hiring More Case Workers... | Medium", "date": "", "ddg_snippet": "The findings, detailed in “ The Value of Prediction in Identifying the Worst - Off ,” challenge the current policy focus on perfecting prediction accuracy.", "subpage_snippet": "", "source": "nyudatascience.medium.com", "link": "https://nyudatascience.medium.com/helping-the-worst-off-when-hiring-more-case-workers-beats-building-better-ai-12e6f968de0b", "content": "The findings, detailed in “ The Value of Prediction in Identifying the Worst - Off ,” challenge the current policy focus on perfecting prediction accuracy."}
+{"idx": 9, "title": "EA Sports FC 26 Tactics Codes for the best formations | VG247", "date": "", "ddg_snippet": "Use these Custom Tactics Codes to unlock some of the best meta formations in FC 26.", "subpage_snippet": "", "source": "www.vg247.com", "link": "https://www.vg247.com/ea-sports-fc-26-tactics-codes-best-formations", "content": "Use these Custom Tactics Codes to unlock some of the best meta formations in FC 26."}

data/sampled_jsons/27tMzmzDjO_A_Table_1_dataset_statistics_user-item_interactions_density.jsonl

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+{"idx": 0, "title": "Statistics of the datasets Dataset 1 # Users # Items # Interactions Density", "date": "", "ddg_snippet": "Download scientific diagram | Statistics of the datasets Dataset 1 # Users # Items # Interactions Density from publication: Latent Structures Mining with Contrastive Modality Fusion for Multimedia ...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/figure/Statistics-of-the-datasets-Dataset-1-Users-Items-Interactions-Density_tbl1_355841189", "content": "Download scientific diagram | Statistics of the datasets Dataset 1 # Users # Items # Interactions Density from publication: Latent Structures Mining with Contrastive Modality Fusion for Multimedia ..."}
+{"idx": 1, "title": "Preparing item interaction data for training - Amazon Personalize", "date": "", "ddg_snippet": "An item interaction is a positive interaction event between a user and an item in your catalogue. For example, a user watching a movie, viewing a listing, or purchasing a pair of shoes. You import data about your users' interactions with your items into a Item interactions dataset .", "subpage_snippet": "", "source": "docs.aws.amazon.com", "link": "https://docs.aws.amazon.com/personalize/latest/dg/interactions-datasets.html", "content": "An item interaction is a positive interaction event between a user and an item in your catalogue. For example, a user watching a movie, viewing a listing, or purchasing a pair of shoes. You import data about your users' interactions with your items into a Item interactions dataset ."}
+{"idx": 2, "title": "Item interactions dataset schema requirements (custom)", "date": "", "ddg_snippet": "An Item interactions dataset stores historical and real-time data from interactions between users and items in your catalog. For information on the types of interactions data Amazon Personalize can use, see Item interaction data. The data you provide for each interaction must match your schema.", "subpage_snippet": "", "source": "docs.aws.amazon.com", "link": "https://docs.aws.amazon.com/personalize/latest/dg/interactions-dataset-requirements.html", "content": "An Item interactions dataset stores historical and real-time data from interactions between users and items in your catalog. For information on the types of interactions data Amazon Personalize can use, see Item interaction data. The data you provide for each interaction must match your schema."}
+{"idx": 3, "title": "KuaiRec | A Fully-observed Dataset for Recommender Systems (Density ...", "date": "", "ddg_snippet": "KuaiRec A Fully-observed Dataset for Recommender Systems ( Density : Almost 100%) View on GitHub KuaiRec is a real-world dataset collected from the recommendation logs of the video-sharing mobile app Kuaishou. For now, it is the first dataset that contains a fully observed user-item interaction matrix.", "subpage_snippet": "", "source": "kuairec.com", "link": "https://kuairec.com/", "content": "KuaiRec A Fully-observed Dataset for Recommender Systems ( Density : Almost 100%) View on GitHub KuaiRec is a real-world dataset collected from the recommendation logs of the video-sharing mobile app Kuaishou. For now, it is the first dataset that contains a fully observed user-item interaction matrix."}
+{"idx": 4, "title": "Find Open Datasets and Machine Learning Projects | Kaggle", "date": "", "ddg_snippet": "Download Open Datasets on 1000s of Projects + Share Projects on One Platform. Explore Popular Topics Like Government, Sports, Medicine, Fintech, Food, More. Flexible Data Ingestion.", "subpage_snippet": "", "source": "www.kaggle.com", "link": "https://www.kaggle.com/datasets", "content": "Download Open Datasets on 1000s of Projects + Share Projects on One Platform. Explore Popular Topics Like Government, Sports, Medicine, Fintech, Food, More. Flexible Data Ingestion."}
+{"idx": 5, "title": "Table 1 : Statistics of the three datasets. The first row of each...", "date": "", "ddg_snippet": "The first row of each dataset corresponds to the numbers of users , items and interactions , while the other rows correspond to the statistics of other relations. ...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/figure/Statistics-of-the-three-datasets-The-first-row-of-each-dataset-corresponds-to-the_tbl1_325964999", "content": "The first row of each dataset corresponds to the numbers of users , items and interactions , while the other rows correspond to the statistics of other relations. ..."}
+{"idx": 6, "title": "Statistics of datasets. Dataset #Interactions #Items #User Mean ...", "date": "", "ddg_snippet": "The two Amazon datasets are sparse and have fewer interactions per user and per item , while MovieLens-1M and Netflix-2M are more dense due to fewer items . ... View in full-text", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/figure/Statistics-of-datasets-Dataset-Interactions-Items-User-Mean-Interactions-user-Mean_tbl1_351685598", "content": "The two Amazon datasets are sparse and have fewer interactions per user and per item , while MovieLens-1M and Netflix-2M are more dense due to fewer items . ... View in full-text"}
+{"idx": 7, "title": "Statistics of the dataset | Download Table - ResearchGate", "date": "", "ddg_snippet": "Download Table | Statistics of the dataset from publication: Explicit Feedbacks Meet with Implicit Feedbacks : A Combined Approach for Recommendation System | Recommender systems recommend items ...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/figure/Statistics-of-the-dataset_tbl1_328451127", "content": "Download Table | Statistics of the dataset from publication: Explicit Feedbacks Meet with Implicit Feedbacks : A Combined Approach for Recommendation System | Recommender systems recommend items ..."}
+{"idx": 8, "title": "KuaiRec: A Fully-observed Dataset and Insights for Evaluating ...", "date": "", "ddg_snippet": "Note that all user-item interactions in the small matrix are excluded from the big matrix to separate the training and evaluation data. Table 1 lists the statistics of KuaiRec. Besides the user-item interaction , we further collect the side information of users and items .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2202.10842", "content": "Note that all user-item interactions in the small matrix are excluded from the big matrix to separate the training and evaluation data. Table 1 lists the statistics of KuaiRec. Besides the user-item interaction , we further collect the side information of users and items ."}
+{"idx": 9, "title": "Data.gov Home - Data.gov", "date": "", "ddg_snippet": "Here you will find data, tools, and resources to conduct research, develop web and mobile applications, design data visualizations, and more.", "subpage_snippet": "", "source": "data.gov", "link": "https://data.gov/", "content": "Here you will find data, tools, and resources to conduct research, develop web and mobile applications, design data visualizations, and more."}

data/sampled_jsons/2uheUFcFsM_Normalizing_Flows_are_Capable_Generative_Models_Equation_6_training_loss.jsonl

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+{"idx": 0, "title": "[2412.06329] Normalizing Flows are Capable Generative Models", "date": "", "ddg_snippet": "Normalizing Flows (NFs) are likelihood-based models for continuous inputs. They have demonstrated promising results on both density estimation and generative modeling tasks, but have received relatively little attention in recent years. In this work, we demonstrate that NFs are more powerful than previously believed. We present TarFlow: a simple and scalable architecture that enables highly ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2412.06329", "content": "Normalizing Flows (NFs) are likelihood-based models for continuous inputs. They have demonstrated promising results on both density estimation and generative modeling tasks, but have received relatively little attention in recent years. In this work, we demonstrate that NFs are more powerful than previously believed. We present TarFlow: a simple and scalable architecture that enables highly ..."}
+{"idx": 1, "title": "Normalizing Flows are Capable Generative Models", "date": "", "ddg_snippet": "The second reason is the presence of a generative method called Normalizing Flows (NF for short). Like GANs and VAEs, this method lost—by knockout—to diffusion models in the battle for dominance in generative modeling. Still, it's a very interesting approach with a precise mathematical formulation and an intuitive core.", "subpage_snippet": "", "source": "aiwithmike.substack.com", "link": "https://aiwithmike.substack.com/p/normalizing-flows-are-capable-generative", "content": "The second reason is the presence of a generative method called Normalizing Flows (NF for short). Like GANs and VAEs, this method lost—by knockout—to diffusion models in the battle for dominance in generative modeling. Still, it's a very interesting approach with a precise mathematical formulation and an intuitive core."}
+{"idx": 2, "title": "Generative Models 3 - Normalizing Flows | Roy Friedman", "date": "", "ddg_snippet": "Problems with Normalizing and Continuous Flows Normalizing flows are a popular class of explicit likelihood generative models . Because the likelihood is baked into the whole definition of normalizing flows , that means that you don't need to approximate it during inference like VAEs or the models in the next few posts.", "subpage_snippet": "", "source": "friedmanroy.github.io", "link": "https://friedmanroy.github.io/blog/2024/gen3/", "content": "Problems with Normalizing and Continuous Flows Normalizing flows are a popular class of explicit likelihood generative models . Because the likelihood is baked into the whole definition of normalizing flows , that means that you don't need to approximate it during inference like VAEs or the models in the next few posts."}
+{"idx": 3, "title": "PDF Generative Models: Normalizing flows and Diffusion Models", "date": "", "ddg_snippet": "Reverse denoising process ( generative ) Sohl-Dickstein et al., Deep Unsupervised Learning using Nonequilibrium Thermodynamics, ICML 2015 Ho et al., Denoising Diffusion Probabilistic Models , NeurIPS 2020 Song et al., Score-Based Generative Modeling through Stochastic Differential Equations , ICLR 2021", "subpage_snippet": "", "source": "cqf.io", "link": "https://cqf.io/EESM5900V/lectures/Lecture17.pdf", "content": "Reverse denoising process ( generative ) Sohl-Dickstein et al., Deep Unsupervised Learning using Nonequilibrium Thermodynamics, ICML 2015 Ho et al., Denoising Diffusion Probabilistic Models , NeurIPS 2020 Song et al., Score-Based Generative Modeling through Stochastic Differential Equations , ICLR 2021"}
+{"idx": 4, "title": "Deep Learning Part 6: Generative Modelling through Normalizing Flows ...", "date": "", "ddg_snippet": "Since a few days ago, I have been learning about normalizing flows . It belongs to the renowned class of generative models used in deep learning and machine learning. As its name implies ...", "subpage_snippet": "", "source": "medium.com", "link": "https://medium.com/@tejpal.abhyuday/deep-learning-part-6-generative-modelling-through-normalizing-flows-c79fffc90091", "content": "Since a few days ago, I have been learning about normalizing flows . It belongs to the renowned class of generative models used in deep learning and machine learning. As its name implies ..."}
+{"idx": 5, "title": "Normalizing Flows are Capable Generative Models - OpenReview", "date": "", "ddg_snippet": "Normalizing Flows (NFs) are likelihood-based models for continuous inputs. They have demonstrated promising results on both density estimation and generative modeling tasks, but have received relatively little attention in recent years.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=2uheUFcFsM", "content": "Normalizing Flows (NFs) are likelihood-based models for continuous inputs. They have demonstrated promising results on both density estimation and generative modeling tasks, but have received relatively little attention in recent years."}
+{"idx": 6, "title": "Normalizing Flows are Capable Generative Models - Apple Machine ...", "date": "", "ddg_snippet": "Normalizing Flows (NFs) are likelihood-based models for continuous inputs. They have demonstrated promising results on both density estimation and generative modeling tasks, but have received relatively little attention in recent years.", "subpage_snippet": "", "source": "machinelearning.apple.com", "link": "https://machinelearning.apple.com/research/normalizing-flows", "content": "Normalizing Flows (NFs) are likelihood-based models for continuous inputs. They have demonstrated promising results on both density estimation and generative modeling tasks, but have received relatively little attention in recent years."}
+{"idx": 7, "title": "Normalizing Flows in PyTorch for Generative Models", "date": "", "ddg_snippet": "This might limit the expressiveness compared to less constrained models , although complex flows can still model intricate distributions. Computational Cost: Calculating Jacobian determinants, even when tractable, can add computational overhead during training , especially for high-dimensional data or deep flows .", "subpage_snippet": "", "source": "apxml.com", "link": "https://apxml.com/courses/advanced-pytorch/chapter-2-advanced-network-architectures/normalizing-flows", "content": "This might limit the expressiveness compared to less constrained models , although complex flows can still model intricate distributions. Computational Cost: Calculating Jacobian determinants, even when tractable, can add computational overhead during training , especially for high-dimensional data or deep flows ."}
+{"idx": 8, "title": "Normalizing Flows are Capable Generative Models - arXiv.org", "date": "", "ddg_snippet": "Abstract Normalizing Flows (NFs) are likelihood-based models for continuous inputs. They have demonstrated promising results on both density estimation and generative modeling tasks, but have received relatively little attention in recent years. In this work, we demonstrate that NFs are more powerful than previously believed.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2412.06329v3", "content": "Abstract Normalizing Flows (NFs) are likelihood-based models for continuous inputs. They have demonstrated promising results on both density estimation and generative modeling tasks, but have received relatively little attention in recent years. In this work, we demonstrate that NFs are more powerful than previously believed."}
+{"idx": 9, "title": "ICML Poster Normalizing Flows are Capable Generative Models", "date": "", "ddg_snippet": "Normalizing Flows are Capable Generative Models Shuangfei Zhai · Ruixiang Zhang · Preetum Nakkiran · David Berthelot · Jiatao Gu · Huangjie Zheng · Tianrong Chen · Miguel Angel Bautista Martin · Navdeep Jaitly · Joshua M Susskind", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/poster/46564", "content": "Normalizing Flows are Capable Generative Models Shuangfei Zhai · Ruixiang Zhang · Preetum Nakkiran · David Berthelot · Jiatao Gu · Huangjie Zheng · Tianrong Chen · Miguel Angel Bautista Martin · Navdeep Jaitly · Joshua M Susskind"}

data/sampled_jsons/33113_Deterministic-to-Stochastic_Diverse_Latent_Feature_Mapping_Algorithm_1_D_Drift.jsonl

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+{"idx": 0, "title": "Stochastic - Wikipedia", "date": "", "ddg_snippet": "Stochastic is the property of being well-described by a random probability distribution. Stochasticity and randomness are technically distinct concepts: the former refers to a modeling approach, while the latter describes phenomena; in everyday conve...", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/Stochastic", "content": "Stochastic is the property of being well-described by a random probability distribution. Stochasticity and randomness are technically distinct concepts: the former refers to a modeling approach, while the latter describes phenomena; in everyday conve..."}
+{"idx": 1, "title": "CVPR Poster Deterministic - to - Stochastic Diverse Latent Feature ...", "date": "", "ddg_snippet": "Deterministic - to - Stochastic Diverse Latent Feature Mapping for Human Motion Synthesis.This stage is achieved by the designed deterministic feature mapping procedure with DerODE and stochastic diverse output generation procedure with DivSDE.", "subpage_snippet": "", "source": "cvpr.thecvf.com", "link": "https://cvpr.thecvf.com/virtual/2025/poster/33113", "content": "Deterministic - to - Stochastic Diverse Latent Feature Mapping for Human Motion Synthesis.This stage is achieved by the designed deterministic feature mapping procedure with DerODE and stochastic diverse output generation procedure with DivSDE."}
+{"idx": 2, "title": "Deterministic - to - Stochastic Diverse Latent Feature Mapping for...", "date": "", "ddg_snippet": "In this paper, we propose a Deterministic - to - Stochastic Diverse Latent Feature Mapping (DSDFM) method for human motion synthesis. DSDFM consists of two stages. The first human motion reconstruction stage aims to learn the latent space distribution of human motions.", "subpage_snippet": "", "source": "synthical.com", "link": "https://synthical.com/article/Deterministic-to-Stochastic-Diverse-Latent-Feature-Mapping-for-Human-Motion-Synthesis-218660a3-37a0-490e-94f1-73766d3ec529", "content": "In this paper, we propose a Deterministic - to - Stochastic Diverse Latent Feature Mapping (DSDFM) method for human motion synthesis. DSDFM consists of two stages. The first human motion reconstruction stage aims to learn the latent space distribution of human motions."}
+{"idx": 3, "title": "Deterministic - to - Stochastic Diverse Latent Feature Mapping for...", "date": "", "ddg_snippet": "In this paper, we propose a Deterministic - to - Stochastic Diverse Latent Feature Mapping (DSDFM) method for human motion synthesis. DSDFM consists of two stages. The first human motion reconstruction stage aims to learn the latent space distribution of human motions.", "subpage_snippet": "", "source": "www.alphaxiv.org", "link": "https://www.alphaxiv.org/abs/2505.00998", "content": "In this paper, we propose a Deterministic - to - Stochastic Diverse Latent Feature Mapping (DSDFM) method for human motion synthesis. DSDFM consists of two stages. The first human motion reconstruction stage aims to learn the latent space distribution of human motions."}
+{"idx": 4, "title": "Deterministic - to - Stochastic Diverse Latent Feature Mapping for...", "date": "", "ddg_snippet": "The first human motion reconstruction stage aims to learn the latent space distribution of human motions.This stage is achieved by the designed deterministic feature mapping procedure with DerODE and stochastic diverse output generation procedure with DivSDE.", "subpage_snippet": "", "source": "papers.cool", "link": "https://papers.cool/venue/Hua_Deterministic-to-Stochastic_Diverse_Latent_Feature_Mapping_for_Human_Motion_Synthesis@CVPR2025@CVF", "content": "The first human motion reconstruction stage aims to learn the latent space distribution of human motions.This stage is achieved by the designed deterministic feature mapping procedure with DerODE and stochastic diverse output generation procedure with DivSDE."}
+{"idx": 5, "title": "DivDiff: A Conditional Diffusion Model for Diverse Human Motion...", "date": "", "ddg_snippet": "Deterministic - to - Stochastic Diverse Latent Feature Mapping for Human Motion Synthesis.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/387413105_DivDiff_A_Conditional_Diffusion_Model_for_Diverse_Human_Motion_Prediction", "content": "Deterministic - to - Stochastic Diverse Latent Feature Mapping for Human Motion Synthesis."}
+{"idx": 6, "title": "Deterministic vs stochastic trends - YouTube", "date": "", "ddg_snippet": "This video explains the difference between stochastic and deterministic trends.", "subpage_snippet": "", "source": "www.youtube.com", "link": "https://www.youtube.com/watch?v=yCM6N8sRtPY", "content": "This video explains the difference between stochastic and deterministic trends."}
+{"idx": 7, "title": "S 1 Appendix Algorithmic details of the stochastic reaction-diffusion...", "date": "", "ddg_snippet": "In addition, unlike most procedures for numerically solving the deterministic reaction-rate equations, this algorithm never approximates infinitesimal time increments dt by finite time steps At.", "subpage_snippet": "", "source": "storage.googleapis.com", "link": "https://storage.googleapis.com/plos-corpus-prod/10.1371/journal.pone.0219055/1/pone.0219055.s001.docx?X-Goog-Algorithm=GOOG4-RSA-SHA256&X-Goog-Credential=wombat-sa@plos-prod.iam.gserviceaccount.com/20250918/auto/storage/goog4_request&X-Goog-Date=20250918T201808Z&X-Goog-Expires=86400&X-Goog-SignedHeaders=host&X-Goog-Signature=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", "content": "In addition, unlike most procedures for numerically solving the deterministic reaction-rate equations, this algorithm never approximates infinitesimal time increments dt by finite time steps At."}
+{"idx": 8, "title": "Deterministic-to-Stochastic Diverse Latent Feature Mapping ... Deterministic-to-Stochastic Diverse Latent Feature Mapping ... Foruck/Awesome-Human-Motion - GitHub Deterministic-to-Stochastic Diverse Latent Feature Mapping ... arXiv:2505.00998v1 [cs.CV] 2 May 2025 Appendix - CVF Open Access Harmonizing Stochasticity and Determinism: Scene-responsive ...", "date": "", "ddg_snippet": "May 2, 2025 · In this paper, we propose a Deterministic -to- Stochastic Diverse Latent Feature Mapping (DSDFM) method for human motion synthesis. DSDFM consists of two stages. The first human motion reconstruction stage aims to learn the latent space distribution of human motions. This stage is achieved by the designed deterministic feature mapping procedure with DerODE and stochastic diverse output generation procedure with DivSDE. DSDFM is easy to train compared to previous SGMs-based methods and can enhance diversity without introducing additional training parameters. (CVPR 2025) DSDFM: Deterministic -to- Stochastic Diverse Latent Feature Mapping for Human Motion Synthesis, Hua et al. (CVPR 2025) EchoMimicV2: Towards Striking, Simplified, and Semi-Body Human Animation, Hua et al. (CVPR 2025) UniPose: A Unified Multimodal Framework for Human Pose Comprehension, Generation and Editing, Li et al. Human motion synthesis aims to generate plausible human motion sequences, which has raised widespread attention in computer animation. Recent score-based generative models (SGMs) have demonstrated impressive results on this task. However, their training process involves complex curvature trajectories, leading to unstable training process. In this paper, we propose a Deterministic -to- Stochastic ... rministic feature map-ping procedure. The deterministic feature mapping proce-dure is designed to model the relationship between Gaussian distribution p(Zt= 1 ) and the latent distributio B. Proof of Proposition 2 position 2. Given the stochastic differential equations dzt = f(zt, t)dt+g(t)dwt with the drift and diffusion terms, with the initial data sample z0 and the noise level η, the prob-ability of data distribution zt is p(xt) = N(( 1 −t)zt, η2t2I) at the time step t when p(z0) = N(z0 Sep 25, 2024 · On top of that, DiMoP3D identifies deterministic factors in the scene and integrates them into the stochastic modeling, making the diverse HMP in realistic scenes become a controllable stochastic generation process.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2505.00998", "content": "May 2, 2025 · In this paper, we propose a Deterministic -to- Stochastic Diverse Latent Feature Mapping (DSDFM) method for human motion synthesis. DSDFM consists of two stages. The first human motion reconstruction stage aims to learn the latent space distribution of human motions. This stage is achieved by the designed deterministic feature mapping procedure with DerODE and stochastic diverse output generation procedure with DivSDE. DSDFM is easy to train compared to previous SGMs-based methods and can enhance diversity without introducing additional training parameters. (CVPR 2025) DSDFM: Deterministic -to- Stochastic Diverse Latent Feature Mapping for Human Motion Synthesis, Hua et al. (CVPR 2025) EchoMimicV2: Towards Striking, Simplified, and Semi-Body Human Animation, Hua et al. (CVPR 2025) UniPose: A Unified Multimodal Framework for Human Pose Comprehension, Generation and Editing, Li et al. Human motion synthesis aims to generate plausible human motion sequences, which has raised widespread attention in computer animation. Recent score-based generative models (SGMs) have demonstrated impressive results on this task. However, their training process involves complex curvature trajectories, leading to unstable training process. In this paper, we propose a Deterministic -to- Stochastic ... rministic feature map-ping procedure. The deterministic feature mapping proce-dure is designed to model the relationship between Gaussian distribution p(Zt= 1 ) and the latent distributio B. Proof of Proposition 2 position 2. Given the stochastic differential equations dzt = f(zt, t)dt+g(t)dwt with the drift and diffusion terms, with the initial data sample z0 and the noise level η, the prob-ability of data distribution zt is p(xt) = N(( 1 −t)zt, η2t2I) at the time step t when p(z0) = N(z0 Sep 25, 2024 · On top of that, DiMoP3D identifies deterministic factors in the scene and integrates them into the stochastic modeling, making the diverse HMP in realistic scenes become a controllable stochastic generation process."}
+{"idx": 9, "title": "Deterministic-to-Stochastic Diverse Latent Feature Mapping ...", "date": "", "ddg_snippet": "This stage is achieved by the designed deterministic feature mapping procedure with DerODE and stochastic diverse output generation procedure with DivSDE. DSDFM is easy to train compared to previous SGMs-based methods and can enhance diversity without introducing additional training parameters.", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2025/papers/Hua_Deterministic-to-Stochastic_Diverse_Latent_Feature_Mapping_for_Human_Motion_Synthesis_CVPR_2025_paper.pdf", "content": "This stage is achieved by the designed deterministic feature mapping procedure with DerODE and stochastic diverse output generation procedure with DivSDE. DSDFM is easy to train compared to previous SGMs-based methods and can enhance diversity without introducing additional training parameters."}

data/sampled_jsons/33113_Deterministic-to-Stochastic_Diverse_Latent_Feature_Mapping_Human_Motion_Synthesis_Algorithm_1__year_2024.jsonl

