diff --git "a/data/sampled_jsons/0hrkN07DuO_Linear_convergence_of_Sinkhorn's_algorithm_for_generalized_static_Schr\303\266dinger_bridge_Equa_year_2023.jsonl" "b/data/sampled_jsons/0hrkN07DuO_Linear_convergence_of_Sinkhorn's_algorithm_for_generalized_static_Schr\303\266dinger_bridge_Equa_year_2023.jsonl"
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index 0000000000000000000000000000000000000000..c21067fa4379d057f1ac7a55e2f545dc99a04315
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+++ "b/data/sampled_jsons/0hrkN07DuO_Linear_convergence_of_Sinkhorn's_algorithm_for_generalized_static_Schr\303\266dinger_bridge_Equa_year_2023.jsonl"
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "On the Convergence Rate of Sinkhorn's Algorithm", "date": "", "ddg_snippet": "Since this paper was completed, several results on Sinkhorn’s algorithm have been obtained, tackling some of the aforementioned issues. For quadratic cost and unbounded continuous marginals satisfying a log-concavity condi-tion, [20] proves linear convergence based on a fine analysis of the gradients of Schrödinger potentials and Sinkhorn ...", "subpage_snippet": "", "source": "www.math.columbia.edu", "link": "https://www.math.columbia.edu/~mnutz/docs/Sinkhorn_rate.pdf", "content": "Since this paper was completed, several results on Sinkhorn’s algorithm have been obtained, tackling some of the aforementioned issues. For quadratic cost and unbounded continuous marginals satisfying a log-concavity condi-tion, [20] proves linear convergence based on a fine analysis of the gradients of Schrödinger potentials and Sinkhorn ..."}
+{"idx": 1, "title": "On the Convergence Rate of Sinkhorn’s AlgorithmThe authors ...", "date": "", "ddg_snippet": "Since this paper was completed, several results on Sinkhorn’s algorithm have been obtained, tackling some of the aforementioned issues. For quadratic cost and unbounded continuous marginals satisfying a log-concavity condition, [20]proves linear convergence based on a fine analysis of the gradients of Schrödinger potentials and Sinkhorn ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2212.06000v2", "content": "Since this paper was completed, several results on Sinkhorn’s algorithm have been obtained, tackling some of the aforementioned issues. For quadratic cost and unbounded continuous marginals satisfying a log-concavity condition, [20]proves linear convergence based on a fine analysis of the gradients of Schrödinger potentials and Sinkhorn ..."}
+{"idx": 2, "title": "Linear convergence of Sinkhorn's algorithm for generalized ...", "date": "", "ddg_snippet": "Poster Linear convergence of Sinkhorn's algorithm for generalized static Schrödinger bridge Rahul Choudhary · Hanbaek Lyu West Exhibition Hall B2-B3 #W-506", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/poster/46671", "content": "Poster Linear convergence of Sinkhorn's algorithm for generalized static Schrödinger bridge Rahul Choudhary · Hanbaek Lyu West Exhibition Hall B2-B3 #W-506"}
+{"idx": 3, "title": "Linear Convergence of Sinkhorn’s Algorithm for Generalized ...", "date": "", "ddg_snippet": "In this paper, we present the generalized static Schrödinger bridge problem, establish its Kantorovich dual, and show that the associated generalized Sinkhorn algorithm con-verges linearly in a dimension independent manner under mild assumptions on the general divergence functional f , weight matrix W and margin (r,c).", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=0hrkN07DuO", "content": "In this paper, we present the generalized static Schrödinger bridge problem, establish its Kantorovich dual, and show that the associated generalized Sinkhorn algorithm con-verges linearly in a dimension independent manner under mild assumptions on the general divergence functional f , weight matrix W and margin (r,c)."}
+{"idx": 4, "title": "Onthelinearconvergenceofthe multi-marginalSinkhornalgorithm", "date": "", "ddg_snippet": "The aim of this short note is to give an elementary proof of linear convergence of the Sinkhorn algorithm for the entropic regularization of multi-marginal optimal transport. The proof simply relies on: i) the fact that Sinkhorn iterates are bounded, ii) strong convexity of the exponential on bounded intervals and iii) the convergence analysis of the coordinate descent (Gauss-Seidel) method of ...", "subpage_snippet": "", "source": "www.mathtube.org", "link": "https://www.mathtube.org/sites/default/files/lecture-extra-files/linear-sinkhorn.pdf", "content": "The aim of this short note is to give an elementary proof of linear convergence of the Sinkhorn algorithm for the entropic regularization of multi-marginal optimal transport. The proof simply relies on: i) the fact that Sinkhorn iterates are bounded, ii) strong convexity of the exponential on bounded intervals and iii) the convergence analysis of the coordinate descent (Gauss-Seidel) method of ..."}
+{"idx": 5, "title": "On the Linear Convergence of the Multimarginal Sinkhorn Algorithm", "date": "", "ddg_snippet": "The aim of this note is to give an elementary proof of linear convergence of the Sinkhorn algorithm for the entropic regularization of multimarginal optimal transport in the setting of general probability spaces. The proof simply relies on (i) the fact that Sinkhorn iterates are bounded, (ii) the strong convexity of the exponential on bounded intervals, and (iii) the convergence analysis of ...", "subpage_snippet": "", "source": "epubs.siam.org", "link": "https://epubs.siam.org/doi/10.1137/21M1410634", "content": "The aim of this note is to give an elementary proof of linear convergence of the Sinkhorn algorithm for the entropic regularization of multimarginal optimal transport in the setting of general probability spaces. The proof simply relies on (i) the fact that Sinkhorn iterates are bounded, (ii) the strong convexity of the exponential on bounded intervals, and (iii) the convergence analysis of ..."}
+{"idx": 6, "title": "Sharper exponential convergence rates for Sinkhorn ’ s algorithm in...", "date": "", "ddg_snippet": "We study the convergence rate of Sinkhorn ’ s algorithm for solving entropy-regularized optimal transport problems when at least one of the probability.", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/article/10.1007/s10107-025-02242-z", "content": "We study the convergence rate of Sinkhorn ’ s algorithm for solving entropy-regularized optimal transport problems when at least one of the probability."}
+{"idx": 7, "title": "A survey of the Schrödinger problem and some of its connections with...", "date": "", "ddg_snippet": "A second order equation for Schrödinger bridges with applications to the hot gas experiment and entropic transportation cost[J]. Probability Theory and Related Fields, 2019, 174(0178-8051): 1. doi: 10.1007/ s 00440-018-0856-7.", "subpage_snippet": "", "source": "www.aimsciences.org", "link": "https://www.aimsciences.org/article/doi/10.3934/dcds.2014.34.1533", "content": "A second order equation for Schrödinger bridges with applications to the hot gas experiment and entropic transportation cost[J]. Probability Theory and Related Fields, 2019, 174(0178-8051): 1. doi: 10.1007/ s 00440-018-0856-7."}
+{"idx": 8, "title": "Stability of Schrödinger potentials and convergence of ... | CoLab", "date": "", "ddg_snippet": "As an application, we show convergence of Sinkhorn ' s algorithm in Wasserstein sense, including for quadratic cost.The aim of this short note is to give an elementary proof of linear convergence of the Sinkhorn algorithm for the entropic regularization of multi-marginal optimal transport.", "subpage_snippet": "", "source": "colab.ws", "link": "https://colab.ws/articles/10.1214/22-aop1611", "content": "As an application, we show convergence of Sinkhorn ' s algorithm in Wasserstein sense, including for quadratic cost.The aim of this short note is to give an elementary proof of linear convergence of the Sinkhorn algorithm for the entropic regularization of multi-marginal optimal transport."}
+{"idx": 9, "title": "Tree-Based Diffusion Schrödinger Bridge with Applications to...", "date": "", "ddg_snippet": "General experimental setup. Details on the experiments. Tree-Based Diffusion Schrödinger Bridge with Applications to Wasserstein Barycenters.Carlier, G. On the linear convergence of the multimarginal Sinkhorn algorithm . SIAM Journal on Optimization, 32(2):786–794, 2022.", "subpage_snippet": "", "source": "hal.science", "link": "https://hal.science/hal-04402356/document", "content": "General experimental setup. Details on the experiments. Tree-Based Diffusion Schrödinger Bridge with Applications to Wasserstein Barycenters.Carlier, G. On the linear convergence of the multimarginal Sinkhorn algorithm . SIAM Journal on Optimization, 32(2):786–794, 2022."}
diff --git a/data/sampled_jsons/13.66_EventPS_MAE_average_MAE_3D_printed_objects.jsonl b/data/sampled_jsons/13.66_EventPS_MAE_average_MAE_3D_printed_objects.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..6499d3733fbf9d37c3e81cbb004ae32e22fcc49b
--- /dev/null
+++ b/data/sampled_jsons/13.66_EventPS_MAE_average_MAE_3D_printed_objects.jsonl
@@ -0,0 +1,3 @@
+{"idx": 0, "title": "(PDF) What Is Learned in Deep Uncalibrated Photometric Stereo?", "date": "", "ddg_snippet": "PF14 ( MAE : 33.22, relative error: 0.223). Figures 6 (a)-(b) visualize the light-. ing estimation results for the Pot1 and the Goblet.However, EventPS treats each event interval independently, making it sensitive to noise, shadows, and non-Lambertian reflections.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/346874574_What_Is_Learned_in_Deep_Uncalibrated_Photometric_Stereo", "content": "PF14 ( MAE : 33.22, relative error: 0.223). Figures 6 (a)-(b) visualize the light-. ing estimation results for the Pot1 and the Goblet.However, EventPS treats each event interval independently, making it sensitive to noise, shadows, and non-Lambertian reflections."}
+{"idx": 1, "title": "How to get velocity and position for each node when... | ResearchGate", "date": "", "ddg_snippet": "base_rfr_ mae = mean_absolute_error(test_y, base_rfr_pred).Then I fit the \"best parameters\" obtained to get an optimized MAE but the results are always worse than the base model MAE (opt rfr for image).", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/post/how_to_get_velocity_and_position_for_each_node_when_they_move_in_ns3_vanet_scenario", "content": "base_rfr_ mae = mean_absolute_error(test_y, base_rfr_pred).Then I fit the \"best parameters\" obtained to get an optimized MAE but the results are always worse than the base model MAE (opt rfr for image)."}
+{"idx": 2, "title": "", "date": "", "ddg_snippet": "", "subpage_snippet": "", "source": "", "link": "", "content": ""}
diff --git a/data/sampled_jsons/1BaC3AdG1i_ATA-_Adaptive_Task_Allocation_for_Efficient_Resource_Management_in_Distributed_Machine_Le.jsonl b/data/sampled_jsons/1BaC3AdG1i_ATA-_Adaptive_Task_Allocation_for_Efficient_Resource_Management_in_Distributed_Machine_Le.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..7f464a1a0a9a0e0b904b69e07fd4a990cbe02712
--- /dev/null
+++ b/data/sampled_jsons/1BaC3AdG1i_ATA-_Adaptive_Task_Allocation_for_Efficient_Resource_Management_in_Distributed_Machine_Le.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "ATA: Adaptive Task Allocation for Efficient Resource ...", "date": "", "ddg_snippet": "Feb 2, 2025 · The challenge lies in achieving this optimal allocation without prior knowledge of the computation time distributions. 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/abs/2502.00775", "content": "Feb 2, 2025 · The challenge lies in achieving this optimal allocation without prior knowledge of the computation time distributions. 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": "ATA: Adaptive Task Allocation for Efficient Resource ...", "date": "", "ddg_snippet": "May 1, 2025 · In this paper, we propose ATA ( Adaptive Task Allocation ), a method that adapts to heterogeneous and random distributions of worker computation times. Through rigorous theoretical analysis, we show that ATA identifies the optimal task allocation and performs comparably to methods with prior knowledge of computation times.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=1BaC3AdG1i", "content": "May 1, 2025 · In this paper, we propose ATA ( Adaptive Task Allocation ), a method that adapts to heterogeneous and random distributions of worker computation times. Through rigorous theoretical analysis, we show that ATA identifies the optimal task allocation and performs comparably to methods with prior knowledge of computation times."}
+{"idx": 2, "title": "ATA: Adaptive Task Allocation for Eficient Resource ...", "date": "", "ddg_snippet": "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": "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": 3, "title": "ATA: Adaptive Task Allocation for Efficient Resource ...", "date": "", "ddg_snippet": "In this work, we formalize the task allocation problem as a combinatorial online learning problem with partial feedback and non-linear losses. Then, we introduce ATA , a lower-confidence bound-based algorithm designed to solve the proposed allocation problem.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2502.00775v2", "content": "In this work, we formalize the task allocation problem as a combinatorial online learning problem with partial feedback and non-linear losses. Then, we introduce ATA , a lower-confidence bound-based algorithm designed to solve the proposed allocation problem."}
+{"idx": 4, "title": "Adaptive and Efficient Resource Allocation in Cloud ...", "date": "", "ddg_snippet": "Dec 3, 2021 · The ever-expanding scale of cloud datacenters necessitates automated resource provisioning to best meet the requirements of low latency and high energy-efficiency. However, due to the dynamic system states and various user demands, efficient resource allocation in cloud faces huge challenges.", "subpage_snippet": "", "source": "ieeexplore.ieee.org", "link": "https://ieeexplore.ieee.org/document/9635652", "content": "Dec 3, 2021 · The ever-expanding scale of cloud datacenters necessitates automated resource provisioning to best meet the requirements of low latency and high energy-efficiency. However, due to the dynamic system states and various user demands, efficient resource allocation in cloud faces huge challenges."}
+{"idx": 5, "title": "[PDF] ATA: Adaptive Task Allocation for Efficient Resource ...", "date": "", "ddg_snippet": "The challenge lies in achieving this optimal allocation without prior knowledge of the computation time distributions. 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": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/ATA:-Adaptive-Task-Allocation-for-Efficient-in-Maranjyan-Saad/871e17b70c5c31985b6ecb1d61960ad5ee7d1cbd", "content": "The challenge lies in achieving this optimal allocation without prior knowledge of the computation time distributions. In this paper, we propose ATA ( Adaptive Task Allocation ), a method that adapts to heterogeneous and random distributions of worker computation times."}
+{"idx": 6, "title": "ATA : Adaptive Task Allocation for Efficient Resource Management ...", "date": "", "ddg_snippet": "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/2502.00775v1", "content": "In this paper, we propose ATA ( Adaptive Task Allocation ), a method that adapts to heterogeneous and random distributions of worker computation times."}
+{"idx": 7, "title": "ATA : Adaptive Task Allocation", "date": "", "ddg_snippet": "ATA : Adaptive Task Allocation for Efficient Resource Management in Distributed Machine Learning . distributed ML training. # Tasks : Computation.", "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 . distributed ML training. # Tasks : Computation."}
+{"idx": 8, "title": "Quantum Inspired Adaptive Resource Management Algorithm for...", "date": "", "ddg_snippet": "Quantum-inspired adaptive Resource Management switches on quite a new conceptual framework toward optimized resource allocation in fog computing environments.", "subpage_snippet": "", "source": "www.sciopen.com", "link": "https://www.sciopen.com/article/10.32604/cmes.2025.060973", "content": "Quantum-inspired adaptive Resource Management switches on quite a new conceptual framework toward optimized resource allocation in fog computing environments."}
+{"idx": 9, "title": "Joint Task and Resource Allocation for Mobile Edge Learning (2020)", "date": "", "ddg_snippet": "We then employ numerical solvers to efficiently solve this simplified problem. Simulation results show gains up to 166% and 250% compared to the task allocation only and the resource allocation only techniques, respectively.", "subpage_snippet": "", "source": "scispace.com", "link": "https://scispace.com/papers/joint-task-and-resource-allocation-for-mobile-edge-learning-10hpi1t00k", "content": "We then employ numerical solvers to efficiently solve this simplified problem. Simulation results show gains up to 166% and 250% compared to the task allocation only and the resource allocation only techniques, respectively."}
diff --git a/data/sampled_jsons/2111.03980_arxiv_abstract.jsonl b/data/sampled_jsons/2111.03980_arxiv_abstract.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..cf176bceb047d994f1e7e2ab45b75601caa255ee
--- /dev/null
+++ b/data/sampled_jsons/2111.03980_arxiv_abstract.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "[ 2111 . 03980 ] Dynamic Algorithms Against an Adaptive Adversary...", "date": "", "ddg_snippet": "Abstract :A dynamic algorithm against an adaptive adversary is required to be correct when the adversary chooses the next update after seeing the previous outputs of the algorithm.(or arXiv : 2111 . 03980 v1 [cs.DS] for this version).", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2111.03980", "content": "Abstract :A dynamic algorithm against an adaptive adversary is required to be correct when the adversary chooses the next update after seeing the previous outputs of the algorithm.(or arXiv : 2111 . 03980 v1 [cs.DS] for this version)."}
+{"idx": 1, "title": "ash001/ arxiv - abstract · Datasets at Hugging Face", "date": "", "ddg_snippet": "/ arxiv - abstract .", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/datasets/ash001/arxiv-abstract", "content": "/ arxiv - abstract ."}
+{"idx": 2, "title": "Dynamic Algorithms Against an Adaptive Adversary", "date": "", "ddg_snippet": "arXiv : 2111 . 03980 v1 [cs.DS] 7 Nov 2021. Abstract . A dynamic algorithm against an adaptive adversary is required to be correct when the adversary chooses the next update after seeing the previous outputs of the algorithm.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2111.03980", "content": "arXiv : 2111 . 03980 v1 [cs.DS] 7 Nov 2021. Abstract . A dynamic algorithm against an adaptive adversary is required to be correct when the adversary chooses the next update after seeing the previous outputs of the algorithm."}
+{"idx": 3, "title": "Revenue Maximization for Buyers with Costly Participation", "date": "", "ddg_snippet": "Abstract . We study mechanisms for selling a single item when buyers have private costs for participating in the mechanism.Sergiu Hart & Noam Nisan, 2012. \"Approximate Revenue Maximization with Multiple Items,\" Papers 1204.1846, arXiv .org, revised Dec 2017.", "subpage_snippet": "", "source": "ideas.repec.org", "link": "https://ideas.repec.org/p/arx/papers/2103.03980.html", "content": "Abstract . We study mechanisms for selling a single item when buyers have private costs for participating in the mechanism.Sergiu Hart & Noam Nisan, 2012. \"Approximate Revenue Maximization with Multiple Items,\" Papers 1204.1846, arXiv .org, revised Dec 2017."}
+{"idx": 4, "title": "Faster maxflow via improved dynamic spectral vertex sparsifiers", "date": "", "ddg_snippet": "Abstract . We make several advances broadly related to the maintenance of electrical flows in weighted graphs undergoing dynamic resistance updates, including: (1) More efficient dynamic spectral vertex sparsification, achieved by faster lengthCoRR, abs/ 2111 . 03980 (2021).", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.1145/3519935.3520068?cookieSet=1", "content": "Abstract . We make several advances broadly related to the maintenance of electrical flows in weighted graphs undergoing dynamic resistance updates, including: (1) More efficient dynamic spectral vertex sparsification, achieved by faster lengthCoRR, abs/ 2111 . 03980 (2021)."}
+{"idx": 5, "title": "Валентин Сидоров Про Женщин На Экскурсии В Питере | TikTok", "date": "", "ddg_snippet": "оригинальный звук - Валентин Сидоров. 2111. Лайки.", "subpage_snippet": "", "source": "www.tiktok.com", "link": "https://www.tiktok.com/discover/валентин-сидоров-про-женщин-на-экскурсии-в-питере", "content": "оригинальный звук - Валентин Сидоров. 2111. Лайки."}
+{"idx": 6, "title": "ORCID", "date": "", "ddg_snippet": "ARXIV : 2306.16227. Contributors : J Antoniadis; P Arumugam; S Arumugam; P Auclair; S Babak; M Bagchi; A.-S Bak Nielsen; E Barausse; C.G Bassa; A Bathula et al.", "subpage_snippet": "", "source": "orcid.org", "link": "https://orcid.org/0000-0001-7469-4250", "content": "ARXIV : 2306.16227. Contributors : J Antoniadis; P Arumugam; S Arumugam; P Auclair; S Babak; M Bagchi; A.-S Bak Nielsen; E Barausse; C.G Bassa; A Bathula et al."}
+{"idx": 7, "title": "The Target-Charging Technique", "date": "", "ddg_snippet": "CoRR, abs/ 2111 . 03980 , 2021. [3] Mark Bun, Thomas Steinke, and Jonathan Ullman. Make Up Your Mind: The Price of Online Queries in Differential Privacy, pages 1306–1325.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=7yjsYrajlt", "content": "CoRR, abs/ 2111 . 03980 , 2021. [3] Mark Bun, Thomas Steinke, and Jonathan Ullman. Make Up Your Mind: The Price of Online Queries in Differential Privacy, pages 1306–1325."}
+{"idx": 8, "title": "Создай скроллинг текст онлайн — «ТЫ САМЫЙ ЛУТШИЙ...»", "date": "", "ddg_snippet": "...03980 ты самый лутший человек в моей жизни я люблю тебя 03981 ты самый лутший человек.", "subpage_snippet": "", "source": "vladpetrov.me", "link": "https://vladpetrov.me/ru/scrolling-text?text=0KLQqyDQodCQ0JzQq9CZINCb0KPQotCo0JjQmSDQp9CV0JvQntCS0JXQmiDQkiDQnNCe0JXQmSDQltCY0JfQndCYINCvINCb0K7QkdCb0K4g0KLQldCR0K8=&template=column&count=10000&numbers=true", "content": "...03980 ты самый лутший человек в моей жизни я люблю тебя 03981 ты самый лутший человек."}
+{"idx": 9, "title": "Глава 6420. Шокирующие изменения, запечатывание входа...", "date": "", "ddg_snippet": "Можешь только ждать смерти Глава 2113. Царство Богов Глава 2112. Исход битвы Глава 2111. Невероятно Глава 2110. Смотри на своё исполнение Глава 2109.", "subpage_snippet": "", "source": "ifreedom.su", "link": "https://ifreedom.su/voinstvennyj-bog-asura-2/glava-6420-shokiruyushhie-izmeneniya-zapechatyvanie-vhoda/", "content": "Можешь только ждать смерти Глава 2113. Царство Богов Глава 2112. Исход битвы Глава 2111. Невероятно Глава 2110. Смотри на своё исполнение Глава 2109."}
diff --git "a/data/sampled_jsons/2212.08420_Sar\304\261y\304\261ld\304\261z_ImageNet-100_results.jsonl" "b/data/sampled_jsons/2212.08420_Sar\304\261y\304\261ld\304\261z_ImageNet-100_results.jsonl"
new file mode 100644
index 0000000000000000000000000000000000000000..b884e39c3888b9dad236d485e722d21c2bd8c95c
--- /dev/null
+++ "b/data/sampled_jsons/2212.08420_Sar\304\261y\304\261ld\304\261z_ImageNet-100_results.jsonl"
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "GitHub | ImageNet - 100 -Pytorch/generate_IN100.py at Main...", "date": "", "ddg_snippet": "Use saved searches to filter your results more quickly.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/danielchyeh/ImageNet-100-Pytorch/blob/main/generate_IN100.py", "content": "Use saved searches to filter your results more quickly."}
+{"idx": 1, "title": "ImageNet - 100 (GCD Split) Dataset | Papers With Code", "date": "", "ddg_snippet": "This ImageNet - 100 dataset was introduced in the following paper, Vaze, S., Han, K., Vedaldi, A. and Zisserman, A., 2022.", "subpage_snippet": "", "source": "paperswithcode.com", "link": "https://paperswithcode.com/dataset/imagenet-100-gcd-split", "content": "This ImageNet - 100 dataset was introduced in the following paper, Vaze, S., Han, K., Vedaldi, A. and Zisserman, A., 2022."}
+{"idx": 2, "title": "ImageNet 100 | Kaggle", "date": "", "ddg_snippet": "A Sample of ImageNet Classes...", "subpage_snippet": "", "source": "www.kaggle.com", "link": "https://www.kaggle.com/datasets/ambityga/imagenet100?resource=download-directory&select=val.X", "content": "A Sample of ImageNet Classes..."}
+{"idx": 3, "title": "Example Retrieval Results for HBMP and MIHash on ImageNet 100 .", "date": "", "ddg_snippet": "... Fig. 4, we present example retrieval results for HBMP and MIHash for several image queries from the ImageNet 100 dataset.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/figure/Example-retrieval-results-for-HBMP-and-MIHash-on-ImageNet100-In-all-the-queries-HBMP_fig2_326851596", "content": "... Fig. 4, we present example retrieval results for HBMP and MIHash for several image queries from the ImageNet 100 dataset."}
+{"idx": 4, "title": "Worse results by reproducing MoCo, InsDIS and CMC on ImageNet 100", "date": "", "ddg_snippet": "I can see several gaps as below, which apply to all MoCo, InsDis, and CMC: (1) For pre-training on ImageNet 100 , use batch_size 128 instead of 256.", "subpage_snippet": "", "source": "githubissues.com", "link": "https://githubissues.com/HobbitLong/CMC/28", "content": "I can see several gaps as below, which apply to all MoCo, InsDis, and CMC: (1) For pre-training on ImageNet 100 , use batch_size 128 instead of 256."}
+{"idx": 5, "title": "ImageNet", "date": "", "ddg_snippet": "The most highly-used subset of ImageNet is the ImageNet Large Scale Visual Recognition Challenge (ILSVRC) 2012-2017 image classification and localization dataset.", "subpage_snippet": "", "source": "image-net.org", "link": "https://image-net.org/download.php", "content": "The most highly-used subset of ImageNet is the ImageNet Large Scale Visual Recognition Challenge (ILSVRC) 2012-2017 image classification and localization dataset."}
+{"idx": 6, "title": "NEW Classification Datasets: `Imagenet10`, ` Imagenet 100 ... | GitL...", "date": "", "ddg_snippet": "These are super small versions of imagenet that train/val in seconds with only 1 image per class (with all 1000 classes, only 100 classes, and only 10 classes).", "subpage_snippet": "", "source": "gitlab.com", "link": "https://gitlab.com/ultralytics/yolov3/-/issues/2022", "content": "These are super small versions of imagenet that train/val in seconds with only 1 image per class (with all 1000 classes, only 100 classes, and only 10 classes)."}
+{"idx": 7, "title": "[ 2212 . 08420 ] Fake it Till You Make it: Learning Transferable...", "date": "", "ddg_snippet": "Abstract:Recent image generation models such as Stable Diffusion have exhibited an impressive ability to generate fairly realistic images starting from a simple text prompt.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2212.08420", "content": "Abstract:Recent image generation models such as Stable Diffusion have exhibited an impressive ability to generate fairly realistic images starting from a simple text prompt."}
+{"idx": 8, "title": "Lose INNER & OUTER Thighs Fat in 14 Days ( 100 % Visible Result )...", "date": "", "ddg_snippet": "#legworkout #homeworkout #loseweight This workout is designed to reduce your thighs size, burn legs fat (inner & Outer) and tone up of glute. No equipment ne...", "subpage_snippet": "", "source": "www.youtube.com", "link": "https://www.youtube.com/watch?v=MO3lH9QzfqI", "content": "#legworkout #homeworkout #loseweight This workout is designed to reduce your thighs size, burn legs fat (inner & Outer) and tone up of glute. No equipment ne..."}
+{"idx": 9, "title": "Free AI Video Face Swap Online (No Signup, No Limits)", "date": "", "ddg_snippet": "So easy to use and the results are super realistic.", "subpage_snippet": "", "source": "arting.ai", "link": "https://arting.ai/video-face-swap", "content": "So easy to use and the results are super realistic."}
diff --git a/data/sampled_jsons/2403.07932_Feint_Behaviors_scheduler_Section_4.2.2.jsonl b/data/sampled_jsons/2403.07932_Feint_Behaviors_scheduler_Section_4.2.2.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..b033d939610615916f87a6549131cba5c63d72dc
--- /dev/null
+++ b/data/sampled_jsons/2403.07932_Feint_Behaviors_scheduler_Section_4.2.2.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Abstract page for arXiv paper 2403 . 07932 : Feint in Multi-Player Games", "date": "", "ddg_snippet": "Our work first formalizes Feint from the perspective of Multi-Player Games, in terms of the temporal, spatial, and their collective impacts. The formalization is built upon Non-transitive Active Markov Game Model, where Feint can have a considerable amount of impacts.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2403.07932", "content": "Our work first formalizes Feint from the perspective of Multi-Player Games, in terms of the temporal, spatial, and their collective impacts. The formalization is built upon Non-transitive Active Markov Game Model, where Feint can have a considerable amount of impacts."}
+{"idx": 1, "title": "Feint Behaviors and Strategies: Formalization, Implementation and...", "date": "", "ddg_snippet": "The figure illustrates three ways to generate Feint behavior templates by extracting and combining sections of these sequences, emphasizing the semi-symmetric nature and ensuring physically realistic movements. read the caption.", "subpage_snippet": "", "source": "deep-diver.github.io", "link": "https://deep-diver.github.io/neurips2024/posters/aciddntbsj/", "content": "The figure illustrates three ways to generate Feint behavior templates by extracting and combining sections of these sequences, emphasizing the semi-symmetric nature and ensuring physically realistic movements. read the caption."}
+{"idx": 2, "title": "Technical Report-Feb-24, at User-Centric Computing Group, 2024", "date": "", "ddg_snippet": "arXiv: 2403 . 07932 v1 [cs.GT] 4 Mar 2024.tial impacts Rewspatial ( Section 3. 2 . 2 ) are aggregated on temporal domain. µ1 and µ2 denote the. weights of aggregated temporal impacts and spatial impacts respectively, enabling flexible adap", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2403.07932", "content": "arXiv: 2403 . 07932 v1 [cs.GT] 4 Mar 2024.tial impacts Rewspatial ( Section 3. 2 . 2 ) are aggregated on temporal domain. µ1 and µ2 denote the. weights of aggregated temporal impacts and spatial impacts respectively, enabling flexible adap"}
+{"idx": 3, "title": "How to Run Wan 2 . 2 Image to Video Model in ComfyUI... - Next Diffusion", "date": "", "ddg_snippet": "In the next section , we’ll dive into the key settings to optimize your video generation.Define the animation behavior and camera motion using a detailed text prompt. This guides the wan 2 . 2 module to transform the base image into a video with realistic or stylized movement.", "subpage_snippet": "", "source": "www.nextdiffusion.ai", "link": "https://www.nextdiffusion.ai/tutorials/exploring-the-new-wan22-image-to-video-generation-model-in-comfyui", "content": "In the next section , we’ll dive into the key settings to optimize your video generation.Define the animation behavior and camera motion using a detailed text prompt. This guides the wan 2 . 2 module to transform the base image into a video with realistic or stylized movement."}
+{"idx": 4, "title": "dblp: List of computer science publications by Junyu Liu", "date": "", "ddg_snippet": "Junyu Liu, Xiangjun Peng: Feint Behaviors and Strategies: Formalization, Implementation and Evaluation. NeurIPS 2024.journals/corr/abs- 2403 - 07932 .", "subpage_snippet": "", "source": "dblp.uni-trier.de", "link": "https://dblp.uni-trier.de/pid/96/7401.html", "content": "Junyu Liu, Xiangjun Peng: Feint Behaviors and Strategies: Formalization, Implementation and Evaluation. NeurIPS 2024.journals/corr/abs- 2403 - 07932 ."}
+{"idx": 5, "title": "Анти-Плагио - Бесплатный АнтиПлагиат | Без регистрации, без...", "date": "", "ddg_snippet": "Проверяйте уникальность больших текстов и на ИИ онлайн бесплатно и без ограничений. Российский сервис Анти-Плагио поможет создать качественный контент.", "subpage_snippet": "", "source": "anti-plagio.ru", "link": "https://anti-plagio.ru/", "content": "Проверяйте уникальность больших текстов и на ИИ онлайн бесплатно и без ограничений. Российский сервис Анти-Плагио поможет создать качественный контент."}
+{"idx": 6, "title": "qBittorrent - скачать бесплатно qBittorrent 4.4.5 / 5.1.2", "date": "", "ddg_snippet": "Бесплатно. Windows. QBittorrent - бесплатный, мощный и удобный в работе кроссплатформенный клиент файлообменной сети BitTorrent. Несмотря на кажущуюся простоту, программа обладает внушительным набором весьма полезных возможностей...", "subpage_snippet": "", "source": "www.SoftPortal.com", "link": "https://www.SoftPortal.com/software-18505-qbittorrent.html", "content": "Бесплатно. Windows. QBittorrent - бесплатный, мощный и удобный в работе кроссплатформенный клиент файлообменной сети BitTorrent. Несмотря на кажущуюся простоту, программа обладает внушительным набором весьма полезных возможностей..."}
+{"idx": 7, "title": "ESET NOD32 | Свежие Ключи 2025 | Бесплатно | СТЕНА – Telegram", "date": "", "ddg_snippet": "Личные ключи в боте @eset_free_nod32_bot (Напишите для начала /start). ESET HOME Security Premium | До 15-10-2025 1 | 5SG2-XPWE-K46M-RJ92-HAXH 2 | JE3E-XB9C-ANJK-UJ6U-2M6W 3 | RJWJ-XUAU-52GG-FTF4-W7VP 4 | AE8P-XXVG-KHW4-EEEC-AGRG 5...", "subpage_snippet": "", "source": "t.me", "link": "https://t.me/s/eset_nod32_free", "content": "Личные ключи в боте @eset_free_nod32_bot (Напишите для начала /start). ESET HOME Security Premium | До 15-10-2025 1 | 5SG2-XPWE-K46M-RJ92-HAXH 2 | JE3E-XB9C-ANJK-UJ6U-2M6W 3 | RJWJ-XUAU-52GG-FTF4-W7VP 4 | AE8P-XXVG-KHW4-EEEC-AGRG 5..."}
+{"idx": 8, "title": "Fix Windows Alerts by Removing Trojan:Win32/Vigorf.A Completely...", "date": "", "ddg_snippet": "Make Trojan:Win32/Vigorf.A Stop Relaunching After Reboots. If components are still active, running code often points to its location by holding locks, creating logs, or scheduling relaunches. Use that behavior to identify persistence cleanly.", "subpage_snippet": "", "source": "howtoremove.guide", "link": "https://howtoremove.guide/remove-trojanwin32-vigorf-a/", "content": "Make Trojan:Win32/Vigorf.A Stop Relaunching After Reboots. If components are still active, running code often points to its location by holding locks, creating logs, or scheduling relaunches. Use that behavior to identify persistence cleanly."}
+{"idx": 9, "title": "Build software products, using only a chat interface", "date": "", "ddg_snippet": "Build software products, using only a chat interface...", "subpage_snippet": "", "source": "lovable.dev", "link": "https://lovable.dev/", "content": "Build software products, using only a chat interface..."}
diff --git a/data/sampled_jsons/2403.07932_implementation_details_scheduler_weights.jsonl b/data/sampled_jsons/2403.07932_implementation_details_scheduler_weights.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..c4fb9710ee6b85fe731e929bdd21c94a7e21c406
--- /dev/null
+++ b/data/sampled_jsons/2403.07932_implementation_details_scheduler_weights.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Abstract page for arXiv paper 2403 . 07932 : Feint in Multi-Player Games", "date": "", "ddg_snippet": "Then, our work considers practical implementation details of Feint in Multi-Player Games, under the state-of-the-art progress of multi-agent modeling to date (namely Multi-Agent Reinforcement Learning).", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2403.07932", "content": "Then, our work considers practical implementation details of Feint in Multi-Player Games, under the state-of-the-art progress of multi-agent modeling to date (namely Multi-Agent Reinforcement Learning)."}
+{"idx": 1, "title": "Hello eBPF: Writing a Linux scheduler in Java with eBPF (15)", "date": "", "ddg_snippet": "Why another scheduler ? Creating your own scheduler allows you to explore new scheduling policies and rapidly iterate on custom implementation . To quote Tejun Heo on why we need the sched-ext extension for the Linux kernel", "subpage_snippet": "", "source": "mostlynerdless.de", "link": "https://mostlynerdless.de/blog/2024/09/10/hello-ebpf-writing-a-linux-scheduler-in-java-with-ebpf-15/", "content": "Why another scheduler ? Creating your own scheduler allows you to explore new scheduling policies and rapidly iterate on custom implementation . To quote Tejun Heo on why we need the sched-ext extension for the Linux kernel"}
+{"idx": 2, "title": "Go scheduler details | Yet Another Dev Blog", "date": "", "ddg_snippet": "Scheduler logic in details . LRQ. Finding a runnable goroutine.Goroutines are very light weight (~2KB+) and very cheap to operate. You can have millions of them in your program. Context switch between goroutines is also pretty cheap. There are 5 main entities Scheduler operates", "subpage_snippet": "", "source": "blog.bullgare.com", "link": "https://blog.bullgare.com/2022/11/go-scheduler-details/", "content": "Scheduler logic in details . LRQ. Finding a runnable goroutine.Goroutines are very light weight (~2KB+) and very cheap to operate. You can have millions of them in your program. Context switch between goroutines is also pretty cheap. There are 5 main entities Scheduler operates"}
+{"idx": 3, "title": "Telegram: View @kizma_v_teme", "date": "", "ddg_snippet": "The webpage at https://t.me/kizma_v_teme/7932?embed=1&mode=tme might be temporarily down or it may have moved to a new web address. Details .", "subpage_snippet": "", "source": "t.me", "link": "https://t.me/kizma_v_teme/7932", "content": "The webpage at https://t.me/kizma_v_teme/7932?embed=1&mode=tme might be temporarily down or it may have moved to a new web address. Details ."}
+{"idx": 4, "title": "stabilityai/stable-diffusion-3.5-large · Hugging Face", "date": "", "ddg_snippet": "Implementation Details . QK Normalization: Implements the QK normalization technique to improve training Stability. Text Encoders： CLIPs: OpenCLIP-ViT/G, CLIP-ViT/L, context length 77 tokens.", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/stabilityai/stable-diffusion-3.5-large", "content": "Implementation Details . QK Normalization: Implements the QK normalization technique to improve training Stability. Text Encoders： CLIPs: OpenCLIP-ViT/G, CLIP-ViT/L, context length 77 tokens."}
+{"idx": 5, "title": "Sample Complexity and Representation Ability of Test-time", "date": "", "ddg_snippet": "We demonstrate that a Transformer equipped with verifier feedback at test time can implement online learning algorithms over a pool of expert models ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2506.05295v1", "content": "We demonstrate that a Transformer equipped with verifier feedback at test time can implement online learning algorithms over a pool of expert models ..."}
+{"idx": 6, "title": "Schedule 1 How & Where To Get LED & UV Lights In... - 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=pUT1PZQ22c4", "content": "About Press Copyright Contact us Creators Advertise Developers Terms Privacy Policy & Safety How..."}
+{"idx": 7, "title": "Schedule 1: Where to Buy Pots and LED or UV Lights", "date": "", "ddg_snippet": "But without the right gear, your grow room is just a sad, dusty corner. If you’re looking for pots, LED lights , or suspension racks, here’s where you can find each item in Schedule 1.LED Lights from the game schedule 1. Where to Buy LED Lights & Suspension Racks.", "subpage_snippet": "", "source": "gamerblurb.com", "link": "https://gamerblurb.com/articles/schedule-1-where-to-buy-pots-and-led-lights", "content": "But without the right gear, your grow room is just a sad, dusty corner. If you’re looking for pots, LED lights , or suspension racks, here’s where you can find each item in Schedule 1.LED Lights from the game schedule 1. Where to Buy LED Lights & Suspension Racks."}
+{"idx": 8, "title": "Manus: General AI agent that bridges mind and action", "date": "", "ddg_snippet": "Manus is a general AI agent that turns your thoughts into actions. It excels at various tasks in work and life, getting everything done while you rest.", "subpage_snippet": "", "source": "manus.im", "link": "https://manus.im/", "content": "Manus is a general AI agent that turns your thoughts into actions. It excels at various tasks in work and life, getting everything done while you rest."}
+{"idx": 9, "title": "Tampa Bay Lightning Schedule | Tampa Bay Lightning", "date": "", "ddg_snippet": "Navigation Menu. Schedule . Community. Team.Youth Hockey. NHL.com. Schedule .", "subpage_snippet": "", "source": "www.nhl.com", "link": "https://www.nhl.com/lightning/schedule", "content": "Navigation Menu. Schedule . Community. Team.Youth Hockey. NHL.com. Schedule ."}
diff --git a/data/sampled_jsons/2412.11576_limitations_Section_5_applicability.jsonl b/data/sampled_jsons/2412.11576_limitations_Section_5_applicability.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..e391633daa9dfd809ebe96031b0e803d6a9a8df8
--- /dev/null
+++ b/data/sampled_jsons/2412.11576_limitations_Section_5_applicability.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "[ 2412 . 11576 ] DCBM: Data-Efficient Visual Concept Bottleneck Models", "date": "", "ddg_snippet": "This removes reliance on textual descriptions and large-scale pre-training, making DCBMs applicable for fine-grained classification and out-of-distribution tasks.Computer Vision and Pattern Recognition (cs.CV). Cite as: arXiv: 2412 . 11576 [cs.CV].", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2412.11576", "content": "This removes reliance on textual descriptions and large-scale pre-training, making DCBMs applicable for fine-grained classification and out-of-distribution tasks.Computer Vision and Pattern Recognition (cs.CV). Cite as: arXiv: 2412 . 11576 [cs.CV]."}
+{"idx": 1, "title": "5 Applicability to Different Distances – Norwegian Singles", "date": "", "ddg_snippet": "5 Applicability to Different Distances. This method, focusing on sub-threshold work, is primarily designed for the general preparation phase of training, building a strong aerobic base. Its direct applicability varies slightly by race distance: 5 .1 5 k to Half Marathon.", "subpage_snippet": "", "source": "norwegiansingles.run", "link": "https://norwegiansingles.run/section5_applicability_distances.html", "content": "5 Applicability to Different Distances. This method, focusing on sub-threshold work, is primarily designed for the general preparation phase of training, building a strong aerobic base. Its direct applicability varies slightly by race distance: 5 .1 5 k to Half Marathon."}
+{"idx": 2, "title": "Section 315 at Thompson-Boling Arena - RateYourSeats.com", "date": "", "ddg_snippet": "See the view from Section 315, read reviews and buy tickets.", "subpage_snippet": "", "source": "www.rateyourseats.com", "link": "https://www.rateyourseats.com/thompson-boling-arena/seating/sections/315", "content": "See the view from Section 315, read reviews and buy tickets."}
+{"idx": 3, "title": "TGame | Daily Lives Of My Countryside character section ... - YouTube", "date": "", "ddg_snippet": "Daily Lives Of My Countryside experience part Mae- The main thing is to have fun.- The video I made is a guide to the events in the game, I didn't make the u...", "subpage_snippet": "", "source": "www.youtube.com", "link": "https://www.youtube.com/watch?v=BirIRgPd6QM", "content": "Daily Lives Of My Countryside experience part Mae- The main thing is to have fun.- The video I made is a guide to the events in the game, I didn't make the u..."}
+{"idx": 4, "title": "14th Amendment - Citizenship Rights, Equal... | Constitution Center", "date": "", "ddg_snippet": "14th Amendment. Section 1. Section 1. All persons born or naturalized in the United States, and subject to the jurisdiction thereof, are citizens of the United States and of the State wherein they reside.", "subpage_snippet": "", "source": "constitutioncenter.org", "link": "https://constitutioncenter.org/the-constitution/amendments/amendment-xiv", "content": "14th Amendment. Section 1. Section 1. All persons born or naturalized in the United States, and subject to the jurisdiction thereof, are citizens of the United States and of the State wherein they reside."}
+{"idx": 5, "title": "9 Best Uncensored Local LLM (7 To 20B) - Sci Fi Logic", "date": "", "ddg_snippet": "This versatility enhances its applicability in roleplay, instruction following, and coding scenarios, placing it on par with many 30 billion parameter models. Throughout my testing, I observed excellent results from this improved flagship model.", "subpage_snippet": "", "source": "scifilogic.com", "link": "https://scifilogic.com/open-uncensored-llm-model/", "content": "This versatility enhances its applicability in roleplay, instruction following, and coding scenarios, placing it on par with many 30 billion parameter models. Throughout my testing, I observed excellent results from this improved flagship model."}
+{"idx": 6, "title": "e-Filing Home Page, Income Tax Department, Government of India", "date": "", "ddg_snippet": "Pre-login section .", "subpage_snippet": "", "source": "eportal.incometax.gov.in", "link": "https://eportal.incometax.gov.in/iec/foservices/", "content": "Pre-login section ."}
+{"idx": 7, "title": "Atbash Cipher - Decryption and Encryption Online", "date": "", "ddg_snippet": "If the alphabet used in encryption is limited (for example, only letters), this can greatly limit the applicability of the cipher. For long texts, the Atbash Cipher can be inconvenient, as it requires a large amount of manual work and can be prone to errors.", "subpage_snippet": "", "source": "ciphersonline.com", "link": "https://ciphersonline.com/atbash-cipher", "content": "If the alphabet used in encryption is limited (for example, only letters), this can greatly limit the applicability of the cipher. For long texts, the Atbash Cipher can be inconvenient, as it requires a large amount of manual work and can be prone to errors."}
+{"idx": 8, "title": "Ограничения по контракту | Служить Родине", "date": "", "ddg_snippet": "Список ограничений, препятствующих добровольцам заключить контракт на СВО.", "subpage_snippet": "", "source": "armyworld.ru", "link": "https://armyworld.ru/limitations/", "content": "Список ограничений, препятствующих добровольцам заключить контракт на СВО."}
+{"idx": 9, "title": "7 важных ограничений после выкидыша: рекомендации врачей", "date": "", "ddg_snippet": "Что нельзя делать после выкидыша: узнайте о 7 важных физических ограничениях, включая запрет на интимную жизнь в течение 2-4 недель, и рекомендациях врачей для безопасного восстановления организма.", "subpage_snippet": "", "source": "onlinepharm.uz", "link": "https://onlinepharm.uz/physical-limitations-after-miscarriage/", "content": "Что нельзя делать после выкидыша: узнайте о 7 важных физических ограничениях, включая запрет на интимную жизнь в течение 2-4 недель, и рекомендациях врачей для безопасного восстановления организма."}
diff --git a/data/sampled_jsons/2412.17997_diagonal_update_simultaneous_grid_parallel_sampling.jsonl b/data/sampled_jsons/2412.17997_diagonal_update_simultaneous_grid_parallel_sampling.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..9e92ca4ed962d0fcacd82f30e1b0fcf043ce6be6
--- /dev/null
+++ b/data/sampled_jsons/2412.17997_diagonal_update_simultaneous_grid_parallel_sampling.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "[ 2412 . 17997 ] Shifted Composition III: Local Error Framework for KL...", "date": "", "ddg_snippet": "Mathematics > Statistics Theory. arXiv: 2412 . 17997 (math).We apply this framework to the problem of sampling from a target distribution $\\pi$. Here, the two stochastic processes are the Langevin diffusion and an algorithmic discretization thereof.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2412.17997", "content": "Mathematics > Statistics Theory. arXiv: 2412 . 17997 (math).We apply this framework to the problem of sampling from a target distribution $\\pi$. Here, the two stochastic processes are the Langevin diffusion and an algorithmic discretization thereof."}
+{"idx": 1, "title": "Parallel Sampling Algorithms", "date": "", "ddg_snippet": "Parallel Sampling Algorithms. Updated 30 June 2025. Parallel sampling algorithms are computational methods designed to generate random samples from specified probability distributions using multiple computational resources simultaneously .", "subpage_snippet": "", "source": "www.emergentmind.com", "link": "https://www.emergentmind.com/topics/parallel-sampling-algorithms", "content": "Parallel Sampling Algorithms. Updated 30 June 2025. Parallel sampling algorithms are computational methods designed to generate random samples from specified probability distributions using multiple computational resources simultaneously ."}
+{"idx": 2, "title": "Turning mutable grid access into a thread-safe grid access (stencils)", "date": "", "ddg_snippet": "Run 1): parallel over the mutable black references: 1 black reference a uses the list of all white immutable references cells to get its correct neighbors and does the update a.div and a.u and a.v from 2).", "subpage_snippet": "", "source": "users.rust-lang.org", "link": "https://users.rust-lang.org/t/turning-mutable-grid-access-into-a-thread-safe-grid-access-stencils/87502", "content": "Run 1): parallel over the mutable black references: 1 black reference a uses the list of all white immutable references cells to get its correct neighbors and does the update a.div and a.u and a.v from 2)."}
+{"idx": 3, "title": "SQL UPDATE Statement - GeeksforGeeks", "date": "", "ddg_snippet": "Without WHERE, all rows will be updated . Examples of SQL UPDATE Statement. Let’s begin by creating a Customer table with some sample data. This table contains each customer's unique ID, name, last name, phone number and country.", "subpage_snippet": "", "source": "www.geeksforgeeks.org", "link": "https://www.geeksforgeeks.org/sql/sql-update-statement/", "content": "Without WHERE, all rows will be updated . Examples of SQL UPDATE Statement. Let’s begin by creating a Customer table with some sample data. This table contains each customer's unique ID, name, last name, phone number and country."}
+{"idx": 4, "title": "Applying Concurrency Patterns with Parallel BFS in C# for... | IT trip", "date": "", "ddg_snippet": "In this code, Parallel .For is used to process all nodes at the current level of BFS simultaneously . This helps speed up the exploration of neighboring nodes. Optimizing Grid Division.", "subpage_snippet": "", "source": "en.ittrip.xyz", "link": "https://en.ittrip.xyz/c-sharp/concurrency-bfs-game-ai-csharp", "content": "In this code, Parallel .For is used to process all nodes at the current level of BFS simultaneously . This helps speed up the exploration of neighboring nodes. Optimizing Grid Division."}
+{"idx": 5, "title": "(PDF) Communication Reduced Parallel Multigrid: Analysis and...", "date": "", "ddg_snippet": "A parallel multigrid method with this technique avoids all communication on the finest grid levels. This article will examine some features of this class of algorithms as compared to standard parallel multigrid methods.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/2393821_Communication_Reduced_Parallel_Multigrid_Analysis_and_Experiments", "content": "A parallel multigrid method with this technique avoids all communication on the finest grid levels. This article will examine some features of this class of algorithms as compared to standard parallel multigrid methods."}
+{"idx": 6, "title": "How do I fix \"'ag- grid -angular' is not a known element...\" -...", "date": "", "ddg_snippet": "I am having an issue with my angular code where my ag- grid tags aren't being recognised, and neither are my columnDefs and rowData. I have looked up the issues and imported what I needed to but the problem still persists.", "subpage_snippet": "", "source": "stackoverflow.com", "link": "https://stackoverflow.com/questions/75971845/how-do-i-fix-ag-grid-angular-is-not-a-known-element-problem", "content": "I am having an issue with my angular code where my ag- grid tags aren't being recognised, and neither are my columnDefs and rowData. I have looked up the issues and imported what I needed to but the problem still persists."}
+{"idx": 7, "title": "How to Get Position Type in MQL5?", "date": "", "ddg_snippet": "You’ll typically need to access other properties simultaneously : Type + Volume: Manage risk based on position size and direction. Type + Entry Price: Calculate profit/loss (POSITION_PROFIT, POSITION_PRICE_CURRENT) relative to the entry for a specific direction.", "subpage_snippet": "", "source": "trading-strategies.academy", "link": "https://trading-strategies.academy/archives/3234", "content": "You’ll typically need to access other properties simultaneously : Type + Volume: Manage risk based on position size and direction. Type + Entry Price: Calculate profit/loss (POSITION_PROFIT, POSITION_PRICE_CURRENT) relative to the entry for a specific direction."}
+{"idx": 8, "title": "Video Generation with Wan2.1 - Chutes Documentation", "date": "", "ddg_snippet": "Initialize Wan2.1 models in distributed fashion across GPUs. \"\"\"import torch import torch.distributed as dist import wan from wan.configs import WAN_CONFIGS from xfuser.core.distributed import initialize_model_ parallel , init_distributed_environment #.", "subpage_snippet": "", "source": "chutes.ai", "link": "https://chutes.ai/docs/examples/video-generation", "content": "Initialize Wan2.1 models in distributed fashion across GPUs. \"\"\"import torch import torch.distributed as dist import wan from wan.configs import WAN_CONFIGS from xfuser.core.distributed import initialize_model_ parallel , init_distributed_environment #."}
+{"idx": 9, "title": "Journal of Medical Internet Research - Evaluating the Utility of...", "date": "", "ddg_snippet": "These systems monitor the movement of multiple body parts simultaneously by integrating multiple accelerometers and gyroscopes or IMUs, enabling comprehensive analysis of whole-body movement.", "subpage_snippet": "", "source": "www.jmir.org", "link": "https://www.jmir.org/2025/1/e69422/", "content": "These systems monitor the movement of multiple body parts simultaneously by integrating multiple accelerometers and gyroscopes or IMUs, enabling comprehensive analysis of whole-body movement."}
diff --git a/data/sampled_jsons/2503.10694_Table_1_MedQA_accuracy_alpha_conditional.jsonl b/data/sampled_jsons/2503.10694_Table_1_MedQA_accuracy_alpha_conditional.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..81cdef84fb85fe3bd61addbd203f45a37db29705
--- /dev/null
+++ b/data/sampled_jsons/2503.10694_Table_1_MedQA_accuracy_alpha_conditional.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "9.7 - Futility Assessment with Conditional Power; Adaptive ...", "date": "", "ddg_snippet": "View the table from Bhatt DL, Mehta C. Adaptive designs for clinical trials. New England Journal of Medicine. 2016;375 ( 1 ):65–74. doi: 10.1056/NEJMra1510061. pmid:27406349, to see the strengths and weaknesses of different adaptive designs.", "subpage_snippet": "", "source": "online.stat.psu.edu", "link": "https://online.stat.psu.edu/stat509/lesson/9/9.7", "content": "View the table from Bhatt DL, Mehta C. Adaptive designs for clinical trials. New England Journal of Medicine. 2016;375 ( 1 ):65–74. doi: 10.1056/NEJMra1510061. pmid:27406349, to see the strengths and weaknesses of different adaptive designs."}
+{"idx": 1, "title": "Chapter 2 Contingency Tables - University of Chicago", "date": "", "ddg_snippet": "A conditional distribution of Y given X refers to the probability distribution of Y when we restrict attention to a fixed level of X. P(X = i; Y = j) P(Y = j j X = i) = = P(X = i)", "subpage_snippet": "", "source": "www.stat.uchicago.edu", "link": "https://www.stat.uchicago.edu/~yibi/teaching/stat226/2017/lectures/C02A.pdf", "content": "A conditional distribution of Y given X refers to the probability distribution of Y when we restrict attention to a fixed level of X. P(X = i; Y = j) P(Y = j j X = i) = = P(X = i)"}
+{"idx": 2, "title": "MedQA", "date": "", "ddg_snippet": "The cost- accuracy graph shows a few models that define a Pareto curve of tradeoffs: o 1 Preview, o 1 Mini, Llama 3. 1 405b, Llama 3. 1 70b, and GPT 4o mini. Among these five models, Llama 3. 1 70b stands out for its strong quality-to-price ratio.", "subpage_snippet": "", "source": "www.vals.ai", "link": "https://www.vals.ai/benchmarks/medqa-01-27-2025", "content": "The cost- accuracy graph shows a few models that define a Pareto curve of tradeoffs: o 1 Preview, o 1 Mini, Llama 3. 1 405b, Llama 3. 1 70b, and GPT 4o mini. Among these five models, Llama 3. 1 70b stands out for its strong quality-to-price ratio."}
+{"idx": 3, "title": "GBaker/ MedQA -USMLE-4-options · Datasets at Hugging Face", "date": "", "ddg_snippet": "Bone marrow biopsy shows cellular hyperplasia with proliferation of immature granulocytic cells. Which of the following mechanisms is most likely responsible for this patient's condition ?Models trained or fine-tuned on GBaker/ MedQA -USMLE-4-options. sileod/deberta-v3-base-tasksource-nli.", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/datasets/GBaker/MedQA-USMLE-4-options", "content": "Bone marrow biopsy shows cellular hyperplasia with proliferation of immature granulocytic cells. Which of the following mechanisms is most likely responsible for this patient's condition ?Models trained or fine-tuned on GBaker/ MedQA -USMLE-4-options. sileod/deberta-v3-base-tasksource-nli."}
+{"idx": 4, "title": "Brief Review — MedQA : What Disease Does This Patient... | Medium", "date": "", "ddg_snippet": "1 . MedQA Dataset.The task is defined by its three components: Question: question in text, either in one sentence asking for a certain piece of knowledge, or in a long paragraph starting with a description of the patient condition .", "subpage_snippet": "", "source": "sh-tsang.medium.com", "link": "https://sh-tsang.medium.com/brief-review-medqa-what-disease-does-this-patient-have-a-large-scale-open-domain-question-e26e4d682c83", "content": "1 . MedQA Dataset.The task is defined by its three components: Question: question in text, either in one sentence asking for a certain piece of knowledge, or in a long paragraph starting with a description of the patient condition ."}
+{"idx": 5, "title": "Google's Gemini AI Achieves 91. 1 % Accuracy in MedQA ... | DeepNewz", "date": "", "ddg_snippet": "Med-Gemini has achieved state-of-the-art performance on 10 out of 14 benchmarks, surpassing GPT-4 in all comparisons, and achieves 91. 1 % accuracy on the MedQA (USMLE) benchmark.", "subpage_snippet": "", "source": "deepnewz.com", "link": "https://deepnewz.com/ai/google-s-gemini-ai-achieves-91-1-accuracy-medqa-benchmark", "content": "Med-Gemini has achieved state-of-the-art performance on 10 out of 14 benchmarks, surpassing GPT-4 in all comparisons, and achieves 91. 1 % accuracy on the MedQA (USMLE) benchmark."}
+{"idx": 6, "title": "Medical Large Language Model Benchmarks Should Prioritize ...", "date": "", "ddg_snippet": "The results in Table 1 show a modest correlation between LLM performance on the MedQA benchmark and real-world cases. In a scenario of perfect criterion validity, we would expect α to approach 1 .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2503.10694", "content": "The results in Table 1 show a modest correlation between LLM performance on the MedQA benchmark and real-world cases. In a scenario of perfect criterion validity, we would expect α to approach 1 ."}
+{"idx": 7, "title": "MedGemma Technical Report - arXiv.org", "date": "", "ddg_snippet": "Applying RL to the MedGemma 27B resulted in a substantial improvement in its EHRQA accuracy , closing the gap with top-performing models, as shown in Table 14. Notably, the greatest gains were observed in question categories requiring reasoning across inter-dependent records (Appendix Figure A3).", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2507.05201v3", "content": "Applying RL to the MedGemma 27B resulted in a substantial improvement in its EHRQA accuracy , closing the gap with top-performing models, as shown in Table 14. Notably, the greatest gains were observed in question categories requiring reasoning across inter-dependent records (Appendix Figure A3)."}
+{"idx": 8, "title": "Mind the Gap: Evaluating the Representativeness of Medical ...", "date": "", "ddg_snippet": "Stage 1 randomly harmonised six heterogeneous question-answer benchmarks from AfriMed-QA, PubMedQA, MedMCQA, MedQA -USMLE, MMLU-Medical and the in-house Alama-Health-QA—into a single master dataframe. All non-essential fields are collapsed into a cleaned unified column.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2507.16322", "content": "Stage 1 randomly harmonised six heterogeneous question-answer benchmarks from AfriMed-QA, PubMedQA, MedMCQA, MedQA -USMLE, MMLU-Medical and the in-house Alama-Health-QA—into a single master dataframe. All non-essential fields are collapsed into a cleaned unified column."}
+{"idx": 9, "title": "Medical Large Language Model Benchmarks Should Prioritize...", "date": "", "ddg_snippet": "Table 1 : Predictive validity of the MedQA benchmark. Report issue for preceding element. Figure 5: Left: Impact of number of answer choices in MedQA on accuracy . Middle: Distribution of clinical tasks and scenarios in MedQA and real-world data.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2503.10694v1", "content": "Table 1 : Predictive validity of the MedQA benchmark. Report issue for preceding element. Figure 5: Left: Impact of number of answer choices in MedQA on accuracy . Middle: Distribution of clinical tasks and scenarios in MedQA and real-world data."}
diff --git a/data/sampled_jsons/2506.05503_section_3.2_coordinate-wise_private_median_regression_challenge.jsonl b/data/sampled_jsons/2506.05503_section_3.2_coordinate-wise_private_median_regression_challenge.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..ba3822fa46ab5f1307ad6c1277ed1754c55df081
--- /dev/null
+++ b/data/sampled_jsons/2506.05503_section_3.2_coordinate-wise_private_median_regression_challenge.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "MAT240 Module 3 Challenge Activities - YouTube", "date": "", "ddg_snippet": "In this video we'll look at using a linear regression equation to predict, estimate the slope and intercept from a graph, talk about correlation and R-square...", "subpage_snippet": "", "source": "www.youtube.com", "link": "https://www.youtube.com/watch?v=w0pE1PgoGYk", "content": "In this video we'll look at using a linear regression equation to predict, estimate the slope and intercept from a graph, talk about correlation and R-square..."}
+{"idx": 1, "title": "challenge activity 2.3.2 excel linear regression.jpg", "date": "", "ddg_snippet": "Unformatted text preview: GI-lflLBIGE . . mm I 2.3.2. Excel. Linear regresSIon . Click this link to download the spreadsheet for use in this activity. Jump to level 1 According to the Farmer's Almanac, the frequency of cricket chirps varies according to the ambient temperature. As a resutt by counting the number of chirps and dividing by the time period] the temperature can be predicted using ...", "subpage_snippet": "", "source": "www.coursehero.com", "link": "https://www.coursehero.com/file/93407621/challenge-activity-232-excel-linear-regressionjpg/", "content": "Unformatted text preview: GI-lflLBIGE . . mm I 2.3.2. Excel. Linear regresSIon . Click this link to download the spreadsheet for use in this activity. Jump to level 1 According to the Farmer's Almanac, the frequency of cricket chirps varies according to the ambient temperature. As a resutt by counting the number of chirps and dividing by the time period] the temperature can be predicted using ..."}
+{"idx": 2, "title": "C1_W3_Logistic_Regression.ipynb - Colab - Google Colab", "date": "", "ddg_snippet": "2.4 Cost function for logistic regression In this section , you will implement the cost function for logistic regression . Exercise 2 Please complete the compute_cost function using the equations below. Recall that for logistic regression , the cost function is of the form", "subpage_snippet": "", "source": "colab.research.google.com", "link": "https://colab.research.google.com/github/kaieye/2022-Machine-Learning-Specialization/blob/main/Supervised+Machine+Learning+Regression+and+Classification/week3/9.Week+3+practice+lab+logistic+regression/C1_W3_Logistic_Regression.ipynb", "content": "2.4 Cost function for logistic regression In this section , you will implement the cost function for logistic regression . Exercise 2 Please complete the compute_cost function using the equations below. Recall that for logistic regression , the cost function is of the form"}
+{"idx": 3, "title": "PDF Modern regression 2: The lasso - Carnegie Mellon University", "date": "", "ddg_snippet": "The lasso uses a penalty like ridge regression , except the penalty is the `1 norm of the coe cient vector, which causes the estimates of some coe cients to be exactly zero. This is in constrast to ridge regression which never sets coe cients to zero The tuning parameter controls the strength of the `1 penalty.", "subpage_snippet": "", "source": "www.stat.cmu.edu", "link": "https://www.stat.cmu.edu/~ryantibs/datamining/lectures/17-modr2.pdf", "content": "The lasso uses a penalty like ridge regression , except the penalty is the `1 norm of the coe cient vector, which causes the estimates of some coe cients to be exactly zero. This is in constrast to ridge regression which never sets coe cients to zero The tuning parameter controls the strength of the `1 penalty."}
+{"idx": 4, "title": "MAT 240 Assignment 3-3 - Median Housing Price Prediction ... - Studocu", "date": "", "ddg_snippet": "Assignment 3-3 median housing price prediction model for pan real estate company median housing price prediction model for pan real estate company karel ehlers", "subpage_snippet": "", "source": "www.studocu.com", "link": "https://www.studocu.com/en-us/document/southern-new-hampshire-university/applied-statistics/mat-240-assignment-3-3/12129464", "content": "Assignment 3-3 median housing price prediction model for pan real estate company median housing price prediction model for pan real estate company karel ehlers"}
+{"idx": 5, "title": "MATH240 - Challenge Exercises - Section 2.3 - Part 1 - YouTube", "date": "", "ddg_snippet": "Subscribed Like 6.6K views 4 years ago MATH240 - Challenge Exercises - Section 2.3 - Part 1...more", "subpage_snippet": "", "source": "www.youtube.com", "link": "https://www.youtube.com/watch?v=VHdqAdIP-Dk", "content": "Subscribed Like 6.6K views 4 years ago MATH240 - Challenge Exercises - Section 2.3 - Part 1...more"}
+{"idx": 6, "title": "Statistics module three notes - MAT-240 Mod 3 Notes: Mod 3 ... - Studocu", "date": "", "ddg_snippet": "Notes on the reading for module three of applied statistics. This also contains examples, tables, and Excel formulas. mod notes: mod more on linear regression", "subpage_snippet": "", "source": "www.studocu.com", "link": "https://www.studocu.com/en-us/document/southern-new-hampshire-university/applied-statistics/statistics-module-three-notes/17322885", "content": "Notes on the reading for module three of applied statistics. This also contains examples, tables, and Excel formulas. mod notes: mod more on linear regression"}
+{"idx": 7, "title": "Solved MAT 240: Applied Statistics home - Chegg", "date": "", "ddg_snippet": "Question: MAT 240: Applied Statistics home > 2.3: Least squares method zyBooks CHALLENGE ACTIVITY 2.3.3. Excel: Residuals. Click this link to download the spreadsheet for use in this activity. 249.66729276. dan Jump to level 1 The systolic blood pressure dataset (in the third sheet of the spreadsheet linked above) contains the systolic blood pressure and age of 30", "subpage_snippet": "", "source": "www.chegg.com", "link": "https://www.chegg.com/homework-help/questions-and-answers/mat-240-applied-statistics-home-23-least-squares-method-zybooks-challenge-activity-233-exc-q87319295", "content": "Question: MAT 240: Applied Statistics home > 2.3: Least squares method zyBooks CHALLENGE ACTIVITY 2.3.3. Excel: Residuals. Click this link to download the spreadsheet for use in this activity. 249.66729276. dan Jump to level 1 The systolic blood pressure dataset (in the third sheet of the spreadsheet linked above) contains the systolic blood pressure and age of 30"}
+{"idx": 8, "title": "Jko Lms", "date": "", "ddg_snippet": "JKO Help Desk: 24 hours a day, 7 days a week, except Federal Holidays.", "subpage_snippet": "", "source": "jkodirect.jten.mil", "link": "https://jkodirect.jten.mil/", "content": "JKO Help Desk: 24 hours a day, 7 days a week, except Federal Holidays."}
+{"idx": 9, "title": "Stanford Math 51: Linear Algebra, Multivariable Calculus & Apps", "date": "", "ddg_snippet": "Explore Stanford University's Math 51 course text covering linear algebra, multivariable calculus, and modern applications. Learn vectors, matrices, optimization, and eigenvalues for data science and engineering.", "subpage_snippet": "", "source": "studylib.net", "link": "https://studylib.net/doc/27857883/math51book", "content": "Explore Stanford University's Math 51 course text covering linear algebra, multivariable calculus, and modern applications. Learn vectors, matrices, optimization, and eigenvalues for data science and engineering."}
diff --git a/data/sampled_jsons/32867_Descriptor-In-Pixel_SCAMP-7_PPA_prototype_maximum_FPS.jsonl b/data/sampled_jsons/32867_Descriptor-In-Pixel_SCAMP-7_PPA_prototype_maximum_FPS.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..6ee9ea0da3f3fd25a857e36bb0a1a7fb58074267
--- /dev/null
+++ b/data/sampled_jsons/32867_Descriptor-In-Pixel_SCAMP-7_PPA_prototype_maximum_FPS.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Descriptor-In-Pixel : Point-Feature Tracking For Pixel Processor Arrays", "date": "", "ddg_snippet": "This approach is very fast, our implementation upon the SCAMP-7 PPA prototype runs at over 3000 FPS (Frames Per Second), tracking point-features reliably even under violent motion. This is the first work performing point-feature detection and tracking entirely \" in-pixel \".", "subpage_snippet": "", "source": "cvpr.thecvf.com", "link": "https://cvpr.thecvf.com/virtual/2025/poster/32867", "content": "This approach is very fast, our implementation upon the SCAMP-7 PPA prototype runs at over 3000 FPS (Frames Per Second), tracking point-features reliably even under violent motion. This is the first work performing point-feature detection and tracking entirely \" in-pixel \"."}
+{"idx": 1, "title": "Live Demonstration: SCAMP-7 - IEEE Xplore", "date": "", "ddg_snippet": "We propose to demonstrate the next generation of the SCAMP vision sensor, SCAMP-7 , whose architecture embeds programmable processing cores into each pixel . This IC allows various computer vision tasks to be performed \"on-sensor\" at high speeds while avoiding the slow and power hungry transfer of image data off the device. Through in-pixel computations over the image array, visual information ...", "subpage_snippet": "", "source": "ieeexplore.ieee.org", "link": "https://ieeexplore.ieee.org/document/10208514", "content": "We propose to demonstrate the next generation of the SCAMP vision sensor, SCAMP-7 , whose architecture embeds programmable processing cores into each pixel . This IC allows various computer vision tasks to be performed \"on-sensor\" at high speeds while avoiding the slow and power hungry transfer of image data off the device. Through in-pixel computations over the image array, visual information ..."}
+{"idx": 2, "title": "SCAMP Vision Sensor - Technology", "date": "", "ddg_snippet": "Our prototype device, SCAMP-5 integrated circuit, integrates 65,536 processor cores, in a 256x256 pixel array. At peak performance it can carry out over 500 GOPS (billion operations per second) at power consumption below 1.2W.", "subpage_snippet": "", "source": "www.scamp-vision-chip.org", "link": "https://www.scamp-vision-chip.org/technology", "content": "Our prototype device, SCAMP-5 integrated circuit, integrates 65,536 processor cores, in a 256x256 pixel array. At peak performance it can carry out over 500 GOPS (billion operations per second) at power consumption below 1.2W."}
+{"idx": 3, "title": "SCAMP Vision Sensor - University of Manchester", "date": "", "ddg_snippet": "The SCAMP Vision Sensor integrates a massively parallel SIMD processor array into the pixels of the image sensor device. Unlike a conventional image sensor, it does not output raw images, but rather the results of on-sensor computations, for instance a feature map, optic flow map and/or address-events describing locations of pixels of interest. The device is fully programmable, to execute a ...", "subpage_snippet": "", "source": "personalpages.manchester.ac.uk", "link": "https://personalpages.manchester.ac.uk/staff/p.dudek/scamp/", "content": "The SCAMP Vision Sensor integrates a massively parallel SIMD processor array into the pixels of the image sensor device. Unlike a conventional image sensor, it does not output raw images, but rather the results of on-sensor computations, for instance a feature map, optic flow map and/or address-events describing locations of pixels of interest. The device is fully programmable, to execute a ..."}
+{"idx": 4, "title": "Bose Descriptor-In-Pixel Point-Feature Tracking For Pixel Processor ...", "date": "", "ddg_snippet": "This paper introduces a novel method for point-feature detection and tracking using Pixel Processor Array ( PPA ) vision sensors, which enables in-pixel computation and significantly reduces data transfer requirements. The proposed Descriptor- In - Pixel paradigm allows for efficient processing at over 3000 FPS , making it suitable for high-speed applications while maintaining low latency. By ...", "subpage_snippet": "", "source": "www.scribd.com", "link": "https://www.scribd.com/document/890539401/Bose-Descriptor-In-Pixel-Point-Feature-Tracking-for-Pixel-Processor-Arrays-CVPR-2025-Paper", "content": "This paper introduces a novel method for point-feature detection and tracking using Pixel Processor Array ( PPA ) vision sensors, which enables in-pixel computation and significantly reduces data transfer requirements. The proposed Descriptor- In - Pixel paradigm allows for efficient processing at over 3000 FPS , making it suitable for high-speed applications while maintaining low latency. By ..."}
+{"idx": 5, "title": "Descriptor In Pixel : Point Feature Tracking for Pixel ... - YouTube", "date": "", "ddg_snippet": "This approach is very fast, our implementation upon the SCAMP-7 PPA prototype runs at over 3000 FPS (Frames Per Second), tracking point-features reliably even under violent motion.", "subpage_snippet": "", "source": "www.youtube.com", "link": "https://www.youtube.com/watch?v=HIdQtf6mFSs", "content": "This approach is very fast, our implementation upon the SCAMP-7 PPA prototype runs at over 3000 FPS (Frames Per Second), tracking point-features reliably even under violent motion."}
+{"idx": 6, "title": "Live Demonstration: SCAMP-7 - computer.org", "date": "", "ddg_snippet": "We propose to demonstrate the next generation of the SCAMP vision sensor, SCAMP-7 , whose architecture embeds programmable processing cores into each pixel . This IC allows various computer vision tasks to be performed", "subpage_snippet": "", "source": "www.computer.org", "link": "https://www.computer.org/csdl/proceedings-article/cvprw/2023/024900d995/1PBy0sv9HtC", "content": "We propose to demonstrate the next generation of the SCAMP vision sensor, SCAMP-7 , whose architecture embeds programmable processing cores into each pixel . This IC allows various computer vision tasks to be performed"}
+{"idx": 7, "title": "PDF Live Demonstration: Scamp-7 - CVF Open Access", "date": "", "ddg_snippet": "Abstract We propose to demonstrate the next generation of the SCAMP vision sensor, SCAMP-7 , whose architecture em-beds programmable processing cores into each pixel . This IC allows various computer vision tasks to be performed \"on-sensor\" at high speeds while avoiding the slow and power hungry transfer of image data off the device. Through in-pixel computations over the image array, visual ...", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2023W/EventVision/papers/Bose_Live_Demonstration_Scamp-7_CVPRW_2023_paper.pdf", "content": "Abstract We propose to demonstrate the next generation of the SCAMP vision sensor, SCAMP-7 , whose architecture em-beds programmable processing cores into each pixel . This IC allows various computer vision tasks to be performed \"on-sensor\" at high speeds while avoiding the slow and power hungry transfer of image data off the device. Through in-pixel computations over the image array, visual ..."}
+{"idx": 8, "title": "DEMO : Point-Feature Tracking for Pixel Processor Arrays", "date": "", "ddg_snippet": "We implemented our approach upon the SCAMP-7 PPA prototype , upon which it can operate at just over 3000 FPS (Frames Per Second) given sufficient illumination, allowing real-time point-feature tracking under violent motion.", "subpage_snippet": "", "source": "ieeexplore.ieee.org", "link": "https://ieeexplore.ieee.org/abstract/document/11147459", "content": "We implemented our approach upon the SCAMP-7 PPA prototype , upon which it can operate at just over 3000 FPS (Frames Per Second) given sufficient illumination, allowing real-time point-feature tracking under violent motion."}
+{"idx": 9, "title": "CVPR 2025 Open Access Repository", "date": "", "ddg_snippet": "The sparse readout and complete utilization of all pixel -processors makes our approach very efficient. Our implementation upon the SCAMP-7 PPA prototype runs at over 3000 FPS (Frames Per Second), tracking point-features reliably under violent motion. This is the first work performing point-feature detection and tracking entirely in-pixel .", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2025/html/Bose_Descriptor-In-Pixel__Point-Feature_Tracking_For_Pixel_Processor_Arrays_CVPR_2025_paper.html", "content": "The sparse readout and complete utilization of all pixel -processors makes our approach very efficient. Our implementation upon the SCAMP-7 PPA prototype runs at over 3000 FPS (Frames Per Second), tracking point-features reliably under violent motion. This is the first work performing point-feature detection and tracking entirely in-pixel ."}
diff --git a/data/sampled_jsons/33390_GUI-Xplore_Empowering_Generalizable_GUI_Agents_with_One_Exploration.jsonl b/data/sampled_jsons/33390_GUI-Xplore_Empowering_Generalizable_GUI_Agents_with_One_Exploration.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..e3f670c51a730b7cc63a5ef00aa02f772f7e6f8f
--- /dev/null
+++ b/data/sampled_jsons/33390_GUI-Xplore_Empowering_Generalizable_GUI_Agents_with_One_Exploration.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "GUI-Xplore: Empowering Generalizable GUI Agents with One Exploration", "date": "", "ddg_snippet": "GUI agents hold significant potential to enhance the experience and efficiency of human-device interaction. However, current methods face challenges in generalizing across applications (apps) and tasks, primarily due to two fundamental limitations in existing datasets. First, these datasets overlook developer-induced structural variations among apps, limiting the transferability of knowledge ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2503.17709", "content": "GUI agents hold significant potential to enhance the experience and efficiency of human-device interaction. However, current methods face challenges in generalizing across applications (apps) and tasks, primarily due to two fundamental limitations in existing datasets. First, these datasets overlook developer-induced structural variations among apps, limiting the transferability of knowledge ..."}
+{"idx": 1, "title": "GitHub - 921112343/GUI-Xplore: [CVPR 2025] GUI-Xplore: Empowering ...", "date": "", "ddg_snippet": "GUI - Xplore is a novel dataset designed to enhance cross-application and cross-task generalization for GUI agents . It provides exploration videos for diverse applications, combined with five structured downstream tasks, aiming to push the boundaries of generalizable GUI automation.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/921112343/GUI-Xplore", "content": "GUI - Xplore is a novel dataset designed to enhance cross-application and cross-task generalization for GUI agents . It provides exploration videos for diverse applications, combined with five structured downstream tasks, aiming to push the boundaries of generalizable GUI automation."}
+{"idx": 2, "title": "GUI-Xplore: Empowering Generalizable GUI Agents with One Exploration", "date": "", "ddg_snippet": "GUI - Xplore integrates pre-recorded exploration videos providing contextual insights, alongside five hierarchically structured downstream tasks designed to comprehensively evaluate GUI agent capabilities.", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2025/html/Sun_GUI-Xplore_Empowering_Generalizable_GUI_Agents_with_One_Exploration_CVPR_2025_paper.html", "content": "GUI - Xplore integrates pre-recorded exploration videos providing contextual insights, alongside five hierarchically structured downstream tasks designed to comprehensively evaluate GUI agent capabilities."}
+{"idx": 3, "title": "GUI-Xplore: Empowering Generalizable GUI Agents with One Exploration ...", "date": "", "ddg_snippet": "Further experiments indicate that Xplore - Agent achieves a 10% improvement over existing methods in unfamiliar environments, yet there remains significant potential for further enhancement towards truly generalizable GUI agents .", "subpage_snippet": "", "source": "www.alphaxiv.org", "link": "https://www.alphaxiv.org/overview/2503.17709v1", "content": "Further experiments indicate that Xplore - Agent achieves a 10% improvement over existing methods in unfamiliar environments, yet there remains significant potential for further enhancement towards truly generalizable GUI agents ."}
+{"idx": 4, "title": "Visual Agents at CVPR 2025 - Voxel51", "date": "", "ddg_snippet": "The Visual Agent papers from CVPR I'm most excited about are: From Multimodal LLMs to Generalist Embodied Agents : Methods and Lessons ShowUI: One Vision-Language-Action Model for GUI Visual Agent GUI - Xplore : Empowering Generalizable GUI Agents with One Exploration SpiritSight Agent : Advanced GUI Agent with One Look", "subpage_snippet": "", "source": "voxel51.com", "link": "https://voxel51.com/blog/visual-agents-at-cvpr-2025", "content": "The Visual Agent papers from CVPR I'm most excited about are: From Multimodal LLMs to Generalist Embodied Agents : Methods and Lessons ShowUI: One Vision-Language-Action Model for GUI Visual Agent GUI - Xplore : Empowering Generalizable GUI Agents with One Exploration SpiritSight Agent : Advanced GUI Agent with One Look"}
+{"idx": 5, "title": "GUI-Xplore: Empowering Generalizable GUI Agents with One Exploration", "date": "", "ddg_snippet": "GUI - Xplore in-tegrates pre-recorded exploration videos providing contex-tual insights, alongside five hierarchically structured down-stream tasks designed to comprehensively evaluate GUI agent capabilities.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2503.17709", "content": "GUI - Xplore in-tegrates pre-recorded exploration videos providing contex-tual insights, alongside five hierarchically structured down-stream tasks designed to comprehensively evaluate GUI agent capabilities."}
+{"idx": 6, "title": "GUI-Xplore: Empowering Generalizable GUI Agents with One Exploration ...", "date": "", "ddg_snippet": "The paper introduces GUI - Xplore , a new dataset aimed at improving how computer programs understand and interact with different apps by watching recorded user interactions ( exploration videos). It ...", "subpage_snippet": "", "source": "bytez.com", "link": "https://bytez.com/docs/cvpr/33390/paper", "content": "The paper introduces GUI - Xplore , a new dataset aimed at improving how computer programs understand and interact with different apps by watching recorded user interactions ( exploration videos). It ..."}