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+{"idx": 0, "title": "Stochastic - Wikipedia", "date": "", "ddg_snippet": "Stochastic is the property of being well-described by a random probability distribution. Stochasticity and randomness are technically distinct concepts: the former refers to a modeling approach, while the latter describes phenomena; in everyday conve...", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/Stochastic", "content": "Stochastic is the property of being well-described by a random probability distribution. Stochasticity and randomness are technically distinct concepts: the former refers to a modeling approach, while the latter describes phenomena; in everyday conve..."}
+{"idx": 1, "title": "Deterministic-to-Stochastic Diverse Latent Feature Mapping ... Foruck/Awesome-Human-Motion - GitHub Appendix - CVF Open Access Towards Efficient and Diverse Generative Model for ... Weiming Liu - CatalyzeX arXiv:2505.00998v1 [cs.CV] 2 May 2025 Harmonizing Stochasticity and Determinism: Scene-responsive ...", "date": "", "ddg_snippet": "May 2, 2025 · In this paper, we propose a Deterministic - to - Stochastic Diverse Latent Feature Mapping (DSDFM) method for human motion synthesis . DSDFM consists of two stages. The first human motion reconstruction stage aims to learn the latent space distribution of human motions. (CVPR 2025) DSDFM: Deterministic - to - Stochastic Diverse Latent Feature Mapping for Human Motion Synthesis , Hua et al. (CVPR 2025) EchoMimicV2: Towards Striking, Simplified, and Semi-Body Human Animation, Hua et al. In this work, we use the following metrics to measure the per-formance of the proposed method for unconditional human motion synthesis and Action- to - Motion tasks. Oct 28, 2024 · To address the issues, we propose an efficient method called MOOT for unconditional human motion synthesis . First, we utilize a reconstruction network based on GRU and transformer to map human motions to latent space. In this paper, we propose a Deterministic - to - Stochastic Diverse Latent Feature Mapping (DSDFM) method for human motion synthesis . DSDFM consists of two stages. The first human motion reconstruction stage aims to learn the latent space distribution of human motions. thesis. DSDFM consists of two stages. The first human motion reconstruction stage aims to learn the laten space distribution of human motions . The second diverse mo-tion generation stage aims to build connections between the Gaussian distribution and the latent space distribution of hu-man motions, thereby enhancing the diversity and ac Sep 25, 2024 · To fill this gap, this work introduces a novel task: predicting diverse human motion within real-world 3D scenes. In contrast to prior works, it requires harmonizing the deterministic constraints imposed by the surrounding 3D scenes with the stochastic aspect of human motion .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2505.00998", "content": "May 2, 2025 · In this paper, we propose a Deterministic - to - Stochastic Diverse Latent Feature Mapping (DSDFM) method for human motion synthesis . DSDFM consists of two stages. The first human motion reconstruction stage aims to learn the latent space distribution of human motions. (CVPR 2025) DSDFM: Deterministic - to - Stochastic Diverse Latent Feature Mapping for Human Motion Synthesis , Hua et al. (CVPR 2025) EchoMimicV2: Towards Striking, Simplified, and Semi-Body Human Animation, Hua et al. In this work, we use the following metrics to measure the per-formance of the proposed method for unconditional human motion synthesis and Action- to - Motion tasks. Oct 28, 2024 · To address the issues, we propose an efficient method called MOOT for unconditional human motion synthesis . First, we utilize a reconstruction network based on GRU and transformer to map human motions to latent space. In this paper, we propose a Deterministic - to - Stochastic Diverse Latent Feature Mapping (DSDFM) method for human motion synthesis . DSDFM consists of two stages. The first human motion reconstruction stage aims to learn the latent space distribution of human motions. thesis. DSDFM consists of two stages. The first human motion reconstruction stage aims to learn the laten space distribution of human motions . The second diverse mo-tion generation stage aims to build connections between the Gaussian distribution and the latent space distribution of hu-man motions, thereby enhancing the diversity and ac Sep 25, 2024 · To fill this gap, this work introduces a novel task: predicting diverse human motion within real-world 3D scenes. In contrast to prior works, it requires harmonizing the deterministic constraints imposed by the surrounding 3D scenes with the stochastic aspect of human motion ."}
+{"idx": 2, "title": "Foruck/Awesome-Human-Motion - GitHub", "date": "", "ddg_snippet": "(CVPR 2025) DSDFM: Deterministic - to - Stochastic Diverse Latent Feature Mapping for Human Motion Synthesis , Hua et al. (CVPR 2025) EchoMimicV2: Towards Striking, Simplified, and Semi-Body Human Animation, Hua et al.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/Foruck/Awesome-Human-Motion", "content": "(CVPR 2025) DSDFM: Deterministic - to - Stochastic Diverse Latent Feature Mapping for Human Motion Synthesis , Hua et al. (CVPR 2025) EchoMimicV2: Towards Striking, Simplified, and Semi-Body Human Animation, Hua et al."}
+{"idx": 3, "title": "Towards Efficient and Diverse Generative Model for ...", "date": "", "ddg_snippet": "Oct 28, 2024 · To address the issues, we propose an efficient method called MOOT for unconditional human motion synthesis . First, we utilize a reconstruction network based on GRU and transformer to map human motions to latent space.", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.1145/3664647.3681093", "content": "Oct 28, 2024 · To address the issues, we propose an efficient method called MOOT for unconditional human motion synthesis . First, we utilize a reconstruction network based on GRU and transformer to map human motions to latent space."}
+{"idx": 4, "title": "Harmonizing Stochasticity and Determinism: Scene-responsive ...", "date": "", "ddg_snippet": "Sep 25, 2024 · To fill this gap, this work introduces a novel task: predicting diverse human motion within real-world 3D scenes. In contrast to prior works, it requires harmonizing the deterministic constraints imposed by the surrounding 3D scenes with the stochastic aspect of human motion .", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=NQCkNM6TES", "content": "Sep 25, 2024 · To fill this gap, this work introduces a novel task: predicting diverse human motion within real-world 3D scenes. In contrast to prior works, it requires harmonizing the deterministic constraints imposed by the surrounding 3D scenes with the stochastic aspect of human motion ."}
+{"idx": 5, "title": "Deterministic - to - Stochastic Diverse Latent Feature Mapping for...", "date": "", "ddg_snippet": "The first human motion reconstruction stage aims to learn the latent space distribution of human motions .In this paper, we propose a Deterministic - to - Stochastic Diverse Latent Feature Mapping (DSDFM) for human motion synthesis .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2505.00998v1", "content": "The first human motion reconstruction stage aims to learn the latent space distribution of human motions .In this paper, we propose a Deterministic - to - Stochastic Diverse Latent Feature Mapping (DSDFM) for human motion synthesis ."}
+{"idx": 6, "title": "Deterministic - to - Stochastic Diverse Latent Feature Mapping for...", "date": "", "ddg_snippet": "In this paper, we propose a Deterministic - to - Stochastic Diverse Latent Feature Mapping (DSDFM) method for human motion synthesis . DSDFM consists of two stages. The first human motion reconstruction stage aims to learn the latent space distribution of human motions .", "subpage_snippet": "", "source": "www.alphaxiv.org", "link": "https://www.alphaxiv.org/abs/2505.00998", "content": "In this paper, we propose a Deterministic - to - Stochastic Diverse Latent Feature Mapping (DSDFM) method for human motion synthesis . DSDFM consists of two stages. The first human motion reconstruction stage aims to learn the latent space distribution of human motions ."}
+{"idx": 7, "title": "Deterministic - to - Stochastic Diverse Latent Feature Mapping for...", "date": "", "ddg_snippet": "Human motion synthesis aims to generate plausible human motion sequences, which has raised widespread attention in computer animation. Recent score-based generative models (SGMs) have demonstrated impressive results on this task.", "subpage_snippet": "", "source": "papers.cool", "link": "https://papers.cool/venue/Hua_Deterministic-to-Stochastic_Diverse_Latent_Feature_Mapping_for_Human_Motion_Synthesis@CVPR2025@CVF", "content": "Human motion synthesis aims to generate plausible human motion sequences, which has raised widespread attention in computer animation. Recent score-based generative models (SGMs) have demonstrated impressive results on this task."}
+{"idx": 8, "title": "DivDiff: A Conditional Diffusion Model for Diverse Human Motion ...", "date": "", "ddg_snippet": "Deterministic - to - Stochastic Diverse Latent Feature Mapping for Human Motion Synthesis .The first human motion reconstruction stage aims to learn the latent space distribution of human motions .", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/387413105_DivDiff_A_Conditional_Diffusion_Model_for_Diverse_Human_Motion_Prediction", "content": "Deterministic - to - Stochastic Diverse Latent Feature Mapping for Human Motion Synthesis .The first human motion reconstruction stage aims to learn the latent space distribution of human motions ."}
+{"idx": 9, "title": "GitHub - Zilize/awesome-text-to- motion : Text-driven human motion ...", "date": "", "ddg_snippet": "DSDFM: \" Deterministic - to - Stochastic Diverse Latent Feature Mapping for Human Motion Synthesis \".MotionGPT: \"MotionGPT: Human Motion Synthesis with Improved Diversity and Realism via GPT-3 Prompting\".", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/Zilize/awesome-text-to-motion", "content": "DSDFM: \" Deterministic - to - Stochastic Diverse Latent Feature Mapping for Human Motion Synthesis \".MotionGPT: \"MotionGPT: Human Motion Synthesis with Improved Diversity and Realism via GPT-3 Prompting\"."}

data/sampled_jsons/34016_EntityErasure_EntityErasure_Erasing_Entity_Cleanly_Amodal_Entity_Segmentation.jsonl

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+{"idx": 0, "title": "PDF EntityErasure: Erasing Entity Cleanly via Amodal Entity Segmentation ...", "date": "", "ddg_snippet": "Abstract This paper presents EntityErasure , a novel diffusion-based inpainting method that can effectively erase entities with-out inducing unwanted sundries. To this end, we pro-pose to address this problem by dividing it into amodal entity segmentation and completion, such that the region to inpaint takes only entities in the non-inpainting area as reference, avoiding the possibility to ...", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2025/papers/Zhu_EntityErasure_Erasing_Entity_Cleanly_via_Amodal_Entity_Segmentation_and_Completion_CVPR_2025_paper.pdf", "content": "Abstract This paper presents EntityErasure , a novel diffusion-based inpainting method that can effectively erase entities with-out inducing unwanted sundries. To this end, we pro-pose to address this problem by dividing it into amodal entity segmentation and completion, such that the region to inpaint takes only entities in the non-inpainting area as reference, avoiding the possibility to ..."}
+{"idx": 1, "title": "GitHub - zyxunh/entity_erasure", "date": "", "ddg_snippet": "EntityErasure : Erasing Entity Cleanly via Amodal Entity Segmentation and Completion [CVPR2025] Introduction This repository contains the official implementation of the paper EntityErasure .", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/zyxunh/entity_erasure", "content": "EntityErasure : Erasing Entity Cleanly via Amodal Entity Segmentation and Completion [CVPR2025] Introduction This repository contains the official implementation of the paper EntityErasure ."}
+{"idx": 2, "title": "entity_erasure/README.md at master · zyxunh/entity_erasure", "date": "", "ddg_snippet": "Contribute to zyxunh/ entity_erasure development by creating an account on GitHub.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/zyxunh/entity_erasure/blob/master/README.md", "content": "Contribute to zyxunh/ entity_erasure development by creating an account on GitHub."}
+{"idx": 3, "title": "EntityErasure: Erasing Entity Cleanly via Amodal Entity Segmentation ...", "date": "", "ddg_snippet": "Abstract: This paper presents EntityErasure , a novel diffusion-based method that can effectively erase entity without inducing unwanted sundries. To this end, we propose to address this problem by dividing it into amodal entity segmentation and completion, such that the region to inpaint takes only entities in the non-inpainting area as reference, avoiding the possibility to generate ...", "subpage_snippet": "", "source": "cvpr.thecvf.com", "link": "https://cvpr.thecvf.com/virtual/2025/poster/34016", "content": "Abstract: This paper presents EntityErasure , a novel diffusion-based method that can effectively erase entity without inducing unwanted sundries. To this end, we propose to address this problem by dividing it into amodal entity segmentation and completion, such that the region to inpaint takes only entities in the non-inpainting area as reference, avoiding the possibility to generate ..."}
+{"idx": 4, "title": "ICCV 2023 Oral | 超越SAM!EntitySeg：更少的数据，更高的分割质量!-CSDN博客", "date": "", "ddg_snippet": "在本文中，High-Quality Entity Segmentation 对分割问题进行了全新的探索，从以下三个方面取得了显著的改进： 更优的分割质量 正如上图所示，EntitySeg在数值指标和视觉表现方面都相对于SAM有更大的优势。", "subpage_snippet": "", "source": "blog.csdn.net", "link": "https://blog.csdn.net/amusi1994/article/details/132750432", "content": "在本文中，High-Quality Entity Segmentation 对分割问题进行了全新的探索，从以下三个方面取得了显著的改进： 更优的分割质量 正如上图所示，EntitySeg在数值指标和视觉表现方面都相对于SAM有更大的优势。"}
+{"idx": 5, "title": "EntityErasure: Erasing Entity Cleanly via Amodal Entity Segmentation ...", "date": "", "ddg_snippet": "This paper presents EntityErasure , a novel diffusion-based method that can effectively erase entity without inducing unwanted sundries. To this end, we propose to address this problem by dividing it into amodal entity segmentation and completion, such that the region to inpaint takes only entities in the non-inpainting area as reference, avoiding the possibility to generate unpredictable ...", "subpage_snippet": "", "source": "zyxunh.github.io", "link": "https://zyxunh.github.io/EntityErasure-ProjectPage/", "content": "This paper presents EntityErasure , a novel diffusion-based method that can effectively erase entity without inducing unwanted sundries. To this end, we propose to address this problem by dividing it into amodal entity segmentation and completion, such that the region to inpaint takes only entities in the non-inpainting area as reference, avoiding the possibility to generate unpredictable ..."}
+{"idx": 6, "title": "unhzyx/entity_erasure at main - Hugging Face", "date": "", "ddg_snippet": "We're on a journey to advance and democratize artificial intelligence through open source and open science.", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/unhzyx/entity_erasure/tree/main", "content": "We're on a journey to advance and democratize artificial intelligence through open source and open science."}
+{"idx": 7, "title": "Publications - zhangqing-home.net", "date": "", "ddg_snippet": "EntityErasure : Erasing Entity Cleanly via Amodal Entity Segmentation and Completion Yixing Zhu, Qing Zhang, Yitong Wang, Yongwei Nie, Wei-Shi Zheng", "subpage_snippet": "", "source": "www.zhangqing-home.net", "link": "https://www.zhangqing-home.net/full_publications.html", "content": "EntityErasure : Erasing Entity Cleanly via Amodal Entity Segmentation and Completion Yixing Zhu, Qing Zhang, Yitong Wang, Yongwei Nie, Wei-Shi Zheng"}
+{"idx": 8, "title": "Qing Zhang - Homepage", "date": "", "ddg_snippet": "EntityErasure : Erasing Entity Cleanly via Amodal Entity Segmentation and Completion Yixing Zhu, Qing Zhang*, Yitong Wang, Yongwei Nie, Wei-Shi Zheng CVPR 2025 Project | Code | Paper RoGSplat: Learning Robust Generalizable Human Gaussian Splatting from Sparse Multi-View Images Junjin Xiao, Qing Zhang*, Yongwei Nie, Lei Zhu, Wei-Shi Zheng CVPR 2025", "subpage_snippet": "", "source": "www.zhangqing-home.net", "link": "https://www.zhangqing-home.net/", "content": "EntityErasure : Erasing Entity Cleanly via Amodal Entity Segmentation and Completion Yixing Zhu, Qing Zhang*, Yitong Wang, Yongwei Nie, Wei-Shi Zheng CVPR 2025 Project | Code | Paper RoGSplat: Learning Robust Generalizable Human Gaussian Splatting from Sparse Multi-View Images Junjin Xiao, Qing Zhang*, Yongwei Nie, Lei Zhu, Wei-Shi Zheng CVPR 2025"}
+{"idx": 9, "title": "AcademicDissect/detailed_paper_collection/Image_Inpainting.md ... - GitHub", "date": "", "ddg_snippet": "EntityErasure : Erasing Entity Cleanly via Amodal Entity Segmentation and Completion Tags: Image Inpainting, Diffusion Models, Amodal Segmentation , Entity Completion, Diffusion-Based Inpainting", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/AcademicDissect/AcademicDissect/blob/main/detailed_paper_collection/Image_Inpainting.md", "content": "EntityErasure : Erasing Entity Cleanly via Amodal Entity Segmentation and Completion Tags: Image Inpainting, Diffusion Models, Amodal Segmentation , Entity Completion, Diffusion-Based Inpainting"}

data/sampled_jsons/35068_XLRS-Bench_LLaVA-1.5_LLaVA-Next_architectural_difference_high-resolution_year_2024.jsonl

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+{"idx": 0, "title": "GitHub - LLaVA -VL/ LLaVA - NeXT", "date": "", "ddg_snippet": "Contribute to LLaVA-VL/ LLaVA - NeXT development by creating an account on GitHub.With additional scaling to LLaVA - 1 . 5 , LLaVA - NeXT -34B outperforms Gemini Pro on some benchmarks.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/LLaVA-VL/LLaVA-NeXT", "content": "Contribute to LLaVA-VL/ LLaVA - NeXT development by creating an account on GitHub.With additional scaling to LLaVA - 1 . 5 , LLaVA - NeXT -34B outperforms Gemini Pro on some benchmarks."}
+{"idx": 1, "title": "GDinesh/ llava - 1 - 5 · Hugging Face", "date": "", "ddg_snippet": "[1/30] LLaVA - NeXT ( LLaVA - 1 .6) is out! With additional scaling to LLaVA - 1 . 5 , LLaVA - NeXT -34B outperforms Gemini Pro on some benchmarks. It can now process 4x more pixels and perform more tasks/applications than before. Check out the blog post, and expl...", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/GDinesh/llava-1-5", "content": "[1/30] LLaVA - NeXT ( LLaVA - 1 .6) is out! With additional scaling to LLaVA - 1 . 5 , LLaVA - NeXT -34B outperforms Gemini Pro on some benchmarks. It can now process 4x more pixels and perform more tasks/applications than before. Check out the blog post, and expl..."}
+{"idx": 2, "title": "(PDF) XLRS - Bench : Could Your Multimodal LLMs Understand...", "date": "", "ddg_snippet": "ple, LLaVA - Next [32] divides high - resolution images into. patches, encodes each patch independently, and then links.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/390354847_XLRS-Bench_Could_Your_Multimodal_LLMs_Understand_Extremely_Large_Ultra-High-Resolution_Remote_Sensing_Imagery", "content": "ple, LLaVA - Next [32] divides high - resolution images into. patches, encodes each patch independently, and then links."}
+{"idx": 3, "title": "XLRS - Bench : Could Your Multimodal LLMs Understand Extremely...", "date": "", "ddg_snippet": "For example, LLaVA - Next [31] divides high - resolution images into patches, en-codes each patch independently, and then links the patch tokens with the global image tokens.", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2025/papers/Wang_XLRS-Bench_Could_Your_Multimodal_LLMs_Understand_Extremely_Large_Ultra-High-Resolution_Remote_CVPR_2025_paper.pdf", "content": "For example, LLaVA - Next [31] divides high - resolution images into patches, en-codes each patch independently, and then links the patch tokens with the global image tokens."}
+{"idx": 4, "title": "LLaVA - NeXT", "date": "", "ddg_snippet": "LLaVa - NeXT (also called LLaVa - 1 .6) improves upon LLaVa by increasing the input image resolution and training on an improved visual instruction tuning dataset to improve OCR and common sense reasoning.", "subpage_snippet": "", "source": "hf.global-rail.com", "link": "https://hf.global-rail.com/docs/transformers/model_doc/llava_next", "content": "LLaVa - NeXT (also called LLaVa - 1 .6) improves upon LLaVa by increasing the input image resolution and training on an improved visual instruction tuning dataset to improve OCR and common sense reasoning."}
+{"idx": 5, "title": "LLaVA -VL/ LLaVA - NeXT - Githubissues", "date": "", "ddg_snippet": "LLaVA - NeXT : Open Large Multimodal Models Release Notes [2024/10/04] LLaVA -Video (formerly LLaVA - NeXT -Video) has undergone a major upgrade! We are excited to release LLaVA -Video-178K, a high -quality synthetic dataset for video instruction tuning.", "subpage_snippet": "", "source": "githubissues.com", "link": "https://githubissues.com/LLaVA-VL/LLaVA-NeXT/readme", "content": "LLaVA - NeXT : Open Large Multimodal Models Release Notes [2024/10/04] LLaVA -Video (formerly LLaVA - NeXT -Video) has undergone a major upgrade! We are excited to release LLaVA -Video-178K, a high -quality synthetic dataset for video instruction tuning."}
+{"idx": 6, "title": "Trying out LLaVA - NeXT", "date": "", "ddg_snippet": "Trying out LLaVA - NeXT . Testing the capabilities of llava :7b-v1.6-mistral-q4_0. Image generated by author. The LLaVA - NeXT model is the latest iteration of LLaVA , a large multimodal model that has gained recognition for its performance.", "subpage_snippet": "", "source": "readmedium.com", "link": "https://readmedium.com/trying-out-llava-next-8d5e74da3017", "content": "Trying out LLaVA - NeXT . Testing the capabilities of llava :7b-v1.6-mistral-q4_0. Image generated by author. The LLaVA - NeXT model is the latest iteration of LLaVA , a large multimodal model that has gained recognition for its performance."}
+{"idx": 7, "title": "llava", "date": "", "ddg_snippet": "New in LLaVA 1 .6: Increasing the input image resolution to up to 4x more pixels, supporting 672x672, 336x1344, 1344x336 resolutions . Better visual reasoning and OCR capability with an improved visual instruction tuning data mixture.", "subpage_snippet": "", "source": "ollama.com", "link": "https://ollama.com/library/llava", "content": "New in LLaVA 1 .6: Increasing the input image resolution to up to 4x more pixels, supporting 672x672, 336x1344, 1344x336 resolutions . Better visual reasoning and OCR capability with an improved visual instruction tuning data mixture."}
+{"idx": 8, "title": "LLaVA -NDiNO: Empowering LLMs with Multimodality for the Italian...", "date": "", "ddg_snippet": "However, the original LLaVA architecture , as well as other LVLMs, struggled with high - resolution images tasks due to the requirements imposed by vision encoders.Regarding datasets used to train these models, for LLaVA 1 . 5 a mixture of English only vision-language datasets was used.", "subpage_snippet": "", "source": "ceur-ws.org", "link": "https://ceur-ws.org/Vol-3877/paper9.pdf", "content": "However, the original LLaVA architecture , as well as other LVLMs, struggled with high - resolution images tasks due to the requirements imposed by vision encoders.Regarding datasets used to train these models, for LLaVA 1 . 5 a mixture of English only vision-language datasets was used."}
+{"idx": 9, "title": "How to Use LLaVa - Next : Elevating Multimodal AI Interactions fxis.ai", "date": "", "ddg_snippet": "LLaVa - Next combines a large pre-trained language model with an advanced vision encoder, allowing you to create sophisticated chatbot interactions that can understand and respond to queries about images. This model builds on the strengths of its predecessor, LLaVa - 1 . 5 , by training on...", "subpage_snippet": "", "source": "fxis.ai", "link": "https://fxis.ai/edu/how-to-use-llava-next-elevating-multimodal-ai-interactions/", "content": "LLaVa - Next combines a large pre-trained language model with an advanced vision encoder, allowing you to create sophisticated chatbot interactions that can understand and respond to queries about images. This model builds on the strengths of its predecessor, LLaVa - 1 . 5 , by training on..."}

data/sampled_jsons/46n3izUNiv_Origin_Identification_Image_Copy_Detection_manually-designed_transformations_SSCD.jsonl

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+{"idx": 0, "title": "Origin Identification for Text-Guided Image-to-Image Diffusion Models", "date": "", "ddg_snippet": "Unlike ICD, which focuses on manually-designed transformations , our ID2aims to find the origin of a query translated by the diffusion model with prompt-guidance.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=46n3izUNiv", "content": "Unlike ICD, which focuses on manually-designed transformations , our ID2aims to find the origin of a query translated by the diffusion model with prompt-guidance."}
+{"idx": 1, "title": "A Self-Supervised Descriptor for Image Copy Detection (SSCD)", "date": "", "ddg_snippet": "A Self-Supervised Descriptor for Image Copy Detection ( SSCD ) This is the open-source codebase for \"A Self-Supervised Descriptor for Image Copy Detection \", recently accepted to CVPR 2022. This work uses self-supervised contrastive learning with strong differential entropy regularization to create a fingerprint for image copy detection .", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/facebookresearch/sscd-copy-detection", "content": "A Self-Supervised Descriptor for Image Copy Detection ( SSCD ) This is the open-source codebase for \"A Self-Supervised Descriptor for Image Copy Detection \", recently accepted to CVPR 2022. This work uses self-supervised contrastive learning with strong differential entropy regularization to create a fingerprint for image copy detection ."}
+{"idx": 2, "title": "PDF A Self-Supervised Descriptor for Image Copy Detection", "date": "", "ddg_snippet": "Abstract Image copy detection is an important task for content moderation. We introduce SSCD , a model that builds on a recent self-supervised contrastive training objective. We adapt this method to the copy detection task by changing the architecture and training objective, including a pooling op-erator from the instance matching literature, and adapting contrastive learning to augmentations ...", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2022/papers/Pizzi_A_Self-Supervised_Descriptor_for_Image_Copy_Detection_CVPR_2022_paper.pdf", "content": "Abstract Image copy detection is an important task for content moderation. We introduce SSCD , a model that builds on a recent self-supervised contrastive training objective. We adapt this method to the copy detection task by changing the architecture and training objective, including a pooling op-erator from the instance matching literature, and adapting contrastive learning to augmentations ..."}
+{"idx": 3, "title": "sscd-copy-detection/README.md at main - GitHub", "date": "", "ddg_snippet": "A Self-Supervised Descriptor for Image Copy Detection ( SSCD ) This is the open-source codebase for \"A Self-Supervised Descriptor for Image Copy Detection \", recently accepted to CVPR 2022. This work uses self-supervised contrastive learning with strong differential entropy regularization to create a fingerprint for image copy detection .", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/facebookresearch/sscd-copy-detection/blob/main/README.md", "content": "A Self-Supervised Descriptor for Image Copy Detection ( SSCD ) This is the open-source codebase for \"A Self-Supervised Descriptor for Image Copy Detection \", recently accepted to CVPR 2022. This work uses self-supervised contrastive learning with strong differential entropy regularization to create a fingerprint for image copy detection ."}
+{"idx": 4, "title": "A Self-Supervised Descriptor for Image Copy Detection", "date": "", "ddg_snippet": "On the recent DISC2021 benchmark, SSCD is shown to outperform both baseline copy detection models and self-supervised architectures designed for image classification by huge margins, in all settings. For example, SSCD out-performs SimCLR descriptors by 48% absolute. Code is available at this https URL.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2202.10261", "content": "On the recent DISC2021 benchmark, SSCD is shown to outperform both baseline copy detection models and self-supervised architectures designed for image classification by huge margins, in all settings. For example, SSCD out-performs SimCLR descriptors by 48% absolute. Code is available at this https URL."}
+{"idx": 5, "title": "Vision transformers are active learners for image copy detection", "date": "", "ddg_snippet": "Abstract Image Copy Detection (ICD) is developed to identify and track duplicated or manipulated images . The majority of existing methods rely on Convolutional Neural Networks (CNNs) and are trained using unsupervised learning techniques, which leads to subpar performance.", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S0925231224004582", "content": "Abstract Image Copy Detection (ICD) is developed to identify and track duplicated or manipulated images . The majority of existing methods rely on Convolutional Neural Networks (CNNs) and are trained using unsupervised learning techniques, which leads to subpar performance."}
+{"idx": 6, "title": "A Self-Supervised Descriptor for Image Copy Detection", "date": "", "ddg_snippet": "Abstract SSCD , a model built on self-supervised contrastive training with instance matching components and entropy regularization, significantly outperforms baseline models and classification-focused architectures in image copy detection .", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/papers/2202.10261", "content": "Abstract SSCD , a model built on self-supervised contrastive training with instance matching components and entropy regularization, significantly outperforms baseline models and classification-focused architectures in image copy detection ."}
+{"idx": 7, "title": "README.md · m3/sscd-copy-detection at main - Hugging Face", "date": "", "ddg_snippet": "We're on a journey to advance and democratize artificial intelligence through open source and open science.", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/m3/sscd-copy-detection/blob/main/README.md", "content": "We're on a journey to advance and democratize artificial intelligence through open source and open science."}
+{"idx": 8, "title": "arXiv:2405.17928v4 [cs.CV] 16 Jul 2024", "date": "", "ddg_snippet": "Abstract Image copy detection is a task of detecting edited copies from any image within a reference database. While previ-ous approaches have shown remarkable progress, the large size of their networks and descriptors remains disadvan-tage, complicating their practical application. In this pa-per, we propose a novel method that achieves a competi-tive performance by using a lightweight ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2405.17928", "content": "Abstract Image copy detection is a task of detecting edited copies from any image within a reference database. While previ-ous approaches have shown remarkable progress, the large size of their networks and descriptors remains disadvan-tage, complicating their practical application. In this pa-per, we propose a novel method that achieves a competi-tive performance by using a lightweight ..."}
+{"idx": 9, "title": "CVPR 2022 Open Access Repository", "date": "", "ddg_snippet": "Statistical information from a background image distribution can be incorporated into the descriptor. On the recent DISC2021 benchmark, SSCD is shown to outperform both baseline copy detection models and self-supervised architectures designed for image classification by huge margins, in all settings.", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2022/html/Pizzi_A_Self-Supervised_Descriptor_for_Image_Copy_Detection_CVPR_2022_paper.html", "content": "Statistical information from a background image distribution can be incorporated into the descriptor. On the recent DISC2021 benchmark, SSCD is shown to outperform both baseline copy detection models and self-supervised architectures designed for image classification by huge margins, in all settings."}

data/sampled_jsons/46n3izUNiv_Origin_Identification_for_Text-Guided_Image-to-Image_Diffusion_Models_full_text.jsonl

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+{"idx": 0, "title": "(PDF) Generalizable Origin Identification for Text - Guided ...", "date": "", "ddg_snippet": "Text - guided image - to - image diffusion models excel in translating images based on textual prompts, allowing for precise and creative visual modifications. However, such a powerful technique can be misused for spreading misinformation, infringing on copyrights...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/387767437_Generalizable_Origin_Identification_for_Text-Guided_Image-to-Image_Diffusion_Models", "content": "Text - guided image - to - image diffusion models excel in translating images based on textual prompts, allowing for precise and creative visual modifications. However, such a powerful technique can be misused for spreading misinformation, infringing on copyrights..."}
+{"idx": 1, "title": "Generalizable Origin Identification for Text - Guided Image - to - Image ...", "date": "", "ddg_snippet": "Text - guided image - to - image diffusion models excel in translating images based on textual prompts, allowing for precise and creative visual modifications.", "subpage_snippet": "", "source": "paperswithcode.com", "link": "https://paperswithcode.com/paper/generalizable-origin-identification-for-text", "content": "Text - guided image - to - image diffusion models excel in translating images based on textual prompts, allowing for precise and creative visual modifications."}
+{"idx": 2, "title": "Origin Identification for Text - Guided Image - to - Image Diffusion ...", "date": "", "ddg_snippet": "Text - guided image - to - image diffusion models excel in translating images based on textual prompts, allowing for precise and creative visual modifications.Subsequently, it is demonstrated that such a simple linear transformation can be generalized across different diffusion models .", "subpage_snippet": "", "source": "synthical.com", "link": "https://synthical.com/article/Origin-Identification-for-Text-Guided-Image-to-Image-Diffusion-Models-cbcf4366-f59f-4199-b877-a2c10f4dc1ed", "content": "Text - guided image - to - image diffusion models excel in translating images based on textual prompts, allowing for precise and creative visual modifications.Subsequently, it is demonstrated that such a simple linear transformation can be generalized across different diffusion models ."}
+{"idx": 3, "title": "Generalizable Origin Identification for Text - Guided Image - to - Image ...", "date": "", "ddg_snippet": "Text - guided image - to - image diffusion models excel in translating images based on textual prompts, allowing for precise and creative visual modifications.", "subpage_snippet": "", "source": "www.chatpaper.ai", "link": "https://www.chatpaper.ai/dashboard/paper/bab4dd04-edc9-4908-a029-7ed4e8b4ede8", "content": "Text - guided image - to - image diffusion models excel in translating images based on textual prompts, allowing for precise and creative visual modifications."}
+{"idx": 4, "title": "Generalizable Origin Identification for Text - Guided Image - to - Image ...", "date": "", "ddg_snippet": "Text - guided image - to - image diffusion models are powerful AI tools that can change images based on text descriptions. While useful, these tools can be misused to spread fake information, violate copyrights, or hide the source of images.", "subpage_snippet": "", "source": "ai-search.io", "link": "https://ai-search.io/papers/generalizable-origin-identification-for-text-guided-image-to-image-diffusion-models", "content": "Text - guided image - to - image diffusion models are powerful AI tools that can change images based on text descriptions. While useful, these tools can be misused to spread fake information, violate copyrights, or hide the source of images."}
+{"idx": 5, "title": "Stable Diffusion AI - AI Image Generator (Free, Unlimited)", "date": "", "ddg_snippet": "Stable Diffusion Text to Image Example.Upload an image and use a text prompt to guide the transformation—refine style, change scenery, reimagine characters, or generate stunning variations with incredible detail and realism.", "subpage_snippet": "", "source": "stabledifffusion.com", "link": "https://stabledifffusion.com/", "content": "Stable Diffusion Text to Image Example.Upload an image and use a text prompt to guide the transformation—refine style, change scenery, reimagine characters, or generate stunning variations with incredible detail and realism."}
+{"idx": 6, "title": "Image to Image AI - Free AI Image Generator From Image", "date": "", "ddg_snippet": "Image to Image AI Generator is a free online photo editor that offers powerful features allowing you to edit, reshape, and restyle images using text prompts.", "subpage_snippet": "", "source": "imgtoimg.ai", "link": "https://imgtoimg.ai/", "content": "Image to Image AI Generator is a free online photo editor that offers powerful features allowing you to edit, reshape, and restyle images using text prompts."}
+{"idx": 7, "title": "Infinite Texture: Text - guided High Resolution Diffusion Texture...", "date": "", "ddg_snippet": "Texture Synthesis. Diffusion Model . Text - to - Image . Deep Learning. 3D Rendering. Infinite Texture.", "subpage_snippet": "", "source": "www.bohrium.com", "link": "https://www.bohrium.com/paper-details/infinite-texture-text-guided-high-resolution-diffusion-texture-synthesis/997747644509978656-108597", "content": "Texture Synthesis. Diffusion Model . Text - to - Image . Deep Learning. 3D Rendering. Infinite Texture."}
+{"idx": 8, "title": "Text - guided depth- to - image generation", "date": "", "ddg_snippet": "Unconditional image generation Text - to - image Image - to - image Inpainting Text or image - to -video Depth- to - image .and get access to the augmented documentation experience. Collaborate on models , datasets and Spaces. Faster examples with accelerated inference.", "subpage_snippet": "", "source": "huggingface.1319lm.top", "link": "https://huggingface.1319lm.top/docs/diffusers/v0.30.3/en/using-diffusers/depth2img", "content": "Unconditional image generation Text - to - image Image - to - image Inpainting Text or image - to -video Depth- to - image .and get access to the augmented documentation experience. Collaborate on models , datasets and Spaces. Faster examples with accelerated inference."}
+{"idx": 9, "title": "WangWenhao0716 (Wenhao Wang) · GitHub", "date": "", "ddg_snippet": "GitHub Models New. Manage and compare prompts. GitHub Advanced Security.[ICML 2025] The official implementation of \" Origin Identification for Text - Guided Image - to - Image Diffusion Models \".", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/WangWenhao0716", "content": "GitHub Models New. Manage and compare prompts. GitHub Advanced Security.[ICML 2025] The official implementation of \" Origin Identification for Text - Guided Image - to - Image Diffusion Models \"."}

data/sampled_jsons/46yLEXtav4_Statistical_Collusion_by_Collectives_on_Learning_Platforms_Algorithm_1_δ_tilde.jsonl

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+{"idx": 0, "title": "Statistical Collusion by Collectives on Learning Platforms", "date": "", "ddg_snippet": "As platforms increasingly rely on learning algorithms , collectives may form and seek ways to influence these platforms to align with their own interests. This can be achieved by coordinated submission of altered data. To evaluate the potential impact of such behavior, it is essential to understand the computations that collectives must perform to impact platforms in this way. In particular ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2502.04879", "content": "As platforms increasingly rely on learning algorithms , collectives may form and seek ways to influence these platforms to align with their own interests. This can be achieved by coordinated submission of altered data. To evaluate the potential impact of such behavior, it is essential to understand the computations that collectives must perform to impact platforms in this way. In particular ..."}
+{"idx": 1, "title": "Statistical Collusion by Collectives on Learning Platforms | Read Paper ...", "date": "", "ddg_snippet": "This paper talks about how groups of people can work together to change the way online platforms use data and learning algorithms to benefit their interests. The authors created a method that help...", "subpage_snippet": "", "source": "bytez.com", "link": "https://bytez.com/docs/icml/46504/paper", "content": "This paper talks about how groups of people can work together to change the way online platforms use data and learning algorithms to benefit their interests. The authors created a method that help..."}
+{"idx": 2, "title": "GitHub - GauthierE/statistical-collusion", "date": "", "ddg_snippet": "statistical-collusion This repository contains the code for reproducing the experiments and figures presented in the paper Statistical Collusion by Collectives on Learning Platforms .", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/GauthierE/statistical-collusion", "content": "statistical-collusion This repository contains the code for reproducing the experiments and figures presented in the paper Statistical Collusion by Collectives on Learning Platforms ."}
+{"idx": 3, "title": "Algorithmic collective action in machine learning", "date": "", "ddg_snippet": "We initiate a principled study of algorithmic collective action on digital platforms that deploy machine learning algorithms . We propose a simple theoretical model of a collective interacting with a firm's learning algorithm . The collective pools the data of participating individuals and executes an algorithmic strategy by instructing participants how to modify their own data to achieve a ...", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.5555/3618408.3618918", "content": "We initiate a principled study of algorithmic collective action on digital platforms that deploy machine learning algorithms . We propose a simple theoretical model of a collective interacting with a firm's learning algorithm . The collective pools the data of participating individuals and executes an algorithmic strategy by instructing participants how to modify their own data to achieve a ..."}
+{"idx": 4, "title": "[论文审查] Statistical Collusion by Collectives on Learning Platforms", "date": "", "ddg_snippet": "相似审查 [论文审查] Algorithmic Collective Action with Two Collectives [论文审查] A Statistical Learning Approach for Feature-Aware Task-to-Core Allocation in Heterogeneous Platforms [论文审查] Reinforcement Learning , Collusion , and the Folk Theorem [论文审查] Collusion Detection with Graph Neural Networks", "subpage_snippet": "", "source": "www.themoonlight.io", "link": "https://www.themoonlight.io/zh/review/statistical-collusion-by-collectives-on-learning-platforms", "content": "相似审查 [论文审查] Algorithmic Collective Action with Two Collectives [论文审查] A Statistical Learning Approach for Feature-Aware Task-to-Core Allocation in Heterogeneous Platforms [论文审查] Reinforcement Learning , Collusion , and the Folk Theorem [论文审查] Collusion Detection with Graph Neural Networks"}
+{"idx": 5, "title": "Statistical Collusion by Collectives on Learning Platforms", "date": "", "ddg_snippet": "A framework is developed that provides a theoretical and algorithmic treatment of the issues of a priori assessments of the effect of the collective before taking action and presents experimental results in a product evaluation domain. As platforms increasingly rely on learning algorithms , collectives may form and seek ways to influence these platforms to align with their own interests. This ...", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/Statistical-Collusion-by-Collectives-on-Learning-Gauthier-Bach/1c45ef9ad56839c3309f0a0bdcff50fbb3ad73f5", "content": "A framework is developed that provides a theoretical and algorithmic treatment of the issues of a priori assessments of the effect of the collective before taking action and presents experimental results in a product evaluation domain. As platforms increasingly rely on learning algorithms , collectives may form and seek ways to influence these platforms to align with their own interests. This ..."}
+{"idx": 6, "title": "Statistical Collusion by Collectives on Learning Platforms", "date": "", "ddg_snippet": "Abstract As platforms increasingly rely on learning algorithms , collectives may form and seek ways to influence these platforms to align with their own interests. This can be achieved by coordinated submission of altered data.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2502.04879v3", "content": "Abstract As platforms increasingly rely on learning algorithms , collectives may form and seek ways to influence these platforms to align with their own interests. This can be achieved by coordinated submission of altered data."}
+{"idx": 7, "title": "Statistical Collusion by Collectives on Learning Platforms", "date": "", "ddg_snippet": "As platforms increasingly rely on learning algo-rithms , collectives may form and seek ways to influence these platforms to align with their own interests. This can be achieved by coordinated submission of altered data.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2502.04879", "content": "As platforms increasingly rely on learning algo-rithms , collectives may form and seek ways to influence these platforms to align with their own interests. This can be achieved by coordinated submission of altered data."}
+{"idx": 8, "title": "(PDF) Collective Intelligence and Learning Analytics for Online ...", "date": "", "ddg_snippet": "It explains collective intelligence learning analytics (CILA) for informed pedagogical decision-making in four aspects, including objectives setting, data collection, data analysis, and ...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/303749271_Collective_Intelligence_and_Learning_Analytics_for_Online_Learning_and_Teaching_Support", "content": "It explains collective intelligence learning analytics (CILA) for informed pedagogical decision-making in four aspects, including objectives setting, data collection, data analysis, and ..."}
+{"idx": 9, "title": "arXiv:2502.04879v1 [stat.ML] 7 Feb 2025", "date": "", "ddg_snippet": "As platforms increasingly rely on learning algorithms , collectives may form and seek ways to influence these platforms to align with their own interests. This can be achieved by coordinated submission of altered data. To evaluate the potential impact of such behavior, it is essential to understand the computations that collectives must perform to impact platforms in this way. In particular ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2502.04879v1", "content": "As platforms increasingly rely on learning algorithms , collectives may form and seek ways to influence these platforms to align with their own interests. This can be achieved by coordinated submission of altered data. To evaluate the potential impact of such behavior, it is essential to understand the computations that collectives must perform to impact platforms in this way. In particular ..."}

data/sampled_jsons/4AmFA0qNQ2_Long-Form_Speech_Generation_with_Spoken_Language_Models_initialization_section_LM_initial.jsonl

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data/sampled_jsons/4Z04wVQ9FY_Linearization_Turns_Neural_Operators_Theorem_3.2_function-valued_Gaussian_processes_multi.jsonl

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+{"idx": 0, "title": "Linearization Turns Neural Operators into Function - Valued ...", "date": "", "ddg_snippet": "Neural operators generalize neural networks to learn mappings between function spaces from data. They are commonly used to learn solution operators of parametric partial differential equations (PDEs) or propagators of time-dependent PDEs.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=4Z04wVQ9FY", "content": "Neural operators generalize neural networks to learn mappings between function spaces from data. They are commonly used to learn solution operators of parametric partial differential equations (PDEs) or propagators of time-dependent PDEs."}
+{"idx": 1, "title": "Linearization Turns Neural Operators into Function-Valued ...", "date": "", "ddg_snippet": "by E Magnani · Cited by 4 — Theorem 3.2 reveals an insight into the abstract concept of function - valued Gaussian processes : Function - valued Gaus- sian processes are equivalent to ( multi - ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=4Z04wVQ9FY", "content": "by E Magnani · Cited by 4 — Theorem 3.2 reveals an insight into the abstract concept of function - valued Gaussian processes : Function - valued Gaus- sian processes are equivalent to ( multi - ..."}
+{"idx": 2, "title": "(PDF) Linearization Turns Neural Operators into Function - Valued ...", "date": "", "ddg_snippet": ". Operator -valued kernels and function - valued Gaussian processes . have been studied in the Hilbert space setting, e.g. by Micchelli and Pontil. [32].", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/381294298_Linearization_Turns_Neural_Operators_into_Function-Valued_Gaussian_Processes", "content": ". Operator -valued kernels and function - valued Gaussian processes . have been studied in the Hilbert space setting, e.g. by Micchelli and Pontil. [32]."}
+{"idx": 3, "title": "Linearization Turns Neural Operators into Function - Valued ...", "date": "", "ddg_snippet": "Currying of Neural Operators : The neural operator , initially mapping between function spaces, is converted into an equivalent neural network that handles inputs as pairs of functions and points. Linearized Laplace Approximation (LLA)...", "subpage_snippet": "", "source": "www.emergentmind.com", "link": "https://www.emergentmind.com/papers/2406.05072", "content": "Currying of Neural Operators : The neural operator , initially mapping between function spaces, is converted into an equivalent neural network that handles inputs as pairs of functions and points. Linearized Laplace Approximation (LLA)..."}
+{"idx": 4, "title": "Operator Learning with Gaussian Processes | AI Research Paper...", "date": "", "ddg_snippet": "The research paper discusses a technique for learning operators using Gaussian Processes (GPs). Operators are mathematical functions that take one function as input and produce another function as output .", "subpage_snippet": "", "source": "www.aimodels.fyi", "link": "https://www.aimodels.fyi/papers/arxiv/operator-learning-gaussian-processes", "content": "The research paper discusses a technique for learning operators using Gaussian Processes (GPs). Operators are mathematical functions that take one function as input and produce another function as output ."}
+{"idx": 5, "title": "Linearization - no idea how to do this • Physics Forums", "date": "", "ddg_snippet": "Can someone point me in the right direction for this problem. I have no idea how to start on this. I know the linearization formula but i don't know if...problem: You want a linearization that will replace the function over an interval that includes the point Xo.", "subpage_snippet": "", "source": "www.physicsforums.com", "link": "https://www.physicsforums.com/threads/linearization-no-idea-how-to-do-this.140027/", "content": "Can someone point me in the right direction for this problem. I have no idea how to start on this. I know the linearization formula but i don't know if...problem: You want a linearization that will replace the function over an interval that includes the point Xo."}
+{"idx": 6, "title": "Gödel's Incompleteness Theorem - Numberphile - YouTube", "date": "", "ddg_snippet": "Marcus du Sautoy discusses Gödel's Incompleteness TheoremMore links & stuff in full description below ↓↓↓Extra Footage Part One: https://youtu.be/mccoBBf0VDM...", "subpage_snippet": "", "source": "www.youtube.com", "link": "https://www.youtube.com/watch?v=O4ndIDcDSGc", "content": "Marcus du Sautoy discusses Gödel's Incompleteness TheoremMore links & stuff in full description below ↓↓↓Extra Footage Part One: https://youtu.be/mccoBBf0VDM..."}
+{"idx": 7, "title": "Emilia Magnani - Google Akademik", "date": "", "ddg_snippet": "Linearization Turns Neural Operators into Function - Valued Gaussian Processes .", "subpage_snippet": "", "source": "scholar.google.co.in", "link": "https://scholar.google.co.in/citations?user=_zPcNdEAAAAJ&hl=tr", "content": "Linearization Turns Neural Operators into Function - Valued Gaussian Processes ."}
+{"idx": 8, "title": "Publications", "date": "", "ddg_snippet": "Publications. Linearization Turns Neural Operators into Function - Valued Gaussian Processes . Emilia Magnani, Marvin Pförtner, Tobias Weber, Philipp Hennig.Uncertainty Quantification for Fourier Neural Operators .", "subpage_snippet": "", "source": "2bys.github.io", "link": "https://2bys.github.io/publications/", "content": "Publications. Linearization Turns Neural Operators into Function - Valued Gaussian Processes . Emilia Magnani, Marvin Pförtner, Tobias Weber, Philipp Hennig.Uncertainty Quantification for Fourier Neural Operators ."}
+{"idx": 9, "title": "Learning Mechanistic Subtypes of Neurodegeneration with...", "date": "", "ddg_snippet": "Here u(x, t) is a scalar- valued function of space, x, and time, t; D is the diusion coecient; and f (x, t) is the reaction term; Ω and ∂Ω are the domain and boundary, respectively; n is the normal unit vector on the surface ∂Ω; and u0(x) is the initi...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2509.15124", "content": "Here u(x, t) is a scalar- valued function of space, x, and time, t; D is the diusion coecient; and f (x, t) is the reaction term; Ω and ∂Ω are the domain and boundary, respectively; n is the normal unit vector on the surface ∂Ω; and u0(x) is the initi..."}

data/sampled_jsons/4Z04wVQ9FY_Linearization_Turns_Neural_Operators_into_Function-Valued_Gaussian_Processes.jsonl

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+{"idx": 0, "title": "Linearization Turns Neural Operators into Function-Valued Gaussian ...", "date": "", "ddg_snippet": "In a case study on Fourier neural operators , we show that, even for a discretized input, our method yields a Gaussian closure--a structured Gaussian process posterior capturing the uncertainty in the output function of the neural operator , which can be evaluated at an arbitrary set of points.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2406.05072v1", "content": "In a case study on Fourier neural operators , we show that, even for a discretized input, our method yields a Gaussian closure--a structured Gaussian process posterior capturing the uncertainty in the output function of the neural operator , which can be evaluated at an arbitrary set of points."}
+{"idx": 1, "title": "Linearization Turns Neural Operators into Function-Valued Gaussian ...", "date": "", "ddg_snippet": "Finally, probabilistic currying transforms f into a function-valued Gaussian process posterior F over the operator learned by the neural operator F (bottom left).", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=4Z04wVQ9FY", "content": "Finally, probabilistic currying transforms f into a function-valued Gaussian process posterior F over the operator learned by the neural operator F (bottom left)."}
+{"idx": 2, "title": "Linearization Turns Neural Operators into Function-Valued Gaussian ...", "date": "", "ddg_snippet": "The paper presents NOLA, a framework that linearizes neural operators into function-valued Gaussian processes for uncertainty quantification in PDE models.", "subpage_snippet": "", "source": "www.emergentmind.com", "link": "https://www.emergentmind.com/papers/2406.05072", "content": "The paper presents NOLA, a framework that linearizes neural operators into function-valued Gaussian processes for uncertainty quantification in PDE models."}
+{"idx": 3, "title": "LUNO: Linearized Predictive Uncertainty in Neural Operators", "date": "", "ddg_snippet": "This repository contains the main algorithm of the paper \" Linearization Turns Neural Operators into Function-Valued Gaussian Processes \" by Magnani et al. (2025).", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/MethodsOfMachineLearning/luno", "content": "This repository contains the main algorithm of the paper \" Linearization Turns Neural Operators into Function-Valued Gaussian Processes \" by Magnani et al. (2025)."}
+{"idx": 4, "title": "Linearization Turns Neural Operators into Function-Valued Gaussian ...", "date": "", "ddg_snippet": "We want to use model linearization to extend the Gaussian belief over the parameters of a neural network into a (multi-output) Gaussian process belief over the function learned by the neural network.", "subpage_snippet": "", "source": "bytez.com", "link": "https://bytez.com/docs/icml/46474/paper", "content": "We want to use model linearization to extend the Gaussian belief over the parameters of a neural network into a (multi-output) Gaussian process belief over the function learned by the neural network."}
+{"idx": 5, "title": "PDF Abstract Linearization Turns Neural Operators into Function-Valued Gaussian", "date": "", "ddg_snippet": "In Section 2 we provide a brief overview of neural operators , (multi-output) Gaussian processes , and the linearized Laplace approximation. In Section 3 we first develop Gaussian processes taking ...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/381294298_Linearization_Turns_Neural_Operators_into_Function-Valued_Gaussian_Processes/fulltext/6666938685a4ee7261b375f0/Linearization-Turns-Neural-Operators-into-Function-Valued-Gaussian-Processes.pdf", "content": "In Section 2 we provide a brief overview of neural operators , (multi-output) Gaussian processes , and the linearized Laplace approximation. In Section 3 we first develop Gaussian processes taking ..."}
+{"idx": 6, "title": "Linearization Turns Neural Operators into Function-Valued Gaussian ...", "date": "", "ddg_snippet": "Spotlight Poster Linearization Turns Neural Operators into Function-Valued Gaussian Processes Emilia Magnani · Marvin Pförtner · Tobias Weber · Philipp Hennig East Exhibition Hall A-B #E-1207", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/poster/46474", "content": "Spotlight Poster Linearization Turns Neural Operators into Function-Valued Gaussian Processes Emilia Magnani · Marvin Pförtner · Tobias Weber · Philipp Hennig East Exhibition Hall A-B #E-1207"}
+{"idx": 7, "title": "Linearization Turns Neural Operators into Function-Valued Gaussian ...", "date": "", "ddg_snippet": "最近在这个领域中的相关研究包括：1.《Deep Learning for Partial Differential Equations: A Review》；2.《Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations》；3.《Solving High-Dimensional Partial Differential Equations Using ...", "subpage_snippet": "", "source": "hub.baai.ac.cn", "link": "https://hub.baai.ac.cn/paper/a1594339-87b9-4e4a-88e1-b3645371a7f8", "content": "最近在这个领域中的相关研究包括：1.《Deep Learning for Partial Differential Equations: A Review》；2.《Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations》；3.《Solving High-Dimensional Partial Differential Equations Using ..."}
+{"idx": 8, "title": "Linearization Turns Neural Operators into Function-Valued Gaussian ...", "date": "", "ddg_snippet": "Our approach leverages model linearization to push ( Gaussian ) weight-space uncertainty forward to the neural operator's predictions. We show that this can be interpreted as a probabilistic version of the concept of currying from functional programming, yielding a function-valued ( Gaussian ) random process belief.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=4Z04wVQ9FY", "content": "Our approach leverages model linearization to push ( Gaussian ) weight-space uncertainty forward to the neural operator's predictions. We show that this can be interpreted as a probabilistic version of the concept of currying from functional programming, yielding a function-valued ( Gaussian ) random process belief."}
+{"idx": 9, "title": "Neural Operator: Learning Maps Between Function Spaces With ...", "date": "", "ddg_snippet": "We propose a generalization of neural networks to learn operators , termed neural operators , that map between infinite dimensional function spaces. We formulate the neural operator as a composition of linear integral operators and nonlinear activation functions .", "subpage_snippet": "", "source": "jmlr.org", "link": "https://jmlr.org/papers/v24/21-1524.html", "content": "We propose a generalization of neural networks to learn operators , termed neural operators , that map between infinite dimensional function spaces. We formulate the neural operator as a composition of linear integral operators and nonlinear activation functions ."}