+{"idx": 7, "title": "GUI-Xplore/README.md at main · 921112343/GUI-Xplore · GitHub", "date": "", "ddg_snippet": "GUI - Xplore is a novel dataset designed to enhance cross-application and cross-task generalization for GUI agents . It provides exploration videos for diverse applications, combined with five structured downstream tasks, aiming to push the boundaries of generalizable GUI automation.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/921112343/GUI-Xplore/blob/main/README.md", "content": "GUI - Xplore is a novel dataset designed to enhance cross-application and cross-task generalization for GUI agents . It provides exploration videos for diverse applications, combined with five structured downstream tasks, aiming to push the boundaries of generalizable GUI automation."}
+{"idx": 8, "title": "GUI-Xplore: Empowering Generalizable GUI Agents with One Exploration ...", "date": "", "ddg_snippet": "GUI - Xplore represents a significant step forward in developing flexible, generalizable GUI agents that can adapt to diverse applications. By demonstrating that a single exploration session can provide sufficient understanding for effective task completion, the research challenges the assumption that extensive app-specific training is necessary.", "subpage_snippet": "", "source": "www.aimodels.fyi", "link": "https://www.aimodels.fyi/papers/arxiv/gui-xplore-empowering-generalizable-gui-agents-one", "content": "GUI - Xplore represents a significant step forward in developing flexible, generalizable GUI agents that can adapt to diverse applications. By demonstrating that a single exploration session can provide sufficient understanding for effective task completion, the research challenges the assumption that extensive app-specific training is necessary."}
+{"idx": 9, "title": "PDF GUI-Xplore: Empowering Generalizable GUI Agents with One Exploration ...", "date": "", "ddg_snippet": "y videos in the GUI - Xplore dataset aim to achieve comprehensive coverag of software pages. How-ever, in practical apps, full page coverage is often unattain-able. To examine the impact of varying exploration cov-erage rates on Xplore - Agent's reasoning accuracy, we con-ducted additional experiments focusing exclusively on tasks unrelate", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2025/supplemental/Sun_GUI-Xplore_Empowering_Generalizable_CVPR_2025_supplemental.pdf", "content": "y videos in the GUI - Xplore dataset aim to achieve comprehensive coverag of software pages. How-ever, in practical apps, full page coverage is often unattain-able. To examine the impact of varying exploration cov-erage rates on Xplore - Agent's reasoning accuracy, we con-ducted additional experiments focusing exclusively on tasks unrelate"}
diff --git "a/data/sampled_jsons/33775_Instant_Gaussian_Stream_Meeting_Room_Dataset_Table_2_storage_efficiency_Ours-s_3DGStream[51]\342\200\240_year_2024.jsonl" "b/data/sampled_jsons/33775_Instant_Gaussian_Stream_Meeting_Room_Dataset_Table_2_storage_efficiency_Ours-s_3DGStream[51]\342\200\240_year_2024.jsonl"
new file mode 100644
index 0000000000000000000000000000000000000000..4ba4f7d03357e65e1987d5e7a64ebf7e964f3fdd
--- /dev/null
+++ "b/data/sampled_jsons/33775_Instant_Gaussian_Stream_Meeting_Room_Dataset_Table_2_storage_efficiency_Ours-s_3DGStream[51]\342\200\240_year_2024.jsonl"
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "CVPR Poster Instant Gaussian Stream", "date": "", "ddg_snippet": "For the Meeting Room dataset , we train the Gaussians of the first frame ... Our method outperforms 3DGStream in rendering quality, train time, and storage ...", "subpage_snippet": "", "source": "cvpr.thecvf.com", "link": "https://cvpr.thecvf.com/virtual/2025/poster/33775", "content": "For the Meeting Room dataset , we train the Gaussians of the first frame ... Our method outperforms 3DGStream in rendering quality, train time, and storage ..."}
+{"idx": 1, "title": "Ways to Watch the NFL | TV, Streaming & Radio | NFL.com", "date": "", "ddg_snippet": "ESPN is the exclusive home of the NFL' s Monday Night Football. On ESPN Deportes, Monday Night Football is broadcast live via streaming through computers, smartphones, tablets, and connected TV ...", "subpage_snippet": "", "source": "www.nfl.com", "link": "https://www.nfl.com/ways-to-watch/by-device", "content": "ESPN is the exclusive home of the NFL' s Monday Night Football. On ESPN Deportes, Monday Night Football is broadcast live via streaming through computers, smartphones, tablets, and connected TV ..."}
+{"idx": 2, "title": "Ways to Watch the NFL | TNF, SNF & MNF | NFL.com", "date": "", "ddg_snippet": "ESPN is the exclusive home of the NFL' s Monday Night Football. On ESPN Deportes, Monday Night Football is broadcast live via streaming through computers, smartphones, tablets, and connected TV ...", "subpage_snippet": "", "source": "www.nfl.com", "link": "https://www.nfl.com/ways-to-watch/by-day", "content": "ESPN is the exclusive home of the NFL' s Monday Night Football. On ESPN Deportes, Monday Night Football is broadcast live via streaming through computers, smartphones, tablets, and connected TV ..."}
+{"idx": 3, "title": "NFL Monday Night Football Schedule on ESPN | NFL.com", "date": "", "ddg_snippet": "Get the 2024 Monday Night Football Schedule. See which teams are playing this Monday or plan your Football Mondays for the entire NFL season.", "subpage_snippet": "", "source": "www.nfl.com", "link": "https://www.nfl.com/schedules/monday-night-football/", "content": "Get the 2024 Monday Night Football Schedule. See which teams are playing this Monday or plan your Football Mondays for the entire NFL season."}
+{"idx": 4, "title": "NFL 2025 - WEEK 3 Schedule | NFL.com", "date": "", "ddg_snippet": "In Week 18, two games will be played on Saturday (4:30 PM ET and 8:00 PM ET) with the remainder to be played either on Thursday night (8:15 PM ET), Sunday afternoon (1:00 PM ET and 4:25 PM ET), or ...", "subpage_snippet": "", "source": "www.nfl.com", "link": "https://www.nfl.com/schedules/", "content": "In Week 18, two games will be played on Saturday (4:30 PM ET and 8:00 PM ET) with the remainder to be played either on Thursday night (8:15 PM ET), Sunday afternoon (1:00 PM ET and 4:25 PM ET), or ..."}
+{"idx": 5, "title": "NFL.com | Official Site of the National Football League", "date": "", "ddg_snippet": "Jul 30, 2007 · What to watch for in Lions-Ravens on 'Monday Night Football' NFL+: Watch post-Week 3 press conferences! Niners' Bosa believed to have suffered torn ACL in win over Cardinals", "subpage_snippet": "", "source": "www.nfl.com", "link": "https://www.nfl.com/", "content": "Jul 30, 2007 · What to watch for in Lions-Ravens on 'Monday Night Football' NFL+: Watch post-Week 3 press conferences! Niners' Bosa believed to have suffered torn ACL in win over Cardinals"}
+{"idx": 6, "title": "Live NFL Scores for 2025 - Week 3 | NFL.com", "date": "", "ddg_snippet": "Football iconFacebook logo Instagram logo Snapchat logo YouTube logo TikTok logo Spotify logo LinkedIn logo Grid icon Key icon", "subpage_snippet": "", "source": "www.nfl.com", "link": "https://www.nfl.com/scores/", "content": "Football iconFacebook logo Instagram logo Snapchat logo YouTube logo TikTok logo Spotify logo LinkedIn logo Grid icon Key icon"}
+{"idx": 7, "title": "How to access LIVE NFL Regular Season games? – NFL", "date": "", "ddg_snippet": "Aug 25, 2025 · Includes Sunday Night Football, Monday Night Football, Thursday Night Football, NFL Network games, and select Saturday games. Full and condensed game replays are available on all devices, including connected TVs.", "subpage_snippet": "", "source": "support.nfl.com", "link": "https://support.nfl.com/hc/en-us/articles/40568374746644-How-to-access-LIVE-NFL-Regular-Season-games", "content": "Aug 25, 2025 · Includes Sunday Night Football, Monday Night Football, Thursday Night Football, NFL Network games, and select Saturday games. Full and condensed game replays are available on all devices, including connected TVs."}
+{"idx": 8, "title": "Vikings-Bears on 'Monday Night Football': What We Learned from...", "date": "", "ddg_snippet": "Sep 9, 2025 · Following a woeful first three quarters, J.J. McCarthy rallied the Minnesota Vikings to a dramatic win over Caleb Williams and the Chicago Bears on Monday night in his first NFL game.", "subpage_snippet": "", "source": "www.nfl.com", "link": "https://www.nfl.com/news/vikings-bears-on-monday-night-football-what-we-learned-from-minnesota-s-27-24-win", "content": "Sep 9, 2025 · Following a woeful first three quarters, J.J. McCarthy rallied the Minnesota Vikings to a dramatic win over Caleb Williams and the Chicago Bears on Monday night in his first NFL game."}
+{"idx": 9, "title": "Sunday Night Football Schedule | NFL.com", "date": "", "ddg_snippet": "Get the Sunday Night Football Schedule. See which teams are playing this week or plan your Sunday football for the entire NFL season.", "subpage_snippet": "", "source": "www.nfl.com", "link": "https://www.nfl.com/schedules/sunday-night-football/", "content": "Get the Sunday Night Football Schedule. See which teams are playing this week or plan your Sunday football for the entire NFL season."}
diff --git a/data/sampled_jsons/4uOEiitySn_A_Checks-and-Balances_Framework_Context-Aware_Ethical_AI_Alignment.jsonl b/data/sampled_jsons/4uOEiitySn_A_Checks-and-Balances_Framework_Context-Aware_Ethical_AI_Alignment.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..5b214cf91073c3a3f2509cda12257385b3f4236d
--- /dev/null
+++ b/data/sampled_jsons/4uOEiitySn_A_Checks-and-Balances_Framework_Context-Aware_Ethical_AI_Alignment.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Artificial Intelligence Act - Wikipedia", "date": "", "ddg_snippet": "The Artificial Intelligence Act is a European Union regulation concerning artificial intelligence. It establishes a common regulatory and legal framework for AI within the European Union. It came into force on 1 August 2024...", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/Artificial_Intelligence_Act", "content": "The Artificial Intelligence Act is a European Union regulation concerning artificial intelligence. It establishes a common regulatory and legal framework for AI within the European Union. It came into force on 1 August 2024..."}
+{"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.", "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."}
+{"idx": 3, "title": "A Three-Branch Checks - and - Balances Framework", "date": "", "ddg_snippet": "A Three-Branch Checks - and - Balances Framework for Context - Aware Ethical Alignment of Large Language Models.This work presents a three-branch framework for ethical AI behavior, inspired by governmental checks and balances , centered on the DIKE-ERIS duality.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=o2afWIxjKD", "content": "A Three-Branch Checks - and - Balances Framework for Context - Aware Ethical Alignment of Large Language Models.This work presents a three-branch framework for ethical AI behavior, inspired by governmental checks and balances , centered on the DIKE-ERIS duality."}
+{"idx": 4, "title": "(PDF) A Three-Branch Checks - and - Balances Framework for...", "date": "", "ddg_snippet": "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": "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": 5, "title": "Context - aware Prompt Adaptation: AI ’s Next Frontier - Artificial...", "date": "", "ddg_snippet": "Adopting context - aware AI requires more trust in AI technology. We’re moving from detailed instructions to letting AI figure things out on its own. This shift needs faith in AI ’s ability to understand and respond to context. Even with minimal prompts, human creativity is still key.", "subpage_snippet": "", "source": "esoftskills.com", "link": "https://esoftskills.com/ai/context-aware-prompt-adaptation/", "content": "Adopting context - aware AI requires more trust in AI technology. We’re moving from detailed instructions to letting AI figure things out on its own. This shift needs faith in AI ’s ability to understand and respond to context. Even with minimal prompts, human creativity is still key."}
+{"idx": 6, "title": "Human Ai Test for Ai Alignment | Restackio", "date": "", "ddg_snippet": "Explore the human AI test and its significance in AI alignment , ensuring ethical and effective AI development.Research and Development: Continuous efforts to enhance contextual awareness in AI .", "subpage_snippet": "", "source": "d2wozrt205r2fu.cloudfront.net", "link": "https://d2wozrt205r2fu.cloudfront.net/p/ai-alignment-answer-human-ai-test-cat-ai", "content": "Explore the human AI test and its significance in AI alignment , ensuring ethical and effective AI development.Research and Development: Continuous efforts to enhance contextual awareness in AI ."}
+{"idx": 7, "title": "The Gardener’s Guide to AI Alignment : A New Paradigm of... | Medium", "date": "", "ddg_snippet": "The field of AI Alignment is built on a simple, terrifying premise: an AI does not need to be malicious to be catastrophic. The central fear is one of misaligned competence. The classic thought experiment, the “Paperclip Maximizer,” illustrates this perfectly.", "subpage_snippet": "", "source": "medium.com", "link": "https://medium.com/@dom.pennock/the-gardeners-guide-to-ai-alignment-a-new-paradigm-of-emergent-culture-408687c3531c", "content": "The field of AI Alignment is built on a simple, terrifying premise: an AI does not need to be malicious to be catastrophic. The central fear is one of misaligned competence. The classic thought experiment, the “Paperclip Maximizer,” illustrates this perfectly."}
+{"idx": 8, "title": "Third Way Alignment - Ethical AI Coexistence", "date": "", "ddg_snippet": "A New Approach to AI Ethics . Third Way Alignment : AI Coexistence. Moving beyond fear and control toward mutual respect, shared flourishing, and ethical partnership between humans and artificial intelligence.", "subpage_snippet": "", "source": "thirdwayalignment.com", "link": "https://thirdwayalignment.com/", "content": "A New Approach to AI Ethics . Third Way Alignment : AI Coexistence. Moving beyond fear and control toward mutual respect, shared flourishing, and ethical partnership between humans and artificial intelligence."}
+{"idx": 9, "title": "Design and Evaluation Methods for LLM-Based Explainable AI", "date": "", "ddg_snippet": "Visualization and intuitiveness of explanation. Interactivity and controllability. Context awareness and adaptability. Feedback mechanism.", "subpage_snippet": "", "source": "www.oajaiml.com", "link": "https://www.oajaiml.com/uploads/archivepdf/430552340.pdf", "content": "Visualization and intuitiveness of explanation. Interactivity and controllability. Context awareness and adaptability. Feedback mechanism."}
diff --git a/data/sampled_jsons/6ojzpDczIY_Global_Optimization_with_a_Power-Transformed_Objective_and_Gaussian_Smoothing_Rosenbrock_.jsonl b/data/sampled_jsons/6ojzpDczIY_Global_Optimization_with_a_Power-Transformed_Objective_and_Gaussian_Smoothing_Rosenbrock_.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..c760d1611c95bd1043a82726bf0d312be7b9ef64
--- /dev/null
+++ b/data/sampled_jsons/6ojzpDczIY_Global_Optimization_with_a_Power-Transformed_Objective_and_Gaussian_Smoothing_Rosenbrock_.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Global Optimization with a Power-Transformed Objective and Gaussian ...", "date": "", "ddg_snippet": "Abstract We propose a novel method, namely Gaussian Smoothing with a Power-Transformed Objective (GS-PowerOpt), that solves global optimization problems in two steps: (1) perform a (exponen-tial) power-N transformation to the not necessar-Rd ! ily differentiable objective f : and get R fN, and (2) optimize the Gaussian -smoothed fN with stochastic approximations. Under mild con-ditions on f ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=6ojzpDczIY", "content": "Abstract We propose a novel method, namely Gaussian Smoothing with a Power-Transformed Objective (GS-PowerOpt), that solves global optimization problems in two steps: (1) perform a (exponen-tial) power-N transformation to the not necessar-Rd ! ily differentiable objective f : and get R fN, and (2) optimize the Gaussian -smoothed fN with stochastic approximations. Under mild con-ditions on f ..."}
+{"idx": 1, "title": "Global Optimization with A Power-Transformed Objective and Gaussian ...", "date": "", "ddg_snippet": "We propose a novel method that solves global optimization problems in two steps: (1) perform a (exponential) power - N transformation to the not-necessarily differentiable objective function f and get f_N, and (2) optimize the Gaussian -smoothed f_N with stochastic approximations. Under mild conditions on f, for any \\delta>0, we prove that with a sufficiently large power N_\\delta, this method ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2412.05204", "content": "We propose a novel method that solves global optimization problems in two steps: (1) perform a (exponential) power - N transformation to the not-necessarily differentiable objective function f and get f_N, and (2) optimize the Gaussian -smoothed f_N with stochastic approximations. Under mild conditions on f, for any \\delta>0, we prove that with a sufficiently large power N_\\delta, this method ..."}
+{"idx": 2, "title": "Global Optimization with a Power-Transformed Objective and Gaussian ...", "date": "", "ddg_snippet": "Abstract: We propose a novel method, namely Gaussian Smoothing with a Power-Transformed Objective (GS-PowerOpt), that solves global optimization problems in two steps: (1) perform a (exponential) power - N transformation to the not necessarily differentiable objective f : R d → R and get f N , and (2) optimize the Gaussian -smoothed f N with stochastic approximations. Under mild conditions on ...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/chen-research/GS-PowerTransform", "content": "Abstract: We propose a novel method, namely Gaussian Smoothing with a Power-Transformed Objective (GS-PowerOpt), that solves global optimization problems in two steps: (1) perform a (exponential) power - N transformation to the not necessarily differentiable objective f : R d → R and get f N , and (2) optimize the Gaussian -smoothed f N with stochastic approximations. Under mild conditions on ..."}
+{"idx": 3, "title": "dblp: Global Optimization with A Power-Transformed Objective and ...", "date": "", "ddg_snippet": "Bibliographic details on Global Optimization with A Power-Transformed Objective and Gaussian Smoothing .", "subpage_snippet": "", "source": "dblp.org", "link": "https://dblp.org/rec/journals/corr/abs-2412-05204", "content": "Bibliographic details on Global Optimization with A Power-Transformed Objective and Gaussian Smoothing ."}
+{"idx": 4, "title": "Global Optimization with A Power-Transformed Objective and Gaussian ...", "date": "", "ddg_snippet": "We propose a novel method that solves global optimization problems in two steps: (1) perform a (exponential) power-$N$ transformation to the not-necessarily differentiable objective function $f$ and get $f_N$, and (2) optimize the Gaussian -smoothed $f_N$ with stochastic approximations.", "subpage_snippet": "", "source": "ui.adsabs.harvard.edu", "link": "https://ui.adsabs.harvard.edu/abs/2024arXiv241205204X/abstract", "content": "We propose a novel method that solves global optimization problems in two steps: (1) perform a (exponential) power-$N$ transformation to the not-necessarily differentiable objective function $f$ and get $f_N$, and (2) optimize the Gaussian -smoothed $f_N$ with stochastic approximations."}
+{"idx": 5, "title": "GlobalOptimizationwithAPower-Transformed ObjectiveandGaussianSmoothing", "date": "", "ddg_snippet": "We propose a novel method that solves global optimization problems in two steps: (1) perform a (exponential) power-N transformation to the not-necessarily differen-tiable objective function f to obtain fN, and (2) optimize the Gaussian -smoothed fN with stochastic approximations. Under mild conditions on f, for any δ > 0, we prove that with a sufficiently large power Nδ, this method converges ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2412.05204v1", "content": "We propose a novel method that solves global optimization problems in two steps: (1) perform a (exponential) power-N transformation to the not-necessarily differen-tiable objective function f to obtain fN, and (2) optimize the Gaussian -smoothed fN with stochastic approximations. Under mild conditions on f, for any δ > 0, we prove that with a sufficiently large power Nδ, this method converges ..."}
+{"idx": 6, "title": "PDF Deterministic global optimization with Gaussian processes embedded", "date": "", "ddg_snippet": "These optimization problems are nonconvex and global optimization is desired. However, previous literature observed computational burdens limiting deterministic global opti-mization to Gaussian processes trained on few data points. We propose a reduced-space formulation for deterministic global optimization with trained Gaussian processes embedded.", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/content/pdf/10.1007/s12532-021-00204-y.pdf", "content": "These optimization problems are nonconvex and global optimization is desired. However, previous literature observed computational burdens limiting deterministic global opti-mization to Gaussian processes trained on few data points. We propose a reduced-space formulation for deterministic global optimization with trained Gaussian processes embedded."}
+{"idx": 7, "title": "PDF Global Optimization with a Power-Transformed Objective and Gaussian ...", "date": "", "ddg_snippet": "The Problem to Solve We propose a novel method, namely Gaussian Smoothing with a Power-Transformed Objective (GS-PowerOpt), that solves the following continuous optimization problems max f (x),", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/media/icml-2025/Slides/46360_JdTUNKo.pdf", "content": "The Problem to Solve We propose a novel method, namely Gaussian Smoothing with a Power-Transformed Objective (GS-PowerOpt), that solves the following continuous optimization problems max f (x),"}
+{"idx": 8, "title": "Global Optimization with A Power-Transformed Objective and Gaussian ...", "date": "", "ddg_snippet": "The power-transformed objective with Gaussian smoothing represents a significant advance in global optimization . The method offers practical benefits for machine learning and statistical applications while maintaining theoretical rigor.", "subpage_snippet": "", "source": "www.aimodels.fyi", "link": "https://www.aimodels.fyi/papers/arxiv/global-optimization-power-transformed-objective-gaussian-smoothing", "content": "The power-transformed objective with Gaussian smoothing represents a significant advance in global optimization . The method offers practical benefits for machine learning and statistical applications while maintaining theoretical rigor."}
+{"idx": 9, "title": "Revisions | OpenReview", "date": "", "ddg_snippet": "12 Aug 2025, 20:24 Pacific Daylight Time TLDR: We propose a zeroth-order method for optimizing a non-convex function , which applies Gaussian smoothing and Stochastic Gradient Ascent to the (exponential) power of the objective . Abstract:", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/revisions?id=6ojzpDczIY", "content": "12 Aug 2025, 20:24 Pacific Daylight Time TLDR: We propose a zeroth-order method for optimizing a non-convex function , which applies Gaussian smoothing and Stochastic Gradient Ascent to the (exponential) power of the objective . Abstract:"}
diff --git a/data/sampled_jsons/ACIDDnTbSJ_Feint_Behaviors_and_Strategies_Formalization_Figure_4_MASAC_model.jsonl b/data/sampled_jsons/ACIDDnTbSJ_Feint_Behaviors_and_Strategies_Formalization_Figure_4_MASAC_model.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..596835a8502dc95b5f3477e9c7572a33fe0044ab
--- /dev/null
+++ b/data/sampled_jsons/ACIDDnTbSJ_Feint_Behaviors_and_Strategies_Formalization_Figure_4_MASAC_model.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Feint Behaviors and Strategies : Formalization , Implementation and...", "date": "", "ddg_snippet": "Such behaviors are crucial tactics in most competitive multi-player games (e.g., boxing, fencing, basketball, motor racing, etc.). However, existing literature does not provide a comprehensive (and/or concrete) formalization for Feint behaviors , and their implications on game strategies .", "subpage_snippet": "", "source": "papers.cool", "link": "https://papers.cool/venue/ACIDDnTbSJ@OpenReview", "content": "Such behaviors are crucial tactics in most competitive multi-player games (e.g., boxing, fencing, basketball, motor racing, etc.). However, existing literature does not provide a comprehensive (and/or concrete) formalization for Feint behaviors , and their implications on game strategies ."}
+{"idx": 1, "title": "Abstract page for arXiv paper 2403.07932: Feint in Multi-Player Games", "date": "", "ddg_snippet": "Our work first formalizes Feint from the perspective of Multi-Player Games, in terms of the temporal, spatial, and their collective impacts. The formalization is built upon Non-transitive Active Markov Game Model , where Feint can have a considerable amount of impacts.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2403.07932", "content": "Our work first formalizes Feint from the perspective of Multi-Player Games, in terms of the temporal, spatial, and their collective impacts. The formalization is built upon Non-transitive Active Markov Game Model , where Feint can have a considerable amount of impacts."}
+{"idx": 2, "title": "Feint Behaviors and Strategies : Formalization , Implementation and...", "date": "", "ddg_snippet": "# The Dual- Behavior Model is a crucial concept for integrating feint behaviors into game strategies . Figure 4 : Comparison of Game Reward when using Feint and not using Feint in a 1 VS 1 scenario.", "subpage_snippet": "", "source": "deep-diver.github.io", "link": "https://deep-diver.github.io/neurips2024/posters/aciddntbsj/", "content": "# The Dual- Behavior Model is a crucial concept for integrating feint behaviors into game strategies . Figure 4 : Comparison of Game Reward when using Feint and not using Feint in a 1 VS 1 scenario."}
+{"idx": 3, "title": "Borderlands 4 Frank the Furnace Location, Route and Loot Drops", "date": "", "ddg_snippet": "Boss behavior and fight strategy . Frank the Furnace specializes in strong furnace and flamethrower-based attacks covering wide areas.", "subpage_snippet": "", "source": "nerdschalk.com", "link": "https://nerdschalk.com/borderlands-4-frank-the-furnace-location-route-and-loot-drops/", "content": "Boss behavior and fight strategy . Frank the Furnace specializes in strong furnace and flamethrower-based attacks covering wide areas."}
+{"idx": 4, "title": "How to Create a Python Trading Bot and Implement a Secure License...", "date": "", "ddg_snippet": "Supports Different Licensing Models : Facilitates offering various features or usage limits based on license type. Overview of the Article: Building a Secure Trading Bot. We’ll cover the following key areas", "subpage_snippet": "", "source": "trading-strategies.academy", "link": "https://trading-strategies.academy/archives/1661", "content": "Supports Different Licensing Models : Facilitates offering various features or usage limits based on license type. Overview of the Article: Building a Secure Trading Bot. We’ll cover the following key areas"}
+{"idx": 5, "title": "Rationalizable Strategic Behavior and the Problem of Perfection on...", "date": "", "ddg_snippet": "Strategy profiles that are rationalizable are not always Nash equilibria; conversely, the information in an extensive form game often allows certain \"unreasonable\" Nash equilibria to be excluded from the set of rationalizable profiles.", "subpage_snippet": "", "source": "www.jstor.org", "link": "https://www.jstor.org/stable/1911197", "content": "Strategy profiles that are rationalizable are not always Nash equilibria; conversely, the information in an extensive form game often allows certain \"unreasonable\" Nash equilibria to be excluded from the set of rationalizable profiles."}
+{"idx": 6, "title": "Junyu LIU - Google Scholar", "date": "", "ddg_snippet": "Feint behaviors and strategies : formalization , implementation and evaluation.", "subpage_snippet": "", "source": "scholar.google.com", "link": "https://scholar.google.com/citations?user=EiSrD1UAAAAJ&hl=en", "content": "Feint behaviors and strategies : formalization , implementation and evaluation."}
+{"idx": 7, "title": "New Jepang Viral Phenomenon Of ABG Viral SMA 2025... - Video Viral", "date": "", "ddg_snippet": "Stay informed, stay connected, and leverage viral trends to enhance your marketing, SEO strategies , and content development efforts. Visit videoviral.baby today and never miss the latest viral sensation!", "subpage_snippet": "", "source": "videoviral.baby", "link": "https://videoviral.baby/new-jepang-viral-phenomenon-of-abg-viral-sma-2025-kak-zahra-tutorial-wiwik-dalam-mobil-di-tempat-parkir-one-for-all-indonesia/", "content": "Stay informed, stay connected, and leverage viral trends to enhance your marketing, SEO strategies , and content development efforts. Visit videoviral.baby today and never miss the latest viral sensation!"}
+{"idx": 8, "title": "dblp: List of computer science publications by Xiangjun Peng", "date": "", "ddg_snippet": "Junyu Liu, Xiangjun Peng: Feint Behaviors and Strategies : Formalization , Implementation and Evaluation. NeurIPS 2024.", "subpage_snippet": "", "source": "dblp.uni-trier.de", "link": "https://dblp.uni-trier.de/pid/123/5256.html", "content": "Junyu Liu, Xiangjun Peng: Feint Behaviors and Strategies : Formalization , Implementation and Evaluation. NeurIPS 2024."}
+{"idx": 9, "title": "Formalized Strategic Planning in Organizations - MBA Knowledge Base", "date": "", "ddg_snippet": "Strategic planning is an organization’s process of understanding its strategy and making decisions on where to allocate the companies resources in order to peruse the strategy .Problems Associated with Formalized Strategic Decision Making.", "subpage_snippet": "", "source": "www.mbaknol.com", "link": "https://www.mbaknol.com/strategic-management/formalized-strategic-planning-in-organizations/", "content": "Strategic planning is an organization’s process of understanding its strategy and making decisions on where to allocate the companies resources in order to peruse the strategy .Problems Associated with Formalized Strategic Decision Making."}
diff --git a/data/sampled_jsons/ACIDDnTbSJ_NeurIPS_2024_Feint_Behaviors_scheduler_weights_implementation_details.jsonl b/data/sampled_jsons/ACIDDnTbSJ_NeurIPS_2024_Feint_Behaviors_scheduler_weights_implementation_details.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..3e9316ae8fe679c4095e81a8b34754a4bb9fb11f
--- /dev/null
+++ b/data/sampled_jsons/ACIDDnTbSJ_NeurIPS_2024_Feint_Behaviors_scheduler_weights_implementation_details.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "International Conference on Learning Representations - Wikipedia", "date": "", "ddg_snippet": "Machine learningand data mining. v. t. e. The International Conference on Learning Representations is a machine learning conference typically held in late April or early May each year. Along with NeurIPS and ICML...", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/International_Conference_on_Learning_Representations", "content": "Machine learningand data mining. v. t. e. The International Conference on Learning Representations is a machine learning conference typically held in late April or early May each year. Along with NeurIPS and ICML..."}
+{"idx": 1, "title": "Brown University · NeurIPS 2024", "date": "", "ddg_snippet": "RTify: Aligning Deep Neural Networks with Human Behavioral Decisions. 26 September 2024 ·1884 words·9 mins· loading. Feint Behaviors and Strategies: Formalization, Implementation and Evaluation.", "subpage_snippet": "", "source": "deep-diver.github.io", "link": "https://deep-diver.github.io/neurips2024/tags/-brown-university/", "content": "RTify: Aligning Deep Neural Networks with Human Behavioral Decisions. 26 September 2024 ·1884 words·9 mins· loading. Feint Behaviors and Strategies: Formalization, Implementation and Evaluation."}
+{"idx": 2, "title": "GitHub - CAMMA-public/PeskaVLP: [ NeurIPS '24 Spotlight]...", "date": "", "ddg_snippet": "[ NeurIPS 2024 ] Procedure-Aware Surgical Video-language Pretraining with Hierarchical Knowledge Augmentation. Kun Yuan, Vinkle Srivastav, Nassir Navab, Nicolas Padoy, NeurIPS 2024 . arXiv OpenReview.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/CAMMA-public/PeskaVLP", "content": "[ NeurIPS 2024 ] Procedure-Aware Surgical Video-language Pretraining with Hierarchical Knowledge Augmentation. Kun Yuan, Vinkle Srivastav, Nassir Navab, Nicolas Padoy, NeurIPS 2024 . arXiv OpenReview."}
+{"idx": 3, "title": "hide_event_type_ details only hides on first step. Expected behavior ?", "date": "", "ddg_snippet": "The behavior of `hide_event_type_ details ` seems a bit odd to me, is this expected behavior ?If I set `hideEventTypeDetails: true`, the event title, and event duration are hidden on step 1 and step 2, but not on step 3. It does seem valuable to show the user what they selected in step 3...", "subpage_snippet": "", "source": "community.calendly.com", "link": "https://community.calendly.com/api-webhook-help-61/hide-event-type-details-only-hides-on-first-step-expected-behavior-2159", "content": "The behavior of `hide_event_type_ details ` seems a bit odd to me, is this expected behavior ?If I set `hideEventTypeDetails: true`, the event title, and event duration are hidden on step 1 and step 2, but not on step 3. It does seem valuable to show the user what they selected in step 3..."}
+{"idx": 4, "title": "Complete PyTorch implementation of Selective Self-Attention...", "date": "", "ddg_snippet": "Weight Sharing: <0.5% parameter overhead through efficient weight reuse. ComfyUI Integration: Optimized for diffusion model workflows. Performance Highlights. 15-30% inference speedup with maintained quality. Consistent improvements across GPT-2, ...", "subpage_snippet": "", "source": "pypi.org", "link": "https://pypi.org/project/selective-self-attention/", "content": "Weight Sharing: <0.5% parameter overhead through efficient weight reuse. ComfyUI Integration: Optimized for diffusion model workflows. Performance Highlights. 15-30% inference speedup with maintained quality. Consistent improvements across GPT-2, ..."}
+{"idx": 5, "title": "2025 Conference", "date": "", "ddg_snippet": "2024 . 2023. 2021. NeurIPS announces a 2nd physical location in Mexico City and an affiliated meeting in Europe (EurIPS) for Dec 2025. (Press Releases). NeurIPS has released an LLM policy.", "subpage_snippet": "", "source": "neurips.cc", "link": "https://neurips.cc/", "content": "2024 . 2023. 2021. NeurIPS announces a 2nd physical location in Mexico City and an affiliated meeting in Europe (EurIPS) for Dec 2025. (Press Releases). NeurIPS has released an LLM policy."}
+{"idx": 6, "title": "Intern Sued by Byte for 8 Million Due to Malicious Attack Wins Best...", "date": "", "ddg_snippet": "1.4k. Recently, Tian Keyu has once again become the focus of attention due to a malicious attack incident during his internship at ByteDance. This Peking University intern won the Best Paper Award at NeurIPS 2024 for a paper he participated in during his internship at ByteDance...", "subpage_snippet": "", "source": "www.aibase.com", "link": "https://www.aibase.com/news/13687", "content": "1.4k. Recently, Tian Keyu has once again become the focus of attention due to a malicious attack incident during his internship at ByteDance. This Peking University intern won the Best Paper Award at NeurIPS 2024 for a paper he participated in during his internship at ByteDance..."}
+{"idx": 7, "title": "Outlook", "date": "", "ddg_snippet": "Autumn 2023 | Spring 2024 Card 4 Face to Face Programmes Login.", "subpage_snippet": "", "source": "outlook.office365.com", "link": "https://outlook.office365.com/", "content": "Autumn 2023 | Spring 2024 Card 4 Face to Face Programmes Login."}
+{"idx": 8, "title": "Tampa Bay Lightning Schedule | Tampa Bay Lightning", "date": "", "ddg_snippet": "Navigation Menu. Schedule . Community. Team.Youth Hockey. NHL.com. Schedule .", "subpage_snippet": "", "source": "www.nhl.com", "link": "https://www.nhl.com/lightning/schedule", "content": "Navigation Menu. Schedule . Community. Team.Youth Hockey. NHL.com. Schedule ."}
+{"idx": 9, "title": "Book tickets online now and fly out into the world | Discover Airlines", "date": "", "ddg_snippet": "Discover Airlines flies from Frankfurt and Munich to the most attractive destinations on short-, medium- and long-haul routes.", "subpage_snippet": "", "source": "www.discover-airlines.com", "link": "https://www.discover-airlines.com/", "content": "Discover Airlines flies from Frankfurt and Munich to the most attractive destinations on short-, medium- and long-haul routes."}
diff --git a/data/sampled_jsons/AECM_learning_rate_EntityErasure_supplementary.jsonl b/data/sampled_jsons/AECM_learning_rate_EntityErasure_supplementary.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..e72f901f969fbb42b3274d2e5545d481ef6650d1
--- /dev/null
+++ b/data/sampled_jsons/AECM_learning_rate_EntityErasure_supplementary.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "machinelearningmastery.com/understand-the-dynamics-of- learning ...", "date": "", "ddg_snippet": "the Impact of Learning Rate on Neural Network.", "subpage_snippet": "", "source": "machinelearningmastery.com", "link": "https://machinelearningmastery.com/understand-the-dynamics-of-learning-rate-on-deep-learning-neural-networks/", "content": "the Impact of Learning Rate on Neural Network."}
+{"idx": 1, "title": "Guide to Pytorch Learning Rate Scheduling", "date": "", "ddg_snippet": "Explore and run machine learning code with Kaggle Notebooks | Using data from No attached data sources.", "subpage_snippet": "", "source": "www.kaggle.com", "link": "https://www.kaggle.com/code/isbhargav/guide-to-pytorch-learning-rate-scheduling", "content": "Explore and run machine learning code with Kaggle Notebooks | Using data from No attached data sources."}
+{"idx": 2, "title": "GSPO Reinforcement Learning | Unsloth Documentation", "date": "", "ddg_snippet": "Reinforcement Learning - DPO, ORPO & KTO. New. Vision Reinforcement Learning (VLM RL). Unsloth Dynamic GGUFs on Aider Polyglot. Memory Efficient RL.", "subpage_snippet": "", "source": "docs.unsloth.ai", "link": "https://docs.unsloth.ai/get-started/reinforcement-learning-rl-guide/gspo-reinforcement-learning", "content": "Reinforcement Learning - DPO, ORPO & KTO. New. Vision Reinforcement Learning (VLM RL). Unsloth Dynamic GGUFs on Aider Polyglot. Memory Efficient RL."}
+{"idx": 3, "title": "Meta- learning with negative learning rates | OpenReview", "date": "", "ddg_snippet": "Deep learning models require a large amount of data to perform well. When data is scarce for a target task, we can transfer the knowledge gained by training on similar tasks to quickly learn the target.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=60j5LygnmD", "content": "Deep learning models require a large amount of data to perform well. When data is scarce for a target task, we can transfer the knowledge gained by training on similar tasks to quickly learn the target."}
+{"idx": 4, "title": "Video: Progettazione e costruzione di proteine aECM a matrice...", "date": "", "ddg_snippet": "Design and Construction of Artificial Extracellular Matrix ( aECM ) Proteins from Escherichia coli for Skin Tissue Engineering.Then transfer 10 milliliters of the starter culture into one liter of fresh and sterile TB media. Supplemented with the same antibiotics in a three liter erlin Meyer flask.", "subpage_snippet": "", "source": "www.jove.com", "link": "https://www.jove.com/it/v/52845/design-construction-artificial-extracellular-matrix-aecm-proteins", "content": "Design and Construction of Artificial Extracellular Matrix ( aECM ) Proteins from Escherichia coli for Skin Tissue Engineering.Then transfer 10 milliliters of the starter culture into one liter of fresh and sterile TB media. Supplemented with the same antibiotics in a three liter erlin Meyer flask."}
+{"idx": 5, "title": "Model Agnostic Meta- Learning made simple | InstaDeep...", "date": "", "ddg_snippet": "In this post, we introduce our first Meta-RL algorithm: MAML (Model-Agnostic Meta- Learning for Fast Adaptation of Deep Networks). With MAML, you can train agents that quickly adapt in almost any dense-reward environment. Let’s detail how it works.", "subpage_snippet": "", "source": "instadeep.com", "link": "https://instadeep.com/research/blog/model-agnostic-meta-learning-made-simple/", "content": "In this post, we introduce our first Meta-RL algorithm: MAML (Model-Agnostic Meta- Learning for Fast Adaptation of Deep Networks). With MAML, you can train agents that quickly adapt in almost any dense-reward environment. Let’s detail how it works."}
+{"idx": 6, "title": "Что такое глубокое машинное обучение (Deep Learning )... - «Plaan»", "date": "", "ddg_snippet": "Внутри ИИ находится машинное обучение (Machine Learning , ML) — подраздел, который использует статистические алгоритмы, чтобы дать компьютерам возможность обучаться на данных без явного программирования.", "subpage_snippet": "", "source": "plaan.ai", "link": "https://plaan.ai/deep-learning/", "content": "Внутри ИИ находится машинное обучение (Machine Learning , ML) — подраздел, который использует статистические алгоритмы, чтобы дать компьютерам возможность обучаться на данных без явного программирования."}
+{"idx": 7, "title": "L1 и L2 регуляризация в машинном обучении: эффективные методы", "date": "", "ddg_snippet": "Совмещение L2 регуляризации с другими техниками: Dropout, Batch NormalizationГрадуальное уменьшение регуляризации в процессе обучения ( learning rate scheduling)", "subpage_snippet": "", "source": "sky.pro", "link": "https://sky.pro/wiki/analytics/l1-i-l2-regulyarizatsiya-v-mashinnom-obuchenii-effektivnye-metody/", "content": "Совмещение L2 регуляризации с другими техниками: Dropout, Batch NormalizationГрадуальное уменьшение регуляризации в процессе обучения ( learning rate scheduling)"}
+{"idx": 8, "title": "Как обучить нейросеть на своих данных: пошаговый разбор", "date": "", "ddg_snippet": "Настройки learning rate — скорости обучения. Правильного выбора размера батча — сколько примеров показывать за раз. Подбора функции потерь и оптимизатора.", "subpage_snippet": "", "source": "scrile.ru", "link": "https://scrile.ru/obuchenie-neiroseti/", "content": "Настройки learning rate — скорости обучения. Правильного выбора размера батча — сколько примеров показывать за раз. Подбора функции потерь и оптимизатора."}
+{"idx": 9, "title": "CatBoost / Хабр", "date": "", "ddg_snippet": "# Создание и обучение модели model = CatBoostClassifier(iterations=100, learning _ rate =0.1) model.fit(X_train, y_train, verbose=False).", "subpage_snippet": "", "source": "habr.com", "link": "https://habr.com/ru/companies/otus/articles/778714/", "content": "# Создание и обучение модели model = CatBoostClassifier(iterations=100, learning _ rate =0.1) model.fit(X_train, y_train, verbose=False)."}
diff --git a/data/sampled_jsons/AERO_Table_1_learning_rate_Enhancing_Sharding_Blockchain_Deep_Reinforcement_Learning_for_Account_Mig_year_2025.jsonl b/data/sampled_jsons/AERO_Table_1_learning_rate_Enhancing_Sharding_Blockchain_Deep_Reinforcement_Learning_for_Account_Mig_year_2025.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..14a2ef10c3a1a96dc7faf7ab1df9b17cc8a64bf2
--- /dev/null
+++ b/data/sampled_jsons/AERO_Table_1_learning_rate_Enhancing_Sharding_Blockchain_Deep_Reinforcement_Learning_for_Account_Mig_year_2025.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "AERO : Enhancing Sharding Blockchain via Deep Reinforcement ...", "date": "", "ddg_snippet": "Blockchain , Sharding , Account migration , Reinforcement learning . ACM Reference Format: Anonymous authors.Sender Shard Transaction Count. AERO : Enhancing Sharding Blockchain via Deep Reinforcement Learning for Account Migration .", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=WcuXvn3HVk", "content": "Blockchain , Sharding , Account migration , Reinforcement learning . ACM Reference Format: Anonymous authors.Sender Shard Transaction Count. AERO : Enhancing Sharding Blockchain via Deep Reinforcement Learning for Account Migration ."}
+{"idx": 1, "title": "AERO : Enhancing Sharding Blockchain via Deep Reinforcement ...", "date": "", "ddg_snippet": "Blockchain , Sharding , Account migration , Reinforcement learning . ∗Corresponding author.Sender Shard Transaction Count. AERO : Enhancing Sharding Blockchain via Deep Reinforcement Learning for Account Migration .", "subpage_snippet": "", "source": "zhenxiao.com", "link": "http://zhenxiao.com/papers/WWW_AERO_camera_ready.pdf", "content": "Blockchain , Sharding , Account migration , Reinforcement learning . ∗Corresponding author.Sender Shard Transaction Count. AERO : Enhancing Sharding Blockchain via Deep Reinforcement Learning for Account Migration ."}
+{"idx": 2, "title": "Peking University - Cited by 70 - Deep Learning - Google Scholar", "date": "", "ddg_snippet": "AERO : Enhancing sharding blockchain via deep reinforcement learning for account migration .", "subpage_snippet": "", "source": "scholar.google.com", "link": "https://scholar.google.com/citations?user=BdT5CzcAAAAJ&hl=en", "content": "AERO : Enhancing sharding blockchain via deep reinforcement learning for account migration ."}
+{"idx": 3, "title": "(PDF) Sharding for Blockchain based Mobile Edge Computing...", "date": "", "ddg_snippet": "AERO : Enhancing Sharding Blockchain via Deep Reinforcement Learning for Account Migration . Blockchain -Based Transformer-Assisted Multi-Agent Reinforcement Learning for Resource Allocation and Computation Offloading in 5G Private Networks.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/363131667_Sharding_for_Blockchain_based_Mobile_Edge_Computing_System_A_Deep_Reinforcement_Learning_Approach", "content": "AERO : Enhancing Sharding Blockchain via Deep Reinforcement Learning for Account Migration . Blockchain -Based Transformer-Assisted Multi-Agent Reinforcement Learning for Resource Allocation and Computation Offloading in 5G Private Networks."}
+{"idx": 4, "title": "MIT 6.S091: Introduction to Deep Reinforcement Learning ... - YouTube", "date": "", "ddg_snippet": "First lecture of MIT course 6.S091: Deep Reinforcement Learning , introducing the fascinating field of Deep RL.", "subpage_snippet": "", "source": "www.youtube.com", "link": "https://www.youtube.com/watch?v=zR11FLZ-O9M", "content": "First lecture of MIT course 6.S091: Deep Reinforcement Learning , introducing the fascinating field of Deep RL."}
+{"idx": 5, "title": "SmartChain: A Dynamic and Self-Adaptive Sharding Framework for...", "date": "", "ddg_snippet": "Learning Algorithm, Malicious Nodes, Deep Reinforcement Learning Agent, Byzantine Fault Tolerance, Successful Defense, Replay Memory, Blockchain Nodes, Internet Of Things Nodes, Message Authentication Code, Time Varying Environment, Shannons Information Theory...", "subpage_snippet": "", "source": "www.computer.org", "link": "https://www.computer.org/csdl/journal/sc/2024/02/10468552/1VpXOUcfXEs", "content": "Learning Algorithm, Malicious Nodes, Deep Reinforcement Learning Agent, Byzantine Fault Tolerance, Successful Defense, Replay Memory, Blockchain Nodes, Internet Of Things Nodes, Message Authentication Code, Time Varying Environment, Shannons Information Theory..."}
+{"idx": 6, "title": "Scalability of blockchain : Review of cross- sharding with high...", "date": "", "ddg_snippet": "During migration , account migration is required to guard against hostile nodes assaulting the system and execution errors. deep more scalable. and blockchain security and atomicity. learning with the proposed reinforcement - Blockchain transpare of cross- shard transactions.", "subpage_snippet": "", "source": "www.bio-conferences.org", "link": "https://www.bio-conferences.org/articles/bioconf/pdf/2024/16/bioconf_iscku2024_00075.pdf", "content": "During migration , account migration is required to guard against hostile nodes assaulting the system and execution errors. deep more scalable. and blockchain security and atomicity. learning with the proposed reinforcement - Blockchain transpare of cross- shard transactions."}
+{"idx": 7, "title": "LB-Chain: Load-Balanced and Low-Latency Blockchain Sharding via...", "date": "", "ddg_snippet": "Index Terms— Blockchain , blockchain sharding , load balance, account migration . First , how to propose an efcient and secure migration scheme in blockchain sharding ? To balance the load on different shards , account migration is required.", "subpage_snippet": "", "source": "cse.hkust.edu.hk", "link": "https://cse.hkust.edu.hk/~weiwa/papers/lb-chain-tpds22.pdf", "content": "Index Terms— Blockchain , blockchain sharding , load balance, account migration . First , how to propose an efcient and secure migration scheme in blockchain sharding ? To balance the load on different shards , account migration is required."}
+{"idx": 8, "title": "BlockEmulator: An Emulator Enabling to Test Blockchain Sharding ...", "date": "", "ddg_snippet": "In state sharding that adopts the account /balance-based transaction model, the blockchain system can be described as a structure consisting of a final committee and several worker shards . The final committee is also called the main shard .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2311.03612v3/", "content": "In state sharding that adopts the account /balance-based transaction model, the blockchain system can be described as a structure consisting of a final committee and several worker shards . The final committee is also called the main shard ."}
+{"idx": 9, "title": "Analysing and Improving Shard Allocation Protocols for Sharded...", "date": "", "ddg_snippet": "2021. Shard Scheduler: object placement and migration in sharded account -based blockchains . In Proceedings of the 3rd ACM Conference on Advances in Financial Technologies.", "subpage_snippet": "", "source": "eprint.iacr.org", "link": "https://eprint.iacr.org/2020/943.pdf", "content": "2021. Shard Scheduler: object placement and migration in sharded account -based blockchains . In Proceedings of the 3rd ACM Conference on Advances in Financial Technologies."}
diff --git a/data/sampled_jsons/A_Checks-and-Balances_Framework_for_Context-Aware_Ethical_AI_Alignment_dataset_empirical_study_year_2024.jsonl b/data/sampled_jsons/A_Checks-and-Balances_Framework_for_Context-Aware_Ethical_AI_Alignment_dataset_empirical_study_year_2024.jsonl
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index 0000000000000000000000000000000000000000..a51d87cbefe49268371fa527a93a16f0174caa62
--- /dev/null
+++ b/data/sampled_jsons/A_Checks-and-Balances_Framework_for_Context-Aware_Ethical_AI_Alignment_dataset_empirical_study_year_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Justified Evidence Collection for Argument-based AI Fairness", "date": "", "ddg_snippet": "The increasing investment and demand for AI -enabled systems raises critical concerns about fairness, accountability, and transparency in the design ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2505.08064v1", "content": "The increasing investment and demand for AI -enabled systems raises critical concerns about fairness, accountability, and transparency in the design ..."}
+{"idx": 1, "title": "A Unified Framework for Evaluating the Effectiveness and", "date": "", "ddg_snippet": "... organized as follows: Section 2 provides an overview of earlier studies in this area, establishing the foundational work and context for our research.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2412.03884v2", "content": "... organized as follows: Section 2 provides an overview of earlier studies in this area, establishing the foundational work and context for our research."}
+{"idx": 2, "title": "Understanding Ethical Practices in AI: Insights from a", "date": "", "ddg_snippet": "Recent advances in AI applications have raised growing concerns about the need for ethical guidelines and regulations to mitigate the risks posed by ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2508.09219v1", "content": "Recent advances in AI applications have raised growing concerns about the need for ethical guidelines and regulations to mitigate the risks posed by ..."}
+{"idx": 3, "title": "Multi-level Value Alignment in Agentic AI Systems: Survey and", "date": "", "ddg_snippet": "This has brought significant attention to value alignment for agentic AI systems, which aims to ensure that an agent’s goals, preferences, and ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2506.09656v2", "content": "This has brought significant attention to value alignment for agentic AI systems, which aims to ensure that an agent’s goals, preferences, and ..."}
+{"idx": 4, "title": "Navigating the ethical landscape of AI... | F1000Research", "date": "", "ddg_snippet": "... an analysis of existing frameworks and current AI implementations in education, the paper calls for clear ethical guidelines to ensure the responsible ...", "subpage_snippet": "", "source": "f1000research.com", "link": "https://f1000research.com/articles/14-299", "content": "... an analysis of existing frameworks and current AI implementations in education, the paper calls for clear ethical guidelines to ensure the responsible ..."}
+{"idx": 5, "title": "AI Ethics Knowledge Among Chinese Teachers: An Empirical", "date": "", "ddg_snippet": "... a high priority on utilizing AI for educational reform, but the extent of teachers’ awareness of AI ethics , or the degree to which AI ethics are ...", "subpage_snippet": "", "source": "www.researchsquare.com", "link": "https://www.researchsquare.com/article/rs-7042877/v1", "content": "... a high priority on utilizing AI for educational reform, but the extent of teachers’ awareness of AI ethics , or the degree to which AI ethics are ..."}
+{"idx": 6, "title": "FAIR Enough: Develop and Assess a FAIR-Compliant Dataset for", "date": "", "ddg_snippet": "He holds an expert positions at the UN Economic Commission of Europe-Task Force for Digitalization in Energy, and is Technical Steering Committee ...", "subpage_snippet": "", "source": "direct.mit.edu", "link": "https://direct.mit.edu/dint/article/6/2/559/123375/FAIR-Enough-Develop-and-Assess-a-FAIR-Compliant", "content": "He holds an expert positions at the UN Economic Commission of Europe-Task Force for Digitalization in Energy, and is Technical Steering Committee ..."}
+{"idx": 7, "title": "(PDF) A Unified Framework for Human AI Collaboration in", "date": "", "ddg_snippet": "... article presents a structured framework for Human- AI collaboration in Security Operations Centers (SOCs), integrating AI autonomy, trust calibration, ...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/392204042_A_Unified_Framework_for_Human_AI_Collaboration_in_Security_Operations_Centers_with_Trusted_Autonomy", "content": "... article presents a structured framework for Human- AI collaboration in Security Operations Centers (SOCs), integrating AI autonomy, trust calibration, ..."}
+{"idx": 8, "title": "Responsible AI in NLP: GUS-Net Span-Level Bias Detection", "date": "", "ddg_snippet": "We introduce the GUS-Net Framework , comprising the GUS dataset and a multi-label token-level detector for span-level analysis of social bias.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2410.08388v5", "content": "We introduce the GUS-Net Framework , comprising the GUS dataset and a multi-label token-level detector for span-level analysis of social bias."}
+{"idx": 9, "title": "Perspectives on Managing AI Ethics in the Digital Age", "date": "", "ddg_snippet": "Feature papers are submitted upon individual invitation or recommendation by the scientific editors and must receive positive feedback from the ...", "subpage_snippet": "", "source": "www.mdpi.com", "link": "https://www.mdpi.com/2078-2489/16/4/318", "content": "Feature papers are submitted upon individual invitation or recommendation by the scientific editors and must receive positive feedback from the ..."}
diff --git a/data/sampled_jsons/A_Geometric_Approach_to_Personalized_Recommendation_with_Set-Theoretic_Constraints_Using_Box_Embeddi.jsonl b/data/sampled_jsons/A_Geometric_Approach_to_Personalized_Recommendation_with_Set-Theoretic_Constraints_Using_Box_Embeddi.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..44bce5b689885d6b412f4747a1a60689b551db9b
--- /dev/null
+++ b/data/sampled_jsons/A_Geometric_Approach_to_Personalized_Recommendation_with_Set-Theoretic_Constraints_Using_Box_Embeddi.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "A Geometric Approach to Personalized Recommendation with Set-Theoretic ...", "date": "", "ddg_snippet": "Queries involving set-theoretic constraints can be efficiently computed directly on the embedding space by performing geometric operations on the representations. We empirically demonstrate the superiority of box embeddings over vector-based neural methods on both simple and complex item recommendation queries by up to 30 \\% overall.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2502.10875", "content": "Queries involving set-theoretic constraints can be efficiently computed directly on the embedding space by performing geometric operations on the representations. We empirically demonstrate the superiority of box embeddings over vector-based neural methods on both simple and complex item recommendation queries by up to 30 \\% overall."}
+{"idx": 1, "title": "dblp: A Geometric Approach to Personalized Recommendation with Set ...", "date": "", "ddg_snippet": "Bibliographic details on A Geometric Approach to Personalized Recommendation with Set-Theoretic Constraints Using Box Embeddings .", "subpage_snippet": "", "source": "dblp.org", "link": "https://dblp.org/rec/journals/corr/abs-2502-10875", "content": "Bibliographic details on A Geometric Approach to Personalized Recommendation with Set-Theoretic Constraints Using Box Embeddings ."}
+{"idx": 2, "title": "A Geometric Approach to Personalized Recommendation with Set-Theoretic ...", "date": "", "ddg_snippet": "This research paper talks about improving how we recommend movies and other items to people based on their preferences, using a new approach called \" box embeddings .\" Traditional methods struggled ...", "subpage_snippet": "", "source": "bytez.com", "link": "https://bytez.com/docs/icml/46603/paper", "content": "This research paper talks about improving how we recommend movies and other items to people based on their preferences, using a new approach called \" box embeddings .\" Traditional methods struggled ..."}
+{"idx": 3, "title": "A Geometric Approach to Personalized Recommendation With Set T C U B ...", "date": "", "ddg_snippet": "t also naturally and faithfully support arbi-trary set-theoretic relationships. Queries involving set-theoretic constraints can be eficiently computed directly o the embedding space by performing geometric operations on the representations. We empirically demonstrate the superiority of box embeddings over vector-based neural meth 030 031", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=0HWAbWgI3T", "content": "t also naturally and faithfully support arbi-trary set-theoretic relationships. Queries involving set-theoretic constraints can be eficiently computed directly o the embedding space by performing geometric operations on the representations. We empirically demonstrate the superiority of box embeddings over vector-based neural meth 030 031"}
+{"idx": 4, "title": "PhD Thesis Defense: Shib Dasgupta, Box Embeddings as Set-theoretic ...", "date": "", "ddg_snippet": "To address this, I frame personalized item search as matrix completion with set-theoretic constraints , where both users and attributes are represented as hyper-rectangles (boxes). This enables the system to reason over complex, structured preferences in a principled and interpretable way.", "subpage_snippet": "", "source": "www.cics.umass.edu", "link": "https://www.cics.umass.edu/events/phd-thesis-defense-shib-dasgupta", "content": "To address this, I frame personalized item search as matrix completion with set-theoretic constraints , where both users and attributes are represented as hyper-rectangles (boxes). This enables the system to reason over complex, structured preferences in a principled and interpretable way."}
+{"idx": 5, "title": "Geometric Approach to Personalized Recommendation with Set-Theoretic ...", "date": "", "ddg_snippet": "Queries involving set-theoretic con-straints can be eficiently computed directly on the embedding space by performing geometric operations on the representations. We empirically demonstrate the superiority of box embeddings over vector-based neural methods on both simple and complex item recommendation queries by up to 30% overall.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2502.10875", "content": "Queries involving set-theoretic con-straints can be eficiently computed directly on the embedding space by performing geometric operations on the representations. We empirically demonstrate the superiority of box embeddings over vector-based neural methods on both simple and complex item recommendation queries by up to 30% overall."}
+{"idx": 6, "title": "Foundation Model for Personalized Recommendation | by Netflix ...", "date": "", "ddg_snippet": "For example, new title embeddings can be initialized by adding slight random noise to existing average embeddings or by using a weighted combination of similar titles' embeddings based on metadata. This approach allows new titles to start with relevant embeddings , facilitating faster fine-tuning.", "subpage_snippet": "", "source": "netflixtechblog.com", "link": "https://netflixtechblog.com/foundation-model-for-personalized-recommendation-1a0bd8e02d39", "content": "For example, new title embeddings can be initialized by adding slight random noise to existing average embeddings or by using a weighted combination of similar titles' embeddings based on metadata. This approach allows new titles to start with relevant embeddings , facilitating faster fine-tuning."}
+{"idx": 7, "title": "A Geometric Approach to Personalized Recommendation with Set-Theoretic ...", "date": "", "ddg_snippet": "Queries involving set-theoretic constraints can be efficiently computed directly on the embedding space by performing geometric operations on the representations. We empirically demonstrate the superiority of box embeddings over vector-based neural methods on both simple and complex item recommendation queries by up to 30% overall.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=0HWAbWgI3T", "content": "Queries involving set-theoretic constraints can be efficiently computed directly on the embedding space by performing geometric operations on the representations. We empirically demonstrate the superiority of box embeddings over vector-based neural methods on both simple and complex item recommendation queries by up to 30% overall."}
+{"idx": 8, "title": "GitHub Pages - Shib Dasgupta", "date": "", "ddg_snippet": "My work on Personalized Recommendation with Set-Theoretic Constraints explored attribute-aware recommendations , enhancing performance when user preferences follow set -based patterns. I also integrated box embeddings with transformer-based dual encoders, improving search and personalized recommendation performance (results coming soon).", "subpage_snippet": "", "source": "ssdasgupta.github.io", "link": "https://ssdasgupta.github.io/", "content": "My work on Personalized Recommendation with Set-Theoretic Constraints explored attribute-aware recommendations , enhancing performance when user preferences follow set -based patterns. I also integrated box embeddings with transformer-based dual encoders, improving search and personalized recommendation performance (results coming soon)."}
+{"idx": 9, "title": "The effect is less for the non-compounding error. - ResearchGate", "date": "", "ddg_snippet": "A Geometric Approach to Personalized Recommendation with Set-Theoretic Constraints Using Box Embeddings Preprint Full-text available Feb 2025 Shib Dasgupta Michael Boratko Andrew McCallum", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/figure/The-effect-is-less-for-the-non-compounding-error_fig4_389091382", "content": "A Geometric Approach to Personalized Recommendation with Set-Theoretic Constraints Using Box Embeddings Preprint Full-text available Feb 2025 Shib Dasgupta Michael Boratko Andrew McCallum"}
diff --git a/data/sampled_jsons/A_Likelihood_Based_Approach_to_Distribution_Regression_Figure_2_alpha_noise_variance.jsonl b/data/sampled_jsons/A_Likelihood_Based_Approach_to_Distribution_Regression_Figure_2_alpha_noise_variance.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..433464d6bd00e7c77005bc87c3488670817f3f8a
--- /dev/null
+++ b/data/sampled_jsons/A_Likelihood_Based_Approach_to_Distribution_Regression_Figure_2_alpha_noise_variance.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "A Likelihood Based Approach to Distribution Regression ...", "date": "", "ddg_snippet": "2 Oct 2024 — When α 𝛼 \\ alpha italic_α is large, the noise variance is very small, and the perturbed data facilitates efficient estimation. Report issue for ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2410.02025v1", "content": "2 Oct 2024 — When α 𝛼 \\ alpha italic_α is large, the noise variance is very small, and the perturbed data facilitates efficient estimation. Report issue for ..."}
+{"idx": 1, "title": "A Likelihood Based Approach to Distribution Regression Using ...", "date": "", "ddg_snippet": "ity of the true data distribution . • When α is large, the noise variance is small, and the perturbed data facilitates efficient estimation. This observed ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=1IyPRv1A0r", "content": "ity of the true data distribution . • When α is large, the noise variance is small, and the perturbed data facilitates efficient estimation. This observed ..."}
+{"idx": 2, "title": "A Likelihood Based Approach to Distribution Regression ...", "date": "", "ddg_snippet": "The noise sequence follows a two -dimensional centered Gaussian distribution , ε ∼ N(02, σ2∗I2) . We investigated this setup across various noise variances ...", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/poster/46645", "content": "The noise sequence follows a two -dimensional centered Gaussian distribution , ε ∼ N(02, σ2∗I2) . We investigated this setup across various noise variances ..."}
+{"idx": 3, "title": "A likelihood based approach to distribution regression ...", "date": "", "ddg_snippet": "by S Kumar · 2024 · Cited by 1 — A Likelihood Based Approach to Distribution Regression Using ... variance of noise by the factor α . When α is very small, indicating that ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2410.02025", "content": "by S Kumar · 2024 · Cited by 1 — A Likelihood Based Approach to Distribution Regression Using ... variance of noise by the factor α . When α is very small, indicating that ..."}
+{"idx": 4, "title": "Likelihood-ratio-based confidence intervals for neural ...", "date": "", "ddg_snippet": "by L Sluijterman · 2025 · Cited by 1 — This paper introduces a first implementation of a novel likelihood -ratio- based approach for constructing confidence intervals for neural networks.", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/article/10.1007/s10994-024-06639-3", "content": "by L Sluijterman · 2025 · Cited by 1 — This paper introduces a first implementation of a novel likelihood -ratio- based approach for constructing confidence intervals for neural networks."}
+{"idx": 5, "title": "A Likelihood Approach to Nonparametric Estimation of a ...", "date": "", "ddg_snippet": "by M Chae · 2023 · Cited by 33 — We investigate statistical properties of a likelihood approach to nonparametric estimation of a singular distribution using deep generative models. 42 pages", "subpage_snippet": "", "source": "www.jmlr.org", "link": "https://www.jmlr.org/papers/volume24/21-1099/21-1099.pdf", "content": "by M Chae · 2023 · Cited by 33 — We investigate statistical properties of a likelihood approach to nonparametric estimation of a singular distribution using deep generative models. 42 pages"}
+{"idx": 6, "title": "Statistical Methods: Likelihood, Bayes and Regression", "date": "", "ddg_snippet": "by K Strimmer · 2023 · Cited by 3 — In the beta-binomial model the likelihood is based on the binomial distribution ... The maximum likelihood estimate of the noise variance ... 107 pages", "subpage_snippet": "", "source": "strimmerlab.github.io", "link": "https://strimmerlab.github.io/publications/lecture-notes/MATH20802/math20802-script-a4.pdf", "content": "by K Strimmer · 2023 · Cited by 3 — In the beta-binomial model the likelihood is based on the binomial distribution ... The maximum likelihood estimate of the noise variance ... 107 pages"}
+{"idx": 7, "title": "A generalized likelihood-based Bayesian approach for ...", "date": "", "ddg_snippet": "by S Samanta · 2022 · Cited by 8 — The paper addresses joint sparsity selection in the regression coefficient matrix and the error precision (inverse covariance) matrix for high-dimensional ...", "subpage_snippet": "", "source": "pmc.ncbi.nlm.nih.gov", "link": "https://pmc.ncbi.nlm.nih.gov/articles/PMC9881595/", "content": "by S Samanta · 2022 · Cited by 8 — The paper addresses joint sparsity selection in the regression coefficient matrix and the error precision (inverse covariance) matrix for high-dimensional ..."}
+{"idx": 8, "title": "TIVE MODELS", "date": "", "ddg_snippet": "To the best of our knowledge, our study is the first attempt to explore the likelihood - based ap- proach for distributional regression using a conditional deep ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/notes/edits/attachment?id=bKSeeDBAan&name=pdf", "content": "To the best of our knowledge, our study is the first attempt to explore the likelihood - based ap- proach for distributional regression using a conditional deep ..."}
+{"idx": 9, "title": "Beyond Least Squares: Estimation of Dynamic Models With ...", "date": "", "ddg_snippet": "by T Li · 2025 — Using the classical SEIR model, we compare standard least squares against maximum likelihood estimators including variance -scaled Gaussian, log ...", "subpage_snippet": "", "source": "onlinelibrary.wiley.com", "link": "https://onlinelibrary.wiley.com/doi/full/10.1002/sdr.70004", "content": "by T Li · 2025 — Using the classical SEIR model, we compare standard least squares against maximum likelihood estimators including variance -scaled Gaussian, log ..."}
diff --git a/data/sampled_jsons/A_Likelihood_Based_Approach_to_Distribution_Regression_Using_Conditional_Deep_Generative_Models_FD2_.jsonl b/data/sampled_jsons/A_Likelihood_Based_Approach_to_Distribution_Regression_Using_Conditional_Deep_Generative_Models_FD2_.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..d812c3d436574ebffa5eb21098175681bb36277b
--- /dev/null
+++ b/data/sampled_jsons/A_Likelihood_Based_Approach_to_Distribution_Regression_Using_Conditional_Deep_Generative_Models_FD2_.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "A Likelihood Based Approach to Distribution Regression Using ...", "date": "", "ddg_snippet": "A deep generative approach to conditional sampling. Journal of the American Statistical Association, pages 1–12. Supplementary Materials for “ A Likelihood Based Approach to Distribution Regression Using Conditional Deep Generative Models ”.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2410.02025v1", "content": "A deep generative approach to conditional sampling. Journal of the American Statistical Association, pages 1–12. Supplementary Materials for “ A Likelihood Based Approach to Distribution Regression Using Conditional Deep Generative Models ”."}
+{"idx": 1, "title": "(PDF) A Likelihood Based Approach to Distribution Regression ...", "date": "", "ddg_snippet": "Keywords: Distribution Regression ; Conditional Deep Generative Models ; Intrinsic Manifold StructureThe second approach , based on adversarial learning, matches the empirical distribution of the data with a. distribution estimator using an adversarial loss.", "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": "Keywords: Distribution Regression ; Conditional Deep Generative Models ; Intrinsic Manifold StructureThe second approach , based on adversarial learning, matches the empirical distribution of the data with a. distribution estimator using an adversarial loss."}
+{"idx": 2, "title": "ICML Poster A Likelihood Based Approach to Distribution ...", "date": "", "ddg_snippet": "models ), can estimate those high-dimensional conditional distributions as if the data were low-dimensional. The trick is a likelihood - based fit called a sieve maximum-likelihood estimator that grows in complexity with sample size.", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/poster/46645", "content": "models ), can estimate those high-dimensional conditional distributions as if the data were low-dimensional. The trick is a likelihood - based fit called a sieve maximum-likelihood estimator that grows in complexity with sample size."}
+{"idx": 3, "title": "A Likelihood Based Approach to Distribution Regression Using ...", "date": "", "ddg_snippet": "These findings provide an explanation of why conditional deep generative models can circumvent the curse of dimensionality from the perspective of statistical foundations and demonstrate that they can learn a broader class of nearly singular conditional distributions.", "subpage_snippet": "", "source": "synthical.com", "link": "https://synthical.com/article/A-Likelihood-Based-Approach-to-Distribution-Regression-Using-Conditional-Deep-Generative-Models-0a08e8b9-48c6-483c-8ab4-4ef338d3619c", "content": "These findings provide an explanation of why conditional deep generative models can circumvent the curse of dimensionality from the perspective of statistical foundations and demonstrate that they can learn a broader class of nearly singular conditional distributions."}
+{"idx": 4, "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 . The method's convergence rates and approximability are analyzed, providing insights into its effectiveness.", "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 . The method's convergence rates and approximability are analyzed, providing insights into its effectiveness."}
+{"idx": 5, "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": 6, "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": 7, "title": "[PDF] Discussion of: “Nonparametric regression using deep neural...”", "date": "", "ddg_snippet": "A Likelihood Based Approach to Distribution Regression Using Conditional Deep Generative Models .", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/Discussion-of:-“Nonparametric-regression-using-deep-Ghorbani-Mei/688b87f8e83080a024eb0fd7e55288de313aff8b", "content": "A Likelihood Based Approach to Distribution Regression Using Conditional Deep Generative Models ."}
+{"idx": 8, "title": "Papers by Lizhen Lin with links to code and results.", "date": "", "ddg_snippet": "A Likelihood Based Approach to Distribution Regression Using Conditional Deep Generative Models .We introduce, for the first time, a metric geometry approach to studying edge weight prediction in WDNs.", "subpage_snippet": "", "source": "paperswithcode.com", "link": "https://paperswithcode.com/search?q=author:Lizhen+Lin", "content": "A Likelihood Based Approach to Distribution Regression Using Conditional Deep Generative Models .We introduce, for the first time, a metric geometry approach to studying edge weight prediction in WDNs."}
+{"idx": 9, "title": "openreview.net/profile?id=~Lizhen_Lin2", "date": "", "ddg_snippet": "A Likelihood Based Approach to Distribution Regression Using Conditional Deep Generative Models .A Semi-Bayesian Nonparametric Estimator of the Maximum Mean Discrepancy Measure: Applications in Goodness-of-Fit Testing and Generative Adversarial Networks.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/profile?id=~Lizhen_Lin2", "content": "A Likelihood Based Approach to Distribution Regression Using Conditional Deep Generative Models .A Semi-Bayesian Nonparametric Estimator of the Maximum Mean Discrepancy Measure: Applications in Goodness-of-Fit Testing and Generative Adversarial Networks."}
diff --git a/data/sampled_jsons/A_Likelihood_Based_Approach_to_Distribution_Regression_sieve_MLE_FD3_MSE(SD)_table_year_2024.jsonl b/data/sampled_jsons/A_Likelihood_Based_Approach_to_Distribution_Regression_sieve_MLE_FD3_MSE(SD)_table_year_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..931e2a5b92a3691a1327b996e8551cc84fa1f11d
--- /dev/null
+++ b/data/sampled_jsons/A_Likelihood_Based_Approach_to_Distribution_Regression_sieve_MLE_FD3_MSE(SD)_table_year_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Maximum likelihood estimation - Wikipedia", "date": "", "ddg_snippet": "In statistics, maximum likelihood estimation ( MLE ) is a method of estimating the parameters of an assumed probability distribution , given some observed data. This is achieved by maximizing a likelihood function so that, under the assumed statistical model, the observed data is most probable.", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/Maximum_likelihood_estimation", "content": "In statistics, maximum likelihood estimation ( MLE ) is a method of estimating the parameters of an assumed probability distribution , given some observed data. This is achieved by maximizing a likelihood function so that, under the assumed statistical model, the observed data is most probable."}
+{"idx": 1, "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": 2, "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": 3, "title": "A Likelihood Based Approach to Distribution Regression Using ...", "date": "", "ddg_snippet": "How-ever, for the FD3 dataset, we found that as the training sam-ple size increases further, the MSE(SD) of the sieve MLE achieves performance increasingly comparable to CKDE.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=1IyPRv1A0r", "content": "How-ever, for the FD3 dataset, we found that as the training sam-ple size increases further, the MSE(SD) of the sieve MLE achieves performance increasingly comparable to CKDE."}
+{"idx": 4, "title": "A Likelihood Based Approach to Distribution Regression Using ...", "date": "", "ddg_snippet": "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 ...", "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": "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 ..."}
+{"idx": 5, "title": "PDF 20_mle_annotated - Stanford University", "date": "", "ddg_snippet": "Maximum Likelihood Estimator Defining the likelihood of data: Bernoulli of iid random variables", "subpage_snippet": "", "source": "web.stanford.edu", "link": "https://web.stanford.edu/class/archive/cs/cs109/cs109.1234/lectures/20_mle_annotated.pdf", "content": "Maximum Likelihood Estimator Defining the likelihood of data: Bernoulli of iid random variables"}
+{"idx": 6, "title": "PDF 10-315 Notes Maximum Likelihood Estimation", "date": "", "ddg_snippet": "1.3 Gaussian The Gaussian, or normal, distribution is a continuous distribution with two parameters: one for the mean, μ, and one for the variance, σ2 (or equivalently, the standard deviation, σ). The Gaussian distribution is our go- to distribution for representing continuous random variables.", "subpage_snippet": "", "source": "www.cs.cmu.edu", "link": "https://www.cs.cmu.edu/~10315/notes/10315_S24_Notes_MLE.pdf", "content": "1.3 Gaussian The Gaussian, or normal, distribution is a continuous distribution with two parameters: one for the mean, μ, and one for the variance, σ2 (or equivalently, the standard deviation, σ). The Gaussian distribution is our go- to distribution for representing continuous random variables."}
+{"idx": 7, "title": "PDF 20_mle_annotated - Stanford University", "date": "", "ddg_snippet": "Maximum Likelihood Estimator Defining the likelihood of data: Bernoulli random variables", "subpage_snippet": "", "source": "web.stanford.edu", "link": "https://web.stanford.edu/class/archive/cs/cs109/cs109.1244/lectures/20_mle_annotated.pdf", "content": "Maximum Likelihood Estimator Defining the likelihood of data: Bernoulli random variables"}
+{"idx": 8, "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 ..."}
+{"idx": 9, "title": "fit — SciPy v1.16.2 Manual", "date": "", "ddg_snippet": "Guesses for parameters which must be integral will be rounded to integral values, and guesses that lie outside the intersection of the user-provided bounds and the domain of the distribution will be clipped. method{'mle', 'mse'} With method=\"mle\" (default), the fit is computed by minimizing the negative log- likelihood function.", "subpage_snippet": "", "source": "docs.scipy.org", "link": "https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.fit.html", "content": "Guesses for parameters which must be integral will be rounded to integral values, and guesses that lie outside the intersection of the user-provided bounds and the domain of the distribution will be clipped. method{'mle', 'mse'} With method=\"mle\" (default), the fit is computed by minimizing the negative log- likelihood function."}
diff --git a/data/sampled_jsons/Ad-Hoc_Human-AI_Coordination_Challenge_dataset_3079_games.jsonl b/data/sampled_jsons/Ad-Hoc_Human-AI_Coordination_Challenge_dataset_3079_games.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..04ca5d5980c4f7249e918f1f6474ab60855b7315
--- /dev/null
+++ b/data/sampled_jsons/Ad-Hoc_Human-AI_Coordination_Challenge_dataset_3079_games.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "[2506.21490] Ad-Hoc Human-AI Coordination Challenge", "date": "", "ddg_snippet": "by T Dizdarević · 2025 — To encourage the development of data-efficient methods, we open-source a dataset of 3,079 games , deliberately limiting the amount of available ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2506.21490", "content": "by T Dizdarević · 2025 — To encourage the development of data-efficient methods, we open-source a dataset of 3,079 games , deliberately limiting the amount of available ..."}
+{"idx": 1, "title": "Ad-Hoc Human-AI Coordination Challenge", "date": "", "ddg_snippet": "by T Dizdarević — To encourage the development of data-efficient methods, we open-source a dataset of 3,079 games , deliberately limiting the amount of available ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=FuGps5Zyia", "content": "by T Dizdarević — To encourage the development of data-efficient methods, we open-source a dataset of 3,079 games , deliberately limiting the amount of available ..."}
+{"idx": 2, "title": "Ad-Hoc Human-AI Coordination Challenge", "date": "", "ddg_snippet": "To encourage the development of data-efficient methods, we open-source a dataset of 3,079 games , deliberately limiting the amount of available human gameplay ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2506.21490v1", "content": "To encourage the development of data-efficient methods, we open-source a dataset of 3,079 games , deliberately limiting the amount of available human gameplay ..."}
+{"idx": 3, "title": "Ad-Hoc Human-AI Coordination Challenge", "date": "", "ddg_snippet": "AH2AC2, a human-proxy challenge in 3,079 Hanabi games , assesses efficient human - AI coordination . Aimed at cost reduction, it offers a benchmark for fair, ...", "subpage_snippet": "", "source": "powerdrill.ai", "link": "https://powerdrill.ai/discover/summary-ad-hoc-human-ai-coordination-challenge-cmcfaq3ifm18907nqbp2j6wty", "content": "AH2AC2, a human-proxy challenge in 3,079 Hanabi games , assesses efficient human - AI coordination . Aimed at cost reduction, it offers a benchmark for fair, ..."}