data/sampled_jsons/4gWE7CMOlH_Soft_Reasoning_r_coherence(y)_formula_page_4.jsonl

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+{"idx": 0, "title": "GitHub - alickzhu/Soft-Reasoning: code for paper: Soft Reasoning ...", "date": "", "ddg_snippet": "code for paper: Soft Reasoning : Navigating Solution Spaces in Large Language Models through Controlled Embedding Exploration - alickzhu/Soft- Reasoning", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/alickzhu/Soft-Reasoning", "content": "code for paper: Soft Reasoning : Navigating Solution Spaces in Large Language Models through Controlled Embedding Exploration - alickzhu/Soft- Reasoning"}
+{"idx": 1, "title": "Soft Reasoning: Navigating Solution Spaces in Large ... - OpenReview", "date": "", "ddg_snippet": "TL;DR: We propose an embedding-based search framework that optimises the first token's embedding through controlled perturbation and Bayesian refinement to enhance reasoning accuracy and coherence in large language models with minimal computation.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=4gWE7CMOlH", "content": "TL;DR: We propose an embedding-based search framework that optimises the first token's embedding through controlled perturbation and Bayesian refinement to enhance reasoning accuracy and coherence in large language models with minimal computation."}
+{"idx": 2, "title": "[2505.24688] Soft Reasoning: Navigating Solution Spaces in Large ...", "date": "", "ddg_snippet": "Large Language Models (LLMs) struggle with complex reasoning due to limited diversity and inefficient search. We propose Soft Reasoning , an embedding-based search framework that optimises the embedding of the first token to guide generation. It combines (1) embedding perturbation for controlled exploration and (2) Bayesian optimisation to refine embeddings via a verifier-guided objective ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2505.24688", "content": "Large Language Models (LLMs) struggle with complex reasoning due to limited diversity and inefficient search. We propose Soft Reasoning , an embedding-based search framework that optimises the embedding of the first token to guide generation. It combines (1) embedding perturbation for controlled exploration and (2) Bayesian optimisation to refine embeddings via a verifier-guided objective ..."}
+{"idx": 3, "title": "Soft Reasoning: Navigating Solution Spaces in Large Language Models ...", "date": "", "ddg_snippet": "Abstract Large Language Models (LLMs) struggle with complex reasoning due to limited diversity and inefficient search. We propose Soft Reasoning , an embedding-based search framework that optimises the embedding of the first token to guide generation. It combines (1) embedding perturbation for controlled exploration and (2) Bayesian optimisation to refine embeddings via a verifier-guided ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2505.24688", "content": "Abstract Large Language Models (LLMs) struggle with complex reasoning due to limited diversity and inefficient search. We propose Soft Reasoning , an embedding-based search framework that optimises the embedding of the first token to guide generation. It combines (1) embedding perturbation for controlled exploration and (2) Bayesian optimisation to refine embeddings via a verifier-guided ..."}
+{"idx": 4, "title": "Reasoning datasets - a open-r1 Collection - Hugging Face", "date": "", "ddg_snippet": "Datasets with reasoning traces for math and code released by the community", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/collections/open-r1/reasoning-datasets-67980cac6e816a0eda98c678", "content": "Datasets with reasoning traces for math and code released by the community"}
+{"idx": 5, "title": "(PDF) Soft Reasoning: Navigating Solution Spaces in Large Language ...", "date": "", "ddg_snippet": "Soft Reasoning : Navigating Solution Spaces in Large Language Models through Controlled Embedding Exploration Based on the given question and the previous answers, please provide your analysis and ...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/392314552_Soft_Reasoning_Navigating_Solution_Spaces_in_Large_Language_Models_through_Controlled_Embedding_Exploration", "content": "Soft Reasoning : Navigating Solution Spaces in Large Language Models through Controlled Embedding Exploration Based on the given question and the previous answers, please provide your analysis and ..."}
+{"idx": 6, "title": "Soft Reasoning: Navigating Solution Spaces in Large Language Models ...", "date": "", "ddg_snippet": "Abstract Large Language Models (LLMs) struggle with complex reasoning due to limited diversity and ineficient search. We propose Soft Reason-ing , an embedding-based search framework that optimises the embedding of the first token to guide generation. It combines (1) embed-ding perturbation for controlled exploration and (2) Bayesian optimisation to refine embeddings via a verifier-guided ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2505.24688", "content": "Abstract Large Language Models (LLMs) struggle with complex reasoning due to limited diversity and ineficient search. We propose Soft Reason-ing , an embedding-based search framework that optimises the embedding of the first token to guide generation. It combines (1) embed-ding perturbation for controlled exploration and (2) Bayesian optimisation to refine embeddings via a verifier-guided ..."}
+{"idx": 7, "title": "Musr: Testing the Limits of Chain-of-thought With Multistep Soft Reasoning", "date": "", "ddg_snippet": "In this work, we present MuSR: Multistep Soft Reasoning , a dataset focused on tasks involving reasoning based on text narratives. The narratives in our dataset are hundreds of words long and present evidence in ways that require commonsense knowledge to unpack.", "subpage_snippet": "", "source": "par.nsf.gov", "link": "https://par.nsf.gov/servlets/purl/10516573", "content": "In this work, we present MuSR: Multistep Soft Reasoning , a dataset focused on tasks involving reasoning based on text narratives. The narratives in our dataset are hundreds of words long and present evidence in ways that require commonsense knowledge to unpack."}
+{"idx": 8, "title": "Soft Thinking: Unlocking the Reasoning Potential of LLMs in ...", "date": "", "ddg_snippet": "🧠 Soft Thinking: Unlocking the Reasoning Potential of LLMs in Continuous Concept Space", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/eric-ai-lab/Soft-Thinking", "content": "🧠 Soft Thinking: Unlocking the Reasoning Potential of LLMs in Continuous Concept Space"}
+{"idx": 9, "title": "Soft Thinking: Unlocking the Reasoning Potential of LLMs in Continuous ...", "date": "", "ddg_snippet": "Qualitative analysis further reveals that Soft Thinking outputs remain highly interpretable and readable, highlighting the potential of Soft Thinking to break the inherent bottleneck of discrete language-based reasoning . Code is available at this https URL.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2505.15778", "content": "Qualitative analysis further reveals that Soft Thinking outputs remain highly interpretable and readable, highlighting the potential of Soft Thinking to break the inherent bottleneck of discrete language-based reasoning . Code is available at this https URL."}

data/sampled_jsons/4uOEiitySn_A_Checks-and-Balances_Framework_Context-Aware_Ethical_AI_Alignment_Section_3.2_four-step_.jsonl

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+{"idx": 0, "title": "A Checks-and-Balances Framework for Context-Aware Ethical AI Alignment", "date": "", "ddg_snippet": "This paper introduces a checks - and - balances framework for ethical alignment of Large Language Models (LLMs), inspired by three-branch governmental systems. It implements three independent yet interacting components: LLMs as the executive branch for knowledge generation, DIKE as the legislative branch establishing ethical guardrails, and ERIS as the judicial branch for contextual interpretation ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2502.00136", "content": "This paper introduces a checks - and - balances framework for ethical alignment of Large Language Models (LLMs), inspired by three-branch governmental systems. It implements three independent yet interacting components: LLMs as the executive branch for knowledge generation, DIKE as the legislative branch establishing ethical guardrails, and ERIS as the judicial branch for contextual interpretation ..."}
+{"idx": 1, "title": "A Checks-and-Balances Framework for Context-Aware Ethical AI Alignment", "date": "", "ddg_snippet": "This paper introduces a checks - and - balances framework for ethical alignment of Large Lan-guage Models (LLMs), inspired by three-branch governmental systems. It implements three in-dependent yet interacting components: LLMs as the executive branch for knowledge generation, Dike as the legislative branch that establishes eth-ical guardrails, and ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=4uOEiitySn", "content": "This paper introduces a checks - and - balances framework for ethical alignment of Large Lan-guage Models (LLMs), inspired by three-branch governmental systems. It implements three in-dependent yet interacting components: LLMs as the executive branch for knowledge generation, Dike as the legislative branch that establishes eth-ical guardrails, and ..."}
+{"idx": 2, "title": "A Three-Branch Checks-and-Balances Frameworkfor Context-Aware Ethical ...", "date": "", "ddg_snippet": "This paper introduces a three-branch checks - and - balances framework for ethical alignment of Large Language Models (LLMs), inspired by governmental systems. It implements three independent yet interacting components: LLMs as the executive branch for knowledge generation, DIKE as the legislative branch establishing ethical guardrails, and ERIS as ...", "subpage_snippet": "", "source": "researchtrend.ai", "link": "https://researchtrend.ai/papers/2502.00136", "content": "This paper introduces a three-branch checks - and - balances framework for ethical alignment of Large Language Models (LLMs), inspired by governmental systems. It implements three independent yet interacting components: LLMs as the executive branch for knowledge generation, DIKE as the legislative branch establishing ethical guardrails, and ERIS as ..."}
+{"idx": 3, "title": "PDF An Adversarial Behavior Model for Contextual Ethical Alignment in Large ...", "date": "", "ddg_snippet": "Our pilot studies have shown promising results, indicating the effectiveness of self-supervised learning and adversarial processes in refining AI's interaction with ethically and culturally ...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/profile/Edward-Chang-22/publication/380515639_A_Three-Branch_Checks-and-Balances_Framework_for_Context-Aware_Ethical_Alignment_of_Large_Language_Models/links/671b315b55a5271cded9457e/A-Three-Branch-Checks-and-Balances-Framework-for-Context-Aware-Ethical-Alignment-of-Large-Language-Models.pdf", "content": "Our pilot studies have shown promising results, indicating the effectiveness of self-supervised learning and adversarial processes in refining AI's interaction with ethically and culturally ..."}
+{"idx": 4, "title": "Benchmarking, ethical alignment, and evaluation framework for ...", "date": "", "ddg_snippet": "Adaptive Standards and Intelligent Evaluation: This research paper proposes a comprehensive framework for evaluating ChatGPT that includes adaptive standards to keep pace with the dynamic nature of conversational AI . The framework incorporates ethical considerations, context adaptability, and community collaboration.", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S2772485923000534", "content": "Adaptive Standards and Intelligent Evaluation: This research paper proposes a comprehensive framework for evaluating ChatGPT that includes adaptive standards to keep pace with the dynamic nature of conversational AI . The framework incorporates ethical considerations, context adaptability, and community collaboration."}
+{"idx": 5, "title": "A Three-Branch Checks-and-Balances Frameworkfor Context-Aware Ethical ...", "date": "", "ddg_snippet": "A three-branch checks - and - balances framework for ethical alignment of Large Language Models, inspired by governmental systems, demonstrates how emotional modeling can guide linguistic behaviors toward ethical outcomes while preserving independence across knowledge generation, ethical oversight, and contextual interpretation.", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/A-Three-Branch-Checks-and-Balances-Frameworkfor-of-Chang/5918a91419cf95db8599b086590facf63f124702/figure/4", "content": "A three-branch checks - and - balances framework for ethical alignment of Large Language Models, inspired by governmental systems, demonstrates how emotional modeling can guide linguistic behaviors toward ethical outcomes while preserving independence across knowledge generation, ethical oversight, and contextual interpretation."}
+{"idx": 6, "title": "PDF A Checks-and-Balances Framework for Context-Aware Ethical AI Alignment", "date": "", "ddg_snippet": "A Checks - and - Balances Framework for Context-Aware Ethical AI Alignment Susceptible to social biases Vulnerable to reward hacking \"Whack-A-Mole\" reactive approach Catastrophic forgetting issues", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/media/icml-2025/Slides/46461.pdf", "content": "A Checks - and - Balances Framework for Context-Aware Ethical AI Alignment Susceptible to social biases Vulnerable to reward hacking \"Whack-A-Mole\" reactive approach Catastrophic forgetting issues"}
+{"idx": 7, "title": "A Checks-and-Balances Framework for Context-Aware Ethical AI Alignment", "date": "", "ddg_snippet": "Abstract This paper introduces a checks - and - balances framework for ethical alignment of Large Lan-guage Models (LLMs), inspired by three-branch governmental systems. It implements three in-dependent yet interacting components: LLMs as the executive branch for knowledge generation, Dike as the legislative branch that establishes eth-ical guardrails, and Eris as the judicial branch for ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2502.00136", "content": "Abstract This paper introduces a checks - and - balances framework for ethical alignment of Large Lan-guage Models (LLMs), inspired by three-branch governmental systems. It implements three in-dependent yet interacting components: LLMs as the executive branch for knowledge generation, Dike as the legislative branch that establishes eth-ical guardrails, and Eris as the judicial branch for ..."}
+{"idx": 8, "title": "infolab.stanford.edu", "date": "", "ddg_snippet": "@article{chang2025threebranch, title={A Three-Branch Checks - and - Balances Framework for Context-Aware Ethical Alignment of Large Language Models}, author={Chang ...", "subpage_snippet": "", "source": "infolab.stanford.edu", "link": "http://infolab.stanford.edu/~echang/Behavior2024.bib", "content": "@article{chang2025threebranch, title={A Three-Branch Checks - and - Balances Framework for Context-Aware Ethical Alignment of Large Language Models}, author={Chang ..."}
+{"idx": 9, "title": "Edward Y. Chang - Stanford University", "date": "", "ddg_snippet": "A Three-Branch Checks - and - Balances Framework for Context-Aware Ethical Alignment of Large Language Models Edward Y. Chang NeurIPS AI Safety, Decmber 2024 PDF | BibTex Behavioral Emotion Analysis Model for Large Language Models Edward Y. Chang IEEE Conference on Multimedia Information Processing and Retrieval (MIPR) (invited paper), August 2024", "subpage_snippet": "", "source": "infolab.stanford.edu", "link": "http://infolab.stanford.edu/~echang/", "content": "A Three-Branch Checks - and - Balances Framework for Context-Aware Ethical Alignment of Large Language Models Edward Y. Chang NeurIPS AI Safety, Decmber 2024 PDF | BibTex Behavioral Emotion Analysis Model for Large Language Models Edward Y. Chang IEEE Conference on Multimedia Information Processing and Retrieval (MIPR) (invited paper), August 2024"}

data/sampled_jsons/4uOEiitySn_A_Section_3.2_self-supervised_learning_behaviors_emotions_four_steps.jsonl

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+{"idx": 0, "title": "", "date": "", "ddg_snippet": "", "subpage_snippet": "", "source": "", "link": "", "content": ""}

data/sampled_jsons/51x0dfsD8A_Section_4_Time_complexity_Algorithm_2.jsonl

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+{"idx": 0, "title": "Dynamic Time Warping Under Limited Warping Path Length", "date": "", "ddg_snippet": "4 .8. Time Complexity and Parallelization In this subsection, we discuss the time complexity and running time of LDTW in comparison with. other well-established methods.Memory and time improvements in a dynamic programming algorithm for matching speech patterns.", "subpage_snippet": "", "source": "hal.science", "link": "https://hal.science/hal-01470554/document", "content": "4 .8. Time Complexity and Parallelization In this subsection, we discuss the time complexity and running time of LDTW in comparison with. other well-established methods.Memory and time improvements in a dynamic programming algorithm for matching speech patterns."}
+{"idx": 1, "title": "Reinforcement learning-guided Animated Oat Optimization Algorithm ...", "date": "", "ddg_snippet": "Section 2 reviews reinforcement learning and the Animated Oat Optimization Algorithm , and identifies the corresponding research gaps. In Section 3, the RLDN-AOO algorithm is described in detail.", "subpage_snippet": "", "source": "www.aimspress.com", "link": "https://www.aimspress.com/article/doi/10.3934/era.2025248", "content": "Section 2 reviews reinforcement learning and the Animated Oat Optimization Algorithm , and identifies the corresponding research gaps. In Section 3, the RLDN-AOO algorithm is described in detail."}
+{"idx": 2, "title": "Enumerating Graphlets with Amortized Time Complexity Independent...", "date": "", "ddg_snippet": "We first consider the time complexity of each node X, i.e., of a single recursive call.A slight variation of what we propose in this section also gives an enumeration algorithm for all k-subtrees with the same time and space complexity.", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/article/10.1007/s00453-025-01312-0", "content": "We first consider the time complexity of each node X, i.e., of a single recursive call.A slight variation of what we propose in this section also gives an enumeration algorithm for all k-subtrees with the same time and space complexity."}
+{"idx": 3, "title": "A Pattern and Summarization Based Optimization Algorithm to QoS", "date": "", "ddg_snippet": "In Algorithm 2 , for each dimension i of a new wolf. (WSC), represented as wω-new.cai (see Section 4 .2.1), a candidate is selected. The sum of the quality attributes (QAs) for the ith dimension of all wolves is then computed to identify the best candidate for that dimension (service).", "subpage_snippet": "", "source": "jad.shahroodut.ac.ir", "link": "https://jad.shahroodut.ac.ir/article_3415_050f4e9a0bfd40c2253b58f06f17f6e3.pdf", "content": "In Algorithm 2 , for each dimension i of a new wolf. (WSC), represented as wω-new.cai (see Section 4 .2.1), a candidate is selected. The sum of the quality attributes (QAs) for the ith dimension of all wolves is then computed to identify the best candidate for that dimension (service)."}
+{"idx": 4, "title": "Deterministic Dynamic Maximal Matching in Sublinear Update Time", "date": "", "ddg_snippet": "For ease of exposition, we first present the algorithm in a decremental setting, where the input graph only undergoes edge deletions. We will show how to handle fully dynamic updates without increasing update time at the end in Section 4 .7.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2504.20780", "content": "For ease of exposition, we first present the algorithm in a decremental setting, where the input graph only undergoes edge deletions. We will show how to handle fully dynamic updates without increasing update time at the end in Section 4 .7."}
+{"idx": 5, "title": "CCCG 2010, Winnipeg MB, August 9–11, 2010", "date": "", "ddg_snippet": "Section 4 concludes.1. 2 Preliminaries. We recall some denitions from [9] and [27].5The source code can be downloaded at www.mi.auckland.ac. nz/; follow the link at the 2009 MI-tech Report 51. CCCG 2010, Winnipeg MB, August 9–11, 2010.", "subpage_snippet": "", "source": "cerv.aut.ac.nz", "link": "https://cerv.aut.ac.nz/wp-content/uploads/2015/08/MItech-TR-56.pdf", "content": "Section 4 concludes.1. 2 Preliminaries. We recall some denitions from [9] and [27].5The source code can be downloaded at www.mi.auckland.ac. nz/; follow the link at the 2009 MI-tech Report 51. CCCG 2010, Winnipeg MB, August 9–11, 2010."}
+{"idx": 6, "title": "Proof of Theorem 1", "date": "", "ddg_snippet": "E Optimizing Split Time Complexity . Algorithm 2 gives pseudocode for finding the optimal split for a given feature.Table 8: Training time mean (sd) in seconds for a single tree random forest.", "subpage_snippet": "", "source": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper_files/paper/2022/file/98257285340854262185500e59bc0f28-Supplemental-Conference.pdf", "content": "E Optimizing Split Time Complexity . Algorithm 2 gives pseudocode for finding the optimal split for a given feature.Table 8: Training time mean (sd) in seconds for a single tree random forest."}
+{"idx": 7, "title": "Constraint Programming for Mining Borders of Frequent Itemsets", "date": "", "ddg_snippet": "Section 4 analyzes the problem of mining constrained MFIs or MIIs.Theorem 3 Given a transaction dataset D of n items and m transactions, Algorithm 1 has an O(n 2 × m) time complexity .", "subpage_snippet": "", "source": "hal-lirmm.ccsd.cnrs.fr", "link": "https://hal-lirmm.ccsd.cnrs.fr/lirmm-02310629/document", "content": "Section 4 analyzes the problem of mining constrained MFIs or MIIs.Theorem 3 Given a transaction dataset D of n items and m transactions, Algorithm 1 has an O(n 2 × m) time complexity ."}
+{"idx": 8, "title": "Minimum Displacement in Existing Moment... | Preprints.org", "date": "", "ddg_snippet": "4 . Time Complexity Analysis of the MDEM Algorithm . In this section , we will analyze the time complexity of our proposed algorithm both in training and testing phase.", "subpage_snippet": "", "source": "www.preprints.org", "link": "https://www.preprints.org/manuscript/202501.0999/v1", "content": "4 . Time Complexity Analysis of the MDEM Algorithm . In this section , we will analyze the time complexity of our proposed algorithm both in training and testing phase."}
+{"idx": 9, "title": "Main-memory Triangle Computations for", "date": "", "ddg_snippet": "Notation for precise space complexity . In the context of complex network studies, the dierence between an algorithm with a given time com", "subpage_snippet": "", "source": "www-complexnetworks.lip6.fr", "link": "https://www-complexnetworks.lip6.fr/~latapy/Publis/triangles.pdf", "content": "Notation for precise space complexity . In the context of complex network studies, the dierence between an algorithm with a given time com"}

data/sampled_jsons/583klsIjNx_ELITE_Enhanced_Language-Image_Toxicity_Evaluation_E-ASR_gap_VLGuard_reason.jsonl

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+{"idx": 0, "title": "ICML Poster ELITE : Enhanced Language - Image Toxicity Evaluation ...", "date": "", "ddg_snippet": "ELITE : Enhanced Language - Image Toxicity Evaluation for Safety.The ELITE evaluator explicitly incorporates a toxicity score to accurately assess harmfulness in multimodal contexts, where VLMs often provide specific, convincing, but unharmful descriptions of images.", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/poster/46445", "content": "ELITE : Enhanced Language - Image Toxicity Evaluation for Safety.The ELITE evaluator explicitly incorporates a toxicity score to accurately assess harmfulness in multimodal contexts, where VLMs often provide specific, convincing, but unharmful descriptions of images."}
+{"idx": 1, "title": "Understanding and Mitigating Toxicity in Image -Text Pretraining...", "date": "", "ddg_snippet": "ELITE [12] evaluator explicitly incorporates a toxicity score to accurately assess harmfulness in multimodal contexts, where VLMs often provide specific, convincing, but unharmful descriptions of images. Elite : Enhanced language - image toxicity evaluation for safety, 2025.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2505.06356v1", "content": "ELITE [12] evaluator explicitly incorporates a toxicity score to accurately assess harmfulness in multimodal contexts, where VLMs often provide specific, convincing, but unharmful descriptions of images. Elite : Enhanced language - image toxicity evaluation for safety, 2025."}
+{"idx": 2, "title": "[Literature Review] Beautiful Images , Toxic Words: Understanding and...", "date": "", "ddg_snippet": "[Literature Review] ELITE : Enhanced Language - Image Toxicity Evaluation for Safety. [Literature Review] QLIP: Text-Aligned Visual Tokenization Unifies Auto-Regressive Multimodal Understanding and Generation.", "subpage_snippet": "", "source": "www.themoonlight.io", "link": "https://www.themoonlight.io/en/review/beautiful-images-toxic-words-understanding-and-addressing-offensive-text-in-generated-images", "content": "[Literature Review] ELITE : Enhanced Language - Image Toxicity Evaluation for Safety. [Literature Review] QLIP: Text-Aligned Visual Tokenization Unifies Auto-Regressive Multimodal Understanding and Generation."}
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+{"idx": 9, "title": "How to Increase UVH Level | Borderlands 4｜Game8", "date": "", "ddg_snippet": "Create your free account today and unlock all our premium features and tools to enhance your gaming experience.Borderlands 4 Walkthrough Team. This article was created by Game8's elite team of writers and gamers.", "subpage_snippet": "", "source": "game8.co", "link": "https://game8.co/games/Borderlands-4/archives/551972", "content": "Create your free account today and unlock all our premium features and tools to enhance your gaming experience.Borderlands 4 Walkthrough Team. This article was created by Game8's elite team of writers and gamers."}

data/sampled_jsons/5EbiopWH6e_Implicit_Language_Models_are_RNNs_Balancing_Parallelization_and_Expressivity.jsonl

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+{"idx": 0, "title": "Implicit Language Models are RNNs: Balancing Parallelization ...", "date": "", "ddg_snippet": "Abstract State-space models (SSMs) and transformers dominate the language modeling landscape. How-ever, they are constrained to a lower computa-tional complexity than classical recurrent neu-ral networks ( RNNs ), limiting their expressiv-ity . In contrast, RNNs lack parallelization dur-ing training, raising fundamental questions about the trade off between parallelization and expres-sivity . We ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=5EbiopWH6e", "content": "Abstract State-space models (SSMs) and transformers dominate the language modeling landscape. How-ever, they are constrained to a lower computa-tional complexity than classical recurrent neu-ral networks ( RNNs ), limiting their expressiv-ity . In contrast, RNNs lack parallelization dur-ing training, raising fundamental questions about the trade off between parallelization and expres-sivity . We ..."}
+{"idx": 1, "title": "Implicit Language Models are RNNs: Balancing Parallelization ... Implicit Language Models are RNNs - GitHub Implicit Language Models are RNNs: Balancing Parallelization ... dblp: Implicit Language Models are RNNs: Balancing ... Implicit Language Models are RNNs: Balancing Parallelization ... Results from our latest preprint \"Implicit Language Models ... Implicit Language Models are RNNs: Balancing Parallelization ...", "date": "", "ddg_snippet": "Feb 10, 2025 · State-space models (SSMs) and transformers dominate the language modeling landscape. However, they are constrained to a lower computational complexity than classical recurrent neural networks ( RNNs ), limiting their expressivity . In contrast, RNNs lack parallelization during training, raising fundamental questions about the trade off between parallelization and expressivity . We propose implicit ... Implicit Language Models are RNNs Balancing Parallelization and Expressivity Mark Schöne 1,2 *, Babak Rahmani 2 *, Heiner Kremer 2, Fabian Falck 2, Hitesh Ballani 2, Jannes Gladrow 2 † 1 TU Dresden, Germany, 2 Microsoft Research, Cambridge, UK (*) Equal contribution. ( † ) Corresponding author. Abstract State-space models (SSMs) and transformers dominate the language modeling landscape. How-ever, they are constrained to a lower computa-tional complexity than classical recurrent neu-ral networks ( RNNs ), limiting their expressiv-ity . In contrast, RNNs lack parallelization dur-ing training, raising fundamental questions about the trade off between parallelization and expres-sivity . We ... Mar 12, 2025 · Bibliographic details on Implicit Language Models are RNNs : Balancing Parallelization and Expressivity . Feb 10, 2025 · This work proposes implicit SSMs, which iterate a transformation until convergence to a fixed point, and shows that only approximate fixed-point convergence suffices, enabling the design of a scalable training curriculum that largely retains parallelization , with full convergence required only for a small subset of tokens. State-space models (SSMs) and transformers dominate the language ... 💡 Results from our latest preprint \" Implicit Language Models are RNNs : Balancing Parallelization and Expressivity \" with Microsoft Research. We explored the algorithmic properties of ... In contrast, RNNs lack parallelization during training, raising fundamental questions about the trade off between parallelization and expressivity . We propose implicit SSMs, which iterate a transformation until convergence to a fixed point. Theoretically, we show that implicit SSMs implement the non-linear state-transitions of RNNs .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2502.07827", "content": "Feb 10, 2025 · State-space models (SSMs) and transformers dominate the language modeling landscape. However, they are constrained to a lower computational complexity than classical recurrent neural networks ( RNNs ), limiting their expressivity . In contrast, RNNs lack parallelization during training, raising fundamental questions about the trade off between parallelization and expressivity . We propose implicit ... Implicit Language Models are RNNs Balancing Parallelization and Expressivity Mark Schöne 1,2 *, Babak Rahmani 2 *, Heiner Kremer 2, Fabian Falck 2, Hitesh Ballani 2, Jannes Gladrow 2 † 1 TU Dresden, Germany, 2 Microsoft Research, Cambridge, UK (*) Equal contribution. ( † ) Corresponding author. Abstract State-space models (SSMs) and transformers dominate the language modeling landscape. How-ever, they are constrained to a lower computa-tional complexity than classical recurrent neu-ral networks ( RNNs ), limiting their expressiv-ity . In contrast, RNNs lack parallelization dur-ing training, raising fundamental questions about the trade off between parallelization and expres-sivity . We ... Mar 12, 2025 · Bibliographic details on Implicit Language Models are RNNs : Balancing Parallelization and Expressivity . Feb 10, 2025 · This work proposes implicit SSMs, which iterate a transformation until convergence to a fixed point, and shows that only approximate fixed-point convergence suffices, enabling the design of a scalable training curriculum that largely retains parallelization , with full convergence required only for a small subset of tokens. State-space models (SSMs) and transformers dominate the language ... 💡 Results from our latest preprint \" Implicit Language Models are RNNs : Balancing Parallelization and Expressivity \" with Microsoft Research. We explored the algorithmic properties of ... In contrast, RNNs lack parallelization during training, raising fundamental questions about the trade off between parallelization and expressivity . We propose implicit SSMs, which iterate a transformation until convergence to a fixed point. Theoretically, we show that implicit SSMs implement the non-linear state-transitions of RNNs ."}
+{"idx": 2, "title": "Implicit Language Models are RNNs - GitHub", "date": "", "ddg_snippet": "Implicit Language Models are RNNs Balancing Parallelization and Expressivity Mark Schöne 1,2 *, Babak Rahmani 2 *, Heiner Kremer 2, Fabian Falck 2, Hitesh Ballani 2, Jannes Gladrow 2 † 1 TU Dresden, Germany, 2 Microsoft Research, Cambridge, UK (*) Equal contribution. ( † ) Corresponding author.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/microsoft/implicit_languagemodels/blob/main/README.md", "content": "Implicit Language Models are RNNs Balancing Parallelization and Expressivity Mark Schöne 1,2 *, Babak Rahmani 2 *, Heiner Kremer 2, Fabian Falck 2, Hitesh Ballani 2, Jannes Gladrow 2 † 1 TU Dresden, Germany, 2 Microsoft Research, Cambridge, UK (*) Equal contribution. ( † ) Corresponding author."}
+{"idx": 3, "title": "dblp: Implicit Language Models are RNNs: Balancing ...", "date": "", "ddg_snippet": "Mar 12, 2025 · Bibliographic details on Implicit Language Models are RNNs : Balancing Parallelization and Expressivity .", "subpage_snippet": "", "source": "dblp.org", "link": "https://dblp.org/rec/journals/corr/abs-2502-07827", "content": "Mar 12, 2025 · Bibliographic details on Implicit Language Models are RNNs : Balancing Parallelization and Expressivity ."}
+{"idx": 4, "title": "Implicit Language Models are RNNs: Balancing Parallelization ...", "date": "", "ddg_snippet": "Feb 10, 2025 · This work proposes implicit SSMs, which iterate a transformation until convergence to a fixed point, and shows that only approximate fixed-point convergence suffices, enabling the design of a scalable training curriculum that largely retains parallelization , with full convergence required only for a small subset of tokens. State-space models (SSMs) and transformers dominate the language ...", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/Implicit-Language-Models-are-RNNs:-Balancing-and-Schöne-Rahmani/263d2563523f744b63a558aa35a7d4442d7d1da0", "content": "Feb 10, 2025 · This work proposes implicit SSMs, which iterate a transformation until convergence to a fixed point, and shows that only approximate fixed-point convergence suffices, enabling the design of a scalable training curriculum that largely retains parallelization , with full convergence required only for a small subset of tokens. State-space models (SSMs) and transformers dominate the language ..."}
+{"idx": 5, "title": "Results from our latest preprint \"Implicit Language Models ...", "date": "", "ddg_snippet": "💡 Results from our latest preprint \" Implicit Language Models are RNNs : Balancing Parallelization and Expressivity \" with Microsoft Research. We explored the algorithmic properties of ...", "subpage_snippet": "", "source": "www.linkedin.com", "link": "https://www.linkedin.com/feed/update/urn:li:activity:7297205635211821056/", "content": "💡 Results from our latest preprint \" Implicit Language Models are RNNs : Balancing Parallelization and Expressivity \" with Microsoft Research. We explored the algorithmic properties of ..."}
+{"idx": 6, "title": "Implicit Language Models are RNNs: Balancing Parallelization ...", "date": "", "ddg_snippet": "In contrast, RNNs lack parallelization during training, raising fundamental questions about the trade off between parallelization and expressivity . We propose implicit SSMs, which iterate a transformation until convergence to a fixed point. Theoretically, we show that implicit SSMs implement the non-linear state-transitions of RNNs .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2502.07827", "content": "In contrast, RNNs lack parallelization during training, raising fundamental questions about the trade off between parallelization and expressivity . We propose implicit SSMs, which iterate a transformation until convergence to a fixed point. Theoretically, we show that implicit SSMs implement the non-linear state-transitions of RNNs ."}
+{"idx": 7, "title": "Implicit Language Models are RNNs : Balancing Parallelization and ...", "date": "", "ddg_snippet": "In contrast, RNNs lack parallelization during training, raising fundamental questions about the trade off between parallelization and expressivity . We propose implicit SSMs, which iterate a transformation until convergence to a fixed point.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2502.07827v3", "content": "In contrast, RNNs lack parallelization during training, raising fundamental questions about the trade off between parallelization and expressivity . We propose implicit SSMs, which iterate a transformation until convergence to a fixed point."}
+{"idx": 8, "title": "(PDF) Implicit Language Models are RNNs : Balancing ...", "date": "", "ddg_snippet": "implicit models that combine the expressive power of RNNs .All models were evaluated using one H100 80GB GPU. 20. Implicit Language Models are RNNs : Balancing Parallelization and Expressivity .", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/388955170_Implicit_Language_Models_are_RNNs_Balancing_Parallelization_and_Expressivity", "content": "implicit models that combine the expressive power of RNNs .All models were evaluated using one H100 80GB GPU. 20. Implicit Language Models are RNNs : Balancing Parallelization and Expressivity ."}
+{"idx": 9, "title": "GitHub - microsoft/ implicit _languagemodels", "date": "", "ddg_snippet": "MIT license. Security. Implicit Language Models are RNNs . Balancing Parallelization and Expressivity .", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/microsoft/implicit_languagemodels", "content": "MIT license. Security. Implicit Language Models are RNNs . Balancing Parallelization and Expressivity ."}

data/sampled_jsons/5IpVe9PH14_Catoni_Contextual_Bandits_are_Robust_to_Heavy-tailed_Rewards.jsonl

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+{"idx": 0, "title": "Catoni Contextual Bandits are Robust to Heavy - tailed Rewards", "date": "", "ddg_snippet": "bandit algorithm carefully peels the samples based on their uncertainty and utilizes a plug-in estimator for the sum of variances. The algorithm also obtains a variance-based bound depending on R logarithmically, but has a worse dependence on the eluder dimension.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=5IpVe9PH14", "content": "bandit algorithm carefully peels the samples based on their uncertainty and utilizes a plug-in estimator for the sum of variances. The algorithm also obtains a variance-based bound depending on R logarithmically, but has a worse dependence on the eluder dimension."}
+{"idx": 1, "title": "ICML Poster Catoni Contextual Bandits are Robust to Heavy - tailed ...", "date": "", "ddg_snippet": "Abstract: Typical contextual bandit algorithms assume that the rewards at each round lie in some fixed range $[0, R]$, and their regret scales polynomially with this reward range $R$.", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/poster/46438", "content": "Abstract: Typical contextual bandit algorithms assume that the rewards at each round lie in some fixed range $[0, R]$, and their regret scales polynomially with this reward range $R$."}
+{"idx": 2, "title": "Multi-Armed Bandits | Papers With Code", "date": "", "ddg_snippet": "Catoni Contextual Bandits are Robust to Heavy - tailed Rewards .Multi-agent Multi-armed Bandit with Fully Heavy - tailed Dynamics.", "subpage_snippet": "", "source": "paperswithcode.com", "link": "https://paperswithcode.com/task/multi-armed-bandits/codeless?page=4", "content": "Catoni Contextual Bandits are Robust to Heavy - tailed Rewards .Multi-agent Multi-armed Bandit with Fully Heavy - tailed Dynamics."}
+{"idx": 3, "title": "Bandits Corrupted by Nature: Lower Bounds on Regret and Robust ...", "date": "", "ddg_snippet": "Since the rewards are heavy - tailed and corrupted in this setting, we have to use a robust estimator of mean.In this setting, we prove lower bounds on the regret that shows the heavy - tailed bandits and corrupted bandits are strictly harder than the usual sub-Gaussian bandits .", "subpage_snippet": "", "source": "hal.science", "link": "https://hal.science/hal-04615733v1/document", "content": "Since the rewards are heavy - tailed and corrupted in this setting, we have to use a robust estimator of mean.In this setting, we prove lower bounds on the regret that shows the heavy - tailed bandits and corrupted bandits are strictly harder than the usual sub-Gaussian bandits ."}
+{"idx": 4, "title": "On Private and Robust Bandits", "date": "", "ddg_snippet": "We study private and robust multi-armed bandits (MABs), where the agent receives Huber’s contaminated heavy - tailed rewards and meanwhile needs to ensure dif-ferential privacy.", "subpage_snippet": "", "source": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper_files/paper/2023/file/6d13e085b79d454da5910e4ca82a3d9d-Supplemental-Conference.pdf", "content": "We study private and robust multi-armed bandits (MABs), where the agent receives Huber’s contaminated heavy - tailed rewards and meanwhile needs to ensure dif-ferential privacy."}
+{"idx": 5, "title": "(PDF) Heavy - Tailed Reinforcement Learning With Penalized Robust ...", "date": "", "ddg_snippet": "Bandits for Heavy Tail Rewards ’’. In: IEEE Trans- 620. actions on Neural Networks and Learning Systems 621.same, we only introduce the extra regret incurred by heavy- 856. tailed noise PH.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/382095728_Heavy-Tailed_Reinforcement_Learning_with_Penalized_Robust_Estimator", "content": "Bandits for Heavy Tail Rewards ’’. In: IEEE Trans- 620. actions on Neural Networks and Learning Systems 621.same, we only introduce the extra regret incurred by heavy- 856. tailed noise PH."}
+{"idx": 6, "title": "Cooperative Multi-Agent Bandits with Heavy Tails", "date": "", "ddg_snippet": "Cooperative Multiagent Bandits with Heavy Tails . tailed effects, which is the central theme of this paper.For the specic case of (1 + ε)- heavy tailed rewards , the single-agent lower bound provided by (Bubeck et al., 2013) can be easily extended to the cooperative multi-agent case.", "subpage_snippet": "", "source": "proceedings.mlr.press", "link": "https://proceedings.mlr.press/v119/dubey20a/dubey20a.pdf", "content": "Cooperative Multiagent Bandits with Heavy Tails . tailed effects, which is the central theme of this paper.For the specic case of (1 + ε)- heavy tailed rewards , the single-agent lower bound provided by (Bubeck et al., 2013) can be easily extended to the cooperative multi-agent case."}
+{"idx": 7, "title": "Robust Lipschitz Bandits to Adversarial Corruptions", "date": "", "ddg_snippet": "bandits that are robust to adversarial corruptions under both weak and strong adversaries.Another line of work on the robust bandit problem focuses on a more challenging setting with strong adversaries who could observe current actions before attacking rewards .", "subpage_snippet": "", "source": "papers.nips.cc", "link": "https://papers.nips.cc/paper_files/paper/2023/file/238f3b98bbe998b4f2234443907fe663-Paper-Conference.pdf", "content": "bandits that are robust to adversarial corruptions under both weak and strong adversaries.Another line of work on the robust bandit problem focuses on a more challenging setting with strong adversaries who could observe current actions before attacking rewards ."}
+{"idx": 8, "title": "Chenlu Ye - Google Akademik", "date": "", "ddg_snippet": "Sharp analysis for kl-regularized contextual bandits and rlhf.2025. Catoni Contextual Bandits are Robust to Heavy - tailed Rewards .", "subpage_snippet": "", "source": "scholar.google.es", "link": "https://scholar.google.es/citations?user=c8yK5XsAAAAJ&hl=tr", "content": "Sharp analysis for kl-regularized contextual bandits and rlhf.2025. Catoni Contextual Bandits are Robust to Heavy - tailed Rewards ."}
+{"idx": 9, "title": "Chenlu Ye", "date": "", "ddg_snippet": "Catoni Contextual Bandits are Robust to Heavy - tailed Rewards Chenlu Ye*, Yujia Jin, Alekh Agarwal, Tong Zhang, Preprint.", "subpage_snippet": "", "source": "chenluye99.github.io", "link": "https://chenluye99.github.io/", "content": "Catoni Contextual Bandits are Robust to Heavy - tailed Rewards Chenlu Ye*, Yujia Jin, Alekh Agarwal, Tong Zhang, Preprint."}

data/sampled_jsons/9m87e9Keq1_RL_Incorrect_Synthetic_Data_Scales_LLM_Math_Reasoning.jsonl

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+{"idx": 0, "title": "RL on Incorrect Synthetic Data Scales the Efficiency of LLM Math ...", "date": "", "ddg_snippet": "Training on model-generated synthetic data is a promising approach for finetuning LLMs , but it remains unclear when it helps or hurts. In this paper, we investigate this question for math reasoning via an empirical study, followed by building a conceptual understanding of our observations. First, we find that while the typical approach of finetuning a model on synthetic correct or positive ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2406.14532", "content": "Training on model-generated synthetic data is a promising approach for finetuning LLMs , but it remains unclear when it helps or hurts. In this paper, we investigate this question for math reasoning via an empirical study, followed by building a conceptual understanding of our observations. First, we find that while the typical approach of finetuning a model on synthetic correct or positive ..."}
+{"idx": 1, "title": "RL on Incorrect Synthetic Data Scales the Efficiency of LLM Math ...", "date": "", "ddg_snippet": "To provide clarity on how synthetic data contributes to performance, we aim to understand its impact on LLM capabilities via a study on math reasoning , a prevalent scenario where synthetic data is used. Typically, in this setting, synthetic data corresponds to correct or positive model-generated responses for a novel set of initial problems synthesized by prompting capable models [29, 31]. The ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=9m87e9Keq1", "content": "To provide clarity on how synthetic data contributes to performance, we aim to understand its impact on LLM capabilities via a study on math reasoning , a prevalent scenario where synthetic data is used. Typically, in this setting, synthetic data corresponds to correct or positive model-generated responses for a novel set of initial problems synthesized by prompting capable models [29, 31]. The ..."}
+{"idx": 2, "title": "PDF Reinforcement Learning for LLM Reasoning", "date": "", "ddg_snippet": "RL on Incorrect Synthetic Data Scales the Efficiency of LLM Math Reasoning by Eight-Fold. Setlur, Garg, Geng, Garg, Smith, Kumar. NeurIPS 2024 Rewarding Progress: Scaling up Automated Process Supervision for LLM Reasoning", "subpage_snippet": "", "source": "cs224r.stanford.edu", "link": "https://cs224r.stanford.edu/slides/10_cs224r-rl_for_reasoning_lecture.pdf", "content": "RL on Incorrect Synthetic Data Scales the Efficiency of LLM Math Reasoning by Eight-Fold. Setlur, Garg, Geng, Garg, Smith, Kumar. NeurIPS 2024 Rewarding Progress: Scaling up Automated Process Supervision for LLM Reasoning"}
+{"idx": 3, "title": "RL on Incorrect Synthetic Data Scales the Efficiency of LLM Math ...", "date": "", "ddg_snippet": "RL on Incorrect S ynthetic Data Scales the Efficien cy of LLM Math R easo ning by Eight-F old Amrith Setlur 1, Saurabh Garg 1, Xinyang (Young) Geng 2, N aman Garg 3, Virginia Smith 1 and Aviral ...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/381604579_RL_on_Incorrect_Synthetic_Data_Scales_the_Efficiency_of_LLM_Math_Reasoning_by_Eight-Fold", "content": "RL on Incorrect S ynthetic Data Scales the Efficien cy of LLM Math R easo ning by Eight-F old Amrith Setlur 1, Saurabh Garg 1, Xinyang (Young) Geng 2, N aman Garg 3, Virginia Smith 1 and Aviral ..."}
+{"idx": 4, "title": "RL on Incorrect Synthetic Data Scales the Efficiency of LLM Math ...", "date": "", "ddg_snippet": "Abstract Training on model-generated synthetic data is a promising approach for finetuning LLMs , but it remains unclear when it helps or hurts. In this paper, we investigate this question for math reasoning via an empirical study, followed by building a conceptual understanding of our observations.", "subpage_snippet": "", "source": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper_files/paper/2024/hash/4b77d5b896c321a29277524a98a50215-Abstract-Conference.html", "content": "Abstract Training on model-generated synthetic data is a promising approach for finetuning LLMs , but it remains unclear when it helps or hurts. In this paper, we investigate this question for math reasoning via an empirical study, followed by building a conceptual understanding of our observations."}
+{"idx": 5, "title": "RL on Incorrect Synthetic Data Scales the Efficiency of LLM Math ...", "date": "", "ddg_snippet": "This paper investigates the use of synthetic data for enhancing LLM math reasoning capabilities. The researchers discovered that this approach leads to only modest gains, and in some cases, even performance degradation. The study introduces a novel approach that utilizes both positive and negative synthetic data .", "subpage_snippet": "", "source": "deep-diver.github.io", "link": "https://deep-diver.github.io/neurips2024/posters/9m87e9keq1/", "content": "This paper investigates the use of synthetic data for enhancing LLM math reasoning capabilities. The researchers discovered that this approach leads to only modest gains, and in some cases, even performance degradation. The study introduces a novel approach that utilizes both positive and negative synthetic data ."}
+{"idx": 6, "title": "scaling-LLM-math-synthetic-data/README.md at master - GitHub", "date": "", "ddg_snippet": "Code and data used in the paper: \"Training on Incorrect Synthetic Data via RL Scales LLM Math Reasoning Eight-Fold\"", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/ars22/scaling-LLM-math-synthetic-data/blob/master/README.md", "content": "Code and data used in the paper: \"Training on Incorrect Synthetic Data via RL Scales LLM Math Reasoning Eight-Fold\""}
+{"idx": 7, "title": "RL on Incorrect Synthetic Data Scales the Efficiency of LLM Math ...", "date": "", "ddg_snippet": "Abstract Training on model-generated synthetic data is a promising approach for finetuning LLMs , but it remains unclear when it helps or hurts. In this paper, we investigate this question for math reasoning via an empirical study, followed by building a conceptual understanding of our observations. First, we find that while the typical approach of finetuning a model on synthetic correct or ...", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/papers/2406.14532", "content": "Abstract Training on model-generated synthetic data is a promising approach for finetuning LLMs , but it remains unclear when it helps or hurts. In this paper, we investigate this question for math reasoning via an empirical study, followed by building a conceptual understanding of our observations. First, we find that while the typical approach of finetuning a model on synthetic correct or ..."}
+{"idx": 8, "title": "RL on Incorrect Synthetic Data Scales the Efficiency of LLM Math ...", "date": "", "ddg_snippet": "AI-generated Key Points Authors explore training language models on model-generated synthetic data for math reasoning tasks Sampling more correct solutions from the finetuned learner and fine-tuning on self-generated data doubles efficiency in solving synthetic problems Constructing negative responses to mitigate potential pitfalls of training on model-generated positives leads to consistent ...", "subpage_snippet": "", "source": "www.summarizepaper.com", "link": "https://www.summarizepaper.com/en/arxiv-id/2406.14532v1/", "content": "AI-generated Key Points Authors explore training language models on model-generated synthetic data for math reasoning tasks Sampling more correct solutions from the finetuned learner and fine-tuning on self-generated data doubles efficiency in solving synthetic problems Constructing negative responses to mitigate potential pitfalls of training on model-generated positives leads to consistent ..."}
+{"idx": 9, "title": "RLonIncorrectSyntheticDataScalesthe EfficiencyofLLMMathReasoningbyEight ...", "date": "", "ddg_snippet": "RLonIncorrectSyntheticDataScalestheEfficiencyofLLMMathReasoningbyEight-Fold SFTbase Policy ! QnA pairs sampled from GPT/Gemini Synthetic Data Positive Data Correct answers \"! Negative Data Finetune policy RFT : SFT on self-generated correct answers !\" RL with step-level rewards on all answers #!\" e.g., preference - based RL Incorrect answers \"!", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2406.14532", "content": "RLonIncorrectSyntheticDataScalestheEfficiencyofLLMMathReasoningbyEight-Fold SFTbase Policy ! QnA pairs sampled from GPT/Gemini Synthetic Data Positive Data Correct answers \"! Negative Data Finetune policy RFT : SFT on self-generated correct answers !\" RL with step-level rewards on all answers #!\" e.g., preference - based RL Incorrect answers \"!"}

data/sampled_jsons/9m87e9Keq1_RL_Incorrect_Synthetic_Data_Scales_LLM_Math_Reasoning_Per-step_DPO_algorithm.jsonl