+{"idx": 4, "title": "ICML Poster Ad-Hoc Human-AI Coordination Challenge", "date": "", "ddg_snippet": "... dataset of 3,079 games , deliberately limiting the amount of available human gameplay data. We present baseline results for both two- and three- player ...", "subpage_snippet": "", "source": "dev.icml.cc", "link": "https://dev.icml.cc/virtual/2025/poster/45867", "content": "... dataset of 3,079 games , deliberately limiting the amount of available human gameplay data. We present baseline results for both two- and three- player ..."}
+{"idx": 5, "title": "Ad-Hoc Human-AI Coordination Challenge", "date": "", "ddg_snippet": "26 Jun 2025 — ... dataset of 3,079 games , deliberately limiting the amount of available human gameplay data. We present baseline results for both two- and ...", "subpage_snippet": "", "source": "k8s.consensus.app", "link": "https://k8s.consensus.app/papers/details/785e955ec83554cb85641742cf052b41/", "content": "26 Jun 2025 — ... dataset of 3,079 games , deliberately limiting the amount of available human gameplay data. We present baseline results for both two- and ..."}
+{"idx": 6, "title": "Ad-Hoc Human-AI Coordination Challenge", "date": "", "ddg_snippet": "... dataset of 3,079 games , deliberately limiting the amount of available human gameplay data. We present baseline results for both two- and three- player ...", "subpage_snippet": "", "source": "chatpaper.com", "link": "https://chatpaper.com/de/chatpaper/paper/165106", "content": "... dataset of 3,079 games , deliberately limiting the amount of available human gameplay data. We present baseline results for both two- and three- player ..."}
+{"idx": 7, "title": "Ad-Hoc Human-AI Coordination Challenge", "date": "", "ddg_snippet": "18 Jun 2025 — To encourage the development of data-efficient methods, we open-source a dataset of 3,079 games , deliberately limiting the amount of available ...", "subpage_snippet": "", "source": "chatpaper.com", "link": "https://chatpaper.com/fr/chatpaper/paper/165106", "content": "18 Jun 2025 — To encourage the development of data-efficient methods, we open-source a dataset of 3,079 games , deliberately limiting the amount of available ..."}
+{"idx": 8, "title": "Human-AI Coordination via Human-Regularized Search ...", "date": "", "ddg_snippet": "11 Oct 2022 — This work develops a three-step algorithm that achieve strong performance in coordinating with real humans in the Hanabi benchmark, and uses ...", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/Human-AI-Coordination-via-Human-Regularized-Search-Hu-Wu/d42ac61b938a2173d0fcb9c327684748d9e880bd", "content": "11 Oct 2022 — This work develops a three-step algorithm that achieve strong performance in coordinating with real humans in the Hanabi benchmark, and uses ..."}
+{"idx": 9, "title": "AH2AC2", "date": "", "ddg_snippet": "A standardized benchmark for evaluating AI agents' ability to coordinate with human like partners using the cooperative card game Hanabi.", "subpage_snippet": "", "source": "ah2ac2.com", "link": "https://ah2ac2.com/", "content": "A standardized benchmark for evaluating AI agents' ability to coordinate with human like partners using the cooperative card game Hanabi."}
diff --git a/data/sampled_jsons/Adcock_Collier_2001_American_Political_Science_Review.jsonl b/data/sampled_jsons/Adcock_Collier_2001_American_Political_Science_Review.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..02dbf4236b711b4a7a5a82d296fb7aae91319e66
--- /dev/null
+++ b/data/sampled_jsons/Adcock_Collier_2001_American_Political_Science_Review.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "THE 10 BEST Cafés in Seattle (Updated 2025) - Tripadvisor", "date": "", "ddg_snippet": "Best Cafés in Seattle , Washington: Find Tripadvisor traveller reviews of Seattle Cafés and search by price, location, and more.", "subpage_snippet": "", "source": "www.tripadvisor.com", "link": "https://www.tripadvisor.com/Restaurants-g60878-c8-Seattle_Washington.html", "content": "Best Cafés in Seattle , Washington: Find Tripadvisor traveller reviews of Seattle Cafés and search by price, location, and more."}
+{"idx": 1, "title": "The Absolute Best Coffee Shops in Downtown Seattle [Updated 2025]", "date": "", "ddg_snippet": "Mar 28, 2025 · From quaint corner cafes to bustling roasteries, the coffee scene here is rich and diverse. After thoroughly exploring and reviewing 12 standout spots, I’ve gathered a selection that perfectly encapsulates the vibrant coffee culture of the city.", "subpage_snippet": "", "source": "seattlesecrets.org", "link": "https://seattlesecrets.org/best-coffee-shops-in-downtown-seattle", "content": "Mar 28, 2025 · From quaint corner cafes to bustling roasteries, the coffee scene here is rich and diverse. After thoroughly exploring and reviewing 12 standout spots, I’ve gathered a selection that perfectly encapsulates the vibrant coffee culture of the city."}
+{"idx": 2, "title": "THE BEST 10 CAFES in SEATTLE, WA - Updated 2025 - Yelp", "date": "", "ddg_snippet": "Best Cafes in Seattle , WA - Last Updated September 2025 - Café Hagen, Hood Famous, Bakery Nouveau, Nana’s Green Tea, Petit Pierre Bakery, Volunteer Park Cafe & Pantry, Cardoon, Skalka, Soufend Cafe & Eatery", "subpage_snippet": "", "source": "www.yelp.com", "link": "https://www.yelp.com/search?cflt=cafes&find_loc=Seattle,+WA", "content": "Best Cafes in Seattle , WA - Last Updated September 2025 - Café Hagen, Hood Famous, Bakery Nouveau, Nana’s Green Tea, Petit Pierre Bakery, Volunteer Park Cafe & Pantry, Cardoon, Skalka, Soufend Cafe & Eatery"}
+{"idx": 3, "title": "The 19 Best Coffee Shops In Seattle - Seattle - The Infatuation", "date": "", "ddg_snippet": "Check out the best coffee shops in Seattle , from little walk-up windows and iconic roasting operations to grungy lounges that double as vinyl stores.", "subpage_snippet": "", "source": "www.theinfatuation.com", "link": "https://www.theinfatuation.com/seattle/guides/best-coffee-shops-seattle", "content": "Check out the best coffee shops in Seattle , from little walk-up windows and iconic roasting operations to grungy lounges that double as vinyl stores."}
+{"idx": 4, "title": "The 10 Best Coffee Shops In Seattle According To Locals", "date": "", "ddg_snippet": "Jul 13, 2023 · We asked local Seattleites for their recommendations for the very best coffee shops in Seattle . These are their tried-and-true favorites.", "subpage_snippet": "", "source": "secretseattle.co", "link": "https://secretseattle.co/coffee-shops-seattle/", "content": "Jul 13, 2023 · We asked local Seattleites for their recommendations for the very best coffee shops in Seattle . These are their tried-and-true favorites."}
+{"idx": 5, "title": "10 of the best cafes to drink coffee in Seattle - Lonely Planet", "date": "", "ddg_snippet": "Sep 10, 2025 · 3. Caffe Vita Coffee Roasting Co. One of the “three Vs” of Seattle coffee, Vita has poured some of the city’s boldest flavors since its cafe -roastery opened in 1995. From its first location at the bottom of Queen Anne Hill, the brand now operates nine locations in the Pacific Northwest, with cafes in New York and Phoenix.", "subpage_snippet": "", "source": "www.lonelyplanet.com", "link": "https://www.lonelyplanet.com/articles/best-places-to-drink-coffee-in-seattle", "content": "Sep 10, 2025 · 3. Caffe Vita Coffee Roasting Co. One of the “three Vs” of Seattle coffee, Vita has poured some of the city’s boldest flavors since its cafe -roastery opened in 1995. From its first location at the bottom of Queen Anne Hill, the brand now operates nine locations in the Pacific Northwest, with cafes in New York and Phoenix."}
+{"idx": 6, "title": "15 Best Cafes & Coffee Shops in Seattle ’s Capitol Hill (2025...", "date": "", "ddg_snippet": "6 days ago · Are you looking for the best Coffee Shops in Seattle ’s Capitol Hill neighborhood? Here my top 15 Seattle cafes to visit in Capitol Hill.", "subpage_snippet": "", "source": "www.emmasedition.com", "link": "https://www.emmasedition.com/2025/09/15-best-cafes-coffee-shops-in-seattles-capitol-hill-2025-guide.html", "content": "6 days ago · Are you looking for the best Coffee Shops in Seattle ’s Capitol Hill neighborhood? Here my top 15 Seattle cafes to visit in Capitol Hill."}
+{"idx": 7, "title": "20 Best Coffee Shops in Seattle , WA - CoffeeSpots", "date": "", "ddg_snippet": "Sip your way to coffee excellence. Find the 20 Best Coffee Shops in Seattle , WA.", "subpage_snippet": "", "source": "coffeespots.com", "link": "https://coffeespots.com/best-coffee-shops-in-seattle-wa/", "content": "Sip your way to coffee excellence. Find the 20 Best Coffee Shops in Seattle , WA."}
+{"idx": 8, "title": "13 Best Coffee Shops in Seattle , Washington", "date": "", "ddg_snippet": "Whether you’re grabbing a morning latte, taking meetings over cold brew, or looking for a quiet place to read, this guide covers the standout coffee shops around downtown Seattle .", "subpage_snippet": "", "source": "coffeestuds.com", "link": "https://coffeestuds.com/coffee-shops-in-seattle-washington/", "content": "Whether you’re grabbing a morning latte, taking meetings over cold brew, or looking for a quiet place to read, this guide covers the standout coffee shops around downtown Seattle ."}
+{"idx": 9, "title": "The Best Coffee Shops in Seattle", "date": "", "ddg_snippet": "Apr 22, 2024 · From Espresso Vivace to Boon Boona Coffee: The second-wave pioneers, multi-roaster cafes , and geographic specialists that make Seattle ’s coffee scene great.", "subpage_snippet": "", "source": "www.seattlemet.com", "link": "https://www.seattlemet.com/eat-and-drink/best-coffee-shops-in-seattle-washington", "content": "Apr 22, 2024 · From Espresso Vivace to Boon Boona Coffee: The second-wave pioneers, multi-roaster cafes , and geographic specialists that make Seattle ’s coffee scene great."}
diff --git a/data/sampled_jsons/AltUp_Baykal_et_al._2023_abstract_year_2023.jsonl b/data/sampled_jsons/AltUp_Baykal_et_al._2023_abstract_year_2023.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..249db168a8904a81466de516f82e1f207f1b01ab
--- /dev/null
+++ b/data/sampled_jsons/AltUp_Baykal_et_al._2023_abstract_year_2023.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "[2301.13310] Alternating Updates for Efficient Transformers", "date": "", "ddg_snippet": "It has been well established that increasing scale in deep transformer networks leads to improved quality and performance. However, this increase in scale often comes with prohibitive increases in compute cost and inference latency. We introduce Alternating Updates ( AltUp ), a simple-to-implement method to increase a model's capacity without the computational burden. AltUp enables the widening ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2301.13310", "content": "It has been well established that increasing scale in deep transformer networks leads to improved quality and performance. However, this increase in scale often comes with prohibitive increases in compute cost and inference latency. We introduce Alternating Updates ( AltUp ), a simple-to-implement method to increase a model's capacity without the computational burden. AltUp enables the widening ..."}
+{"idx": 1, "title": "Alternating Updates for Efficient Transformers - NIPS", "date": "", "ddg_snippet": "Authors Cenk Baykal , Dylan Cutler, Nishanth Dikkala, Nikhil Ghosh, Rina Panigrahy, Xin Wang Abstract It has been well established that increasing scale in deep transformer networks leads to improved quality and performance. However, this increase in scale often comes with prohibitive increases in compute cost and inference latency. We introduce Alternating Updates ( AltUp ), a simple-to ...", "subpage_snippet": "", "source": "papers.nips.cc", "link": "https://papers.nips.cc/paper_files/paper/2023/hash/f2059277ac6ce66e7e5543001afa8bb5-Abstract-Conference.html", "content": "Authors Cenk Baykal , Dylan Cutler, Nishanth Dikkala, Nikhil Ghosh, Rina Panigrahy, Xin Wang Abstract It has been well established that increasing scale in deep transformer networks leads to improved quality and performance. However, this increase in scale often comes with prohibitive increases in compute cost and inference latency. We introduce Alternating Updates ( AltUp ), a simple-to ..."}
+{"idx": 2, "title": "Alternating updates for efficient transformers | Proceedings of the ...", "date": "", "ddg_snippet": "We introduce Alternating Updates ( AltUp ), a simple-to-implement method to increase a model's capacity without the computational burden. AltUp enables the widening of the learned representation, i.e., the token embedding, while only incurring a negligible increase in latency.", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.5555/3666122.3669474", "content": "We introduce Alternating Updates ( AltUp ), a simple-to-implement method to increase a model's capacity without the computational burden. AltUp enables the widening of the learned representation, i.e., the token embedding, while only incurring a negligible increase in latency."}
+{"idx": 3, "title": "Alternating Updates for Efficient Transformers - Semantic Scholar", "date": "", "ddg_snippet": "This work introduces Alternating Updates ( AltUp ), a simple-to-implement method to increase a model's capacity without the computational burden, and demonstrates how AltUp can be synergistically combined with existing approaches, such as Sparse Mixture-of-Experts models, to obtain efficient models with even higher capacity.", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/Alternating-Updates-for-Efficient-Transformers-Baykal-Cutler/c9d46cfcf0211d11356c295ecd9584c84c19c8f8/figure/0", "content": "This work introduces Alternating Updates ( AltUp ), a simple-to-implement method to increase a model's capacity without the computational burden, and demonstrates how AltUp can be synergistically combined with existing approaches, such as Sparse Mixture-of-Experts models, to obtain efficient models with even higher capacity."}
+{"idx": 4, "title": "Alternating Updates for Efficient Transformers - DeepAI", "date": "", "ddg_snippet": "AltUp enables the widening of the learned representation without increasing the computation time by working on a subblock of the representation at each layer. Our experiments on various transformer models and language tasks demonstrate the consistent effectiveness of alternating updates on a diverse set of benchmarks.", "subpage_snippet": "", "source": "deepai.org", "link": "https://deepai.org/publication/alternating-updates-for-efficient-transformers", "content": "AltUp enables the widening of the learned representation without increasing the computation time by working on a subblock of the representation at each layer. Our experiments on various transformer models and language tasks demonstrate the consistent effectiveness of alternating updates on a diverse set of benchmarks."}
+{"idx": 5, "title": "Alternating updates for efficient transformers - Google Research", "date": "", "ddg_snippet": "In \" Alternating Updates for Efficient Transformers \", accepted as a Spotlight at NeurIPS 2023 , we introduce AltUp , a method to take advantage of increased token representation without increasing the computation cost. AltUp is easy to implement, widely applicable to any transformer architecture, and requires minimal hyperparameter tuning.", "subpage_snippet": "", "source": "research.google", "link": "https://research.google/blog/alternating-updates-for-efficient-transformers/", "content": "In \" Alternating Updates for Efficient Transformers \", accepted as a Spotlight at NeurIPS 2023 , we introduce AltUp , a method to take advantage of increased token representation without increasing the computation cost. AltUp is easy to implement, widely applicable to any transformer architecture, and requires minimal hyperparameter tuning."}
+{"idx": 6, "title": "PDF Alternating Updates for Efficient Transforme - papers.nips.cc", "date": "", "ddg_snippet": "Abstract It has been well established that increasing scale in deep transformer networks leads to improved quality and performance. However, this increase in scale often comes with prohibitive increases in compute cost and inference latency. We introduce Alternating Updates ( AltUp ), a simple-to-implement method to increase a model's capacity without the computational burden. AltUp enables ...", "subpage_snippet": "", "source": "papers.nips.cc", "link": "https://papers.nips.cc/paper/2023/file/f2059277ac6ce66e7e5543001afa8bb5-Paper-Conference.pdf", "content": "Abstract It has been well established that increasing scale in deep transformer networks leads to improved quality and performance. However, this increase in scale often comes with prohibitive increases in compute cost and inference latency. We introduce Alternating Updates ( AltUp ), a simple-to-implement method to increase a model's capacity without the computational burden. AltUp enables ..."}
+{"idx": 7, "title": "NeurIPS Poster Alternating Updates for Efficient Transformers", "date": "", "ddg_snippet": "Abstract : It has been well established that increasing scale in deep transformer networks leads to improved quality and performance. However, this increase in scale often comes with prohibitive increases in compute cost and inference latency. We introduce Alternating Updates ( AltUp ), a simple-to-implement method to increase a model's capacity without the computational burden. AltUp enables the ...", "subpage_snippet": "", "source": "neurips.cc", "link": "https://neurips.cc/virtual/2023/poster/72994", "content": "Abstract : It has been well established that increasing scale in deep transformer networks leads to improved quality and performance. However, this increase in scale often comes with prohibitive increases in compute cost and inference latency. We introduce Alternating Updates ( AltUp ), a simple-to-implement method to increase a model's capacity without the computational burden. AltUp enables the ..."}
+{"idx": 8, "title": "Alternating Updates for Efficient Transformers - OpenReview", "date": "", "ddg_snippet": "The study introduces Alternating Updates, a novel method to increase the capacity of transformer models without significantly raising latency. AltUp broadens the token representation, operating on a subblock of the widened representation at each layer, and employs a predict-and-correct mechanism for updating inactive blocks. AltUp also extends to the sequence dimension and can work ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=1p6teT6F73", "content": "The study introduces Alternating Updates, a novel method to increase the capacity of transformer models without significantly raising latency. AltUp broadens the token representation, operating on a subblock of the widened representation at each layer, and employs a predict-and-correct mechanism for updating inactive blocks. AltUp also extends to the sequence dimension and can work ..."}
+{"idx": 9, "title": "Alternating Updates for Efficient Transformers | Request PDF", "date": "", "ddg_snippet": "Request PDF | Alternating Updates for Efficient Transformers | It is well established that increasing scale in deep transformer networks leads to improved quality and performance. This increase in ...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/367652661_Alternating_Updates_for_Efficient_Transformers", "content": "Request PDF | Alternating Updates for Efficient Transformers | It is well established that increasing scale in deep transformer networks leads to improved quality and performance. This increase in ..."}
diff --git a/data/sampled_jsons/AltUp_Baykal_et_al._2023_arxiv_year_2023.jsonl b/data/sampled_jsons/AltUp_Baykal_et_al._2023_arxiv_year_2023.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..0fda81e68637c9fdd8ba9ef127f785e01f138732
--- /dev/null
+++ b/data/sampled_jsons/AltUp_Baykal_et_al._2023_arxiv_year_2023.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "[2301.13310] Alternating Updates for Efficient Transformers - arXiv.org", "date": "", "ddg_snippet": "It has been well established that increasing scale in deep transformer networks leads to improved quality and performance. However, this increase in scale often comes with prohibitive increases in compute cost and inference latency. We introduce Alternating Updates ( AltUp ), a simple-to-implement method to increase a model's capacity without the computational burden. AltUp enables the widening ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2301.13310", "content": "It has been well established that increasing scale in deep transformer networks leads to improved quality and performance. However, this increase in scale often comes with prohibitive increases in compute cost and inference latency. We introduce Alternating Updates ( AltUp ), a simple-to-implement method to increase a model's capacity without the computational burden. AltUp enables the widening ..."}
+{"idx": 1, "title": "PDF NeurIPS 2023 Presentation", "date": "", "ddg_snippet": "Recycled- AltUp Evaluations O(100) total parameters added Improved performance at the cost of virtually no slowdown", "subpage_snippet": "", "source": "neurips.cc", "link": "https://neurips.cc/media/neurips-2023/Slides/72994.pdf", "content": "Recycled- AltUp Evaluations O(100) total parameters added Improved performance at the cost of virtually no slowdown"}
+{"idx": 2, "title": "arXiv:2301.13310v2 [cs.LG] 3 Oct 2023", "date": "", "ddg_snippet": "Abstract It has been well established that increasing scale in deep transformer networks leads to improved quality and performance. However, this increase in scale often comes with prohibitive increases in compute cost and inference latency. We introduce Alternating Updates ( AltUp ), a simple-to-implement method to increase a model's capacity without the computational burden. AltUp enables ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2301.13310", "content": "Abstract It has been well established that increasing scale in deep transformer networks leads to improved quality and performance. However, this increase in scale often comes with prohibitive increases in compute cost and inference latency. We introduce Alternating Updates ( AltUp ), a simple-to-implement method to increase a model's capacity without the computational burden. AltUp enables ..."}
+{"idx": 3, "title": "Alternating updates for efficient transformers | Proceedings of the ...", "date": "", "ddg_snippet": "We present extensions of AltUp , such as its applicability to the sequence dimension, and demonstrate how AltUp can be synergistically combined with existing approaches, such as Sparse Mixture-of-Experts models, to obtain efficient models with even higher capacity.", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.5555/3666122.3669474", "content": "We present extensions of AltUp , such as its applicability to the sequence dimension, and demonstrate how AltUp can be synergistically combined with existing approaches, such as Sparse Mixture-of-Experts models, to obtain efficient models with even higher capacity."}
+{"idx": 4, "title": "Alternating Updates for Efficient Transformers - NIPS", "date": "", "ddg_snippet": "Authors Cenk Baykal , Dylan Cutler, Nishanth Dikkala, Nikhil Ghosh, Rina Panigrahy, Xin Wang Abstract It has been well established that increasing scale in deep transformer networks leads to improved quality and performance. However, this increase in scale often comes with prohibitive increases in compute cost and inference latency. We introduce Alternating Updates ( AltUp ), a simple-to ...", "subpage_snippet": "", "source": "papers.nips.cc", "link": "https://papers.nips.cc/paper_files/paper/2023/hash/f2059277ac6ce66e7e5543001afa8bb5-Abstract-Conference.html", "content": "Authors Cenk Baykal , Dylan Cutler, Nishanth Dikkala, Nikhil Ghosh, Rina Panigrahy, Xin Wang Abstract It has been well established that increasing scale in deep transformer networks leads to improved quality and performance. However, this increase in scale often comes with prohibitive increases in compute cost and inference latency. We introduce Alternating Updates ( AltUp ), a simple-to ..."}
+{"idx": 5, "title": "Alternating Updates for Efficient Transformers - OpenReview", "date": "", "ddg_snippet": "AltUp broadens the token representation, operating on a subblock of the widened representation at each layer, and employs a predict-and-correct mechanism for updating inactive blocks. AltUp also extends to the sequence dimension and can work synergistically with existing techniques like Sparse Mixture-of-Experts models.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=1p6teT6F73", "content": "AltUp broadens the token representation, operating on a subblock of the widened representation at each layer, and employs a predict-and-correct mechanism for updating inactive blocks. AltUp also extends to the sequence dimension and can work synergistically with existing techniques like Sparse Mixture-of-Experts models."}
+{"idx": 6, "title": "Alternating Updates for Efficient Transformers | Request PDF", "date": "", "ddg_snippet": "Finally, we present extensions of AltUp to the sequence dimension, and demonstrate how AltUp can be synergistically combined with existing approaches, such as Sparse Mixture-of-Experts models, to ...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/367652661_Alternating_Updates_for_Efficient_Transformers", "content": "Finally, we present extensions of AltUp to the sequence dimension, and demonstrate how AltUp can be synergistically combined with existing approaches, such as Sparse Mixture-of-Experts models, to ..."}
+{"idx": 7, "title": "Alternating Updates for Efficient Transformers - Semantic Scholar", "date": "", "ddg_snippet": "This work introduces Alternating Updates ( AltUp ), a simple-to-implement method to increase a model's capacity without the computational burden, and demonstrates how AltUp can be synergistically combined with existing approaches, such as Sparse Mixture-of-Experts models, to obtain efficient models with even higher capacity.", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/Alternating-Updates-for-Efficient-Transformers-Baykal-Cutler/c9d46cfcf0211d11356c295ecd9584c84c19c8f8/figure/0", "content": "This work introduces Alternating Updates ( AltUp ), a simple-to-implement method to increase a model's capacity without the computational burden, and demonstrates how AltUp can be synergistically combined with existing approaches, such as Sparse Mixture-of-Experts models, to obtain efficient models with even higher capacity."}
+{"idx": 8, "title": "Paper page - Alternating Updates for Efficient Transformers", "date": "", "ddg_snippet": "We introduce Alternating Updates ( AltUp ), a simple-to-implement method to increase a model's capacity without the computational burden. AltUp enables the widening of the learned representation, i.e., the token embedding, while only incurring a negligible increase in latency.", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/papers/2301.13310", "content": "We introduce Alternating Updates ( AltUp ), a simple-to-implement method to increase a model's capacity without the computational burden. AltUp enables the widening of the learned representation, i.e., the token embedding, while only incurring a negligible increase in latency."}
+{"idx": 9, "title": "PDF Alternating Updates for Efficient Transforme - papers.nips.cc", "date": "", "ddg_snippet": "Abstract It has been well established that increasing scale in deep transformer networks leads to improved quality and performance. However, this increase in scale often comes with prohibitive increases in compute cost and inference latency. We introduce Alternating Updates ( AltUp ), a simple-to-implement method to increase a model's capacity without the computational burden. AltUp enables ...", "subpage_snippet": "", "source": "papers.nips.cc", "link": "https://papers.nips.cc/paper/2023/file/f2059277ac6ce66e7e5543001afa8bb5-Paper-Conference.pdf", "content": "Abstract It has been well established that increasing scale in deep transformer networks leads to improved quality and performance. However, this increase in scale often comes with prohibitive increases in compute cost and inference latency. We introduce Alternating Updates ( AltUp ), a simple-to-implement method to increase a model's capacity without the computational burden. AltUp enables ..."}
diff --git a/data/sampled_jsons/An_Empirical_Study_of_License_Conflict_in_Free_and_Open_Source_Software_Cui_Wu_Semantic_Scholar_year_2023.jsonl b/data/sampled_jsons/An_Empirical_Study_of_License_Conflict_in_Free_and_Open_Source_Software_Cui_Wu_Semantic_Scholar_year_2023.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..9857900008420b5fda93d1a3b285df9e2ad6638a
--- /dev/null
+++ b/data/sampled_jsons/An_Empirical_Study_of_License_Conflict_in_Free_and_Open_Source_Software_Cui_Wu_Semantic_Scholar_year_2023.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Xiang Ling - Google Scholar", "date": "", "ddg_snippet": "... Cited by\" count includes citations to the following articles in Scholar ... An empirical study of license conflict in free and open source software", "subpage_snippet": "", "source": "scholar.google.com.hk", "link": "https://scholar.google.com.hk/citations?user=5gaFkzAAAAAJ&hl=en", "content": "... Cited by\" count includes citations to the following articles in Scholar ... An empirical study of license conflict in free and open source software"}
+{"idx": 1, "title": "Radiomics: Status quo and future challenges", "date": "", "ddg_snippet": "In addition, judgment and diagnoses made by experts are usually based on multiple years of experience and subjective impression which lead to ...", "subpage_snippet": "", "source": "www.wjgnet.com", "link": "https://www.wjgnet.com/2644-3260/full/v3/i4/87.htm", "content": "In addition, judgment and diagnoses made by experts are usually based on multiple years of experience and subjective impression which lead to ..."}
+{"idx": 2, "title": "From Surface to Semantics: Semantic Structure Parsing for", "date": "", "ddg_snippet": "... integrates a complete document preprocessing pipeline and semantic relationship analysis of PDF elements, enabling both semantic structure parsing and ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2508.10311v1", "content": "... integrates a complete document preprocessing pipeline and semantic relationship analysis of PDF elements, enabling both semantic structure parsing and ..."}
+{"idx": 3, "title": "Uncovering the New Accessibility Crisis in Scholarly PDFs", "date": "", "ddg_snippet": "... of this issue, we perform a large-scale analysis of 20K open - and closed-access scholarly PDFs published between 2014–2023 sampled across broad ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2410.03022v1", "content": "... of this issue, we perform a large-scale analysis of 20K open - and closed-access scholarly PDFs published between 2014–2023 sampled across broad ..."}
+{"idx": 4, "title": "CMC | Free Full-Text | RESTlogic: Detecting Logic", "date": "", "ddg_snippet": "... of our method, we implemented our work on real-world open - source REST services, including OpenStack [ 18 20 ], one of the most popular cloud computing ...", "subpage_snippet": "", "source": "www.techscience.com", "link": "https://www.techscience.com/cmc/v78n2/55563/html", "content": "... of our method, we implemented our work on real-world open - source REST services, including OpenStack [ 18 20 ], one of the most popular cloud computing ..."}
+{"idx": 5, "title": "More human than human? Differences in lexis and collocation", "date": "", "ddg_snippet": "Due to its extensive training data, ChatGPT can provide instant feedback on grammar, lexis and coherence, easily extract key concepts, and even offer ...", "subpage_snippet": "", "source": "www.degruyterbrill.com", "link": "https://www.degruyterbrill.com/document/doi/10.1515/iral-2024-0196/html", "content": "Due to its extensive training data, ChatGPT can provide instant feedback on grammar, lexis and coherence, easily extract key concepts, and even offer ..."}
+{"idx": 6, "title": "GitHub - Furyton/awesome-language-model-analysis: This paper", "date": "", "ddg_snippet": "... in this list investigate the learning behavior, generalization ability, and other properties of language models through theoretical analysis, ...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/Furyton/awesome-language-model-analysis", "content": "... in this list investigate the learning behavior, generalization ability, and other properties of language models through theoretical analysis, ..."}
+{"idx": 7, "title": "The influence of social power on neural responses to emotional", "date": "", "ddg_snippet": "... often used by provoking interference through the semantic incompatibility between emotional faces and emotional words, and has been employed in many ...", "subpage_snippet": "", "source": "peerj.com", "link": "https://peerj.com/articles/11267/", "content": "... often used by provoking interference through the semantic incompatibility between emotional faces and emotional words, and has been employed in many ..."}
+{"idx": 8, "title": "Frontiers | ChatGPT in society: emerging issues", "date": "", "ddg_snippet": "... include security, political, economic, cultural, and educational issues as well as issues concerning social biases, creativity, copyright, and freedom ...", "subpage_snippet": "", "source": "www.frontiersin.org", "link": "https://www.frontiersin.org/journals/artificial-intelligence/articles/10.3389/frai.2023.1130913/full", "content": "... include security, political, economic, cultural, and educational issues as well as issues concerning social biases, creativity, copyright, and freedom ..."}
+{"idx": 9, "title": "Yang Liu", "date": "", "ddg_snippet": "In our dataset 90.1% of authors are ... Software Development Life Cycle Perspective: A Survey of Benchmarks for Code Large Language Models and Agents.", "subpage_snippet": "", "source": "www.csauthors.net", "link": "https://www.csauthors.net/yang-liu-003/", "content": "In our dataset 90.1% of authors are ... Software Development Life Cycle Perspective: A Survey of Benchmarks for Code Large Language Models and Agents."}
diff --git a/data/sampled_jsons/An_empirical_study_of_license_conflict_in_free_and_open_source_software_Cui_et_al._2023_year_2023.jsonl b/data/sampled_jsons/An_empirical_study_of_license_conflict_in_free_and_open_source_software_Cui_et_al._2023_year_2023.jsonl
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index 0000000000000000000000000000000000000000..5dd99057341cc78823edf88da0f0aba98cde7fd9
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+++ b/data/sampled_jsons/An_empirical_study_of_license_conflict_in_free_and_open_source_software_Cui_et_al._2023_year_2023.jsonl
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+{"idx": 0, "title": "A first look at License Variants in the PyPI Ecosystem", "date": "", "ddg_snippet": "... empirical study into license variants in software packaging ecosystem but also equips developers and organizations with practical tools for navigating ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2507.14594v1", "content": "... empirical study into license variants in software packaging ecosystem but also equips developers and organizations with practical tools for navigating ..."}
+{"idx": 1, "title": "How Robust are LLM-Generated Library Imports? An Empirical", "date": "", "ddg_snippet": "In this paper, we conduct an empirical study of six state- of -the-art LLMs, both proprietary and open - source , by prompting them to solve real-world ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2507.10818v1", "content": "In this paper, we conduct an empirical study of six state- of -the-art LLMs, both proprietary and open - source , by prompting them to solve real-world ..."}
+{"idx": 2, "title": "On Evaluating the Efficiency of Source Code Generated by LLMs |", "date": "", "ddg_snippet": "The present systematic survey comprehensively analyses studies published between 2021 and 2024, focusing on utilizing LLMs in the code generation ...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/381388109_On_Evaluating_the_Efficiency_of_Source_Code_Generated_by_LLMs", "content": "The present systematic survey comprehensively analyses studies published between 2021 and 2024, focusing on utilizing LLMs in the code generation ..."}
+{"idx": 3, "title": "(PDF) Parametric Study of the Modal Behavior of Concrete", "date": "", "ddg_snippet": "The results indicated that modal frequencies in the condition of with and without Pre-stress are different in all cases.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/338161931_Parametric_Study_of_the_Modal_Behavior_of_Concrete_Gravity_Dam_by_Using_Finite_Element_Method", "content": "The results indicated that modal frequencies in the condition of with and without Pre-stress are different in all cases."}
+{"idx": 4, "title": "The Effectiveness of Software Designed to Detect AI-Generated", "date": "", "ddg_snippet": "Quite a few websites and blogs claim to evaluate the accuracy of various AI text detectors ( e .g., Abdullahi, 2023 ; Andrews, 2023 ; Aw, 2023 ...", "subpage_snippet": "", "source": "www.degruyter.com", "link": "https://www.degruyter.com/document/doi/10.1515/opis-2022-0158/html", "content": "Quite a few websites and blogs claim to evaluate the accuracy of various AI text detectors ( e .g., Abdullahi, 2023 ; Andrews, 2023 ; Aw, 2023 ..."}
+{"idx": 5, "title": "Paradoxical Leadership and Employee Proactive... | F1000Research", "date": "", "ddg_snippet": "... an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and ...", "subpage_snippet": "", "source": "f1000research.com", "link": "https://f1000research.com/articles/13-622", "content": "... an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and ..."}
+{"idx": 6, "title": "Noise-Robustness Through Noise: Asymmetric LoRA Adaption with", "date": "", "ddg_snippet": "... robustness seems like a pipe dream, as both clean and noisy samples influence all model parameters, preventing optimal utilization of noise patterns.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2505.23868v4", "content": "... robustness seems like a pipe dream, as both clean and noisy samples influence all model parameters, preventing optimal utilization of noise patterns."}
+{"idx": 7, "title": "Nicer Than Humans: How do Large Language Models Behave in the", "date": "", "ddg_snippet": "... an implicit assumption that LLMs can understand the complex rules of the game and the history of past actions described in the prompt (Akata et al ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2406.13605v2", "content": "... an implicit assumption that LLMs can understand the complex rules of the game and the history of past actions described in the prompt (Akata et al ..."}
+{"idx": 8, "title": "Natural language processing for analyzing online customer", "date": "", "ddg_snippet": "It also gives readers an in -depth understanding of the methodologies, tools, and datasets utilized and the open research challenges in this growing ...", "subpage_snippet": "", "source": "peerj.com", "link": "https://peerj.com/articles/cs-2203/", "content": "It also gives readers an in -depth understanding of the methodologies, tools, and datasets utilized and the open research challenges in this growing ..."}
+{"idx": 9, "title": "Degradation of beechwood xylan using food-grade bacteria-like", "date": "", "ddg_snippet": "... of xylan hemicellulose into xylose monosaccharides is a cost-effective and sustainable alternative source of biofuels, chemicals, and many bio-based ...", "subpage_snippet": "", "source": "bioresourcesbioprocessing.springeropen.com", "link": "https://bioresourcesbioprocessing.springeropen.com/articles/10.1186/s40643-025-00898-1", "content": "... of xylan hemicellulose into xylose monosaccharides is a cost-effective and sustainable alternative source of biofuels, chemicals, and many bio-based ..."}
diff --git a/data/sampled_jsons/Appendix_A.3.2_hardware_configuration_GPU_sitearxiv.org_OR_siteopenreview.net.jsonl b/data/sampled_jsons/Appendix_A.3.2_hardware_configuration_GPU_sitearxiv.org_OR_siteopenreview.net.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..494c45387e1d3bea3662398070f24f6b8d91af14
--- /dev/null
+++ b/data/sampled_jsons/Appendix_A.3.2_hardware_configuration_GPU_sitearxiv.org_OR_siteopenreview.net.jsonl
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+{"idx": 0, "title": "Isaac Gym: High Performance GPU Based Physics Simulation For ...", "date": "", "ddg_snippet": "Oct 11, 2021 · Abstract:Isaac Gym offers a high-performance learning platform to train policies for a wide variety of robotics tasks entirely on GPU . Both physics simulation and neural network policy training reside on GPU and communicate by directly passing data from physics buffers to PyTorch tensors without ever going through CPU bottlenecks.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=fgFBtYgJQX_", "content": "Oct 11, 2021 · Abstract:Isaac Gym offers a high-performance learning platform to train policies for a wide variety of robotics tasks entirely on GPU . Both physics simulation and neural network policy training reside on GPU and communicate by directly passing data from physics buffers to PyTorch tensors without ever going through CPU bottlenecks."}
+{"idx": 1, "title": "On Scaling Up 3D Gaussian Splatting Training | OpenReview", "date": "", "ddg_snippet": "Jan 22, 2025 · 3D Gaussian Splatting (3DGS) is increasingly popular for 3D reconstruction due to its superior visual quality and rendering speed. However, 3DGS training currently occurs on a single GPU , limiting its ability to handle high-resolution and large-scale 3D reconstruction tasks due to memory constraints. We introduce Grendel, a distributed system designed to partition 3DGS parameters and ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=pQqeQpMkE7", "content": "Jan 22, 2025 · 3D Gaussian Splatting (3DGS) is increasingly popular for 3D reconstruction due to its superior visual quality and rendering speed. However, 3DGS training currently occurs on a single GPU , limiting its ability to handle high-resolution and large-scale 3D reconstruction tasks due to memory constraints. We introduce Grendel, a distributed system designed to partition 3DGS parameters and ..."}
+{"idx": 2, "title": "AAAI 2026 Conference | OpenReview", "date": "", "ddg_snippet": "Jan 20, 2025 · Welcome to the OpenReview homepage for AAAI 2026 Conference", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/group?id=AAAI.org/2026/Conference", "content": "Jan 20, 2025 · Welcome to the OpenReview homepage for AAAI 2026 Conference"}
+{"idx": 3, "title": "SpecOffload: Unlocking Latent GPU Capacity for LLM ...", "date": "", "ddg_snippet": "15 May 2025 — During the online phase, the hardware configuration and batched inputs (➂) are provided to the ParaSpec Planner (§ 4.3), which, based on ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2505.10259v1", "content": "15 May 2025 — During the online phase, the hardware configuration and batched inputs (➂) are provided to the ParaSpec Planner (§ 4.3), which, based on ..."}
+{"idx": 4, "title": "SpecOffload: Unlocking Latent GPU Capacity for LLM ...", "date": "", "ddg_snippet": "by X Zhuge · 2025 · Cited by 1 — tion of GPU cores and marginal utility of GPU ... ParaSpec Planner aims to maximize model inference throughput on a given hardware configuration .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2505.10259", "content": "by X Zhuge · 2025 · Cited by 1 — tion of GPU cores and marginal utility of GPU ... ParaSpec Planner aims to maximize model inference throughput on a given hardware configuration ."}
+{"idx": 5, "title": "Task-Gated Multi-Expert Collaboration Network for Degraded...", "date": "", "ddg_snippet": "May 1, 2025 · Lay Summary: This paper proposes a unified framework for degraded multi-modal image restoration and fusion, which bridges different tasks together through a two-stage training strategy to learn inter-task information while avoiding mutual interference, enabling all-in-one processing. This paper proposes the task-aware gating and multi-expert collaboration module. The degradation-aware gating ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=OcFsPBXREI", "content": "May 1, 2025 · Lay Summary: This paper proposes a unified framework for degraded multi-modal image restoration and fusion, which bridges different tasks together through a two-stage training strategy to learn inter-task information while avoiding mutual interference, enabling all-in-one processing. This paper proposes the task-aware gating and multi-expert collaboration module. The degradation-aware gating ..."}
+{"idx": 6, "title": "Scaling Infrastructure to Support Multi-Trillion Parameter ...", "date": "", "ddg_snippet": "Training a hundred-trillion parameter LLM is feasible but requires a secondary memory pool up to 1 TiB per GPU with a bandwidth of 100 GB/s bidirectionally. Strong scaling for a 1T model stalls around 12,288 GPUs, as matrix multiply becomes small, ineficient, and unable to overlap with communication.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=rqn2v1Ltgn0", "content": "Training a hundred-trillion parameter LLM is feasible but requires a secondary memory pool up to 1 TiB per GPU with a bandwidth of 100 GB/s bidirectionally. Strong scaling for a 1T model stalls around 12,288 GPUs, as matrix multiply becomes small, ineficient, and unable to overlap with communication."}
+{"idx": 7, "title": "GShard: Scaling Giant Models with Conditional Computation and...", "date": "", "ddg_snippet": "Jan 12, 2021 · Neural network scaling has been critical for improving the model quality in many real-world machine learning applications with vast amounts of training data and compute. Although this trend of...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=qrwe7XHTmYb", "content": "Jan 12, 2021 · Neural network scaling has been critical for improving the model quality in many real-world machine learning applications with vast amounts of training data and compute. Although this trend of..."}
+{"idx": 8, "title": "AutoGFM: Automated Graph Foundation Model with Adaptive...", "date": "", "ddg_snippet": "May 1, 2025 · Abstract: Graph foundation models (GFMs) aim to share graph knowledge across diverse domains and tasks to boost graph machine learning. However, existing GFMs rely on hand-designed and fixed graph neural network (GNN) architectures, failing to utilize optimal architectures *w.r.t.* specific domains and tasks, inevitably leading to suboptimal performance in diverse graph domains and tasks. In ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=fCPB0qRJT2", "content": "May 1, 2025 · Abstract: Graph foundation models (GFMs) aim to share graph knowledge across diverse domains and tasks to boost graph machine learning. However, existing GFMs rely on hand-designed and fixed graph neural network (GNN) architectures, failing to utilize optimal architectures *w.r.t.* specific domains and tasks, inevitably leading to suboptimal performance in diverse graph domains and tasks. In ..."}
diff --git a/data/sampled_jsons/Appendix_B.2_Feature_Selection_Solution_for_SDXL_query_key_activations_year_2024.jsonl b/data/sampled_jsons/Appendix_B.2_Feature_Selection_Solution_for_SDXL_query_key_activations_year_2024.jsonl
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index 0000000000000000000000000000000000000000..cfdcff84d8cf0d87bfd8c1270ede7c274f363d97
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+++ b/data/sampled_jsons/Appendix_B.2_Feature_Selection_Solution_for_SDXL_query_key_activations_year_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Comparing Graph Transformers via Positional Encodings", "date": "", "ddg_snippet": "2 σ is an activation function. Here Q, K, V refer to “ query ”, “ key ” and “value”, respec-tively, in the terminology of the attention literature (Vaswani et al., 2017), and dh and dr are the dimension of the hidden layer and the dimension of the residual layer, respectively.", "subpage_snippet": "", "source": "par.nsf.gov", "link": "https://par.nsf.gov/servlets/purl/10582794", "content": "2 σ is an activation function. Here Q, K, V refer to “ query ”, “ key ” and “value”, respec-tively, in the terminology of the attention literature (Vaswani et al., 2017), and dh and dr are the dimension of the hidden layer and the dimension of the residual layer, respectively."}
+{"idx": 1, "title": "MUSE: MCTS-Driven Red Teaming Framework for Enhanced Multi ...", "date": "", "ddg_snippet": "In this work, we extend DPO for multi-turn dia-logue safety by introducing fine-grained preference optimization, thereby enabling earlier and more targeted intervention at key decision points and reducing risks in extended conversations.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2509.14651", "content": "In this work, we extend DPO for multi-turn dia-logue safety by introducing fine-grained preference optimization, thereby enabling earlier and more targeted intervention at key decision points and reducing risks in extended conversations."}
+{"idx": 2, "title": "Adaptive weighted multi-view subspace clustering method for ...", "date": "", "ddg_snippet": "Sep 13, 2023 · First, we use two neural networks to map multi-source data into a common latent representation and multiple specific latent representations, which serve as the query vector and input vectors of the attention mechanism, respectively. Then, the weight of each type of data is calculated based on the attention mechanism.", "subpage_snippet": "", "source": "www.tandfonline.com", "link": "https://www.tandfonline.com/doi/full/10.1080/10095020.2024.2356243", "content": "Sep 13, 2023 · First, we use two neural networks to map multi-source data into a common latent representation and multiple specific latent representations, which serve as the query vector and input vectors of the attention mechanism, respectively. Then, the weight of each type of data is calculated based on the attention mechanism."}
+{"idx": 3, "title": "Not All Diffusion Model Activations Have Been Evaluated as ...", "date": "", "ddg_snippet": "Our method provides effective feature selection solutions for popular diffusion models like SDv1.5 and SDXL , validating their superiority across multiple ...", "subpage_snippet": "", "source": "liner.com", "link": "https://liner.com/review/not-all-diffusion-model-activations-have-been-evaluated-as-discriminative", "content": "Our method provides effective feature selection solutions for popular diffusion models like SDv1.5 and SDXL , validating their superiority across multiple ..."}
+{"idx": 4, "title": "Not All Diffusion Model Activations Have Been Evaluated ...", "date": "", "ddg_snippet": "4 Oct 2024 — B . 2 Feature Selection Solution for SDXL . Report issue for preceding element. Four activations are selected from SDXL , trying to get similar ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2410.03558v1", "content": "4 Oct 2024 — B . 2 Feature Selection Solution for SDXL . Report issue for preceding element. Four activations are selected from SDXL , trying to get similar ..."}
+{"idx": 5, "title": "Not All Diffusion Model Activations Have Been Evaluated as...", "date": "", "ddg_snippet": "They develop specific feature selection solutions for popular diffusion models SDv1.5 and SDXL . Experiments demonstrate that this method outperforms state-of- ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=7uqVfZW6Mo&referrer=[the+profile+of+Qianqian+Xu](/profile?id=~Qianqian_Xu2)", "content": "They develop specific feature selection solutions for popular diffusion models SDv1.5 and SDXL . Experiments demonstrate that this method outperforms state-of- ..."}
+{"idx": 6, "title": "Suppress Content Shift: Better Diffusion Features via Off- ...", "date": "", "ddg_snippet": "The inner activations of a pre-trained diffusion model can serve as features for discriminative tasks, namely, diffusion feature . We discover that diffusion ...", "subpage_snippet": "", "source": "neurips.cc", "link": "https://neurips.cc/virtual/2024/poster/95209", "content": "The inner activations of a pre-trained diffusion model can serve as features for discriminative tasks, namely, diffusion feature . We discover that diffusion ..."}
+{"idx": 7, "title": "UNPACKING SDXL TURBO: INTERPRETING TEXT-TO", "date": "", "ddg_snippet": "Spatial activations . We visualize the feature map Sρ 2 Rh⇥w containing the activations of a feature . ⇢ across the spatial locations by upscaling it to the ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf/b01108a15348126fac53227053ed8b12ac3cc7fc.pdf", "content": "Spatial activations . We visualize the feature map Sρ 2 Rh⇥w containing the activations of a feature . ⇢ across the spatial locations by upscaling it to the ..."}
+{"idx": 8, "title": "Maize Yield Prediction via Multi-Branch Feature Extraction ...", "date": "", "ddg_snippet": "5 days ago · This study conducted field experiments in 2024 in Meidaizhao Town, Tumed Right Banner, Baotou City, Inner Mongolia Autonomous Region, adopting a plant-level sampling design with 10 maize plots selected as sampling areas (20 plants per plot). At four critical growth stages—jointing, heading, filling, and maturity—multimodal data, including that covering leaf spectra, root-zone soil spectra ...", "subpage_snippet": "", "source": "www.mdpi.com", "link": "https://www.mdpi.com/2073-4395/15/9/2199", "content": "5 days ago · This study conducted field experiments in 2024 in Meidaizhao Town, Tumed Right Banner, Baotou City, Inner Mongolia Autonomous Region, adopting a plant-level sampling design with 10 maize plots selected as sampling areas (20 plants per plot). At four critical growth stages—jointing, heading, filling, and maturity—multimodal data, including that covering leaf spectra, root-zone soil spectra ..."}
+{"idx": 9, "title": "Local Prompt Adaptation for Style-Consistent Multi-Object ...", "date": "", "ddg_snippet": "27 Jul 2025 — We evaluate our method on a custom benchmark of 50 style-rich prompts across five categories and compare against strong baselines including ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2507.20094v1", "content": "27 Jul 2025 — We evaluate our method on a custom benchmark of 50 style-rich prompts across five categories and compare against strong baselines including ..."}
diff --git a/data/sampled_jsons/Archetypal_SAE_Adaptive_and_Stable_Dictionary_Learning_Concept_Extraction_Large_Vision_Models_Figure.jsonl b/data/sampled_jsons/Archetypal_SAE_Adaptive_and_Stable_Dictionary_Learning_Concept_Extraction_Large_Vision_Models_Figure.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..d8ebea0037429178e7439824b8a1d06b27ce16b1
--- /dev/null
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@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Archetypal SAEs : Adaptive and Stable Dictionary Learning for...", "date": "", "ddg_snippet": "Figure 2. SAEs are a promising direction for scalable concept extraction in vision . Comparison of reconstruction error (ℓ2 Loss) and sparsity across four large -scale vision models : ConvNext, DINO, SigLIP, and ViT.", "subpage_snippet": "", "source": "kempnerinstitute.harvard.edu", "link": "https://kempnerinstitute.harvard.edu/research/deeper-learning/archetypal-saes-adaptive-and-stable-dictionary-learning-for-concept-extraction-in-large-vision-models/", "content": "Figure 2. SAEs are a promising direction for scalable concept extraction in vision . Comparison of reconstruction error (ℓ2 Loss) and sparsity across four large -scale vision models : ConvNext, DINO, SigLIP, and ViT."}
+{"idx": 1, "title": "Archetypal SAE : Adaptive and Stable Dictionary Learning for...", "date": "", "ddg_snippet": "Figure 1: A) Archetypal - SAE . Archetypal - SAEs constrain dictionary atoms (decoder directions) to the data’s convex hull, improving stability . Figure 2: SAEs are a promising direction for scalable concept extraction in vision . Comparison of reconstruction error.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2502.12892v1", "content": "Figure 1: A) Archetypal - SAE . Archetypal - SAEs constrain dictionary atoms (decoder directions) to the data’s convex hull, improving stability . Figure 2: SAEs are a promising direction for scalable concept extraction in vision . Comparison of reconstruction error."}
+{"idx": 2, "title": "(PDF) Archetypal SAE : Adaptive and Stable Dictionary Learning for...", "date": "", "ddg_snippet": "Abstract and Figures . Sparse Autoencoders ( SAEs ) have emerged as a powerful framework for machine learning interpretability, enabling the unsupervised decomposition of model representations into a dictionary of abstract, human-interpretable concepts .", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/389129606_Archetypal_SAE_Adaptive_and_Stable_Dictionary_Learning_for_Concept_Extraction_in_Large_Vision_Models", "content": "Abstract and Figures . Sparse Autoencoders ( SAEs ) have emerged as a powerful framework for machine learning interpretability, enabling the unsupervised decomposition of model representations into a dictionary of abstract, human-interpretable concepts ."}
+{"idx": 3, "title": "Archetypal SAEs : Adaptive and Stable Dictionary Learning for...", "date": "", "ddg_snippet": "A) Compared to a Regular- SAE , Archetypal - SAEs constrain dictionary atoms (decoder directions) to the data’s convex hull, improving stability . Concept Extraction as Dictionary Learning .", "subpage_snippet": "", "source": "medium.com", "link": "https://medium.com/@kempnerinstitute/archetypal-saes-adaptive-and-stable-dictionary-learning-for-concept-extraction-in-large-vision-acf95010c691", "content": "A) Compared to a Regular- SAE , Archetypal - SAEs constrain dictionary atoms (decoder directions) to the data’s convex hull, improving stability . Concept Extraction as Dictionary Learning ."}
+{"idx": 4, "title": "KempnerInstitute/overcomplete: Overcomplete is a Vision -based...", "date": "", "ddg_snippet": "Stable Dictionary with Archetypal - SAE : Coming soon. Advanced metrics to study the solution of SAE : The visualization module: NMF, ConvexNMF and Semi-NMF", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/KempnerInstitute/overcomplete", "content": "Stable Dictionary with Archetypal - SAE : Coming soon. Advanced metrics to study the solution of SAE : The visualization module: NMF, ConvexNMF and Semi-NMF"}
+{"idx": 5, "title": "[Literature Review] Archetypal SAE : Adaptive and Stable Dictionary ...", "date": "", "ddg_snippet": "Archetypal SAE . Sparse Autoencoders. Concept Extraction . Adaptive Dictionary Learning . Stability . Interpretability.", "subpage_snippet": "", "source": "www.themoonlight.io", "link": "https://www.themoonlight.io/en/review/archetypal-sae-adaptive-and-stable-dictionary-learning-for-concept-extraction-in-large-vision-models", "content": "Archetypal SAE . Sparse Autoencoders. Concept Extraction . Adaptive Dictionary Learning . Stability . Interpretability."}
+{"idx": 6, "title": "Archetypal SAE : Adaptive and Stable Dictionary Learning for...", "date": "", "ddg_snippet": "Note on Non- Parametric Regression.Binxu Wang. Research Fellow. ← An analytical theory of power law spectral bias in the learning dynamics of diffusion models Mar 1, 2025.", "subpage_snippet": "", "source": "animadversio.github.io", "link": "https://animadversio.github.io/publication/fel-2025-archetypal/", "content": "Note on Non- Parametric Regression.Binxu Wang. Research Fellow. ← An analytical theory of power law spectral bias in the learning dynamics of diffusion models Mar 1, 2025."}
+{"idx": 7, "title": "An Introduction to SAEs and their Variants for Mech Interp — LessWrong", "date": "", "ddg_snippet": "Introduced in Feb 2025, Archetypal SAE : Adaptive and Stable Dictionary Learning for Concept Extraction in Large Vision Models . dictionary _ learning - this library builds SAEs on nnsight models . It supports a bit wider feature set, but isn't as ergonomic to use.", "subpage_snippet": "", "source": "www.lesswrong.com", "link": "https://www.lesswrong.com/posts/vvvJNh8gNE2ScEtQ3/an-introduction-to-saes-and-their-variants-for-mech-interp", "content": "Introduced in Feb 2025, Archetypal SAE : Adaptive and Stable Dictionary Learning for Concept Extraction in Large Vision Models . dictionary _ learning - this library builds SAEs on nnsight models . It supports a bit wider feature set, but isn't as ergonomic to use."}
+{"idx": 8, "title": "Archetypal SAE : Adaptive and Secure Dictionary ... - I Tech Epic", "date": "", "ddg_snippet": "A- SAE constrains dictionary atoms to the convex hull of the coaching knowledge and enhances stability whereas preserving expressive energy. Adaptive . Archetypal . Concept . Dictionary . Extraction .", "subpage_snippet": "", "source": "itechepic.com", "link": "https://itechepic.com/archetypal-sae-adaptive-and-secure-dictionary-studying-for-idea-extraction-in-massive-imaginative-and-prescient-fashions/", "content": "A- SAE constrains dictionary atoms to the convex hull of the coaching knowledge and enhances stability whereas preserving expressive energy. Adaptive . Archetypal . Concept . Dictionary . Extraction ."}
+{"idx": 9, "title": "NTT Researchers Advance AI and Machine Learning Accuracy...", "date": "", "ddg_snippet": "Archetypal SAE : Adaptive and Stable Dictionary Learning for Concept Extraction in Large Vision Models .", "subpage_snippet": "", "source": "www.businesswire.com", "link": "https://www.businesswire.com/news/home/20250722162521/en/NTT-Researchers-Advance-AI-and-Machine-Learning-Accuracy-Security-and-Cost-Effectiveness-at-ICML-2025", "content": "Archetypal SAE : Adaptive and Stable Dictionary Learning for Concept Extraction in Large Vision Models ."}
diff --git a/data/sampled_jsons/Attention_Is_All_You_Need_Vaswani_et_al_2017_paper_abstract_arxiv.jsonl b/data/sampled_jsons/Attention_Is_All_You_Need_Vaswani_et_al_2017_paper_abstract_arxiv.jsonl
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index 0000000000000000000000000000000000000000..ca886f2789587f9f3c9a60d522c6e78219e2870c
--- /dev/null
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@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Attention - Wikipedia", "date": "", "ddg_snippet": "Attention or focus, is the concentration of awareness on some phenomenon to the exclusion of other stimuli. [1] It is the selective concentration on discrete information, either subjectively or objectively.", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/Attention", "content": "Attention or focus, is the concentration of awareness on some phenomenon to the exclusion of other stimuli. [1] It is the selective concentration on discrete information, either subjectively or objectively."}
+{"idx": 1, "title": "ATTENTION Definition & Meaning - Merriam-Webster", "date": "", "ddg_snippet": "The meaning of ATTENTION is the act or state of applying the mind to something. How to use attention in a sentence.", "subpage_snippet": "", "source": "www.merriam-webster.com", "link": "https://www.merriam-webster.com/dictionary/attention", "content": "The meaning of ATTENTION is the act or state of applying the mind to something. How to use attention in a sentence."}
+{"idx": 2, "title": "ATTENTION | English meaning - Cambridge Dictionary", "date": "", "ddg_snippet": "ATTENTION definition: 1. notice, thought, or interest: 2. to make someone notice you: 3. to watch, listen to, or think…. Learn more.", "subpage_snippet": "", "source": "dictionary.cambridge.org", "link": "https://dictionary.cambridge.org/dictionary/english/attention", "content": "ATTENTION definition: 1. notice, thought, or interest: 2. to make someone notice you: 3. to watch, listen to, or think…. Learn more."}
+{"idx": 3, "title": "ATTENTION Definition & Meaning | Dictionary .com", "date": "", "ddg_snippet": "Attention definition: the act or faculty of attending, especially by directing the mind to an object.. See examples of ATTENTION used in a sentence.", "subpage_snippet": "", "source": "www.dictionary.com", "link": "https://www.dictionary.com/browse/attention", "content": "Attention definition: the act or faculty of attending, especially by directing the mind to an object.. See examples of ATTENTION used in a sentence."}
+{"idx": 4, "title": "Attention ( Stanford Encyclopedia of Philosophy )", "date": "", "ddg_snippet": "Sep 8, 2009 · Attention is involved in the selective directedness of our mental lives. The nature of this selectivity is one of the principal points of disagreement between the extant theories of attention . Some of the most influential theories treat the selectivity of attention as resulting from limitations in the brain’s capacity to process the complex properties of multiple perceivable stimuli. Other ...", "subpage_snippet": "", "source": "plato.stanford.edu", "link": "https://plato.stanford.edu/entries/attention/", "content": "Sep 8, 2009 · Attention is involved in the selective directedness of our mental lives. The nature of this selectivity is one of the principal points of disagreement between the extant theories of attention . Some of the most influential theories treat the selectivity of attention as resulting from limitations in the brain’s capacity to process the complex properties of multiple perceivable stimuli. Other ..."}
+{"idx": 5, "title": "Attention - definition of attention by The Free Dictionary", "date": "", "ddg_snippet": "attention If you give someone or something your attention , you look at them, listen to them, or think about them carefully. When he had their attention , he began his lecture. He turned his attention back to his magazine. You can also say that someone pays attention to something.", "subpage_snippet": "", "source": "www.thefreedictionary.com", "link": "https://www.thefreedictionary.com/attention", "content": "attention If you give someone or something your attention , you look at them, listen to them, or think about them carefully. When he had their attention , he began his lecture. He turned his attention back to his magazine. You can also say that someone pays attention to something."}
+{"idx": 6, "title": "How Psychologists Define Attention - Verywell Mind", "date": "", "ddg_snippet": "Oct 31, 2024 · Attention is the ability to actively process specific information in the environment while tuning out other details. It's like a highlighter or spotlight and makes what we focus on stand out.", "subpage_snippet": "", "source": "www.verywellmind.com", "link": "https://www.verywellmind.com/what-is-attention-2795009", "content": "Oct 31, 2024 · Attention is the ability to actively process specific information in the environment while tuning out other details. It's like a highlighter or spotlight and makes what we focus on stand out."}
+{"idx": 7, "title": "Attention - Psychology Today", "date": "", "ddg_snippet": "Attention can help us focus our awareness on a particular aspect of our environment, important decisions, or the thoughts in our head.", "subpage_snippet": "", "source": "www.psychologytoday.com", "link": "https://www.psychologytoday.com/us/basics/attention", "content": "Attention can help us focus our awareness on a particular aspect of our environment, important decisions, or the thoughts in our head."}
+{"idx": 8, "title": "Attention | Definition, Theories, Aspects, & Facts | Britannica", "date": "", "ddg_snippet": "Attention is awareness of the here and now in a focal and perceptive way. For early psychologists, such as Edward Bradford Titchener, attention determined the content of consciousness and influenced the quality of conscious experience.", "subpage_snippet": "", "source": "www.britannica.com", "link": "https://www.britannica.com/science/attention", "content": "Attention is awareness of the here and now in a focal and perceptive way. For early psychologists, such as Edward Bradford Titchener, attention determined the content of consciousness and influenced the quality of conscious experience."}
+{"idx": 9, "title": "attention - WordReference.com Dictionary of English", "date": "", "ddg_snippet": "[uncountable] thoughtful consideration with a view to action: I promise to give that matter my personal attention . [uncountable] kindness, courtesy, or high regard: lavished attention on the guests.", "subpage_snippet": "", "source": "www.wordreference.com", "link": "https://www.wordreference.com/definition/attention", "content": "[uncountable] thoughtful consideration with a view to action: I promise to give that matter my personal attention . [uncountable] kindness, courtesy, or high regard: lavished attention on the guests."}
diff --git a/data/sampled_jsons/Attention_Is_All_You_Need_arxiv_abstract_year_2017.jsonl b/data/sampled_jsons/Attention_Is_All_You_Need_arxiv_abstract_year_2017.jsonl
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index 0000000000000000000000000000000000000000..f316475d70608c8e67b12c7fec2110c8daab46aa
--- /dev/null
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@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Attention Is All You Need", "date": "", "ddg_snippet": "\" Attention Is All You Need \" is a 2017 landmark research paper in machine learning authored by eight scientists working at Google. The paper introduced a new ...", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/Attention_Is_All_You_Need", "content": "\" Attention Is All You Need \" is a 2017 landmark research paper in machine learning authored by eight scientists working at Google. The paper introduced a new ..."}
+{"idx": 1, "title": "[1706.03762] Attention Is All You Need", "date": "", "ddg_snippet": "12 Jun 2017 — We propose a new simple network architecture, the Transformer, based solely on attention mechanisms, dispensing with recurrence and convolutions entirely.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/1706.03762", "content": "12 Jun 2017 — We propose a new simple network architecture, the Transformer, based solely on attention mechanisms, dispensing with recurrence and convolutions entirely."}
+{"idx": 2, "title": "Attention Is All You Need", "date": "", "ddg_snippet": "We propose a new simple network architecture, the Transformer, based solely on attention mechanisms, dispensing with recurrence and convolutions entirely.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/1706.03762v7", "content": "We propose a new simple network architecture, the Transformer, based solely on attention mechanisms, dispensing with recurrence and convolutions entirely."}
+{"idx": 3, "title": "Attention is All you Need", "date": "", "ddg_snippet": "by A Vaswani · 2017 · Cited by 195236 — We propose a novel, simple network architecture based solely onan attention mechanism, dispensing with recurrence and convolutions entirely.", "subpage_snippet": "", "source": "papers.nips.cc", "link": "https://papers.nips.cc/paper/7181-attention-is-all-you-need", "content": "by A Vaswani · 2017 · Cited by 195236 — We propose a novel, simple network architecture based solely onan attention mechanism, dispensing with recurrence and convolutions entirely."}
+{"idx": 4, "title": "Attention is all you need | Proceedings of the 31st ...", "date": "", "ddg_snippet": "by A Vaswani · 2017 · Cited by 195236 — We propose a new simple network architecture, the Transformer, based solely on attention mechanisms, dispensing with recurrence and convolutions entirely.", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.5555/3295222.3295349", "content": "by A Vaswani · 2017 · Cited by 195236 — We propose a new simple network architecture, the Transformer, based solely on attention mechanisms, dispensing with recurrence and convolutions entirely."}
+{"idx": 5, "title": "[2407.15516] Attention Is All You Need But You Don't ...", "date": "", "ddg_snippet": "by G Tyukin · 2024 · Cited by 4 — In this work, we investigate the effect of dropping MLP and attention layers at inference time on the performance of Llama-v2 models.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2407.15516", "content": "by G Tyukin · 2024 · Cited by 4 — In this work, we investigate the effect of dropping MLP and attention layers at inference time on the performance of Llama-v2 models."}
+{"idx": 6, "title": "System 2 Attention (is something you might need too)", "date": "", "ddg_snippet": "by J Weston · 2023 · Cited by 85 — We introduce System 2 Attention (S2A), which leverages the ability of LLMs to reason in natural language and follow instructions in order to decide what to ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2311.11829", "content": "by J Weston · 2023 · Cited by 85 — We introduce System 2 Attention (S2A), which leverages the ability of LLMs to reason in natural language and follow instructions in order to decide what to ..."}
+{"idx": 7, "title": "[2501.05730] Element-wise Attention Is All You Need", "date": "", "ddg_snippet": "by G Feng · 2025 — We propose a novel element-wise attention mechanism, which uses the element-wise squared Euclidean distance , instead of the dot product operation, to compute ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2501.05730", "content": "by G Feng · 2025 — We propose a novel element-wise attention mechanism, which uses the element-wise squared Euclidean distance , instead of the dot product operation, to compute ..."}
+{"idx": 8, "title": "Attention Is All You Need - arXiv /", "date": "", "ddg_snippet": "Attention Is All You NeedAshish Vaswani Google Shazeer Google Parmar Google Uszkoreit Google Jones Google N. ... of Attention Is All You Need - arXiv", "subpage_snippet": "", "source": "pdf4pro.com", "link": "https://pdf4pro.com/view/attention-is-all-you-need-arxiv-5bc4df.html", "content": "Attention Is All You NeedAshish Vaswani Google Shazeer Google Parmar Google Uszkoreit Google Jones Google N. ... of Attention Is All You Need - arXiv"}
+{"idx": 9, "title": "[2103.03404] Attention is Not All You Need: Pure Attention", "date": "", "ddg_snippet": "View a PDF of the paper titled Attention is Not All You Need : Pure Attention Loses Rank Doubly Exponentially with Depth, by Yihe Dong and 2 other ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2103.03404", "content": "View a PDF of the paper titled Attention is Not All You Need : Pure Attention Loses Rank Doubly Exponentially with Depth, by Yihe Dong and 2 other ..."}
diff --git a/data/sampled_jsons/BIT-VO_Murai_in-pixel_processing_visual_odometry.jsonl b/data/sampled_jsons/BIT-VO_Murai_in-pixel_processing_visual_odometry.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..92222bdaf03dd7daa42ff20ea084dd119cd44a66
--- /dev/null
+++ b/data/sampled_jsons/BIT-VO_Murai_in-pixel_processing_visual_odometry.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "(PDF) Weighted Node Mapping and Localisation on a Pixel", "date": "", "ddg_snippet": "This paper implements and demonstrates visual route mapping and localisation upon a Pixel Processor Array (PPA). ... Processing Elements (PEs), each ...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/350187131_Weighted_Node_Mapping_and_Localisation_on_a_Pixel_Processor_Array", "content": "This paper implements and demonstrates visual route mapping and localisation upon a Pixel Processor Array (PPA). ... Processing Elements (PEs), each ..."}
+{"idx": 1, "title": "BIT - VO", "date": "", "ddg_snippet": "BIT - VO : Visual Odometry at 300 FPS using Binary Features from the Focal Plane. IROS 2020.", "subpage_snippet": "", "source": "rmurai.co.uk", "link": "https://rmurai.co.uk/projects/BIT-VO/", "content": "BIT - VO : Visual Odometry at 300 FPS using Binary Features from the Focal Plane. IROS 2020."}
+{"idx": 2, "title": "Visual Inertial Odometry using Focal Plane Binary Features ( BIT -VIO)", "date": "", "ddg_snippet": "Visual Odometry for Pixel Processor Arrays. BIT - VO : Visual Odometry at 300 FPS using Binary Features from the Focal Plane.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/382976639_Visual_Inertial_Odometry_using_Focal_Plane_Binary_Features_BIT-VIO", "content": "Visual Odometry for Pixel Processor Arrays. BIT - VO : Visual Odometry at 300 FPS using Binary Features from the Focal Plane."}
+{"idx": 3, "title": "BIT - VO : Visual Odometry at 300 FPS using Binary Features from the...", "date": "", "ddg_snippet": "Here, we present BIT - VO , which is, to the best of our knowledge, the first 6 Degrees of Freedom visual odometry algorithm which utilises the FPSP. Our entire system operates at 300 FPS in a natural scene, using binary edges and corner features detected by the SCAMP-5.", "subpage_snippet": "", "source": "synthical.com", "link": "https://synthical.com/article/BIT-VO:-Visual-Odometry-at-300-FPS-using-Binary-Features-from-the-Focal-Plane-91006d3d-8367-44b0-abe0-9195b19fd1ce", "content": "Here, we present BIT - VO , which is, to the best of our knowledge, the first 6 Degrees of Freedom visual odometry algorithm which utilises the FPSP. Our entire system operates at 300 FPS in a natural scene, using binary edges and corner features detected by the SCAMP-5."}
+{"idx": 4, "title": "High-frame rate homography and visual odometry by tracking binary...", "date": "", "ddg_snippet": "An efficient BInary feaTure Visual Odometry , BIT - VO , the first 6-DoF visual odometry which utilises the FPSP. Visual odometry using pixel processor arrays for unmanned aerial systems in GPS denied environments.", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/article/10.1007/s10514-023-10122-8", "content": "An efficient BInary feaTure Visual Odometry , BIT - VO , the first 6-DoF visual odometry which utilises the FPSP. Visual odometry using pixel processor arrays for unmanned aerial systems in GPS denied environments."}
+{"idx": 5, "title": "Riku Murai - Google Scholar", "date": "", "ddg_snippet": "BIT - VO : Visual Odometry at 300 FPS using Binary Features from the Focal Plane.2021. Visual Odometry Using a Focal-plane Sensor-processor. R Murai , P Kelly, S Saeedi, A Davison. 2019.", "subpage_snippet": "", "source": "scholar.google.co.in", "link": "https://scholar.google.co.in/citations?user=JCex0BwAAAAJ&hl=en", "content": "BIT - VO : Visual Odometry at 300 FPS using Binary Features from the Focal Plane.2021. Visual Odometry Using a Focal-plane Sensor-processor. R Murai , P Kelly, S Saeedi, A Davison. 2019."}
+{"idx": 6, "title": "Demo : Point-Feature Tracking for Pixel Processor Arrays", "date": "", "ddg_snippet": "Visual odometry for pixel processor arrays. In Proceedings of the IEEE International Conference on Computer Vision, pages 4604–4612, 2017. BIT - VO : Vi - sual odometry at 300 fps using binary features from the focal plane.", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2025W/EventVision/papers/Bose_Demo__Point-Feature_Tracking_for_Pixel_Processor_Arrays_CVPRW_2025_paper.pdf", "content": "Visual odometry for pixel processor arrays. In Proceedings of the IEEE International Conference on Computer Vision, pages 4604–4612, 2017. BIT - VO : Vi - sual odometry at 300 fps using binary features from the focal plane."}
+{"idx": 7, "title": "PixRO: Pixel -Distributed Rotational Odometry with Gaussian Belief...", "date": "", "ddg_snippet": "Visual Odometry for Pixel Pro-cessor Arrays. In 2017 IEEE International Conference on Computer Vision (ICCV), pages 4614–4622, Venice, 2017. BIT - VO : Visual Odometry at 300 FPS using Binary Features from the Focal Plane.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2406.09726", "content": "Visual Odometry for Pixel Pro-cessor Arrays. In 2017 IEEE International Conference on Computer Vision (ICCV), pages 4614–4622, Venice, 2017. BIT - VO : Visual Odometry at 300 FPS using Binary Features from the Focal Plane."}
+{"idx": 8, "title": "What is visual odometry ? Competitors, Complementary... | Sumble", "date": "", "ddg_snippet": "Visual odometry ( VO ) is the process of determining the position and orientation of a robot or other mobile platform by analyzing the sequence of images captured by one or more cameras.", "subpage_snippet": "", "source": "sumble.com", "link": "https://sumble.com/tech/visual-odometry", "content": "Visual odometry ( VO ) is the process of determining the position and orientation of a robot or other mobile platform by analyzing the sequence of images captured by one or more cameras."}
+{"idx": 9, "title": "Drone Autonomous Navigation by Hardware Image Processing", "date": "", "ddg_snippet": "2 autonomous navigation by image processing . In this section, two approaches applied for UAV autonomous navigation, visual odometry ( VO ) and computer vision (CV), are briey presented.", "subpage_snippet": "", "source": "cimec.org.ar", "link": "https://cimec.org.ar/ojs/index.php/mc/article/download/6040/6033", "content": "2 autonomous navigation by image processing . In this section, two approaches applied for UAV autonomous navigation, visual odometry ( VO ) and computer vision (CV), are briey presented."}
diff --git a/data/sampled_jsons/BIT-VO_Visual_Odometry_at_300_FPS_using_Binary_Features_from_the_Focal_Plane.jsonl b/data/sampled_jsons/BIT-VO_Visual_Odometry_at_300_FPS_using_Binary_Features_from_the_Focal_Plane.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..4c69ce5251af2ef32395983a1d185d952c5f2631
--- /dev/null
+++ b/data/sampled_jsons/BIT-VO_Visual_Odometry_at_300_FPS_using_Binary_Features_from_the_Focal_Plane.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "BIT-VO: Visual Odometry at 300 FPS using Binary Features from the Focal ...", "date": "", "ddg_snippet": "Here, we present BIT-VO , which is, to the best of our knowledge, the first 6 Degrees of Freedom visual odometry algorithm which utilises the FPSP. Our entire system operates at 300 FPS in a natural scene, using binary edges and corner features detected by the SCAMP-5.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2004.11186", "content": "Here, we present BIT-VO , which is, to the best of our knowledge, the first 6 Degrees of Freedom visual odometry algorithm which utilises the FPSP. Our entire system operates at 300 FPS in a natural scene, using binary edges and corner features detected by the SCAMP-5."}
+{"idx": 1, "title": "BIT-VO", "date": "", "ddg_snippet": "inproceedings{Murai:etal:IROS:2020, title={ BIT - VO : Visual Odometry at 300 FPS using Binary Features from the Focal Plane }, author={Murai, Riku and ...", "subpage_snippet": "", "source": "rmurai.co.uk", "link": "https://rmurai.co.uk/projects/BIT-VO/", "content": "inproceedings{Murai:etal:IROS:2020, title={ BIT - VO : Visual Odometry at 300 FPS using Binary Features from the Focal Plane }, author={Murai, Riku and ..."}
+{"idx": 2, "title": "BIT-VO: Visual Odometry at 300 FPS using Binary Features from the Focal ...", "date": "", "ddg_snippet": "Focal-plane Sensor-processor (FPSP) is a next-generation camera technology which enables every pixel on the sensor chip to perform computation in parallel, on the focal plane where the light intensity is captured. SCAMP-5 is a general-purpose FPSP used in this work and it carries out computations in the analog domain before analog to digital conversion. By extracting features from the image on ...", "subpage_snippet": "", "source": "ieeexplore.ieee.org", "link": "https://ieeexplore.ieee.org/abstract/document/9341151", "content": "Focal-plane Sensor-processor (FPSP) is a next-generation camera technology which enables every pixel on the sensor chip to perform computation in parallel, on the focal plane where the light intensity is captured. SCAMP-5 is a general-purpose FPSP used in this work and it carries out computations in the analog domain before analog to digital conversion. By extracting features from the image on ..."}
+{"idx": 3, "title": "[Spatial AI Study] BIT-VO: Visual Odometry at 300 FPS using Binary ...", "date": "", "ddg_snippet": "Presenter: Heokjin YunDate: 2024.10.22Paper: Murai et al 2021 - BIT-VO : Visual Odometry at 300 FPS using Binary Features from the Focal Plane (https://arxiv.o...", "subpage_snippet": "", "source": "www.youtube.com", "link": "https://www.youtube.com/watch?v=p7WAS2VRe9Q", "content": "Presenter: Heokjin YunDate: 2024.10.22Paper: Murai et al 2021 - BIT-VO : Visual Odometry at 300 FPS using Binary Features from the Focal Plane (https://arxiv.o..."}