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+{"idx": 0, "title": "RL on Incorrect Synthetic Data Scales the Efficiency of LLM Math ...", "date": "", "ddg_snippet": "To provide clarity on how synthetic data contributes to performance, we aim to understand its impact on LLM capabilities via a study on math reasoning , a prevalent scenario where synthetic data is used. Typically, in this setting, synthetic data corresponds to correct or positive model-generated responses for a novel set of initial problems synthesized by prompting capable models [29, 31]. The ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=9m87e9Keq1", "content": "To provide clarity on how synthetic data contributes to performance, we aim to understand its impact on LLM capabilities via a study on math reasoning , a prevalent scenario where synthetic data is used. Typically, in this setting, synthetic data corresponds to correct or positive model-generated responses for a novel set of initial problems synthesized by prompting capable models [29, 31]. The ..."}
+{"idx": 1, "title": "RL on Incorrect Synthetic Data Scales the Efficiency of LLM Math ...", "date": "", "ddg_snippet": "Training on model-generated synthetic data is a promising approach for finetuning LLMs , but it remains unclear when it helps or hurts. In this paper, we investigate this question for math reasoning via an empirical study, followed by building a conceptual understanding of our observations. First, we find that while the typical approach of finetuning a model on synthetic correct or positive ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2406.14532", "content": "Training on model-generated synthetic data is a promising approach for finetuning LLMs , but it remains unclear when it helps or hurts. In this paper, we investigate this question for math reasoning via an empirical study, followed by building a conceptual understanding of our observations. First, we find that while the typical approach of finetuning a model on synthetic correct or positive ..."}
+{"idx": 2, "title": "RL on Incorrect Synthetic Data · MinWoo Park", "date": "", "ddg_snippet": "We show that training on per-step negatives can help to unlearn spurious correlations in the positive data , and is equivalent to advantage-weighted reinforcement learning (RL), implying that it inherits robustness benefits of RL over imitating positive data alone.", "subpage_snippet": "", "source": "dsdanielpark.github.io", "link": "https://dsdanielpark.github.io/llm/2024-06-25-RLonIncorrectSyntheticData.html", "content": "We show that training on per-step negatives can help to unlearn spurious correlations in the positive data , and is equivalent to advantage-weighted reinforcement learning (RL), implying that it inherits robustness benefits of RL over imitating positive data alone."}
+{"idx": 3, "title": "PDF Reinforcement Learning for LLM Reasoning", "date": "", "ddg_snippet": "RL on Incorrect Synthetic Data Scales the Efficiency of LLM Math Reasoning by Eight-Fold. Setlur, Garg, Geng, Garg, Smith, Kumar. NeurIPS 2024 Rewarding Progress: Scaling up Automated Process Supervision for LLM Reasoning", "subpage_snippet": "", "source": "cs224r.stanford.edu", "link": "https://cs224r.stanford.edu/slides/10_cs224r-rl_for_reasoning_lecture.pdf", "content": "RL on Incorrect Synthetic Data Scales the Efficiency of LLM Math Reasoning by Eight-Fold. Setlur, Garg, Geng, Garg, Smith, Kumar. NeurIPS 2024 Rewarding Progress: Scaling up Automated Process Supervision for LLM Reasoning"}
+{"idx": 4, "title": "RL on Incorrect Synthetic Data Scales the Efficiency of LLM Math ...", "date": "", "ddg_snippet": "With this per-step scheme, we are able to attain consistent gains over only positive data , attaining performance similar to amplifying the amount of synthetic data by $\\mathbf {8 \\times}$.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/381604579_RL_on_Incorrect_Synthetic_Data_Scales_the_Efficiency_of_LLM_Math_Reasoning_by_Eight-Fold", "content": "With this per-step scheme, we are able to attain consistent gains over only positive data , attaining performance similar to amplifying the amount of synthetic data by $\\mathbf {8 \\times}$."}
+{"idx": 5, "title": "RL on Incorrect Synthetic Data Scales the Efficiency of LLM Math ...", "date": "", "ddg_snippet": "Abstract Training on model-generated synthetic data is a promising approach for finetuning LLMs , but it remains unclear when it helps or hurts. In this paper, we investigate this question for math reasoning via an empirical study, followed by building a conceptual understanding of our observations. First, we find that while the typical approach of finetuning a model on synthetic correct or ...", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/papers/2406.14532", "content": "Abstract Training on model-generated synthetic data is a promising approach for finetuning LLMs , but it remains unclear when it helps or hurts. In this paper, we investigate this question for math reasoning via an empirical study, followed by building a conceptual understanding of our observations. First, we find that while the typical approach of finetuning a model on synthetic correct or ..."}
+{"idx": 6, "title": "RLonIncorrectSyntheticDataScalesthe EfficiencyofLLMMathReasoningbyEight ...", "date": "", "ddg_snippet": "RLonIncorrectSyntheticDataScalestheEfficiencyofLLMMathReasoningbyEight-Fold SFTbase Policy ! QnA pairs sampled from GPT/Gemini Synthetic Data Positive Data Correct answers \"! Negative Data Finetune policy RFT : SFT on self-generated correct answers !\" RL with step -level rewards on all answers #!\" e.g., preference - based RL Incorrect answers \"!", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2406.14532", "content": "RLonIncorrectSyntheticDataScalestheEfficiencyofLLMMathReasoningbyEight-Fold SFTbase Policy ! QnA pairs sampled from GPT/Gemini Synthetic Data Positive Data Correct answers \"! Negative Data Finetune policy RFT : SFT on self-generated correct answers !\" RL with step -level rewards on all answers #!\" e.g., preference - based RL Incorrect answers \"!"}
+{"idx": 7, "title": "RL on Incorrect Synthetic Data Scales the Efficiency of LLM Math ...", "date": "", "ddg_snippet": "View recent discussion. Abstract: Training on model-generated synthetic data is a promising approach for finetuning LLMs , but it remains unclear when it helps or hurts. In this paper, we investigate this question for math reasoning via an empirical study, followed by building a conceptual understanding of our observations. First, we find that while the typical approach of finetuning a model on ...", "subpage_snippet": "", "source": "www.alphaxiv.org", "link": "https://www.alphaxiv.org/overview/2406.14532v1", "content": "View recent discussion. Abstract: Training on model-generated synthetic data is a promising approach for finetuning LLMs , but it remains unclear when it helps or hurts. In this paper, we investigate this question for math reasoning via an empirical study, followed by building a conceptual understanding of our observations. First, we find that while the typical approach of finetuning a model on ..."}
+{"idx": 8, "title": "RL on Incorrect Synthetic Data Scales the Efficiency of LLM Math ...", "date": "", "ddg_snippet": "This paper investigates the use of synthetic data for enhancing LLM math reasoning capabilities. The researchers discovered that this approach leads to only modest gains, and in some cases, even performance degradation. The study introduces a novel approach that utilizes both positive and negative synthetic data .", "subpage_snippet": "", "source": "deep-diver.github.io", "link": "https://deep-diver.github.io/neurips2024/posters/9m87e9keq1/", "content": "This paper investigates the use of synthetic data for enhancing LLM math reasoning capabilities. The researchers discovered that this approach leads to only modest gains, and in some cases, even performance degradation. The study introduces a novel approach that utilizes both positive and negative synthetic data ."}
+{"idx": 9, "title": "AI-Powered Paper Summarization about the arXiv paper 2406.14532v1", "date": "", "ddg_snippet": "Easy-to-read summary of the arXiv paper 2406.14532v1 entitled RL on Incorrect Synthetic Data Scales the Efficiency of LLM Math Reasoning by Eight-Fold", "subpage_snippet": "", "source": "www.summarizepaper.com", "link": "https://www.summarizepaper.com/en/arxiv-id/2406.14532v1/", "content": "Easy-to-read summary of the arXiv paper 2406.14532v1 entitled RL on Incorrect Synthetic Data Scales the Efficiency of LLM Math Reasoning by Eight-Fold"}

data/sampled_jsons/ACIDDnTbSJ_Rew_short_equation_(1)_sum_alpha_Feint_alpha_attack.jsonl

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+{"idx": 0, "title": "Equation Solver: Step-by-Step Calculator - Wolfram| Alpha", "date": "", "ddg_snippet": "Online Equation Solver. Solve linear, quadratic and polynomial systems of equations with Wolfram| Alpha .", "subpage_snippet": "", "source": "www.wolframalpha.com", "link": "https://www.wolframalpha.com/calculators/equation-solver-calculator", "content": "Online Equation Solver. Solve linear, quadratic and polynomial systems of equations with Wolfram| Alpha ."}
+{"idx": 1, "title": "How can I have linebreaks in my long LaTeX equations ?", "date": "", "ddg_snippet": "There are a couple ways you can deal with this. First , and perhaps best, is to rework your equation so that it is not so long; it is likely unreadable if it is that long. If it must be so, check out the AMS Short Math Guide for some ways to handle it. (on the second page).", "subpage_snippet": "", "source": "stackoverflow.com", "link": "https://stackoverflow.com/questions/2860145/how-can-i-have-linebreaks-in-my-long-latex-equations", "content": "There are a couple ways you can deal with this. First , and perhaps best, is to rework your equation so that it is not so long; it is likely unreadable if it is that long. If it must be so, check out the AMS Short Math Guide for some ways to handle it. (on the second page)."}
+{"idx": 2, "title": "nt.number theory - Sum of powers identities for Stirling... - MathOverflow", "date": "", "ddg_snippet": "The short answer is yes.I conjecture that sums of resulting integer coefficients are always equal $(- 1 )^{m(k-1)}$. Here is the PARI/GP program to check it numerically", "subpage_snippet": "", "source": "mathoverflow.net", "link": "https://mathoverflow.net/questions/486366/sum-of-powers-identities-for-stirling-numbers-of-the-second-kind-in-the-m-th-p", "content": "The short answer is yes.I conjecture that sums of resulting integer coefficients are always equal $(- 1 )^{m(k-1)}$. Here is the PARI/GP program to check it numerically"}
+{"idx": 3, "title": "The sum , of the coefficients of the first 50terms in the binomial of", "date": "", "ddg_snippet": "Step 3: Group the Terms Notice that the sum can be grouped as follows: S=49∑r=0(100r)(− 1 )r This is half of the total sum of the coefficients when we consider the entire expansion up to r=100: S=12(100∑r=0(100r)(− 1 )r).", "subpage_snippet": "", "source": "www.doubtnut.com", "link": "https://www.doubtnut.com/qna/649667907", "content": "Step 3: Group the Terms Notice that the sum can be grouped as follows: S=49∑r=0(100r)(− 1 )r This is half of the total sum of the coefficients when we consider the entire expansion up to r=100: S=12(100∑r=0(100r)(− 1 )r)."}
+{"idx": 4, "title": "Linear hyperbolic partial differential equation and system", "date": "", "ddg_snippet": "A partial differential equation of the form. $$ \\tag{ 1 } \\ sum _ {| \\ alpha | \\leq m } a _ \\ alpha D ^ \\ alpha u = f $$. for which at any point $ x = $ of its domain of definition $ \\Omega $ one can distinguish among the real variables $ y _ {0}, \\dots, ...", "subpage_snippet": "", "source": "encyclopediaofmath.org", "link": "https://encyclopediaofmath.org/wiki/Linear_hyperbolic_partial_differential_equation_and_system", "content": "A partial differential equation of the form. $$ \\tag{ 1 } \\ sum _ {| \\ alpha | \\leq m } a _ \\ alpha D ^ \\ alpha u = f $$. for which at any point $ x = $ of its domain of definition $ \\Omega $ one can distinguish among the real variables $ y _ {0}, \\dots, ..."}
+{"idx": 5, "title": "Комплексные числа. Пошаговый калькулятор", "date": "", "ddg_snippet": "integral icon Интегралы. equation icon Уравнения. limit icon Предел функции.•lambda — lambda. •pi — pi. alpha — alpha .", "subpage_snippet": "", "source": "mathdf.com", "link": "https://mathdf.com/com/ru/", "content": "integral icon Интегралы. equation icon Уравнения. limit icon Предел функции.•lambda — lambda. •pi — pi. alpha — alpha ."}
+{"idx": 6, "title": "Обзор математики для начинающего ML-инженера / Хабр", "date": "", "ddg_snippet": "линейно независим, если равенство возможно только при \\ alpha _ i =0 . Элементарные преобразования матриц. перестановка строк", "subpage_snippet": "", "source": "habr.com", "link": "https://habr.com/ru/articles/942114/", "content": "линейно независим, если равенство возможно только при \\ alpha _ i =0 . Элементарные преобразования матриц. перестановка строк"}
+{"idx": 7, "title": "Curl P 1 -R vs Feint Long | Форум", "date": "", "ddg_snippet": "Curl P 1 -R vs Feint Long. 25 Авг 2016, 11:06:40. Собственно, такая история.ОХ супер играет на Peter Korbel и на моей Defence alpha . 1 . 1 для меня многовато было, но тем не менее в этой толщине я отыграл ими более 2,5 лет.", "subpage_snippet": "", "source": "www.tt-maximum.com", "link": "https://www.tt-maximum.com/forum/index.php?topic=7666.0", "content": "Curl P 1 -R vs Feint Long. 25 Авг 2016, 11:06:40. Собственно, такая история.ОХ супер играет на Peter Korbel и на моей Defence alpha . 1 . 1 для меня многовато было, но тем не менее в этой толщине я отыграл ими более 2,5 лет."}
+{"idx": 8, "title": "The Ram | 99 Nights in the Forest Wiki | Fandom", "date": "", "ddg_snippet": "The Ram is a hostile entity that replaced The Owl in 99 Nights in the Forest . When a night begins there is a chance of a short scene involving The Ram. Afterwards, the following text will appear: \"The Ram has woken up\". Once the scene has ...", "subpage_snippet": "", "source": "99-nights-in-the-forest.fandom.com", "link": "https://99-nights-in-the-forest.fandom.com/wiki/The_Ram", "content": "The Ram is a hostile entity that replaced The Owl in 99 Nights in the Forest . When a night begins there is a chance of a short scene involving The Ram. Afterwards, the following text will appear: \"The Ram has woken up\". Once the scene has ..."}
+{"idx": 9, "title": "Как Сделать Отстрелы На Мопед Альфа | TikTok", "date": "", "ddg_snippet": "#альфа #атстрелы #2008 #аааоаоаоао 2008 Alpha Attacks : Key Insights and Analysis. Explore the significance of the 2008 Alpha attacks and their impact.", "subpage_snippet": "", "source": "www.tiktok.com", "link": "https://www.tiktok.com/discover/как-сделать-отстрелы-на-мопед-альфа", "content": "#альфа #атстрелы #2008 #аааоаоаоао 2008 Alpha Attacks : Key Insights and Analysis. Explore the significance of the 2008 Alpha attacks and their impact."}

data/sampled_jsons/AERO_Enhancing_Sharding_Blockchain_via_Deep_Reinforcement_Learning_Table_1_learning_rate.jsonl

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+{"idx": 0, "title": "Aero (American airline) - Wikipedia", "date": "", "ddg_snippet": "Aero , legally Aero Technologies, Inc., is an American air carrier headquartered in Van Nuys, California. The airline operates point-to-point flights between and within California, Colorado, Idaho, Nevada, New Jersey, and Utah in the United States and Baja California Sur in Mexico.", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/Aero_(American_airline)", "content": "Aero , legally Aero Technologies, Inc., is an American air carrier headquartered in Van Nuys, California. The airline operates point-to-point flights between and within California, Colorado, Idaho, Nevada, New Jersey, and Utah in the United States and Baja California Sur in Mexico."}
+{"idx": 1, "title": "Apartments in Tacoma, WA | Aero Apartments | Home", "date": "", "ddg_snippet": "Aero Apartments offers versatile 1, 2 & 3-bedroom apartments in Tacoma, WA. View our floor plans, discover the perks, and schedule a tour!", "subpage_snippet": "", "source": "www.liveaeroapartments.com", "link": "https://www.liveaeroapartments.com/", "content": "Aero Apartments offers versatile 1, 2 & 3-bedroom apartments in Tacoma, WA. View our floor plans, discover the perks, and schedule a tour!"}
+{"idx": 2, "title": "Book by the seat | The Private Jet Experience | Aero ™", "date": "", "ddg_snippet": "A beyond first-class experience awaits, no membership required. Aero effortlessly merges the worlds of hospitality, design, and travel. Enjoy spacious premium seats, private terminals with no lines or crowds, a dedicated concierge team, and curated amenities.", "subpage_snippet": "", "source": "aero.com", "link": "https://aero.com/", "content": "A beyond first-class experience awaits, no membership required. Aero effortlessly merges the worlds of hospitality, design, and travel. Enjoy spacious premium seats, private terminals with no lines or crowds, a dedicated concierge team, and curated amenities."}
+{"idx": 3, "title": "Aero Contractors the Reliable Way to Fly - Book Affordable ...", "date": "", "ddg_snippet": "About Vision & Mission History Profile Privacy Policy Cookie Policy Offices Conditions of Carriage Contact +234 807 200 5691 +234 915 539 0873 +234 (0) 201 330 2660 +234 (0) 201 330 2666 Help Desk PMB 21090 Murtala Mohammed Domestic Airport Private Terminal Ikeja, Lagos. Aero Contractors Company Of Nigeria Limited © 2025. All rights reserved ...", "subpage_snippet": "", "source": "flyaero.com", "link": "https://flyaero.com/", "content": "About Vision & Mission History Profile Privacy Policy Cookie Policy Offices Conditions of Carriage Contact +234 807 200 5691 +234 915 539 0873 +234 (0) 201 330 2660 +234 (0) 201 330 2666 Help Desk PMB 21090 Murtala Mohammed Domestic Airport Private Terminal Ikeja, Lagos. Aero Contractors Company Of Nigeria Limited © 2025. All rights reserved ..."}
+{"idx": 4, "title": "Seats. aero - Home", "date": "", "ddg_snippet": "Seats. aero is the fastest search engine for award travel. Explore availability across entire regions, search with instant results, create free alerts and more to find the best flights for your points.", "subpage_snippet": "", "source": "seats.aero", "link": "https://seats.aero/", "content": "Seats. aero is the fastest search engine for award travel. Explore availability across entire regions, search with instant results, create free alerts and more to find the best flights for your points."}
+{"idx": 5, "title": "Aero | Apartments in Tacoma, WA | Contact Us", "date": "", "ddg_snippet": "Learn more about Aero in Tacoma, WA and schedule a visit.", "subpage_snippet": "", "source": "www.liveaeroapartments.com", "link": "https://www.liveaeroapartments.com/contactus", "content": "Learn more about Aero in Tacoma, WA and schedule a visit."}
+{"idx": 6, "title": "Aircraft Fleet | Aero ™", "date": "", "ddg_snippet": "\"I’ve done a lot of traveling in my life, and I’ve never experienced transportation quite like Aero . Exceptional service, luxurious terminal, high quality amenities, top notch staff.\"", "subpage_snippet": "", "source": "aero.com", "link": "https://aero.com/fleet", "content": "\"I’ve done a lot of traveling in my life, and I’ve never experienced transportation quite like Aero . Exceptional service, luxurious terminal, high quality amenities, top notch staff.\""}
+{"idx": 7, "title": "1, 2 & 3-Bedroom Apartments in Tacoma | Aero Apartments", "date": "", "ddg_snippet": "Aero Apartments offers 1, 2 & 3-bedroom apartments in Tacoma, WA, designed around your needs. Browse our floor plans and pick the one that suits you!", "subpage_snippet": "", "source": "www.liveaeroapartments.com", "link": "https://www.liveaeroapartments.com/floorplans", "content": "Aero Apartments offers 1, 2 & 3-bedroom apartments in Tacoma, WA, designed around your needs. Browse our floor plans and pick the one that suits you!"}
+{"idx": 8, "title": "The Private Jet Experience | Aero ™", "date": "", "ddg_snippet": "Aero is ideal for all kinds of travelers—pets are welcome onboard, and our spacious jets are perfect for individuals, families with children, groups of friends, and more.", "subpage_snippet": "", "source": "aero.com", "link": "https://aero.com/the-experience", "content": "Aero is ideal for all kinds of travelers—pets are welcome onboard, and our spacious jets are perfect for individuals, families with children, groups of friends, and more."}
+{"idx": 9, "title": "Explore Destinations | The Private Jet Experience | Aero ™", "date": "", "ddg_snippet": "Jet to a collection of sought-after leisure destinations or seek out an elevated travel experience to the world's largest entertainment and sporting events. Wherever you choose to wander, fly in signature Aero style.", "subpage_snippet": "", "source": "aero.com", "link": "https://aero.com/where-we-fly", "content": "Jet to a collection of sought-after leisure destinations or seek out an elevated travel experience to the world's largest entertainment and sporting events. Wherever you choose to wander, fly in signature Aero style."}

data/sampled_jsons/AI_alignment_paper_executive_branch.jsonl

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+{"idx": 0, "title": "Solving alignment isn't enough for a flourishing future — LessWrong", "date": "", "ddg_snippet": "AI alignment is commonly explained as aligning advanced AI systems with human values.This paper proposes three categories for AI alignment : alignment to task preferences, alignment to human values, and alignment to ideal values.", "subpage_snippet": "", "source": "www.lesswrong.com", "link": "https://www.lesswrong.com/posts/uHcJyKcszugFkwhFs/solving-alignment-isn-t-enough-for-a-flourishing-future", "content": "AI alignment is commonly explained as aligning advanced AI systems with human values.This paper proposes three categories for AI alignment : alignment to task preferences, alignment to human values, and alignment to ideal values."}
+{"idx": 1, "title": "Navigating the Landscape of AI Alignment , Part 1 | by... | Medium", "date": "", "ddg_snippet": "AI Alignment is a critical objective of AI Safety, focused on designing safe AI systems that align with human values and intentions. In this blog post series, we’ll navigate the field of AI alignment by exploring its key objectives, core components, and the methods used to build AI systems...", "subpage_snippet": "", "source": "vijayasriiyer.medium.com", "link": "https://vijayasriiyer.medium.com/navigating-the-landscape-of-ai-alignment-part-1-95fc3089d8c3", "content": "AI Alignment is a critical objective of AI Safety, focused on designing safe AI systems that align with human values and intentions. In this blog post series, we’ll navigate the field of AI alignment by exploring its key objectives, core components, and the methods used to build AI systems..."}
+{"idx": 2, "title": "AI Alignment vs AI Ethical Treatment: Ten Challenges", "date": "", "ddg_snippet": "Risk-weighted taxation and legal protections for AI entities. Why it matters? This paper reframes the AI safety debate by insisting that who the AI is matters as much as what the AI does. Traditional alignment research assumes AI systems are tools to...", "subpage_snippet": "", "source": "www.aigl.blog", "link": "https://www.aigl.blog/ai-alignment-vs-ai-ethical-treatment-ten-challenges/", "content": "Risk-weighted taxation and legal protections for AI entities. Why it matters? This paper reframes the AI safety debate by insisting that who the AI is matters as much as what the AI does. Traditional alignment research assumes AI systems are tools to..."}
+{"idx": 3, "title": "AI Alignment : Why the Core Challenge Is Both Technical and...", "date": "", "ddg_snippet": "At its heart, AI alignment is a technical problem. It asks: Can we design robust algorithms, architectures, and training protocols such that advanced AIs will act in alignment with human goals, even as their capabilities surpass our own on many dimensions?", "subpage_snippet": "", "source": "www.linkedin.com", "link": "https://www.linkedin.com/pulse/ai-alignment-why-core-challenge-both-technical-gary-ramah-y5w0c", "content": "At its heart, AI alignment is a technical problem. It asks: Can we design robust algorithms, architectures, and training protocols such that advanced AIs will act in alignment with human goals, even as their capabilities surpass our own on many dimensions?"}
+{"idx": 4, "title": "OpenAI's New Alignment Paper | ml-news – Weights & Biases", "date": "", "ddg_snippet": "OpenAI's New Alignment Paper . Advancing AI Alignment Through Weak-to-Strong Generalization.Traditional AI models are aligned with human expectations through Reinforcement Learning from Human Feedback (RLHF), where human evaluators guide model behavior.", "subpage_snippet": "", "source": "wandb.ai", "link": "https://wandb.ai/byyoung3/ml-news/reports/OpenAI-s-New-Alignment-Paper--Vmlldzo2MzA0NzQ3", "content": "OpenAI's New Alignment Paper . Advancing AI Alignment Through Weak-to-Strong Generalization.Traditional AI models are aligned with human expectations through Reinforcement Learning from Human Feedback (RLHF), where human evaluators guide model behavior."}
+{"idx": 5, "title": "AI Alignment Research Paper | Restackio", "date": "", "ddg_snippet": "AI Alignment Research Paper . Last updated on 10/01/24. Build your AI product with Restack.Key Insights from Research. Recent studies indicate that aligning AI systems with human values is achievable, particularly with advanced models like GPT-4.", "subpage_snippet": "", "source": "www.restack.io", "link": "https://www.restack.io/p/ai-alignment-answer-research-paper-cat-ai", "content": "AI Alignment Research Paper . Last updated on 10/01/24. Build your AI product with Restack.Key Insights from Research. Recent studies indicate that aligning AI systems with human values is achievable, particularly with advanced models like GPT-4."}
+{"idx": 6, "title": "AI Alignment with Changing and Influenceable Reward Functions...", "date": "", "ddg_snippet": "We hope our work can provide conceptual clarity and constitute a first step towards AI alignment practices which explicitly account for (and contend with) the changing and influenceable nature of human preferences. Please find the paper here.", "subpage_snippet": "", "source": "humancompatible.ai", "link": "https://humancompatible.ai/news/2024/07/23/ai-alignment-with-changing-and-influenceable-reward-functions/", "content": "We hope our work can provide conceptual clarity and constitute a first step towards AI alignment practices which explicitly account for (and contend with) the changing and influenceable nature of human preferences. Please find the paper here."}
+{"idx": 7, "title": "Murphys Laws of AI Alignment : Why the Gap Always Wins - Paper ...", "date": "", "ddg_snippet": "Quick Read (beta). loading the full paper ...", "subpage_snippet": "", "source": "deeplearn.org", "link": "https://deeplearn.org/arxiv/635901/murphys-laws-of-ai-alignment:-why-the-gap-always-wins", "content": "Quick Read (beta). loading the full paper ..."}
+{"idx": 8, "title": "GitHub - coinsspor/0G- AI - Alignment -Node---Simple-Setup-Guide", "date": "", "ddg_snippet": "Contribute to coinsspor/0G- AI - Alignment -Node---Simple-Setup-Guide development by creating an account on GitHub.Quick and easy setup for 0G AI Alignment Node with systemd service. Requirements. Ubuntu/Debian VPS.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/coinsspor/0G-AI-Alignment-Node---Simple-Setup-Guide", "content": "Contribute to coinsspor/0G- AI - Alignment -Node---Simple-Setup-Guide development by creating an account on GitHub.Quick and easy setup for 0G AI Alignment Node with systemd service. Requirements. Ubuntu/Debian VPS."}
+{"idx": 9, "title": "AI Alignment at Your Discretion | AI Research Paper Details", "date": "", "ddg_snippet": "Extended to AI alignment , discretion is required when alignment principles and rules are (inevitably) conflicting or indecisive. We present a set of metrics to systematically analyze when and how discretion in AI alignment is exercised, such that both risks (i) and (ii) can be observed.", "subpage_snippet": "", "source": "www.aimodels.fyi", "link": "https://www.aimodels.fyi/papers/arxiv/ai-alignment-at-your-discretion", "content": "Extended to AI alignment , discretion is required when alignment principles and rules are (inevitably) conflicting or indecisive. We present a set of metrics to systematically analyze when and how discretion in AI alignment is exercised, such that both risks (i) and (ii) can be observed."}

data/sampled_jsons/ATA_Adaptive_Task_Allocation_Maranjyan_Saad_Richtarik_Orabona_GTA_strategy.jsonl

@@ -0,0 +1,10 @@
+{"idx": 0, "title": "ATA : Adaptive Task Allocation for Efficient Resource Management in...", "date": "", "ddg_snippet": "Artavazd Maranjyan El Mehdi Saad Peter Richtárik Francesco Orabona .In this paper, we propose ATA ( Adaptive Task Allocation ), a method that adapts to heterogeneous and random distributions of worker computation times.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2502.00775v2", "content": "Artavazd Maranjyan El Mehdi Saad Peter Richtárik Francesco Orabona .In this paper, we propose ATA ( Adaptive Task Allocation ), a method that adapts to heterogeneous and random distributions of worker computation times."}
+{"idx": 1, "title": "Peter Richtarik", "date": "", "ddg_snippet": "[274] Artavazd Maranjyan , El Mehdi Saad , Peter Richtárik , and Francesco Orabona ATA : Adaptive task allocation for efficient resource management in distributed machine learning 42nd International Conference on Machine Learning (ICML 2025) Asynchronous Optimization [arXiv]...", "subpage_snippet": "", "source": "richtarik.org", "link": "https://richtarik.org/i_papers.html", "content": "[274] Artavazd Maranjyan , El Mehdi Saad , Peter Richtárik , and Francesco Orabona ATA : Adaptive task allocation for efficient resource management in distributed machine learning 42nd International Conference on Machine Learning (ICML 2025) Asynchronous Optimization [arXiv]..."}
+{"idx": 2, "title": "Arto Maranjyan - Google Akademik", "date": "", "ddg_snippet": "Ata : Adaptive task allocation for efficient resource management in distributed machine learning. A Maranjyan , EM Saad , P Richtárik , F Orabona . arXiv preprint arXiv:2502.00775, 2025.", "subpage_snippet": "", "source": "scholar.google.ru", "link": "https://scholar.google.ru/citations?user=93WEFj8AAAAJ&hl=tr", "content": "Ata : Adaptive task allocation for efficient resource management in distributed machine learning. A Maranjyan , EM Saad , P Richtárik , F Orabona . arXiv preprint arXiv:2502.00775, 2025."}
+{"idx": 3, "title": "openreview.net/profile?id=~Francesco_ Orabona 2", "date": "", "ddg_snippet": "ATA : Adaptive Task Allocation for Efficient Resource Management in Distributed Machine Learning. Arto Maranjyan , El Mehdi Saad , Peter Richtárik , Francesco Orabona .", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/profile?id=~Francesco_Orabona2", "content": "ATA : Adaptive Task Allocation for Efficient Resource Management in Distributed Machine Learning. Arto Maranjyan , El Mehdi Saad , Peter Richtárik , Francesco Orabona ."}
+{"idx": 4, "title": "ATA : Adaptive Task Allocation", "date": "", "ddg_snippet": "ATA : Adaptive Task Allocation for Efficient Resource Management in Distributed Machine Learning. Artavazd Maranjyan , El Mehdi Saad , Peter Richtárik , Francesco Orabona .", "subpage_snippet": "", "source": "artomaranjyan.github.io", "link": "https://artomaranjyan.github.io/assets/pdf/posters/ATA_ICML.pdf", "content": "ATA : Adaptive Task Allocation for Efficient Resource Management in Distributed Machine Learning. Artavazd Maranjyan , El Mehdi Saad , Peter Richtárik , Francesco Orabona ."}
+{"idx": 5, "title": "Arto Maranjyan (@ArtoMaranjyan) | Aguea", "date": "", "ddg_snippet": "Arto Maranjyan retweeted. Francesco Orabona .New paper out “ ATA : Adaptive Task Allocation for Efficient Resource Management in Distributed Machine Learning”.", "subpage_snippet": "", "source": "aguea.net", "link": "https://aguea.net/ArtoMaranjyan", "content": "Arto Maranjyan retweeted. Francesco Orabona .New paper out “ ATA : Adaptive Task Allocation for Efficient Resource Management in Distributed Machine Learning”."}
+{"idx": 6, "title": "Assistant / adaptive", "date": "", "ddg_snippet": "Remix on Adaptive adaptive .ai.", "subpage_snippet": "", "source": "q3q37tabrx.on.adaptive.ai", "link": "https://q3q37tabrx.on.adaptive.ai/", "content": "Remix on Adaptive adaptive .ai."}
+{"idx": 7, "title": "Peter RICHTÁRIK | Professor (Full) | Professor | King Abdullah...", "date": "", "ddg_snippet": "ATA : Adaptive Task Allocation for Efficient Resource Management in Distributed Machine Learning.Konstantin Mishchenko. Peter Richtárik . In this work, we propose new adaptive step size strategies that improve several stochastic gradient methods.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/profile/Peter-Richtarik-2", "content": "ATA : Adaptive Task Allocation for Efficient Resource Management in Distributed Machine Learning.Konstantin Mishchenko. Peter Richtárik . In this work, we propose new adaptive step size strategies that improve several stochastic gradient methods."}
+{"idx": 8, "title": "Articles by Peter Richtárik | Synthical", "date": "", "ddg_snippet": "ATA : Adaptive Task Allocation for Efficient Resource Management in Distributed Machine Learning.On the Convergence of DP-SGD with Adaptive Clipping. 27 December 2024 by Egor Shulgin and Peter Richtárik .", "subpage_snippet": "", "source": "synthical.com", "link": "https://synthical.com/search/by_author/Peter+Richtárik", "content": "ATA : Adaptive Task Allocation for Efficient Resource Management in Distributed Machine Learning.On the Convergence of DP-SGD with Adaptive Clipping. 27 December 2024 by Egor Shulgin and Peter Richtárik ."}
+{"idx": 9, "title": "dblp: List of computer science publications by Peter Richtárik", "date": "", "ddg_snippet": "Artavazd Maranjyan , El Mehdi Saad , Peter Richtárik , Francesco Orabona : ATA : Adaptive Task Allocation for Efficient Resource Management in Distributed Machine Learning.150. Francesco Orabona .", "subpage_snippet": "", "source": "dblp.uni-trier.de", "link": "https://dblp.uni-trier.de/pid/62/8001.html", "content": "Artavazd Maranjyan , El Mehdi Saad , Peter Richtárik , Francesco Orabona : ATA : Adaptive Task Allocation for Efficient Resource Management in Distributed Machine Learning.150. Francesco Orabona ."}

data/sampled_jsons/ATA_Adaptive_Task_Allocation_Theorem_6.1_regret_bound_year_2024.jsonl

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+{"idx": 0, "title": "ATA: Adaptive Task Allocation for Efficient Resource ...", "date": "", "ddg_snippet": "We present theoretical guarantees for ATA -Empiricalby providing an upper bound on the expected cumulative regret (9). As discussed in Section 4, ATA -Empiricalleverages lower confidence bounds derived from a novel data-dependent concentration inequality introduced below.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2502.00775v2", "content": "We present theoretical guarantees for ATA -Empiricalby providing an upper bound on the expected cumulative regret (9). As discussed in Section 4, ATA -Empiricalleverages lower confidence bounds derived from a novel data-dependent concentration inequality introduced below."}
+{"idx": 1, "title": "ATA: Adaptive Task Allocation for Efficient Resource ...", "date": "", "ddg_snippet": "May 1 , 2025 · It recasts task allocation as a combinatorial bandit problem with rigorous theoretical guarantees, ensuring logarithmic regret and near-optimal performance, and shows through experiments that both ATA and its variant, ATA -Empirical, substantially reduce wasted computation compared to greedy methods.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=1BaC3AdG1i", "content": "May 1 , 2025 · It recasts task allocation as a combinatorial bandit problem with rigorous theoretical guarantees, ensuring logarithmic regret and near-optimal performance, and shows through experiments that both ATA and its variant, ATA -Empirical, substantially reduce wasted computation compared to greedy methods."}
+{"idx": 2, "title": "A Closer Look at Adaptive Regret - Journal of Machine ... ATA: Adaptive Task Allocation for Eficient Resource ... Lecture 22: Adaptive Methods / Regret Minimization CS292FStatRLLecture 8 Exploration in Bandits Bandits: Regret Lower Bound and Instance-Dependent Regret", "date": "", "ddg_snippet": "For the prediction with expert advice setting, we consider methods to construct algorithms that have low adaptive regret . The adaptive regret of an algorithm on a time interval [t1; t2] is the loss of the algorithm minus the loss of the best expert over that interval. Adaptive regret measures how well the algorithm approximates the best expert loca... See full list on jmlr.org To discuss the rst method we need a simple extension of the mix-loss prediction proto-col to the case of specialist experts, who are absent at some steps (\\are asleep\"). At the beginning of each round t the subset At f1; : : : ; Ng of experts who are awake is re-vealed, and the other experts are said to be asleep. The algorithm is required to assig... See full list on jmlr.org which is exactly the standard Fixed Share tracking bound ( 6 ). So we see that the reason why Fixed Share can e ectively compete with switching sequences is that it can, in fact, e ectively compete with any expert on any interval, that is, has small adaptive regret . See full list on jmlr.org In this paper, we propose ATA ( Adaptive Task Allocation ), a method that adapts to het-erogeneous and random distributions of worker computation times. Through rigorous theoretical analysis, we show that ATA identifies the optimal task allocation and performs comparably to meth-ods with prior knowledge of computation times. The key idea is to bound the regret of algorithm in hindsight. We develop these ideas in the next section. The key result is that with mild assumptions (convexity, bounded gradients, √ bounded distance between iterates) we can show that Adam achieves O( T ) bound on the regret R(T ). T 13 / 21 Plan of the proof 1. First prove the Proposition that bounds the sum of square regret • By bounding instantaneous regret e um of squares with “Information 2. Prove the uniform confidence bound Want to construct a lower bound regret ? on the achievable regret So far we our theoretical analysis has always considered a fixed algorithm and analyzed it (by deriving a regret upper bound with high probability) To get a lower bound , we need to consider what regret could be achieved by algorithm, and show it can’t be better than some rate any", "subpage_snippet": "", "source": "jmlr.org", "link": "https://jmlr.org/papers/volume17/13-533/13-533.pdf", "content": "For the prediction with expert advice setting, we consider methods to construct algorithms that have low adaptive regret . The adaptive regret of an algorithm on a time interval [t1; t2] is the loss of the algorithm minus the loss of the best expert over that interval. Adaptive regret measures how well the algorithm approximates the best expert loca... See full list on jmlr.org To discuss the rst method we need a simple extension of the mix-loss prediction proto-col to the case of specialist experts, who are absent at some steps (\\are asleep\"). At the beginning of each round t the subset At f1; : : : ; Ng of experts who are awake is re-vealed, and the other experts are said to be asleep. The algorithm is required to assig... See full list on jmlr.org which is exactly the standard Fixed Share tracking bound ( 6 ). So we see that the reason why Fixed Share can e ectively compete with switching sequences is that it can, in fact, e ectively compete with any expert on any interval, that is, has small adaptive regret . See full list on jmlr.org In this paper, we propose ATA ( Adaptive Task Allocation ), a method that adapts to het-erogeneous and random distributions of worker computation times. Through rigorous theoretical analysis, we show that ATA identifies the optimal task allocation and performs comparably to meth-ods with prior knowledge of computation times. The key idea is to bound the regret of algorithm in hindsight. We develop these ideas in the next section. The key result is that with mild assumptions (convexity, bounded gradients, √ bounded distance between iterates) we can show that Adam achieves O( T ) bound on the regret R(T ). T 13 / 21 Plan of the proof 1. First prove the Proposition that bounds the sum of square regret • By bounding instantaneous regret e um of squares with “Information 2. Prove the uniform confidence bound Want to construct a lower bound regret ? on the achievable regret So far we our theoretical analysis has always considered a fixed algorithm and analyzed it (by deriving a regret upper bound with high probability) To get a lower bound , we need to consider what regret could be achieved by algorithm, and show it can’t be better than some rate any"}
+{"idx": 3, "title": "ATA: Adaptive Task Allocation for Eficient Resource ...", "date": "", "ddg_snippet": "In this paper, we propose ATA ( Adaptive Task Allocation ), a method that adapts to het-erogeneous and random distributions of worker computation times. Through rigorous theoretical analysis, we show that ATA identifies the optimal task allocation and performs comparably to meth-ods with prior knowledge of computation times.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2502.00775", "content": "In this paper, we propose ATA ( Adaptive Task Allocation ), a method that adapts to het-erogeneous and random distributions of worker computation times. Through rigorous theoretical analysis, we show that ATA identifies the optimal task allocation and performs comparably to meth-ods with prior knowledge of computation times."}
+{"idx": 4, "title": "Lecture 22: Adaptive Methods / Regret Minimization", "date": "", "ddg_snippet": "The key idea is to bound the regret of algorithm in hindsight. We develop these ideas in the next section. The key result is that with mild assumptions (convexity, bounded gradients, √ bounded distance between iterates) we can show that Adam achieves O( T ) bound on the regret R(T ).", "subpage_snippet": "", "source": "www.cs.cmu.edu", "link": "https://www.cs.cmu.edu/~mgormley/courses/10425//slides/lecture22-adam.pdf", "content": "The key idea is to bound the regret of algorithm in hindsight. We develop these ideas in the next section. The key result is that with mild assumptions (convexity, bounded gradients, √ bounded distance between iterates) we can show that Adam achieves O( T ) bound on the regret R(T )."}
+{"idx": 5, "title": "Bandits: Regret Lower Bound and Instance-Dependent Regret", "date": "", "ddg_snippet": "Want to construct a lower bound regret ? on the achievable regret So far we our theoretical analysis has always considered a fixed algorithm and analyzed it (by deriving a regret upper bound with high probability) To get a lower bound , we need to consider what regret could be achieved by algorithm, and show it can’t be better than some rate any", "subpage_snippet": "", "source": "shamulent.github.io", "link": "https://shamulent.github.io/RL_2022/Lectures/Lecture4_prelecture.pdf", "content": "Want to construct a lower bound regret ? on the achievable regret So far we our theoretical analysis has always considered a fixed algorithm and analyzed it (by deriving a regret upper bound with high probability) To get a lower bound , we need to consider what regret could be achieved by algorithm, and show it can’t be better than some rate any"}
+{"idx": 6, "title": "ATA: Adaptive Task Allocation for Efficient Resource ...", "date": "", "ddg_snippet": "18 Jun 2025 — We introduce ATA ( Adaptive Task Allocation ), a method that learns how fast each machine is over time and adapts the task assignment accordingly.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=1BaC3AdG1i&noteId=hkH8Wi9zZm", "content": "18 Jun 2025 — We introduce ATA ( Adaptive Task Allocation ), a method that learns how fast each machine is over time and adapts the task assignment accordingly."}
+{"idx": 7, "title": "ATA: Adaptive Task Allocation for Efficient Resource ...", "date": "", "ddg_snippet": "2 Feb 2025 — We will give its full specifics in the regret upper bound of Theorem 6.1 . Report issue for preceding element. The bound in Theorem 4.2 shows ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2502.00775v1", "content": "2 Feb 2025 — We will give its full specifics in the regret upper bound of Theorem 6.1 . Report issue for preceding element. The bound in Theorem 4.2 shows ..."}
+{"idx": 8, "title": "ATA: Adaptive Task Allocation for Efficient Resource ...", "date": "", "ddg_snippet": "In the regret bound of Theorem 6.1 , the term α2 appears instead of α2i ... The following theorem provides an upper bound on the regret of ATA -Empirical.", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/poster/46650", "content": "In the regret bound of Theorem 6.1 , the term α2 appears instead of α2i ... The following theorem provides an upper bound on the regret of ATA -Empirical."}
+{"idx": 9, "title": "cherryATA", "date": "", "ddg_snippet": "In the regret bound of Theorem 6.1 , the term α2 appears in- stead of α2 i because the learner's prior knowledge is limited to an upper bound α ≥ maxi ∥Xi∥ψ1.", "subpage_snippet": "", "source": "www.arxiv.org", "link": "https://www.arxiv.org/pdf/2502.00775v1", "content": "In the regret bound of Theorem 6.1 , the term α2 appears in- stead of α2 i because the learner's prior knowledge is limited to an upper bound α ≥ maxi ∥Xi∥ψ1."}

data/sampled_jsons/A_Checks-and-Balances_Framework_ETHICS_dataset_reasons_sitearxiv.org.jsonl

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+{"idx": 0, "title": "A Checks - and - Balances Framework for Context-Aware Ethical AI...", "date": "", "ddg_snippet": "1.2. Checks and Balances for Emotion-Guided Ethics . Central to this approach is the synergy between Dike and Eris, reflecting the internal conflict often present in the regu-lation of human emotions.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2502.00136", "content": "1.2. Checks and Balances for Emotion-Guided Ethics . Central to this approach is the synergy between Dike and Eris, reflecting the internal conflict often present in the regu-lation of human emotions."}
+{"idx": 1, "title": "A Study on the Framework for Evaluating the Ethics ...", "date": "", "ddg_snippet": "by C Jeong · 2025 — Through this approach, the study seeks to ensure a balanced evaluation of both ethics and trustworthiness, thereby guiding the development of ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2509.00398", "content": "by C Jeong · 2025 — Through this approach, the study seeks to ensure a balanced evaluation of both ethics and trustworthiness, thereby guiding the development of ..."}
+{"idx": 2, "title": "Building Better Datasets: Seven Recommendations for ...", "date": "", "ddg_snippet": "30 Aug 2024 — Processes for assessing the ethical concerns of datasets — including privacy, copyright, and consent — are also important open challenges.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2409.00252v1", "content": "30 Aug 2024 — Processes for assessing the ethical concerns of datasets — including privacy, copyright, and consent — are also important open challenges."}
+{"idx": 3, "title": "Integrating Emotional and Linguistic Models for Ethical ...", "date": "", "ddg_snippet": "11 May 2024 — This research develops advanced methodologies for Large Language Models (LLMs) to better manage linguistic behaviors related to emotions and ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2405.07076v1", "content": "11 May 2024 — This research develops advanced methodologies for Large Language Models (LLMs) to better manage linguistic behaviors related to emotions and ..."}
+{"idx": 4, "title": "On the ETHOS of AI Agents: An Ethical Technology and ...", "date": "", "ddg_snippet": "24 Dec 2024 — Ethical grounding is a necessary condition for AI agents to operate in a manner that respects fundamental human values, dignity, and rights ( ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2412.17114v2", "content": "24 Dec 2024 — Ethical grounding is a necessary condition for AI agents to operate in a manner that respects fundamental human values, dignity, and rights ( ..."}
+{"idx": 5, "title": "A Framework of Fundamental Values for Human-AI ...", "date": "", "ddg_snippet": "We introduce \\system, a framework of fundamental values, grounded in psychological theory and a systematic review, to identify and evaluate human-AI alignment.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2409.09586v1", "content": "We introduce \\system, a framework of fundamental values, grounded in psychological theory and a systematic review, to identify and evaluate human-AI alignment."}
+{"idx": 6, "title": "A Conceptual Framework for Ethical Evaluation of Machine ...", "date": "", "ddg_snippet": "20 Aug 2024 — We conceptualize ethics -related concerns in standard ML evaluation techniques. Specifically, we present a utility framework , characterizing the ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2408.10239", "content": "20 Aug 2024 — We conceptualize ethics -related concerns in standard ML evaluation techniques. Specifically, we present a utility framework , characterizing the ..."}
+{"idx": 7, "title": "Advancing AI with Integrity: Ethical Challenges and ...", "date": "", "ddg_snippet": "1 Apr 2024 — In this review, we will center our attention on the ethical dimensions encompassing NMT, for both high-resource and low-resource languages.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2404.01070v1", "content": "1 Apr 2024 — In this review, we will center our attention on the ethical dimensions encompassing NMT, for both high-resource and low-resource languages."}
+{"idx": 8, "title": "Identifying AI incidents Related to Diversity and Inclusion", "date": "", "ddg_snippet": "Wei et al. presented a detailed analysis of real-world AI ethical issues, drawn from the AI Incident Database , identifying 13 prevalent application areas and 8 ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2408.01438v1", "content": "Wei et al. presented a detailed analysis of real-world AI ethical issues, drawn from the AI Incident Database , identifying 13 prevalent application areas and 8 ..."}
+{"idx": 9, "title": "A Review of Ethical and Robust Large Language Models", "date": "", "ddg_snippet": "1 Jun 2024 — This comprehensive review examines the critical trust issues in LLMs, focusing on concerns such as unintentional harms, lack of transparency, vulnerability to ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2407.13934v1", "content": "1 Jun 2024 — This comprehensive review examines the critical trust issues in LLMs, focusing on concerns such as unintentional harms, lack of transparency, vulnerability to ..."}

data/sampled_jsons/A_Checks-and-Balances_Framework_for_Context-Aware_Ethical_AI_Alignment_Edward_Chang_limitations_year_2024.jsonl

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+{"idx": 1, "title": "A Checks - and - Balances Framework for Context - Aware Ethical AI ...", "date": "", "ddg_snippet": "This work introduces a checks - and - balances framework for ethical AI behavior. By delineating the responsibilities: LLM (executive), Dike (legislative), and Eris (judicial), the framework enables robust ethical oversight while...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2502.00136", "content": "This work introduces a checks - and - balances framework for ethical AI behavior. By delineating the responsibilities: LLM (executive), Dike (legislative), and Eris (judicial), the framework enables robust ethical oversight while..."}
+{"idx": 2, "title": "(PDF) Checks - and - Balances Framework for Context - Aware Ethical ...", "date": "", "ddg_snippet": "This paper introduces a checks - and - balances framework for ethical alignment of Large Language Models (LLMs), inspired by three-branch governmental systems. Edward Y. Chang 1. Abstract. This paper introduces a checks - and - balances .", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/380515639_Checks-and-Balances_Framework_for_Context-Aware_Ethical_AI_Alignment", "content": "This paper introduces a checks - and - balances framework for ethical alignment of Large Language Models (LLMs), inspired by three-branch governmental systems. Edward Y. Chang 1. Abstract. This paper introduces a checks - and - balances ."}
+{"idx": 3, "title": "A Three-Branch Checks - and - Balances Framework for ... | OpenReview", "date": "", "ddg_snippet": "This paper introduces a three-branch checks - and - balances framework for ethical alignment of Large Language Models (LLMs), inspired by the idea of collaborative intelligence.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=o2afWIxjKD", "content": "This paper introduces a three-branch checks - and - balances framework for ethical alignment of Large Language Models (LLMs), inspired by the idea of collaborative intelligence."}
+{"idx": 4, "title": "infolab.stanford.edu/~echang/Behavior2024.bib", "date": "", "ddg_snippet": "@article{ chang 2025threebranch, title={A Three-Branch Checks - and - Balances Framework for Context - Aware Ethical Alignment of Large Language Models}, author={ Chang , Edward Y.}, journal={arXiv preprint arXiv:2502.00136}, year={2024}, url={https...", "subpage_snippet": "", "source": "infolab.stanford.edu", "link": "http://infolab.stanford.edu/~echang/Behavior2024.bib", "content": "@article{ chang 2025threebranch, title={A Three-Branch Checks - and - Balances Framework for Context - Aware Ethical Alignment of Large Language Models}, author={ Chang , Edward Y.}, journal={arXiv preprint arXiv:2502.00136}, year={2024}, url={https..."}
+{"idx": 5, "title": "(PDF) A Three-Branch Checks - and - Balances Framework for ...", "date": "", "ddg_snippet": "Edward Y. Chang .This work , which will be presented at NeurIPS this week, proposes a paradigm shift: using three LLM modules to perform checks and balances to represent knowledge, legislative, and judicial functions.", "subpage_snippet": "", "source": "www.linkedin.com", "link": "https://www.linkedin.com/posts/edward-y-chang-218b182_pdf-a-three-branch-checks-and-balances-activity-7272792943923458050-RO6k", "content": "Edward Y. Chang .This work , which will be presented at NeurIPS this week, proposes a paradigm shift: using three LLM modules to perform checks and balances to represent knowledge, legislative, and judicial functions."}
+{"idx": 6, "title": "AI Is Changing The Way We Learn At Work - BW People", "date": "", "ddg_snippet": "Building AI literacy, ethics , and role-based skills to prepare India’s workforce for responsible, high-impact AI adoption at scale. Free Source.As AI processes data at scale, human judgment— contextual awareness , ethical reasoning, empathy—becomes more critical.", "subpage_snippet": "", "source": "www.bwpeople.in", "link": "https://www.bwpeople.in/article/ai-is-changing-the-way-we-learn-at-wirk-567559", "content": "Building AI literacy, ethics , and role-based skills to prepare India’s workforce for responsible, high-impact AI adoption at scale. Free Source.As AI processes data at scale, human judgment— contextual awareness , ethical reasoning, empathy—becomes more critical."}
+{"idx": 7, "title": "AI Detector and AI Checker Tool – Free and Accurate AI Detection.", "date": "", "ddg_snippet": "No sign-up or limits ! Merlin's AI Detector spots content generated by GPT-4o, Claude 3.5, and Gemini accurately while humanizing text instantly.", "subpage_snippet": "", "source": "www.getmerlin.in", "link": "https://www.getmerlin.in/ai-detection", "content": "No sign-up or limits ! Merlin's AI Detector spots content generated by GPT-4o, Claude 3.5, and Gemini accurately while humanizing text instantly."}
+{"idx": 8, "title": "AI Ethics and Fairness: AI Alignment | Restackio", "date": "", "ddg_snippet": "Understanding AI Alignment in Ethical AI Challenges of Bias and Fairness in AI SystemsIn summary, the challenges of bias and fairness in AI systems necessitate a proactive approach...", "subpage_snippet": "", "source": "d2wozrt205r2fu.cloudfront.net", "link": "https://d2wozrt205r2fu.cloudfront.net/p/ai-ethics-and-fairness-answer-ai-alignment-cat-ai", "content": "Understanding AI Alignment in Ethical AI Challenges of Bias and Fairness in AI SystemsIn summary, the challenges of bias and fairness in AI systems necessitate a proactive approach..."}
+{"idx": 9, "title": "Balancing Human Creativity and AI Efficiency: Strategies for Effective...", "date": "", "ddg_snippet": "Challenges and Limitations of AI in Content CreationFuture Trends in AI and Human Collaboration for SEOHowever, maintaining a balance between AI tools and human creativity is essential to ensure...", "subpage_snippet": "", "source": "spreadbot.ai", "link": "https://spreadbot.ai/blog/balancing-human-creativity-and-ai-efficiency-strategies-for-effective-seo-driven-content-automation-in-digital-marketing/", "content": "Challenges and Limitations of AI in Content CreationFuture Trends in AI and Human Collaboration for SEOHowever, maintaining a balance between AI tools and human creativity is essential to ensure..."}

data/sampled_jsons/A_Checks-and-Balances_Framework_for_Context-Aware_Ethical_AI_Alignment_OpenReview_dataset.jsonl