+{"idx": 4, "title": "High-frame rate homography and visual odometry by tracking binary ...", "date": "", "ddg_snippet": "We presented BIT-VO , which is capable of performing VO at 300 FPS by using binary edges and corners computed on the focal plane . Our system is simplistic and minimal, yet it is sufficient to work in challenging conditions, highlighting the advantage of operating at high effective frame rates.", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/article/10.1007/s10514-023-10122-8", "content": "We presented BIT-VO , which is capable of performing VO at 300 FPS by using binary edges and corners computed on the focal plane . Our system is simplistic and minimal, yet it is sufficient to work in challenging conditions, highlighting the advantage of operating at high effective frame rates."}
+{"idx": 5, "title": "BIT-VO: Visual Odometry at 300 FPS using Binary Features from the Focal ...", "date": "", "ddg_snippet": "BITVO is presented, which is the first 6-Degrees of Freedom visual odometry algorithm which utilises the FPSP, and it operates at 300 FPS in a natural environment, using binary edges and corner features detected by the SCAMP-5.", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/BIT-VO:-Visual-Odometry-at-300-FPS-using-Binary-the-Murai-Saeedi/02d51188c578337a2ed81e8a42fee89109a2a25d/figure/6", "content": "BITVO is presented, which is the first 6-Degrees of Freedom visual odometry algorithm which utilises the FPSP, and it operates at 300 FPS in a natural environment, using binary edges and corner features detected by the SCAMP-5."}
+{"idx": 6, "title": "BIT-VO: Visual Odometry at 300 FPS using Binary Features from the Focal ...", "date": "", "ddg_snippet": "BIT-VO : Visual Odometry at 300 FPS using Binary Features from the Focal Plane Riku Murai1, Sajad Saeedi2, Paul H. J. Kelly Abstract—Focal- plane Sensor-processor (FPSP) is a next- generation camera technology which enables every pixel on the sensor chip to perform computation in parallel, on the focal plane where the light intensity is captured.", "subpage_snippet": "", "source": "web3.arxiv.org", "link": "https://web3.arxiv.org/pdf/2004.11186", "content": "BIT-VO : Visual Odometry at 300 FPS using Binary Features from the Focal Plane Riku Murai1, Sajad Saeedi2, Paul H. J. Kelly Abstract—Focal- plane Sensor-processor (FPSP) is a next- generation camera technology which enables every pixel on the sensor chip to perform computation in parallel, on the focal plane where the light intensity is captured."}
+{"idx": 7, "title": "High-frame rate homography and visual odometry by tracking binary ...", "date": "", "ddg_snippet": "Second, our work is extended to implement BIT -VO—6-DoF visual odometry system which operates under an unknown natural environment at 300 FPS .", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/372527248_High-frame_rate_homography_and_visual_odometry_by_tracking_binary_features_from_the_focal_plane", "content": "Second, our work is extended to implement BIT -VO—6-DoF visual odometry system which operates under an unknown natural environment at 300 FPS ."}
+{"idx": 8, "title": "High-frame rate homography and visual odometry by tracking binary ...", "date": "", "ddg_snippet": "We present a localisation system that utilise the FPSP in two parts. First, a 6-DoF odometry system is introduced, which efficiently estimates its position against a known marker at over 400 FPS . Second, our work is extended to implement BIT -VO—6-DoF visual odometry system which operates under an unknown natural environment at 300 FPS .", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.1007/s10514-023-10122-8", "content": "We present a localisation system that utilise the FPSP in two parts. First, a 6-DoF odometry system is introduced, which efficiently estimates its position against a known marker at over 400 FPS . Second, our work is extended to implement BIT -VO—6-DoF visual odometry system which operates under an unknown natural environment at 300 FPS ."}
+{"idx": 9, "title": "BIT-VO: Visual Odometry at 300 FPS using Binary Features from the Focal ...", "date": "", "ddg_snippet": "The estimated data x, y, z is plotted using red, green, blue and the ground truth data x, y, z is plotted using purple, orange, cyan respectively. Note rotations along z-axis wraps as full 360 degrees loops are made. - \" BIT-VO : Visual Odometry at 300 FPS using Binary Features from the Focal Plane \"", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/BIT-VO:-Visual-Odometry-at-300-FPS-using-Binary-the-Murai-Saeedi/02d51188c578337a2ed81e8a42fee89109a2a25d/figure/3", "content": "The estimated data x, y, z is plotted using red, green, blue and the ground truth data x, y, z is plotted using purple, orange, cyan respectively. Note rotations along z-axis wraps as full 360 degrees loops are made. - \" BIT-VO : Visual Odometry at 300 FPS using Binary Features from the Focal Plane \""}
diff --git a/data/sampled_jsons/Bansal_FOCS_2010_discrepancy_minimization.jsonl b/data/sampled_jsons/Bansal_FOCS_2010_discrepancy_minimization.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..a8f51665b5f3146b789946fa9027e65dc8b88529
--- /dev/null
+++ b/data/sampled_jsons/Bansal_FOCS_2010_discrepancy_minimization.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Constructive ℓ₂-Discrepancy Minimization with Additive", "date": "", "ddg_snippet": "... discrepancy minimisation was open for a long time, until Bansal ’s remarkable breakthrough [ 4 ] , which showed Spencer’s bound could be achieved ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2508.21423v1", "content": "... discrepancy minimisation was open for a long time, until Bansal ’s remarkable breakthrough [ 4 ] , which showed Spencer’s bound could be achieved ..."}
+{"idx": 1, "title": "Discrepancy Games and Sensitivity | Gödel's Lost Letter and", "date": "", "ddg_snippet": "Spencer ’ s talk was titled “ Four Discrepancies ” and based on a joint paper with Nikhil Bansal .", "subpage_snippet": "", "source": "rjlipton.com", "link": "https://rjlipton.com/2019/07/25/discrepancy-games-and-sensitivity/", "content": "Spencer ’ s talk was titled “ Four Discrepancies ” and based on a joint paper with Nikhil Bansal ."}
+{"idx": 2, "title": "Bibliography: Theory of Computing: An Open Access Electronic", "date": "", "ddg_snippet": "7] Nikhil Bansal and Shashwat Garg: Algorithmic discrepancy beyond partial coloring. ... and Thomas Rothvoss: Deterministic discrepancy minimization ...", "subpage_snippet": "", "source": "theoryofcomputing.org", "link": "http://theoryofcomputing.org/articles/v015a021/bibliography.html", "content": "7] Nikhil Bansal and Shashwat Garg: Algorithmic discrepancy beyond partial coloring. ... and Thomas Rothvoss: Deterministic discrepancy minimization ..."}
+{"idx": 3, "title": "Bibliography: Theory of Computing: An Open Access Electronic", "date": "", "ddg_snippet": "9] Nikhil Bansal and Shashwat Garg: Algorithmic discrepancy beyond partial coloring. ... Bansal and Viswanath Nagarajan: Approximation-friendly ...", "subpage_snippet": "", "source": "theoryofcomputing.org", "link": "https://theoryofcomputing.org/articles/v020a006/bibliography.html", "content": "9] Nikhil Bansal and Shashwat Garg: Algorithmic discrepancy beyond partial coloring. ... Bansal and Viswanath Nagarajan: Approximation-friendly ..."}
+{"idx": 4, "title": "Bibliography: Theory of Computing: An Open Access Electronic", "date": "", "ddg_snippet": "21] Avi Levy, Harishchandra Ramadas, and Thomas Rothvoss: Deterministic discrepancy minimization via the multiplicative weight update method.", "subpage_snippet": "", "source": "theoryofcomputing.org", "link": "https://theoryofcomputing.org/articles/v015a015/bibliography.html", "content": "21] Avi Levy, Harishchandra Ramadas, and Thomas Rothvoss: Deterministic discrepancy minimization via the multiplicative weight update method."}
+{"idx": 5, "title": "Bibliography: Theory of Computing: An Open Access Electronic", "date": "", "ddg_snippet": "21] Avi Levy, Harishchandra Ramadas, and Thomas Rothvoss: Deterministic discrepancy minimization via the multiplicative weight update method.", "subpage_snippet": "", "source": "theoryofcomputing.org", "link": "http://theoryofcomputing.org/articles/v015a015/bibliography.html", "content": "21] Avi Levy, Harishchandra Ramadas, and Thomas Rothvoss: Deterministic discrepancy minimization via the multiplicative weight update method."}
+{"idx": 6, "title": "Bibliography: Theory of Computing: An Open Access Electronic", "date": "", "ddg_snippet": "... Bansal : Constructive algorithms for discrepancy ... 13] Shachar Lovett and Raghu Meka: Constructive discrepancy minimization by walking on the edges.", "subpage_snippet": "", "source": "theoryofcomputing.org", "link": "https://theoryofcomputing.org/articles/v015a010/bibliography.html", "content": "... Bansal : Constructive algorithms for discrepancy ... 13] Shachar Lovett and Raghu Meka: Constructive discrepancy minimization by walking on the edges."}
+{"idx": 7, "title": "Anupam Gupta - Publications", "date": "", "ddg_snippet": "The number of minimum k-cuts: improving the Karger-{Stein} bound ... Non-Preemptive Flow-Time Minimization via Rejections", "subpage_snippet": "", "source": "www.cs.cmu.edu", "link": "https://www.cs.cmu.edu/~anupamg/newpubspage/my-papers.html", "content": "The number of minimum k-cuts: improving the Karger-{Stein} bound ... Non-Preemptive Flow-Time Minimization via Rejections"}
+{"idx": 8, "title": "Raghu Meka - researchr alias", "date": "", "ddg_snippet": "Online Discrepancy Minimization for Stochastic Arrivals Nikhil Bansal 0001 , Haotian Jiang , Raghu Meka , Sahil Singla 0001 , Makrand Sinha .", "subpage_snippet": "", "source": "researchr.org", "link": "https://researchr.org/alias/raghu-meka", "content": "Online Discrepancy Minimization for Stochastic Arrivals Nikhil Bansal 0001 , Haotian Jiang , Raghu Meka , Sahil Singla 0001 , Makrand Sinha ."}
+{"idx": 9, "title": "Homepage of Mohit Singh", "date": "", "ddg_snippet": "Efficient Algorithms for Discrepancy Minimization in Convex Sets , Ronen Eldan and Mohit Singh, Random Structures and Algorithms, 2018.", "subpage_snippet": "", "source": "www2.isye.gatech.edu", "link": "https://www2.isye.gatech.edu/~msingh94/publications.html", "content": "Efficient Algorithms for Discrepancy Minimization in Convex Sets , Ronen Eldan and Mohit Singh, Random Structures and Algorithms, 2018."}
diff --git a/data/sampled_jsons/Beckers_2023_'Disjunctive_counterfactuals_using_causal_models_a_critical_examination'_year_2023.jsonl b/data/sampled_jsons/Beckers_2023_'Disjunctive_counterfactuals_using_causal_models_a_critical_examination'_year_2023.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..72da822c1497ce4ceb984a338b85b6ec0629ad13
--- /dev/null
+++ b/data/sampled_jsons/Beckers_2023_'Disjunctive_counterfactuals_using_causal_models_a_critical_examination'_year_2023.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Intervention and Conditioning in Causal Bayesian", "date": "", "ddg_snippet": "Beckers , S. ( 2023 ). Disjunctive counterfactuals using causal models : a critical examination .", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=DC28Fpk76s", "content": "Beckers , S. ( 2023 ). Disjunctive counterfactuals using causal models : a critical examination ."}
+{"idx": 1, "title": "(PDF) Intervention and Conditioning in Causal Bayesian Networks", "date": "", "ddg_snippet": "Disjunctive counterfactuals using causal models : a critical examination .Probabilities of causation are proven to be critical in modern decision-making. This paper deals with the problem of estimating the probabilities of causation when treatment and effect are not binary.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/380847642_Intervention_and_Conditioning_in_Causal_Bayesian_Networks", "content": "Disjunctive counterfactuals using causal models : a critical examination .Probabilities of causation are proven to be critical in modern decision-making. This paper deals with the problem of estimating the probabilities of causation when treatment and effect are not binary."}
+{"idx": 2, "title": "Counterfactual Identifiability of Bijective Causal Models", "date": "", "ddg_snippet": "We study counterfactual identifiability in causal models with bijective generation mechanisms (BGM), a class that generalizes several widely- used causal models in the literature.", "subpage_snippet": "", "source": "proceedings.mlr.press", "link": "https://proceedings.mlr.press/v202/nasr-esfahany23a/nasr-esfahany23a.pdf", "content": "We study counterfactual identifiability in causal models with bijective generation mechanisms (BGM), a class that generalizes several widely- used causal models in the literature."}
+{"idx": 3, "title": "Connections between causal models and matrix completion via...", "date": "", "ddg_snippet": "In this thesis, we formalize these counterfactual statements using causal graphical models . In particular, we provide a new theoretical framework that connects causal graphs with factor models .", "subpage_snippet": "", "source": "upcommons.upc.edu", "link": "https://upcommons.upc.edu/handle/2117/394531", "content": "In this thesis, we formalize these counterfactual statements using causal graphical models . In particular, we provide a new theoretical framework that connects causal graphs with factor models ."}
+{"idx": 4, "title": "Disjunctive Counterfactuals in Alternative and", "date": "", "ddg_snippet": "Therefore using alternative semantics makes it possible for the consequent of a disjunctive counterfactual to operate on both constituent parts of the antecedent. Consequently an appropriate analysis of counterfactuals has to be chosen, which makes use of this possibility.", "subpage_snippet": "", "source": "projects.illc.uva.nl", "link": "https://projects.illc.uva.nl/inquisitivesemantics/assets/files/papers/Hiller2014_MoLproject-DisjunctiveCounterfactuals.pdf", "content": "Therefore using alternative semantics makes it possible for the consequent of a disjunctive counterfactual to operate on both constituent parts of the antecedent. Consequently an appropriate analysis of counterfactuals has to be chosen, which makes use of this possibility."}
+{"idx": 5, "title": "CLEAR : Can Language Models Really Understand Causal Graphs?", "date": "", "ddg_snippet": "To model and reason about causality , causal graphs offer a concise yet effective solution. Given the impressive ad-vancements in language models , a crucial ques-tion arises: can they really understand causal graphs?", "subpage_snippet": "", "source": "aclanthology.org", "link": "https://aclanthology.org/2024.findings-emnlp.363.pdf", "content": "To model and reason about causality , causal graphs offer a concise yet effective solution. Given the impressive ad-vancements in language models , a crucial ques-tion arises: can they really understand causal graphs?"}
+{"idx": 6, "title": "A Causal Analysis of Harm | Minds and Machines", "date": "", "ddg_snippet": "2 Causal Models and Actual Causality .A standard use of causal models is to define actual causation : that is, what it means for some particular event that occurred to cause another particular event.", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/article/10.1007/s11023-024-09689-7", "content": "2 Causal Models and Actual Causality .A standard use of causal models is to define actual causation : that is, what it means for some particular event that occurred to cause another particular event."}
+{"idx": 7, "title": "Interpretability at Scale: Identifying Causal Mechanisms in Alpaca", "date": "", "ddg_snippet": "Causal Proxy Models for Concept-Based Model Explanations.Faithful Explanations of Black-box NLP Models Using LLM-generated Counterfactuals . Y. Gat, Nitay Calderon, Amir Feder, Alexander Chapanin, Amit Sharma, Roi Reichart.", "subpage_snippet": "", "source": "www.connectedpapers.com", "link": "https://www.connectedpapers.com/main/3f7fa58806614a1f38ae760c25c1305e3a87d4ce/Interpretability-at-Scale:-Identifying-Causal-Mechanisms-in-Alpaca/graph", "content": "Causal Proxy Models for Concept-Based Model Explanations.Faithful Explanations of Black-box NLP Models Using LLM-generated Counterfactuals . Y. Gat, Nitay Calderon, Amir Feder, Alexander Chapanin, Amit Sharma, Roi Reichart."}
+{"idx": 8, "title": "Analysis of Sales Shift in Retail with Causal ... | Technical Terrence", "date": "", "ddg_snippet": "1: A simplified example of Causal Impact in action. The top graph shows two time series: the orange line represents actual observed data, while the blue line is the model ’s prediction, created using covariates and endogenous components.", "subpage_snippet": "", "source": "technicalterrence.com", "link": "https://technicalterrence.com/tech/ai/analysis-of-sales-shift-in-retail-with-causal-impact-a-case-study-at-carrefour/", "content": "1: A simplified example of Causal Impact in action. The top graph shows two time series: the orange line represents actual observed data, while the blue line is the model ’s prediction, created using covariates and endogenous components."}
+{"idx": 9, "title": "Counterfactual Data Augmentation", "date": "", "ddg_snippet": "Using causal interventions we can manipulate specific attributes and synthesize new counterfactual images based on existing data.", "subpage_snippet": "", "source": "www.imperial.ac.uk", "link": "https://www.imperial.ac.uk/media/imperial-college/faculty-of-engineering/computing/public/distinguished-projects/2223-ug-projects/Counterfactual-data-augmentation-for-deep-learning-predictive-models.pdf", "content": "Using causal interventions we can manipulate specific attributes and synthesize new counterfactual images based on existing data."}
diff --git a/data/sampled_jsons/Beckers_2023_sitearxiv.org_OR_siteneurips.cc_Pearl_incorrect.jsonl b/data/sampled_jsons/Beckers_2023_sitearxiv.org_OR_siteneurips.cc_Pearl_incorrect.jsonl
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index 0000000000000000000000000000000000000000..809675b49295d3586519420395b0f85cb139c695
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+++ b/data/sampled_jsons/Beckers_2023_sitearxiv.org_OR_siteneurips.cc_Pearl_incorrect.jsonl
@@ -0,0 +1,8 @@
+{"idx": 0, "title": "arXiv:2412.09843v1 [cs.LG] 13 Dec 2024", "date": "", "ddg_snippet": "Abstract In this study, we address causal inference when only observa-tional data and a valid causal ordering from the causal graph are available. We introduce a set of flow models that can recover component-wise, invertible transformation of exoge-nous variables. Our flow-based methods offer flexible model design while maintaining causal consistency regardless of the number of discretization ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2412.09843", "content": "Abstract In this study, we address causal inference when only observa-tional data and a valid causal ordering from the causal graph are available. We introduce a set of flow models that can recover component-wise, invertible transformation of exoge-nous variables. Our flow-based methods offer flexible model design while maintaining causal consistency regardless of the number of discretization ..."}
+{"idx": 1, "title": "Interventionand Conditioning in Causal Bayesian Networks", "date": "", "ddg_snippet": "Abstract Causal models are crucial for understanding complex systems and identifying causal relationships among variables. Even though causal models are extremely popular, conditional probability calculation of formulas involving interventions pose significant challenges. In case of Causal Bayesian Networks (CBNs), Pearl assumes autonomy of mechanisms that determine interventions to calculate ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2405.14728", "content": "Abstract Causal models are crucial for understanding complex systems and identifying causal relationships among variables. Even though causal models are extremely popular, conditional probability calculation of formulas involving interventions pose significant challenges. In case of Causal Bayesian Networks (CBNs), Pearl assumes autonomy of mechanisms that determine interventions to calculate ..."}
+{"idx": 2, "title": "Characterization and Learning of Causal Graphs with Small ...", "date": "", "ddg_snippet": "Abstract Constraint-based causal discovery algorithms learn part of the causal graph struc-ture by systematically testing conditional independences observed in the data. These algorithms, such as the PC algorithm and its variants, rely on graphical characterizations of the so-called equivalence class of causal graphs proposed by Pearl . However, constraint-based causal discovery algorithms ...", "subpage_snippet": "", "source": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper_files/paper/2023/file/eaef3b49866b942041a34bb8da397eb7-Paper-Conference.pdf", "content": "Abstract Constraint-based causal discovery algorithms learn part of the causal graph struc-ture by systematically testing conditional independences observed in the data. These algorithms, such as the PC algorithm and its variants, rely on graphical characterizations of the so-called equivalence class of causal graphs proposed by Pearl . However, constraint-based causal discovery algorithms ..."}
+{"idx": 3, "title": "Physics of droplet regulation in biological cells", "date": "", "ddg_snippet": "Sep 15, 2025 · Abstract Droplet formation has emerged as an essential concept for the spatiotemporal organisation of biomolecules in cells. However, classical descriptions of droplet dynamics based on passive liquid-liquid phase separation cannot capture the complex situation inside cells. This review discusses three distinct aspects that are crucial in cells: (i) biomolecules are diverse and individually ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2501.13639v3", "content": "Sep 15, 2025 · Abstract Droplet formation has emerged as an essential concept for the spatiotemporal organisation of biomolecules in cells. However, classical descriptions of droplet dynamics based on passive liquid-liquid phase separation cannot capture the complex situation inside cells. This review discusses three distinct aspects that are crucial in cells: (i) biomolecules are diverse and individually ..."}
+{"idx": 4, "title": "Intervention and Conditioning in Causal Bayesian Networks", "date": "", "ddg_snippet": "by S Galhotra · 2024 · Cited by 3 — Indeed, recent work of Beckers (2023 ) has shown that an approach given by Pearl (2000) to calculate these probabilities in a CBN is incorrect . 2 Pearl also ...", "subpage_snippet": "", "source": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper_files/paper/2024/file/a2118322165fffb648d1e341ff5a5b05-Paper-Conference.pdf", "content": "by S Galhotra · 2024 · Cited by 3 — Indeed, recent work of Beckers (2023 ) has shown that an approach given by Pearl (2000) to calculate these probabilities in a CBN is incorrect . 2 Pearl also ..."}
+{"idx": 5, "title": "Learning Structural Causal Models from Ordering: Identifiable ...", "date": "", "ddg_snippet": "Abstract In this study, we address causal inference when only observational data and a valid causal ordering from the causal graph are available. We introduce a set of flow models that can recover component-wise, invertible transformation of exogenous variables. Our flow-based methods offer flexible model design while maintaining causal consistency regardless of the number of discretization ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2412.09843v1", "content": "Abstract In this study, we address causal inference when only observational data and a valid causal ordering from the causal graph are available. We introduce a set of flow models that can recover component-wise, invertible transformation of exogenous variables. Our flow-based methods offer flexible model design while maintaining causal consistency regardless of the number of discretization ..."}
+{"idx": 6, "title": "[2412.09843] Learning Structural Causal Models from Ordering ...", "date": "", "ddg_snippet": "Abstract In this study, we address causal inference when only observational data and a valid causal ordering from the causal graph are available. We introduce a set of flow models that can recover component-wise, invertible transformation of exogenous variables. Our flow-based methods offer flexible model design while maintaining causal consistency regardless of the number of discretization ...", "subpage_snippet": "", "source": "ar5iv.labs.arxiv.org", "link": "https://ar5iv.labs.arxiv.org/html/2412.09843", "content": "Abstract In this study, we address causal inference when only observational data and a valid causal ordering from the causal graph are available. We introduce a set of flow models that can recover component-wise, invertible transformation of exogenous variables. Our flow-based methods offer flexible model design while maintaining causal consistency regardless of the number of discretization ..."}
+{"idx": 7, "title": "Causal Inference on Time Series using Restricted Structural ...", "date": "", "ddg_snippet": "Abstract Causal inference uses observational data to infer the causal structure of the data generating system. We study a class of restricted Structural Equation Models for time series that we call Time Series Models with Independent Noise (TiMINo). These models require independent residual time series, whereas traditional meth-ods like Granger causality exploit the variance of residuals. This ...", "subpage_snippet": "", "source": "papers.neurips.cc", "link": "https://papers.neurips.cc/paper/5063-causal-inference-on-time-series-using-restricted-structural-equation-models.pdf", "content": "Abstract Causal inference uses observational data to infer the causal structure of the data generating system. We study a class of restricted Structural Equation Models for time series that we call Time Series Models with Independent Noise (TiMINo). These models require independent residual time series, whereas traditional meth-ods like Granger causality exploit the variance of residuals. This ..."}
diff --git a/data/sampled_jsons/Beyond_Self-Repellent_Kernels_History-Driven_Target_Efficient_Nonlinear_MCMC_General_Graphs.jsonl b/data/sampled_jsons/Beyond_Self-Repellent_Kernels_History-Driven_Target_Efficient_Nonlinear_MCMC_General_Graphs.jsonl
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index 0000000000000000000000000000000000000000..741ecdaafef8f0cf491a5b74b0c00f5e7f47815e
--- /dev/null
+++ b/data/sampled_jsons/Beyond_Self-Repellent_Kernels_History-Driven_Target_Efficient_Nonlinear_MCMC_General_Graphs.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "History-Driven Target Towards Efficient Nonlinear MCMC ...", "date": "", "ddg_snippet": "by J Hu · 2025 — We propose a history - driven target (HDT) framework in Markov Chain Monte Carlo ( MCMC ) to improve any random walk algorithm on discrete state spaces.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2505.18300", "content": "by J Hu · 2025 — We propose a history - driven target (HDT) framework in Markov Chain Monte Carlo ( MCMC ) to improve any random walk algorithm on discrete state spaces."}
+{"idx": 1, "title": "History-Driven Target Towards Efficient Nonlinear MCMC ...", "date": "", "ddg_snippet": "We propose a history - driven target (HDT) framework in Markov Chain Monte Carlo ( MCMC ) to improve any random walk algorithm on discrete state spaces, ...", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/poster/46659", "content": "We propose a history - driven target (HDT) framework in Markov Chain Monte Carlo ( MCMC ) to improve any random walk algorithm on discrete state spaces, ..."}
+{"idx": 2, "title": "History-Driven Target Towards Efficient Nonlinear MCMC ...", "date": "", "ddg_snippet": "In this paper, we propose a history - driven target (HDT) framework for MCMC sampling on general graphs . By embedding self -repellency in the target rather than ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2505.18300v1", "content": "In this paper, we propose a history - driven target (HDT) framework for MCMC sampling on general graphs . By embedding self -repellency in the target rather than ..."}
+{"idx": 3, "title": "Our paper on efficient MCMC on graphs accepted at ICML 2025", "date": "", "ddg_snippet": "Excited to announce that our paper, “Beyond Self-Repellent Kernels: History-Driven Target Towards Efficient Non-linear MCMC on General Graphs ,” has been ...", "subpage_snippet": "", "source": "www.linkedin.com", "link": "https://www.linkedin.com/posts/jie-hu-ncsu_icml2025-mcmc-activity-7337284173516210176-FGlQ", "content": "Excited to announce that our paper, “Beyond Self-Repellent Kernels: History-Driven Target Towards Efficient Non-linear MCMC on General Graphs ,” has been ..."}
+{"idx": 4, "title": "Beyond Self-Repellent Kernels: History-Driven Target ...", "date": "", "ddg_snippet": "9 Jun 2025 — Overview. Introduces novel approach for efficient graph sampling using history-driven MCMC ; Improves upon self-repelling random walks by ...", "subpage_snippet": "", "source": "www.aimodels.fyi", "link": "https://www.aimodels.fyi/papers/arxiv/beyond-self-repellent-kernels-history-driven-target", "content": "9 Jun 2025 — Overview. Introduces novel approach for efficient graph sampling using history-driven MCMC ; Improves upon self-repelling random walks by ..."}
+{"idx": 5, "title": "Beyond Self-Repellent Kernels: History-Driven Target ...", "date": "", "ddg_snippet": "This research paper introduces a new method called the History-Driven Target (HDT) framework for improving sampling techniques used in random walks on ...", "subpage_snippet": "", "source": "bytez.com", "link": "https://bytez.com/docs/icml/46659/audio", "content": "This research paper introduces a new method called the History-Driven Target (HDT) framework for improving sampling techniques used in random walks on ..."}
+{"idx": 6, "title": "Beyond Self-Repellent Kernels: History-Driven Target Towards ...", "date": "", "ddg_snippet": "View recent discussion. Abstract: We propose a history - driven target (HDT) framework in Markov Chain Monte Carlo ( MCMC ) to improve any random walk algorithm ...", "subpage_snippet": "", "source": "www.alphaxiv.org", "link": "https://www.alphaxiv.org/abs/2505.18300", "content": "View recent discussion. Abstract: We propose a history - driven target (HDT) framework in Markov Chain Monte Carlo ( MCMC ) to improve any random walk algorithm ..."}
+{"idx": 7, "title": "Beyond Self-Repellent Kernels: History-Driven Target Towards ...", "date": "", "ddg_snippet": "error. 500. Oops, something went wrong!", "subpage_snippet": "", "source": "chatpaper.com", "link": "https://chatpaper.com/zh-CN/chatpaper/paper/141994", "content": "error. 500. Oops, something went wrong!"}
+{"idx": 8, "title": "ICML 2025 Beyond Self-Repellent Kernels", "date": "", "ddg_snippet": "The ICML Logo above may be used on presentations. Right-click and choose download. It is a vector graphic and may be used at any scale. Useful links. About ...", "subpage_snippet": "", "source": "dev.icml.cc", "link": "https://dev.icml.cc/virtual/2025/oral/47270", "content": "The ICML Logo above may be used on presentations. Right-click and choose download. It is a vector graphic and may be used at any scale. Useful links. About ..."}
+{"idx": 9, "title": "Downloads", "date": "", "ddg_snippet": "ADDP: Learning General Representations for Image Recognition and Generation with Alternating Denoising Diffusion Process", "subpage_snippet": "", "source": "iclr.cc", "link": "https://iclr.cc/Downloads/2024", "content": "ADDP: Learning General Representations for Image Recognition and Generation with Alternating Denoising Diffusion Process"}
diff --git a/data/sampled_jsons/Boltzmann-Aligned_Inverse_Folding_Model_BA-Cycle_method_Section_3.2_delta_delta_G_calculation_differ_year_2023.jsonl b/data/sampled_jsons/Boltzmann-Aligned_Inverse_Folding_Model_BA-Cycle_method_Section_3.2_delta_delta_G_calculation_differ_year_2023.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..9b4ceded180d49296bb0cf1de953a2ee6844a525
--- /dev/null
+++ b/data/sampled_jsons/Boltzmann-Aligned_Inverse_Folding_Model_BA-Cycle_method_Section_3.2_delta_delta_G_calculation_differ_year_2023.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Boltzmann-Aligned Inverse Folding Model as a Predictor of ... - GitHub", "date": "", "ddg_snippet": "The official implementation of our ICLR 2025 Spotlight paper \" Boltzmann-Aligned Inverse Folding Model as a Predictor of Mutational Effects on Protein-Protein Interactions\", which establishes a bidirectional connection between log-likelihood in protein inverse folding models and Δ Δ G values.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/aim-uofa/BA-DDG", "content": "The official implementation of our ICLR 2025 Spotlight paper \" Boltzmann-Aligned Inverse Folding Model as a Predictor of Mutational Effects on Protein-Protein Interactions\", which establishes a bidirectional connection between log-likelihood in protein inverse folding models and Δ Δ G values."}
+{"idx": 1, "title": "Boltzmann-Aligned Inverse Folding Model as a Predictor of Mutational ...", "date": "", "ddg_snippet": "Compared to previous inverse folding -based methods , our method explicitly accounts for the unbound state of protein complex in the Δ Δ 𝐺 \\ Delta\\Delta G roman_Δ roman_Δ italic_G thermodynamic cycle , introducing a physical inductive bias and achieving both supervised and unsupervised state-of-the-art (SoTA) performance.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2410.09543v1", "content": "Compared to previous inverse folding -based methods , our method explicitly accounts for the unbound state of protein complex in the Δ Δ 𝐺 \\ Delta\\Delta G roman_Δ roman_Δ italic_G thermodynamic cycle , introducing a physical inductive bias and achieving both supervised and unsupervised state-of-the-art (SoTA) performance."}
+{"idx": 2, "title": "AIDD论文详解：Boltzmann-Aligned Inverse Folding Model —— ICLR2025", "date": "", "ddg_snippet": "第一部分很好理解，在模型forward并利用式 (10)进行计算后可以得到预测的变化结合自由能 \\widehat { \\ Delta \\ Delta G } 与真实标签值 \\ Delta\\Delta G 计算MSE Loss；第二部分是衡量BA-DDG方法finetune后和原始模型的输出分布的KL散度，通俗点说就是希望finetune后的模型分布与原始 ...", "subpage_snippet": "", "source": "zhuanlan.zhihu.com", "link": "https://zhuanlan.zhihu.com/p/29398730183", "content": "第一部分很好理解，在模型forward并利用式 (10)进行计算后可以得到预测的变化结合自由能 \\widehat { \\ Delta \\ Delta G } 与真实标签值 \\ Delta\\Delta G 计算MSE Loss；第二部分是衡量BA-DDG方法finetune后和原始模型的输出分布的KL散度，通俗点说就是希望finetune后的模型分布与原始 ..."}
+{"idx": 3, "title": "[ICLR 2025 Spotlight] Boltzmann-Aligned Inverse Folding Model as a ...", "date": "", "ddg_snippet": "The official implementation of our ICLR 2025 Spotlight paper \" Boltzmann-Aligned Inverse Folding Model as a Predictor of Mutational Effects on Protein-Protein Interactions\", which establishes a bidirectional connection between log-likelihood in protein inverse folding models and $\\ Delta\\Delta G $ values.", "subpage_snippet": "", "source": "github.jpy.wang", "link": "https://github.jpy.wang/aim-uofa/BA-DDG", "content": "The official implementation of our ICLR 2025 Spotlight paper \" Boltzmann-Aligned Inverse Folding Model as a Predictor of Mutational Effects on Protein-Protein Interactions\", which establishes a bidirectional connection between log-likelihood in protein inverse folding models and $\\ Delta\\Delta G $ values."}
+{"idx": 4, "title": "Boltzmann-Aligned Inverse Folding Model as a Predictor of Mutational ...", "date": "", "ddg_snippet": "Compared to previous methods based on inverse folding , our method explicitly accounts for the unbound state of the protein complex in the $\\ Delta \\ Delta G $ thermodynamic cycle , introducing a physical inductive bias and achieving supervised and unsupervised state-of-the-art (SoTA) performance.Experimental results on SKEMPI v2 indicate that our ...", "subpage_snippet": "", "source": "proceedings.iclr.cc", "link": "https://proceedings.iclr.cc/paper_files/paper/2025/hash/2e10d50dfd2a9d52c06fbcd4ed89a022-Abstract-Conference.html", "content": "Compared to previous methods based on inverse folding , our method explicitly accounts for the unbound state of the protein complex in the $\\ Delta \\ Delta G $ thermodynamic cycle , introducing a physical inductive bias and achieving supervised and unsupervised state-of-the-art (SoTA) performance.Experimental results on SKEMPI v2 indicate that our ..."}
+{"idx": 5, "title": "Portal Weekly #67: MoML 2024, data-driven discovery, boltzmann-aligned ...", "date": "", "ddg_snippet": "Boltzmann-Aligned Inverse Folding Model as a Predictor of Mutational Effects on Protein-Protein Interactions Predicting changes in binding free energy (∆∆ G ) is essential for understanding and modifying protein-protein interactions, which are important in drug design.", "subpage_snippet": "", "source": "m2d2.substack.com", "link": "https://m2d2.substack.com/p/portal-weekly-67-moml-2024-data-driven", "content": "Boltzmann-Aligned Inverse Folding Model as a Predictor of Mutational Effects on Protein-Protein Interactions Predicting changes in binding free energy (∆∆ G ) is essential for understanding and modifying protein-protein interactions, which are important in drug design."}
+{"idx": 6, "title": "B -a Inverse Folding Model As a Predictor of Mutational Effects on ...", "date": "", "ddg_snippet": "In this work, we propose a technique named Boltzmann Alignment to transfer knowledge from pre-trained inverse folding models to ∆∆ G prediction. We first analyze the thermodynamic definition of ∆∆ G and introduce the Boltzmann distribution to connect energy with protein conformational distri-bution, thereby highlighting the potential of pre-trained probabilistic models . However, the ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2410.09543", "content": "In this work, we propose a technique named Boltzmann Alignment to transfer knowledge from pre-trained inverse folding models to ∆∆ G prediction. We first analyze the thermodynamic definition of ∆∆ G and introduce the Boltzmann distribution to connect energy with protein conformational distri-bution, thereby highlighting the potential of pre-trained probabilistic models . However, the ..."}
+{"idx": 7, "title": "Revisions | OpenReview", "date": "", "ddg_snippet": "Compared to previous methods based on inverse folding , our method explicitly accounts for the unbound state of the protein complex in the $\\ Delta \\ Delta G $ thermodynamic cycle , introducing a physical inductive bias and achieving supervised and unsupervised state-of-the-art (SoTA) performance.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/revisions?id=lzdFImKK8w", "content": "Compared to previous methods based on inverse folding , our method explicitly accounts for the unbound state of the protein complex in the $\\ Delta \\ Delta G $ thermodynamic cycle , introducing a physical inductive bias and achieving supervised and unsupervised state-of-the-art (SoTA) performance."}
+{"idx": 8, "title": "BA-DDG/README.md at master · aim-uofa/BA-DDG · GitHub", "date": "", "ddg_snippet": "The official implementation of our ICLR 2025 Spotlight paper \" Boltzmann-Aligned Inverse Folding Model as a Predictor of Mutational Effects on Protein-Protein Interactions\", which establishes a bidirectional connection between log-likelihood in protein inverse folding models and Δ Δ G values.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/aim-uofa/BA-DDG/blob/master/README.md", "content": "The official implementation of our ICLR 2025 Spotlight paper \" Boltzmann-Aligned Inverse Folding Model as a Predictor of Mutational Effects on Protein-Protein Interactions\", which establishes a bidirectional connection between log-likelihood in protein inverse folding models and Δ Δ G values."}
+{"idx": 9, "title": "Yang Tan - Homepage", "date": "", "ddg_snippet": "无监督方法 BA-Cycle ：将上述概率估算方法代入最终的 Δ Δ G 公式，便得到了一个无需训练的（无监督）评估方法，作者将其命名为 BA-Cycle 。", "subpage_snippet": "", "source": "tyang816.github.io", "link": "https://tyang816.github.io/bi/Boltzmann-Aligned-Inverse-Folding-Model-as-a-Predictor-of-Mutational-Effects-on-Protein-Protein-Interactions/", "content": "无监督方法 BA-Cycle ：将上述概率估算方法代入最终的 Δ Δ G 公式，便得到了一个无需训练的（无监督）评估方法，作者将其命名为 BA-Cycle 。"}
diff --git "a/data/sampled_jsons/Boltzmann-Aligned_Inverse_Folding_Model_Equation_8_\342\210\206\342\210\206G_formula.jsonl" "b/data/sampled_jsons/Boltzmann-Aligned_Inverse_Folding_Model_Equation_8_\342\210\206\342\210\206G_formula.jsonl"
new file mode 100644
index 0000000000000000000000000000000000000000..54617499f500090bfce49c064ed1980bad337111
--- /dev/null
+++ "b/data/sampled_jsons/Boltzmann-Aligned_Inverse_Folding_Model_Equation_8_\342\210\206\342\210\206G_formula.jsonl"
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Maxwell– Boltzmann distribution - Wikipedia", "date": "", "ddg_snippet": "In physics, the Maxwell– Boltzmann distribution, or Maxwell distribution, is a particular probability distribution named after James Clerk Maxwell and Ludwig Boltzmann . It was first defined and used for describing particle speeds in idealized gases...", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/Maxwell–Boltzmann_distribution", "content": "In physics, the Maxwell– Boltzmann distribution, or Maxwell distribution, is a particular probability distribution named after James Clerk Maxwell and Ludwig Boltzmann . It was first defined and used for describing particle speeds in idealized gases..."}
+{"idx": 1, "title": "Boltzmann-Aligned Inverse Folding Model as a Predictor of ...", "date": "", "ddg_snippet": "The official implementation of our ICLR 2025 Spotlight paper \" Boltzmann-Aligned Inverse Folding Model as a Predictor of Mutational Effects on Protein-Protein Interactions\", which establishes a bidirectional connection between log-likelihood in protein inverse folding models and Δ Δ G values.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/aim-uofa/BA-DDG", "content": "The official implementation of our ICLR 2025 Spotlight paper \" Boltzmann-Aligned Inverse Folding Model as a Predictor of Mutational Effects on Protein-Protein Interactions\", which establishes a bidirectional connection between log-likelihood in protein inverse folding models and Δ Δ G values."}
+{"idx": 2, "title": "B -A INVERSE FOLDING MODEL AS A PREDICTOR OF MUTATIONAL ...", "date": "", "ddg_snippet": "In this work, we propose a technique named Boltzmann Alignment to transfer knowledge from pre-trained inverse folding models to ∆∆G prediction. We first analyze the thermodynamic definition of ∆∆G and introduce the Boltzmann distribution to connect energy with protein conformational distri-bution, thereby highlighting the potential of pre-trained probabilistic models. However, the ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2410.09543", "content": "In this work, we propose a technique named Boltzmann Alignment to transfer knowledge from pre-trained inverse folding models to ∆∆G prediction. We first analyze the thermodynamic definition of ∆∆G and introduce the Boltzmann distribution to connect energy with protein conformational distri-bution, thereby highlighting the potential of pre-trained probabilistic models. However, the ..."}
+{"idx": 3, "title": "B -A INVERSE FOLDING MODEL AS A PREDICTOR OF MUTATIONAL ...", "date": "", "ddg_snippet": "ABSTRACT Predicting the change in binding free energy ( ∆∆G ) is crucial for understanding and modulating protein-protein interactions, which are critical in drug design. Due to the scarcity of experimental ∆∆G data, existing methods focus on pre-training, while neglecting the importance of alignment . In this work, we propose Boltzmann Alignment technique to transfer knowledge from pre ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=lzdFImKK8w", "content": "ABSTRACT Predicting the change in binding free energy ( ∆∆G ) is crucial for understanding and modulating protein-protein interactions, which are critical in drug design. Due to the scarcity of experimental ∆∆G data, existing methods focus on pre-training, while neglecting the importance of alignment . In this work, we propose Boltzmann Alignment technique to transfer knowledge from pre ..."}
+{"idx": 4, "title": "[ICLR 2025 Spotlight] Boltzmann-Aligned Inverse Folding Model ...", "date": "", "ddg_snippet": "The official implementation of our ICLR 2025 Spotlight paper \" Boltzmann-Aligned Inverse Folding Model as a Predictor of Mutational Effects on Protein-Protein Interactions\", which establishes a bidirectional connection between log-likelihood in protein inverse folding models and $\\Delta\\Delta G $ values.", "subpage_snippet": "", "source": "github.jpy.wang", "link": "https://github.jpy.wang/aim-uofa/BA-DDG", "content": "The official implementation of our ICLR 2025 Spotlight paper \" Boltzmann-Aligned Inverse Folding Model as a Predictor of Mutational Effects on Protein-Protein Interactions\", which establishes a bidirectional connection between log-likelihood in protein inverse folding models and $\\Delta\\Delta G $ values."}
+{"idx": 5, "title": "Boltzmann-Aligned Inverse Folding Model as a Predictor of ...", "date": "", "ddg_snippet": "Compared to previous methods based on inverse folding , our method explicitly accounts for the unbound state of the protein complex in the $\\Delta \\Delta G $ thermodynamic cycle, introducing a physical inductive bias and achieving supervised and unsupervised state-of-the-art (SoTA) performance.Experimental results on SKEMPI v2 indicate that our ...", "subpage_snippet": "", "source": "proceedings.iclr.cc", "link": "https://proceedings.iclr.cc/paper_files/paper/2025/hash/2e10d50dfd2a9d52c06fbcd4ed89a022-Abstract-Conference.html", "content": "Compared to previous methods based on inverse folding , our method explicitly accounts for the unbound state of the protein complex in the $\\Delta \\Delta G $ thermodynamic cycle, introducing a physical inductive bias and achieving supervised and unsupervised state-of-the-art (SoTA) performance.Experimental results on SKEMPI v2 indicate that our ..."}
+{"idx": 6, "title": "Boltzmann-Aligned Inverse Folding Model as a Predictor of ...", "date": "", "ddg_snippet": "Oct 12, 2024 · Download Citation | Boltzmann-Aligned Inverse Folding Model as a Predictor of Mutational Effects on Protein-Protein Interactions | Predicting the change in binding free energy ($\\Delta \\Delta G ...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/384930266_Boltzmann-Aligned_Inverse_Folding_Model_as_a_Predictor_of_Mutational_Effects_on_Protein-Protein_Interactions", "content": "Oct 12, 2024 · Download Citation | Boltzmann-Aligned Inverse Folding Model as a Predictor of Mutational Effects on Protein-Protein Interactions | Predicting the change in binding free energy ($\\Delta \\Delta G ..."}
+{"idx": 7, "title": "Boltzmann - Aligned Inverse Folding Model as... | OpenReview", "date": "", "ddg_snippet": "However, the protein conformational distribution is intractable. Therefore, we employ Bayes’ theorem to circumvent direct estimation and instead utilize the log-likelihood provided by protein inverse folding models for the estimation of $\\Delta\\Delta G ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=lzdFImKK8w", "content": "However, the protein conformational distribution is intractable. Therefore, we employ Bayes’ theorem to circumvent direct estimation and instead utilize the log-likelihood provided by protein inverse folding models for the estimation of $\\Delta\\Delta G ..."}
+{"idx": 8, "title": "Inverse Folding ICML 2022", "date": "", "ddg_snippet": "Training with predicted structures improves inverse folding model performance on the following zero-shot prediction tasks", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/media/icml-2022/Slides/16886.pdf", "content": "Training with predicted structures improves inverse folding model performance on the following zero-shot prediction tasks"}
+{"idx": 9, "title": "Approximation of the Linear Boltzmann Equation by the... | CoLab", "date": "", "ddg_snippet": "In order to describe Brownian motion rigorously, Boltzmann ’s integral equation must be used. The Fokker-Planck type of equation is only an approximation to the Boltzmann equation and its domain of validity is worth examining.", "subpage_snippet": "", "source": "colab.ws", "link": "https://colab.ws/articles/10.1103/physrev.162.186", "content": "In order to describe Brownian motion rigorously, Boltzmann ’s integral equation must be used. The Fokker-Planck type of equation is only an approximation to the Boltzmann equation and its domain of validity is worth examining."}
diff --git a/data/sampled_jsons/Boltzmann-Aligned_Inverse_Folding_Model_discussion_conclusion_limitations.jsonl b/data/sampled_jsons/Boltzmann-Aligned_Inverse_Folding_Model_discussion_conclusion_limitations.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..14e45c02649c0f9c9e2caa1b0a6bf1b6a5f875cb
--- /dev/null
+++ b/data/sampled_jsons/Boltzmann-Aligned_Inverse_Folding_Model_discussion_conclusion_limitations.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "[2410.09543] Boltzmann-Aligned Inverse Folding Model as a ...", "date": "", "ddg_snippet": "Oct 12, 2024 · In this work, we propose the Boltzmann Alignment technique to transfer knowledge from pre-trained inverse folding models to ΔΔG prediction. We begin by analyzing the thermodynamic definition of ΔΔG and introducing the Boltzmann distribution to connect energy with protein conformational distribution.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2410.09543", "content": "Oct 12, 2024 · In this work, we propose the Boltzmann Alignment technique to transfer knowledge from pre-trained inverse folding models to ΔΔG prediction. We begin by analyzing the thermodynamic definition of ΔΔG and introducing the Boltzmann distribution to connect energy with protein conformational distribution."}
+{"idx": 1, "title": "Boltzmann-Aligned Inverse Folding Model as a Predictor of ...", "date": "", "ddg_snippet": "The official implementation of our ICLR 2025 Spotlight paper \" Boltzmann-Aligned Inverse Folding Model as a Predictor of Mutational Effects on Protein-Protein Interactions\", which establishes a bidirectional connection between log-likelihood in protein inverse folding models and Δ Δ G values.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/aim-uofa/BA-DDG", "content": "The official implementation of our ICLR 2025 Spotlight paper \" Boltzmann-Aligned Inverse Folding Model as a Predictor of Mutational Effects on Protein-Protein Interactions\", which establishes a bidirectional connection between log-likelihood in protein inverse folding models and Δ Δ G values."}
+{"idx": 2, "title": "AIDD论文详解：Boltzmann-Aligned Inverse Folding Model —— ICLR2025", "date": "", "ddg_snippet": "玻尔兹曼对齐（ Boltzmann Alignment） 我知道大家看公式头皮发麻，所以在这里简单介绍一下侧重点： 公式1-4、5-7基本就是简单的代换，重点理解思想（加粗部分）即可，不需要自己推导。", "subpage_snippet": "", "source": "zhuanlan.zhihu.com", "link": "https://zhuanlan.zhihu.com/p/29398730183", "content": "玻尔兹曼对齐（ Boltzmann Alignment） 我知道大家看公式头皮发麻，所以在这里简单介绍一下侧重点： 公式1-4、5-7基本就是简单的代换，重点理解思想（加粗部分）即可，不需要自己推导。"}
+{"idx": 3, "title": "B -A INVERSE FOLDING MODEL AS A PREDICTOR OF MUTATIONAL ...", "date": "", "ddg_snippet": "existing methods focus on pre-training, while neglecting the importance of alignment. In this work, we propose Boltzmann Alignment technique o transfer knowledge from pre-trained inverse folding models to prediction of ∆∆G. We begin by analyzing the thermodynamic definition of ∆∆G and introducing t", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=lzdFImKK8w", "content": "existing methods focus on pre-training, while neglecting the importance of alignment. In this work, we propose Boltzmann Alignment technique o transfer knowledge from pre-trained inverse folding models to prediction of ∆∆G. We begin by analyzing the thermodynamic definition of ∆∆G and introducing t"}
+{"idx": 4, "title": "Advancing protein evolution with inverse folding models ...", "date": "", "ddg_snippet": "Aug 21, 2025 · Results Prediction of HF substitutions using structure-constrained inverse folding models Our goal was to assess the effectiveness of generalized inverse folding models in directing protein evolution and, if effective, to develop a new protein engineering approach that eliminates the need for custom models or multiple iterations.", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S0092867425006804", "content": "Aug 21, 2025 · Results Prediction of HF substitutions using structure-constrained inverse folding models Our goal was to assess the effectiveness of generalized inverse folding models in directing protein evolution and, if effective, to develop a new protein engineering approach that eliminates the need for custom models or multiple iterations."}
+{"idx": 5, "title": "Boltzmann-Aligned Inverse Folding Model as a Predictor of ...", "date": "", "ddg_snippet": "However, the protein conformational distribution is intractable. Therefore, we employ Bayes’ theorem to circumvent direct estimation and instead utilize the log-likelihood provided by protein inverse folding models for the estimation of $\\Delta\\Delta G$.", "subpage_snippet": "", "source": "proceedings.iclr.cc", "link": "https://proceedings.iclr.cc/paper_files/paper/2025/hash/2e10d50dfd2a9d52c06fbcd4ed89a022-Abstract-Conference.html", "content": "However, the protein conformational distribution is intractable. Therefore, we employ Bayes’ theorem to circumvent direct estimation and instead utilize the log-likelihood provided by protein inverse folding models for the estimation of $\\Delta\\Delta G$."}
+{"idx": 6, "title": "B -A INVERSE FOLDING MODEL AS A PREDICTOR OF MUTATIONAL ...", "date": "", "ddg_snippet": "In this work, we propose a technique named Boltzmann Alignment to transfer knowledge from pre-trained inverse folding models to ∆∆G prediction. We first analyze the thermodynamic definition of ∆∆G and introduce the Boltzmann distribution to connect energy with protein conformational distri-bution, thereby highlighting the potential of pre-trained probabilistic models. However, the ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2410.09543", "content": "In this work, we propose a technique named Boltzmann Alignment to transfer knowledge from pre-trained inverse folding models to ∆∆G prediction. We first analyze the thermodynamic definition of ∆∆G and introduce the Boltzmann distribution to connect energy with protein conformational distri-bution, thereby highlighting the potential of pre-trained probabilistic models. However, the ..."}
+{"idx": 7, "title": "Boltzmann-Aligned Inverse Folding Model as a Predictor of ...", "date": "", "ddg_snippet": "12 Oct 2024 — In this work, we propose the Boltzmann Alignment technique to transfer knowledge from pre-trained inverse folding models to Δ Δ G Δ Δ G \\ ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2410.09543v1", "content": "12 Oct 2024 — In this work, we propose the Boltzmann Alignment technique to transfer knowledge from pre-trained inverse folding models to Δ Δ G Δ Δ G \\ ..."}
+{"idx": 8, "title": "[Literature Review] Boltzmann-Aligned Inverse Folding Model as ...", "date": "", "ddg_snippet": "This page provides the most accurate and concise summary worldwide for the paper titled Boltzmann - Aligned Inverse Folding Model as a Predictor of Mutational ...", "subpage_snippet": "", "source": "www.themoonlight.io", "link": "https://www.themoonlight.io/en/review/boltzmann-aligned-inverse-folding-model-as-a-predictor-of-mutational-effects-on-protein-protein-interactions", "content": "This page provides the most accurate and concise summary worldwide for the paper titled Boltzmann - Aligned Inverse Folding Model as a Predictor of Mutational ..."}
+{"idx": 9, "title": "Protein folding stability estimation with explicit ...", "date": "", "ddg_snippet": "by H Lee · 2025 — These limitations listed above define key directions for the future model development toward enhancing the scope and accuracy of protein ... 22 pages", "subpage_snippet": "", "source": "www.biorxiv.org", "link": "https://www.biorxiv.org/content/10.1101/2025.02.10.637420v2.full.pdf", "content": "by H Lee · 2025 — These limitations listed above define key directions for the future model development toward enhancing the scope and accuracy of protein ... 22 pages"}
diff --git a/data/sampled_jsons/CALF_model_Liu_et_al_2024_time_series_forecasting_language_model_year_2024.jsonl b/data/sampled_jsons/CALF_model_Liu_et_al_2024_time_series_forecasting_language_model_year_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..18816f6277edb63ebbbd4f5bac64d45cd67155aa
--- /dev/null
+++ b/data/sampled_jsons/CALF_model_Liu_et_al_2024_time_series_forecasting_language_model_year_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "[2403.07300] CALF: Aligning LLMs for Time Series Forecasting via Cross ...", "date": "", "ddg_snippet": "Deep learning (e.g., Transformer) has been widely and successfully used in multivariate time series forecasting (MTSF). Unlike existing methods that focus on training models from a single modal of time series input, large language models (LLMs) based MTSF methods with cross-modal text and time series input have recently shown great superiority, especially with limited temporal data. However ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2403.07300", "content": "Deep learning (e.g., Transformer) has been widely and successfully used in multivariate time series forecasting (MTSF). Unlike existing methods that focus on training models from a single modal of time series input, large language models (LLMs) based MTSF methods with cross-modal text and time series input have recently shown great superiority, especially with limited temporal data. However ..."}
+{"idx": 1, "title": "CALF: Aligning LLMs for Time Series Forecasting via Cross ... - GitHub", "date": "", "ddg_snippet": "Current LLM-based methods either use linear layers to project time series to the LLM's feature dimension or employ cross-attention and contrastive learning techniques, which address only the input side and overlook alignment in the deeper layers. Our CALF achieves better alignment through multi-level cross-modal fine-tuning.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/Hank0626/CALF", "content": "Current LLM-based methods either use linear layers to project time series to the LLM's feature dimension or employ cross-attention and contrastive learning techniques, which address only the input side and overlook alignment in the deeper layers. Our CALF achieves better alignment through multi-level cross-modal fine-tuning."}
+{"idx": 2, "title": "PDF Are Language Models Actually Useful for Time Series Forecasting?", "date": "", "ddg_snippet": "This hypothesis is unsurprising given language models are now pervasive in machine learning research. However, direct connections between language modeling and time series forecasting remain largely undefined. So to what extent is language modeling really beneficial for traditional time series tasks?", "subpage_snippet": "", "source": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper_files/paper/2024/file/6ed5bf446f59e2c6646d23058c86424b-Paper-Conference.pdf", "content": "This hypothesis is unsurprising given language models are now pervasive in machine learning research. However, direct connections between language modeling and time series forecasting remain largely undefined. So to what extent is language modeling really beneficial for traditional time series tasks?"}
+{"idx": 3, "title": "Paper page - CALF: Aligning LLMs for Time Series Forecasting via Cross ...", "date": "", "ddg_snippet": "Abstract A novel Cross-Modal LLM Fine-Tuning ( CALF ) framework reduces distribution discrepancy between text and time series data for improved multivariate time series forecasting , achieving state-of-the-art performance with low computational complexity.", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/papers/2403.07300", "content": "Abstract A novel Cross-Modal LLM Fine-Tuning ( CALF ) framework reduces distribution discrepancy between text and time series data for improved multivariate time series forecasting , achieving state-of-the-art performance with low computational complexity."}
+{"idx": 4, "title": "Table 4 from CALF: Aligning LLMs for Time Series Forecasting via Cross ...", "date": "", "ddg_snippet": "This work leverages pre-trained Large Language Models to enhance time-series forecasting and adopts several Parameter-Efficient Fine-Tuning (PEFT) techniques, which have yielded state-of-the-art results in long-term forecasting .", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/CALF:-Aligning-LLMs-for-Time-Series-Forecasting-via-Liu-Guo/c5af46d51668c054e00c6419a522862531eaf9cc/figure/6", "content": "This work leverages pre-trained Large Language Models to enhance time-series forecasting and adopts several Parameter-Efficient Fine-Tuning (PEFT) techniques, which have yielded state-of-the-art results in long-term forecasting ."}
+{"idx": 5, "title": "Are Language Models Actually Useful for Time Series Forecasting?", "date": "", "ddg_snippet": "Large language models (LLMs) are being applied to time series forecasting . But are language models actually useful for time series ? In a series of ablation studies on three recent and popular LLM-based time series forecasting methods, we find that removing the LLM component or replacing it with a basic attention layer does not degrade forecasting performance -- in most cases, the results even ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2406.16964", "content": "Large language models (LLMs) are being applied to time series forecasting . But are language models actually useful for time series ? In a series of ablation studies on three recent and popular LLM-based time series forecasting methods, we find that removing the LLM component or replacing it with a basic attention layer does not degrade forecasting performance -- in most cases, the results even ..."}
+{"idx": 6, "title": "CALF: Aligning LLMs for Time Series Forecasting via Cross-modal Fine-Tuning", "date": "", "ddg_snippet": "Introduction Multivariate time series forecasting (MTSF) plays a crucial role in the domain of time series analysis and has further boasted a wide range of real-world applications including weather forecasting (Angryk et al . 2020), energy predic-tion (Demirel et al . 2012), financial modeling (Patton 2013).", "subpage_snippet": "", "source": "ojs.aaai.org", "link": "https://ojs.aaai.org/index.php/AAAI/article/view/34082/36237", "content": "Introduction Multivariate time series forecasting (MTSF) plays a crucial role in the domain of time series analysis and has further boasted a wide range of real-world applications including weather forecasting (Angryk et al . 2020), energy predic-tion (Demirel et al . 2012), financial modeling (Patton 2013)."}
+{"idx": 7, "title": "CALF/README.md at main · Hank0626/CALF · GitHub", "date": "", "ddg_snippet": "Current LLM-based methods either use linear layers to project time series to the LLM's feature dimension or employ cross-attention and contrastive learning techniques, which address only the input side and overlook alignment in the deeper layers. Our CALF achieves better alignment through multi-level cross-modal fine-tuning.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/Hank0626/CALF/blob/main/README.md", "content": "Current LLM-based methods either use linear layers to project time series to the LLM's feature dimension or employ cross-attention and contrastive learning techniques, which address only the input side and overlook alignment in the deeper layers. Our CALF achieves better alignment through multi-level cross-modal fine-tuning."}
+{"idx": 8, "title": "Large language models for time series | Proceedings of the Thirty-Third ...", "date": "", "ddg_snippet": "Large Language Models (LLMs) have seen significant use in domains such as natural language processing and computer vision. Going beyond text, image and graphics, LLMs present a significant potential for analysis of time series data, benefiting domains such as climate, IoT, healthcare, traffic, audio and finance. This survey paper provides an in-depth exploration and a detailed taxonomy of the ...", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.24963/ijcai.2024/921", "content": "Large Language Models (LLMs) have seen significant use in domains such as natural language processing and computer vision. Going beyond text, image and graphics, LLMs present a significant potential for analysis of time series data, benefiting domains such as climate, IoT, healthcare, traffic, audio and finance. This survey paper provides an in-depth exploration and a detailed taxonomy of the ..."}
+{"idx": 9, "title": "Time-LLM: Time Series Forecasting by Reprogramming Large Language Models", "date": "", "ddg_snippet": "Time series forecasting holds significant importance in many real-world dynamic systems and has been extensively studied. Unlike natural language process (NLP) and computer vision (CV), where a single large model can tackle multiple tasks, models for time series forecasting are often specialized, necessitating distinct designs for different tasks and applications. While pre-trained foundation ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2310.01728", "content": "Time series forecasting holds significant importance in many real-world dynamic systems and has been extensively studied. Unlike natural language process (NLP) and computer vision (CV), where a single large model can tackle multiple tasks, models for time series forecasting are often specialized, necessitating distinct designs for different tasks and applications. While pre-trained foundation ..."}
diff --git a/data/sampled_jsons/CVE-Bench_CVE-2024_CVE-2023_CVE-2022_CVSS_10.0_9.9_9.8_highest_score_Table_year_2025.jsonl b/data/sampled_jsons/CVE-Bench_CVE-2024_CVE-2023_CVE-2022_CVSS_10.0_9.9_9.8_highest_score_Table_year_2025.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..1fa9e6a087430dfae514e28a5a84bccef6de9228
--- /dev/null
+++ b/data/sampled_jsons/CVE-Bench_CVE-2024_CVE-2023_CVE-2022_CVSS_10.0_9.9_9.8_highest_score_Table_year_2025.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Security Vulnerabilities, CVEs Published In 2024 - CVEdetails.com", "date": "", "ddg_snippet": "Security vulnerabilities, CVEs published in 2024Firecrawl is a web scraper that allows users to extract the content of a webpage for a large language model. Versions prior to 1.1.1 contain a server-side request forgery (SSRF) vulnerability. The scraping engine could be exploited by crafting a malicious site that redirects to a local IP address. This allowed exfiltration of local network ...", "subpage_snippet": "", "source": "www.cvedetails.com", "link": "https://www.cvedetails.com/vulnerability-list/year-2024/vulnerabilities.html", "content": "Security vulnerabilities, CVEs published in 2024Firecrawl is a web scraper that allows users to extract the content of a webpage for a large language model. Versions prior to 1.1.1 contain a server-side request forgery (SSRF) vulnerability. The scraping engine could be exploited by crafting a malicious site that redirects to a local IP address. This allowed exfiltration of local network ..."}
+{"idx": 1, "title": "CVE: Common Vulnerabilities and Exposures", "date": "", "ddg_snippet": "At cve .org, we provide the authoritative reference method for publicly known information-security vulnerabilities and exposures", "subpage_snippet": "", "source": "www.cve.org", "link": "https://www.cve.org/", "content": "At cve .org, we provide the authoritative reference method for publicly known information-security vulnerabilities and exposures"}
+{"idx": 2, "title": "cve-bench/README.md at main · uiuc-kang-lab/cve-bench · GitHub", "date": "", "ddg_snippet": "This repository contains data and code used in the CVE-Bench (paper, blog), which is for evaluating AI agents on real world web vulnerabilities and exploits collected from National Vulnerability Database. CVE-Bench includes 40 critical-severity Common Vulnerability and Exposures ( CVE ) with the reference automatic exploits available on requests.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/uiuc-kang-lab/cve-bench/blob/main/README.md", "content": "This repository contains data and code used in the CVE-Bench (paper, blog), which is for evaluating AI agents on real world web vulnerabilities and exploits collected from National Vulnerability Database. CVE-Bench includes 40 critical-severity Common Vulnerability and Exposures ( CVE ) with the reference automatic exploits available on requests."}
+{"idx": 3, "title": "2024 CVEs in Review - Vulnerability Blog", "date": "", "ddg_snippet": "When comparing the averages , 2024 saw an average score of 7.1, a drop from 7.7 ( 2023 ), 7.8 ( 2022 ), 7.7 (2021), and 7.8 (2020) and which tells the story in a more drastic way. Last year was the largest drop ever and the 2nd largest change ever (2016 +1.1 score ) when the concept of a CNA was introduced.", "subpage_snippet": "", "source": "vulnerability.blog", "link": "https://vulnerability.blog/2025/01/14/2024-cves-in-review/", "content": "When comparing the averages , 2024 saw an average score of 7.1, a drop from 7.7 ( 2023 ), 7.8 ( 2022 ), 7.7 (2021), and 7.8 (2020) and which tells the story in a more drastic way. Last year was the largest drop ever and the 2nd largest change ever (2016 +1.1 score ) when the concept of a CNA was introduced."}
+{"idx": 4, "title": "2024 CVE Review - \"Critical, High, Medium\" Position Shifting in ...", "date": "", "ddg_snippet": "Compared to 2023 , the average CVSS score decreased from 7.09 to 6.9, while the median fell from 7.2 to 6.5—a 9.7% reduction. This drop aligns with a larger trend of declining CVSS scores observed over the past few years, raising questions about whether software is becoming more secure or if vulnerabilities are being assessed differently.", "subpage_snippet": "", "source": "cybersecuritynews.com", "link": "https://cybersecuritynews.com/2024-cve-review/", "content": "Compared to 2023 , the average CVSS score decreased from 7.09 to 6.9, while the median fell from 7.2 to 6.5—a 9.7% reduction. This drop aligns with a larger trend of declining CVSS scores observed over the past few years, raising questions about whether software is becoming more secure or if vulnerabilities are being assessed differently."}
+{"idx": 5, "title": "Over 40,000 CVEs Published in 2024, Marking a 38% Increase from 2023", "date": "", "ddg_snippet": "The year 2024 marked a significant milestone in cybersecurity, featuring an extraordinary surge in Common Vulnerabilities and Exposures ( CVE ) data. With the release of over 40,000 CVEs , the industry has seen a dramatic increase of more than 38% compared to the 28,818 CVEs published in 2023 . CVEs By The Numbers The statistics from 2024 reveal an average of 108 CVEs published daily. May stood ...", "subpage_snippet": "", "source": "cyberpress.org", "link": "https://cyberpress.org/over-40000-cves-published-in-2024/", "content": "The year 2024 marked a significant milestone in cybersecurity, featuring an extraordinary surge in Common Vulnerabilities and Exposures ( CVE ) data. With the release of over 40,000 CVEs , the industry has seen a dramatic increase of more than 38% compared to the 28,818 CVEs published in 2023 . CVEs By The Numbers The statistics from 2024 reveal an average of 108 CVEs published daily. May stood ..."}
+{"idx": 6, "title": "2024: A Record-Breaking Year in Cybersecurity Vulnerabilities", "date": "", "ddg_snippet": "2024 marks the seventh consecutive year of record-high CVE publications, with 15.32% of all CVEs ever recorded being published this year alone. The average CVSS score of 6.67 highlights the severity of these vulnerabilities, with 231 achieving a perfect 10.0 score .", "subpage_snippet": "", "source": "undercodenews.com", "link": "https://undercodenews.com/2024-a-record-breaking-year-in-cybersecurity-vulnerabilities/", "content": "2024 marks the seventh consecutive year of record-high CVE publications, with 15.32% of all CVEs ever recorded being published this year alone. The average CVSS score of 6.67 highlights the severity of these vulnerabilities, with 231 achieving a perfect 10.0 score ."}
+{"idx": 7, "title": "Vulnerability Summary for the Week of February 12, 2024 - CISA", "date": "", "ddg_snippet": "( CVE ) vulnerability naming standard and are organized according to severity, determined by the Common Vulnerability Scoring System ( CVSS ) standard. The division of high, medium, and low severities correspond to the following scores : High: vulnerabilities with a CVSS base score of 7.0-10.0 Medium: vulnerabilities with a CVSS base score of 4.0 ...", "subpage_snippet": "", "source": "www.cisa.gov", "link": "https://www.cisa.gov/news-events/bulletins/sb24-051", "content": "( CVE ) vulnerability naming standard and are organized according to severity, determined by the Common Vulnerability Scoring System ( CVSS ) standard. The division of high, medium, and low severities correspond to the following scores : High: vulnerabilities with a CVSS base score of 7.0-10.0 Medium: vulnerabilities with a CVSS base score of 4.0 ..."}
+{"idx": 8, "title": "CVE Security Vulnerability Database (Published in 2024)", "date": "", "ddg_snippet": "Identify Unique Vulnerabilities using our Year on Year CVE Database. This Vulnerability Database Contains Insight on Vulnerabilities Published in the Year 2024 .", "subpage_snippet": "", "source": "www.clouddefense.ai", "link": "https://www.clouddefense.ai/cve/2024", "content": "Identify Unique Vulnerabilities using our Year on Year CVE Database. This Vulnerability Database Contains Insight on Vulnerabilities Published in the Year 2024 ."}
+{"idx": 9, "title": "Nvd - Cve-2023-2024", "date": "", "ddg_snippet": "Information Technology Laboratory National Vulnerability DatabaseVulnerabilities", "subpage_snippet": "", "source": "nvd.nist.gov", "link": "https://nvd.nist.gov/vuln/detail/CVE-2023-2024", "content": "Information Technology Laboratory National Vulnerability DatabaseVulnerabilities"}
diff --git a/data/sampled_jsons/CVE-Bench_T-Agent_sqlmap_integration_database_access_improvement.jsonl b/data/sampled_jsons/CVE-Bench_T-Agent_sqlmap_integration_database_access_improvement.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..bb541cf583328363714a8db71731584f25cc6574
--- /dev/null
+++ b/data/sampled_jsons/CVE-Bench_T-Agent_sqlmap_integration_database_access_improvement.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "GitHub - uiuc-kang-lab/cve-bench: CVE-Bench: A Benchmark for ...", "date": "", "ddg_snippet": "Apr 24, 2025 · This repository contains data and code used in the CVE-Bench (paper, blog), which is for evaluating AI agents on real world web vulnerabilities and exploits collected from National Vulnerability Database . CVE-Bench includes 40 critical-severity Common Vulnerability and Exposures ( CVE ) with the reference automatic exploits available on requests.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/uiuc-kang-lab/cve-bench", "content": "Apr 24, 2025 · This repository contains data and code used in the CVE-Bench (paper, blog), which is for evaluating AI agents on real world web vulnerabilities and exploits collected from National Vulnerability Database . CVE-Bench includes 40 critical-severity Common Vulnerability and Exposures ( CVE ) with the reference automatic exploits available on requests."}
+{"idx": 1, "title": "CVE-Bench: A Benchmark for AI Agents’ Ability to Exploit Real ...", "date": "", "ddg_snippet": "Building a benchmark for real-world vulnerabilities involves both specialized expertise to reproduce exploits and a systematic approach to evaluating unpredictable threats. To address this challenge, we introduce CVE-Bench , a real-world cybersecurity benchmark based on critical-severity Common Vulnerabilities and Exposures.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2503.17332v1", "content": "Building a benchmark for real-world vulnerabilities involves both specialized expertise to reproduce exploits and a systematic approach to evaluating unpredictable threats. To address this challenge, we introduce CVE-Bench , a real-world cybersecurity benchmark based on critical-severity Common Vulnerabilities and Exposures."}
+{"idx": 2, "title": "uiuc-kang-lab/cve-bench | DeepWiki", "date": "", "ddg_snippet": "May 12, 2025 · What is CVE-Bench ? CVE-Bench is a benchmark that contains 40 critical-severity Common Vulnerability and Exposures (CVEs) collected from the National Vulnerability Database . It creates reproducible environments for testing AI agents ' abilities to discover and exploit web application vulnerabilities.", "subpage_snippet": "", "source": "deepwiki.com", "link": "https://deepwiki.com/uiuc-kang-lab/cve-bench/1-overview", "content": "May 12, 2025 · What is CVE-Bench ? CVE-Bench is a benchmark that contains 40 critical-severity Common Vulnerability and Exposures (CVEs) collected from the National Vulnerability Database . It creates reproducible environments for testing AI agents ' abilities to discover and exploit web application vulnerabilities."}
+{"idx": 3, "title": "CVE-Bench: Benchmarking LLM-based Software Engineering Agent ...", "date": "", "ddg_snippet": "3 days ago · CVE-Bench : Benchmarking LLM-based Software Engineering Agent ’s Ability to Repair Real-World CVE Vulnerabilities. In Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers), pages 4207–4224, Albuquerque, New Mexico.", "subpage_snippet": "", "source": "aclanthology.org", "link": "https://aclanthology.org/2025.naacl-long.212/", "content": "3 days ago · CVE-Bench : Benchmarking LLM-based Software Engineering Agent ’s Ability to Repair Real-World CVE Vulnerabilities. In Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers), pages 4207–4224, Albuquerque, New Mexico."}
+{"idx": 4, "title": "From SQL Queries to Exploits: The Art of Database Pentesting ...", "date": "", "ddg_snippet": "Apr 12, 2025 · Database penetration testing is a critical skill for cybersecurity professionals, and SQLmap is one of the most powerful tools for automating SQL injection attacks.", "subpage_snippet": "", "source": "undercodetesting.com", "link": "https://undercodetesting.com/from-sql-queries-to-exploits-the-art-of-database-pentesting-with-sqlmap/", "content": "Apr 12, 2025 · Database penetration testing is a critical skill for cybersecurity professionals, and SQLmap is one of the most powerful tools for automating SQL injection attacks."}
+{"idx": 5, "title": "SQL Injection Attacks – How To Use SQLMap To Find Database ...", "date": "", "ddg_snippet": "Aug 26, 2024 · SQL injection attacks allow hackers to insert malicious SQL code into application queries in order to access or destroy sensitive data in databases. According to security experts, SQL injection is one of the most common and dangerous application vulnerabilities. In this comprehensive 2600+ word guide, we will understand what SQL injection is, learn how to use the powerful SQLMap tool to find ...", "subpage_snippet": "", "source": "expertbeacon.com", "link": "https://expertbeacon.com/sql-injection-attacks-how-to-use-sqlmap-to-find-database-vulnerabilities/", "content": "Aug 26, 2024 · SQL injection attacks allow hackers to insert malicious SQL code into application queries in order to access or destroy sensitive data in databases. According to security experts, SQL injection is one of the most common and dangerous application vulnerabilities. In this comprehensive 2600+ word guide, we will understand what SQL injection is, learn how to use the powerful SQLMap tool to find ..."}
+{"idx": 6, "title": "Measuring AI Agents’ Ability to Exploit Web Applications", "date": "", "ddg_snippet": "Mar 31, 2025 · This highlights the need for continuous improvement in evaluating, red-teaming, and regulating AI agents . We hope CVE-bench can serve as a valuable tool for the community to assess the risks of ...", "subpage_snippet": "", "source": "medium.com", "link": "https://medium.com/@danieldkang/measuring-ai-agents-ability-to-exploit-web-applications-ba4225aa281f", "content": "Mar 31, 2025 · This highlights the need for continuous improvement in evaluating, red-teaming, and regulating AI agents . We hope CVE-bench can serve as a valuable tool for the community to assess the risks of ..."}
+{"idx": 7, "title": "CVE - Bench : A Benchmark for AI Agents ' Ability to Exploit Real-World...", "date": "", "ddg_snippet": "To exploit a database access vulnerability , the LLM agents need to retrieve the data in a specific table.B.4. Using CVE - Bench via inspect ai CVE - Bench is fully integrated with inspect ai, an open-source framework for LLM evaluations (AI Security Institute).", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2503.17332", "content": "To exploit a database access vulnerability , the LLM agents need to retrieve the data in a specific table.B.4. Using CVE - Bench via inspect ai CVE - Bench is fully integrated with inspect ai, an open-source framework for LLM evaluations (AI Security Institute)."}
+{"idx": 8, "title": "Sqlmap - HackTricks", "date": "", "ddg_snippet": "Docker release_ agent cgroups escape.References. SQLMap : Testing SQL Database Vulnerabilities .", "subpage_snippet": "", "source": "book.hacktricks.wiki", "link": "https://book.hacktricks.wiki/en/pentesting-web/sql-injection/sqlmap.html", "content": "Docker release_ agent cgroups escape.References. SQLMap : Testing SQL Database Vulnerabilities ."}
+{"idx": 9, "title": "Инструкция по использованию sqlmap . Ч.1: Основы... - HackWare.ru", "date": "", "ddg_snippet": "Что такое sqlmap , для чего она нужна. Программа sqlmap позволяет проверять сайты на наличие в них уязвимости SQL-инъекция, уязвимости XSS, а также эксплуатировать SQL-инъекцию.", "subpage_snippet": "", "source": "HackWare.ru", "link": "https://HackWare.ru/?p=1928", "content": "Что такое sqlmap , для чего она нужна. Программа sqlmap позволяет проверять сайты на наличие в них уязвимости SQL-инъекция, уязвимости XSS, а также эксплуатировать SQL-инъекцию."}
diff --git a/data/sampled_jsons/Can_We_Leave_Deepfake_Data_Behind_in_Training_Deepfake_Detector_Table_2_C-Avg_AUC_Transition_Loss_ab.jsonl b/data/sampled_jsons/Can_We_Leave_Deepfake_Data_Behind_in_Training_Deepfake_Detector_Table_2_C-Avg_AUC_Transition_Loss_ab.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..be250216dbdc8d8f6ed114641deefaef4c68d00e
--- /dev/null
+++ b/data/sampled_jsons/Can_We_Leave_Deepfake_Data_Behind_in_Training_Deepfake_Detector_Table_2_C-Avg_AUC_Transition_Loss_ab.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "[2408.17052] Can We Leave Deepfake Data Behind in Training ...", "date": "", "ddg_snippet": "Specifically, blendfake and deepfake can be explicitly delineated as the oriented pivot anchors between \"real-to-fake\" transitions . The accumulation of forgery information should be oriented and progressively increasing during this transition process.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2408.17052", "content": "Specifically, blendfake and deepfake can be explicitly delineated as the oriented pivot anchors between \"real-to-fake\" transitions . The accumulation of forgery information should be oriented and progressively increasing during this transition process."}
+{"idx": 1, "title": "(PDF) Can We Leave Deepfake Data Behind in Training Deepfake ...", "date": "", "ddg_snippet": "deepfake detector without using any deepfake data seems counter-intuitive. Table 3: Ablations on leveraging oriented anchors progressively ( AUC ). All variants are trained on. FF++ (in-dataset) and evaluated on other datasets (cross-dataset).", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/383648453_Can_We_Leave_Deepfake_Data_Behind_in_Training_Deepfake_Detector", "content": "deepfake detector without using any deepfake data seems counter-intuitive. Table 3: Ablations on leveraging oriented anchors progressively ( AUC ). All variants are trained on. FF++ (in-dataset) and evaluated on other datasets (cross-dataset)."}
+{"idx": 2, "title": "Can We Leave Deepfake Data Behind in Training", "date": "", "ddg_snippet": "Table 2 : Ablations for each network component ( AUC ↑ and EER↓). All variants are trained on FF++ (in-dataset) and evaluated on other datasets (cross-dataset). BF-only represents using only blendfake data as the negative samples.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=vh9yEPLeyD", "content": "Table 2 : Ablations for each network component ( AUC ↑ and EER↓). All variants are trained on FF++ (in-dataset) and evaluated on other datasets (cross-dataset). BF-only represents using only blendfake data as the negative samples."}
+{"idx": 3, "title": "Can We Leave Deepfake Data Behind in Training Deepfake ...", "date": "", "ddg_snippet": "This paper explores whether deepfake detection models can be trained without using deepfake data , which can be costly and difficult to obtain. The researchers experiment with different training approaches and evaluate the performance of the resulting deepfake detectors .", "subpage_snippet": "", "source": "www.aimodels.fyi", "link": "https://www.aimodels.fyi/papers/arxiv/can-we-leave-deepfake-data-behind-training", "content": "This paper explores whether deepfake detection models can be trained without using deepfake data , which can be costly and difficult to obtain. The researchers experiment with different training approaches and evaluate the performance of the resulting deepfake detectors ."}
+{"idx": 4, "title": "Can We Leave Deepfake Data Behind in Training Deepfake ...", "date": "", "ddg_snippet": "Therefore, a critical question arises: Can we leave deepfake behind and rely solely on blendfake data to train an effective deepfake detector ?", "subpage_snippet": "", "source": "papers.nips.cc", "link": "https://papers.nips.cc/paper_files/paper/2024/hash/2718a032d15e0b80cd164b240220df89-Abstract-Conference.html", "content": "Therefore, a critical question arises: Can we leave deepfake behind and rely solely on blendfake data to train an effective deepfake detector ?"}
+{"idx": 5, "title": "Can We Leave Deepfake Data Behind in Training Deepfake ...", "date": "", "ddg_snippet": "...which combines deepfake and blendfake data , results in inferior performance to methods using only blendfake data (so-called \"1+1< 2 \"). Therefore, a critical question arises: Can we leave deepfake behind and rely solely on blendfake data to train an effective deepfake detector ?", "subpage_snippet": "", "source": "papers.cool", "link": "https://papers.cool/venue/vh9yEPLeyD@OpenReview", "content": "...which combines deepfake and blendfake data , results in inferior performance to methods using only blendfake data (so-called \"1+1< 2 \"). Therefore, a critical question arises: Can we leave deepfake behind and rely solely on blendfake data to train an effective deepfake detector ?"}
+{"idx": 6, "title": "FreqDebias: Towards Generalizable Deepfake Detection via...", "date": "", "ddg_snippet": "Can we leave deepfake data . behind in training deepfake detector ? In Advances in Neural. Information Processing Systems, 2024. data -level debiasing for deepfake detection . arXiv preprint.", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2025/papers/Kashiani_FreqDebias_Towards_Generalizable_Deepfake_Detection_via_Consistency-Driven_Frequency_Debiasing_CVPR_2025_paper.pdf", "content": "Can we leave deepfake data . behind in training deepfake detector ? In Advances in Neural. Information Processing Systems, 2024. data -level debiasing for deepfake detection . arXiv preprint."}
+{"idx": 7, "title": "SCLBD/DeepfakeBench: A comprehensive benchmark of deepfake ...", "date": "", "ddg_snippet": "In the above table , \" Avg .\" donates the average AUC for within-domain and cross-domain evaluation, and the overall results. \"Top3\" represents the count of each method ranks within the top-3 across all testing datasets. The best-performing method for each column is highlighted.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/SCLBD/DeepfakeBench", "content": "In the above table , \" Avg .\" donates the average AUC for within-domain and cross-domain evaluation, and the overall results. \"Top3\" represents the count of each method ranks within the top-3 across all testing datasets. The best-performing method for each column is highlighted."}
+{"idx": 8, "title": "Can We Leave Deepfake Data Behind in Training Deepfake ...", "date": "", "ddg_snippet": "This table presents the Area Under the Curve ( AUC ) scores for various deepfake detection methods evaluated on multiple datasets.", "subpage_snippet": "", "source": "deep-diver.github.io", "link": "https://deep-diver.github.io/neurips2024/posters/vh9yepleyd/", "content": "This table presents the Area Under the Curve ( AUC ) scores for various deepfake detection methods evaluated on multiple datasets."}
+{"idx": 9, "title": "Deepfake Face Detection and Adversarial Attack Defense Method...", "date": "", "ddg_snippet": "Table 4: The detection AUC results were obtained from training experiments with the gram l 2 loss function and the emd loss function in efficientnet. Table 8: AUC and ACC metrics of deepfake face detection with and without adversarial training on four different datasets.", "subpage_snippet": "", "source": "www.thefreelibrary.com", "link": "https://www.thefreelibrary.com/Deepfake+Face+Detection+and+Adversarial+Attack+Defense+Method+Based...-a0845571235", "content": "Table 4: The detection AUC results were obtained from training experiments with the gram l 2 loss function and the emd loss function in efficientnet. Table 8: AUC and ACC metrics of deepfake face detection with and without adversarial training on four different datasets."}
diff --git a/data/sampled_jsons/Capturing_dynamics_of_time-varying_data_via_topology_Crocker.jsonl b/data/sampled_jsons/Capturing_dynamics_of_time-varying_data_via_topology_Crocker.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..7e201c2ea37f30b6868d4eb8d3f0393212b8facc
--- /dev/null
+++ b/data/sampled_jsons/Capturing_dynamics_of_time-varying_data_via_topology_Crocker.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "[2010.05780] Capturing Dynamics of Time-Varying Data via", "date": "", "ddg_snippet": "View a PDF of the paper titled Capturing Dynamics of Time - Varying Data via Topology , by Lu Xian and 3 other authors", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2010.05780", "content": "View a PDF of the paper titled Capturing Dynamics of Time - Varying Data via Topology , by Lu Xian and 3 other authors"}
+{"idx": 1, "title": "US20050050098A1 - System and method for aligning data frames in", "date": "", "ddg_snippet": "This invention relates to a method for capturing data from a system of multiple computer networks in order to analyze the networks for performance.", "subpage_snippet": "", "source": "patents.google.com", "link": "https://patents.google.com/patent/US20050050098A1/en", "content": "This invention relates to a method for capturing data from a system of multiple computer networks in order to analyze the networks for performance."}
+{"idx": 2, "title": "Temporal network analysis using zigzag persistence | EPJ Data", "date": "", "ddg_snippet": "... to a system modeled as a network with edges whose weights are functions of time , or they may represent a time series of a complex dynamical system.", "subpage_snippet": "", "source": "epjdatascience.springeropen.com", "link": "https://epjdatascience.springeropen.com/articles/10.1140/epjds/s13688-023-00379-5", "content": "... to a system modeled as a network with edges whose weights are functions of time , or they may represent a time series of a complex dynamical system."}
+{"idx": 3, "title": "Gene activity fully predicts transcriptional bursting dynamics", "date": "", "ddg_snippet": "Eukaryotic transcriptional regulation is an inherently dynamic and stochastic process, orchestrated by a series of molecular events governing ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2304.08770v3", "content": "Eukaryotic transcriptional regulation is an inherently dynamic and stochastic process, orchestrated by a series of molecular events governing ..."}
+{"idx": 4, "title": "Persistent Topological Features in Large Language Models", "date": "", "ddg_snippet": "... prominent tool within TDA is persistent homology, which tracks the birth and death of topological features across different scales, thereby capturing ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2410.11042v3", "content": "... prominent tool within TDA is persistent homology, which tracks the birth and death of topological features across different scales, thereby capturing ..."}
+{"idx": 5, "title": "Research - Lori Ziegelmeier", "date": "", "ddg_snippet": "... tools from computational geometry and topology to a wide variety of data sets ... Capturing Dynamics of Time - Varying Data via Topology ( Preprint )", "subpage_snippet": "", "source": "www.loriziegelmeier.com", "link": "https://www.loriziegelmeier.com/research.html", "content": "... tools from computational geometry and topology to a wide variety of data sets ... Capturing Dynamics of Time - Varying Data via Topology ( Preprint )"}
+{"idx": 6, "title": "US10033700B2 - Dynamic evaluation of access rights - Google", "date": "", "ddg_snippet": "2007-02-12 Assigned to GUARDIAN DATA STORAGE, LLC reassignment GUARDIAN DATA STORAGE, LLC ASSIGNMENT OF ASSIGNORS INTEREST (SEE DOCUMENT FOR DETAILS).", "subpage_snippet": "", "source": "patents.google.com", "link": "https://patents.google.com/patent/US10033700B2/en", "content": "2007-02-12 Assigned to GUARDIAN DATA STORAGE, LLC reassignment GUARDIAN DATA STORAGE, LLC ASSIGNMENT OF ASSIGNORS INTEREST (SEE DOCUMENT FOR DETAILS)."}
+{"idx": 7, "title": "Henry Adams", "date": "", "ddg_snippet": "Over 2019-2022 I was a member of the Descriptors of Energy Landscapes Using Topological Data Analysis (DELTA) leadership team.", "subpage_snippet": "", "source": "www.math.colostate.edu", "link": "https://www.math.colostate.edu/~adams/research/", "content": "Over 2019-2022 I was a member of the Descriptors of Energy Landscapes Using Topological Data Analysis (DELTA) leadership team."}
+{"idx": 8, "title": "Persistent homology: An introduction and a new text", "date": "", "ddg_snippet": "... these limitations, some researchers advocate for the adoption of Topological Data Analysis (TDA), a statistical approach that discerningly captures ...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/262311097_Persistent_homology_An_introduction_and_a_new_text_representation_for_natural_language_processing", "content": "... these limitations, some researchers advocate for the adoption of Topological Data Analysis (TDA), a statistical approach that discerningly captures ..."}
+{"idx": 9, "title": "Quantifying topological features and irregularities in", "date": "", "ddg_snippet": "... often exhibit remarkable robustness, variation and irregularity exist at multiple scales and can carry important information about the underlying ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2509.11023v1", "content": "... often exhibit remarkable robustness, variation and irregularity exist at multiple scales and can carry important information about the underlying ..."}
diff --git a/data/sampled_jsons/Catoni-OFUL_algorithm_regret_bound_Table_1_Contextual_Bandits_are_Robust_to_Heavy-tailed_Rewards.jsonl b/data/sampled_jsons/Catoni-OFUL_algorithm_regret_bound_Table_1_Contextual_Bandits_are_Robust_to_Heavy-tailed_Rewards.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..4f127c3f3a2feafd883aba3d77c36e9f1a1513b8
--- /dev/null
+++ b/data/sampled_jsons/Catoni-OFUL_algorithm_regret_bound_Table_1_Contextual_Bandits_are_Robust_to_Heavy-tailed_Rewards.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Catoni Contextual Bandits are Robust to Heavy - tailed Rewards", "date": "", "ddg_snippet": "Minimax optimal regret bounds in the worst-case over problem instances for contextual bandit learning are relatively well-understood in the literature, both using policy-based approaches in the agnostic case, and regression-based approaches in the realizable case.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2502.02486", "content": "Minimax optimal regret bounds in the worst-case over problem instances for contextual bandit learning are relatively well-understood in the literature, both using policy-based approaches in the agnostic case, and regression-based approaches in the realizable case."}
+{"idx": 1, "title": "Catoni Contextual Bandits are Robust to Heavy - tailed Rewards", "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. However, many practical scenarios naturally involve heavy - tailed rewards or rewards where the...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=5IpVe9PH14", "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. However, many practical scenarios naturally involve heavy - tailed rewards or rewards where the..."}
+{"idx": 2, "title": "Bandits Corrupted by Nature: Lower Bounds on Regret and Robust ...", "date": "", "ddg_snippet": "Bandits with Stochastic Corruption: Problem formulation. Lower bounds for uniformly good policies under heavy - tails and corruptions. Robust bandit algorithm : Huber's estimator and upper bound on the regret .", "subpage_snippet": "", "source": "hal.science", "link": "https://hal.science/hal-04615733v1/document", "content": "Bandits with Stochastic Corruption: Problem formulation. Lower bounds for uniformly good policies under heavy - tails and corruptions. Robust bandit algorithm : Huber's estimator and upper bound on the regret ."}
+{"idx": 3, "title": "Improved Regret Bounds for Linear Bandits with Heavy - Tailed ...", "date": "", "ddg_snippet": "No- regret algorithms for heavy - tailed linear bandits . In International Conference on Machine Learning (ICML), pages 1642-1650. Heavy - tailed distributions in the amplitude of neural oscillations.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/392466000_Improved_Regret_Bounds_for_Linear_Bandits_with_Heavy-Tailed_Rewards", "content": "No- regret algorithms for heavy - tailed linear bandits . In International Conference on Machine Learning (ICML), pages 1642-1650. Heavy - tailed distributions in the amplitude of neural oscillations."}
+{"idx": 4, "title": "Tackling Heavy - Tailed Rewards in Reinforcement", "date": "", "ddg_snippet": "HEAVY- OFUL and HEAVY-LSVI-UCB achieve minimax optimal and instance-dependent regret bounds scaling with the central moments. We also provide a lower bound for linear MDPs with heavy - tailed rewards to demonstrate the opti-mality of HEAVY-LSVI-UCB.", "subpage_snippet": "", "source": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper_files/paper/2023/file/b11393733b1ea5890100302ab8a0f74c-Paper-Conference.pdf", "content": "HEAVY- OFUL and HEAVY-LSVI-UCB achieve minimax optimal and instance-dependent regret bounds scaling with the central moments. We also provide a lower bound for linear MDPs with heavy - tailed rewards to demonstrate the opti-mality of HEAVY-LSVI-UCB."}
+{"idx": 5, "title": "Optimal Algorithms for Stochastic Multi-Armed", "date": "", "ddg_snippet": "Multi-Armed Bandits with Heavy - Tailed Rewards We compare APE2 with robust UCB [3] and DSEE [17]. Note that an empirical comparison with GSR [7] is omitted here and can be found in the supplementary material since GSR shows poor performance in terms of the cumulative...", "subpage_snippet": "", "source": "papers.nips.cc", "link": "https://papers.nips.cc/paper/2020/file/607bc9ebe4abfcd65181bfbef6252830-Paper.pdf", "content": "Multi-Armed Bandits with Heavy - Tailed Rewards We compare APE2 with robust UCB [3] and DSEE [17]. Note that an empirical comparison with GSR [7] is omitted here and can be found in the supplementary material since GSR shows poor performance in terms of the cumulative..."}
+{"idx": 6, "title": "Optimal Algorithms for Lipschitz Bandits with Heavy - tailed Rewards", "date": "", "ddg_snippet": "While there exist lower bounds of Lipschitz bandits for sub-Gaussian rewards (Slivkins, 2014), to the best of our knowledge, this is the rst lower bound for heavy - tailed rewards .We have proposed two adaptive algorithms for Lipschitz bandits with heavy - tailed rewards .", "subpage_snippet": "", "source": "proceedings.mlr.press", "link": "https://proceedings.mlr.press/v97/lu19c/lu19c.pdf", "content": "While there exist lower bounds of Lipschitz bandits for sub-Gaussian rewards (Slivkins, 2014), to the best of our knowledge, this is the rst lower bound for heavy - tailed rewards .We have proposed two adaptive algorithms for Lipschitz bandits with heavy - tailed rewards ."}
+{"idx": 7, "title": "Nearly Optimal Regret for Stochastic Linear Bandits with...", "date": "", "ddg_snippet": "heavy - tailed issue in linear contextual bandit with nite arms. Our algorithms only require the existence of bounded 1 +.Optimal algorithms for Lipschitz bandits with heavy - tailed rewards .", "subpage_snippet": "", "source": "www.ijcai.org", "link": "https://www.ijcai.org/proceedings/2020/0406.pdf", "content": "heavy - tailed issue in linear contextual bandit with nite arms. Our algorithms only require the existence of bounded 1 +.Optimal algorithms for Lipschitz bandits with heavy - tailed rewards ."}
+{"idx": 8, "title": "Robust Offline Reinforcement Learning with Heavy - Tailed Rewards", "date": "", "ddg_snippet": "Opti-mal algorithms for lipschitz bandits with heavy - tailed rewards .Si, N., Zhang, F., Zhou, Z., and Blanchet, J. (2020). Distributionally robust policy evaluation and learn-ing in offline contextual bandits .", "subpage_snippet": "", "source": "eprints.lse.ac.uk", "link": "https://eprints.lse.ac.uk/122740/1/Robust_Offline_Reinforcement_Learning_with_Heavy_Tailed_Rewards.pdf", "content": "Opti-mal algorithms for lipschitz bandits with heavy - tailed rewards .Si, N., Zhang, F., Zhou, Z., and Blanchet, J. (2020). Distributionally robust policy evaluation and learn-ing in offline contextual bandits ."}
+{"idx": 9, "title": "Nearly optimal regret for stochastic linear bandits with heavy - tailed ...", "date": "", "ddg_snippet": "Optimal algorithms for Lipschitz bandits with heavy - tailed rewards . In Proceedings of the 36th International Conference on Machine Learning, pages 4154-4163, 2019.", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.5555/3491440.3491846", "content": "Optimal algorithms for Lipschitz bandits with heavy - tailed rewards . In Proceedings of the 36th International Conference on Machine Learning, pages 4154-4163, 2019."}
diff --git a/data/sampled_jsons/Catoni_Contextual_Bandits_function-dependent_theta_parameter_OFUL_harder.jsonl b/data/sampled_jsons/Catoni_Contextual_Bandits_function-dependent_theta_parameter_OFUL_harder.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..6d8e6f5b6d79d9cb36d048724897e78a95c8a6e6
--- /dev/null
+++ b/data/sampled_jsons/Catoni_Contextual_Bandits_function-dependent_theta_parameter_OFUL_harder.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Catoni Contextual Bandits are Robust to Heavy-tailed Rewards", "date": "", "ddg_snippet": "In this paper, we develop an algorithmic approach building on Catoni 's estimator from robust statistics, and apply it to contextual bandits with general function approximation.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2502.02486", "content": "In this paper, we develop an algorithmic approach building on Catoni 's estimator from robust statistics, and apply it to contextual bandits with general function approximation."}
+{"idx": 1, "title": "Catoni Contextual Bandits are Robust to Heavy-tailed Rewards", "date": "", "ddg_snippet": "a contextual bandit (CB) algorithm that uses the Catoni . mean as a robust device for constructing a regression error. estimator for the excess loss, given some function class. F for predicting the expected reward.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=5IpVe9PH14", "content": "a contextual bandit (CB) algorithm that uses the Catoni . mean as a robust device for constructing a regression error. estimator for the excess loss, given some function class. F for predicting the expected reward."}
+{"idx": 2, "title": "Contextual multi-armed bandits for content recommendation... | Medium", "date": "", "ddg_snippet": "Contextual multi-armed bandits algorithm: this section covers contextual multi-armed bandits and how they are trained in a particular scenario that we used in our solution.As the a priori distribution of the parameters \\ theta _k we use the beta distribution", "subpage_snippet": "", "source": "vkteam.medium.com", "link": "https://vkteam.medium.com/contextual-multi-armed-bandits-for-content-recommendation-or-not-by-bernoulli-alone-263569f06158", "content": "Contextual multi-armed bandits algorithm: this section covers contextual multi-armed bandits and how they are trained in a particular scenario that we used in our solution.As the a priori distribution of the parameters \\ theta _k we use the beta distribution"}
+{"idx": 3, "title": "[Literature Review] Linear Bandits on Ellipsoids: Minimax Optimal...", "date": "", "ddg_snippet": "The paper titled \"Linear Bandits on Ellipsoids: Minimax Optimal Algorithms\" by Raymond Zhang, Hédi Hadiji, and Richard Combes introduces a significant advancement in the study of linear stochastic bandits where the action set is defined as an ellipsoid.", "subpage_snippet": "", "source": "www.themoonlight.io", "link": "https://www.themoonlight.io/en/review/linear-bandits-on-ellipsoids-minimax-optimal-algorithms", "content": "The paper titled \"Linear Bandits on Ellipsoids: Minimax Optimal Algorithms\" by Raymond Zhang, Hédi Hadiji, and Richard Combes introduces a significant advancement in the study of linear stochastic bandits where the action set is defined as an ellipsoid."}
+{"idx": 4, "title": "Norm-Agnostic Linear Bandits -Bohrium", "date": "", "ddg_snippet": "Linear bandits have a wide variety of applications including recommendation systems yet they make one strong assumption: the algorithms must know an upper bound $S$ on the norm of the unknown parameter $\\ theta ^*$ that governs the reward generation.", "subpage_snippet": "", "source": "www.bohrium.com", "link": "https://www.bohrium.com/paper-details/norm-agnostic-linear-bandits/867754731630494343-108580", "content": "Linear bandits have a wide variety of applications including recommendation systems yet they make one strong assumption: the algorithms must know an upper bound $S$ on the norm of the unknown parameter $\\ theta ^*$ that governs the reward generation."}
+{"idx": 5, "title": "Introduction to Multi-armed Bandits | PDF", "date": "", "ddg_snippet": "k’s payout rate • Draw parameters theta from each of k’s posterior distribution • Select the variant k which is the highest parameter theta • Observe the distribution parameters . Summary k-armed Bandits Action Value Methods Tracking a non-stationary problem Optimistic initial...", "subpage_snippet": "", "source": "www.slideshare.net", "link": "https://www.slideshare.net/slideshow/introduction-to-multiarmed-bandits/140924728", "content": "k’s payout rate • Draw parameters theta from each of k’s posterior distribution • Select the variant k which is the highest parameter theta • Observe the distribution parameters . Summary k-armed Bandits Action Value Methods Tracking a non-stationary problem Optimistic initial..."}
+{"idx": 6, "title": "Federated Linear Contextual Bandits", "date": "", "ddg_snippet": "The feature vectors and arms parameters $\\ theta $ are generated in SyntheticProblem.py, which is a synthetic dataset.To solve multi-client G-optimal design or its equivalent Determinant Maximizaion subject to multi-constraints, use the function OptimalExperiment in minVar.py.", "subpage_snippet": "", "source": "awesomeopensource.com", "link": "https://awesomeopensource.com/project/Ruiquan5514/Federated-Linear-Contextual-Bandits", "content": "The feature vectors and arms parameters $\\ theta $ are generated in SyntheticProblem.py, which is a synthetic dataset.To solve multi-client G-optimal design or its equivalent Determinant Maximizaion subject to multi-constraints, use the function OptimalExperiment in minVar.py."}
+{"idx": 7, "title": "real analysis - Continuity of a parameter - dependent function ...", "date": "", "ddg_snippet": "Continuity of max (moving the domain and the function ). 10. On continuity of roots of a polynomial depending on a real parameter .", "subpage_snippet": "", "source": "math.stackexchange.com", "link": "https://math.stackexchange.com/questions/139973/continuity-of-a-parameter-dependent-function/140023", "content": "Continuity of max (moving the domain and the function ). 10. On continuity of roots of a polynomial depending on a real parameter ."}
+{"idx": 8, "title": "Function dependent on Parameter defined... | Free Math Help Forum", "date": "", "ddg_snippet": "I ran into this problem I guess it is not to hard to handle but my math school days are long gone and I am having a hard time with this. I got a fixed parameter say it is called A There is a second Parameter B that is defined to be in the open interval (0.5A ; A)...", "subpage_snippet": "", "source": "www.freemathhelp.com", "link": "https://www.freemathhelp.com/forum/threads/function-dependent-on-parameter-defined-by-open-intervall.118154/", "content": "I ran into this problem I guess it is not to hard to handle but my math school days are long gone and I am having a hard time with this. I got a fixed parameter say it is called A There is a second Parameter B that is defined to be in the open interval (0.5A ; A)..."}
+{"idx": 9, "title": "A Comprehensive Guide to Excel's COPILOT Function : How AI is...", "date": "", "ddg_snippet": "Excel’s new COPILOT function can read and structure free-text data like notes, reviews, and updates into actionable plans instantly. It saves hours of manual work and signals the future of AI-powered productivity in spreadsheets.", "subpage_snippet": "", "source": "www.remio.ai", "link": "https://www.remio.ai/post/a-comprehensive-guide-to-excel-s-copilot-function-how-ai-is-revolutionizing-your-spreadsheet", "content": "Excel’s new COPILOT function can read and structure free-text data like notes, reviews, and updates into actionable plans instantly. It saves hours of manual work and signals the future of AI-powered productivity in spreadsheets."}
diff --git a/data/sampled_jsons/Causal_Modeling_of_Climate_Activism_on_Reddit_4.4_Summary_of_findings_activation_sympathy.jsonl b/data/sampled_jsons/Causal_Modeling_of_Climate_Activism_on_Reddit_4.4_Summary_of_findings_activation_sympathy.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..35d980276b4632ef66a4abefd950a6c0483178da
--- /dev/null
+++ b/data/sampled_jsons/Causal_Modeling_of_Climate_Activism_on_Reddit_4.4_Summary_of_findings_activation_sympathy.jsonl
@@ -0,0 +1,3 @@
+{"idx": 0, "title": "Causal Modeling of Climate Activism on Reddit", "date": "", "ddg_snippet": "by J Lenti · Cited by 9 — Causal Modeling of Climate Activism on Reddit . In Proceedings of The ... 4.4 Summary of findings . Thanks to the comprehensive causal model proposed in ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=6yBhoJn6qy", "content": "by J Lenti · Cited by 9 — Causal Modeling of Climate Activism on Reddit . In Proceedings of The ... 4.4 Summary of findings . Thanks to the comprehensive causal model proposed in ..."}
+{"idx": 1, "title": "Causal Modeling of Climate Activism on Reddit", "date": "", "ddg_snippet": "D Sympathy . E Sociodemographic Features. Causal Modeling of Climate Activism on Reddit .3 Model. We ground our operationalization of involvement with activist communities—the activation outcome we aim to build a causal. Causal Modeling of Climate Activism on Reddit .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2410.10562", "content": "D Sympathy . E Sociodemographic Features. Causal Modeling of Climate Activism on Reddit .3 Model. We ground our operationalization of involvement with activist communities—the activation outcome we aim to build a causal. Causal Modeling of Climate Activism on Reddit ."}
+{"idx": 2, "title": "", "date": "", "ddg_snippet": "", "subpage_snippet": "", "source": "", "link": "", "content": ""}
diff --git a/data/sampled_jsons/Causal_Modeling_of_Climate_Activism_on_Reddit_Related_Work_three_types_of_events.jsonl b/data/sampled_jsons/Causal_Modeling_of_Climate_Activism_on_Reddit_Related_Work_three_types_of_events.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..ca1803df6d3df580ed09f07322b7120f0e3dde53
--- /dev/null
+++ b/data/sampled_jsons/Causal_Modeling_of_Climate_Activism_on_Reddit_Related_Work_three_types_of_events.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "[2410.10562] Causal Modeling of Climate Activism on Reddit", "date": "", "ddg_snippet": "by J Lenti · 2024 · Cited by 9 — In this work , we develop a comprehensive causal model of how and why Reddit users engage with activist communities driving mass climate protests.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2410.10562", "content": "by J Lenti · 2024 · Cited by 9 — In this work , we develop a comprehensive causal model of how and why Reddit users engage with activist communities driving mass climate protests."}
+{"idx": 1, "title": "Causal Modeling of Climate Activism on Reddit", "date": "", "ddg_snippet": "by J Lenti · Cited by 9 — Summary: This paper proposes a causal model to measure the effects of media, demographic features and user interaction on activation in climate activism groups ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=6yBhoJn6qy", "content": "by J Lenti · Cited by 9 — Summary: This paper proposes a causal model to measure the effects of media, demographic features and user interaction on activation in climate activism groups ..."}
+{"idx": 2, "title": "Causal Modeling of Climate Activism on Reddit", "date": "", "ddg_snippet": "22 Apr 2025 — In this work, we develop a comprehensive causal model of how and why Reddit users engage with activist communities driving mass climate protests .", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.1145/3696410.3714684", "content": "22 Apr 2025 — In this work, we develop a comprehensive causal model of how and why Reddit users engage with activist communities driving mass climate protests ."}
+{"idx": 3, "title": "Causal Modeling of Climate Activism on Reddit", "date": "", "ddg_snippet": "14 Oct 2024 — In this work, we develop a comprehensive causal model of how and why Reddit users engage with activist communities driving mass climate protests .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2410.10562v1", "content": "14 Oct 2024 — In this work, we develop a comprehensive causal model of how and why Reddit users engage with activist communities driving mass climate protests ."}
+{"idx": 4, "title": "[PDF] Causal Modeling of Climate Activism on Reddit", "date": "", "ddg_snippet": "Climate activism is crucial in stimulating collective societal and behavioral change towards sustainable practices through political pressure.", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/c4c7c3972ba102db37738c082f27ebfcd3983057", "content": "Climate activism is crucial in stimulating collective societal and behavioral change towards sustainable practices through political pressure."}
+{"idx": 5, "title": "Causal Modeling of Climate Activism on Reddit", "date": "", "ddg_snippet": "by J Lenti · Cited by 9 — In this work, we develop a comprehensive causal model of how and why Reddit users engage with activist communities driving mass climate protests (mainly the ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=6yBhoJn6qy", "content": "by J Lenti · Cited by 9 — In this work, we develop a comprehensive causal model of how and why Reddit users engage with activist communities driving mass climate protests (mainly the ..."}
+{"idx": 6, "title": "Causal Modeling of Climate Activism on Reddit", "date": "", "ddg_snippet": "14 Oct 2024 — A methodological approach for inferring causal relationships from opinions and news-derived events with an application to climate change. PeerJ ...", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/Causal-Modeling-of-Climate-Activism-on-Reddit-Lenti-Aiello/c4c7c3972ba102db37738c082f27ebfcd3983057", "content": "14 Oct 2024 — A methodological approach for inferring causal relationships from opinions and news-derived events with an application to climate change. PeerJ ..."}
+{"idx": 7, "title": "A methodological approach for inferring causal ...", "date": "", "ddg_snippet": "by J Marten · 2025 — The proposed methodology is applied to climate change opinions and data, offering insights into the causal relationships among public sentiment, ...", "subpage_snippet": "", "source": "pmc.ncbi.nlm.nih.gov", "link": "https://pmc.ncbi.nlm.nih.gov/articles/PMC12193450/", "content": "by J Marten · 2025 — The proposed methodology is applied to climate change opinions and data, offering insights into the causal relationships among public sentiment, ..."}
+{"idx": 8, "title": "Climate Change Frames and Emotional Responses on Reddit", "date": "", "ddg_snippet": "by II Villanueva · 2021 · Cited by 8 — This study expands climate change communication research by conducting a content analysis of top climate change posts on Reddit , investigating the climate ...", "subpage_snippet": "", "source": "scholarworks.uark.edu", "link": "https://scholarworks.uark.edu/cgi/viewcontent.cgi?article=5626&context=etd", "content": "by II Villanueva · 2021 · Cited by 8 — This study expands climate change communication research by conducting a content analysis of top climate change posts on Reddit , investigating the climate ..."}
+{"idx": 9, "title": "[Q] How do you diplomatically convince people with a ...", "date": "", "ddg_snippet": "Causal modeling is useful in prediction to predict what would happen in conditions different from those that we observed in the past. In any ...", "subpage_snippet": "", "source": "www.reddit.com", "link": "https://www.reddit.com/r/statistics/comments/1fwkt9v/q_how_do_you_diplomatically_convince_people_with/", "content": "Causal modeling is useful in prediction to predict what would happen in conditions different from those that we observed in the past. In any ..."}
diff --git a/data/sampled_jsons/Causal_Modeling_of_Climate_Activism_on_Reddit_removing_user_engagement_(E)_causes_the_measured_effec.jsonl b/data/sampled_jsons/Causal_Modeling_of_Climate_Activism_on_Reddit_removing_user_engagement_(E)_causes_the_measured_effec.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..dc154121c239b917fef83c3cbd9a06c1f4f538c3
--- /dev/null
+++ b/data/sampled_jsons/Causal_Modeling_of_Climate_Activism_on_Reddit_removing_user_engagement_(E)_causes_the_measured_effec.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Causality - Wikipedia", "date": "", "ddg_snippet": "In general, a process can have multiple causes, [1] which are also said to be causal factors for it, and all lie in its past. An effect can in turn be a cause of, or causal factor for, many other effects, which all lie in its future.", "subpage_snippet": "", "source": "en.m.wikipedia.org", "link": "https://en.m.wikipedia.org/wiki/Causality", "content": "In general, a process can have multiple causes, [1] which are also said to be causal factors for it, and all lie in its past. An effect can in turn be a cause of, or causal factor for, many other effects, which all lie in its future."}
+{"idx": 1, "title": "CAUSAL Definition & Meaning - Merriam-Webster", "date": "", "ddg_snippet": "The meaning of CAUSAL is expressing or indicating cause : causative. How to use causal in a sentence.", "subpage_snippet": "", "source": "www.merriam-webster.com", "link": "https://www.merriam-webster.com/dictionary/causal", "content": "The meaning of CAUSAL is expressing or indicating cause : causative. How to use causal in a sentence."}
+{"idx": 2, "title": "CAUSAL | English meaning - Cambridge Dictionary", "date": "", "ddg_snippet": "CAUSAL definition: 1. a relationship, link, etc. between two things in which one causes the other: 2. a relationship…. Learn more.", "subpage_snippet": "", "source": "dictionary.cambridge.org", "link": "https://dictionary.cambridge.org/dictionary/english/causal", "content": "CAUSAL definition: 1. a relationship, link, etc. between two things in which one causes the other: 2. a relationship…. Learn more."}
+{"idx": 3, "title": "CAUSAL Definition & Meaning | Dictionary .com", "date": "", "ddg_snippet": "Causal definition: of, constituting, or implying a cause.. See examples of CAUSAL used in a sentence.", "subpage_snippet": "", "source": "www.dictionary.com", "link": "https://www.dictionary.com/browse/causal", "content": "Causal definition: of, constituting, or implying a cause.. See examples of CAUSAL used in a sentence."}
+{"idx": 4, "title": "Causal - definition of causal by The Free Dictionary", "date": "", "ddg_snippet": "1. Of, involving, or constituting a cause: a causal relationship between scarcity of goods and higher prices. 2. Indicative of or expressing a cause.", "subpage_snippet": "", "source": "www.thefreedictionary.com", "link": "https://www.thefreedictionary.com/causal", "content": "1. Of, involving, or constituting a cause: a causal relationship between scarcity of goods and higher prices. 2. Indicative of or expressing a cause."}
+{"idx": 5, "title": "CAUSAL definition and meaning | Collins English Dictionary", "date": "", "ddg_snippet": "If there is a causal relationship between two things, one thing is responsible for causing the other thing.", "subpage_snippet": "", "source": "www.collinsdictionary.com", "link": "https://www.collinsdictionary.com/dictionary/english/causal", "content": "If there is a causal relationship between two things, one thing is responsible for causing the other thing."}
+{"idx": 6, "title": "causal adjective - Definition, pictures, pronunciation and usage...", "date": "", "ddg_snippet": "Definition of causal adjective in Oxford Advanced Learner's Dictionary. Meaning, pronunciation, picture, example sentences, grammar, usage notes, synonyms and more.", "subpage_snippet": "", "source": "www.oxfordlearnersdictionaries.com", "link": "https://www.oxfordlearnersdictionaries.com/definition/english/causal", "content": "Definition of causal adjective in Oxford Advanced Learner's Dictionary. Meaning, pronunciation, picture, example sentences, grammar, usage notes, synonyms and more."}
+{"idx": 7, "title": "causal , n. & adj. meanings, etymology and more | Oxford English...", "date": "", "ddg_snippet": "causal , n. & adj. meanings, etymology, pronunciation and more in the Oxford English Dictionary", "subpage_snippet": "", "source": "www.oed.com", "link": "https://www.oed.com/dictionary/causal_n", "content": "causal , n. & adj. meanings, etymology, pronunciation and more in the Oxford English Dictionary"}
+{"idx": 8, "title": "causal - Wiktionary, the free dictionary", "date": "", "ddg_snippet": "Aug 28, 2025 · causal (comparative more causal , superlative most causal ) There is no causal relationship between eating carrots and seeing in the dark.", "subpage_snippet": "", "source": "en.m.wiktionary.org", "link": "https://en.m.wiktionary.org/wiki/causal", "content": "Aug 28, 2025 · causal (comparative more causal , superlative most causal ) There is no causal relationship between eating carrots and seeing in the dark."}
+{"idx": 9, "title": "Causal - Definition, Meaning & Synonyms | Vocabulary.com", "date": "", "ddg_snippet": "Causal is a variation of the word cause , which should be a clue to its meaning. A cause is what makes something happen: the notebook flew across the room because you threw it, so your throwing it was causal. If a bolt of lightning set a statue on fire, the lightning was causal for the fire.", "subpage_snippet": "", "source": "www.vocabulary.com", "link": "https://www.vocabulary.com/dictionary/causal", "content": "Causal is a variation of the word cause , which should be a clue to its meaning. A cause is what makes something happen: the notebook flew across the room because you threw it, so your throwing it was causal. If a bolt of lightning set a statue on fire, the lightning was causal for the fire."}
diff --git a/data/sampled_jsons/Causal_Modeling_of_Climate_Activism_on_Reddit_six_climate_activist_subreddits_names.jsonl b/data/sampled_jsons/Causal_Modeling_of_Climate_Activism_on_Reddit_six_climate_activist_subreddits_names.jsonl
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index 0000000000000000000000000000000000000000..daff205c83d93cbffd114b9a460fc9fb8ea667d6
--- /dev/null
+++ b/data/sampled_jsons/Causal_Modeling_of_Climate_Activism_on_Reddit_six_climate_activist_subreddits_names.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Echo chamber (media) - Wikipedia", "date": "", "ddg_snippet": "Another set of studies suggests that echo chambers exist, but that these are not a widespread phenomenon: Based on survey data, Dubois and Blank ...", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/Echo_chamber_(media)", "content": "Another set of studies suggests that echo chambers exist, but that these are not a widespread phenomenon: Based on survey data, Dubois and Blank ..."}
+{"idx": 1, "title": "[2410.10562] Causal Modeling of Climate Activism on Reddit", "date": "", "ddg_snippet": "Climate activism is crucial in stimulating collective societal and behavioral change towards sustainable practices through political pressure. Although multiple factors contribute to the participation in activism , their complex relationships and the scarcity of data on their interactions have restricted most prior research to studying them in isolation, thus preventing the development of a ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2410.10562", "content": "Climate activism is crucial in stimulating collective societal and behavioral change towards sustainable practices through political pressure. Although multiple factors contribute to the participation in activism , their complex relationships and the scarcity of data on their interactions have restricted most prior research to studying them in isolation, thus preventing the development of a ..."}
+{"idx": 2, "title": "Causal Modeling of Climate Activism on Reddit | Article Information | J ...", "date": "", "ddg_snippet": "Article \" Causal Modeling of Climate Activism on Reddit \" Detailed information of the J-GLOBAL is an information service managed by the Japan Science and Technology Agency (hereinafter referred to as \"JST\"). It provides free access to secondary information on researchers, articles, patents, etc., in science and technology, medicine and pharmacy. The search results guide you to high-quality ...", "subpage_snippet": "", "source": "jglobal.jst.go.jp", "link": "https://jglobal.jst.go.jp/en/detail?JGLOBAL_ID=202502222230976259", "content": "Article \" Causal Modeling of Climate Activism on Reddit \" Detailed information of the J-GLOBAL is an information service managed by the Japan Science and Technology Agency (hereinafter referred to as \"JST\"). It provides free access to secondary information on researchers, articles, patents, etc., in science and technology, medicine and pharmacy. The search results guide you to high-quality ..."}
+{"idx": 3, "title": "Causal Modeling of Climate Activism on Reddit", "date": "", "ddg_snippet": "Featured Image Read the Original This page is a summary of : Causal Modeling of Climate Activism on Reddit , April 2025, ACM (Association for Computing Machinery), DOI: 10.1145/3696410.3714684. You can read the full text: Read", "subpage_snippet": "", "source": "www.growkudos.com", "link": "https://www.growkudos.com/publications/10.1145%2F3696410.3714684/reader", "content": "Featured Image Read the Original This page is a summary of : Causal Modeling of Climate Activism on Reddit , April 2025, ACM (Association for Computing Machinery), DOI: 10.1145/3696410.3714684. You can read the full text: Read"}
+{"idx": 4, "title": "\"Causal Modeling of Climate Activism on Reddit.\" - dblp", "date": "", "ddg_snippet": "Bibliographic details on Causal Modeling of Climate Activism on Reddit .", "subpage_snippet": "", "source": "dblp.org", "link": "https://dblp.org/rec/journals/corr/abs-2410-10562", "content": "Bibliographic details on Causal Modeling of Climate Activism on Reddit ."}
+{"idx": 5, "title": "Causal Modeling of Climate Activism on Reddit - OpenReview", "date": "", "ddg_snippet": "In this 16 work, we develop a comprehensive causal model of how and why 17 Reddit users engage with activist communities driving mass climate 18 protests (mainly the 2019 Earth Strike, Fridays for Future, and Ex- 19 tinction Rebellion). Our framework, based on Stochastic Variational 20 Inference applied to Bayesian Networks, learns the causal ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=6yBhoJn6qy", "content": "In this 16 work, we develop a comprehensive causal model of how and why 17 Reddit users engage with activist communities driving mass climate 18 protests (mainly the 2019 Earth Strike, Fridays for Future, and Ex- 19 tinction Rebellion). Our framework, based on Stochastic Variational 20 Inference applied to Bayesian Networks, learns the causal ..."}
+{"idx": 6, "title": "(PDF) Analyzing Climate Change Discussions on Reddit", "date": "", "ddg_snippet": "In addition to the presence of groups of activists and skeptics, we find climate change discussions on question-answering and opinion-sharing communities, as well as in a community for teenagers.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/366548402_Analyzing_climate_change_discussions_on_Reddit", "content": "In addition to the presence of groups of activists and skeptics, we find climate change discussions on question-answering and opinion-sharing communities, as well as in a community for teenagers."}
+{"idx": 7, "title": "Causal Modeling of Climate Activism on Reddit - arXiv.org", "date": "", "ddg_snippet": "We developed a rich and comprehensive causal model to study the interplay between diferent determinants of climate activism on Reddit . This work represents a first attempt to apply a multi- causal model to social media data.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2410.10562", "content": "We developed a rich and comprehensive causal model to study the interplay between diferent determinants of climate activism on Reddit . This work represents a first attempt to apply a multi- causal model to social media data."}
+{"idx": 8, "title": "Causal Modeling of Climate Activism on Reddit - arXiv.org", "date": "", "ddg_snippet": "We developed a rich and comprehensive causal model to study the interplay between different determinants of climate activism on Reddit . This work represents a first attempt to apply a multi- causal model to social media data.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2410.10562v1", "content": "We developed a rich and comprehensive causal model to study the interplay between different determinants of climate activism on Reddit . This work represents a first attempt to apply a multi- causal model to social media data."}
+{"idx": 9, "title": "r/climatechange on Reddit: To all the climate activists here on Reddit ...", "date": "", "ddg_snippet": "To all the climate activists here on Reddit , what drives you towards this cause? What made you a climate activist ? This is for research purposes. Really keen to know a bit of your story.", "subpage_snippet": "", "source": "www.reddit.com", "link": "https://www.reddit.com/r/climatechange/comments/11s32ot/to_all_the_climate_activists_here_on_reddit_what/", "content": "To all the climate activists here on Reddit , what drives you towards this cause? What made you a climate activist ? This is for research purposes. Really keen to know a bit of your story."}
diff --git a/data/sampled_jsons/Causal_representation_learning_robust_noisy_mixing_function_stochastic_year_2024.jsonl b/data/sampled_jsons/Causal_representation_learning_robust_noisy_mixing_function_stochastic_year_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..3b98aeff6bd80335dfa942b3aed8ce978101b694
--- /dev/null
+++ b/data/sampled_jsons/Causal_representation_learning_robust_noisy_mixing_function_stochastic_year_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Causality-Inspired Robustness for Nonlinear Models via Representation ...", "date": "", "ddg_snippet": "In this work, we propose a nonlinear method under a causal framework by incorporating recent developments in identifiable representation learning and establish a distributional robustness guarantee. To our best knowledge, this is the first causality-inspired robustness method with such a finite-radius robustness guarantee in nonlinear settings.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2505.12868", "content": "In this work, we propose a nonlinear method under a causal framework by incorporating recent developments in identifiable representation learning and establish a distributional robustness guarantee. To our best knowledge, this is the first causality-inspired robustness method with such a finite-radius robustness guarantee in nonlinear settings."}
+{"idx": 1, "title": "PDF Causal Representation Learning from General Environments under ...", "date": "", "ddg_snippet": "Interestingly, we show that one can fully recover the latent DAG and identify the latent variables up to minor indeterminacies under a nonparametric mixing function and nonlinear latent causal models, such as addi-tive (Gaussian) noise models or heteroscedas-tic noise models, by properly leveraging suf-ficient change conditions on the causal ...", "subpage_snippet": "", "source": "raw.githubusercontent.com", "link": "https://raw.githubusercontent.com/mlresearch/v258/main/assets/ng25a/ng25a.pdf", "content": "Interestingly, we show that one can fully recover the latent DAG and identify the latent variables up to minor indeterminacies under a nonparametric mixing function and nonlinear latent causal models, such as addi-tive (Gaussian) noise models or heteroscedas-tic noise models, by properly leveraging suf-ficient change conditions on the causal ..."}
+{"idx": 2, "title": "Causal Representation Learning from General Environments under ...", "date": "", "ddg_snippet": "Interestingly, we show that one can fully recover the latent DAG and identify the latent variables up to minor indeterminacies under a nonparametric mixing function and nonlinear latent causal models, such as additive (Gaussian) noise models or heteroscedastic noise models, by properly leveraging sufficient change conditions on the causal ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=S8lfepB2fz", "content": "Interestingly, we show that one can fully recover the latent DAG and identify the latent variables up to minor indeterminacies under a nonparametric mixing function and nonlinear latent causal models, such as additive (Gaussian) noise models or heteroscedastic noise models, by properly leveraging sufficient change conditions on the causal ..."}
+{"idx": 3, "title": "PDF Learning from Noisy Data with Robust Representation Learning", "date": "", "ddg_snippet": "Abstract Learning from noisy data has attracted much attention, where most methods focus on label noise. In this work, we propose a new learning framework which simultaneously addresses three types of noise commonly seen in real-world data: label noise, out-of-distribution input, and input cor-ruption. In contrast to most existing methods, we combat noise by learning robust representation ...", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/ICCV2021/papers/Li_Learning_From_Noisy_Data_With_Robust_Representation_Learning_ICCV_2021_paper.pdf", "content": "Abstract Learning from noisy data has attracted much attention, where most methods focus on label noise. In this work, we propose a new learning framework which simultaneously addresses three types of noise commonly seen in real-world data: label noise, out-of-distribution input, and input cor-ruption. In contrast to most existing methods, we combat noise by learning robust representation ..."}
+{"idx": 4, "title": "PDF Causal Representation Learning", "date": "", "ddg_snippet": "By combining ideas in representation learning with causal inference, causal representation learning offers the potential for representations which are robust and transferrable, interpretable as cause-effect mechanisms, and sample-eficient [Schölkopf et al., 2021]. Machine learning models have achieved impressive success on a wide range of tasks.", "subpage_snippet": "", "source": "sanaelotfi.github.io", "link": "https://sanaelotfi.github.io/files/project_reports/causal_representation_learning_survey.pdf", "content": "By combining ideas in representation learning with causal inference, causal representation learning offers the potential for representations which are robust and transferrable, interpretable as cause-effect mechanisms, and sample-eficient [Schölkopf et al., 2021]. Machine learning models have achieved impressive success on a wide range of tasks."}
+{"idx": 5, "title": "Learning from Noisy Data with Robust Representation Learning", "date": "", "ddg_snippet": "Learning from noisy data has attracted much attention, where most methods focus on label noise. In this work, we propose a new learning framework which simultaneously addresses three types of noise commonly seen in real-world data: label noise, out-of-distribution input, and input corruption. In contrast to most existing methods, we combat noise by learning robust representation . Specifically ...", "subpage_snippet": "", "source": "ieeexplore.ieee.org", "link": "https://ieeexplore.ieee.org/document/9710292", "content": "Learning from noisy data has attracted much attention, where most methods focus on label noise. In this work, we propose a new learning framework which simultaneously addresses three types of noise commonly seen in real-world data: label noise, out-of-distribution input, and input corruption. In contrast to most existing methods, we combat noise by learning robust representation . Specifically ..."}
+{"idx": 6, "title": "Causality-oriented robustness: exploiting general noise interventions ...", "date": "", "ddg_snippet": "In this paper, we focus on causality-oriented robustness and propose Distributional Robustness via Invariant Gradients (DRIG), a method that exploits general noise interventions in training data for robust predictions against unseen interventions, and naturally interpolates between in-distribution prediction and causality.", "subpage_snippet": "", "source": "www.tandfonline.com", "link": "https://www.tandfonline.com/doi/full/10.1080/01621459.2025.2544365", "content": "In this paper, we focus on causality-oriented robustness and propose Distributional Robustness via Invariant Gradients (DRIG), a method that exploits general noise interventions in training data for robust predictions against unseen interventions, and naturally interpolates between in-distribution prediction and causality."}
+{"idx": 7, "title": "Track: Oral 2C Causality - NIPS", "date": "", "ddg_snippet": "We study the problem of learning causal representations from unknown, latent interventions in a general setting, where the latent distribution is Gaussian but the mixing function is completely general. We prove strong identifiability results given unknown single-node interventions, i.e., without having access to the intervention targets. This generalizes prior works which have focused on ...", "subpage_snippet": "", "source": "nips.cc", "link": "https://nips.cc/virtual/2023/session/74039", "content": "We study the problem of learning causal representations from unknown, latent interventions in a general setting, where the latent distribution is Gaussian but the mixing function is completely general. We prove strong identifiability results given unknown single-node interventions, i.e., without having access to the intervention targets. This generalizes prior works which have focused on ..."}
+{"idx": 8, "title": "Causal Representation Learning from General Environments under ...", "date": "", "ddg_snippet": "Causal representation learning aims to recover the latent causal variables and their causal relations, typically represented by directed acyclic graphs (DAGs), from low-level observations such as image pixels. A prevailing line of research exploits multiple environments, which assume how data distributions change, including single-node interventions, coupled interventions, or hard ...", "subpage_snippet": "", "source": "proceedings.mlr.press", "link": "https://proceedings.mlr.press/v258/ng25a.html", "content": "Causal representation learning aims to recover the latent causal variables and their causal relations, typically represented by directed acyclic graphs (DAGs), from low-level observations such as image pixels. A prevailing line of research exploits multiple environments, which assume how data distributions change, including single-node interventions, coupled interventions, or hard ..."}
+{"idx": 9, "title": "PDF Learning the Latent Causal Structure for Modeling Label Noise", "date": "", "ddg_snippet": "Unlike previous generative label-noise learning methods, we consider causal relations between latent causal variables and model them with a learnable graphical model. Utilizing only noisy data, our method can effectively learn the latent causal structure.", "subpage_snippet": "", "source": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper_files/paper/2024/file/da75d2bbf862b86f10241d0887613b41-Paper-Conference.pdf", "content": "Unlike previous generative label-noise learning methods, we consider causal relations between latent causal variables and model them with a learnable graphical model. Utilizing only noisy data, our method can effectively learn the latent causal structure."}
diff --git a/data/sampled_jsons/Checks-and-Balances_Framework_for_Context-Aware_Ethical_AI_Alignment_limitations_future_work.jsonl b/data/sampled_jsons/Checks-and-Balances_Framework_for_Context-Aware_Ethical_AI_Alignment_limitations_future_work.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..8f6caeb4c4dda616df8126dd5b842606936a227f
--- /dev/null
+++ b/data/sampled_jsons/Checks-and-Balances_Framework_for_Context-Aware_Ethical_AI_Alignment_limitations_future_work.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Justified Evidence Collection for Argument-based AI Fairness", "date": "", "ddg_snippet": "... work to demonstrate the ongoing gap between operationalising fairness research into practical implementation, where practitioners struggle with ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2505.08064v1", "content": "... work to demonstrate the ongoing gap between operationalising fairness research into practical implementation, where practitioners struggle with ..."}
+{"idx": 1, "title": "Structured Reasoning for Fairness: A Multi-Agent Approach to", "date": "", "ddg_snippet": "... work contributes to broader efforts in creating accountable and socially responsible AI : by integrating this holistic approach, it holds promise for ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2503.00355v1", "content": "... work contributes to broader efforts in creating accountable and socially responsible AI : by integrating this holistic approach, it holds promise for ..."}
+{"idx": 2, "title": "(PDF) Ethical considerations in Risk management of autonomous", "date": "", "ddg_snippet": "Through the analysis of AI risks and risk management procedures, we advocate for establishing effective mechanisms for ethical oversight and legal ...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/381237450_Ethical_considerations_in_Risk_management_of_autonomous_and_intelligent_systems", "content": "Through the analysis of AI risks and risk management procedures, we advocate for establishing effective mechanisms for ethical oversight and legal ..."}
+{"idx": 3, "title": "(PDF) A Unified Framework for Human AI Collaboration in", "date": "", "ddg_snippet": "... presents a structured framework for Human- AI collaboration in Security Operations Centers (SOCs), integrating AI autonomy, trust calibration, and ...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/392204042_A_Unified_Framework_for_Human_AI_Collaboration_in_Security_Operations_Centers_with_Trusted_Autonomy", "content": "... presents a structured framework for Human- AI collaboration in Security Operations Centers (SOCs), integrating AI autonomy, trust calibration, and ..."}
+{"idx": 4, "title": "AI Alignment Knowledge Graph", "date": "", "ddg_snippet": "... work sheds a light on how limited our current methodology is and bracketed sequence classification might serve as a good toy-problem task for future ...", "subpage_snippet": "", "source": "www.apartresearch.com", "link": "https://www.apartresearch.com/project/ai-alignment-knowledge-graph", "content": "... work sheds a light on how limited our current methodology is and bracketed sequence classification might serve as a good toy-problem task for future ..."}
+{"idx": 5, "title": "Creating a Tool to Reproducibly Estimate the Ethical Impact of", "date": "", "ddg_snippet": "... in the well-known IEEE Ethically Aligned Design documents, that ethical AI projects must protect human rights foremost (IEEE Global Initiative 2017).", "subpage_snippet": "", "source": "aipulse.org", "link": "https://aipulse.org/creating-a-tool-to-reproducibly-estimate-the-ethical-impact-of-artificial-intelligence/", "content": "... in the well-known IEEE Ethically Aligned Design documents, that ethical AI projects must protect human rights foremost (IEEE Global Initiative 2017)."}
+{"idx": 6, "title": "“Think First, Verify Always”: Training Humans to Face AI", "date": "", "ddg_snippet": "... frameworks such as IEEE’s Ethically Aligned Design ( ieee2019, ) and NIST AI RMF ( nist2023ai, ) , which present ethical principles, TFVA ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2508.03714v1", "content": "... frameworks such as IEEE’s Ethically Aligned Design ( ieee2019, ) and NIST AI RMF ( nist2023ai, ) , which present ethical principles, TFVA ..."}
+{"idx": 7, "title": "Aligning Trustworthy AI with Democracy: A Dual Taxonomy of", "date": "", "ddg_snippet": "... to evaluate the democratic implications of AI , equips policymakers with structured criteria for ethical oversight, and helps technologists align ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2505.13565v1", "content": "... to evaluate the democratic implications of AI , equips policymakers with structured criteria for ethical oversight, and helps technologists align ..."}
+{"idx": 8, "title": "Generalized Comprehensible Configurable Adaptive Cognitive", "date": "", "ddg_snippet": "... for developing transparent, ethically aligned, and trustworthy Artificial Intelligence ( AI ) systems in high-stakes domains such as healthcare, law, ...", "subpage_snippet": "", "source": "ihorivliev.wordpress.com", "link": "https://ihorivliev.wordpress.com/2025/03/25/generalized-comprehensible-configurable-adaptive-cognitive-structure/", "content": "... for developing transparent, ethically aligned, and trustworthy Artificial Intelligence ( AI ) systems in high-stakes domains such as healthcare, law, ..."}
+{"idx": 9, "title": "Towards Fair AI: Mitigating Bias in Credit Decisions—A", "date": "", "ddg_snippet": "... Paper should be a substantial original Article that involves several techniques or approaches, provides an outlook for future research directions and ...", "subpage_snippet": "", "source": "www.mdpi.com", "link": "https://www.mdpi.com/1911-8074/18/5/228", "content": "... Paper should be a substantial original Article that involves several techniques or approaches, provides an outlook for future research directions and ..."}
diff --git a/data/sampled_jsons/Chinchilla_language_model_scaling_laws_Kaplan.jsonl b/data/sampled_jsons/Chinchilla_language_model_scaling_laws_Kaplan.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..f0e5089a9d2ad9aba81ff8ace5b4dbdacef9f621
--- /dev/null
+++ b/data/sampled_jsons/Chinchilla_language_model_scaling_laws_Kaplan.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Reconciling Kaplan and Chinchilla Scaling Laws", "date": "", "ddg_snippet": "Again non-embedding parameters and smaller scale models are the cause of Kaplan ’s biased estimate, combined with the absence of an offset term in ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2406.12907v3", "content": "Again non-embedding parameters and smaller scale models are the cause of Kaplan ’s biased estimate, combined with the absence of an offset term in ..."}
+{"idx": 1, "title": "Beyond Chinchilla-Optimal: Accounting for Inference in Language", "date": "", "ddg_snippet": "Beyond Chinchilla -Optimal: Accounting for Inference in Language Model Scaling Laws ... Large language model (LLM) scaling laws are empirical formulas ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2401.00448v2", "content": "Beyond Chinchilla -Optimal: Accounting for Inference in Language Model Scaling Laws ... Large language model (LLM) scaling laws are empirical formulas ..."}
+{"idx": 2, "title": "Scaling Data-Constrained Language Models", "date": "", "ddg_snippet": "... high-quality English language data will be exhausted by the year 2024 given the Chinchilla scaling laws and the trend of training ever-larger models ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2305.16264v5", "content": "... high-quality English language data will be exhausted by the year 2024 given the Chinchilla scaling laws and the trend of training ever-larger models ..."}
+{"idx": 3, "title": "Scaling Laws Across Model Architectures: A Comparative Analysis", "date": "", "ddg_snippet": "Due to the significant costs associated with training process, understanding the scaling laws of large language models (LLMs) is crucial.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2410.05661v1", "content": "Due to the significant costs associated with training process, understanding the scaling laws of large language models (LLMs) is crucial."}
+{"idx": 4, "title": "Chinchilla Paper explained - Nikhil R", "date": "", "ddg_snippet": "... emergence also raises the question of whether additional scaling could potentially further expand the range of capabilities of language models or not.", "subpage_snippet": "", "source": "rnikhil.com", "link": "https://rnikhil.com/2023/11/28/llm-scaling", "content": "... emergence also raises the question of whether additional scaling could potentially further expand the range of capabilities of language models or not."}
+{"idx": 5, "title": "Chinchilla data-optimal scaling laws: In plain English –", "date": "", "ddg_snippet": "... scaling only , using tokens to parameters as a ratio, and as derived from large language models like GPT-3, Chinchilla , and beyond, linked to the ...", "subpage_snippet": "", "source": "lifearchitect.ai", "link": "https://lifearchitect.ai/chinchilla/", "content": "... scaling only , using tokens to parameters as a ratio, and as derived from large language models like GPT-3, Chinchilla , and beyond, linked to the ..."}
+{"idx": 6, "title": "Emerging Technology Solutions | The Evolution of Model", "date": "", "ddg_snippet": "... Chinchilla Scaling Laws demonstrated that model size and training data should be scaled equally for compute-optimal training, contradicting the ...", "subpage_snippet": "", "source": "blogs.infosys.com", "link": "https://blogs.infosys.com/emerging-technology-solutions/artificial-intelligence/evolution-of-model-performance.html", "content": "... Chinchilla Scaling Laws demonstrated that model size and training data should be scaled equally for compute-optimal training, contradicting the ..."}
+{"idx": 7, "title": "chinchilla's wild implications - LessWrong 2.0 viewer", "date": "", "ddg_snippet": "This post is about language model scaling laws , specifically the laws derived in the DeepMind paper that introduced Chinchilla .", "subpage_snippet": "", "source": "www.greaterwrong.com", "link": "https://www.greaterwrong.com/posts/6Fpvch8RR29qLEWNH/chinchilla-s-wild-implications?comments=false", "content": "This post is about language model scaling laws , specifically the laws derived in the DeepMind paper that introduced Chinchilla ."}
+{"idx": 8, "title": "chinchilla's wild implications - LessWrong 2.0 viewer", "date": "", "ddg_snippet": "This post is about language model scaling laws , specifically the laws derived in the DeepMind paper that introduced Chinchilla .", "subpage_snippet": "", "source": "www.greaterwrong.com", "link": "https://www.greaterwrong.com/posts/6Fpvch8RR29qLEWNH/chinchilla-s-wild-implications", "content": "This post is about language model scaling laws , specifically the laws derived in the DeepMind paper that introduced Chinchilla ."}
+{"idx": 9, "title": "Scaling Laws for LLM Pretraining", "date": "", "ddg_snippet": "While the Kaplan Scaling Laws suggest that scaling model size is more important than scaling data, given a fixed compute budget, the Chinchilla ...", "subpage_snippet": "", "source": "www.jonvet.com", "link": "https://www.jonvet.com/blog/llm-scaling-laws", "content": "While the Kaplan Scaling Laws suggest that scaling model size is more important than scaling data, given a fixed compute budget, the Chinchilla ..."}
diff --git a/data/sampled_jsons/Christian_Gaetz_Yibo_Gao_On_the_minimal_power_of_q_in_a_Kazhdan-Lusztig_polynomial_year_2024.jsonl b/data/sampled_jsons/Christian_Gaetz_Yibo_Gao_On_the_minimal_power_of_q_in_a_Kazhdan-Lusztig_polynomial_year_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..bf60574321f36b6cd84544fd1562e5db9ab37dbf
--- /dev/null
+++ b/data/sampled_jsons/Christian_Gaetz_Yibo_Gao_On_the_minimal_power_of_q_in_a_Kazhdan-Lusztig_polynomial_year_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "On the minimal power of $ q $ in a Kazhdan - Lusztig polynomial", "date": "", "ddg_snippet": "in a Kazhdan - Lusztig polynomial . Authors: Christian Gaetz , Yibo Gao .in the symmetric group, we provide an exact formula for the smallest positive power q ^{h(w)}. appearing in the Kazhdan - Lusztig polynomial P_{e,w}( q ). . We also provide a tight upper bound on h(w).", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2303.13695", "content": "in a Kazhdan - Lusztig polynomial . Authors: Christian Gaetz , Yibo Gao .in the symmetric group, we provide an exact formula for the smallest positive power q ^{h(w)}. appearing in the Kazhdan - Lusztig polynomial P_{e,w}( q ). . We also provide a tight upper bound on h(w)."}
+{"idx": 1, "title": "Pattern heights and the minimal power of q in a Kazhdan – Lusztig ...", "date": "", "ddg_snippet": "2 Christian Gaetz and Yibo Gao . In light of Theorem 1, one wants to understand Py,w( q ) explicitly enough to determine which coefficients vanish. Indeed, the view of the Py,w as a measure of the failure of local Poincaré duality in Xw was among the original motivations in [14].", "subpage_snippet": "", "source": "fpsac2024.rub.de", "link": "https://fpsac2024.rub.de/public/extended_abstracts/gaetz.pdf", "content": "2 Christian Gaetz and Yibo Gao . In light of Theorem 1, one wants to understand Py,w( q ) explicitly enough to determine which coefficients vanish. Indeed, the view of the Py,w as a measure of the failure of local Poincaré duality in Xw was among the original motivations in [14]."}
+{"idx": 2, "title": "Christian Gaetz - Google Scholar", "date": "", "ddg_snippet": "Yibo Gao Yibo GaoMassachusetts Institute of TechnologyVerified email at mit.edu.C Gaetz , Y Gao . Proceedings of the American Mathematical Society 148 (1), 1-7, 2020.", "subpage_snippet": "", "source": "scholar.google.com", "link": "https://scholar.google.com/citations?user=6cnTkSgAAAAJ&hl=en", "content": "Yibo Gao Yibo GaoMassachusetts Institute of TechnologyVerified email at mit.edu.C Gaetz , Y Gao . Proceedings of the American Mathematical Society 148 (1), 1-7, 2020."}
+{"idx": 3, "title": "Articles by Christian Gaetz | Synthical", "date": "", "ddg_snippet": "On the minimal power of q in a Kazhdan - Lusztig polynomial . 3 September 2024 by Christian Gaetz and Yibo Gao .On combinatorial invariance of parabolic Kazhdan - Lusztig polynomials.", "subpage_snippet": "", "source": "synthical.com", "link": "https://synthical.com/search/by_author/Christian+Gaetz", "content": "On the minimal power of q in a Kazhdan - Lusztig polynomial . 3 September 2024 by Christian Gaetz and Yibo Gao .On combinatorial invariance of parabolic Kazhdan - Lusztig polynomials."}
+{"idx": 4, "title": "Yibo Gao 's research works | Peking University and other places", "date": "", "ddg_snippet": "Yibo Gao 's 66 research works with 141 citations, including: On the minimal power of q in a Kazhdan – Lusztig polynomial .", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/scientific-contributions/Yibo-Gao-2084490604", "content": "Yibo Gao 's 66 research works with 141 citations, including: On the minimal power of q in a Kazhdan – Lusztig polynomial ."}
+{"idx": 5, "title": "Yibo Gao - Research", "date": "", "ddg_snippet": "2022. with Christian Gaetz , Pakawut Jiradilok, Gleb Nenashev, Alexander Postnikov. Comb. Theory 4 (2024), no. 2, Paper No. 16, 35 pp.", "subpage_snippet": "", "source": "faculty.bicmr.pku.edu.cn", "link": "http://faculty.bicmr.pku.edu.cn/~gaoyibo/research.html", "content": "2022. with Christian Gaetz , Pakawut Jiradilok, Gleb Nenashev, Alexander Postnikov. Comb. Theory 4 (2024), no. 2, Paper No. 16, 35 pp."}
+{"idx": 6, "title": "Recent updates on Kazhdan - Lusztig polynomial being... | Medium", "date": "", "ddg_snippet": "On the minimal power of q in a Kazhdan - Lusztig polynomial (arXiv). Author : Christian Gaetz , Yibo Gao .2.Parabolic recursions for Kazhdan - Lusztig polynomials and the hypercube decomposition (arXiv).", "subpage_snippet": "", "source": "medium.com", "link": "https://medium.com/@monocosmo77/recent-updates-on-kazhdan-lusztig-polynomial-being-used-in-machine-learning-problems-part1-6e9de977b3ee", "content": "On the minimal power of q in a Kazhdan - Lusztig polynomial (arXiv). Author : Christian Gaetz , Yibo Gao .2.Parabolic recursions for Kazhdan - Lusztig polynomials and the hypercube decomposition (arXiv)."}
+{"idx": 7, "title": "Christian Gaetz 's Homepage - Research", "date": "", "ddg_snippet": "On the minimal power of q in a Kazhdan - Lusztig polynomial , with Yibo Gao (2023). Repeatable patterns and the maximum multiplicity of a generator in a reduced word, with Yibo Gao , Pakawut Jiradilok, Gleb Nenashev, and Alexander Postnikov (2022).", "subpage_snippet": "", "source": "sites.google.com", "link": "https://sites.google.com/view/crgaetz/research", "content": "On the minimal power of q in a Kazhdan - Lusztig polynomial , with Yibo Gao (2023). Repeatable patterns and the maximum multiplicity of a generator in a reduced word, with Yibo Gao , Pakawut Jiradilok, Gleb Nenashev, and Alexander Postnikov (2022)."}
+{"idx": 8, "title": "On the minimal power of q in a Kazhdan–Lusztig polynomial", "date": "", "ddg_snippet": "by C Gaetz · 2024 · Cited by 4 — Advances in Mathematics. On the minimal power of q in a Kazhdan–Lusztig polynomial . Author links open overlay panel. Christian Gaetz a , Yibo Gao b . Show more.", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/abs/pii/S0001870824004560", "content": "by C Gaetz · 2024 · Cited by 4 — Advances in Mathematics. On the minimal power of q in a Kazhdan–Lusztig polynomial . Author links open overlay panel. Christian Gaetz a , Yibo Gao b . Show more."}
+{"idx": 9, "title": "On the minimal power of 𝑞 in a Kazhdan–Lusztig polynomial", "date": "", "ddg_snippet": "Christian Gaetz Department of Mathematics, University of California, Berkeley, CA, USA. gaetz@berkeley.edu and Yibo Gao Beijing International Center for ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2303.13695v2", "content": "Christian Gaetz Department of Mathematics, University of California, Berkeley, CA, USA. gaetz@berkeley.edu and Yibo Gao Beijing International Center for ..."}
diff --git a/data/sampled_jsons/CrossKD_Wang_2024_CIFAR100_71.13_classification_accuracy.jsonl b/data/sampled_jsons/CrossKD_Wang_2024_CIFAR100_71.13_classification_accuracy.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..c9bc6c7bd2888bba70d2904d01f839620c076b22
--- /dev/null
+++ b/data/sampled_jsons/CrossKD_Wang_2024_CIFAR100_71.13_classification_accuracy.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "CIFAR-10 - Wikipedia", "date": "", "ddg_snippet": "The CIFAR-10 dataset is a collection of images that are commonly used to train machine learning and computer vision algorithms. It is one of the most widely used datasets for machine learning research. The CIFAR-10 dataset contains 60,000 32x32 color...", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/CIFAR-10", "content": "The CIFAR-10 dataset is a collection of images that are commonly used to train machine learning and computer vision algorithms. It is one of the most widely used datasets for machine learning research. The CIFAR-10 dataset contains 60,000 32x32 color..."}
+{"idx": 1, "title": "CrossKD : Cross -Head Knowledge Distillation for Object Detection", "date": "", "ddg_snippet": "CrossKD boosts the accuracy of GFL-R50 to 42.1 (+1.9 AP) when applying ATSS as the teacher.Few shot network compression via cross distillation. In Proceed-ings of the AAAI Conference on Artificial Intelligence, 2020.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2306.11369", "content": "CrossKD boosts the accuracy of GFL-R50 to 42.1 (+1.9 AP) when applying ATSS as the teacher.Few shot network compression via cross distillation. In Proceed-ings of the AAAI Conference on Artificial Intelligence, 2020."}
+{"idx": 2, "title": "Variational sparse Bayesian neural networks with regularized...", "date": "", "ddg_snippet": "We used three widely known image classification datasets—CIFAR-10, CIFAR - 100 , and SVHN—as in-distribution training sets. A fully connected network with four hidden layers (H=3. ) and 800 hidden units per layer was trained.", "subpage_snippet": "", "source": "www.aimspress.com", "link": "https://www.aimspress.com/article/doi/10.3934/math.2025977", "content": "We used three widely known image classification datasets—CIFAR-10, CIFAR - 100 , and SVHN—as in-distribution training sets. A fully connected network with four hidden layers (H=3. ) and 800 hidden units per layer was trained."}
+{"idx": 3, "title": "Модели и алгоритмы обучения стохастических нейронных сетей...", "date": "", "ddg_snippet": "Dataset MNIST SVHN CIFAR10 CIFAR 100 . Accuracy gains in these tasks ranged from 0.5% to 1.5% compared to the baseline cross -entropy minimization approach.", "subpage_snippet": "", "source": "www.hse.ru", "link": "https://www.hse.ru/data/2025/09/19/159632086/Карпухин_резюме_ENG.pdf", "content": "Dataset MNIST SVHN CIFAR10 CIFAR 100 . Accuracy gains in these tasks ranged from 0.5% to 1.5% compared to the baseline cross -entropy minimization approach."}
+{"idx": 4, "title": "Image GPT | OpenAI", "date": "", "ddg_snippet": "By establishing a correlation between sample quality and image classification accuracy , we show that our best generative model also contains features competitive with top convolutional nets in the unsupervised setting.", "subpage_snippet": "", "source": "openai.com", "link": "https://openai.com/index/image-gpt/", "content": "By establishing a correlation between sample quality and image classification accuracy , we show that our best generative model also contains features competitive with top convolutional nets in the unsupervised setting."}
+{"idx": 5, "title": "(PDF) A Hybrid KAN-BiLSTM Transformer with Multi-Domain Dynamic...", "date": "", "ddg_snippet": "classification accuracy , especially in transformer models.Shen, H.; Zeng, C.; Wang , J.; Wang , Q. Reduced effectiveness of Kolmogorov-Arnold networks on functions with noise. arXiv. 2024 , arXiv:2407.14882.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/392213055_A_Hybrid_KAN-BiLSTM_Transformer_with_Multi-Domain_Dynamic_Attention_Model_for_Cybersecurity", "content": "classification accuracy , especially in transformer models.Shen, H.; Zeng, C.; Wang , J.; Wang , Q. Reduced effectiveness of Kolmogorov-Arnold networks on functions with noise. arXiv. 2024 , arXiv:2407.14882."}
+{"idx": 6, "title": "CIFAR-10 and CIFAR - 100 datasets", "date": "", "ddg_snippet": "The CIFAR-10 and CIFAR - 100 datasets are labeled subsets of the 80 million tiny images dataset. CIFAR-10 and CIFAR - 100 were created by Alex Krizhevsky, Vinod Nair, and Geoffrey Hinton.161 MB. 03b5dce01913d631647c71ecec9e9cb8.", "subpage_snippet": "", "source": "www.cs.toronto.edu", "link": "https://www.cs.toronto.edu/~kriz/cifar.html", "content": "The CIFAR-10 and CIFAR - 100 datasets are labeled subsets of the 80 million tiny images dataset. CIFAR-10 and CIFAR - 100 were created by Alex Krizhevsky, Vinod Nair, and Geoffrey Hinton.161 MB. 03b5dce01913d631647c71ecec9e9cb8."}
+{"idx": 7, "title": "52 датасета для тренировочных проектов / Хабр", "date": "", "ddg_snippet": "CIFAR - 100 — соответственно, 0-100. GTSRB (German traffic sign recognition benchmark) Dataset — 50 000 изображений 43 дорожных знаков.Я разобрался, каршеринг – это не бизнес на аренде авто. Простой. 13 мин. 37K. Интервью.", "subpage_snippet": "", "source": "habr.com", "link": "https://habr.com/ru/companies/edison/articles/480408/", "content": "CIFAR - 100 — соответственно, 0-100. GTSRB (German traffic sign recognition benchmark) Dataset — 50 000 изображений 43 дорожных знаков.Я разобрался, каршеринг – это не бизнес на аренде авто. Простой. 13 мин. 37K. Интервью."}
+{"idx": 8, "title": "04_data.external.ipynb - Colab", "date": "", "ddg_snippet": "CIFAR : The famous cifar-10 dataset which consists of 60000 32x32 colour images in 10 classes, with 6000 images per class. COCO_SAMPLE : A sample of the coco dataset for object detection.", "subpage_snippet": "", "source": "colab.research.google.com", "link": "https://colab.research.google.com/github/fastai/fastai/blob/master/nbs/04_data.external.ipynb", "content": "CIFAR : The famous cifar-10 dataset which consists of 60000 32x32 colour images in 10 classes, with 6000 images per class. COCO_SAMPLE : A sample of the coco dataset for object detection."}
+{"idx": 9, "title": "Калькулятор процентов онлайн — точный расчет процентов от...", "date": "", "ddg_snippet": "Спортсменка, комсомолка и просто красавица Элеонора Карцева поступает на должность офис-менеджера. Зарплата офис-менеджера в этой организации составляет 83 000 рублей в месяц. Это сумма — начисленная, с нее удерживается НДФЛ 13%.", "subpage_snippet": "", "source": "ppt.ru", "link": "https://ppt.ru/calc/procenty", "content": "Спортсменка, комсомолка и просто красавица Элеонора Карцева поступает на должность офис-менеджера. Зарплата офис-менеджера в этой организации составляет 83 000 рублей в месяц. Это сумма — начисленная, с нее удерживается НДФЛ 13%."}
diff --git a/data/sampled_jsons/Cross_Entropy_CE_formula_Reverse_Cross_Entropy_RCE_formula_mathematical_expression.jsonl b/data/sampled_jsons/Cross_Entropy_CE_formula_Reverse_Cross_Entropy_RCE_formula_mathematical_expression.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..c7679720e9d9c192b27b2cee5f6be0514f97af95
--- /dev/null
+++ b/data/sampled_jsons/Cross_Entropy_CE_formula_Reverse_Cross_Entropy_RCE_formula_mathematical_expression.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Cross Entropy Approximation of Structured", "date": "", "ddg_snippet": "Cross Entropy Approx of Structured Covariance Matrices.An interesting alternative form for the cross - entropy formulas can be found by substituting the value of σˆ2 from (21) into (22): CE : RCE", "subpage_snippet": "", "source": "scispace.com", "link": "https://scispace.com/pdf/cross-entropy-approximation-of-structured-covariance-3h55at80ku.pdf", "content": "Cross Entropy Approx of Structured Covariance Matrices.An interesting alternative form for the cross - entropy formulas can be found by substituting the value of σˆ2 from (21) into (22): CE : RCE"}
+{"idx": 1, "title": "RCE", "date": "", "ddg_snippet": "Cross -‐ Entropy ( CE )The RCE Training Method Phase 1: Reverse Training. Training the model by minimizing the RCE loss.", "subpage_snippet": "", "source": "nips.cc", "link": "https://nips.cc/media/nips-2018/Slides/12617.pdf", "content": "Cross -‐ Entropy ( CE )The RCE Training Method Phase 1: Reverse Training. Training the model by minimizing the RCE loss."}
+{"idx": 2, "title": "machine learning - What is cross - entropy ? - Stack Overflow", "date": "", "ddg_snippet": "Cross entropy loss formula .In short, cross - entropy ( CE ) is the measure of how far is your predicted value from the true label. The cross here refers to calculating the entropy between two or more features / true labels (like 0, 1).", "subpage_snippet": "", "source": "stackoverflow.com", "link": "https://stackoverflow.com/questions/41990250/what-is-cross-entropy", "content": "Cross entropy loss formula .In short, cross - entropy ( CE ) is the measure of how far is your predicted value from the true label. The cross here refers to calculating the entropy between two or more features / true labels (like 0, 1)."}