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+{"idx": 0, "title": "A Checks-and-Balances Framework for Context-Aware Ethical AI Alignment ...", "date": "", "ddg_snippet": "This paper introduces a checks - and - balances framework for ethical alignment of Large Language Models (LLMs), inspired by three-branch governmental systems. It implements three independent yet...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=4uOEiitySn", "content": "This paper introduces a checks - and - balances framework for ethical alignment of Large Language Models (LLMs), inspired by three-branch governmental systems. It implements three independent yet..."}
+{"idx": 1, "title": "A Checks-and-Balances Framework for Context-Aware Ethical AI Alignment", "date": "", "ddg_snippet": "This paper introduces a checks - and - balances framework for ethical alignment of Large Language Models (LLMs), inspired by three-branch governmental systems. It implements three independent yet interacting components: LLMs as the executive branch for knowledge generation, DIKE as the legislative branch establishing ethical guardrails, and ERIS as the judicial branch for contextual interpretation ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2502.00136", "content": "This paper introduces a checks - and - balances framework for ethical alignment of Large Language Models (LLMs), inspired by three-branch governmental systems. It implements three independent yet interacting components: LLMs as the executive branch for knowledge generation, DIKE as the legislative branch establishing ethical guardrails, and ERIS as the judicial branch for contextual interpretation ..."}
+{"idx": 2, "title": "PDF An Adversarial Behavior Model for Contextual Ethical Alignment in Large ...", "date": "", "ddg_snippet": "Abstract This research introduces DIKE, a novel framework for aligning Large Language Models (LLMs) with human values through emotion-guided behavioral control. Inspired by the checks and balances ...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/profile/Edward-Chang-22/publication/380515639_A_Three-Branch_Checks-and-Balances_Framework_for_Context-Aware_Ethical_Alignment_of_Large_Language_Models/links/671b315b55a5271cded9457e/A-Three-Branch-Checks-and-Balances-Framework-for-Context-Aware-Ethical-Alignment-of-Large-Language-Models.pdf", "content": "Abstract This research introduces DIKE, a novel framework for aligning Large Language Models (LLMs) with human values through emotion-guided behavioral control. Inspired by the checks and balances ..."}
+{"idx": 3, "title": "A Three-Branch Checks-and-Balances Frameworkfor Context-Aware Ethical ...", "date": "", "ddg_snippet": "A three-branch checks - and - balances framework for ethical alignment of Large Language Models, inspired by governmental systems, demonstrates how emotional modeling can guide linguistic behaviors toward ethical outcomes while preserving independence across knowledge generation, ethical oversight, and contextual interpretation.", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/A-Three-Branch-Checks-and-Balances-Frameworkfor-of-Chang/5918a91419cf95db8599b086590facf63f124702/figure/4", "content": "A three-branch checks - and - balances framework for ethical alignment of Large Language Models, inspired by governmental systems, demonstrates how emotional modeling can guide linguistic behaviors toward ethical outcomes while preserving independence across knowledge generation, ethical oversight, and contextual interpretation."}
+{"idx": 4, "title": "infolab.stanford.edu", "date": "", "ddg_snippet": "@article{chang2025threebranch, title={A Three-Branch Checks - and - Balances Framework for Context-Aware Ethical Alignment of Large Language Models}, author={Chang ...", "subpage_snippet": "", "source": "infolab.stanford.edu", "link": "http://infolab.stanford.edu/~echang/Behavior2024.bib", "content": "@article{chang2025threebranch, title={A Three-Branch Checks - and - Balances Framework for Context-Aware Ethical Alignment of Large Language Models}, author={Chang ..."}
+{"idx": 5, "title": "A Checks-and-Balances Framework for Context-Aware Ethical AI Alignment ...", "date": "", "ddg_snippet": "This paper presents a new way to ensure that large AI language models behave ethically, using a system inspired by government branches. It has three parts: the AI (executive) generates knowledge, ...", "subpage_snippet": "", "source": "bytez.com", "link": "https://bytez.com/docs/icml/46461/paper", "content": "This paper presents a new way to ensure that large AI language models behave ethically, using a system inspired by government branches. It has three parts: the AI (executive) generates knowledge, ..."}
+{"idx": 6, "title": "Benchmarking, ethical alignment, and evaluation framework for ...", "date": "", "ddg_snippet": "Adaptive Standards and Intelligent Evaluation: This research paper proposes a comprehensive framework for evaluating ChatGPT that includes adaptive standards to keep pace with the dynamic nature of conversational AI . The framework incorporates ethical considerations, context adaptability, and community collaboration.", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S2772485923000534", "content": "Adaptive Standards and Intelligent Evaluation: This research paper proposes a comprehensive framework for evaluating ChatGPT that includes adaptive standards to keep pace with the dynamic nature of conversational AI . The framework incorporates ethical considerations, context adaptability, and community collaboration."}
+{"idx": 7, "title": "A Three-Branch Checks-and-Balances Framework for Context-Aware Ethical ...", "date": "", "ddg_snippet": "This paper introduces a three-branch checks - and - balances framework for ethical alignment of Large Language Models (LLMs), inspired by the idea of collaborative intelligence. It implements three...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=o2afWIxjKD", "content": "This paper introduces a three-branch checks - and - balances framework for ethical alignment of Large Language Models (LLMs), inspired by the idea of collaborative intelligence. It implements three..."}
+{"idx": 8, "title": "A Three-Branch Checks-and-Balances Frameworkfor Context-Aware Ethical ...", "date": "", "ddg_snippet": "Conclusion This checks - and - balances approach offers a promising direction for building more ethically- aware AI systems. The framework's ability to handle cultural differences while maintaining ethical standards could help develop AI systems that work responsibly across global contexts .", "subpage_snippet": "", "source": "www.aimodels.fyi", "link": "https://www.aimodels.fyi/papers/arxiv/three-branch-checks-balances-frameworkfor-context-aware", "content": "Conclusion This checks - and - balances approach offers a promising direction for building more ethically- aware AI systems. The framework's ability to handle cultural differences while maintaining ethical standards could help develop AI systems that work responsibly across global contexts ."}
+{"idx": 9, "title": "A Three-Branch Checks-and-Balances Frameworkfor Context-Aware Ethical ...", "date": "", "ddg_snippet": "This paper introduces a three-branch checks - and - balances framework for ethical alignment of Large Language Models (LLMs), inspired by governmental systems. It implements three independent yet interacting components: LLMs as the executive branch for knowledge generation, DIKE as the legislative branch establishing ethical guardrails, and ERIS as the judicial branch for contextual interpretation ...", "subpage_snippet": "", "source": "paperreading.club", "link": "https://paperreading.club/page?id=281349", "content": "This paper introduces a three-branch checks - and - balances framework for ethical alignment of Large Language Models (LLMs), inspired by governmental systems. It implements three independent yet interacting components: LLMs as the executive branch for knowledge generation, DIKE as the legislative branch establishing ethical guardrails, and ERIS as the judicial branch for contextual interpretation ..."}

data/sampled_jsons/A_Checks-and-Balances_Framework_for_Context-Aware_Ethical_AI_Alignment_full_text_algorithm.jsonl

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+{"idx": 1, "title": "Context Reasoner: Incentivizing Reasoning Capability for", "date": "", "ddg_snippet": "With the CI framework , we are able to align LLMs with established legal frameworks , including GDPR, the EU AI Act, and HIPAA.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2505.14585v2", "content": "With the CI framework , we are able to align LLMs with established legal frameworks , including GDPR, the EU AI Act, and HIPAA."}
+{"idx": 2, "title": "Redefining Elderly Care with Agentic AI: Challenges and", "date": "", "ddg_snippet": "Personalized tracking of health, cognitive care, and environmental management, all aimed at enhancing independence and high-level living for older ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2507.14912v1", "content": "Personalized tracking of health, cognitive care, and environmental management, all aimed at enhancing independence and high-level living for older ..."}
+{"idx": 3, "title": "Laws, norms, and ethics for AI in health - Microsoft Research", "date": "", "ddg_snippet": "When we were writing our book, Carey, Zak, and I didn’ t claim that putting frameworks in place to allow for innovation and adoption while ...", "subpage_snippet": "", "source": "www.microsoft.com", "link": "https://www.microsoft.com/en-us/research/podcast/laws-norms-and-ethics-for-ai-in-health/", "content": "When we were writing our book, Carey, Zak, and I didn’ t claim that putting frameworks in place to allow for innovation and adoption while ..."}
+{"idx": 4, "title": "How Explainable Is Explainability? Towards Better Metrics for", "date": "", "ddg_snippet": "The paper underscores the critical role of xAI in addressing opacity issues within machine learning algorithms and sets the stage for further ...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/377059445_How_Explainable_Is_Explainability_Towards_Better_Metrics_for_Explainable_AI", "content": "The paper underscores the critical role of xAI in addressing opacity issues within machine learning algorithms and sets the stage for further ..."}
+{"idx": 5, "title": "(PDF) AI-ENABLED DECISION SUPPORT SYSTEMS FOR SMARTER", "date": "", "ddg_snippet": "... framework emphasizes dynamic scheduling, risk forecasting, lifecycle asset management, and compliance monitoring as core functional pillars of AI -DSS ...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/393655215_AI-ENABLED_DECISION_SUPPORT_SYSTEMS_FOR_SMARTER_INFRASTRUCTURE_PROJECT_MANAGEMENT_IN_PUBLIC_WORKS", "content": "... framework emphasizes dynamic scheduling, risk forecasting, lifecycle asset management, and compliance monitoring as core functional pillars of AI -DSS ..."}
+{"idx": 6, "title": "DaLiF: a data lifecycle framework for data-driven governments |", "date": "", "ddg_snippet": "While achieving this requires various functions, roles, responsibilities, abilities, and skills for both technology and people, it is critical to pay ...", "subpage_snippet": "", "source": "journalofbigdata.springeropen.com", "link": "https://journalofbigdata.springeropen.com/articles/10.1186/s40537-021-00481-3", "content": "While achieving this requires various functions, roles, responsibilities, abilities, and skills for both technology and people, it is critical to pay ..."}
+{"idx": 7, "title": "The Algorithmic Couch: An In-Depth Analysis of Efficacy and", "date": "", "ddg_snippet": "This “ human-in-the-loop ” approach leverages AI for tasks such as administrative support, psychoeducation, and between-session skill ...", "subpage_snippet": "", "source": "uplatz.com", "link": "https://uplatz.com/blog/the-algorithmic-couch-an-in-depth-analysis-of-efficacy-and-ethics-in-ai-powered-mental-health-support/", "content": "This “ human-in-the-loop ” approach leverages AI for tasks such as administrative support, psychoeducation, and between-session skill ..."}
+{"idx": 8, "title": "How do I use ChatGPT for a resume?", "date": "", "ddg_snippet": "QuillBot ’ s AI Checker Android App and AI Checker iOS App can help you ensure that the writing you submit for class assignments is based on ...", "subpage_snippet": "", "source": "quillbot.com", "link": "https://quillbot.com/blog/frequently-asked-questions/how-do-i-use-chatgpt-for-a-resume/", "content": "QuillBot ’ s AI Checker Android App and AI Checker iOS App can help you ensure that the writing you submit for class assignments is based on ..."}
+{"idx": 9, "title": "Frontiers | Artificial intelligence in variant calling: a review", "date": "", "ddg_snippet": "To address these challenges, increasingly sophisticated computational pipelines and algorithms have been developed ( McKenna et al., 2010 ; Garrison ...", "subpage_snippet": "", "source": "www.frontiersin.org", "link": "https://www.frontiersin.org/journals/bioinformatics/articles/10.3389/fbinf.2025.1574359/full", "content": "To address these challenges, increasingly sophisticated computational pipelines and algorithms have been developed ( McKenna et al., 2010 ; Garrison ..."}

data/sampled_jsons/A_First_Look_at_Public_Service_Websites_from_the_Affordability_Lens_Habib_research_paper.jsonl

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+{"idx": 0, "title": "A First Look at Public Service Websites from the Affordability Lens", "date": "", "ddg_snippet": "The research found that communication and information mechanisms are at the forefront of the services provided to citizens on metropolitan municipalities' official websites .", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/370413005_A_First_Look_at_Public_Service_Websites_from_the_Affordability_Lens", "content": "The research found that communication and information mechanisms are at the forefront of the services provided to citizens on metropolitan municipalities' official websites ."}
+{"idx": 1, "title": "GitHub - nsgLUMS/ public - service -sites", "date": "", "ddg_snippet": "A First Look at Public Service Websites from the Affordability Lens Dataset.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/nsgLUMS/public-service-sites", "content": "A First Look at Public Service Websites from the Affordability Lens Dataset."}
+{"idx": 2, "title": "Rumaisa Habib - CS PhD Student @ Stanford | LinkedIn", "date": "", "ddg_snippet": "A First Look at Public Service Websites from the Affordability Lens .This paper presents the first large-scale analysis of the affordability of public service websites …", "subpage_snippet": "", "source": "www.linkedin.com", "link": "https://www.linkedin.com/in/rumaisahabib", "content": "A First Look at Public Service Websites from the Affordability Lens .This paper presents the first large-scale analysis of the affordability of public service websites …"}
+{"idx": 3, "title": "Dr. Ihsan Ayyub Qazi @ LUMS - Publications", "date": "", "ddg_snippet": "A First Look at Public Service Websites from the Affordability Lens .", "subpage_snippet": "", "source": "www.ihsanqazi.com", "link": "https://www.ihsanqazi.com/publications", "content": "A First Look at Public Service Websites from the Affordability Lens ."}
+{"idx": 4, "title": "dblp: List of computer science publications by Aimen Inam", "date": "", "ddg_snippet": "Rumaisa Habib , Aimen Inam , Ayesha Ali , Ihsan Ayyub Qazi , Zafar Ayyub Qazi : A First Look at Public Service Websites from the Affordability Lens .", "subpage_snippet": "", "source": "dblp.org", "link": "https://dblp.org/pid/345/6218.html", "content": "Rumaisa Habib , Aimen Inam , Ayesha Ali , Ihsan Ayyub Qazi , Zafar Ayyub Qazi : A First Look at Public Service Websites from the Affordability Lens ."}
+{"idx": 5, "title": "Paper Digest: WWW 2023 Highlights – Resources | Paper Digest", "date": "", "ddg_snippet": "To browse papers by author, here is a list of top authors (WWW-2023). You may also like to explore our \"Best Paper \" Digest (WWW), which lists the most influential WWW.", "subpage_snippet": "", "source": "resources.paperdigest.org", "link": "https://resources.paperdigest.org/2023/04/www-2023-highlights/", "content": "To browse papers by author, here is a list of top authors (WWW-2023). You may also like to explore our \"Best Paper \" Digest (WWW), which lists the most influential WWW."}
+{"idx": 6, "title": "Uncovering the Hidden Data Costs of Mobile YouTube Video Ads", "date": "", "ddg_snippet": "In this work, we conducted the first large-scale empirical analysis of YouTube with the goal of understanding the data costs of video ads through an affordability lens .", "subpage_snippet": "", "source": "emaanatique.github.io", "link": "https://emaanatique.github.io/files/ytafford-www'24.pdf", "content": "In this work, we conducted the first large-scale empirical analysis of YouTube with the goal of understanding the data costs of video ads through an affordability lens ."}
+{"idx": 7, "title": "CS PhD student at Stanford University", "date": "", "ddg_snippet": "A First Look at Public Service Websites from the Affordability Lens .This paper presents the first large-scale analysis of the afordability of public service websites in developing countries.", "subpage_snippet": "", "source": "rumaisahabib.com", "link": "https://rumaisahabib.com/", "content": "A First Look at Public Service Websites from the Affordability Lens .This paper presents the first large-scale analysis of the afordability of public service websites in developing countries."}
+{"idx": 8, "title": "Zafar Ayyub Qazi - Google Scholar", "date": "", "ddg_snippet": "4. 2016. A First Look at Public Service Websites from the Affordability Lens .", "subpage_snippet": "", "source": "scholar.google.com.ru", "link": "https://scholar.google.com.ru/citations?user=O5uXfioAAAAJ&hl=en", "content": "4. 2016. A First Look at Public Service Websites from the Affordability Lens ."}
+{"idx": 9, "title": "Of Choices and Control - A Comparative Analysis of Government Hosting", "date": "", "ddg_snippet": "2023. A First Look at Public Service Websites from the Affordability Lens .", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/abs/10.1145/3646547.3688447", "content": "2023. A First Look at Public Service Websites from the Affordability Lens ."}

data/sampled_jsons/A_Likelihood_Based_Approach_to_Distribution_Regression_Table_1_Sieve_MLE_FD3.jsonl

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+{"idx": 0, "title": "A Likelihood Based Approach to Distribution Regression Using ...", "date": "", "ddg_snippet": "More specifically, we study the large-sample properties of a likelihood-based approach for estimating these models. Our results lead to the convergence rate of a sieve maximum likelihood estimator ( MLE ) for estimating the conditional distribution (and its devolved counterpart) of the response given predictors in the Hellinger (Wasserstein) metric.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2410.02025", "content": "More specifically, we study the large-sample properties of a likelihood-based approach for estimating these models. Our results lead to the convergence rate of a sieve maximum likelihood estimator ( MLE ) for estimating the conditional distribution (and its devolved counterpart) of the response given predictors in the Hellinger (Wasserstein) metric."}
+{"idx": 1, "title": "PDF 10-315 Notes Maximum Likelihood Estimation", "date": "", "ddg_snippet": "Maximum likelihood estimation ( MLE ) is trying to find the best parameters for a specific dataset, D. Specifically, we want to find the parameters ˆθ MLE that maximize the likelihood for D.", "subpage_snippet": "", "source": "www.cs.cmu.edu", "link": "https://www.cs.cmu.edu/~10315/notes/10315_S24_Notes_MLE.pdf", "content": "Maximum likelihood estimation ( MLE ) is trying to find the best parameters for a specific dataset, D. Specifically, we want to find the parameters ˆθ MLE that maximize the likelihood for D."}
+{"idx": 2, "title": "PDF A Likelihood Approach to Nonparametric Estimation of a Singular ...", "date": "", "ddg_snippet": "In this work, we focus on the likelihood-based approach and study statistical proper-ties of a sieve maximum likelihood estimator ( MLE ) of deep generative models under the assumption that P is the distribution of X = f (Z) +", "subpage_snippet": "", "source": "jmlr.org", "link": "https://jmlr.org/papers/volume24/21-1099/21-1099.pdf", "content": "In this work, we focus on the likelihood-based approach and study statistical proper-ties of a sieve maximum likelihood estimator ( MLE ) of deep generative models under the assumption that P is the distribution of X = f (Z) +"}
+{"idx": 3, "title": "PDF Maximum Likelihood Estimation (MLE)", "date": "", "ddg_snippet": "Maximum Likelihood—Approach I: Grid Search We can find the MLE with grid-search—we evaluate log likelihood (4) for a range of possible values of μ and choose the one that maximizes log likelihood .", "subpage_snippet": "", "source": "www.fsb.miamioh.edu", "link": "https://www.fsb.miamioh.edu/lij14/572_slide_mle.pdf", "content": "Maximum Likelihood—Approach I: Grid Search We can find the MLE with grid-search—we evaluate log likelihood (4) for a range of possible values of μ and choose the one that maximizes log likelihood ."}
+{"idx": 4, "title": "3 Likelihood-based inference - MATH 60604A - Statistical Modelling", "date": "", "ddg_snippet": "3 Likelihood-based inference This chapter is dedicated to the basics of statistical modelling using likelihood-based inference, arguably the most popular estimation paradigm in statistics.", "subpage_snippet": "", "source": "lbelzile.github.io", "link": "https://lbelzile.github.io/math60604a/likelihood.html", "content": "3 Likelihood-based inference This chapter is dedicated to the basics of statistical modelling using likelihood-based inference, arguably the most popular estimation paradigm in statistics."}
+{"idx": 5, "title": "A Likelihood Based Approach to Distribution Regression Using ...", "date": "", "ddg_snippet": "he large-sample properties of a likelihood-based approach for estimating these models. Our results lead to the convergence rate of a sieve maximum likelihood estimator ( MLE ) for estimating the conditional distribution (and its devolv d counterpart) of the response given predictors in the Hellinger (Wasserstein) metric. Our rates depend", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=V6hhhXoTSq", "content": "he large-sample properties of a likelihood-based approach for estimating these models. Our results lead to the convergence rate of a sieve maximum likelihood estimator ( MLE ) for estimating the conditional distribution (and its devolv d counterpart) of the response given predictors in the Hellinger (Wasserstein) metric. Our rates depend"}
+{"idx": 6, "title": "(PDF) Convergence Rate of Sieve Estimates - ResearchGate", "date": "", "ddg_snippet": "This method is called the method of sieves . In the case of the maximum likelihood estimation, an MLE based on a sieve is called a sieve MLE .", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/38357549_Convergence_Rate_of_Sieve_Estimates", "content": "This method is called the method of sieves . In the case of the maximum likelihood estimation, an MLE based on a sieve is called a sieve MLE ."}
+{"idx": 7, "title": "Sieve Maximum Likelihood Estimation for Regression Models with ...", "date": "", "ddg_snippet": "Abstract and details Missing covariates are common in regression problems. We propose a new semiparametric method based on a fully nonparametric distribution for the missing covariates that are assumed to be missing at random. The method of sieve maximum likelihood estimation is used to obtain the estimators of thr regression coefficients.", "subpage_snippet": "", "source": "www.jstor.org", "link": "https://www.jstor.org/stable/27639981", "content": "Abstract and details Missing covariates are common in regression problems. We propose a new semiparametric method based on a fully nonparametric distribution for the missing covariates that are assumed to be missing at random. The method of sieve maximum likelihood estimation is used to obtain the estimators of thr regression coefficients."}
+{"idx": 8, "title": "Convergence Rate of Sieve Estimates - Project Euclid", "date": "", "ddg_snippet": "In this paper, we develop a general theory for the convergence rate of sieve estimates, maximum likelihood estimates ( MLE's ) and related estimates obtained by optimizing certain empirical criteria in general parameter spaces.", "subpage_snippet": "", "source": "projecteuclid.org", "link": "https://projecteuclid.org/journals/annals-of-statistics/volume-22/issue-2/Convergence-Rate-of-Sieve-Estimates/10.1214/aos/1176325486.full", "content": "In this paper, we develop a general theory for the convergence rate of sieve estimates, maximum likelihood estimates ( MLE's ) and related estimates obtained by optimizing certain empirical criteria in general parameter spaces."}
+{"idx": 9, "title": "arXiv:2410.02025v1 [math.ST] 2 Oct 2024", "date": "", "ddg_snippet": "In this work, we explore the theoretical properties of conditional deep generative models under the statistical framework of distribution regression where the response variable lies in a high-dimensional am-bient space but concentrates around a potentially lower-dimensional manifold. More specifically, we study the large-sample properties of a likelihood-based approach for estimating these ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2410.02025", "content": "In this work, we explore the theoretical properties of conditional deep generative models under the statistical framework of distribution regression where the response variable lies in a high-dimensional am-bient space but concentrates around a potentially lower-dimensional manifold. More specifically, we study the large-sample properties of a likelihood-based approach for estimating these ..."}

data/sampled_jsons/A_Likelihood_Based_Approach_to_Distribution_Regression_Using_Conditional_Deep_Generative_Models_Coro_year_2023.jsonl

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+{"idx": 0, "title": "A Likelihood Based Approach to Distribution Regression ...", "date": "", "ddg_snippet": "by S Kumar · Cited by 1 — In this work, we explore the theoretical properties of conditional deep generative models under the statistical framework of distribution ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=1IyPRv1A0r", "content": "by S Kumar · Cited by 1 — In this work, we explore the theoretical properties of conditional deep generative models under the statistical framework of distribution ..."}
+{"idx": 1, "title": "A Likelihood Based Approach to Distribution Regression ...", "date": "", "ddg_snippet": "In this work, we explore the theoretical properties of conditional deep generative models under the statistical framework of distribution regression where ...", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/poster/46645", "content": "In this work, we explore the theoretical properties of conditional deep generative models under the statistical framework of distribution regression where ..."}
+{"idx": 2, "title": "A Likelihood Based Approach to Distribution Regression Using ...", "date": "", "ddg_snippet": "In this work, we explore the theoretical properties of conditional deep generative models under the statistical framework of distribution regression where the ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2410.02025", "content": "In this work, we explore the theoretical properties of conditional deep generative models under the statistical framework of distribution regression where the ..."}
+{"idx": 3, "title": "A Likelihood Based Approach to Distribution Regression Using ...", "date": "", "ddg_snippet": "A Likelihood Based Approach to Distribution Regression Using Conditional Deep Generative Models . Table 2 . Mean W1 distance (± SD) between generated and test ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=1IyPRv1A0r", "content": "A Likelihood Based Approach to Distribution Regression Using Conditional Deep Generative Models . Table 2 . Mean W1 distance (± SD) between generated and test ..."}
+{"idx": 4, "title": "A likelihood based approach to distribution regression ...", "date": "", "ddg_snippet": "by S Kumar · 2024 · Cited by 1 — We investigated statistical properties of a likelihood - based conditional deep generative model for distribution regression in a scenario where ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2410.02025", "content": "by S Kumar · 2024 · Cited by 1 — We investigated statistical properties of a likelihood - based conditional deep generative model for distribution regression in a scenario where ..."}
+{"idx": 5, "title": "Bayesian likelihood-based regression for estimation of optimal ...", "date": "", "ddg_snippet": "by W Yu · 2023 · Cited by 6 — In this paper, we propose a Bayesian likelihood - based dynamic treatment regime model that incorporates regression specifications to yield interpretable ...", "subpage_snippet": "", "source": "academic.oup.com", "link": "https://academic.oup.com/jrsssb/article/85/3/551/7092905", "content": "by W Yu · 2023 · Cited by 6 — In this paper, we propose a Bayesian likelihood - based dynamic treatment regime model that incorporates regression specifications to yield interpretable ..."}
+{"idx": 6, "title": "Probabilistic Conformal Prediction Using Conditional Random ...", "date": "", "ddg_snippet": "Table 2 : Summary results of Multi-Target Regression experiments, where Marg. C and Cond. C denote the marginal coverage and approximated conditional coverage.", "subpage_snippet": "", "source": "proceedings.mlr.press", "link": "https://proceedings.mlr.press/v206/wang23n/wang23n.pdf", "content": "Table 2 : Summary results of Multi-Target Regression experiments, where Marg. C and Cond. C denote the marginal coverage and approximated conditional coverage."}
+{"idx": 7, "title": "Toward Understanding Generative Data Augmentation", "date": "", "ddg_snippet": "by C Zheng · 2023 · Cited by 49 — Generative data augmentation, which scales datasets by obtaining fake labeled examples from a trained conditional generative model , boosts classification ...", "subpage_snippet": "", "source": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper_files/paper/2023/file/a94a8800a4b0af45600bab91164849df-Paper-Conference.pdf", "content": "by C Zheng · 2023 · Cited by 49 — Generative data augmentation, which scales datasets by obtaining fake labeled examples from a trained conditional generative model , boosts classification ..."}
+{"idx": 8, "title": "Selective Amnesia: A Continual Learning Approach to ...", "date": "", "ddg_snippet": "by A Heng · 2023 · Cited by 159 — Selective Amnesia can be applied to conditional variational likelihood models , which encompass a variety of popular deep generative frameworks, including ...", "subpage_snippet": "", "source": "papers.nips.cc", "link": "https://papers.nips.cc/paper_files/paper/2023/file/376276a95781fa17c177b1ccdd0a03ac-Paper-Conference.pdf", "content": "by A Heng · 2023 · Cited by 159 — Selective Amnesia can be applied to conditional variational likelihood models , which encompass a variety of popular deep generative frameworks, including ..."}
+{"idx": 9, "title": "SynC2S: An Efficient Method for Synthesizing Tabular Data ...", "date": "", "ddg_snippet": "by J Kim · 2024 · Cited by 1 — In this study, we propose an efficient and theoretically principled method based on a deep generative model to effectively generate high-quality ... 20 pages", "subpage_snippet": "", "source": "ieeexplore.ieee.org", "link": "https://ieeexplore.ieee.org/iel8/6287639/10820123/10704657.pdf", "content": "by J Kim · 2024 · Cited by 1 — In this study, we propose an efficient and theoretically principled method based on a deep generative model to effectively generate high-quality ... 20 pages"}