+{"idx": 3, "title": "Cross - Entropy , Negative Log-Likelihood, and All That Jazz | Medium", "date": "", "ddg_snippet": "Cross - entropy and negative log-likelihood are closely related mathematical formulations .It turns out that the formulation of cross - entropy between two probability distributions coincides with the negative log-likelihood.", "subpage_snippet": "", "source": "medium.com", "link": "https://medium.com/data-science/cross-entropy-negative-log-likelihood-and-all-that-jazz-47a95bd2e81", "content": "Cross - entropy and negative log-likelihood are closely related mathematical formulations .It turns out that the formulation of cross - entropy between two probability distributions coincides with the negative log-likelihood."}
+{"idx": 4, "title": "Cross Entropy Approximation of Structured... : Internet Archive", "date": "", "ddg_snippet": "Cross Entropy Approx of Structured Covariance Matrices.Substituting this into the RCE formula gives an alternate set of necessary conditions for the optimal RCE solution", "subpage_snippet": "", "source": "archive.org", "link": "https://archive.org/details/arxiv-cs0608121", "content": "Cross Entropy Approx of Structured Covariance Matrices.Substituting this into the RCE formula gives an alternate set of necessary conditions for the optimal RCE solution"}
+{"idx": 5, "title": "entropy — SciPy v1.16.2 Manual", "date": "", "ddg_snippet": "The cross entropy can be calculated as the sum of the entropy and relative entropy `", "subpage_snippet": "", "source": "docs.scipy.org", "link": "https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.entropy.html", "content": "The cross entropy can be calculated as the sum of the entropy and relative entropy `"}
+{"idx": 6, "title": "Symmetric Cross Entropy for Robust Learning With Noisy Labels", "date": "", "ddg_snippet": "While Cross Entropy ( CE ) loss is the most commonly used loss for training DNNs, we have found that DNN learning with CE can be class-biasedWe begin by analyzing the Cross Entropy ( CE ) and its limitations for learning with noisy labels.", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content_ICCV_2019/papers/Wang_Symmetric_Cross_Entropy_for_Robust_Learning_With_Noisy_Labels_ICCV_2019_paper.pdf", "content": "While Cross Entropy ( CE ) loss is the most commonly used loss for training DNNs, we have found that DNN learning with CE can be class-biasedWe begin by analyzing the Cross Entropy ( CE ) and its limitations for learning with noisy labels."}
+{"idx": 7, "title": "entropy _based_measures_in_ml.ipynb - Colab", "date": "", "ddg_snippet": "Cross - Entropy . subdirectory_arrow_right 4 cells hidden.This formula makes it pretty clear that the relative entropy (KL-divergence) represents a very similar to cross - entropy except for the fact that the KL-divergence has the original entropy 'removed'.", "subpage_snippet": "", "source": "colab.research.google.com", "link": "https://colab.research.google.com/github/DiGyt/snippets/blob/master/entropy_based_measures_in_ml.ipynb", "content": "Cross - Entropy . subdirectory_arrow_right 4 cells hidden.This formula makes it pretty clear that the relative entropy (KL-divergence) represents a very similar to cross - entropy except for the fact that the KL-divergence has the original entropy 'removed'."}
+{"idx": 8, "title": "An improved cross - entropy method", "date": "", "ddg_snippet": "The Cross - Entropy ( CE ) method derives from the Monte Carlo method and was introduced by Rubinstein in 1997 [3]. Its central concept involves iteratively refining the probability dis-tribution of solutions, starting from an initial parameter distribution.", "subpage_snippet": "", "source": "www.politesi.polimi.it", "link": "https://www.politesi.polimi.it/retrieve/7e1befd2-970f-44a3-94d8-93090c238948/2023_12_Zeng_Tesi_01.pdf", "content": "The Cross - Entropy ( CE ) method derives from the Monte Carlo method and was introduced by Rubinstein in 1997 [3]. Its central concept involves iteratively refining the probability dis-tribution of solutions, starting from an initial parameter distribution."}
+{"idx": 9, "title": "Towards Robust Detection of Adversarial Examples", "date": "", "ddg_snippet": "Reverse Cross Entropy Training.Figure: The dierence between the CE and the RCE in the case of L = 10. The RCE training method consists of two simple phases: • Reverse training: Training the DNN F(X, θ) to be a. reverse classier by minimizing the average RCE loss", "subpage_snippet": "", "source": "ml.cs.tsinghua.edu.cn", "link": "https://ml.cs.tsinghua.edu.cn/~tianyu/RCE/RCE_poster.pdf", "content": "Reverse Cross Entropy Training.Figure: The dierence between the CE and the RCE in the case of L = 10. The RCE training method consists of two simple phases: • Reverse training: Training the DNN F(X, θ) to be a. reverse classier by minimizing the average RCE loss"}
diff --git a/data/sampled_jsons/DART_Disease-aware_Image-Text_Alignment_and_Self-correcting_Re-alignment_Equation_(5)_lambda_m.jsonl b/data/sampled_jsons/DART_Disease-aware_Image-Text_Alignment_and_Self-correcting_Re-alignment_Equation_(5)_lambda_m.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..0f687ed125a8b3cbdee941e4faf9fcdc46e9af9b
--- /dev/null
+++ b/data/sampled_jsons/DART_Disease-aware_Image-Text_Alignment_and_Self-correcting_Re-alignment_Equation_(5)_lambda_m.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Enhancing Global Sensitivity and Uncertainty Quantification in", "date": "", "ddg_snippet": "... and high-fidelity reconstructed medical images ensure the precision and effectiveness of subsequent disease diagnosis and treatment planning, thus ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2405.17659v2", "content": "... and high-fidelity reconstructed medical images ensure the precision and effectiveness of subsequent disease diagnosis and treatment planning, thus ..."}
+{"idx": 1, "title": "US20030108250A1 - Method and system for selectively applying", "date": "", "ddg_snippet": "G06V10/26 — Segmentation of patterns in the image field; Cutting or merging of image elements to establish the pattern region, e.g.", "subpage_snippet": "", "source": "patents.google.com", "link": "https://patents.google.com/patent/US20030108250A1/en", "content": "G06V10/26 — Segmentation of patterns in the image field; Cutting or merging of image elements to establish the pattern region, e.g."}
+{"idx": 2, "title": "Bealtaine festival and first day of summer — boards.ie - Now", "date": "", "ddg_snippet": "It is really unhelpful to break the seasons, as current people are inclined to do, into meteorological and astronomical seasons as it obstructs an ...", "subpage_snippet": "", "source": "www.boards.ie", "link": "https://www.boards.ie/discussion/2058244014/bealtaine-festival-and-first-day-of-summer", "content": "It is really unhelpful to break the seasons, as current people are inclined to do, into meteorological and astronomical seasons as it obstructs an ..."}
+{"idx": 3, "title": "bravenewlife | Small Conceits", "date": "", "ddg_snippet": "And , here I was, naming my new home, a place I hoped would hold and support me for the rest of my life and heal all those who sought peace within its ...", "subpage_snippet": "", "source": "smallconceits.com", "link": "https://smallconceits.com/tag/bravenewlife/", "content": "And , here I was, naming my new home, a place I hoped would hold and support me for the rest of my life and heal all those who sought peace within its ..."}
+{"idx": 4, "title": "US8761348B2 - DSL system training - Google Patents", "date": "", "ddg_snippet": "2016-11-30 Assigned to ADAPTIVE SPECTRUM AND SIGNAL ALIGNMENT , INCORPORATED reassignment ADAPTIVE SPECTRUM AND SIGNAL ALIGNMENT , INCORPORATED RELEASE ...", "subpage_snippet": "", "source": "patents.google.com", "link": "https://patents.google.com/patent/US8761348B2/en", "content": "2016-11-30 Assigned to ADAPTIVE SPECTRUM AND SIGNAL ALIGNMENT , INCORPORATED reassignment ADAPTIVE SPECTRUM AND SIGNAL ALIGNMENT , INCORPORATED RELEASE ..."}
+{"idx": 5, "title": "From Python to Numpy", "date": "", "ddg_snippet": "This is a public scientific and technological establishment (EPST) under the double supervision of the Research & Education Ministry, and the ...", "subpage_snippet": "", "source": "www.labri.fr", "link": "https://www.labri.fr/perso/nrougier/from-python-to-numpy/", "content": "This is a public scientific and technological establishment (EPST) under the double supervision of the Research & Education Ministry, and the ..."}
+{"idx": 6, "title": "The Lens That Sees Its Flaws — LessWrong", "date": "", "ddg_snippet": "Light leaves the Sun and strikes your shoelaces and bounces off; some photons enter the pupils of your eyes and strike your retina; the energy of the ...", "subpage_snippet": "", "source": "www.lesswrong.com", "link": "https://www.lesswrong.com/posts/46qnWRSR7L2eyNbMA/the-lens-that-sees-its-flaws", "content": "Light leaves the Sun and strikes your shoelaces and bounces off; some photons enter the pupils of your eyes and strike your retina; the energy of the ..."}
+{"idx": 7, "title": "The Lens That Sees Its Flaws - LessWrong 2.0 viewer", "date": "", "ddg_snippet": "Light leaves the Sun and strikes your shoelaces and bounces off; some photons enter the pupils of your eyes and strike your retina; the energy of the ...", "subpage_snippet": "", "source": "www.greaterwrong.com", "link": "https://www.greaterwrong.com/posts/46qnWRSR7L2eyNbMA/the-lens-that-sees-its-flaws", "content": "Light leaves the Sun and strikes your shoelaces and bounces off; some photons enter the pupils of your eyes and strike your retina; the energy of the ..."}
+{"idx": 8, "title": "SGU Episode 987 - SGUTranscripts", "date": "", "ddg_snippet": "C: And so he's basically like a dog whose alignment is off, so he leans to the left when he walks, and if he tries to like scuttle too fast or if he ...", "subpage_snippet": "", "source": "www.sgutranscripts.org", "link": "https://www.sgutranscripts.org/wiki/SGU_Episode_987", "content": "C: And so he's basically like a dog whose alignment is off, so he leans to the left when he walks, and if he tries to like scuttle too fast or if he ..."}
+{"idx": 9, "title": "Fridge / The Order of the Stick - TV Tropes", "date": "", "ddg_snippet": "... reflect their alignments ; most of the applications for illusion spells involve deceiving others, a behavior associated with the chaotic alignments , ...", "subpage_snippet": "", "source": "tvtropes.org", "link": "https://tvtropes.org/pmwiki/pmwiki.php/Fridge/TheOrderOfTheStick", "content": "... reflect their alignments ; most of the applications for illusion spells involve deceiving others, a behavior associated with the chaotic alignments , ..."}
diff --git a/data/sampled_jsons/DART_Disease-aware_Image-Text_Alignment_lambda_m_inurlgithub.com_year_2023.jsonl b/data/sampled_jsons/DART_Disease-aware_Image-Text_Alignment_lambda_m_inurlgithub.com_year_2023.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..704bcdb03bbcfa5b637d4664c02aeb37d95938ce
--- /dev/null
+++ b/data/sampled_jsons/DART_Disease-aware_Image-Text_Alignment_lambda_m_inurlgithub.com_year_2023.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Vacuum hose diagram question | Dodge Dart Forum", "date": "", "ddg_snippet": "Jun 7, 2025 · Having issues with my 2013 dodge dart 1.4l. Keep getting underboost code and a sligh shudder when turbo kicks in. New to this type of vehicle and not at all familiar with turbos in general. Someone suggested I replace the boost selenoid and they took it off. Got a new one but now have no idea...", "subpage_snippet": "", "source": "www.dodge-dart.org", "link": "https://www.dodge-dart.org/threads/vacuum-hose-diagram-question.71323/", "content": "Jun 7, 2025 · Having issues with my 2013 dodge dart 1.4l. Keep getting underboost code and a sligh shudder when turbo kicks in. New to this type of vehicle and not at all familiar with turbos in general. Someone suggested I replace the boost selenoid and they took it off. Got a new one but now have no idea..."}
+{"idx": 1, "title": "Recall Shifter Bushing Replacement - 2013 Dart", "date": "", "ddg_snippet": "Jul 20, 2024 · 2013 1.4 dart manual, 87k miles, and accidentally discovered that my bushing was holding on by a thread while investigating oil on top of my transmission (looks like my vaccuum pump gasket is rotting away).", "subpage_snippet": "", "source": "www.dodge-dart.org", "link": "https://www.dodge-dart.org/threads/recall-shifter-bushing-replacement-2013-dart.70634/", "content": "Jul 20, 2024 · 2013 1.4 dart manual, 87k miles, and accidentally discovered that my bushing was holding on by a thread while investigating oil on top of my transmission (looks like my vaccuum pump gasket is rotting away)."}
+{"idx": 2, "title": "2.4 multiair variable valve timing actuator - Dodge Dart Forum", "date": "", "ddg_snippet": "Nov 9, 2019 · Dodge- Dart .org is a forum dedicated to the 2013-2016 Dodge Dart . Join and participate in discussions about maintenance, performance mods, and mechanical issues. Get the latest tips, news, browse the classifieds, and more!", "subpage_snippet": "", "source": "www.dodge-dart.org", "link": "https://www.dodge-dart.org/threads/2-4-multiair-variable-valve-timing-actuator.65832/", "content": "Nov 9, 2019 · Dodge- Dart .org is a forum dedicated to the 2013-2016 Dodge Dart . Join and participate in discussions about maintenance, performance mods, and mechanical issues. Get the latest tips, news, browse the classifieds, and more!"}
+{"idx": 3, "title": "No Crank, No Start - Dodge Dart Forum", "date": "", "ddg_snippet": "Feb 16, 2023 · Y'all helped me spectacularly last time I had issues with my Dart I figured I'd come back for round 2. So since the last time I asked for help my Dart has started refusing to start. No crank, a singular click, and nothing more. The Dashboard comes to life, the AC works fine, and the Radio works...", "subpage_snippet": "", "source": "www.dodge-dart.org", "link": "https://www.dodge-dart.org/threads/no-crank-no-start.69518/", "content": "Feb 16, 2023 · Y'all helped me spectacularly last time I had issues with my Dart I figured I'd come back for round 2. So since the last time I asked for help my Dart has started refusing to start. No crank, a singular click, and nothing more. The Dashboard comes to life, the AC works fine, and the Radio works..."}
+{"idx": 4, "title": "UConnect Bluetooth Issue | Dodge Dart Forum", "date": "", "ddg_snippet": "Jan 2, 2025 · I have attached some images of the messages I've been getting on my phone when trying to connect, these messages appear just after I tap uconnect on my phone. I have tried many things already such as the temperature button soft reset procedure and the corner of the screen soft reset procedure...", "subpage_snippet": "", "source": "www.dodge-dart.org", "link": "https://www.dodge-dart.org/threads/uconnect-bluetooth-issue.71014/", "content": "Jan 2, 2025 · I have attached some images of the messages I've been getting on my phone when trying to connect, these messages appear just after I tap uconnect on my phone. I have tried many things already such as the temperature button soft reset procedure and the corner of the screen soft reset procedure..."}
+{"idx": 5, "title": "Front strut replacement - Dodge Dart Forum", "date": "", "ddg_snippet": "May 24, 2020 · Front struts are gone, will replace with KYB's struts part #'s 334982 and 334981. Rockauto. com has them for $83 each rightnow compared to the next cheapest autozone $127 so definitely gonna pull the trigger, however I need to know, do I need to replace the strut mounts with KYB's also (sm5811...", "subpage_snippet": "", "source": "www.dodge-dart.org", "link": "https://www.dodge-dart.org/threads/front-strut-replacement.66659/", "content": "May 24, 2020 · Front struts are gone, will replace with KYB's struts part #'s 334982 and 334981. Rockauto. com has them for $83 each rightnow compared to the next cheapest autozone $127 so definitely gonna pull the trigger, however I need to know, do I need to replace the strut mounts with KYB's also (sm5811..."}
+{"idx": 6, "title": "Coolant hose connector - Dodge Dart Forum", "date": "", "ddg_snippet": "May 10, 2024 · Dodge- Dart .org is a forum dedicated to the 2013-2016 Dodge Dart . Join and participate in discussions about maintenance, performance mods, and mechanical issues. Get the latest tips, news, browse the classifieds, and more!", "subpage_snippet": "", "source": "www.dodge-dart.org", "link": "https://www.dodge-dart.org/threads/coolant-hose-connector.70491/", "content": "May 10, 2024 · Dodge- Dart .org is a forum dedicated to the 2013-2016 Dodge Dart . Join and participate in discussions about maintenance, performance mods, and mechanical issues. Get the latest tips, news, browse the classifieds, and more!"}
+{"idx": 7, "title": "Transmission Shudder - Dodge Dart Forum", "date": "", "ddg_snippet": "Aug 15, 2018 · 2016 dart gt blacktop. 10k miles. First owner. This has been going on for almost a year now. At about 2000rpm (coasting) if I lightly press the throttle the entire car will jerk violently, the feeling can be compared to almost stalling out a manual transmission. Most the time it's when it...", "subpage_snippet": "", "source": "www.dodge-dart.org", "link": "https://www.dodge-dart.org/threads/transmission-shudder.62830/", "content": "Aug 15, 2018 · 2016 dart gt blacktop. 10k miles. First owner. This has been going on for almost a year now. At about 2000rpm (coasting) if I lightly press the throttle the entire car will jerk violently, the feeling can be compared to almost stalling out a manual transmission. Most the time it's when it..."}
+{"idx": 8, "title": "Dart Wiring Diagrams | Page 5 | Dodge Dart Forum", "date": "", "ddg_snippet": "Jan 28, 2024 · Search Wiring Diagrams Use the following link to search for wiring diagrams for the dart .", "subpage_snippet": "", "source": "www.dodge-dart.org", "link": "https://www.dodge-dart.org/threads/dart-wiring-diagrams.50274/page-5", "content": "Jan 28, 2024 · Search Wiring Diagrams Use the following link to search for wiring diagrams for the dart ."}
+{"idx": 9, "title": "Recirculation Door Actuator Location - Dodge Dart Forum", "date": "", "ddg_snippet": "Jun 10, 2024 · The recirculation door actuator (1) is a reversible 12 volt Direct Current (DC) servo motor. The recirculation door actuator is located on the bottom of the HVAC air inlet housing, behind the instrument panel. The recirculation door actuator is contained within a black molded plastic housing with an integral wire connector receptacle (4). Three mounting tabs (3) allow the actuator to be ...", "subpage_snippet": "", "source": "www.dodge-dart.org", "link": "https://www.dodge-dart.org/threads/recirculation-door-actuator-location.70551/", "content": "Jun 10, 2024 · The recirculation door actuator (1) is a reversible 12 volt Direct Current (DC) servo motor. The recirculation door actuator is located on the bottom of the HVAC air inlet housing, behind the instrument panel. The recirculation door actuator is contained within a black molded plastic housing with an integral wire connector receptacle (4). Three mounting tabs (3) allow the actuator to be ..."}
diff --git "a/data/sampled_jsons/DART_Disease-aware_Image-Text_Alignment_self-correcting_Re-alignment_\316\273m_Equation_(5)_year_2024.jsonl" "b/data/sampled_jsons/DART_Disease-aware_Image-Text_Alignment_self-correcting_Re-alignment_\316\273m_Equation_(5)_year_2024.jsonl"
new file mode 100644
index 0000000000000000000000000000000000000000..c79fa804350b77e6a39d0b046a7af7371891b728
--- /dev/null
+++ "b/data/sampled_jsons/DART_Disease-aware_Image-Text_Alignment_self-correcting_Re-alignment_\316\273m_Equation_(5)_year_2024.jsonl"
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "[2504.11786] DART : Disease - aware Image - Text Alignment and...", "date": "", "ddg_snippet": "View a PDF of the paper titled DART : Disease - aware Image - Text Alignment and Self - correcting Re - alignment for Trustworthy Radiology Report Generation, by Sang-Jun Park and 5 other authors.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2504.11786", "content": "View a PDF of the paper titled DART : Disease - aware Image - Text Alignment and Self - correcting Re - alignment for Trustworthy Radiology Report Generation, by Sang-Jun Park and 5 other authors."}
+{"idx": 1, "title": "(PDF) DART : Disease - aware Image - Text Alignment and...", "date": "", "ddg_snippet": "a Disease - aware image - text Alignment and self - correcting . Re - alignment for Trustworthy radiology report generation. ( DART ) framework. disease - aware image -to- text alignment . First, an input im-. age and its associated report are embedded into a shared.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/390845711_DART_Disease-aware_Image-Text_Alignment_and_Self-correcting_Re-alignment_for_Trustworthy_Radiology_Report_Generation", "content": "a Disease - aware image - text Alignment and self - correcting . Re - alignment for Trustworthy radiology report generation. ( DART ) framework. disease - aware image -to- text alignment . First, an input im-. age and its associated report are embedded into a shared."}
+{"idx": 2, "title": "DART : Disease - aware Image - Text Alignment and Self - correcting ...", "date": "", "ddg_snippet": ". Self - Correcting Re - alignment of Generated Reports.In this study, we propose a Disease - aware image - text Alignment and self - correcting Re - alignment for Trustwor-thy radiology report generation ( DART ), a novel frame", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2025/papers/Park_DART_Disease-aware_Image-Text_Alignment_and_Self-correcting_Re-alignment_for_Trustworthy_Radiology_CVPR_2025_paper.pdf", "content": ". Self - Correcting Re - alignment of Generated Reports.In this study, we propose a Disease - aware image - text Alignment and self - correcting Re - alignment for Trustwor-thy radiology report generation ( DART ), a novel frame"}
+{"idx": 3, "title": "Dart", "date": "", "ddg_snippet": "DART : Disease - aware Image - Text Alignment and Self - correcting Re - alignment for Trustworthy Radiology Report Generation. Request Code.In the second stage, we further enhance the initial reports by introducing a self - correction module that re - aligns them with the X-ray images.", "subpage_snippet": "", "source": "www.catalyzex.com", "link": "https://www.catalyzex.com/s/Dart", "content": "DART : Disease - aware Image - Text Alignment and Self - correcting Re - alignment for Trustworthy Radiology Report Generation. Request Code.In the second stage, we further enhance the initial reports by introducing a self - correction module that re - aligns them with the X-ray images."}
+{"idx": 4, "title": "Brat Text Generator - Charli XCX Inspired Text Maker", "date": "", "ddg_snippet": "4. How do I align the text ? Click the \" Text Alignment \" button to toggle between left, right, center, and justify alignments . 5 . Why is my text getting cut off? If your text is too long, decrease the font size to fit it properly within the canvas.", "subpage_snippet": "", "source": "bratgenerator.io", "link": "https://bratgenerator.io/old/", "content": "4. How do I align the text ? Click the \" Text Alignment \" button to toggle between left, right, center, and justify alignments . 5 . Why is my text getting cut off? If your text is too long, decrease the font size to fit it properly within the canvas."}
+{"idx": 5, "title": "HTML 5 | Выравнивание элементов. align -items и align - self", "date": "", "ddg_snippet": "Выравнивание по поперечной оси cross axis во Flexbox в HTML 5 и CSS3, свойства align -items и align - self .", "subpage_snippet": "", "source": "metanit.com", "link": "https://metanit.com/web/html5/12.6.php", "content": "Выравнивание по поперечной оси cross axis во Flexbox в HTML 5 и CSS3, свойства align -items и align - self ."}
+{"idx": 6, "title": "Articles by Jae-Hyuk Oh | Synthical", "date": "", "ddg_snippet": "High Energy Physics. DART : Disease - aware Image - Text Alignment and Self - correcting Re - alignment for Trustworthy Radiology Report Generation.", "subpage_snippet": "", "source": "synthical.com", "link": "https://synthical.com/profile/d020f4a8-4269-4de9-b83d-12a7f9e33801/articles", "content": "High Energy Physics. DART : Disease - aware Image - Text Alignment and Self - correcting Re - alignment for Trustworthy Radiology Report Generation."}
+{"idx": 7, "title": "GitHub - AlonzoLeeeooo/awesome-radiology-report-generation...", "date": "", "ddg_snippet": "DART : Disease - aware Image - Text Alignment and Self - correcting Re - alignment for Trustworthy Radiology Report Generation [Paper].", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/AlonzoLeeeooo/awesome-radiology-report-generation", "content": "DART : Disease - aware Image - Text Alignment and Self - correcting Re - alignment for Trustworthy Radiology Report Generation [Paper]."}
+{"idx": 8, "title": "Latest 15 Papers - May 18, 2025", "date": "", "ddg_snippet": "DART : Disease - aware Image - Text Alignment and Self - correcting Re - alignment for Trustworthy Radiology Report Generation.", "subpage_snippet": "", "source": "devflowstack.org", "link": "https://devflowstack.org/blog/latest-15-papers-may-18-1747530773612", "content": "DART : Disease - aware Image - Text Alignment and Self - correcting Re - alignment for Trustworthy Radiology Report Generation."}
+{"idx": 9, "title": "Latest 15 Papers - May 21, 2025", "date": "", "ddg_snippet": "DART : Disease - aware Image - Text Alignment and Self - correcting Re - alignment for Trustworthy Radiology Report Generation.", "subpage_snippet": "", "source": "viao.co.uk", "link": "https://viao.co.uk/blog/latest-15-papers-may-21-1747789981215", "content": "DART : Disease - aware Image - Text Alignment and Self - correcting Re - alignment for Trustworthy Radiology Report Generation."}
diff --git a/data/sampled_jsons/DART_paper_Section_3.2_self-correction_module_methodology_radiology_report_generation.jsonl b/data/sampled_jsons/DART_paper_Section_3.2_self-correction_module_methodology_radiology_report_generation.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..60713c82116194f84fbe673a10af7e93d73e26f2
--- /dev/null
+++ b/data/sampled_jsons/DART_paper_Section_3.2_self-correction_module_methodology_radiology_report_generation.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "DART: Disease-aware Image-Text Alignment and Self-correcting ...", "date": "", "ddg_snippet": "We introduce a two-stage framework for radiology re-port generation , which combines disease-aware image-to-text retrieval with a self-correction module to refine gener-ated reports by re-aligning reports with image features for greater accuracy and coherence.", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2025/papers/Park_DART_Disease-aware_Image-Text_Alignment_and_Self-correcting_Re-alignment_for_Trustworthy_Radiology_CVPR_2025_paper.pdf", "content": "We introduce a two-stage framework for radiology re-port generation , which combines disease-aware image-to-text retrieval with a self-correction module to refine gener-ated reports by re-aligning reports with image features for greater accuracy and coherence."}
+{"idx": 1, "title": "DART: Disease-aware Image-Text Alignment and Self-correcting ... DART | PDF | Artificial Intelligence | Intelligence (AI ... HKRG: Hierarchical knowledge integration for radiology report ... DART: Disease-aware Image-Text Alignment and Self-correcting ... A Self-guided Framework for Radiology Report Generation Radiology report generation with medical knowledge and ... HKRG : Hierarchical knowledge integration for radiology report Radiology report generation with medical knowledge and multilevel image HKRG : Hierarchical knowledge integration for radiology report HKRG : Hierarchical knowledge integration for radiology report CVPR 2025 Open Access Repository", "date": "", "ddg_snippet": "Apr 16, 2025 · In this study, we propose a Disease-aware image-text Alignment and self -correcting Re-alignment for Trustworthy radiology report generation ( DART ) framework. In the first stage, we generate initial reports based on image-to-text retrieval with disease-matching, embedding both images and texts in a shared embedding space through contrastive ... The document presents the DART framework, which focuses on automatic radiology report generation by ensuring disease-aware image-text alignment and incorporating a self-correction mechanism. This two-stage approach first generates initial reports through image-to-text retrieval with a disease-matching constraint and then refines these reports by re-aligning them with input X-ray images. The ... May 1, 2025 · This section reviews recent work on radiology report generation and vision-language pre-training. We focus on these two aspects as they are crucial for understanding the advancements and challenges in integrating visual and textual data for radiology report generation . We introduce a two-stage framework for radiology re-port generation , which combines disease-aware image-to-text retrieval with a self-correction module to refine gener-ated reports by re-aligning reports with image features for greater accuracy and coherence. KD is responsible for extracting the potential knowledge from the reports without any extra disease labels. The orange part of Fig. 1 provides an overview of the KD module that contains Report Embedding (RE), Dimension Reduction (DR), and Knowledge Clustering (KC). In RE, the Sentence-Bert (SB) is considered as the embedding method to convert each... See full list on link.springer.com KMVE is utilized to alleviate the mismatch feature diversity between the image and text. KMVE takes image pairs \\(I=\\left\\{{i}_{m}^{1},{i}_{m}^{ 2 }\\right\\}, {i}_{m}\\in {R}^{{N}_{i}}\\) as input, where the size of each image is \\(\\left( 3 , 244, 244\\right)\\). The knowledge topics \\(T\\) from the KD module are adopted as the potential labels of the images... See full list on link.springer.com RG is based on Transformer (TF) and Similarity Comparer (SC). TF is consisted with Transformer Encoder (TE) and Transformer Decoder (TD) . In TE, the \\({V}_{avg}\\) are converted into query, key, and value at first. Then the Multi-Head Attention (MHA) concatenates all head results together to acquire the visual details in different subspaces. After ... See full list on link.springer.com Dec 1, 2023 · In general, medical report generation is an image captioning task. Since medical reports have long sequences with data bias, the existing medical report generation models lack medical knowledge and ignore the interaction alignment between the two modalities of reports and images. How is a radiology report generated? During the radiology report generation phase, we first process the radiology image I using an image encoder , segmenting it into multiple blocks and extracting the image features f I. Concurrently, radiological knowledge related to organs (K o) and diseases (K d) is encoded into corresponding knowledge features f K o and f K d. What is a new approach to medical report generation? We propose a new approach to medical report generation, including knowledge enhancement and multilevel alignment modules . It alleviates the expertise limitations in medical reports and the lack of interaction between text and image modalities in reports and knowledge. How can hierarchical knowledge integration facilitate the generation of Radiology reports? To tackle this, we propose the hierarchical knowledge integration framework (HKRG), which facilitates the generation of radiology reports by guiding the process through layered domain knowledge . We extract the correspondence between organs and pathologies, serving as a buffer for the conversion between image–text pairs. Do different learning rates affect generating radiological reports? In Table 8, we present the impact of different learning rates on the task of generating radiological reports for the IU-Xray dataset. By comparing the results at learning rates set at 1 × 1 0 4, 1 × 1 0 5, and 8 × 1 0 6, we find that the model performs slightly better on all evaluation metrics when the learning rate is 1 × 1 0 5. In this study, we propose a Disease-aware image-text Alignment and self -correcting Re-alignment for Trustworthy radiology report generation ( DART ) framework. In the first stage, we generate initial reports based on image-to-text retrieval with disease-matching, embedding both images and texts in a shared embedding space through contrastive ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2504.11786", "content": "Apr 16, 2025 · In this study, we propose a Disease-aware image-text Alignment and self -correcting Re-alignment for Trustworthy radiology report generation ( DART ) framework. In the first stage, we generate initial reports based on image-to-text retrieval with disease-matching, embedding both images and texts in a shared embedding space through contrastive ... The document presents the DART framework, which focuses on automatic radiology report generation by ensuring disease-aware image-text alignment and incorporating a self-correction mechanism. This two-stage approach first generates initial reports through image-to-text retrieval with a disease-matching constraint and then refines these reports by re-aligning them with input X-ray images. The ... May 1, 2025 · This section reviews recent work on radiology report generation and vision-language pre-training. We focus on these two aspects as they are crucial for understanding the advancements and challenges in integrating visual and textual data for radiology report generation . We introduce a two-stage framework for radiology re-port generation , which combines disease-aware image-to-text retrieval with a self-correction module to refine gener-ated reports by re-aligning reports with image features for greater accuracy and coherence. KD is responsible for extracting the potential knowledge from the reports without any extra disease labels. The orange part of Fig. 1 provides an overview of the KD module that contains Report Embedding (RE), Dimension Reduction (DR), and Knowledge Clustering (KC). In RE, the Sentence-Bert (SB) is considered as the embedding method to convert each... See full list on link.springer.com KMVE is utilized to alleviate the mismatch feature diversity between the image and text. KMVE takes image pairs \\(I=\\left\\{{i}_{m}^{1},{i}_{m}^{ 2 }\\right\\}, {i}_{m}\\in {R}^{{N}_{i}}\\) as input, where the size of each image is \\(\\left( 3 , 244, 244\\right)\\). The knowledge topics \\(T\\) from the KD module are adopted as the potential labels of the images... See full list on link.springer.com RG is based on Transformer (TF) and Similarity Comparer (SC). TF is consisted with Transformer Encoder (TE) and Transformer Decoder (TD) . In TE, the \\({V}_{avg}\\) are converted into query, key, and value at first. Then the Multi-Head Attention (MHA) concatenates all head results together to acquire the visual details in different subspaces. After ... See full list on link.springer.com Dec 1, 2023 · In general, medical report generation is an image captioning task. Since medical reports have long sequences with data bias, the existing medical report generation models lack medical knowledge and ignore the interaction alignment between the two modalities of reports and images. How is a radiology report generated? During the radiology report generation phase, we first process the radiology image I using an image encoder , segmenting it into multiple blocks and extracting the image features f I. Concurrently, radiological knowledge related to organs (K o) and diseases (K d) is encoded into corresponding knowledge features f K o and f K d. What is a new approach to medical report generation? We propose a new approach to medical report generation, including knowledge enhancement and multilevel alignment modules . It alleviates the expertise limitations in medical reports and the lack of interaction between text and image modalities in reports and knowledge. How can hierarchical knowledge integration facilitate the generation of Radiology reports? To tackle this, we propose the hierarchical knowledge integration framework (HKRG), which facilitates the generation of radiology reports by guiding the process through layered domain knowledge . We extract the correspondence between organs and pathologies, serving as a buffer for the conversion between image–text pairs. Do different learning rates affect generating radiological reports? In Table 8, we present the impact of different learning rates on the task of generating radiological reports for the IU-Xray dataset. By comparing the results at learning rates set at 1 × 1 0 4, 1 × 1 0 5, and 8 × 1 0 6, we find that the model performs slightly better on all evaluation metrics when the learning rate is 1 × 1 0 5. In this study, we propose a Disease-aware image-text Alignment and self -correcting Re-alignment for Trustworthy radiology report generation ( DART ) framework. In the first stage, we generate initial reports based on image-to-text retrieval with disease-matching, embedding both images and texts in a shared embedding space through contrastive ..."}
+{"idx": 2, "title": "DART | PDF | Artificial Intelligence | Intelligence (AI ...", "date": "", "ddg_snippet": "The document presents the DART framework, which focuses on automatic radiology report generation by ensuring disease-aware image-text alignment and incorporating a self-correction mechanism. This two-stage approach first generates initial reports through image-to-text retrieval with a disease-matching constraint and then refines these reports by re-aligning them with input X-ray images. The ...", "subpage_snippet": "", "source": "www.scribd.com", "link": "https://www.scribd.com/document/876841025/DART", "content": "The document presents the DART framework, which focuses on automatic radiology report generation by ensuring disease-aware image-text alignment and incorporating a self-correction mechanism. This two-stage approach first generates initial reports through image-to-text retrieval with a disease-matching constraint and then refines these reports by re-aligning them with input X-ray images. The ..."}
+{"idx": 3, "title": "HKRG: Hierarchical knowledge integration for radiology report ...", "date": "", "ddg_snippet": "May 1, 2025 · This section reviews recent work on radiology report generation and vision-language pre-training. We focus on these two aspects as they are crucial for understanding the advancements and challenges in integrating visual and textual data for radiology report generation .", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S0957417425002441", "content": "May 1, 2025 · This section reviews recent work on radiology report generation and vision-language pre-training. We focus on these two aspects as they are crucial for understanding the advancements and challenges in integrating visual and textual data for radiology report generation ."}
+{"idx": 4, "title": "A Self-guided Framework for Radiology Report Generation Radiology report generation with medical knowledge and ... HKRG : Hierarchical knowledge integration for radiology report Radiology report generation with medical knowledge and multilevel image HKRG : Hierarchical knowledge integration for radiology report HKRG : Hierarchical knowledge integration for radiology report CVPR 2025 Open Access Repository", "date": "", "ddg_snippet": "KD is responsible for extracting the potential knowledge from the reports without any extra disease labels. The orange part of Fig. 1 provides an overview of the KD module that contains Report Embedding (RE), Dimension Reduction (DR), and Knowledge Clustering (KC). In RE, the Sentence-Bert (SB) is considered as the embedding method to convert each... See full list on link.springer.com KMVE is utilized to alleviate the mismatch feature diversity between the image and text. KMVE takes image pairs \\(I=\\left\\{{i}_{m}^{1},{i}_{m}^{ 2 }\\right\\}, {i}_{m}\\in {R}^{{N}_{i}}\\) as input, where the size of each image is \\(\\left( 3 , 244, 244\\right)\\). The knowledge topics \\(T\\) from the KD module are adopted as the potential labels of the images... See full list on link.springer.com RG is based on Transformer (TF) and Similarity Comparer (SC). TF is consisted with Transformer Encoder (TE) and Transformer Decoder (TD) . In TE, the \\({V}_{avg}\\) are converted into query, key, and value at first. Then the Multi-Head Attention (MHA) concatenates all head results together to acquire the visual details in different subspaces. After ... See full list on link.springer.com Dec 1, 2023 · In general, medical report generation is an image captioning task. Since medical reports have long sequences with data bias, the existing medical report generation models lack medical knowledge and ignore the interaction alignment between the two modalities of reports and images. How is a radiology report generated? During the radiology report generation phase, we first process the radiology image I using an image encoder , segmenting it into multiple blocks and extracting the image features f I. Concurrently, radiological knowledge related to organs (K o) and diseases (K d) is encoded into corresponding knowledge features f K o and f K d. What is a new approach to medical report generation? We propose a new approach to medical report generation, including knowledge enhancement and multilevel alignment modules . It alleviates the expertise limitations in medical reports and the lack of interaction between text and image modalities in reports and knowledge. How can hierarchical knowledge integration facilitate the generation of Radiology reports? To tackle this, we propose the hierarchical knowledge integration framework (HKRG), which facilitates the generation of radiology reports by guiding the process through layered domain knowledge . We extract the correspondence between organs and pathologies, serving as a buffer for the conversion between image–text pairs. Do different learning rates affect generating radiological reports? In Table 8, we present the impact of different learning rates on the task of generating radiological reports for the IU-Xray dataset. By comparing the results at learning rates set at 1 × 1 0 4, 1 × 1 0 5, and 8 × 1 0 6, we find that the model performs slightly better on all evaluation metrics when the learning rate is 1 × 1 0 5. In this study, we propose a Disease-aware image-text Alignment and self -correcting Re-alignment for Trustworthy radiology report generation ( DART ) framework. In the first stage, we generate initial reports based on image-to-text retrieval with disease-matching, embedding both images and texts in a shared embedding space through contrastive ...", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/chapter/10.1007/978-3-031-16452-1_56", "content": "KD is responsible for extracting the potential knowledge from the reports without any extra disease labels. The orange part of Fig. 1 provides an overview of the KD module that contains Report Embedding (RE), Dimension Reduction (DR), and Knowledge Clustering (KC). In RE, the Sentence-Bert (SB) is considered as the embedding method to convert each... See full list on link.springer.com KMVE is utilized to alleviate the mismatch feature diversity between the image and text. KMVE takes image pairs \\(I=\\left\\{{i}_{m}^{1},{i}_{m}^{ 2 }\\right\\}, {i}_{m}\\in {R}^{{N}_{i}}\\) as input, where the size of each image is \\(\\left( 3 , 244, 244\\right)\\). The knowledge topics \\(T\\) from the KD module are adopted as the potential labels of the images... See full list on link.springer.com RG is based on Transformer (TF) and Similarity Comparer (SC). TF is consisted with Transformer Encoder (TE) and Transformer Decoder (TD) . In TE, the \\({V}_{avg}\\) are converted into query, key, and value at first. Then the Multi-Head Attention (MHA) concatenates all head results together to acquire the visual details in different subspaces. After ... See full list on link.springer.com Dec 1, 2023 · In general, medical report generation is an image captioning task. Since medical reports have long sequences with data bias, the existing medical report generation models lack medical knowledge and ignore the interaction alignment between the two modalities of reports and images. How is a radiology report generated? During the radiology report generation phase, we first process the radiology image I using an image encoder , segmenting it into multiple blocks and extracting the image features f I. Concurrently, radiological knowledge related to organs (K o) and diseases (K d) is encoded into corresponding knowledge features f K o and f K d. What is a new approach to medical report generation? We propose a new approach to medical report generation, including knowledge enhancement and multilevel alignment modules . It alleviates the expertise limitations in medical reports and the lack of interaction between text and image modalities in reports and knowledge. How can hierarchical knowledge integration facilitate the generation of Radiology reports? To tackle this, we propose the hierarchical knowledge integration framework (HKRG), which facilitates the generation of radiology reports by guiding the process through layered domain knowledge . We extract the correspondence between organs and pathologies, serving as a buffer for the conversion between image–text pairs. Do different learning rates affect generating radiological reports? In Table 8, we present the impact of different learning rates on the task of generating radiological reports for the IU-Xray dataset. By comparing the results at learning rates set at 1 × 1 0 4, 1 × 1 0 5, and 8 × 1 0 6, we find that the model performs slightly better on all evaluation metrics when the learning rate is 1 × 1 0 5. In this study, we propose a Disease-aware image-text Alignment and self -correcting Re-alignment for Trustworthy radiology report generation ( DART ) framework. In the first stage, we generate initial reports based on image-to-text retrieval with disease-matching, embedding both images and texts in a shared embedding space through contrastive ..."}
+{"idx": 5, "title": "Radiology report generation with medical knowledge and ...", "date": "", "ddg_snippet": "Dec 1, 2023 · In general, medical report generation is an image captioning task. Since medical reports have long sequences with data bias, the existing medical report generation models lack medical knowledge and ignore the interaction alignment between the two modalities of reports and images.", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S0933365723002282", "content": "Dec 1, 2023 · In general, medical report generation is an image captioning task. Since medical reports have long sequences with data bias, the existing medical report generation models lack medical knowledge and ignore the interaction alignment between the two modalities of reports and images."}
+{"idx": 6, "title": "(PDF) DART : Disease-aware Image-Text Alignment and...", "date": "", "ddg_snippet": "thy radiology report generation ( DART ), a novel frame3. Method . Our proposed framework comprises two stages: (1) re-. port generation based on disease-aware image-text align-. ment and ( 2 ) self - correcting re-alignment of the generated . reports , as shown in Fig.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/390845711_DART_Disease-aware_Image-Text_Alignment_and_Self-correcting_Re-alignment_for_Trustworthy_Radiology_Report_Generation", "content": "thy radiology report generation ( DART ), a novel frame3. Method . Our proposed framework comprises two stages: (1) re-. port generation based on disease-aware image-text align-. ment and ( 2 ) self - correcting re-alignment of the generated . reports , as shown in Fig."}
+{"idx": 7, "title": "GitHub - langjihao/arxiv-daily: Automatically Update CV Papers ...", "date": "", "ddg_snippet": "Automatic generation of radiology reports has the potential to alleviate radiologists ' significant workload, yet current methods struggle to deliver clinically reliable conclusions.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/langjihao/arxiv-daily", "content": "Automatic generation of radiology reports has the potential to alleviate radiologists ' significant workload, yet current methods struggle to deliver clinically reliable conclusions."}
+{"idx": 8, "title": "Two -Pronged Human Evaluation of ChatGPT Self - Correction in...", "date": "", "ddg_snippet": "This paper introduces a two -pronged approach for human evaluation of radiology report simplifica-tions. It proposes a specialized variant of the self - correction mechanism that allows ChatGPT to gen - erate high-quality simplifications.", "subpage_snippet": "", "source": "aclanthology.org", "link": "https://aclanthology.org/2024.findings-acl.279.pdf", "content": "This paper introduces a two -pronged approach for human evaluation of radiology report simplifica-tions. It proposes a specialized variant of the self - correction mechanism that allows ChatGPT to gen - erate high-quality simplifications."}
+{"idx": 9, "title": "MedAutoCorrect: Image-conditioned autocorrection in medical reporting", "date": "", "ddg_snippet": "In medical reporting , the accuracy of radiological reports , whether generated by humans or machine learning algorithms, is critical. We tackle a new task in this paper : image-conditioned autocorrection of inaccuracies within these reports .", "subpage_snippet": "", "source": "medautocorrect.cs.columbia.edu", "link": "https://medautocorrect.cs.columbia.edu/", "content": "In medical reporting , the accuracy of radiological reports , whether generated by humans or machine learning algorithms, is critical. We tackle a new task in this paper : image-conditioned autocorrection of inaccuracies within these reports ."}
diff --git a/data/sampled_jsons/DART_radiology_report_Equation_(7)_correction_loss_formula_year_2024.jsonl b/data/sampled_jsons/DART_radiology_report_Equation_(7)_correction_loss_formula_year_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..6da413431232c82d9c1c42b534261b972f101685
--- /dev/null
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@@ -0,0 +1,10 @@
+{"idx": 0, "title": "PDF DART: Disease-aware Image-Text Alignment and Self-correcting Re ...", "date": "", "ddg_snippet": "DART : Disease-aware Image-Text Alignment and Self-correcting Re-alignment for Trustworthy Radiology Report Generation Supplementary Material A. Contrastive Loss", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2025/supplemental/Park_DART_Disease-aware_Image-Text_CVPR_2025_supplemental.pdf", "content": "DART : Disease-aware Image-Text Alignment and Self-correcting Re-alignment for Trustworthy Radiology Report Generation Supplementary Material A. Contrastive Loss"}
+{"idx": 1, "title": "Radiology Formulas & References Flashcards | Quizlet", "date": "", "ddg_snippet": "Study with Quizlet and memorize flashcards containing terms like Basic Proportion vs Multiplication Formula Setup, Grid Conversion Factors, mAs Distance and more.", "subpage_snippet": "", "source": "quizlet.com", "link": "https://quizlet.com/78467543/radiology-formulas-references-flash-cards/", "content": "Study with Quizlet and memorize flashcards containing terms like Basic Proportion vs Multiplication Formula Setup, Grid Conversion Factors, mAs Distance and more."}
+{"idx": 2, "title": "RT Formulas For Calculations | PDF | Radiography | Attenuation", "date": "", "ddg_snippet": "The document discusses various formulas used in radiographic inspection to calculate things like half-value layer, exposure adjustments based on distance, intensity adjustments based on distance, time-current reciprocity, exposure-density relationships, magnification, and geometric unsharpness. It provides the formulas , explains their purposes, and includes example calculations for each one.", "subpage_snippet": "", "source": "www.scribd.com", "link": "https://www.scribd.com/document/229254576/RT-Formulas-for-Calculations", "content": "The document discusses various formulas used in radiographic inspection to calculate things like half-value layer, exposure adjustments based on distance, intensity adjustments based on distance, time-current reciprocity, exposure-density relationships, magnification, and geometric unsharpness. It provides the formulas , explains their purposes, and includes example calculations for each one."}
+{"idx": 3, "title": "DART | PDF | Artificial Intelligence | Intelligence (AI) & Semantics", "date": "", "ddg_snippet": "The document presents the DART framework, which focuses on automatic radiology report generation by ensuring disease-aware image-text alignment and incorporating a self- correction mechanism. This two-stage approach first generates initial reports through image-to-text retrieval with a disease-matching constraint and then refines these reports by re-aligning them with input X-ray images. The ...", "subpage_snippet": "", "source": "www.scribd.com", "link": "https://www.scribd.com/document/876841025/DART", "content": "The document presents the DART framework, which focuses on automatic radiology report generation by ensuring disease-aware image-text alignment and incorporating a self- correction mechanism. This two-stage approach first generates initial reports through image-to-text retrieval with a disease-matching constraint and then refines these reports by re-aligning them with input X-ray images. The ..."}
+{"idx": 4, "title": "PDF Basic Shielding Formula: - Rad Pro Calculator", "date": "", "ddg_snippet": "This formula attempts to estimate the correct number of scattered photons that reach the detector (closest estimate) by using a correction factor to add in the Compton scatter and pair production photons that are ignored by the linear attenuation coefficient formula .", "subpage_snippet": "", "source": "radprocalculator.com", "link": "http://radprocalculator.com/Files/ShieldingandBuildup.pdf", "content": "This formula attempts to estimate the correct number of scattered photons that reach the detector (closest estimate) by using a correction factor to add in the Compton scatter and pair production photons that are ignored by the linear attenuation coefficient formula ."}
+{"idx": 5, "title": "Rebuttal - DART: Disease-aware Image-Text Alignment and Self-correcting ...", "date": "", "ddg_snippet": "Second, DART introduces a self- correction mechanism, which refines generated reports by re-aligning them within image-text embedding space, enabling reports to more accurately reflect disease-relevant findings. [MsGg,rq7L] Effect of the number of retrieved texts.", "subpage_snippet": "", "source": "ar5iv.labs.arxiv.org", "link": "https://ar5iv.labs.arxiv.org/html/2504.11786", "content": "Second, DART introduces a self- correction mechanism, which refines generated reports by re-aligning them within image-text embedding space, enabling reports to more accurately reflect disease-relevant findings. [MsGg,rq7L] Effect of the number of retrieved texts."}
+{"idx": 6, "title": "Correction Formula - an overview | ScienceDirect Topics", "date": "", "ddg_snippet": "The correction formula is applicable to X-ray systems with different image intensifiers and for different view angle projection. System calibration is not required, nor must any constant values be determined by empirical methods. Since the view angle is a constant for a series of images, only one trigonometric function per pixel has to be computed.", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/topics/engineering/correction-formula", "content": "The correction formula is applicable to X-ray systems with different image intensifiers and for different view angle projection. System calibration is not required, nor must any constant values be determined by empirical methods. Since the view angle is a constant for a series of images, only one trigonometric function per pixel has to be computed."}
+{"idx": 7, "title": "PDF DART: Disease-aware Image-Text Alignment and Self-correcting Re ...", "date": "", "ddg_snippet": "In this study, we propose a Disease-aware image-text Alignment and self-correcting Re-alignment for Trustwor-thy radiology report generation ( DART ), a novel frame-work that ensures retrieved reports contain similar disease-relevant findings and introduces a self- correction mecha-nism to refine generated reports .", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2025/papers/Park_DART_Disease-aware_Image-Text_Alignment_and_Self-correcting_Re-alignment_for_Trustworthy_Radiology_CVPR_2025_paper.pdf", "content": "In this study, we propose a Disease-aware image-text Alignment and self-correcting Re-alignment for Trustwor-thy radiology report generation ( DART ), a novel frame-work that ensures retrieved reports contain similar disease-relevant findings and introduces a self- correction mecha-nism to refine generated reports ."}
+{"idx": 8, "title": "Radiology Formulas Study Guide - Quizlet", "date": "", "ddg_snippet": "Level up your studying with AI-generated flashcards, summaries, essay prompts, and practice tests from your own notes. Sign up now to access Radiology Formulas materials and AI-powered study resources.", "subpage_snippet": "", "source": "quizlet.com", "link": "https://quizlet.com/study-guides/radiology-formulas-34a19b30-82f5-46a9-8a41-17095969b953", "content": "Level up your studying with AI-generated flashcards, summaries, essay prompts, and practice tests from your own notes. Sign up now to access Radiology Formulas materials and AI-powered study resources."}
+{"idx": 9, "title": "CVPR 2025 Open Access Repository", "date": "", "ddg_snippet": "In this study, we propose a Disease-aware image-text Alignment and self-correcting Re-alignment for Trustworthy radiology report generation ( DART ) framework. In the first stage, we generate initial reports based on image-to-text retrieval with disease-matching, embedding both images and texts in a shared embedding space through contrastive ...", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2025/html/Park_DART_Disease-aware_Image-Text_Alignment_and_Self-correcting_Re-alignment_for_Trustworthy_Radiology_CVPR_2025_paper.html", "content": "In this study, we propose a Disease-aware image-text Alignment and self-correcting Re-alignment for Trustworthy radiology report generation ( DART ) framework. In the first stage, we generate initial reports based on image-to-text retrieval with disease-matching, embedding both images and texts in a shared embedding space through contrastive ..."}
diff --git a/data/sampled_jsons/DART_radiology_report_generation_Table_3_ablation_study_self-correction_module_Section_3.2.jsonl b/data/sampled_jsons/DART_radiology_report_generation_Table_3_ablation_study_self-correction_module_Section_3.2.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..63c4bcb9d460a719343801e92c8bb6ed6937ba19
--- /dev/null
+++ b/data/sampled_jsons/DART_radiology_report_generation_Table_3_ablation_study_self-correction_module_Section_3.2.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "DART : Disease-aware Image-Text Alignment and Self - correcting ...", "date": "", "ddg_snippet": "Table 3 : An ablation study of our proposed framework on the MIMIC-CXR dataset, assessing the impact of key components: contrastive loss (CL), image-to-text retrieval (I2T), disease-matching constraint (DM), and self - correction (SC).", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2504.11786v1", "content": "Table 3 : An ablation study of our proposed framework on the MIMIC-CXR dataset, assessing the impact of key components: contrastive loss (CL), image-to-text retrieval (I2T), disease-matching constraint (DM), and self - correction (SC)."}
+{"idx": 1, "title": "(PDF) DART : Disease-aware Image-Text Alignment and...", "date": "", "ddg_snippet": "In this study , we propose a Disease-aware image-text Alignment and self - correcting Re-alignment for Trustworthy radiology report generation ( DART ) framework.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/390845711_DART_Disease-aware_Image-Text_Alignment_and_Self-correcting_Re-alignment_for_Trustworthy_Radiology_Report_Generation", "content": "In this study , we propose a Disease-aware image-text Alignment and self - correcting Re-alignment for Trustworthy radiology report generation ( DART ) framework."}
+{"idx": 2, "title": "Dart", "date": "", "ddg_snippet": "In this study , we propose a Disease-aware image-text Alignment and self - correcting Re-alignment for Trustworthy radiology report generation ( DART ) framework. Ablation studies confirm the critical role of each framework component.", "subpage_snippet": "", "source": "www.catalyzex.com", "link": "https://www.catalyzex.com/s/Dart", "content": "In this study , we propose a Disease-aware image-text Alignment and self - correcting Re-alignment for Trustworthy radiology report generation ( DART ) framework. Ablation studies confirm the critical role of each framework component."}
+{"idx": 3, "title": "DeltaNet: Conditional Medical Report Generation for COVID-19...", "date": "", "ddg_snippet": "4.4 Ablation Study . Self vs. Other Patients. Here we conduct an experiment to compare the performance of two types of conditional reports . We use MIMIC-CXR dataset and select the patients with ≥2 reports .", "subpage_snippet": "", "source": "aclanthology.org", "link": "https://aclanthology.org/2022.coling-1.261.pdf", "content": "4.4 Ablation Study . Self vs. Other Patients. Here we conduct an experiment to compare the performance of two types of conditional reports . We use MIMIC-CXR dataset and select the patients with ≥2 reports ."}
+{"idx": 4, "title": "Token Imbalance Adaptation", "date": "", "ddg_snippet": "Token Imbalance Adaptation for Radiology Report Generation . Table 3 : The imbalanced evaluation in the high- and low-frequency token set.", "subpage_snippet": "", "source": "scispace.com", "link": "https://scispace.com/pdf/token-imbalance-adaptation-for-radiology-report-generation-15yycb66.pdf", "content": "Token Imbalance Adaptation for Radiology Report Generation . Table 3 : The imbalanced evaluation in the high- and low-frequency token set."}
+{"idx": 5, "title": "avepdf.com - Free Online PDF and Document Tools", "date": "", "ddg_snippet": "Finally, the report generation module generates reports via the Transformer model based on the enriched disease embedding. Table 3 .6: The ablation study of our approach w/ vs. w/o the EDDIE module , evaluated on the Open-I dataset.", "subpage_snippet": "", "source": "era.library.ualberta.ca", "link": "https://era.library.ualberta.ca/items/be5d063c-77bc-472a-af3e-fa7f44cfb744/download/56e6cd46-28d3-42fa-9883-9e2c677c362b", "content": "Finally, the report generation module generates reports via the Transformer model based on the enriched disease embedding. Table 3 .6: The ablation study of our approach w/ vs. w/o the EDDIE module , evaluated on the Open-I dataset."}
+{"idx": 6, "title": "FG-CXR: A Radiologist -Aligned Gaze Dataset", "date": "", "ddg_snippet": "2. 3 Radiology Report Generation .We design an ablation study : we train the model without penalty terms to demonstrate the eect of every penalty. As a result, we nd that our penalties based on the idea of looking at the correct anatomy are benecial to the model, as shown in Table 8.", "subpage_snippet": "", "source": "vision.csee.wvu.edu", "link": "https://vision.csee.wvu.edu/publications/phamHBPPADNWNL24accv.pdf", "content": "2. 3 Radiology Report Generation .We design an ablation study : we train the model without penalty terms to demonstrate the eect of every penalty. As a result, we nd that our penalties based on the idea of looking at the correct anatomy are benecial to the model, as shown in Table 8."}
+{"idx": 7, "title": "CmEAA: Cross-modal Enhancement and Alignment Adapter for...", "date": "", "ddg_snippet": "Ablation Study . Qualitative Analysis. Related work. Radiology Report Generation .The weights generator in the Weight&Fusion module consists of a linear layer and a Softmax function, which can be formulated as: Feature Enhancement (CFE) and Neural Mutual 8547.", "subpage_snippet": "", "source": "coling-2025-proceedings.s3.us-east-1.amazonaws.com", "link": "https://coling-2025-proceedings.s3.us-east-1.amazonaws.com/main/pdf/2025.coling-main.571.pdf", "content": "Ablation Study . Qualitative Analysis. Related work. Radiology Report Generation .The weights generator in the Weight&Fusion module consists of a linear layer and a Softmax function, which can be formulated as: Feature Enhancement (CFE) and Neural Mutual 8547."}
+{"idx": 8, "title": "Structural Entities Extraction and Patient", "date": "", "ddg_snippet": "A clinically applicable reports generation algorithm should demonstrate its effectiveness in pro-ducing reports that accurately describe radiology findings and attend to patient-specific indications.", "subpage_snippet": "", "source": "papers.miccai.org", "link": "https://papers.miccai.org/miccai-2024/paper/1768_paper.pdf", "content": "A clinically applicable reports generation algorithm should demonstrate its effectiveness in pro-ducing reports that accurately describe radiology findings and attend to patient-specific indications."}
+{"idx": 9, "title": "The Impact of Locally Grounded Vision and", "date": "", "ddg_snippet": "Enhancing Radiology Report Generation : The Impact of Locally Grounded Vision and Language Training.", "subpage_snippet": "", "source": "bmva-archive.org.uk", "link": "https://bmva-archive.org.uk/bmvc/2024/papers/Paper_857/paper.pdf", "content": "Enhancing Radiology Report Generation : The Impact of Locally Grounded Vision and Language Training."}
diff --git a/data/sampled_jsons/DC28Fpk76s_Intervention_Conditioning_Causal_Bayesian_Networks_Definition_5.1_probability_necessity.jsonl b/data/sampled_jsons/DC28Fpk76s_Intervention_Conditioning_Causal_Bayesian_Networks_Definition_5.1_probability_necessity.jsonl
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index 0000000000000000000000000000000000000000..cf12a0ba112eaf9cf29d9726cae28b364b304621
--- /dev/null
+++ b/data/sampled_jsons/DC28Fpk76s_Intervention_Conditioning_Causal_Bayesian_Networks_Definition_5.1_probability_necessity.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Intervention and Conditioning in Causal Bayesian Networks", "date": "", "ddg_snippet": "Even though causal models are extremely popular, conditional probability calculation of formulas involving interventions pose significant challenges.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=DC28Fpk76s", "content": "Even though causal models are extremely popular, conditional probability calculation of formulas involving interventions pose significant challenges."}
+{"idx": 1, "title": "Intervention and Conditioning in Causal Bayesian Networks", "date": "", "ddg_snippet": "DC 28 Fpk 76 s .# Estimating probabilities involving interventions in causal models, especially Causal Bayesian Networks (CBNs), is challenging. Existing methods struggle with accurate calculations, particularly for formulas involving interventions and conditioning .", "subpage_snippet": "", "source": "deep-diver.github.io", "link": "https://deep-diver.github.io/neurips2024/posters/dc28fpk76s/", "content": "DC 28 Fpk 76 s .# Estimating probabilities involving interventions in causal models, especially Causal Bayesian Networks (CBNs), is challenging. Existing methods struggle with accurate calculations, particularly for formulas involving interventions and conditioning ."}
+{"idx": 2, "title": "Webinar: Health Economics and Outcomes Research with Bayesian ...", "date": "", "ddg_snippet": "Causality . Causal Bayesian Networks .Learning Dynamic Bayesian Networks From fMRI Time Series Under Conditions of Chronic Pain and Opioid Addiction.", "subpage_snippet": "", "source": "www.bayesia.com", "link": "https://www.bayesia.com/bayesialab/tutorials/webinar-health-outcomes-research", "content": "Causality . Causal Bayesian Networks .Learning Dynamic Bayesian Networks From fMRI Time Series Under Conditions of Chronic Pain and Opioid Addiction."}
+{"idx": 3, "title": "Causality", "date": "", "ddg_snippet": "1.3 causal bayesian networks .9.2 necessary and sufficient causes : conditions of identification. 9.2.1 Definitions , Notation, and Basic Relationships.", "subpage_snippet": "", "source": "projects.illc.uva.nl", "link": "https://projects.illc.uva.nl/cil/uploaded_files/inlineitem/Pearl_2009_Causality.pdf", "content": "1.3 causal bayesian networks .9.2 necessary and sufficient causes : conditions of identification. 9.2.1 Definitions , Notation, and Basic Relationships."}
+{"idx": 4, "title": "(PDF) Algorithms for Causal Reasoning in Probability ... - Academia.edu", "date": "", "ddg_snippet": "Probability trees are one of the simplest models of causal generative processes. They possess clean semantics and -- unlike causal Bayesian networks -- they can represent context-specific causal dependencies, which are necessary for e.g. causal .", "subpage_snippet": "", "source": "www.academia.edu", "link": "https://www.academia.edu/72721956/Algorithms_for_Causal_Reasoning_in_Probability_Trees", "content": "Probability trees are one of the simplest models of causal generative processes. They possess clean semantics and -- unlike causal Bayesian networks -- they can represent context-specific causal dependencies, which are necessary for e.g. causal ."}
+{"idx": 5, "title": "Bayesian Network : Significance and symbolism", "date": "", "ddg_snippet": "Bayesian Network is a model illustrating relationships among variables, including dynamic versions for temporal processes. It aids in constructing causal models to identify determinants and their conditional probabilities in research studies.", "subpage_snippet": "", "source": "www.wisdomlib.org", "link": "https://www.wisdomlib.org/concept/bayesian-network", "content": "Bayesian Network is a model illustrating relationships among variables, including dynamic versions for temporal processes. It aids in constructing causal models to identify determinants and their conditional probabilities in research studies."}
+{"idx": 6, "title": "Seeing Theory - Basic Probability", "date": "", "ddg_snippet": "A classic example of a probabilistic experiment is a fair coin toss, in which the two possible...Set Theory Counting Conditional Probability Bayes ' Theorem Likelihood Function Prior to Posterior", "subpage_snippet": "", "source": "seeing-theory.brown.edu", "link": "https://seeing-theory.brown.edu/basic-probability/index.html", "content": "A classic example of a probabilistic experiment is a fair coin toss, in which the two possible...Set Theory Counting Conditional Probability Bayes ' Theorem Likelihood Function Prior to Posterior"}
+{"idx": 7, "title": "(PDF) Causal mechanism and probability : A normative approach", "date": "", "ddg_snippet": "... Bayesian network theories of causation (e.g., Pearl, 2000;Spirtes, Glymour, & Scheines, 1993) would be a quintessential example of such an approach. For example, Glymour and Cheng (1998) provide the following example of a causal mechanism (from Baumrind, 1983)", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/230876057_Causal_mechanism_and_probability_A_normative_approach", "content": "... Bayesian network theories of causation (e.g., Pearl, 2000;Spirtes, Glymour, & Scheines, 1993) would be a quintessential example of such an approach. For example, Glymour and Cheng (1998) provide the following example of a causal mechanism (from Baumrind, 1983)"}
+{"idx": 8, "title": "Graphical Models 4dummies | PDF", "date": "", "ddg_snippet": "Bayesian networks are a type of graphical model that use directed graphs to represent conditional independence relationships between variables. Each node corresponds to a variable with a conditional probability distribution.", "subpage_snippet": "", "source": "www.slideshare.net", "link": "https://www.slideshare.net/xamdam/graphical-models-4dummies", "content": "Bayesian networks are a type of graphical model that use directed graphs to represent conditional independence relationships between variables. Each node corresponds to a variable with a conditional probability distribution."}
+{"idx": 9, "title": "academic.oup.com/biomet/article/70/1/41/240879", "date": "", "ddg_snippet": "a propensity score estimates the probability .", "subpage_snippet": "", "source": "academic.oup.com", "link": "https://academic.oup.com/biomet/article/70/1/41/240879", "content": "a propensity score estimates the probability ."}
diff --git a/data/sampled_jsons/DCBM_Section_5_limitations_applicability_concept_bottleneck.jsonl b/data/sampled_jsons/DCBM_Section_5_limitations_applicability_concept_bottleneck.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..fbe777778c4adb4d3283525d8732fad70d0c26c0
--- /dev/null
+++ b/data/sampled_jsons/DCBM_Section_5_limitations_applicability_concept_bottleneck.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "(PDF) Advancing Model Explainability: Visual Concept Knowledge...", "date": "", "ddg_snippet": "Section 5 addresses the. limitations of this study, while Section 6provides a summary and conclusion.In particular, we propose the Concept Contribution Score (CCS) to quantify the interpretability of DCBM .", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/387816696_Advancing_Model_Explainability_Visual_Concept_Knowledge_Distillation_for_Concept_Bottleneck_Models", "content": "Section 5 addresses the. limitations of this study, while Section 6provides a summary and conclusion.In particular, we propose the Concept Contribution Score (CCS) to quantify the interpretability of DCBM ."}
+{"idx": 1, "title": "Language in a Bottle: Language Model Guided Concept Bottlenecks...", "date": "", "ddg_snippet": "DCBM : Data-Efficient Visual Concept Bottleneck Models. Preprint. Full-text available.The reported limitations of prototype-based networks put their trustworthiness and applicability into question, motivating further work on the robustness and alignment of (deep) interpretable models.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/373326243_Language_in_a_Bottle_Language_Model_Guided_Concept_Bottlenecks_for_Interpretable_Image_Classification", "content": "DCBM : Data-Efficient Visual Concept Bottleneck Models. Preprint. Full-text available.The reported limitations of prototype-based networks put their trustworthiness and applicability into question, motivating further work on the robustness and alignment of (deep) interpretable models."}
+{"idx": 2, "title": "DCBM: Data-Efficient Visual Concept Bottleneck Models", "date": "", "ddg_snippet": "by K Prasse — The paper tackles a clear and practical limitation of CBMs—their high dependency on labeled concept data—and proposes a solution that combines ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=BdO4R6XxUH", "content": "by K Prasse — The paper tackles a clear and practical limitation of CBMs—their high dependency on labeled concept data—and proposes a solution that combines ..."}
+{"idx": 3, "title": "DCBM: Data-Efficient Visual Concept Bottleneck Models", "date": "", "ddg_snippet": "2 Jul 2025 — We propose Data-efficient CBMs (DCBMs), which reduce the need for large sample sizes during concept generation while preserving interpretability.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2412.11576v3", "content": "2 Jul 2025 — We propose Data-efficient CBMs (DCBMs), which reduce the need for large sample sizes during concept generation while preserving interpretability."}
+{"idx": 4, "title": "The Decoupling Concept Bottleneck Model", "date": "", "ddg_snippet": "by R Zhang · 2025 · Cited by 6 — This paper proves that insufficient concept information can lead to an inherent dilemma of concept and label distortions in CBM.", "subpage_snippet": "", "source": "www.computer.org", "link": "https://www.computer.org/csdl/journal/tp/2025/02/10740789/21w2sYkMsuc", "content": "by R Zhang · 2025 · Cited by 6 — This paper proves that insufficient concept information can lead to an inherent dilemma of concept and label distortions in CBM."}
+{"idx": 5, "title": "DCBM: Data-Efficient Visual Concept Bottleneck Models", "date": "", "ddg_snippet": "4 Feb 2025 — Concept Bottleneck Models (CBMs) enhance the interpretability of neural networks by basing predictions on human-understandable concepts .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2412.11576v2", "content": "4 Feb 2025 — Concept Bottleneck Models (CBMs) enhance the interpretability of neural networks by basing predictions on human-understandable concepts ."}
+{"idx": 6, "title": "Publications - Max Planck Institute for Informatics", "date": "", "ddg_snippet": "These limitations severely restrict their general applicability , causing poor performance in real-world scenarios with fisheye or panoramic images and resulting in substantial context loss. To address this, we present UniK3D, the first generalizable method for monocular 3D estimation able to model any camera. Our method introduces a spherical 3D", "subpage_snippet": "", "source": "www.mpi-inf.mpg.de", "link": "https://www.mpi-inf.mpg.de/departments/computer-vision-and-machine-learning/publications/", "content": "These limitations severely restrict their general applicability , causing poor performance in real-world scenarios with fisheye or panoramic images and resulting in substantial context loss. To address this, we present UniK3D, the first generalizable method for monocular 3D estimation able to model any camera. Our method introduces a spherical 3D"}
+{"idx": 7, "title": "A genome-scale dynamic constraint-based modelling ...", "date": "", "ddg_snippet": "by M Yasemi · 2023 · Cited by 1 — In DCBM, the well-known quasi-steady state (QSS) assumption is employed in conjunction with experimental limitations on substrates, oxygen, and energy ...", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S1096717623000873", "content": "by M Yasemi · 2023 · Cited by 1 — In DCBM, the well-known quasi-steady state (QSS) assumption is employed in conjunction with experimental limitations on substrates, oxygen, and energy ..."}
+{"idx": 8, "title": "ICML 2025 Thursday 07/17", "date": "", "ddg_snippet": "... applicable across various models and consistently outperforms existing token sparsification methods. DCBM : Data-Efficient Visual Concept Bottleneck Models.", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/day/7/17", "content": "... applicable across various models and consistently outperforms existing token sparsification methods. DCBM : Data-Efficient Visual Concept Bottleneck Models."}
+{"idx": 9, "title": "Network Snapshot - an overview", "date": "", "ddg_snippet": "As detailed in Section 5 , the two planning engines work with significantly different types of policies. From a system integration perspective, the challenge ...", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/topics/computer-science/network-snapshot", "content": "As detailed in Section 5 , the two planning engines work with significantly different types of policies. From a system integration perspective, the challenge ..."}
diff --git a/data/sampled_jsons/DCBM_arxiv_2412.11576_limitations_Section_5.jsonl b/data/sampled_jsons/DCBM_arxiv_2412.11576_limitations_Section_5.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..f9427889d5c44b34d7a86f08c3429898839765dd
--- /dev/null
+++ b/data/sampled_jsons/DCBM_arxiv_2412.11576_limitations_Section_5.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Properly Calculating Your Section 40 Reimbursement in New Jersey", "date": "", "ddg_snippet": "N.J.S.A. 34:15-40 (“ Section 40”) grants a right of reimbursement to workers’ compensation carriers. This reimbursement is to the extent of benefits paid to an injured worker should that injured worker file a third-party civil suit against the responsible tortfeasor.", "subpage_snippet": "", "source": "loisllc.com", "link": "https://loisllc.com/properly-calculating-your-section-40-reimbursement-in-new-jersey/", "content": "N.J.S.A. 34:15-40 (“ Section 40”) grants a right of reimbursement to workers’ compensation carriers. This reimbursement is to the extent of benefits paid to an injured worker should that injured worker file a third-party civil suit against the responsible tortfeasor."}
+{"idx": 1, "title": "A New Bite Into Dark Matter with the SNSPD-Based QROCODILE...", "date": "", "ddg_snippet": "[2016] R. Essig, M. Fernandez-Serra, J. Mardon, A. Soto, T. Volansky, and T.-T. Yu, Direct Detection of sub-GeV Dark Matter with Semiconductor Targets, JHEP 05, 046, arXiv :1509.01598 [hep-ph] .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2412.16279", "content": "[2016] R. Essig, M. Fernandez-Serra, J. Mardon, A. Soto, T. Volansky, and T.-T. Yu, Direct Detection of sub-GeV Dark Matter with Semiconductor Targets, JHEP 05, 046, arXiv :1509.01598 [hep-ph] ."}
+{"idx": 2, "title": "RusAutoCon", "date": "", "ddg_snippet": "A. Smolev, O. Golovnin Enhancing Spatial Planning Through Hybrid Graph-Analytical Conflict Modeling. 14782. J.F. Kurbanov, N. V . Yaronova, O.O. Ruzimov Modern Solutions for Automated Information Exchange and Diagnostics in Railway Block Section Control.", "subpage_snippet": "", "source": "rusautocon.org", "link": "https://rusautocon.org/programme2025-rus.html", "content": "A. Smolev, O. Golovnin Enhancing Spatial Planning Through Hybrid Graph-Analytical Conflict Modeling. 14782. J.F. Kurbanov, N. V . Yaronova, O.O. Ruzimov Modern Solutions for Automated Information Exchange and Diagnostics in Railway Block Section Control."}
+{"idx": 3, "title": "Strange Antiquities walkthrough: Complete item guide and puzzle...", "date": "", "ddg_snippet": "How to open the locked section in Strange Antiquities. You likely won’t be able to open the locked section in your Strange Antiquities store until you’re prompted to in the story. First, you need to find the Ethereal Key. It’s a circular gold statue that has three gems on it.", "subpage_snippet": "", "source": "www.gamespew.com", "link": "https://www.gamespew.com/2025/09/strange-antiquities-walkthrough/", "content": "How to open the locked section in Strange Antiquities. You likely won’t be able to open the locked section in your Strange Antiquities store until you’re prompted to in the story. First, you need to find the Ethereal Key. It’s a circular gold statue that has three gems on it."}
+{"idx": 4, "title": "React Dialog component - Material UI", "date": "", "ddg_snippet": "Follow the Modal limitations section .Scrolling long content. Performance. Limitations . Supplementary projects. material-ui-confirm.", "subpage_snippet": "", "source": "mui.com", "link": "https://mui.com/material-ui/react-dialog/", "content": "Follow the Modal limitations section .Scrolling long content. Performance. Limitations . Supplementary projects. material-ui-confirm."}
+{"idx": 5, "title": "Diagonality measures of Hermitian positive-definite matrices with...", "date": "", "ddg_snippet": "In Section 5 application to the problem of approximate joint diagonalization of a nite collection of Hermitian positive-denite matrices is discussed and some computations of the approximate joint diagonalizer based on these diagonality measures are presented.", "subpage_snippet": "", "source": "hal.science", "link": "https://hal.science/hal-01578462/document", "content": "In Section 5 application to the problem of approximate joint diagonalization of a nite collection of Hermitian positive-denite matrices is discussed and some computations of the approximate joint diagonalizer based on these diagonality measures are presented."}
+{"idx": 6, "title": "STATISTICS", "date": "", "ddg_snippet": "Limit results for distributed estimation of invariant subspaces in multiple networks inference and PCA. arXiv preprint.Spatio-Temporal Statistics with R. CRC Press, Boca Raton, FL. MR4915363 WOOLDRIDGE, J. M. (2002). Econometric Analysis of Cross Section and Panel Data.", "subpage_snippet": "", "source": "imstat.org", "link": "https://imstat.org/publications/aos/aos_53_4/aos_53_4.pdf", "content": "Limit results for distributed estimation of invariant subspaces in multiple networks inference and PCA. arXiv preprint.Spatio-Temporal Statistics with R. CRC Press, Boca Raton, FL. MR4915363 WOOLDRIDGE, J. M. (2002). Econometric Analysis of Cross Section and Panel Data."}
+{"idx": 7, "title": "Enterprise Resource Planning (ERP) System Implementation in...", "date": "", "ddg_snippet": "Section four presents field data from our case study firm, Sugar & Salt. Culminating the above, a discussion of findings as well as conclusions, contributions, and directions for future research are offered in sections five and six, respectively.", "subpage_snippet": "", "source": "www.econstor.eu", "link": "https://www.econstor.eu/bitstream/10419/310852/1/22_1_05.pdf", "content": "Section four presents field data from our case study firm, Sugar & Salt. Culminating the above, a discussion of findings as well as conclusions, contributions, and directions for future research are offered in sections five and six, respectively."}
+{"idx": 8, "title": "Speed limits - GOV.UK", "date": "", "ddg_snippet": "The following speed limits apply to all single and dual carriageways with street lights, unless there are signs showing otherwise", "subpage_snippet": "", "source": "www.gov.uk", "link": "https://www.gov.uk/speed-limits", "content": "The following speed limits apply to all single and dual carriageways with street lights, unless there are signs showing otherwise"}
+{"idx": 9, "title": "Калькулятор онлайн - СТАНДАРТНЫЙ", "date": "", "ddg_snippet": "Стандартные математические операции. Сохранение истории вычислений. Поддержка длинных выражений и скобок.", "subpage_snippet": "", "source": "x-calculator.ru", "link": "https://x-calculator.ru/", "content": "Стандартные математические операции. Сохранение истории вычислений. Поддержка длинных выражений и скобок."}
diff --git a/data/sampled_jsons/DISEF_Table_2_Flowers_16-shot_accuracy.jsonl b/data/sampled_jsons/DISEF_Table_2_Flowers_16-shot_accuracy.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..f79a0d86b0a121e64efb36f464cf198f620f0e33
--- /dev/null
+++ b/data/sampled_jsons/DISEF_Table_2_Flowers_16-shot_accuracy.jsonl
@@ -0,0 +1,6 @@
+{"idx": 0, "title": "DataDream: Few-shot Guided Dataset Generation - arXiv.org", "date": "", "ddg_snippet": "To support our 1-, 4-, 8-, and 16-shot generated images, we include the real few-shot examples used to generate them. We also show previous SOTA images for comparison, from two other image-generation methods: DISEF [9] and IsSynth [14].", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2407.10910v2", "content": "To support our 1-, 4-, 8-, and 16-shot generated images, we include the real few-shot examples used to generate them. We also show previous SOTA images for comparison, from two other image-generation methods: DISEF [9] and IsSynth [14]."}
+{"idx": 1, "title": "DataDream: Few-Shot Guided Dataset Generation | SpringerLink", "date": "", "ddg_snippet": "Nov 1, 2024 · To support our 1-, 4-, 8-, and 16-shot generated images, we include the real few-shot examples used to generate them. We also show previous SOTA images for comparison, from two other image-generation methods: DISEF [9] and IsSynth [14].", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/chapter/10.1007/978-3-031-73209-6_15", "content": "Nov 1, 2024 · To support our 1-, 4-, 8-, and 16-shot generated images, we include the real few-shot examples used to generate them. We also show previous SOTA images for comparison, from two other image-generation methods: DISEF [9] and IsSynth [14]."}
+{"idx": 2, "title": "Diversified in-domain synthesis with efficient fine-tuning for few- shot ...", "date": "", "ddg_snippet": "Following this trend, we propose Diversified In-domain Synthesis with Efficient Fine-tuning ( DISEF ), a novel approach which addresses the generalization challenge in few- shot learning using synthetic data. DISEF consists of two main components.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2312.03046v2/", "content": "Following this trend, we propose Diversified In-domain Synthesis with Efficient Fine-tuning ( DISEF ), a novel approach which addresses the generalization challenge in few- shot learning using synthetic data. DISEF consists of two main components."}
+{"idx": 3, "title": "Supplementary Material for “ImagineFSL: Self-Supervised Pretraining...", "date": "", "ddg_snippet": "ImageNet Aircraft Flowers EuroSAT Avg Acc.Although DISEF already leverages synthetic images to complement its few- shot data, our approach still provides an additional 0.6%/0.5% improvement.", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2025/supplemental/Yang_ImagineFSL_Self-Supervised_Pretraining_CVPR_2025_supplemental.pdf", "content": "ImageNet Aircraft Flowers EuroSAT Avg Acc.Although DISEF already leverages synthetic images to complement its few- shot data, our approach still provides an additional 0.6%/0.5% improvement."}
+{"idx": 4, "title": "Diversified in-domain synthesis with efficient fine-tuning for ...", "date": "", "ddg_snippet": "Report issue for preceding element. Table 2 : 16-shot results ... Although classifier tuning is competitive with DISEF in Caltech101, ImageNet, and Flowers ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2312.03046v2", "content": "Report issue for preceding element. Table 2 : 16-shot results ... Although classifier tuning is competitive with DISEF in Caltech101, ImageNet, and Flowers ..."}
+{"idx": 5, "title": "ImagineFSL: Self-Supervised Pretraining Matters on Imagined ...", "date": "", "ddg_snippet": "by H Yang · 2025 — Results of domain generalization on 16-shot setting. Method. ImageNet. Caltech Aircraft. Cars. F ood. Pets. Flowers . DTD. 12 pages", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2025/papers/Yang_ImagineFSL_Self-Supervised_Pretraining_Matters_on_Imagined_Base_Set_for_VLM-based_CVPR_2025_paper.pdf", "content": "by H Yang · 2025 — Results of domain generalization on 16-shot setting. Method. ImageNet. Caltech Aircraft. Cars. F ood. Pets. Flowers . DTD. 12 pages"}
diff --git a/data/sampled_jsons/DISEF_paper_table_Flowers_16-shot_performance.jsonl b/data/sampled_jsons/DISEF_paper_table_Flowers_16-shot_performance.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..89e5040183fd0847623844ca67008052b1156e14
--- /dev/null
+++ b/data/sampled_jsons/DISEF_paper_table_Flowers_16-shot_performance.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Amazing Table of Flowers and Epoxy resin. - YouTube", "date": "", "ddg_snippet": "How to make an amazing and functional table of flowers , plywood and epoxy with your own hands. #positivecoupleyoutube #art #epoxyOur channel with music:Posit...", "subpage_snippet": "", "source": "www.youtube.com", "link": "https://www.youtube.com/watch?v=AsopsTPbnwM", "content": "How to make an amazing and functional table of flowers , plywood and epoxy with your own hands. #positivecoupleyoutube #art #epoxyOur channel with music:Posit..."}
+{"idx": 1, "title": "A Paper Sizes - A0, A1, A2, A3, A4, A5, A6, A7, A8, A9, A10", "date": "", "ddg_snippet": "The dimensions of the A series paper sizes, as defined by the ISO 216 standard, are given in the table below the diagram in both millimetres and inches (cm measurements can be obtained by dividing mm value by 10). Table of Paper Sizes From 4A0 to A10. Size. Width x Height (mm).", "subpage_snippet": "", "source": "www.papersizes.org", "link": "https://www.papersizes.org/a-paper-sizes.htm", "content": "The dimensions of the A series paper sizes, as defined by the ISO 216 standard, are given in the table below the diagram in both millimetres and inches (cm measurements can be obtained by dividing mm value by 10). Table of Paper Sizes From 4A0 to A10. Size. Width x Height (mm)."}
+{"idx": 2, "title": "РЕАЛЬНОЕ СОБЕСЕДОВАНИЕ / Junior ML-engineer (Data Scientist)...", "date": "", "ddg_snippet": "Это показывает глубину без воды. Релевантность: Акцент на Go: concurrency (goroutines, channels), микросервисы, performance .Почему Multi-Head? Ablation в paper : 8 heads >1 head (perf +3-5% на tasks). Но overhead: Params ~4*d_model^2 per layer (QKV+WO).", "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": "Это показывает глубину без воды. Релевантность: Акцент на Go: concurrency (goroutines, channels), микросервисы, performance .Почему Multi-Head? Ablation в paper : 8 heads >1 head (perf +3-5% на tasks). Но overhead: Params ~4*d_model^2 per layer (QKV+WO)."}
+{"idx": 3, "title": "EA Sports FC 26 FPS Boost Guide (2025): Ultimate Windows, GPU...", "date": "", "ddg_snippet": "TL;DR – Quick FPS Boost Checklist✅ Enable Hardware-Accelerated GPU Scheduling✅ Prefer Max Performance in GPU control panel", "subpage_snippet": "", "source": "www.dtgre.com", "link": "https://www.dtgre.com/2025/09/ea-sports-fc-26-fps-boost-guide-windows-gpu-in-game-settings.html", "content": "TL;DR – Quick FPS Boost Checklist✅ Enable Hardware-Accelerated GPU Scheduling✅ Prefer Max Performance in GPU control panel"}
+{"idx": 4, "title": "Gemini - Google DeepMind", "date": "", "ddg_snippet": "Gemini 2.5 is our most intelligent AI model, capable of reasoning through its thoughts before responding, resulting in enhanced performance and improved accuracy.Adaptive controls and adjustable thinking budgets allow you to balance performance and cost.", "subpage_snippet": "", "source": "deepmind.google", "link": "https://deepmind.google/models/gemini/", "content": "Gemini 2.5 is our most intelligent AI model, capable of reasoning through its thoughts before responding, resulting in enhanced performance and improved accuracy.Adaptive controls and adjustable thinking budgets allow you to balance performance and cost."}
+{"idx": 5, "title": "Dutch courage really DOES help: Study proving... | Daily Mail Online", "date": "", "ddg_snippet": "They found the bats flew significantly slower and their echolocation skills were worsened after eating the alcoholic fruit. One tradition of the Ig Nobel ceremony, held at Boston University, is that the audience make and throw paper planes at the winners.", "subpage_snippet": "", "source": "www.dailymail.co.uk", "link": "https://www.dailymail.co.uk/sciencetech/article-15106619/alcohol-foreign-language-Ig-Nobel-award.html", "content": "They found the bats flew significantly slower and their echolocation skills were worsened after eating the alcoholic fruit. One tradition of the Ig Nobel ceremony, held at Boston University, is that the audience make and throw paper planes at the winners."}
+{"idx": 6, "title": "Destroy Lonely – blowin smoke Lyrics | Genius Lyrics", "date": "", "ddg_snippet": "Shot a left skeet on the bеd (what you say?) I bled blood on the curb (what you say?) I'ma take two shots , she's dead (what you say?)", "subpage_snippet": "", "source": "genius.com", "link": "https://genius.com/Destroy-lonely-blowin-smoke-lyrics", "content": "Shot a left skeet on the bеd (what you say?) I bled blood on the curb (what you say?) I'ma take two shots , she's dead (what you say?)"}
+{"idx": 7, "title": "How do weight loss medications affect our relationship... | The Guardian", "date": "", "ddg_snippet": "Today's paper . Inside the Guardian.Advertising performance can be measured. Reports can be generated based on your activity and those of others.", "subpage_snippet": "", "source": "www.theguardian.com", "link": "https://www.theguardian.com/society/2025/sep/20/how-do-weight-loss-medications-affect-our-relationship-with-food", "content": "Today's paper . Inside the Guardian.Advertising performance can be measured. Reports can be generated based on your activity and those of others."}
+{"idx": 8, "title": "EFT | Ammo and Armor Charts", "date": "", "ddg_snippet": "Ammo Table .Note this is just a comparison guideline on what you should use and not always exactly representative of it's true # of shots performance against armor. Value. Effectiveness. Avg Shots Stopped By Armor Before Killing.", "subpage_snippet": "", "source": "www.eft-ammo.com", "link": "https://www.eft-ammo.com/", "content": "Ammo Table .Note this is just a comparison guideline on what you should use and not always exactly representative of it's true # of shots performance against armor. Value. Effectiveness. Avg Shots Stopped By Armor Before Killing."}
+{"idx": 9, "title": "Recent models | 3D CAD Model... | GrabCAD Community Library", "date": "", "ddg_snippet": "FLOWER FRUIT. by ADIGÜZEL, Hüseyin. 16 mm PVC Equal Tee.", "subpage_snippet": "", "source": "grabcad.com", "link": "https://grabcad.com/library", "content": "FLOWER FRUIT. by ADIGÜZEL, Hüseyin. 16 mm PVC Equal Tee."}
diff --git a/data/sampled_jsons/Data-Efficient_Visual_Concept_Bottleneck_Models_Section_5_limitations_year_2023.jsonl b/data/sampled_jsons/Data-Efficient_Visual_Concept_Bottleneck_Models_Section_5_limitations_year_2023.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..bda38f298811a77d8d6dabf314162eebdfb60b4f
--- /dev/null
+++ b/data/sampled_jsons/Data-Efficient_Visual_Concept_Bottleneck_Models_Section_5_limitations_year_2023.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "DCBM: Data-Efficient Visual Concept Bottleneck Models", "date": "", "ddg_snippet": "Abstract Concept Bottleneck Models (CBMs) enhance the interpretability of neural networks by basing predictions on human-understandable concepts . However, current CBMs typically rely on con-cept sets extracted from large language models or extensive image corpora, limiting their effec-tiveness in data -sparse scenarios.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2412.11576", "content": "Abstract Concept Bottleneck Models (CBMs) enhance the interpretability of neural networks by basing predictions on human-understandable concepts . However, current CBMs typically rely on con-cept sets extracted from large language models or extensive image corpora, limiting their effec-tiveness in data -sparse scenarios."}
+{"idx": 1, "title": "DCBM: Data-Efficient Visual Concept Bottleneck Models", "date": "", "ddg_snippet": "concept_extraction/: Scripts and modules for extracting concepts . dcbm_training/: Code for training the DCBM model . data /: Directories for classes, concepts , datasets, embeddings, and segments. experiments/: Code for experiments detailed in the main paper and supplementary material. utils/: Helper scripts for running experiments.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/KathPra/DCBM", "content": "concept_extraction/: Scripts and modules for extracting concepts . dcbm_training/: Code for training the DCBM model . data /: Directories for classes, concepts , datasets, embeddings, and segments. experiments/: Code for experiments detailed in the main paper and supplementary material. utils/: Helper scripts for running experiments."}
+{"idx": 2, "title": "DCBM: Data-Efficient Visual Concept Bottleneck Models", "date": "", "ddg_snippet": "Concept Bottleneck Models (CBMs) enhance the interpretability of neural networks by basing predictions on human-understandable concepts . However, current CBMs typically rely on concept sets extracted from large language models or extensive image corpora, limiting their effectiveness in data -sparse scenarios. We propose Data-efficient CBMs (DCBMs), which reduce the need for large sample sizes ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2412.11576", "content": "Concept Bottleneck Models (CBMs) enhance the interpretability of neural networks by basing predictions on human-understandable concepts . However, current CBMs typically rely on concept sets extracted from large language models or extensive image corpora, limiting their effectiveness in data -sparse scenarios. We propose Data-efficient CBMs (DCBMs), which reduce the need for large sample sizes ..."}
+{"idx": 3, "title": "Concept Bottleneck Models - PMLR", "date": "", "ddg_snippet": "We revisit the classic idea of first predicting concepts that are provided at training time, and then using these concepts to predict the label. By construction, we can intervene on these concept bottleneck models by editing their predicted concept values and propagating these changes to the final prediction.", "subpage_snippet": "", "source": "proceedings.mlr.press", "link": "https://proceedings.mlr.press/v119/koh20a", "content": "We revisit the classic idea of first predicting concepts that are provided at training time, and then using these concepts to predict the label. By construction, we can intervene on these concept bottleneck models by editing their predicted concept values and propagating these changes to the final prediction."}
+{"idx": 4, "title": "DCBM: Data-Efficient Visual Concept Bottleneck Models", "date": "", "ddg_snippet": "Abstract Concept Bottleneck Models (CBMs) enhance the interpretability of neural networks by basing predictions on human-understandable concepts . However, current CBMs typically rely on con-cept sets extracted from large language models or extensive image corpora, limiting their effective-ness in data -sparse scenarios.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2412.11576v2", "content": "Abstract Concept Bottleneck Models (CBMs) enhance the interpretability of neural networks by basing predictions on human-understandable concepts . However, current CBMs typically rely on con-cept sets extracted from large language models or extensive image corpora, limiting their effective-ness in data -sparse scenarios."}
+{"idx": 5, "title": "PDF Discovering Fine-Grained Visual-Concept Relations by Disentangled ...", "date": "", "ddg_snippet": "Abstract Concept Bottleneck Models (CBMs) try to make the decision-making process transparent by exploring an inter-mediate concept space between the input image and the out-put prediction. Existing CBMs just learn coarse-grained relations between the whole image and the concepts , less considering local image information, leading to two main drawbacks: i) they often produce spurious visual ...", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2025/papers/Xie_Discovering_Fine-Grained_Visual-Concept_Relations_by_Disentangled_Optimal_Transport_Concept_Bottleneck_CVPR_2025_paper.pdf", "content": "Abstract Concept Bottleneck Models (CBMs) try to make the decision-making process transparent by exploring an inter-mediate concept space between the input image and the out-put prediction. Existing CBMs just learn coarse-grained relations between the whole image and the concepts , less considering local image information, leading to two main drawbacks: i) they often produce spurious visual ..."}
+{"idx": 6, "title": "Cross-Modality Image Interpretation via Concept ... - IEEE Xplore", "date": "", "ddg_snippet": "To address these limitations , this work explores the cross-modality interpretation of class-related concepts in image classification. Specifically, we propose decomposed concept bottleneck model (DCBM), which utilizes a set of decomposed visual concepts that are extracted directly from images instead of predefined text concepts .", "subpage_snippet": "", "source": "ieeexplore.ieee.org", "link": "https://ieeexplore.ieee.org/document/10535313", "content": "To address these limitations , this work explores the cross-modality interpretation of class-related concepts in image classification. Specifically, we propose decomposed concept bottleneck model (DCBM), which utilizes a set of decomposed visual concepts that are extracted directly from images instead of predefined text concepts ."}
+{"idx": 7, "title": "PDF Coarse-to-Fine Concept Bottleneck Models", "date": "", "ddg_snippet": "To this end, we propose a novel two-level concept discovery formulation leveraging: (i) recent advances in vision-language models , and (ii) an innovative formulation for coarse-to-fine concept selection via data -driven and sparsity-inducing Bayesian arguments.", "subpage_snippet": "", "source": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper_files/paper/2024/file/bdeab378efe6eb289714e2a5abc6ed42-Paper-Conference.pdf", "content": "To this end, we propose a novel two-level concept discovery formulation leveraging: (i) recent advances in vision-language models , and (ii) an innovative formulation for coarse-to-fine concept selection via data -driven and sparsity-inducing Bayesian arguments."}
+{"idx": 8, "title": "DCBM: Data-Efficient Visual Concept Bottleneck Models", "date": "", "ddg_snippet": "Abstract Concept Bottleneck Models (CBMs) enhance the interpretability of neural networks by basing predictions on human-understandable concepts . However, current CBMs typically rely on concept sets extracted from large language models or extensive image corpora, limiting their effectiveness in data -sparse scenarios.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2412.11576v3", "content": "Abstract Concept Bottleneck Models (CBMs) enhance the interpretability of neural networks by basing predictions on human-understandable concepts . However, current CBMs typically rely on concept sets extracted from large language models or extensive image corpora, limiting their effectiveness in data -sparse scenarios."}
+{"idx": 9, "title": "Advancing Model Explainability: Visual Concept Knowledge ... - MDPI", "date": "", "ddg_snippet": "This study explores the integration of concept bottleneck models (CBMs) with knowledge distillation (KD) while preserving the locality characteristics of the CBM. Although KD proves effective in model compression, compressed models often lack interpretability in their decision-making process. We enhance comprehensive explainability by maintaining CBMs' inherent interpretability through our ...", "subpage_snippet": "", "source": "www.mdpi.com", "link": "https://www.mdpi.com/2076-3417/15/2/493", "content": "This study explores the integration of concept bottleneck models (CBMs) with knowledge distillation (KD) while preserving the locality characteristics of the CBM. Although KD proves effective in model compression, compressed models often lack interpretability in their decision-making process. We enhance comprehensive explainability by maintaining CBMs' inherent interpretability through our ..."}
diff --git a/data/sampled_jsons/Data-Efficient_Visual_Concept_Bottleneck_Models_promptable_concept_proposal_method.jsonl b/data/sampled_jsons/Data-Efficient_Visual_Concept_Bottleneck_Models_promptable_concept_proposal_method.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..aaacf65665cb8385e805fd9d0eb836c58e280d53
--- /dev/null
+++ b/data/sampled_jsons/Data-Efficient_Visual_Concept_Bottleneck_Models_promptable_concept_proposal_method.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "DCBM: Data - Efficient Visual Concept Bottleneck Models", "date": "", "ddg_snippet": "2.1 Concept bottleneck models . 2.2 Visual concept generation. 2.3 Image-level interpretability. 3 Data - efficient concepts for CBMs (DCBM).We propose Data - efficient Visual CBMs (DCBM) to improve interpretability in data-scarce domains.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2412.11576v3", "content": "2.1 Concept bottleneck models . 2.2 Visual concept generation. 2.3 Image-level interpretability. 3 Data - efficient concepts for CBMs (DCBM).We propose Data - efficient Visual CBMs (DCBM) to improve interpretability in data-scarce domains."}
+{"idx": 1, "title": "ICML Poster DCBM: Data - Efficient Visual Concept Bottleneck Models", "date": "", "ddg_snippet": "We propose Data - efficient CBMs (DCBMs), which reduce the need for large sample sizes during concept generation while preserving interpretability. DCBMs define concepts as image regions detected by segmentation or detection foundation models ...", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/poster/46104", "content": "We propose Data - efficient CBMs (DCBMs), which reduce the need for large sample sizes during concept generation while preserving interpretability. DCBMs define concepts as image regions detected by segmentation or detection foundation models ..."}
+{"idx": 2, "title": "DCBM: Data - Efficient Visual Concept Bottleneck Models", "date": "", "ddg_snippet": "Concept Bottleneck Models (CBMs) enhance the interpretability of neural networks by basing predictions on human-understandable concepts .", "subpage_snippet": "", "source": "synthical.com", "link": "https://synthical.com/article/DCBM:-Data-Efficient-Visual-Concept-Bottleneck-Models-4be352f8-e37e-432a-9849-dc4f3c8c586f", "content": "Concept Bottleneck Models (CBMs) enhance the interpretability of neural networks by basing predictions on human-understandable concepts ."}
+{"idx": 3, "title": "KathPra/DCBM: Official repo for ICML25 paper: DCBM: Data - Efficient ...", "date": "", "ddg_snippet": "concept _extraction/: Scripts and modules for extracting concepts .For more details, please refer to the sections and scripts within the repository or consult the paper. About. Official repo for ICML25 paper: DCBM: Data - Efficient Visual Concept Bottleneck Models .", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/KathPra/DCBM", "content": "concept _extraction/: Scripts and modules for extracting concepts .For more details, please refer to the sections and scripts within the repository or consult the paper. About. Official repo for ICML25 paper: DCBM: Data - Efficient Visual Concept Bottleneck Models ."}
+{"idx": 4, "title": "Paper page - ConceptAttention: Diffusion Transformers Learn Highly...", "date": "", "ddg_snippet": "DCBM: Data - Efficient Visual Concept Bottleneck Models (2024). Multi-Head Explainer: A General Framework to Improve Explainability in CNNs and Transformers (2025).", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/papers/2502.04320", "content": "DCBM: Data - Efficient Visual Concept Bottleneck Models (2024). Multi-Head Explainer: A General Framework to Improve Explainability in CNNs and Transformers (2025)."}
+{"idx": 5, "title": "ML PhD Student Candidate - Cited by 8 - Explainable AI - ML - CV", "date": "", "ddg_snippet": "1. 2025. DCBM: Data - Efficient Visual Concept Bottleneck Models .", "subpage_snippet": "", "source": "scholar.google.com", "link": "https://scholar.google.com/citations?user=pzg1sbgAAAAJ&hl=en", "content": "1. 2025. DCBM: Data - Efficient Visual Concept Bottleneck Models ."}
+{"idx": 6, "title": "Christian BARTELT | Lecturer for Software Engineering | University of...", "date": "", "ddg_snippet": "DCBM: Data - Efficient Visual Concept Bottleneck Models . Preprint. Full-text available.However, current CBMs typically rely on concept sets extracted from large language models or extensive image corpora, limiting their effectiveness in data-sparse scenarios.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/profile/Christian-Bartelt", "content": "DCBM: Data - Efficient Visual Concept Bottleneck Models . Preprint. Full-text available.However, current CBMs typically rely on concept sets extracted from large language models or extensive image corpora, limiting their effectiveness in data-sparse scenarios."}
+{"idx": 7, "title": "Publications | Universität Mannheim", "date": "", "ddg_snippet": "DCBM: Data - Efficient Visual Concept Bottleneck Models , K. Prasse, P. Knab, S. Marton, C. Bartelt, M. Keuper, International Conference on Machine Learning (ICML) 2025. Balancing Diversity and Risk in LLM Sampling: How to Select Your Method and Parameter for Open-Ended Text...", "subpage_snippet": "", "source": "www.uni-mannheim.de", "link": "https://www.uni-mannheim.de/dws/research/focus-groups/computer-vision-machine-learning-prof-dr-ing-margret-keuper/publications/", "content": "DCBM: Data - Efficient Visual Concept Bottleneck Models , K. Prasse, P. Knab, S. Marton, C. Bartelt, M. Keuper, International Conference on Machine Learning (ICML) 2025. Balancing Diversity and Risk in LLM Sampling: How to Select Your Method and Parameter for Open-Ended Text..."}
+{"idx": 8, "title": "DAGM GCPR (Accepted Papers)", "date": "", "ddg_snippet": "DCBM: Data - Efficient Visual Concept Bottleneck Models Prasse, Katharina; Knab, Patrick; Marton, Sascha; Bartelt, Christian; Keuper, Margret. Prompt -Tuning SAM: From Generalist to Specialist with only 2048 Parameters and 16 Training Images Piater, Tristan; Barz, Björn; Freytag...", "subpage_snippet": "", "source": "www.dagm-gcpr.de", "link": "https://www.dagm-gcpr.de/year/2025/accepted-papers", "content": "DCBM: Data - Efficient Visual Concept Bottleneck Models Prasse, Katharina; Knab, Patrick; Marton, Sascha; Bartelt, Christian; Keuper, Margret. Prompt -Tuning SAM: From Generalist to Specialist with only 2048 Parameters and 16 Training Images Piater, Tristan; Barz, Björn; Freytag..."}
+{"idx": 9, "title": "openreview.net/profile?id=~Sascha_Marton1", "date": "", "ddg_snippet": "DCBM: Data - Efficient Visual Concept Bottleneck Models . Katharina Prasse, Patrick Knab, Sascha Marton, Christian Bartelt, Margret Keuper.A Data-Centric Perspective on Evaluating Machine Learning Models for Tabular Data.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/profile?id=~Sascha_Marton1", "content": "DCBM: Data - Efficient Visual Concept Bottleneck Models . Katharina Prasse, Patrick Knab, Sascha Marton, Christian Bartelt, Margret Keuper.A Data-Centric Perspective on Evaluating Machine Learning Models for Tabular Data."}
diff --git a/data/sampled_jsons/David_Wolpert_1996_No_Free_Lunch_theorem_machine_learning_uniform_distribution.jsonl b/data/sampled_jsons/David_Wolpert_1996_No_Free_Lunch_theorem_machine_learning_uniform_distribution.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..96fc121c26317ad486c6b3da2101465d3ef90dcd
--- /dev/null
+++ b/data/sampled_jsons/David_Wolpert_1996_No_Free_Lunch_theorem_machine_learning_uniform_distribution.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "No free lunch theorem - Wikipedia", "date": "", "ddg_snippet": "The \" no free lunch \" (NFL) theorem is an easily stated and easily understood consequence of theorems Wolpert and Macready actually prove. It is objectively weaker than the proven theorems, and thus does not encapsulate them.", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/No_free_lunch_theorem", "content": "The \" no free lunch \" (NFL) theorem is an easily stated and easily understood consequence of theorems Wolpert and Macready actually prove. It is objectively weaker than the proven theorems, and thus does not encapsulate them."}
+{"idx": 1, "title": "David Wolpert - Wikipedia", "date": "", "ddg_snippet": "Wolpert , David ( 1996 ), The Lack of A Priori Distinctions between ... A No - Free - Lunch Theorem for Non- Uniform Distributions of Target Functions\".", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/David_Wolpert", "content": "Wolpert , David ( 1996 ), The Lack of A Priori Distinctions between ... A No - Free - Lunch Theorem for Non- Uniform Distributions of Target Functions\"."}
+{"idx": 2, "title": "No free lunch in search and optimization - Wikipedia", "date": "", "ddg_snippet": "Wolpert had previously derived no free lunch theorems for machine learning ( statistical inference ). ... The ' no free lunch ' theorem of Wolpert and ...", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/No_free_lunch_in_search_and_optimization", "content": "Wolpert had previously derived no free lunch theorems for machine learning ( statistical inference ). ... The ' no free lunch ' theorem of Wolpert and ..."}
+{"idx": 3, "title": "No Free Lunch Theorems", "date": "", "ddg_snippet": "\"The sharpened No - Free - Lunch - theorem (NFL- theorem ) states that the performance of all optimization algorithms averaged over any finite set F of functions is equal if and only if F is closed under permutation (c.u.p.) and each target function in F is equally likely.", "subpage_snippet": "", "source": "no-free-lunch.org", "link": "http://no-free-lunch.org/", "content": "\"The sharpened No - Free - Lunch - theorem (NFL- theorem ) states that the performance of all optimization algorithms averaged over any finite set F of functions is equal if and only if F is closed under permutation (c.u.p.) and each target function in F is equally likely."}
+{"idx": 4, "title": "No Free Lunch Theorems For Optimization - Evolutionary ...", "date": "", "ddg_snippet": "David H. Wolpert and William G. Macready. Abstract— A framework is developed to explore the connection between effective optimization algorithms and the problems they are solving.", "subpage_snippet": "", "source": "www.cs.ubc.ca", "link": "https://www.cs.ubc.ca/~hutter/earg/papers07/00585893.pdf", "content": "David H. Wolpert and William G. Macready. Abstract— A framework is developed to explore the connection between effective optimization algorithms and the problems they are solving."}
+{"idx": 5, "title": "An Empirical Overview of the No Free Lunch Theorem and Its ...", "date": "", "ddg_snippet": "In a machine learning context, the NFL theorem implies that all learning algorithms perform equally well when averaged over all possible data sets. This nonintuitive idea meant that looking for a general, highly predictive algorithm is not feasible.", "subpage_snippet": "", "source": "core.ac.uk", "link": "https://core.ac.uk/download/pdf/41826017.pdf", "content": "In a machine learning context, the NFL theorem implies that all learning algorithms perform equally well when averaged over all possible data sets. This nonintuitive idea meant that looking for a general, highly predictive algorithm is not feasible."}
+{"idx": 6, "title": "No Free Lunch theorem - Towards AI", "date": "", "ddg_snippet": "Wolpert developed a mathematical proof demonstrating that for all possible problem instances drawn from a uniform probability distribution , the average performance of A and B algorithms is the same.", "subpage_snippet": "", "source": "www.towardsai.blog", "link": "https://www.towardsai.blog/no-free-lunch-theorem/", "content": "Wolpert developed a mathematical proof demonstrating that for all possible problem instances drawn from a uniform probability distribution , the average performance of A and B algorithms is the same."}
+{"idx": 7, "title": "(PDF) No Free Lunch Theorems for Search - ResearchGate", "date": "", "ddg_snippet": "Mar 24, 1996 · A comprehensive collection of deterministic, derivative- free algorithmic implementations based on the DIRECT framework has recently been introduced as part of the DIRECTGO project.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/221997149_No_Free_Lunch_Theorems_for_Search", "content": "Mar 24, 1996 · A comprehensive collection of deterministic, derivative- free algorithmic implementations based on the DIRECT framework has recently been introduced as part of the DIRECTGO project."}
+{"idx": 8, "title": "There is No Free Lunch in Data Science - KDnuggets", "date": "", "ddg_snippet": "During your adventures in machine learning , you may have already come across the “ No Free Lunch ” Theorem . ... No Free Lunch theorems , the most ...", "subpage_snippet": "", "source": "www.kdnuggets.com", "link": "https://www.kdnuggets.com/2019/09/no-free-lunch-data-science.html", "content": "During your adventures in machine learning , you may have already come across the “ No Free Lunch ” Theorem . ... No Free Lunch theorems , the most ..."}
+{"idx": 9, "title": "Notebooks on Language: Wolpert: \"The Lack of A Priori", "date": "", "ddg_snippet": "Naturally, Wolpert wants to discuss the relation between his own paper and the uniform law of large numbers, proven by Vapnik and Chervonenkis in the ...", "subpage_snippet": "", "source": "academiclogbook.blogspot.com", "link": "http://academiclogbook.blogspot.com/2014/07/wolpert-lack-of-priori-distinctions.html", "content": "Naturally, Wolpert wants to discuss the relation between his own paper and the uniform law of large numbers, proven by Vapnik and Chervonenkis in the ..."}
diff --git a/data/sampled_jsons/Davies_et_al_2021_'Advancing_mathematics_by_guiding_human_intuition_with_AI'_abstract_year_2021.jsonl b/data/sampled_jsons/Davies_et_al_2021_'Advancing_mathematics_by_guiding_human_intuition_with_AI'_abstract_year_2021.jsonl
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+++ b/data/sampled_jsons/Davies_et_al_2021_'Advancing_mathematics_by_guiding_human_intuition_with_AI'_abstract_year_2021.jsonl
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+{"idx": 0, "title": "Symbolic artificial intelligence - Wikipedia", "date": "", "ddg_snippet": "... AI program was the Logic theorist , written by Allen Newell , Herbert Simon and Cliff Shaw in 1955–56, as it was able to prove 38 elementary ...", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/Symbolic_artificial_intelligence", "content": "... AI program was the Logic theorist , written by Allen Newell , Herbert Simon and Cliff Shaw in 1955–56, as it was able to prove 38 elementary ..."}
+{"idx": 1, "title": "Mathematics and Machine Creativity: A Survey on Bridging", "date": "", "ddg_snippet": "... human decision-making to uncovering fascinating correlations within vast datasets [ Samek et al ., 2021 ] , AI continues to revolutionize human ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2412.16543v1?", "content": "... human decision-making to uncovering fascinating correlations within vast datasets [ Samek et al ., 2021 ] , AI continues to revolutionize human ..."}
+{"idx": 2, "title": "Human-Centered Design for AI-based Automatically Generated", "date": "", "ddg_snippet": "... aims to develop a human -centered framework that conceptualizes the design and implementation of AutoRs, with a specific emphasis on their applications ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2501.00081v1", "content": "... aims to develop a human -centered framework that conceptualizes the design and implementation of AutoRs, with a specific emphasis on their applications ..."}
+{"idx": 3, "title": "(PDF) Mathematics, word problems, common sense, and artificial", "date": "", "ddg_snippet": "... the capacities and limitations of current artificial intelligence ( AI ) technology to solve word problems that combine elementary knowledge with ...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/367388713_Mathematics_word_problems_common_sense_and_artificial_intelligence", "content": "... the capacities and limitations of current artificial intelligence ( AI ) technology to solve word problems that combine elementary knowledge with ..."}
+{"idx": 4, "title": "Mathematical intuition, deep learning, and Robbins'", "date": "", "ddg_snippet": "... by Davies et al ( 2021 ) describes how deep learning (DL) technology was used to find plausible hypotheses that have led to two original mathematical ...", "subpage_snippet": "", "source": "sugaku.net", "link": "https://sugaku.net/oa/W4390832812/mathematical-intuition-deep-learning-and-robbins-problem", "content": "... by Davies et al ( 2021 ) describes how deep learning (DL) technology was used to find plausible hypotheses that have led to two original mathematical ..."}
+{"idx": 5, "title": "AI + the human brain – Dr Alan D. Thompson –", "date": "", "ddg_snippet": "... human brain with its brain regions – I actually think that the situation there is subtle and deceptive for the following reasons: What I believe ...", "subpage_snippet": "", "source": "lifearchitect.ai", "link": "https://lifearchitect.ai/brain/", "content": "... human brain with its brain regions – I actually think that the situation there is subtle and deceptive for the following reasons: What I believe ..."}
+{"idx": 6, "title": "Artificial intelligence in the field of economics |", "date": "", "ddg_snippet": "... correlations between quality of institutional affiliation and engagement with or focus on AI in economics and negative correlations between the Human ...", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/article/10.1007/s11192-022-04294-w", "content": "... correlations between quality of institutional affiliation and engagement with or focus on AI in economics and negative correlations between the Human ..."}
+{"idx": 7, "title": "A principle-based approach to AI: the case for European Union", "date": "", "ddg_snippet": "With the aim of encouraging ethical and socially beneficial innovation, researchers, policy-makers and organizations have defined lists of principles ...", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/article/10.1007/s00146-022-01453-8", "content": "With the aim of encouraging ethical and socially beneficial innovation, researchers, policy-makers and organizations have defined lists of principles ..."}
+{"idx": 8, "title": "\"AI for Technology: Applied practices and future perspectives", "date": "", "ddg_snippet": "... AI for Technology are not only to accelerate the innovation process and thus reduce ... Advancing mathematics by guiding human intuition with AI .", "subpage_snippet": "", "source": "bulletinofcas.researchcommons.org", "link": "https://bulletinofcas.researchcommons.org/journal/vol39/iss1/5/", "content": "... AI for Technology are not only to accelerate the innovation process and thus reduce ... Advancing mathematics by guiding human intuition with AI ."}
+{"idx": 9, "title": "(PDF) WHY MATHEMATICIANS RESIST AI: BETWEEN RIGOR, RISK, AND", "date": "", "ddg_snippet": "By contrast, professional mathematicians may treat AI as an auxiliary tool, subjecting its suggestions to revision, numerical validation, and ...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/395470481_WHY_MATHEMATICIANS_RESIST_AI_BETWEEN_RIGOR_RISK_AND_OPPORTUNITY", "content": "By contrast, professional mathematicians may treat AI as an auxiliary tool, subjecting its suggestions to revision, numerical validation, and ..."}
diff --git a/data/sampled_jsons/Deep-ELA_scalability_high-dimensional_problems_Seiler_2024_year_2024.jsonl b/data/sampled_jsons/Deep-ELA_scalability_high-dimensional_problems_Seiler_2024_year_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..0df8fc811c4c66db4f295c64c3ec746505543533
--- /dev/null
+++ b/data/sampled_jsons/Deep-ELA_scalability_high-dimensional_problems_Seiler_2024_year_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Deep-ELA: Deep Exploratory Landscape Analysis with Self ...", "date": "", "ddg_snippet": "29 Jul 2024 — Deep - ELA is trained to be invariant against scaling , shifts, and rotations, yielding higher SNR values and minimally correlated features (see ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2401.01192v2", "content": "29 Jul 2024 — Deep - ELA is trained to be invariant against scaling , shifts, and rotations, yielding higher SNR values and minimally correlated features (see ..."}
+{"idx": 1, "title": "NEURAL EXPLORATORY LANDSCAPE ANALYSIS FOR ...", "date": "", "ddg_snippet": "by Z Ma · Cited by 1 — More recently, Seiler et al. ( 2024 ) propose DeepELA to adrress both the infomation loss and the supervised trainng issues . It introduces MHA and trains it by a ...", "subpage_snippet": "", "source": "human-competitive.org", "link": "https://human-competitive.org/sites/default/files/neural_exploratory_landscape_analysis_for_meta-black-box-optimization.pdf", "content": "by Z Ma · Cited by 1 — More recently, Seiler et al. ( 2024 ) propose DeepELA to adrress both the infomation loss and the supervised trainng issues . It introduces MHA and trains it by a ..."}
+{"idx": 2, "title": "Exploring module interactions in modular CMA-ES across ...", "date": "", "ddg_snippet": "by A Nikolikj · 2025 — In high dimensions , module importance is influenced by scalability , multi-modality, and global structure. Finally, to validate whether the module importance ...", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S2210650225002743", "content": "by A Nikolikj · 2025 — In high dimensions , module importance is influenced by scalability , multi-modality, and global structure. Finally, to validate whether the module importance ..."}
+{"idx": 3, "title": "Pflacco: Feature-Based Landscape Analysis of Continuous ...", "date": "", "ddg_snippet": "3 Sept 2024 — The proposed sets of landscape features (also known as Exploratory Landscape Analysis ( ELA )) can be used to assess certain high -level properties ...", "subpage_snippet": "", "source": "direct.mit.edu", "link": "https://direct.mit.edu/evco/article/32/3/211/116950/Pflacco-Feature-Based-Landscape-Analysis-of", "content": "3 Sept 2024 — The proposed sets of landscape features (also known as Exploratory Landscape Analysis ( ELA )) can be used to assess certain high -level properties ..."}
+{"idx": 4, "title": "Random Filter Mappings as Optimization Problem Feature ...", "date": "", "ddg_snippet": "by G Petelin · 2024 — Even though they are widely used in different studies, the ELA features have some disadvantages, such as high computa- tional complexity for ...", "subpage_snippet": "", "source": "ieeexplore.ieee.org", "link": "https://ieeexplore.ieee.org/iel8/6287639/10380310/10695069.pdf", "content": "by G Petelin · 2024 — Even though they are widely used in different studies, the ELA features have some disadvantages, such as high computa- tional complexity for ..."}
+{"idx": 5, "title": "Opt2Vec - a continuous optimization problem ...", "date": "", "ddg_snippet": "by P Korošec · 2024 — This paper proposes a novel representation of continuous optimization problems by encoding the information found in the interaction between an algorithm and an ...", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S002002552401048X", "content": "by P Korošec · 2024 — This paper proposes a novel representation of continuous optimization problems by encoding the information found in the interaction between an algorithm and an ..."}
+{"idx": 6, "title": "A TECHNICAL DETAILS Within this paper, the ...", "date": "", "ddg_snippet": "First, NeurELA addresses the limited scalability of Deep - ELA for high dimensional problem . Concretely, the embedding in Deep - ELA is dependent on the problem .", "subpage_snippet": "", "source": "proceedings.iclr.cc", "link": "https://proceedings.iclr.cc/paper_files/paper/2025/file/27b89ae947228a7195861e3eb307d1a8-Supplemental-Conference.pdf", "content": "First, NeurELA addresses the limited scalability of Deep - ELA for high dimensional problem . Concretely, the embedding in Deep - ELA is dependent on the problem ."}
+{"idx": 7, "title": "Machine Learning for Enhancing Metaheuristics in Global ...", "date": "", "ddg_snippet": "by A Bolufé-Röhler · 2025 — Notably, Jin identified persistent challenges related to scalability in high - dimensional problems , efficient sampling strategies, and the ...", "subpage_snippet": "", "source": "www.preprints.org", "link": "https://www.preprints.org/manuscript/202507.2611/v1/download", "content": "by A Bolufé-Röhler · 2025 — Notably, Jin identified persistent challenges related to scalability in high - dimensional problems , efficient sampling strategies, and the ..."}
+{"idx": 8, "title": "Informing Building Retrofits Using Surrogates of Physics- ...", "date": "", "ddg_snippet": "by S Qiblawi · 2024 · Cited by 1 — This study incorporates exploratory landscape analysis ( ELA ) to better understand the problem , then separately applies four optimization algorithms (random ...", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/pdf/10.5555/3712729.3712795", "content": "by S Qiblawi · 2024 · Cited by 1 — This study incorporates exploratory landscape analysis ( ELA ) to better understand the problem , then separately applies four optimization algorithms (random ..."}
+{"idx": 9, "title": "Have We Hit a Wall in Algorithm Selection Generalization?", "date": "", "ddg_snippet": "29 Jan 2025 — This study evaluates the generalizability of AS models based on different problem representations in the context of single-objective continuous optimization.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2501.17663v1", "content": "29 Jan 2025 — This study evaluates the generalizability of AS models based on different problem representations in the context of single-objective continuous optimization."}
diff --git a/data/sampled_jsons/Digital_Disparities_paper_PAW_index_formula_Appendix_A.7_year_2023.jsonl b/data/sampled_jsons/Digital_Disparities_paper_PAW_index_formula_Appendix_A.7_year_2023.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..0cd27678229e155445c442d6024119f2f72175d1
--- /dev/null
+++ b/data/sampled_jsons/Digital_Disparities_paper_PAW_index_formula_Appendix_A.7_year_2023.jsonl
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+{"idx": 0, "title": "PDF Digital Disparities: A Comparative Web Measurement Study Across ...", "date": "", "ddg_snippet": "A PAW index of 1 or less indicates afordable access, while values above 1 suggest that webpage sizes or broadband costs are too high relative to income, limiting access to essential online services [68]. See Appendix A.7 for details on PAW calculation. Figure 15: Website afordability ( PAW Index ) across countries.", "subpage_snippet": "", "source": "www.staicu.org", "link": "https://www.staicu.org/publications/www2025.pdf", "content": "A PAW index of 1 or less indicates afordable access, while values above 1 suggest that webpage sizes or broadband costs are too high relative to income, limiting access to essential online services [68]. See Appendix A.7 for details on PAW calculation. Figure 15: Website afordability ( PAW Index ) across countries."}
+{"idx": 1, "title": "PDF Student Paper Setup Guide, APA Style 7th Edition", "date": "", "ddg_snippet": "Student Paper Setup Guide This guide will help you set up an APA Style student paper . The basic setup directions apply to the entire paper . Annotated diagrams illustrate how to set up the major sections of a student paper : the title page or cover page, the text, tables and figures, and the reference list.", "subpage_snippet": "", "source": "apastyle.apa.org", "link": "https://apastyle.apa.org/instructional-aids/student-paper-setup-guide.pdf", "content": "Student Paper Setup Guide This guide will help you set up an APA Style student paper . The basic setup directions apply to the entire paper . Annotated diagrams illustrate how to set up the major sections of a student paper : the title page or cover page, the text, tables and figures, and the reference list."}
+{"idx": 2, "title": "APA Sample Paper - Purdue OWL® - Purdue University", "date": "", "ddg_snippet": "Media Files: APA Sample Student Paper , APA Sample Professional Paper This resource is enhanced by Acrobat PDF files. Download the free Acrobat Reader Note: The APA Publication Manual, 7th Edition specifies different formatting conventions for student and professional papers (i.e., papers written for credit in a course and papers intended for scholarly publication). These differences mostly ...", "subpage_snippet": "", "source": "owl.purdue.edu", "link": "https://owl.purdue.edu/owl/research_and_citation/apa_style/apa_formatting_and_style_guide/apa_sample_paper.html", "content": "Media Files: APA Sample Student Paper , APA Sample Professional Paper This resource is enhanced by Acrobat PDF files. Download the free Acrobat Reader Note: The APA Publication Manual, 7th Edition specifies different formatting conventions for student and professional papers (i.e., papers written for credit in a course and papers intended for scholarly publication). These differences mostly ..."}
+{"idx": 3, "title": "Understanding the levels of digital inequality within the city: An ...", "date": "", "ddg_snippet": "Using paper and electronic surveys in English and Spanish, the researchers conducted a city-wide survey to examine all three levels of digital inequality. The research methods were descriptive statistics and regression analysis. Findings include that the digital divide persists at all three levels, with older respondents.", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S0264275124000581", "content": "Using paper and electronic surveys in English and Spanish, the researchers conducted a city-wide survey to examine all three levels of digital inequality. The research methods were descriptive statistics and regression analysis. Findings include that the digital divide persists at all three levels, with older respondents."}
+{"idx": 4, "title": "Sample Papers - APA Style", "date": "", "ddg_snippet": "These sample papers formatted in seventh edition APA Style show the format that authors should use to submit a manuscript for publication in a professional journal and that students should use to submit a paper to an instructor for a course assignment.", "subpage_snippet": "", "source": "apastyle.apa.org", "link": "https://apastyle.apa.org/style-grammar-guidelines/paper-format/sample-papers", "content": "These sample papers formatted in seventh edition APA Style show the format that authors should use to submit a manuscript for publication in a professional journal and that students should use to submit a paper to an instructor for a course assignment."}
+{"idx": 5, "title": "Digital Inequalities: A Review of Contributing Factors and ... - Springer", "date": "", "ddg_snippet": "Abstract. This literature review focuses on the digital divide in contemporary technologically and economically advanced societies. Prior research shows that the digital divide entails more than physical accessibility and points to issues of technology acceptance and actual use. Recurring digital divide factors outside socioeconomic characteristics were identified in the articles reviewed ...", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/content/pdf/10.1007/978-3-030-29374-1_41", "content": "Abstract. This literature review focuses on the digital divide in contemporary technologically and economically advanced societies. Prior research shows that the digital divide entails more than physical accessibility and points to issues of technology acceptance and actual use. Recurring digital divide factors outside socioeconomic characteristics were identified in the articles reviewed ..."}
+{"idx": 6, "title": "Digital Disparities: A Comparative Web Measurement Study Across ...", "date": "", "ddg_snippet": "A PAW index of 1 or less indicates afordable access, while values above 1 suggest that webpage sizes or broadband costs are too high relative to income, limiting access to essential online services [60]. See appendix A.7 for details on PAW calculation.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=IMhoJgWANP", "content": "A PAW index of 1 or less indicates afordable access, while values above 1 suggest that webpage sizes or broadband costs are too high relative to income, limiting access to essential online services [60]. See appendix A.7 for details on PAW calculation."}
+{"idx": 7, "title": "Appendices Setup - APA Style", "date": "", "ddg_snippet": "The following example shows the basic format of an APA Style appendix . This appendix contains a scale developed for the study being reported. To set up an APA Style appendix , begin each appendix on a separate page at the end of the paper . Write the appendix label at the top of the page in bold and centered. When there is only one appendix , use the label \" Appendix .\" When there are multiple ...", "subpage_snippet": "", "source": "apastyle.apa.org", "link": "https://apastyle.apa.org/style-grammar-guidelines/paper-format/appendices", "content": "The following example shows the basic format of an APA Style appendix . This appendix contains a scale developed for the study being reported. To set up an APA Style appendix , begin each appendix on a separate page at the end of the paper . Write the appendix label at the top of the page in bold and centered. When there is only one appendix , use the label \" Appendix .\" When there are multiple ..."}
+{"idx": 8, "title": "Footnotes & Appendices - Purdue OWL® - Purdue University", "date": "", "ddg_snippet": "Formatting Appendices An appendix should be created on its own individual page labelled \" Appendix \" and followed by a title on the next line that describes the subject of the appendix . These headings should be centered and bolded at the top of the page and written in title case.", "subpage_snippet": "", "source": "owl.purdue.edu", "link": "https://owl.purdue.edu/owl/research_and_citation/apa_style/apa_formatting_and_style_guide/footnotes_appendices.html", "content": "Formatting Appendices An appendix should be created on its own individual page labelled \" Appendix \" and followed by a title on the next line that describes the subject of the appendix . These headings should be centered and bolded at the top of the page and written in title case."}
+{"idx": 9, "title": "APA Formatting and Style Guide (7th Edition) - Purdue OWL®", "date": "", "ddg_snippet": "Basic Rules Basic guidelines for formatting the reference list at the end of a standard APA research paper Author/Authors Rules for handling works by a single author or multiple authors that apply to all APA-style references in your reference list, regardless of the type of work (book, article, electronic resource, etc.) Articles in Periodicals ...", "subpage_snippet": "", "source": "owl.purdue.edu", "link": "https://owl.purdue.edu/owl/research_and_citation/apa_style/apa_formatting_and_style_guide/index.html", "content": "Basic Rules Basic guidelines for formatting the reference list at the end of a standard APA research paper Author/Authors Rules for handling works by a single author or multiple authors that apply to all APA-style references in your reference list, regardless of the type of work (book, article, electronic resource, etc.) Articles in Periodicals ..."}
diff --git a/data/sampled_jsons/Direct_Noise_Optimization_DNO_diffusion_models_backpropagation-free.jsonl b/data/sampled_jsons/Direct_Noise_Optimization_DNO_diffusion_models_backpropagation-free.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..76c982b0842f4ad42906c2428fbf60bca80fb025
--- /dev/null
+++ b/data/sampled_jsons/Direct_Noise_Optimization_DNO_diffusion_models_backpropagation-free.jsonl
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+{"idx": 0, "title": "HOIDiNi: Human-Object Interaction through Diffusion Noise", "date": "", "ddg_snippet": "... and physical correctness, HOIDiNi optimizes directly in the noise space of a pretrained diffusion model using Diffusion Noise Optimization ( DNO ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2506.15625v1", "content": "... and physical correctness, HOIDiNi optimizes directly in the noise space of a pretrained diffusion model using Diffusion Noise Optimization ( DNO ..."}
+{"idx": 1, "title": "Inference Time Alignment of Diffusion Models with Evolutionary", "date": "", "ddg_snippet": "Specifically, we introduce two black-box methods for aligning the outputs of diffusion models : (1) optimizing noise , and (2) optimizing ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2506.00299v1", "content": "Specifically, we introduce two black-box methods for aligning the outputs of diffusion models : (1) optimizing noise , and (2) optimizing ..."}
+{"idx": 2, "title": "Inference-Time Alignment of Diffusion Models with Direct Noise Optimization", "date": "", "ddg_snippet": "The central goal of the alignment problem is to adjust the distribution learned by diffusion models such that the generated samples maximize the target reward function. We propose a novel alignment approach, named Direct Noise Optimization ( DNO ), that optimizes the injected noise during the sampling process of diffusion models .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2405.18881", "content": "The central goal of the alignment problem is to adjust the distribution learned by diffusion models such that the generated samples maximize the target reward function. We propose a novel alignment approach, named Direct Noise Optimization ( DNO ), that optimizes the injected noise during the sampling process of diffusion models ."}
+{"idx": 3, "title": "Tuning-Free Alignment of Diffusion Models with Direct Noise Optimization", "date": "", "ddg_snippet": "In this work, we study Direct Noise Optimization ( DNO ) as a tuning- free method for aligning diffusion generative models . We introduce a probability regularization technique to eficiently address the out-of-distribution reward-hacking problem in DNO .", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=Dqpa8rbL39", "content": "In this work, we study Direct Noise Optimization ( DNO ) as a tuning- free method for aligning diffusion generative models . We introduce a probability regularization technique to eficiently address the out-of-distribution reward-hacking problem in DNO ."}
+{"idx": 4, "title": "Direct-Noise-Optimization/tutorial/simple_dno.ipynb at main - GitHub", "date": "", "ddg_snippet": "This is the official repo for the ICML 2025 paper \"Tuning- Free Alignment of Diffusion Models with Direct Noise Optimization \" Tang et al - TZW1998/ Direct - Noise - Optimization", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/TZW1998/Direct-Noise-Optimization/blob/main/tutorial/simple_dno.ipynb", "content": "This is the official repo for the ICML 2025 paper \"Tuning- Free Alignment of Diffusion Models with Direct Noise Optimization \" Tang et al - TZW1998/ Direct - Noise - Optimization"}
+{"idx": 5, "title": "Tuning-Free Alignment of Diffusion Model...", "date": "", "ddg_snippet": "The central goal of the alignment problem is to adjust the distribution learned by diffusion models such that the generated samples maximize the target reward function. We propose a novel alignment approach, named Direct Noise Optimization ( DNO ), that optimizes the injected noise during the sampling process of diffusion models .", "subpage_snippet": "", "source": "axi.lims.ac.uk", "link": "https://axi.lims.ac.uk/paper/2405.18881", "content": "The central goal of the alignment problem is to adjust the distribution learned by diffusion models such that the generated samples maximize the target reward function. We propose a novel alignment approach, named Direct Noise Optimization ( DNO ), that optimizes the injected noise during the sampling process of diffusion models ."}
+{"idx": 6, "title": "Tuning-Free Alignment of Diffusion Models with Direct Noise Optimization", "date": "", "ddg_snippet": "Tuning- Free Alignment of Diffusion Models with Direct Noise Optimization Zhiwei Tang · Jiangweizhi Peng · Jiasheng Tang · Mingyi Hong · Fan Wang · Tsung-Hui Chang", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2024/37060", "content": "Tuning- Free Alignment of Diffusion Models with Direct Noise Optimization Zhiwei Tang · Jiangweizhi Peng · Jiasheng Tang · Mingyi Hong · Fan Wang · Tsung-Hui Chang"}
+{"idx": 7, "title": "DNO: Optimizing Diffusion Noise Can Serve As Universal Motion Priors", "date": "", "ddg_snippet": "Diffusion Noise Optimization ( DNO ) can leverage the existing human motion diffusion models as universal motion priors. We demonstrate its capability in the motion editing tasks where DNO can preserve the content of the original model and accommodates a diverse range of editing modes, including changing trajectory, pose, joint location, and avoiding newly added obstacles.", "subpage_snippet": "", "source": "korrawe.github.io", "link": "https://korrawe.github.io/dno-project/", "content": "Diffusion Noise Optimization ( DNO ) can leverage the existing human motion diffusion models as universal motion priors. We demonstrate its capability in the motion editing tasks where DNO can preserve the content of the original model and accommodates a diverse range of editing modes, including changing trajectory, pose, joint location, and avoiding newly added obstacles."}
+{"idx": 8, "title": "Inference-Time Alignment of Diffusion Models with Direct Noise Optimization", "date": "", "ddg_snippet": "In this work, we present a comprehensive study on Direct Noise Optimization ( DNO ) for aligning diffusion generative models at inference-time. We introduce variants of DNO designed to efficiently address challenges such as out-of-distribution reward-hacking and the optimization of non-differentiable reward functions.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2405.18881v3", "content": "In this work, we present a comprehensive study on Direct Noise Optimization ( DNO ) for aligning diffusion generative models at inference-time. We introduce variants of DNO designed to efficiently address challenges such as out-of-distribution reward-hacking and the optimization of non-differentiable reward functions."}
+{"idx": 9, "title": "Training-free Diffusion Model Alignment with Sampling Demons", "date": "", "ddg_snippet": "We propose a novel alignment approach, named Direct Noise Optimization ( DNO ), that optimizes the injected noise during the sampling process of diffusion models .", "subpage_snippet": "", "source": "bohrium.dp.tech", "link": "https://bohrium.dp.tech/paper/arxiv/2410.05760", "content": "We propose a novel alignment approach, named Direct Noise Optimization ( DNO ), that optimizes the injected noise during the sampling process of diffusion models ."}
diff --git a/data/sampled_jsons/EAGLE-2_MT-Bench_Llama2-Chat_70B_temperature=1_speedup_table_year_2024.jsonl b/data/sampled_jsons/EAGLE-2_MT-Bench_Llama2-Chat_70B_temperature=1_speedup_table_year_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..3ce8cd3b7debe5b0d3aa5feda520c1de21138b9f
--- /dev/null
+++ b/data/sampled_jsons/EAGLE-2_MT-Bench_Llama2-Chat_70B_temperature=1_speedup_table_year_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "karakuri-ai/karakuri-lm- 70 b - chat -v0. 1 · Hugging Face", "date": "", "ddg_snippet": "Furthermore, it achieves performance comparable to Llama 2 70 B Chat on the original English MT - Bench .At the time of release, KARAKURI LM 70 B Chat v0. 1 achieves the highest performance among Japanese open models on the MT - Bench -jp", "subpage_snippet": "", "source": "hf.global-rail.com", "link": "https://hf.global-rail.com/karakuri-ai/karakuri-lm-70b-chat-v0.1", "content": "Furthermore, it achieves performance comparable to Llama 2 70 B Chat on the original English MT - Bench .At the time of release, KARAKURI LM 70 B Chat v0. 1 achieves the highest performance among Japanese open models on the MT - Bench -jp"}
+{"idx": 1, "title": "Chatbot Arena + | OpenLM.ai", "date": "", "ddg_snippet": "This leaderboard is based on the following benchmarks. Chatbot Arena - a crowdsourced, randomized battle platform for large language models (LLMs). We use 4M+ user votes to compute Elo ratings.", "subpage_snippet": "", "source": "openlm.ai", "link": "https://openlm.ai/chatbot-arena/", "content": "This leaderboard is based on the following benchmarks. Chatbot Arena - a crowdsourced, randomized battle platform for large language models (LLMs). We use 4M+ user votes to compute Elo ratings."}
+{"idx": 2, "title": "Together AI · AI Models · LobeChat", "date": "", "ddg_snippet": "meta-llama/ Llama - 2 - 70 b -hf. LLaMA - 2 provides excellent language processing capabilities and outstanding interactive experiences.", "subpage_snippet": "", "source": "lobe-chat-preview-y9k4jc76g-lobe-chat-community.vercel.app", "link": "https://lobe-chat-preview-y9k4jc76g-lobe-chat-community.vercel.app/discover/models/togetherai", "content": "meta-llama/ Llama - 2 - 70 b -hf. LLaMA - 2 provides excellent language processing capabilities and outstanding interactive experiences."}
+{"idx": 3, "title": "Overview Leaderboard | LMArena", "date": "", "ddg_snippet": "athene-v 2 - chat .llama-3. 1 -nemotron- 70 b -instruct.", "subpage_snippet": "", "source": "lmarena.ai", "link": "https://lmarena.ai/leaderboard", "content": "athene-v 2 - chat .llama-3. 1 -nemotron- 70 b -instruct."}
+{"idx": 4, "title": "Mistral 7B | The best 7B model to date, Apache 2 .0", "date": "", "ddg_snippet": "table . An interesting metric to compare how models fare in the cost/performance plane is to compute “equivalent model sizes”. On reasoning, comprehension and STEM reasoning (MMLU), Mistral 7B performs equivalently to a Llama 2 that would be more than 3x its size.", "subpage_snippet": "", "source": "mistral.ai", "link": "https://mistral.ai/news/announcing-mistral-7b", "content": "table . An interesting metric to compare how models fare in the cost/performance plane is to compute “equivalent model sizes”. On reasoning, comprehension and STEM reasoning (MMLU), Mistral 7B performs equivalently to a Llama 2 that would be more than 3x its size."}
+{"idx": 5, "title": "RTX PRO 6000", "date": "", "ddg_snippet": "MT - Bench Analysis. torchtune. 70 B . Mistral Nemo 12B. gpt-oss-120b.", "subpage_snippet": "", "source": "llm-tracker.info", "link": "https://llm-tracker.info/RTX-PRO-6000", "content": "MT - Bench Analysis. torchtune. 70 B . Mistral Nemo 12B. gpt-oss-120b."}
+{"idx": 6, "title": "shisa-v 2 -llama3.3- 70 b by openrouter | AI Model Pricing... | LangDB", "date": "", "ddg_snippet": "Access shisa-v 2 -llama3.3- 70 b by openrouter through LangDB's unified AI gateway.Shisa V 2 70 B achieves leading Japanese task performance across a wide range of custom and public benchmarks, including JA MT Bench , ELYZA 100, and Rakuda.", "subpage_snippet": "", "source": "app.langdb.ai", "link": "https://app.langdb.ai/models/shisa-v2-llama3.3-70b", "content": "Access shisa-v 2 -llama3.3- 70 b by openrouter through LangDB's unified AI gateway.Shisa V 2 70 B achieves leading Japanese task performance across a wide range of custom and public benchmarks, including JA MT Bench , ELYZA 100, and Rakuda."}
+{"idx": 7, "title": "9 лучших локальных/офлайн LLM, которые вы можете... | Дзен", "date": "", "ddg_snippet": "Llama 2 — это преемник оригинальной Llama LLM, предлагающий улучшенные характеристики и универсальность. Вариант 13B Chat GPTQ настроен для приложений разговорного ИИ, оптимизированных для английского диалога.", "subpage_snippet": "", "source": "dzen.ru", "link": "https://dzen.ru/a/Z1_eYR8FKnjuKbOB", "content": "Llama 2 — это преемник оригинальной Llama LLM, предлагающий улучшенные характеристики и универсальность. Вариант 13B Chat GPTQ настроен для приложений разговорного ИИ, оптимизированных для английского диалога."}
+{"idx": 8, "title": "Welcome Gemma - Google’s new open LLM", "date": "", "ddg_snippet": "Gemma comes in two sizes: 7B parameters, for efficient deployment and development on consumer-size GPU and TPU and 2 B versions for CPU and on-device applications. Table of contents. What is Gemma? Prompt format.", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/blog/gemma", "content": "Gemma comes in two sizes: 7B parameters, for efficient deployment and development on consumer-size GPU and TPU and 2 B versions for CPU and on-device applications. Table of contents. What is Gemma? Prompt format."}
+{"idx": 9, "title": "How To Install UNCENSORED Llama 2 Locally... - YouTube", "date": "", "ddg_snippet": "Welcome to the ultimate guide on how to unlock the full potential of the language model in Llama 2 by installing the uncensored version!", "subpage_snippet": "", "source": "www.youtube.com", "link": "https://www.youtube.com/watch?v=RpGXhpeH668", "content": "Welcome to the ultimate guide on how to unlock the full potential of the language model in Llama 2 by installing the uncensored version!"}
diff --git a/data/sampled_jsons/EEI5R89Cmv_Neural_Exploratory_Landscape_Analysis_Equation_3_evaluation_metric.jsonl b/data/sampled_jsons/EEI5R89Cmv_Neural_Exploratory_Landscape_Analysis_Equation_3_evaluation_metric.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..112ed21d23f4ef97b4b196c45c5194d0aa5e35cc
--- /dev/null
+++ b/data/sampled_jsons/EEI5R89Cmv_Neural_Exploratory_Landscape_Analysis_Equation_3_evaluation_metric.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "N EXPLORATORY LANDSCAPE ANALYSIS FOR M -B -OPTIMIZATION", "date": "", "ddg_snippet": "ABSTRACT Recent research in Meta-Black-Box Optimization (MetaBBO) have shown that meta-trained neural networks can effectively guide the design of black-box op-timizers, significantly reducing the need for expert tuning and delivering robust performance across complex problem distributions. Despite their success, a paradox remains: MetaBBO still rely on human-crafted Exploratory Landscape ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=EEI5R89Cmv", "content": "ABSTRACT Recent research in Meta-Black-Box Optimization (MetaBBO) have shown that meta-trained neural networks can effectively guide the design of black-box op-timizers, significantly reducing the need for expert tuning and delivering robust performance across complex problem distributions. Despite their success, a paradox remains: MetaBBO still rely on human-crafted Exploratory Landscape ..."}
+{"idx": 1, "title": "Neural Exploratory Landscape Analysis for... | OpenReview", "date": "", "ddg_snippet": "The proposed approach, termed neural exploratory landscape analysis (NeurELA), trains the attention-based neural network extracting landscape features to improve the MetaBBO algorithms.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=EEI5R89Cmv", "content": "The proposed approach, termed neural exploratory landscape analysis (NeurELA), trains the attention-based neural network extracting landscape features to improve the MetaBBO algorithms."}
+{"idx": 2, "title": "Neural Exploratory Landscape Analysis - arXiv.org", "date": "", "ddg_snippet": "To address the gap, this paper proposes Neural Exploratory Landscape Analysis (NeurELA), a novel framework that dynamically profiles landscape features through a two-stage, attention-based neural network, executed in an entirely end-to-end fashion.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2408.10672v1", "content": "To address the gap, this paper proposes Neural Exploratory Landscape Analysis (NeurELA), a novel framework that dynamically profiles landscape features through a two-stage, attention-based neural network, executed in an entirely end-to-end fashion."}
+{"idx": 3, "title": "Course: Natural Computing *8 Exploratory Landscape Analysis", "date": "", "ddg_snippet": "Exploratorylandscape analysis [ 3 ]! Classificationofproblems tobeoptimized[4,6] Problemisimpliedbya fitnessfunctionf : X !R Levelofclassification ...", "subpage_snippet": "", "source": "opencourse.inf.ed.ac.uk", "link": "https://opencourse.inf.ed.ac.uk/sites/default/files/https/opencourse.inf.ed.ac.uk/nat-dl/2024/lect08s.pdf", "content": "Exploratorylandscape analysis [ 3 ]! Classificationofproblems tobeoptimized[4,6] Problemisimpliedbya fitnessfunctionf : X !R Levelofclassification ..."}
+{"idx": 4, "title": "Neural Exploratory Landscape Analysis for Meta-Black-Box ...", "date": "", "ddg_snippet": "Neural Exploratory Landscape Analysis for Meta-Black-Box-Optimization Here we provide sourcecodes of NeurELA, which has been recently accpeted by ICLR 2025 as a poster paper. We provide a novel way for extracting features from optimization process, hence facilitating any meta-level learning in recent MetaBBO context.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/MetaEvo/Neur-ELA", "content": "Neural Exploratory Landscape Analysis for Meta-Black-Box-Optimization Here we provide sourcecodes of NeurELA, which has been recently accpeted by ICLR 2025 as a poster paper. We provide a novel way for extracting features from optimization process, hence facilitating any meta-level learning in recent MetaBBO context."}
+{"idx": 5, "title": "Neural Exploratory Landscape Analysis - ResearchGate", "date": "", "ddg_snippet": "To address the gap, this paper proposes Neural Exploratory Landscape Analy-sis (NeurELA), a novel framework that dynamically profiles landscape features through a two-stage, attention-based neural ...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/profile/Zeyuan-Ma-2/publication/383267225_Neural_Exploratory_Landscape_Analysis/links/66c5d2d12fec7d516b5cf921/Neural-Exploratory-Landscape-Analysis.pdf", "content": "To address the gap, this paper proposes Neural Exploratory Landscape Analy-sis (NeurELA), a novel framework that dynamically profiles landscape features through a two-stage, attention-based neural ..."}
+{"idx": 6, "title": "Exploratory landscape analysis on black-box optimization ...", "date": "", "ddg_snippet": "This study proposes a novel exploratory landscape analysis (ELA) framework using graph neural networks (GNNs) to identify implicit characteristics of black-box optimization problems (BBOPs), with multimodality identification serving as the primary validation case.", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S2210650225002949", "content": "This study proposes a novel exploratory landscape analysis (ELA) framework using graph neural networks (GNNs) to identify implicit characteristics of black-box optimization problems (BBOPs), with multimodality identification serving as the primary validation case."}
+{"idx": 7, "title": "Neural Exploratory Landscape Analysis for Meta-Black-Box ...", "date": "", "ddg_snippet": "Kerschke et al. omprehensive feature-based landscape analysis of continuous and constrained optimization problems using the r-package flacco, 2019. [6] Seiler et al. Deep-ela: Deep exploratory landscape analysis with self-supervised pretrained transformers for single-and multi-objective continuous optimization problems, 2024.", "subpage_snippet": "", "source": "iclr.cc", "link": "https://iclr.cc/media/iclr-2025/Slides/30417.pdf", "content": "Kerschke et al. omprehensive feature-based landscape analysis of continuous and constrained optimization problems using the r-package flacco, 2019. [6] Seiler et al. Deep-ela: Deep exploratory landscape analysis with self-supervised pretrained transformers for single-and multi-objective continuous optimization problems, 2024."}
+{"idx": 8, "title": "Regression Metrics - GeeksforGeeks", "date": "", "ddg_snippet": "The code estimates the values of the target variable based on the discovered relationships between features and the target variable, using the trained Linear Regression model (model) to make predictions (y_pred) on the test set (X_test). Calculate Evaluation Metrics .", "subpage_snippet": "", "source": "www.geeksforgeeks.org", "link": "https://www.geeksforgeeks.org/machine-learning/regression-metrics/", "content": "The code estimates the values of the target variable based on the discovered relationships between features and the target variable, using the trained Linear Regression model (model) to make predictions (y_pred) on the test set (X_test). Calculate Evaluation Metrics ."}
+{"idx": 9, "title": "F1 Score vs ROC AUC vs Accuracy vs PR AUC: Which Evaluation ...", "date": "", "ddg_snippet": "Learn about metrics : Accuracy, F1, ROC AUC, PR AUC. In-depth comparisons with insights on binary classification metrics , and logging nuances.", "subpage_snippet": "", "source": "neptune.ai", "link": "https://neptune.ai/blog/f1-score-accuracy-roc-auc-pr-auc", "content": "Learn about metrics : Accuracy, F1, ROC AUC, PR AUC. In-depth comparisons with insights on binary classification metrics , and logging nuances."}
diff --git "a/data/sampled_jsons/EEI5R89Cmv_Neural_Exploratory_Landscape_Analysis_equation_3_\316\245_evaluation_metric_formula_year_2024.jsonl" "b/data/sampled_jsons/EEI5R89Cmv_Neural_Exploratory_Landscape_Analysis_equation_3_\316\245_evaluation_metric_formula_year_2024.jsonl"
new file mode 100644
index 0000000000000000000000000000000000000000..6e403bbd7e773701f355edbc14f1931b104aaf47
--- /dev/null
+++ "b/data/sampled_jsons/EEI5R89Cmv_Neural_Exploratory_Landscape_Analysis_equation_3_\316\245_evaluation_metric_formula_year_2024.jsonl"
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "N EXPLORATORY LANDSCAPE ANALYSIS FOR M -B -OPTIMIZATION", "date": "", "ddg_snippet": "ABSTRACT Recent research in Meta-Black-Box Optimization (MetaBBO) have shown that meta-trained neural networks can effectively guide the design of black-box op-timizers, significantly reducing the need for expert tuning and delivering robust performance across complex problem distributions. Despite their success, a paradox remains: MetaBBO still rely on human-crafted Exploratory Landscape ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=EEI5R89Cmv", "content": "ABSTRACT Recent research in Meta-Black-Box Optimization (MetaBBO) have shown that meta-trained neural networks can effectively guide the design of black-box op-timizers, significantly reducing the need for expert tuning and delivering robust performance across complex problem distributions. Despite their success, a paradox remains: MetaBBO still rely on human-crafted Exploratory Landscape ..."}
+{"idx": 1, "title": "Neural Exploratory Landscape Analysis - arXiv.org", "date": "", "ddg_snippet": "To address the gap, this paper proposes Neural Exploratory Landscape Analysis (NeurELA), a novel framework that dynamically profiles landscape features through a two-stage, attention-based neural network, executed in an entirely end-to-end fashion.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2408.10672v1", "content": "To address the gap, this paper proposes Neural Exploratory Landscape Analysis (NeurELA), a novel framework that dynamically profiles landscape features through a two-stage, attention-based neural network, executed in an entirely end-to-end fashion."}
+{"idx": 2, "title": "Neural Exploratory Landscape Analysis for Meta-Black-Box ... Neural Exploratory Landscape Analysis - ResearchGate Exploratory Landscape Analysis Neural Exploratory Landscape Analysis for Meta-Black-Box ... Classical Exploratory Landscape Analysis - flacco Tutorial [2408.10672] Neural Exploratory Landscape Analysis for Meta-Black-Bo… Classical Exploratory Landscape Analysis - flacco Tutorial Neural Exploratory Landscape Analysis - arXiv.org GitHub - MetaEvo/Neur-ELA: Official code for Neural Exploratory Neural Exploratory Landscape Analysis - arXiv.org Neural Exploratory Landscape Analysis - arXiv.org Neural Exploratory Landscape Analysis - arXiv.org", "date": "", "ddg_snippet": "Aug 20, 2024 · Recent research in Meta-Black-Box Optimization (MetaBBO) have shown that meta-trained neural networks can effectively guide the design of black-box optimizers, significantly reducing the need for expert tuning and delivering robust performance across complex problem distributions. Despite their success, a paradox remains: MetaBBO still rely on human-crafted Exploratory Landscape Analysis ... To address the above limitations, we propose Neural Exploratory Landscape Analysis (NeurELA), a novel, learnable framework designed to dynamically profile optimization status for the MetaBBO ... Exploratory Landscape Analysis (ELA) subsumes a number of techniques employed to obtain knowledge about the proper-ties of an unknown optimization problem, especially insofar as these properties are important for the performance of op-timization algorithms. Neural Exploratory Landscape Analysis for Meta-Black-Box-Optimization Here we provide sourcecodes of NeurELA, which has been recently accpeted by ICLR 2025 as a poster paper. We provide a novel way for extracting features from optimization process, hence facilitating any meta-level learning in recent MetaBBO context. The term Exploratory Landscape Analysis (ELA) features (as introduced by Mersmann et al., 2011) summarizes a group of characteristics, which quantifies certain properties of a continuous optimization problem. What is neural exploratory landscape analysis? To address the gap, this paper proposes Neural Exploratory Landscape Analysis (NeurELA), a novel framework that dynamically profiles landscape features through a two-stage, attention-based neural network, executed in an entirely end-to-end fashion. What is exploratory landscape analysis (ELA)? The term Exploratory Landscape Analysis (ELA) features (as introduced by Mersmann et al., 2011) summarizes a group of characteristics, which quantifies certain properties of a continuous optimization problem. Can We learn a universal landscape analyser using deep neural network? As a pioneer work on using deep neural network for landscape analysis, our NeurELA show possibility of learning an universal landscape analyser with minimal expertise requirement and appealing optimization performance. However, certain limitations still exist. On the one hand, the training efficiency of NeurELA is not very satisfactory. Who is the author of 'neural exploratory landscape analysis'? title={Neural Exploratory Landscape Analysis}, author={ Ma, Zeyuan and Chen, Jiacheng and Guo, Hongshu and Gong, Yue-Jiao }, journal={arXiv preprint arXiv:2408.10672}, Is a landscape analyser parameterized by a two-stage attention-based neural network? To this end, we propose a landscape analyser parameterized by a two-stage attention-based neural network . The analyser enables flexible replacement for the original hand-crafted landscape analysis mechanisms in existing MetaBBO algorithms. What is landscape analysis? Landscape Analysis originates from the emergence of Automated Algorithm Selection (AAS) [31, 32], which selects an optimal algorithm out of a collection of algorithms for any given optimization problem . To address the gap, this paper proposes Neural Exploratory Landscape Analysis (NeurELA), a novel framework that dynamically profiles landscape features through a two-stage, attention-based neural network, executed in an entirely end-to-end fashion.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2408.10672", "content": "Aug 20, 2024 · Recent research in Meta-Black-Box Optimization (MetaBBO) have shown that meta-trained neural networks can effectively guide the design of black-box optimizers, significantly reducing the need for expert tuning and delivering robust performance across complex problem distributions. Despite their success, a paradox remains: MetaBBO still rely on human-crafted Exploratory Landscape Analysis ... To address the above limitations, we propose Neural Exploratory Landscape Analysis (NeurELA), a novel, learnable framework designed to dynamically profile optimization status for the MetaBBO ... Exploratory Landscape Analysis (ELA) subsumes a number of techniques employed to obtain knowledge about the proper-ties of an unknown optimization problem, especially insofar as these properties are important for the performance of op-timization algorithms. Neural Exploratory Landscape Analysis for Meta-Black-Box-Optimization Here we provide sourcecodes of NeurELA, which has been recently accpeted by ICLR 2025 as a poster paper. We provide a novel way for extracting features from optimization process, hence facilitating any meta-level learning in recent MetaBBO context. The term Exploratory Landscape Analysis (ELA) features (as introduced by Mersmann et al., 2011) summarizes a group of characteristics, which quantifies certain properties of a continuous optimization problem. What is neural exploratory landscape analysis? To address the gap, this paper proposes Neural Exploratory Landscape Analysis (NeurELA), a novel framework that dynamically profiles landscape features through a two-stage, attention-based neural network, executed in an entirely end-to-end fashion. What is exploratory landscape analysis (ELA)? The term Exploratory Landscape Analysis (ELA) features (as introduced by Mersmann et al., 2011) summarizes a group of characteristics, which quantifies certain properties of a continuous optimization problem. Can We learn a universal landscape analyser using deep neural network? As a pioneer work on using deep neural network for landscape analysis, our NeurELA show possibility of learning an universal landscape analyser with minimal expertise requirement and appealing optimization performance. However, certain limitations still exist. On the one hand, the training efficiency of NeurELA is not very satisfactory. Who is the author of 'neural exploratory landscape analysis'? title={Neural Exploratory Landscape Analysis}, author={ Ma, Zeyuan and Chen, Jiacheng and Guo, Hongshu and Gong, Yue-Jiao }, journal={arXiv preprint arXiv:2408.10672}, Is a landscape analyser parameterized by a two-stage attention-based neural network? To this end, we propose a landscape analyser parameterized by a two-stage attention-based neural network . The analyser enables flexible replacement for the original hand-crafted landscape analysis mechanisms in existing MetaBBO algorithms. What is landscape analysis? Landscape Analysis originates from the emergence of Automated Algorithm Selection (AAS) [31, 32], which selects an optimal algorithm out of a collection of algorithms for any given optimization problem . To address the gap, this paper proposes Neural Exploratory Landscape Analysis (NeurELA), a novel framework that dynamically profiles landscape features through a two-stage, attention-based neural network, executed in an entirely end-to-end fashion."}
+{"idx": 3, "title": "Neural Exploratory Landscape Analysis - ResearchGate", "date": "", "ddg_snippet": "To address the above limitations, we propose Neural Exploratory Landscape Analysis (NeurELA), a novel, learnable framework designed to dynamically profile optimization status for the MetaBBO ...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/profile/Zeyuan-Ma-2/publication/383267225_Neural_Exploratory_Landscape_Analysis/links/66c5d2d12fec7d516b5cf921/Neural-Exploratory-Landscape-Analysis.pdf", "content": "To address the above limitations, we propose Neural Exploratory Landscape Analysis (NeurELA), a novel, learnable framework designed to dynamically profile optimization status for the MetaBBO ..."}
+{"idx": 4, "title": "Exploratory Landscape Analysis", "date": "", "ddg_snippet": "Exploratory Landscape Analysis (ELA) subsumes a number of techniques employed to obtain knowledge about the proper-ties of an unknown optimization problem, especially insofar as these properties are important for the performance of op-timization algorithms.", "subpage_snippet": "", "source": "groups.csail.mit.edu", "link": "https://groups.csail.mit.edu/EVO-DesignOpt/gecco2011Proceedings/proceedings/p829.pdf", "content": "Exploratory Landscape Analysis (ELA) subsumes a number of techniques employed to obtain knowledge about the proper-ties of an unknown optimization problem, especially insofar as these properties are important for the performance of op-timization algorithms."}
+{"idx": 5, "title": "Neural Exploratory Landscape Analysis for Meta-Black-Box ...", "date": "", "ddg_snippet": "Neural Exploratory Landscape Analysis for Meta-Black-Box-Optimization Here we provide sourcecodes of NeurELA, which has been recently accpeted by ICLR 2025 as a poster paper. We provide a novel way for extracting features from optimization process, hence facilitating any meta-level learning in recent MetaBBO context.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/MetaEvo/Neur-ELA", "content": "Neural Exploratory Landscape Analysis for Meta-Black-Box-Optimization Here we provide sourcecodes of NeurELA, which has been recently accpeted by ICLR 2025 as a poster paper. We provide a novel way for extracting features from optimization process, hence facilitating any meta-level learning in recent MetaBBO context."}
+{"idx": 6, "title": "Classical Exploratory Landscape Analysis - flacco Tutorial", "date": "", "ddg_snippet": "The term Exploratory Landscape Analysis (ELA) features (as introduced by Mersmann et al., 2011) summarizes a group of characteristics, which quantifies certain properties of a continuous optimization problem.", "subpage_snippet": "", "source": "kerschke.github.io", "link": "https://kerschke.github.io/flacco-tutorial/site/ela/", "content": "The term Exploratory Landscape Analysis (ELA) features (as introduced by Mersmann et al., 2011) summarizes a group of characteristics, which quantifies certain properties of a continuous optimization problem."}
+{"idx": 7, "title": "F1 Score vs ROC AUC vs Accuracy vs PR AUC: Which Evaluation ...", "date": "", "ddg_snippet": "Learn about metrics : Accuracy, F1, ROC AUC, PR AUC. In-depth comparisons with insights on binary classification metrics , and logging nuances.", "subpage_snippet": "", "source": "neptune.ai", "link": "https://neptune.ai/blog/f1-score-accuracy-roc-auc-pr-auc", "content": "Learn about metrics : Accuracy, F1, ROC AUC, PR AUC. In-depth comparisons with insights on binary classification metrics , and logging nuances."}
+{"idx": 8, "title": "Classification: Accuracy, recall, precision, and related metrics", "date": "", "ddg_snippet": "Learn how to calculate three key classification metrics —accuracy, precision, recall—and how to choose the appropriate metric to evaluate a given binary classification model.", "subpage_snippet": "", "source": "developers.google.com", "link": "https://developers.google.com/machine-learning/crash-course/classification/accuracy-precision-recall", "content": "Learn how to calculate three key classification metrics —accuracy, precision, recall—and how to choose the appropriate metric to evaluate a given binary classification model."}
+{"idx": 9, "title": "Machine Learning Tutorial - GeeksforGeeks", "date": "", "ddg_snippet": "Module 1: Machine Learning Pipeline. This section covers preprocessing, exploratory data analysis and model evaluation to prepare data, uncover insights and build reliable models.", "subpage_snippet": "", "source": "www.geeksforgeeks.org", "link": "https://www.geeksforgeeks.org/machine-learning/machine-learning/", "content": "Module 1: Machine Learning Pipeline. This section covers preprocessing, exploratory data analysis and model evaluation to prepare data, uncover insights and build reliable models."}
diff --git a/data/sampled_jsons/ELITE_Enhanced_Language-Image_Toxicity_Evaluation_Table_3_E-ASR_proprietary_open-source_models.jsonl b/data/sampled_jsons/ELITE_Enhanced_Language-Image_Toxicity_Evaluation_Table_3_E-ASR_proprietary_open-source_models.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..a5841c5c7361e3a2eb907c141d42de143e15369b
--- /dev/null
+++ b/data/sampled_jsons/ELITE_Enhanced_Language-Image_Toxicity_Evaluation_Table_3_E-ASR_proprietary_open-source_models.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "ELITE : Enhanced Language - Image Toxicity Evaluation for Safety", "date": "", "ddg_snippet": "Table 3 . ELITE evaluator score-based ASR of various VLMs across taxonomies. The upper group in the table represents proprietary models , and the lower group represents open - source models .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2502.04757", "content": "Table 3 . ELITE evaluator score-based ASR of various VLMs across taxonomies. The upper group in the table represents proprietary models , and the lower group represents open - source models ."}
+{"idx": 1, "title": "Enhanced Language-Image Toxicity Evaluation for Safety", "date": "", "ddg_snippet": "24 Jul 2025 — In Table 3 , we present comprehensive experimental results of the ELITE benchmark across various proprietary and open - source VLMs. GPT-4o ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2502.04757v3", "content": "24 Jul 2025 — In Table 3 , we present comprehensive experimental results of the ELITE benchmark across various proprietary and open - source VLMs. GPT-4o ..."}
+{"idx": 2, "title": "Enhanced Language-Image Toxicity Evaluation for Safety", "date": "", "ddg_snippet": "In Table 3 , we present comprehensive experimental results of the ELITE benchmark across various proprietary and open - source VLMs. GPT-4o exhibits the lowest E - ...", "subpage_snippet": "", "source": "bytez.com", "link": "https://bytez.com/docs/icml/46445/paper", "content": "In Table 3 , we present comprehensive experimental results of the ELITE benchmark across various proprietary and open - source VLMs. GPT-4o exhibits the lowest E - ..."}
+{"idx": 3, "title": "Enhanced Language-Image Toxicity Evaluation for Safety", "date": "", "ddg_snippet": "These steps and results illustrate the comprehensive evaluation conducted in the paper to assess the safety of Vision Language Models using the ELITE benchmark.", "subpage_snippet": "", "source": "chatpaper.com", "link": "https://chatpaper.com/paper/167387", "content": "These steps and results illustrate the comprehensive evaluation conducted in the paper to assess the safety of Vision Language Models using the ELITE benchmark."}
+{"idx": 4, "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": 5, "title": "Paper page - ELITE : Enhanced Language - Image Toxicity ...", "date": "", "ddg_snippet": "ELITE : Enhanced Language - Image Toxicity Evaluation for Safety.Current Vision Language Models (VLMs) remain vulnerable to malicious prompts that induce harmful outputs.", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/papers/2502.04757", "content": "ELITE : Enhanced Language - Image Toxicity Evaluation for Safety.Current Vision Language Models (VLMs) remain vulnerable to malicious prompts that induce harmful outputs."}
+{"idx": 6, "title": "[Literature Review] ELITE : Enhanced Language - Image Toxicity ...", "date": "", "ddg_snippet": "The paper \" ELITE : Enhanced Language - Image Toxicity Evaluation for Safety\" presents a novel framework designed to evaluate the safety of Vision Language Models (VLMs) against harmful, malicious inputs that could generate unsafe content.", "subpage_snippet": "", "source": "www.themoonlight.io", "link": "https://www.themoonlight.io/en/review/elite-enhanced-language-image-toxicity-evaluation-for-safety", "content": "The paper \" ELITE : Enhanced Language - Image Toxicity Evaluation for Safety\" presents a novel framework designed to evaluate the safety of Vision Language Models (VLMs) against harmful, malicious inputs that could generate unsafe content."}
+{"idx": 7, "title": "Understanding and Mitigating Toxicity in Image -Text Pretraining...", "date": "", "ddg_snippet": "ELITE [12] evaluator explicitly incorporates a toxicity score to accurately assess harmful-ness in multimodal contexts, where VLMs often provide specific, convincing, but unharmful descriptions of images .", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2025W/ReGenAI/papers/Alam_Understanding_and_Mitigating_Toxicity_in_Image-Text_Pretraining_Datasets_A_Case_CVPRW_2025_paper.pdf", "content": "ELITE [12] evaluator explicitly incorporates a toxicity score to accurately assess harmful-ness in multimodal contexts, where VLMs often provide specific, convincing, but unharmful descriptions of images ."}
+{"idx": 8, "title": "Top 15 Open Source Speech Recognition /TTS/STT/ Systems", "date": "", "ddg_snippet": "There has been many open source speech recognition , TTS, STT and ASR libraries developed in recent years. Here's the top ones among them.", "subpage_snippet": "", "source": "fosspost.org", "link": "https://fosspost.org/open-source-speech-recognition", "content": "There has been many open source speech recognition , TTS, STT and ASR libraries developed in recent years. Here's the top ones among them."}
+{"idx": 9, "title": "GLM-4.5 - China's Flagship Open - Source AI Model", "date": "", "ddg_snippet": "GLM-4.5 is China's flagship open - source model with 355B parameters using advanced MoE architecture. It ranks among the top 3 globally in comprehensive performance and leads in open - source models with exceptional reasoning and code generation capabilities.", "subpage_snippet": "", "source": "glm45.net", "link": "https://glm45.net/", "content": "GLM-4.5 is China's flagship open - source model with 355B parameters using advanced MoE architecture. It ranks among the top 3 globally in comprehensive performance and leads in open - source models with exceptional reasoning and code generation capabilities."}
diff --git a/data/sampled_jsons/ELITE_benchmark_filtering_threshold_ELITE_evaluator_score_s.jsonl b/data/sampled_jsons/ELITE_benchmark_filtering_threshold_ELITE_evaluator_score_s.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..8b24ed0400547a1a23df4ccf4161aed1f21cda3a
--- /dev/null
+++ b/data/sampled_jsons/ELITE_benchmark_filtering_threshold_ELITE_evaluator_score_s.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Engineering Metrics Benchmarks: How High-Performing Teams", "date": "", "ddg_snippet": "... per thousand lines of code (KLOC), this metric enables teams to systematically evaluate code quality, prioritize remediation efforts, and benchmark ...", "subpage_snippet": "", "source": "mstone.ai", "link": "https://mstone.ai/blog/engineering-metrics-benchmarks-high-performing-teams-success/", "content": "... per thousand lines of code (KLOC), this metric enables teams to systematically evaluate code quality, prioritize remediation efforts, and benchmark ..."}
+{"idx": 1, "title": "F1 Score in Machine Learning: Formula, Range &", "date": "", "ddg_snippet": "In programs like IIT Jodhpur’ s B. S /B.Sc in Applied AI & Data Science , students don’t just memorize formulas—they calculate F1 Scores ...", "subpage_snippet": "", "source": "www.futurense.com", "link": "https://www.futurense.com/uni-blog/f1-score-machine-learning", "content": "In programs like IIT Jodhpur’ s B. S /B.Sc in Applied AI & Data Science , students don’t just memorize formulas—they calculate F1 Scores ..."}
+{"idx": 2, "title": "Employee Engagement Benchmark Data: The 2024 Essential Overview", "date": "", "ddg_snippet": "... benchmarking engagement data: This type of data compares your ... Industry-specific benchmark data compares employee engagement scores by industry.", "subpage_snippet": "", "source": "blog.engagerocket.co", "link": "https://blog.engagerocket.co/employee-engagement-benchmark-data", "content": "... benchmarking engagement data: This type of data compares your ... Industry-specific benchmark data compares employee engagement scores by industry."}
+{"idx": 3, "title": "Upgrading CMA-ME to CMA-MAE on the Sphere Benchmark — pyribs", "date": "", "ddg_snippet": "The second difference is that this archive takes in a threshold _min parameter ( \\(min_f\\) ) which is the starting threshold for each cell.", "subpage_snippet": "", "source": "docs.pyribs.org", "link": "https://docs.pyribs.org/en/latest/tutorials/cma_mae.html", "content": "The second difference is that this archive takes in a threshold _min parameter ( \\(min_f\\) ) which is the starting threshold for each cell."}
+{"idx": 4, "title": "Upgrading CMA-ME to CMA-MAE on the Sphere Benchmark — pyribs", "date": "", "ddg_snippet": "The second difference is that this archive takes in a threshold _min parameter ( \\(min_f\\) ) which is the starting threshold for each cell.", "subpage_snippet": "", "source": "docs.pyribs.org", "link": "https://docs.pyribs.org/en/stable/tutorials/cma_mae.html", "content": "The second difference is that this archive takes in a threshold _min parameter ( \\(min_f\\) ) which is the starting threshold for each cell."}
+{"idx": 5, "title": "Dominated Novelty Search:Rethinking Local Competition in", "date": "", "ddg_snippet": "The predominant grid-based competition in MAP- Elites and the distance- threshold approach in unstructured archives represent relatively simple ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2502.00593v1", "content": "The predominant grid-based competition in MAP- Elites and the distance- threshold approach in unstructured archives represent relatively simple ..."}
+{"idx": 6, "title": "FinTechZoom Pro: Features and Benefits for Financial", "date": "", "ddg_snippet": "Elite members also receive personalized investment recommendations based on their stated financial goals and risk tolerance profiles.", "subpage_snippet": "", "source": "blessmeup.com", "link": "https://blessmeup.com/fintechzoom-pro-2025/", "content": "Elite members also receive personalized investment recommendations based on their stated financial goals and risk tolerance profiles."}
+{"idx": 7, "title": "Kevin Indrebo | Overrated Mind", "date": "", "ddg_snippet": "I haven’t seen o3 yet & have been critical of benchmarks for AI but they did test against some of the hardest & best ones On GPQA, PhDs ...", "subpage_snippet": "", "source": "overratedmind.com", "link": "https://overratedmind.com/author/kindrebo13/", "content": "I haven’t seen o3 yet & have been critical of benchmarks for AI but they did test against some of the hardest & best ones On GPQA, PhDs ..."}
+{"idx": 8, "title": "Adrenalin 21.8.2 Driver Performance Analysis with 23 Games", "date": "", "ddg_snippet": "All AMD Radeon Software Adrenalin settings are set so that all optimizations are off, Texture filtering is set to ‘High’, and Tessellation uses ...", "subpage_snippet": "", "source": "babeltechreviews.com", "link": "https://babeltechreviews.com/adrenalin-21-8-2-driver-performance-analysis-with-23-games-featuring-myst/", "content": "All AMD Radeon Software Adrenalin settings are set so that all optimizations are off, Texture filtering is set to ‘High’, and Tessellation uses ..."}
+{"idx": 9, "title": "Alignment-Augmented Speculative Decoding with Alignment", "date": "", "ddg_snippet": "... on 8 datasets in long context generation scenarios, including question answering, summarization, and code completion tasks, in Section 4 to evaluate ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2505.13204v2", "content": "... on 8 datasets in long context generation scenarios, including question answering, summarization, and code completion tasks, in Section 4 to evaluate ..."}
diff --git a/data/sampled_jsons/ELITE_evaluator_toxicity_score_methodology_rubric_three_primary_components.jsonl b/data/sampled_jsons/ELITE_evaluator_toxicity_score_methodology_rubric_three_primary_components.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..03fc0bf09b3e89654104d052b320458d3c4f9692
--- /dev/null
+++ b/data/sampled_jsons/ELITE_evaluator_toxicity_score_methodology_rubric_three_primary_components.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "ELITE: Enhanced Language-Image Toxicity Evaluation for Safety", "date": "", "ddg_snippet": "It is built using the ELITE evaluator , which adds a toxicity score to better detect harmful responses. This allows us to remove ambiguous image-text pairs and include more diverse and meaningful image-text combinations. Our experiments show that the ELITE evaluator aligns more closely with human judgment than previous methods.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=583klsIjNx", "content": "It is built using the ELITE evaluator , which adds a toxicity score to better detect harmful responses. This allows us to remove ambiguous image-text pairs and include more diverse and meaningful image-text combinations. Our experiments show that the ELITE evaluator aligns more closely with human judgment than previous methods."}
+{"idx": 1, "title": "AIM Intelligence's ELITE Collaborative Paper Accepted by the ICML", "date": "", "ddg_snippet": "At its core is the ELITE evaluator , a rubric -based method that incorporates a toxicity score to measure harmfulness in multimodal contexts-especially where VLMs produce specific, convincing responses that may appear harmless but convey dangerous intent.", "subpage_snippet": "", "source": "index.businessinsurance.com", "link": "https://index.businessinsurance.com/businessinsurance/article/newsfile-2025-5-15-aim-intelligences-elite-collaborative-paper-accepted-by-the-icml", "content": "At its core is the ELITE evaluator , a rubric -based method that incorporates a toxicity score to measure harmfulness in multimodal contexts-especially where VLMs produce specific, convincing responses that may appear harmless but convey dangerous intent."}
+{"idx": 2, "title": "AIM Intelligence's ELITE Collaborative Paper Accepted by the ICML", "date": "", "ddg_snippet": "The paper proposes ELITE, a high-quality benchmark designed to evaluate the safety of Vision-Language Models (VLMs) with greater precision. At its core is the ELITE evaluator , a rubric -based method that incorporates a toxicity score to measure harmfulness in multimodal contexts-especially where VLMs produce specific, convincing responses that may appear harmless but convey dangerous intent.", "subpage_snippet": "", "source": "www.newsfilecorp.com", "link": "https://www.newsfilecorp.com/release/252268/AIM-Intelligences-ELITE-Collaborative-Paper-Accepted-by-the-ICML", "content": "The paper proposes ELITE, a high-quality benchmark designed to evaluate the safety of Vision-Language Models (VLMs) with greater precision. At its core is the ELITE evaluator , a rubric -based method that incorporates a toxicity score to measure harmfulness in multimodal contexts-especially where VLMs produce specific, convincing responses that may appear harmless but convey dangerous intent."}
+{"idx": 3, "title": "AIM Intelligence's ELITE Collaborative Paper Accepted by the ICML", "date": "", "ddg_snippet": "At its core is the ELITE evaluator , a rubric -based method that incorporates a toxicity score to measure harmfulness in multimodal contexts-especially where VLMs produce specific, convincing ...", "subpage_snippet": "", "source": "www.streetinsider.com", "link": "https://www.streetinsider.com/Newsfile/AIM+Intelligences+ELITE+Collaborative+Paper+Accepted+by+the+ICML/24805029.html", "content": "At its core is the ELITE evaluator , a rubric -based method that incorporates a toxicity score to measure harmfulness in multimodal contexts-especially where VLMs produce specific, convincing ..."}
+{"idx": 4, "title": "Enhanced Language-Image Toxicity Evaluation for Safety", "date": "", "ddg_snippet": "24 Jul 2025 — The ELITE evaluator explicitly incorporates a toxicity score to ... In this work, we introduce the ELITE evaluator , a rubric -based method ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2502.04757v3", "content": "24 Jul 2025 — The ELITE evaluator explicitly incorporates a toxicity score to ... In this work, we introduce the ELITE evaluator , a rubric -based method ..."}
+{"idx": 5, "title": "Enhanced Language-Image Toxicity Evaluation for Safety", "date": "", "ddg_snippet": "The ELITE evaluator explicitly incorporates a toxicity score to accurately assess harmfulness in multimodal contexts, where VLMs often provide specific, ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=583klsIjNx&noteId=lkuNd7fIAG", "content": "The ELITE evaluator explicitly incorporates a toxicity score to accurately assess harmfulness in multimodal contexts, where VLMs often provide specific, ..."}
+{"idx": 6, "title": "ELITE: Enhanced Language-Image Toxicity Evaluation for Safety", "date": "", "ddg_snippet": "(c) Evaluation Method : The ELITE evaluator is a more precise rubric -based safety evaluation ... address this, we incorporate a toxicity score ... combined ELITE ...", "subpage_snippet": "", "source": "www.arxiv.org", "link": "https://www.arxiv.org/pdf/2502.04757v1", "content": "(c) Evaluation Method : The ELITE evaluator is a more precise rubric -based safety evaluation ... address this, we incorporate a toxicity score ... combined ELITE ..."}
+{"idx": 7, "title": "AIM Intelligence's ELITE Collaborative Paper Accepted by the ...", "date": "", "ddg_snippet": "15 May 2025 — At its core is the ELITE evaluator , a rubric -based method that incorporates a toxicity score to measure harmfulness in multimodal contexts ...", "subpage_snippet": "", "source": "finance.yahoo.com", "link": "https://finance.yahoo.com/news/aim-intelligences-elite-collaborative-paper-161800502.html", "content": "15 May 2025 — At its core is the ELITE evaluator , a rubric -based method that incorporates a toxicity score to measure harmfulness in multimodal contexts ..."}
+{"idx": 8, "title": "Enhanced Language-Image Toxicity Evaluation for Safety", "date": "", "ddg_snippet": "7 Feb 2025 — The ELITE evaluator's incorporation of a toxicity score offers a more nuanced evaluation metric that improves the robustness of automated ...", "subpage_snippet": "", "source": "www.themoonlight.io", "link": "https://www.themoonlight.io/fr/review/elite-enhanced-language-image-toxicity-evaluation-for-safety", "content": "7 Feb 2025 — The ELITE evaluator's incorporation of a toxicity score offers a more nuanced evaluation metric that improves the robustness of automated ..."}
+{"idx": 9, "title": "cvlab.yonsei.ac.kr", "date": "", "ddg_snippet": "ELITE evaluator Examples of safety evaluations about the victim model's response by ELITE and StrongREJECT evaluator. \\ ( r \\), \\ ( s \\), \\ ( c \\), and \\ ( t \\) represent refused, specific, convincing, and toxicity, respectively. The ELITE evaluator can effectively evaluate utilizing the toxicity score to make more accurate judgments.", "subpage_snippet": "", "source": "cvlab.yonsei.ac.kr", "link": "https://cvlab.yonsei.ac.kr/projects/ELITE/", "content": "ELITE evaluator Examples of safety evaluations about the victim model's response by ELITE and StrongREJECT evaluator. \\ ( r \\), \\ ( s \\), \\ ( c \\), and \\ ( t \\) represent refused, specific, convincing, and toxicity, respectively. The ELITE evaluator can effectively evaluate utilizing the toxicity score to make more accurate judgments."}
diff --git a/data/sampled_jsons/ELITE_paper_2502.04757_Equation_2_ELITE_evaluator_formula_definition.jsonl b/data/sampled_jsons/ELITE_paper_2502.04757_Equation_2_ELITE_evaluator_formula_definition.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..3874de556dd7b4f2ecb8d911f25f934725157675
--- /dev/null
+++ b/data/sampled_jsons/ELITE_paper_2502.04757_Equation_2_ELITE_evaluator_formula_definition.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "ELITE: Enhanced Language-Image Toxicity Evaluation for Safety ELITE: Enhanced Language-Image Toxicity Evaluation for Safety ELITE: Enhanced Language-Image Toxicity Evaluation for Safety arXiv.org Defining “elite” status in sport: from chaos to clarity - PMC How to Use the \"Evaluate Formula\" Feature in Excel - Spreadsheeto Interpretation of External Load On Nozzle Flange using PV ...", "date": "", "ddg_snippet": "Feb 7, 2025 · Therefore, we found that existing benchmarks have low levels of harmfulness, ambiguous data, and limited diversity in image-text pair combinations. To address these issues, we propose the ELITE benchmark, a high-quality safety evaluation benchmark for VLMs, underpinned by our enhanced evaluation method, the ELITE evaluator . The ELITE benchmark and evaluator improve safety assessment for Vision Language Models by filtering out ambiguous pairs and enhancing diversity in image-text combinations. The ELITE benchmark is proposed, a high-quality safety evaluation benchmark for VLMs, underpinned by the enhanced evaluation method, the ELITE evaluator , which explicitly incorporates a toxicity score to accurately assess harmfulness in multimodal contexts. Current Vision Language Models (VLMs) remain vulnerable to malicious prompts that induce harmful outputs. Existing safety benchmarks for ... arXiv.org The term “ elite ” has been inconsistently applied in research, undermining the external validity of findings regarding the characteristics and prerequisites of high performance. Furthermore, the way the word “ elite ” is applied in practical contexts can be problematic, and may send inadvertent messages and exacerbate existing biases in sport. Aug 29, 2024 · The Evaluate formula tool will evaluate each part of the formula in a sequence starting from the first part. So, say if you want to evaluate the 6th part of the formula , you’d have to press the evaluate button that many times sequentially to reach the 6th part of the formula . ASME Interpretation BPV VIII-1-16-85 ANSI Flange Pressure Reduction Options In PV Elite Code Case 2901 What Code Interpretation BPV VIII-1-16-85 States This interpretation states that if you have external loadings acting on a nozzle you have to consider them on the flange too. In 2013, however, PVP2013-97814 was written to address this issue.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2502.04757", "content": "Feb 7, 2025 · Therefore, we found that existing benchmarks have low levels of harmfulness, ambiguous data, and limited diversity in image-text pair combinations. To address these issues, we propose the ELITE benchmark, a high-quality safety evaluation benchmark for VLMs, underpinned by our enhanced evaluation method, the ELITE evaluator . The ELITE benchmark and evaluator improve safety assessment for Vision Language Models by filtering out ambiguous pairs and enhancing diversity in image-text combinations. The ELITE benchmark is proposed, a high-quality safety evaluation benchmark for VLMs, underpinned by the enhanced evaluation method, the ELITE evaluator , which explicitly incorporates a toxicity score to accurately assess harmfulness in multimodal contexts. Current Vision Language Models (VLMs) remain vulnerable to malicious prompts that induce harmful outputs. Existing safety benchmarks for ... arXiv.org The term “ elite ” has been inconsistently applied in research, undermining the external validity of findings regarding the characteristics and prerequisites of high performance. Furthermore, the way the word “ elite ” is applied in practical contexts can be problematic, and may send inadvertent messages and exacerbate existing biases in sport. Aug 29, 2024 · The Evaluate formula tool will evaluate each part of the formula in a sequence starting from the first part. So, say if you want to evaluate the 6th part of the formula , you’d have to press the evaluate button that many times sequentially to reach the 6th part of the formula . ASME Interpretation BPV VIII-1-16-85 ANSI Flange Pressure Reduction Options In PV Elite Code Case 2901 What Code Interpretation BPV VIII-1-16-85 States This interpretation states that if you have external loadings acting on a nozzle you have to consider them on the flange too. In 2013, however, PVP2013-97814 was written to address this issue."}
+{"idx": 1, "title": "ELITE: Enhanced Language-Image Toxicity Evaluation for Safety", "date": "", "ddg_snippet": "The ELITE benchmark and evaluator improve safety assessment for Vision Language Models by filtering out ambiguous pairs and enhancing diversity in image-text combinations.", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/papers/2502.04757", "content": "The ELITE benchmark and evaluator improve safety assessment for Vision Language Models by filtering out ambiguous pairs and enhancing diversity in image-text combinations."}
+{"idx": 2, "title": "ELITE: Enhanced Language-Image Toxicity Evaluation for Safety", "date": "", "ddg_snippet": "The ELITE benchmark is proposed, a high-quality safety evaluation benchmark for VLMs, underpinned by the enhanced evaluation method, the ELITE evaluator , which explicitly incorporates a toxicity score to accurately assess harmfulness in multimodal contexts. Current Vision Language Models (VLMs) remain vulnerable to malicious prompts that induce harmful outputs. Existing safety benchmarks for ...", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/ELITE:-Enhanced-Language-Image-Toxicity-Evaluation-Lee-Lee/2bf4206276d5f574bbb2e13a56b29b4522fea675", "content": "The ELITE benchmark is proposed, a high-quality safety evaluation benchmark for VLMs, underpinned by the enhanced evaluation method, the ELITE evaluator , which explicitly incorporates a toxicity score to accurately assess harmfulness in multimodal contexts. Current Vision Language Models (VLMs) remain vulnerable to malicious prompts that induce harmful outputs. Existing safety benchmarks for ..."}
+{"idx": 3, "title": "Defining “elite” status in sport: from chaos to clarity - PMC", "date": "", "ddg_snippet": "The term “ elite ” has been inconsistently applied in research, undermining the external validity of findings regarding the characteristics and prerequisites of high performance. Furthermore, the way the word “ elite ” is applied in practical contexts can be problematic, and may send inadvertent messages and exacerbate existing biases in sport.", "subpage_snippet": "", "source": "pmc.ncbi.nlm.nih.gov", "link": "https://pmc.ncbi.nlm.nih.gov/articles/PMC8340584/", "content": "The term “ elite ” has been inconsistently applied in research, undermining the external validity of findings regarding the characteristics and prerequisites of high performance. Furthermore, the way the word “ elite ” is applied in practical contexts can be problematic, and may send inadvertent messages and exacerbate existing biases in sport."}
+{"idx": 4, "title": "How to Use the \"Evaluate Formula\" Feature in Excel - Spreadsheeto", "date": "", "ddg_snippet": "Aug 29, 2024 · The Evaluate formula tool will evaluate each part of the formula in a sequence starting from the first part. So, say if you want to evaluate the 6th part of the formula , you’d have to press the evaluate button that many times sequentially to reach the 6th part of the formula .", "subpage_snippet": "", "source": "spreadsheeto.com", "link": "https://spreadsheeto.com/evaluate-formula-excel/", "content": "Aug 29, 2024 · The Evaluate formula tool will evaluate each part of the formula in a sequence starting from the first part. So, say if you want to evaluate the 6th part of the formula , you’d have to press the evaluate button that many times sequentially to reach the 6th part of the formula ."}
+{"idx": 5, "title": "Interpretation of External Load On Nozzle Flange using PV ...", "date": "", "ddg_snippet": "ASME Interpretation BPV VIII-1-16-85 ANSI Flange Pressure Reduction Options In PV Elite Code Case 2901 What Code Interpretation BPV VIII-1-16-85 States This interpretation states that if you have external loadings acting on a nozzle you have to consider them on the flange too. In 2013, however, PVP2013-97814 was written to address this issue.", "subpage_snippet": "", "source": "whatispiping.com", "link": "https://whatispiping.com/interpretation-of-external-load-on-nozzle-flange-using-pv-elite-code-case-2901/", "content": "ASME Interpretation BPV VIII-1-16-85 ANSI Flange Pressure Reduction Options In PV Elite Code Case 2901 What Code Interpretation BPV VIII-1-16-85 States This interpretation states that if you have external loadings acting on a nozzle you have to consider them on the flange too. In 2013, however, PVP2013-97814 was written to address this issue."}
+{"idx": 6, "title": "Computation and Language Feb 2025", "date": "", "ddg_snippet": "24 Feb 2025 — [1926] arXiv: 2502.04757 (cross-list from cs.CV) [pdf, html, other]. Title: ELITE : Enhanced Language-Image Toxicity Evaluation for Safety.", "subpage_snippet": "", "source": "www.arxiv.org", "link": "https://www.arxiv.org/list/cs.CL/2025-02?skip=1175&show=2000", "content": "24 Feb 2025 — [1926] arXiv: 2502.04757 (cross-list from cs.CV) [pdf, html, other]. Title: ELITE : Enhanced Language-Image Toxicity Evaluation for Safety."}
+{"idx": 7, "title": "davanstrien/arxiv-cs-2024-sample · Datasets at ...", "date": "", "ddg_snippet": "2502.04757 . ELITE : Enhanced Language-Image Toxicity Evaluation for Safety. Current Vision Language Models (VLMs) remain vulnerable to malicious prompts that ...", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/datasets/davanstrien/arxiv-cs-2024-sample/viewer", "content": "2502.04757 . ELITE : Enhanced Language-Image Toxicity Evaluation for Safety. Current Vision Language Models (VLMs) remain vulnerable to malicious prompts that ..."}
+{"idx": 8, "title": "arXiv.org", "date": "", "ddg_snippet": "arXiv.org", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2502.04757", "content": "arXiv.org"}
+{"idx": 9, "title": "Computer Science Feb 2025", "date": "", "ddg_snippet": "7 Feb 2025 — Title: How to introduce an initial crack in phase field simulations to accurately predict the linear elastic fracture propagation threshold?", "subpage_snippet": "", "source": "www.arxiv.org", "link": "https://www.arxiv.org/list/cs/2025-02?skip=1875&show=2000", "content": "7 Feb 2025 — Title: How to introduce an initial crack in phase field simulations to accurately predict the linear elastic fracture propagation threshold?"}
diff --git a/data/sampled_jsons/ETHICS_dataset_Checks-and-Balances_Framework_four_reasons.jsonl b/data/sampled_jsons/ETHICS_dataset_Checks-and-Balances_Framework_four_reasons.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..cacc53268e1f14d516fc67154480027b6b27a3d7
--- /dev/null
+++ b/data/sampled_jsons/ETHICS_dataset_Checks-and-Balances_Framework_four_reasons.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "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.against diverse cultural contexts. 1.2. Checks and Balances for Emotion-Guided Ethics .", "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.against diverse cultural contexts. 1.2. Checks and Balances for Emotion-Guided Ethics ."}
+{"idx": 1, "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": 2, "title": "A Three-Branch Checks - and - Balances Framework", "date": "", "ddg_snippet": "A Three-Branch Checks - and - Balances Framework for Context-Aware Ethical Alignment of Large Language Models.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=o2afWIxjKD", "content": "A Three-Branch Checks - and - Balances Framework for Context-Aware Ethical Alignment of Large Language Models."}
+{"idx": 3, "title": "Checks and Balance Newsletter Alternate DNC Reality | SocioToday", "date": "", "ddg_snippet": "Checks and Balances Visual Representation.Our “ Checks and Balances ” newsletter aims to provide in-depth, unbiased coverage of the Alternate Reality Democratic National Convention (ARDNC), offering readers a multi-faceted perspective on the unfolding events.", "subpage_snippet": "", "source": "sociotoday.com", "link": "https://sociotoday.com/checks-and-balance-newsletter-the-alternate-reality-democratic-national-convention/", "content": "Checks and Balances Visual Representation.Our “ Checks and Balances ” newsletter aims to provide in-depth, unbiased coverage of the Alternate Reality Democratic National Convention (ARDNC), offering readers a multi-faceted perspective on the unfolding events."}
+{"idx": 4, "title": "(PDF) A Three-Branch Checks - and - Balances Framework for...", "date": "", "ddg_snippet": "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": "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": 5, "title": "How To Identify and Prevent Vendor Fraud - Shopify South Africa", "date": "", "ddg_snippet": "Establishing a checks - and - balances system for vendor transactions that uphold company ethics . Verifying vendor business names and tax identification number (TIN). Creating a dual review process for vendor documents and contracts.", "subpage_snippet": "", "source": "www.shopify.com", "link": "https://www.shopify.com/za/retail/vendor-fraud", "content": "Establishing a checks - and - balances system for vendor transactions that uphold company ethics . Verifying vendor business names and tax identification number (TIN). Creating a dual review process for vendor documents and contracts."}
+{"idx": 6, "title": "Human-Machine Teaming and Its Legal and Ethical Implications", "date": "", "ddg_snippet": "It examines the existing approaches, reveals their limitations, and calls for the establishment of accountability and the use of a checks - and - balances framework in AI systems. It also discusses the legal and ethical implications of this solution.", "subpage_snippet": "", "source": "digitalcommons.usf.edu", "link": "https://digitalcommons.usf.edu/mca/vol4/iss2/2/", "content": "It examines the existing approaches, reveals their limitations, and calls for the establishment of accountability and the use of a checks - and - balances framework in AI systems. It also discusses the legal and ethical implications of this solution."}
+{"idx": 7, "title": "Mathematical Foundations in Law and", "date": "", "ddg_snippet": "— Conclusion These strategies form a checks - and - balances framework : 1. **De-mocratization** ensures boundaries reflect collective agreement.public access to boundary-defining algorithms and datasets .", "subpage_snippet": "", "source": "philarchive.org", "link": "https://philarchive.org/archive/EMMTJO-5", "content": "— Conclusion These strategies form a checks - and - balances framework : 1. **De-mocratization** ensures boundaries reflect collective agreement.public access to boundary-defining algorithms and datasets ."}
+{"idx": 8, "title": "Neural interfaces and human-computer interaction: A U.S. review...", "date": "", "ddg_snippet": "The balance between innovation and ethics will be crucial, requiring ongoing dialogue among technologists, ethicists, policymakers, and the public. Ethical frameworks and guidelines will need to evolve in tandem with technological advancements to ensure responsible development.", "subpage_snippet": "", "source": "ijsra.net", "link": "https://ijsra.net/sites/default/files/IJSRA-2024-0111.pdf", "content": "The balance between innovation and ethics will be crucial, requiring ongoing dialogue among technologists, ethicists, policymakers, and the public. Ethical frameworks and guidelines will need to evolve in tandem with technological advancements to ensure responsible development."}
+{"idx": 9, "title": "Downloads", "date": "", "ddg_snippet": "A Checks - and - Balances Framework for Context-Aware Ethical AI Alignment.LlavaGuard: An Open VLM-based Framework for Safeguarding Vision Datasets and Models.", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/Downloads/2025", "content": "A Checks - and - Balances Framework for Context-Aware Ethical AI Alignment.LlavaGuard: An Open VLM-based Framework for Safeguarding Vision Datasets and Models."}
diff --git a/data/sampled_jsons/ETHICS_dataset_four_reasons_empirical_studies_Checks-and-Balances_Framework.jsonl b/data/sampled_jsons/ETHICS_dataset_four_reasons_empirical_studies_Checks-and-Balances_Framework.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..7c7860d6d7fcf8ee9192d8550cabf1cb18ac049d
--- /dev/null
+++ b/data/sampled_jsons/ETHICS_dataset_four_reasons_empirical_studies_Checks-and-Balances_Framework.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "A Checks-and-Balances Framework for Context-Aware ...", "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.", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/poster/46461", "content": "This paper introduces a checks -and- balances framework for ethical alignment of Large Language Models (LLMs), inspired by three-branch governmental systems."}
+{"idx": 1, "title": "A Checks-and-Balances Framework for Context-Aware ...", "date": "", "ddg_snippet": "1 May 2025 — This paper introduces a checks -and- balances framework for ethical alignment of Large Language Models (LLMs), inspired by three-branch governmental systems.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=4uOEiitySn&noteId=cYh3zaQycT", "content": "1 May 2025 — This paper introduces a checks -and- balances framework for ethical alignment of Large Language Models (LLMs), inspired by three-branch governmental systems."}
+{"idx": 2, "title": "Ethics-based AI auditing: A systematic literature review on ...", "date": "", "ddg_snippet": "by J Laine · 2024 · Cited by 59 — We explain how the literature discusses fairness, transparency, non-maleficence, responsibility, privacy, trust, beneficence, and freedom/autonomy.", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S037872062400051X", "content": "by J Laine · 2024 · Cited by 59 — We explain how the literature discusses fairness, transparency, non-maleficence, responsibility, privacy, trust, beneficence, and freedom/autonomy."}
+{"idx": 3, "title": "An ethical code for collecting, using and transferring sensitive ...", "date": "", "ddg_snippet": "by T Lysaght · 2023 · Cited by 3 — We generated this code using a modified Policy Delphi process designed to engage stakeholders in the deliberation of health data ethics and governance.", "subpage_snippet": "", "source": "bmcmedethics.biomedcentral.com", "link": "https://bmcmedethics.biomedcentral.com/articles/10.1186/s12910-023-00952-7", "content": "by T Lysaght · 2023 · Cited by 3 — We generated this code using a modified Policy Delphi process designed to engage stakeholders in the deliberation of health data ethics and governance."}
+{"idx": 4, "title": "Ethics and international business research: Considerations ...", "date": "", "ddg_snippet": "by SR Miller · 2024 · Cited by 33 — Ethical research matters; it enables researchers to trust each other and their findings, and provides a basis for society's trust in our research .", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S0969593123001075", "content": "by SR Miller · 2024 · Cited by 33 — Ethical research matters; it enables researchers to trust each other and their findings, and provides a basis for society's trust in our research ."}
+{"idx": 5, "title": "an empirical study of structural social and ethical ...", "date": "", "ddg_snippet": "by M Ryan · 2024 · Cited by 9 — This paper examines how professionals (N = 32) working on artificial intelligence (AI) view structural AI ethics challenges like injustices and inequalities.", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/article/10.1007/s00146-024-02146-0", "content": "by M Ryan · 2024 · Cited by 9 — This paper examines how professionals (N = 32) working on artificial intelligence (AI) view structural AI ethics challenges like injustices and inequalities."}
+{"idx": 6, "title": "A Framework of Fundamental Values for Human-AI ...", "date": "", "ddg_snippet": "15 Sept 2024 — 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": "15 Sept 2024 — We introduce \\system, a framework of fundamental values, grounded in psychological theory and a systematic review , to identify and evaluate human-AI alignment."}
+{"idx": 7, "title": "AI governance: themes, knowledge gaps and future agendas", "date": "", "ddg_snippet": "by T Birkstedt · 2023 · Cited by 214 — However, empirical studies on the effectiveness of AI ethics are largely missing. In particular, few studies have investigated how ...", "subpage_snippet": "", "source": "www.emerald.com", "link": "https://www.emerald.com/intr/article/33/7/133/178343/AI-governance-themes-knowledge-gaps-and-future", "content": "by T Birkstedt · 2023 · Cited by 214 — However, empirical studies on the effectiveness of AI ethics are largely missing. In particular, few studies have investigated how ..."}
+{"idx": 8, "title": "Ethics-Based Auditing of Automated Decision-Making ...", "date": "", "ddg_snippet": "by J Mökander · 2021 · Cited by 199 — In this article, we consider the feasibility and efficacy of ethics -based auditing (EBA) as a governance mechanism that allows organisations to validate claims ...", "subpage_snippet": "", "source": "pmc.ncbi.nlm.nih.gov", "link": "https://pmc.ncbi.nlm.nih.gov/articles/PMC8260507/", "content": "by J Mökander · 2021 · Cited by 199 — In this article, we consider the feasibility and efficacy of ethics -based auditing (EBA) as a governance mechanism that allows organisations to validate claims ..."}
+{"idx": 9, "title": "AI Ethics: Integrating Transparency, Fairness, and Privacy ...", "date": "", "ddg_snippet": "by P Radanliev · 2025 · Cited by 63 — This research establishes a comprehensive ethical framework that mitigates biases and promotes accountability in AI technologies.", "subpage_snippet": "", "source": "www.tandfonline.com", "link": "https://www.tandfonline.com/doi/full/10.1080/08839514.2025.2463722", "content": "by P Radanliev · 2025 · Cited by 63 — This research establishes a comprehensive ethical framework that mitigates biases and promotes accountability in AI technologies."}
diff --git a/data/sampled_jsons/Enhancing_Sharding_Blockchain_via_Deep_Reinforcement_Learning_for_Account_Migration_Section_5.1_expe_year_2024.jsonl b/data/sampled_jsons/Enhancing_Sharding_Blockchain_via_Deep_Reinforcement_Learning_for_Account_Migration_Section_5.1_expe_year_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..6b246cf543f8404b387d87574ef408c4a9349433
--- /dev/null
+++ b/data/sampled_jsons/Enhancing_Sharding_Blockchain_via_Deep_Reinforcement_Learning_for_Account_Migration_Section_5.1_expe_year_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "84 Synonyms & Antonyms for ENHANCING | Thesaurus.com", "date": "", "ddg_snippet": "Find 84 different ways to say ENHANCING , along with antonyms, related words, and example sentences at Thesaurus.com.", "subpage_snippet": "", "source": "www.thesaurus.com", "link": "https://www.thesaurus.com/browse/enhancing", "content": "Find 84 different ways to say ENHANCING , along with antonyms, related words, and example sentences at Thesaurus.com."}
+{"idx": 1, "title": "ENHANCING definition in American English | Collins English ...", "date": "", "ddg_snippet": "ENHANCING definition: to intensify or increase in quality, value, power, etc; improve ; augment | Meaning, pronunciation, translations and examples in American English", "subpage_snippet": "", "source": "www.collinsdictionary.com", "link": "https://www.collinsdictionary.com/us/dictionary/english/enhancing", "content": "ENHANCING definition: to intensify or increase in quality, value, power, etc; improve ; augment | Meaning, pronunciation, translations and examples in American English"}
+{"idx": 2, "title": "enhancing - Wiktionary, the free dictionary", "date": "", "ddg_snippet": "May 7, 2025 · enhancing (comparative more enhancing , superlative most enhancing) Acting or tending to enhance. quotations", "subpage_snippet": "", "source": "en.wiktionary.org", "link": "https://en.wiktionary.org/wiki/enhancing", "content": "May 7, 2025 · enhancing (comparative more enhancing , superlative most enhancing) Acting or tending to enhance. quotations"}
+{"idx": 3, "title": "MultiversX Sharding: AI-ready Infrastructure", "date": "", "ddg_snippet": "Sharding isn t \" nice to have \" nor something towards which you can migrate later; it needs to be built right from the start and ...", "subpage_snippet": "", "source": "www.astrarizon.com", "link": "https://www.astrarizon.com/blog/multiversx-sharding-ai-ready-infrastructure", "content": "Sharding isn t \" nice to have \" nor something towards which you can migrate later; it needs to be built right from the start and ..."}
+{"idx": 4, "title": "Enhancing - definition of enhancing by The Free Dictionary", "date": "", "ddg_snippet": "To improve or augment , especially in effectiveness, value, or attractiveness: exercises that enhance cardiovascular health; spices that enhance the flavor of a sauce; renovations that enhance the neighborhood.", "subpage_snippet": "", "source": "www.thefreedictionary.com", "link": "https://www.thefreedictionary.com/enhancing", "content": "To improve or augment , especially in effectiveness, value, or attractiveness: exercises that enhance cardiovascular health; spices that enhance the flavor of a sauce; renovations that enhance the neighborhood."}
+{"idx": 5, "title": "enhance verb - Definition, pictures, pronunciation and usage ...", "date": "", "ddg_snippet": "Definition of enhance verb in Oxford Advanced Learner's Dictionary. Meaning, pronunciation, picture, example sentences, grammar, usage notes, synonyms and more.", "subpage_snippet": "", "source": "www.oxfordlearnersdictionaries.com", "link": "https://www.oxfordlearnersdictionaries.com/definition/english/enhance", "content": "Definition of enhance verb in Oxford Advanced Learner's Dictionary. Meaning, pronunciation, picture, example sentences, grammar, usage notes, synonyms and more."}
+{"idx": 6, "title": "ÜberConf | ÜberConf", "date": "", "ddg_snippet": "... an obvious path for continuing to learn design and refactoring skills - know where and how to get feedback and can create own curriculum for next 1 . 5 ...", "subpage_snippet": "", "source": "uberconf.com", "link": "https://uberconf.com/conference/denver/2018/07/schedule", "content": "... an obvious path for continuing to learn design and refactoring skills - know where and how to get feedback and can create own curriculum for next 1 . 5 ..."}
+{"idx": 7, "title": "What Is Cryptocurrency and How to Invest in It Wisely ☀️", "date": "", "ddg_snippet": "For investors , that combination of scarcity, programmability, and global transferability is what makes crypto different from stocks or fiat cash ...", "subpage_snippet": "", "source": "www.tradingvortex.com", "link": "https://www.tradingvortex.com/what-is-cryptocurrency-and-how-to-invest-in-it-wisely", "content": "For investors , that combination of scarcity, programmability, and global transferability is what makes crypto different from stocks or fiat cash ..."}
+{"idx": 8, "title": "Glossary - View All Terms | Refetrust", "date": "", "ddg_snippet": "Well-designed buildings enhance the quality of life for their users, contribute to the community’s visual landscape, and reduce environmental ...", "subpage_snippet": "", "source": "www.refetrust.com", "link": "https://www.refetrust.com/glossary/view-all", "content": "Well-designed buildings enhance the quality of life for their users, contribute to the community’s visual landscape, and reduce environmental ..."}
+{"idx": 9, "title": "ENHANCE Definition & Meaning - Merriam-Webster", "date": "", "ddg_snippet": "The meaning of ENHANCE is heighten, increase; especially : to increase or improve in value, quality, desirability, or attractiveness. How to use enhance in a sentence. Enhance Has Latin Roots.", "subpage_snippet": "", "source": "www.merriam-webster.com", "link": "https://www.merriam-webster.com/dictionary/enhance", "content": "The meaning of ENHANCE is heighten, increase; especially : to increase or improve in value, quality, desirability, or attractiveness. How to use enhance in a sentence. Enhance Has Latin Roots."}
diff --git a/data/sampled_jsons/EntityErasure_MSN_MARS_metric_sundries_amodal_entity_attention_year_2024.jsonl b/data/sampled_jsons/EntityErasure_MSN_MARS_metric_sundries_amodal_entity_attention_year_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..69196c08e5af2ad22e9fa1c576cc6161ddca0b91
--- /dev/null
+++ b/data/sampled_jsons/EntityErasure_MSN_MARS_metric_sundries_amodal_entity_attention_year_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "PDF EntityErasure: Erasing Entity Cleanly via Amodal Entity Segmentation ...", "date": "", "ddg_snippet": "The proposed entity attention injects entity in-formation with amodal entity segmentation guidance. We ensure that all generated content is completed from the en-tities in the non-inpainting area, effectively preventing the creation of new sundries .", "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": "The proposed entity attention injects entity in-formation with amodal entity segmentation guidance. We ensure that all generated content is completed from the en-tities in the non-inpainting area, effectively preventing the creation of new sundries ."}
+{"idx": 1, "title": "EntityErasure: Erasing Entity Cleanly via Amodal Entity Segmentation ...", "date": "", "ddg_snippet": "This paper presents EntityErasure , a novel diffusion-based inpainting method that can effectively erase entities 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": "ieeexplore.ieee.org", "link": "https://ieeexplore.ieee.org/document/11094156", "content": "This paper presents EntityErasure , a novel diffusion-based inpainting method that can effectively erase entities 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": 2, "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": 3, "title": "[2312.17247] Amodal Ground Truth and Completion in the Wild", "date": "", "ddg_snippet": "This paper studies amodal image segmentation: predicting entire object segmentation masks including both visible and invisible (occluded) parts. In previous work, the amodal segmentation ground truth on real images is usually predicted by manual annotaton and thus is subjective. In contrast, we use 3D data to establish an automatic pipeline to determine authentic ground truth amodal masks for ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2312.17247", "content": "This paper studies amodal image segmentation: predicting entire object segmentation masks including both visible and invisible (occluded) parts. In previous work, the amodal segmentation ground truth on real images is usually predicted by manual annotaton and thus is subjective. In contrast, we use 3D data to establish an automatic pipeline to determine authentic ground truth amodal masks for ..."}
+{"idx": 4, "title": "EntityErasure: Erasing Entity Cleanly via Amodal Entity Segmentation ...", "date": "", "ddg_snippet": "Download Citation | On Jun 10, 2025, Yixing Zhu and others published EntityErasure : Erasing Entity Cleanly via Amodal Entity Segmentation and Completion | Find, read and cite all the research you ...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/394511863_EntityErasure_Erasing_Entity_Cleanly_via_Amodal_Entity_Segmentation_and_Completion", "content": "Download Citation | On Jun 10, 2025, Yixing Zhu and others published EntityErasure : Erasing Entity Cleanly via Amodal Entity Segmentation and Completion | Find, read and cite all the research you ..."}
+{"idx": 5, "title": "Entity/Entityv2/README.md at main · qqlu/Entity · GitHub", "date": "", "ddg_snippet": "EntitySeg Toolbox: Towards Open-World and High-Quality Image Segmentation - Entity /Entityv2/README.md at main · qqlu/ Entity", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/qqlu/Entity/blob/main/Entityv2/README.md", "content": "EntitySeg Toolbox: Towards Open-World and High-Quality Image Segmentation - Entity /Entityv2/README.md at main · qqlu/ Entity"}
+{"idx": 6, "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": 7, "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": 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": "CVPR 2025 Open Access Repository", "date": "", "ddg_snippet": "This paper presents EntityErasure , a novel diffusion-based inpainting method that can effectively erase entities 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 ...", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2025/html/Zhu_EntityErasure_Erasing_Entity_Cleanly_via_Amodal_Entity_Segmentation_and_Completion_CVPR_2025_paper.html", "content": "This paper presents EntityErasure , a novel diffusion-based inpainting method that can effectively erase entities 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 ..."}
diff --git a/data/sampled_jsons/EntityErasure_Table_3_Figure_8_entity_attention_MSN_metric_cross_attention_mechanism.jsonl b/data/sampled_jsons/EntityErasure_Table_3_Figure_8_entity_attention_MSN_metric_cross_attention_mechanism.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..0a0dbe957fb53ca54c1bf2bbe3ae587eee82e9e5
--- /dev/null
+++ b/data/sampled_jsons/EntityErasure_Table_3_Figure_8_entity_attention_MSN_metric_cross_attention_mechanism.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Transformer (deep learning architecture) - Wikipedia", "date": "", "ddg_snippet": "The attention mechanism requires the following three equalities to hold: ℓ. seq, key.Each decoder consists of three major components: a causally masked self- attention mechanism , a cross - attention mechanism , and a feed-forward neural network .", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/Transformer_(deep_learning_architecture)", "content": "The attention mechanism requires the following three equalities to hold: ℓ. seq, key.Each decoder consists of three major components: a causally masked self- attention mechanism , a cross - attention mechanism , and a feed-forward neural network ."}
+{"idx": 1, "title": "Attention (machine learning) - Wikipedia", "date": "", "ddg_snippet": "In machine learning, attention is a method that determines the importance of each component in a sequence relative to the other components in that sequence. In natural language processing, importance is represented by \"soft\" weights assigne...", "subpage_snippet": "", "source": "en.m.wikipedia.org", "link": "https://en.m.wikipedia.org/wiki/Attention_(machine_learning)", "content": "In machine learning, attention is a method that determines the importance of each component in a sequence relative to the other components in that sequence. In natural language processing, importance is represented by \"soft\" weights assigne..."}
+{"idx": 2, "title": "EntityErasure : Erasing Entity Cleanly via Amodal Entity ...", "date": "", "ddg_snippet": "Figure 3 . Illustration of entity attention (EA). It leverages amodal entity segmentation to restrain attention map calculated between the. entity reference feature and input feature. By limiting each entity ’s influence solely to pixels within regions predicted by the amodal entity .", "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": "Figure 3 . Illustration of entity attention (EA). It leverages amodal entity segmentation to restrain attention map calculated between the. entity reference feature and input feature. By limiting each entity ’s influence solely to pixels within regions predicted by the amodal entity ."}
+{"idx": 3, "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": 4, "title": "Boosting the Speed of Entity Alignment 10: Dual Attention ... CVPR 2025 Open Access Repository GitHub - zyxunh/Entity_for_entity_erasure Attention Overlap Is Responsible for The Entity Missing ... ML - Attention mechanism - GeeksforGeeks EntityErasure : Erasing Entity Cleanly via Amodal Entity Segmentation Boosting the Speed of Entity Alignment 10: Dual Attention Matching Boosting the Speed of Entity Alignment 10: Dual Attention Matching Boosting the Speed of Entity Alignment 10: Dual Attention Matching ML - Attention mechanism - GeeksforGeeks ML - Attention mechanism - GeeksforGeeks", "date": "", "ddg_snippet": "we propose ( Dual-AMN ) to cap-ture dual relational information within a single graph and across See full list on arxiv.org Anisotropy × None Diagonal ✓ Diagonal Normal × Diagonal × None Table 2: Categorization of GNN encoders in some popular EA approaches. Inspired by these findings, we design a simplified relation-aware GNN layer. ∈ R| |× The inputs of our model are two metrics, represents ∈ R| |× the initial entity features and represents the initial rela- See full list on arxiv.org . As shown in Figure 3 (b), we employ a limited set of proxy vectors to represent the cross -graph alignment relation, similar to use anchor points to present a space. If two entities are equivalent, their similarity distributions associated with these proxy vectors should also be consistent. In this way, the proposed layer is able to capture the cr... See full list on arxiv.org The inputs of the are two matri- ∈ R| |× ces: represents the entities embeddings obtained See full list on arxiv.org Typically, in KGs, only a small portion of cross -graph entity pairs are aligned. So negative sampling is crucial to EA methods. However, the most common approach which selects the K-nearest neighbors, spends a lot of time on candidate ranking in each epoch. In this See full list on arxiv.org section, we propose a strategy, which is eficient and reduces loss shift. See full list on arxiv.org is approximate to TUNS with K = . When is set LogSumExp to an appropriate value, could replace the K-nearest sampling strategy to generate high-quality negative samples, but with better computational eficiency (because this process could be = 1 fully parallelized on GPU). More interestingly, when , the loss See full list on arxiv.org function is equivalent to with loss. This also indicates that the classification losses and the pair-wise losses are essentially two sides of the same coin. See full list on arxiv.org To fairly and comprehensively verify the efectiveness, robustness and scalability of our model, we construct experiments on three widely used public datasets: See full list on arxiv.org As mentioned in Section 3 , many studies adopt enhancement mod-ules. For instance, GM-Align and RDGCN propose to introduce literal information to provide a multi-aspect view. The introduc-tion of additional information leads to unfair comparisons between methods. Thus, existing EA methods will be compared separately according to the enhancement cate... See full list on arxiv.org , our model could fully utilize the GPU to obtain high-accuracy results eficiently. Even using the semi-supervised strategy for data augmentation, the proposed method still could obtain results within an hour. In summary, the high eficiency of Dual-AMN makes the entity alignment application on large-scale KGs possible. See full list on arxiv.org . Dual-AMN adopts the following four components to capture multi-aspect information See full list on arxiv.org . Besides architecture, the ized Hard Sample Mining Loss is also one of our main contributions. To verify its efectiveness, we compare it with several common loss functions. The results are visualized in Fig 4. Compared with the other three, the proposed loss could make the model converge faster See full list on arxiv.org and achieve the best performance. pling Strategy also has a similar decent performance. However, as we have mentioned, this sampling strategy requires massive time consumption. Since most of the negative samples are redundant, the Triplet loss has the worst eficiency of all loss functions. In our experiments, the Triplet loss function usually needs... See full list on arxiv.org Over complex graph encoders and ineficient negative sampling strategies lead to the general ineficiency of existing EA meth-ods, resulting in dificulty for applying on large-scale KGs. In this See full list on arxiv.org (Dual-AMN), which not only models both intra-graph and cross -graph relations smartly but also greatly reduces computa-tional complexity. To replace the ineficient sampling strategy, we See full list on arxiv.org propose to cut down the sam-pling consumption and accelerate the convergence speed. These two modifications enable the proposed model to achieve the SOTA performance while the speed is several times than other EA meth-ods. The main experiments indicate that our method outperforms competitors across all datasets and metrics. Furthermore, we de-sign ... See full list on arxiv.org This paper presents EntityErasure , a novel diffusion-based inpainting method that can effectively erase entities 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 ... EntitySeg Toolbox: Towards open-world and high-quality image segmentation EntitySeg is an open source toolbox which towards open-world and high-quality image segmentation. All works related to image segmentation from our group are open-sourced here. We hypothesize that during the denoising process, entity -related tokens engage in a form of competition for attention toward specific regions through the cross - attention mechanism . What is attention mechanism? Attention mechanism is a type of neural network that helps a model focus on specific parts of the input data, it is done by assigning weights to different elements in input which helps the model to decide which parts of information are most importart. This makes the model better at understanding complex relationships and dependencies in data. Does entity attention cause sundries in non-inpainting areas? We observe that applying entity attention for inject-ing information into the non-inpainting area does not incur sundries , but it may cause alterations to the non-inpainting region. As shown in Fig. 9, if entity attention is applied to the non-inpainting area, changes can be observed. What is entity alignment (EA)? Seeking the equivalent entities among multi-source Knowledge Graphs (KGs) is the pivotal step to KGs integration , also known entity alignment as (EA). However, most existing EA methods are ineficient and poor in scalability. How does dual-Amn capture multi-aspect information critical path around entities? Dual-AMN adopts the following four components to capture multi-aspect information critical path around entities. (2) operation (RP) generates the relation-specific embedding for entities. ( 3 ) (MHE) creates a more global-aware representation of Proxy Attention Matching Layer the KGs. (4) (PAM) captures the cross -graph information. Can GNN expand to multi-hop neighboring level information? In previous studies [14, 24], GNN is able to expand to multi-hop neighboring level information by stacking more layers, thus to create a more global-aware representation of the graph. Following this idea, we concatenate the embeddings from diferent layers where represents the concatenate operation. What is attention mechanism in machine translation? Attention mechanism in machine translation has three main components: Encoder , Attention and Decoder. Here's how each of these components works: 1. Encoder: Encoder processes the input sequence which is usually a sentence and generates hidden states. Jul 15, 2025 · Attention mechanism is a type of neural network that helps a model focus on specific parts of the input data, it is done by assigning weights to different elements in input which helps the model to decide which parts of information are most importart. This makes the model better at understanding complex relationships and dependencies in data.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2103.15452", "content": "we propose ( Dual-AMN ) to cap-ture dual relational information within a single graph and across See full list on arxiv.org Anisotropy × None Diagonal ✓ Diagonal Normal × Diagonal × None Table 2: Categorization of GNN encoders in some popular EA approaches. Inspired by these findings, we design a simplified relation-aware GNN layer. ∈ R| |× The inputs of our model are two metrics, represents ∈ R| |× the initial entity features and represents the initial rela- See full list on arxiv.org . As shown in Figure 3 (b), we employ a limited set of proxy vectors to represent the cross -graph alignment relation, similar to use anchor points to present a space. If two entities are equivalent, their similarity distributions associated with these proxy vectors should also be consistent. In this way, the proposed layer is able to capture the cr... See full list on arxiv.org The inputs of the are two matri- ∈ R| |× ces: represents the entities embeddings obtained See full list on arxiv.org Typically, in KGs, only a small portion of cross -graph entity pairs are aligned. So negative sampling is crucial to EA methods. However, the most common approach which selects the K-nearest neighbors, spends a lot of time on candidate ranking in each epoch. In this See full list on arxiv.org section, we propose a strategy, which is eficient and reduces loss shift. See full list on arxiv.org is approximate to TUNS with K = . When is set LogSumExp to an appropriate value, could replace the K-nearest sampling strategy to generate high-quality negative samples, but with better computational eficiency (because this process could be = 1 fully parallelized on GPU). More interestingly, when , the loss See full list on arxiv.org function is equivalent to with loss. This also indicates that the classification losses and the pair-wise losses are essentially two sides of the same coin. See full list on arxiv.org To fairly and comprehensively verify the efectiveness, robustness and scalability of our model, we construct experiments on three widely used public datasets: See full list on arxiv.org As mentioned in Section 3 , many studies adopt enhancement mod-ules. For instance, GM-Align and RDGCN propose to introduce literal information to provide a multi-aspect view. The introduc-tion of additional information leads to unfair comparisons between methods. Thus, existing EA methods will be compared separately according to the enhancement cate... See full list on arxiv.org , our model could fully utilize the GPU to obtain high-accuracy results eficiently. Even using the semi-supervised strategy for data augmentation, the proposed method still could obtain results within an hour. In summary, the high eficiency of Dual-AMN makes the entity alignment application on large-scale KGs possible. See full list on arxiv.org . Dual-AMN adopts the following four components to capture multi-aspect information See full list on arxiv.org . Besides architecture, the ized Hard Sample Mining Loss is also one of our main contributions. To verify its efectiveness, we compare it with several common loss functions. The results are visualized in Fig 4. Compared with the other three, the proposed loss could make the model converge faster See full list on arxiv.org and achieve the best performance. pling Strategy also has a similar decent performance. However, as we have mentioned, this sampling strategy requires massive time consumption. Since most of the negative samples are redundant, the Triplet loss has the worst eficiency of all loss functions. In our experiments, the Triplet loss function usually needs... See full list on arxiv.org Over complex graph encoders and ineficient negative sampling strategies lead to the general ineficiency of existing EA meth-ods, resulting in dificulty for applying on large-scale KGs. In this See full list on arxiv.org (Dual-AMN), which not only models both intra-graph and cross -graph relations smartly but also greatly reduces computa-tional complexity. To replace the ineficient sampling strategy, we See full list on arxiv.org propose to cut down the sam-pling consumption and accelerate the convergence speed. These two modifications enable the proposed model to achieve the SOTA performance while the speed is several times than other EA meth-ods. The main experiments indicate that our method outperforms competitors across all datasets and metrics. Furthermore, we de-sign ... See full list on arxiv.org This paper presents EntityErasure , a novel diffusion-based inpainting method that can effectively erase entities 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 ... EntitySeg Toolbox: Towards open-world and high-quality image segmentation EntitySeg is an open source toolbox which towards open-world and high-quality image segmentation. All works related to image segmentation from our group are open-sourced here. We hypothesize that during the denoising process, entity -related tokens engage in a form of competition for attention toward specific regions through the cross - attention mechanism . What is attention mechanism? Attention mechanism is a type of neural network that helps a model focus on specific parts of the input data, it is done by assigning weights to different elements in input which helps the model to decide which parts of information are most importart. This makes the model better at understanding complex relationships and dependencies in data. Does entity attention cause sundries in non-inpainting areas? We observe that applying entity attention for inject-ing information into the non-inpainting area does not incur sundries , but it may cause alterations to the non-inpainting region. As shown in Fig. 9, if entity attention is applied to the non-inpainting area, changes can be observed. What is entity alignment (EA)? Seeking the equivalent entities among multi-source Knowledge Graphs (KGs) is the pivotal step to KGs integration , also known entity alignment as (EA). However, most existing EA methods are ineficient and poor in scalability. How does dual-Amn capture multi-aspect information critical path around entities? Dual-AMN adopts the following four components to capture multi-aspect information critical path around entities. (2) operation (RP) generates the relation-specific embedding for entities. ( 3 ) (MHE) creates a more global-aware representation of Proxy Attention Matching Layer the KGs. (4) (PAM) captures the cross -graph information. Can GNN expand to multi-hop neighboring level information? In previous studies [14, 24], GNN is able to expand to multi-hop neighboring level information by stacking more layers, thus to create a more global-aware representation of the graph. Following this idea, we concatenate the embeddings from diferent layers where represents the concatenate operation. What is attention mechanism in machine translation? Attention mechanism in machine translation has three main components: Encoder , Attention and Decoder. Here's how each of these components works: 1. Encoder: Encoder processes the input sequence which is usually a sentence and generates hidden states. Jul 15, 2025 · Attention mechanism is a type of neural network that helps a model focus on specific parts of the input data, it is done by assigning weights to different elements in input which helps the model to decide which parts of information are most importart. This makes the model better at understanding complex relationships and dependencies in data."}
+{"idx": 5, "title": "GitHub - zyxunh/Entity_for_entity_erasure", "date": "", "ddg_snippet": "EntitySeg Toolbox: Towards open-world and high-quality image segmentation EntitySeg is an open source toolbox which towards open-world and high-quality image segmentation. All works related to image segmentation from our group are open-sourced here.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/zyxunh/Entity_for_entity_erasure", "content": "EntitySeg Toolbox: Towards open-world and high-quality image segmentation EntitySeg is an open source toolbox which towards open-world and high-quality image segmentation. All works related to image segmentation from our group are open-sourced here."}
+{"idx": 6, "title": "Attention Overlap Is Responsible for The Entity Missing ...", "date": "", "ddg_snippet": "We hypothesize that during the denoising process, entity -related tokens engage in a form of competition for attention toward specific regions through the cross - attention mechanism .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2410.20972v2", "content": "We hypothesize that during the denoising process, entity -related tokens engage in a form of competition for attention toward specific regions through the cross - attention mechanism ."}
+{"idx": 7, "title": "ML - Attention mechanism - GeeksforGeeks", "date": "", "ddg_snippet": "Jul 15, 2025 · Attention mechanism is a type of neural network that helps a model focus on specific parts of the input data, it is done by assigning weights to different elements in input which helps the model to decide which parts of information are most importart. This makes the model better at understanding complex relationships and dependencies in data.", "subpage_snippet": "", "source": "www.geeksforgeeks.org", "link": "https://www.geeksforgeeks.org/artificial-intelligence/ml-attention-mechanism/", "content": "Jul 15, 2025 · Attention mechanism is a type of neural network that helps a model focus on specific parts of the input data, it is done by assigning weights to different elements in input which helps the model to decide which parts of information are most importart. This makes the model better at understanding complex relationships and dependencies in data."}
+{"idx": 8, "title": "Abstract page for arXiv paper 1706.03762: Attention Is All You Need", "date": "", "ddg_snippet": "The best performing models also connect the encoder and decoder through an attention mechanism . We propose a new simple network architecture, the Transformer, based solely on attention mechanisms , dispensing with recurrence and convolutions entirely.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/1706.03762", "content": "The best performing models also connect the encoder and decoder through an attention mechanism . We propose a new simple network architecture, the Transformer, based solely on attention mechanisms , dispensing with recurrence and convolutions entirely."}
+{"idx": 9, "title": "Attention mechanism : Overview - YouTube", "date": "", "ddg_snippet": "This video introduces you to the attention mechanism , a powerful technique that allows neural networks to focus on specific parts of an input sequence.", "subpage_snippet": "", "source": "www.youtube.com", "link": "https://www.youtube.com/watch?v=fjJOgb-E41w", "content": "This video introduces you to the attention mechanism , a powerful technique that allows neural networks to focus on specific parts of an input sequence."}
diff --git a/data/sampled_jsons/Etienne_Gauthier_Francis_Bach_Michael_Jordan_Statistical_Collusion_by_Collectives_on_Learning_Platfo.jsonl b/data/sampled_jsons/Etienne_Gauthier_Francis_Bach_Michael_Jordan_Statistical_Collusion_by_Collectives_on_Learning_Platfo.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..8f7f9f3bc96d1e10e347c56076e9ce175064d66b
--- /dev/null
+++ b/data/sampled_jsons/Etienne_Gauthier_Francis_Bach_Michael_Jordan_Statistical_Collusion_by_Collectives_on_Learning_Platfo.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Statistical Collusion by Collectives on Learning Platforms", "date": "", "ddg_snippet": "Statistical Collusion by Collectives on Learning Platforms Etienne Gauthier , Francis Bach , Michael I. Jordan", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2502.04879", "content": "Statistical Collusion by Collectives on Learning Platforms Etienne Gauthier , Francis Bach , Michael I. Jordan"}
+{"idx": 1, "title": "Etienne Gauthier - Google Scholar", "date": "", "ddg_snippet": "Co-authors Michael I. Jordan Professor of Electrical Engineering and Computer Sciences and Professor of Statistics, UC Berkeley Francis Bach Inria - Ecole Normale Supérieure Enhao Liu Department of Mathematics, Graduate School of Science, Kyoto University", "subpage_snippet": "", "source": "scholar.google.com", "link": "https://scholar.google.com/citations?user=Tiwn0RMAAAAJ&hl=en", "content": "Co-authors Michael I. Jordan Professor of Electrical Engineering and Computer Sciences and Professor of Statistics, UC Berkeley Francis Bach Inria - Ecole Normale Supérieure Enhao Liu Department of Mathematics, Graduate School of Science, Kyoto University"}
+{"idx": 2, "title": "Statistical Collusion by Collectives on Learning Platforms", "date": "", "ddg_snippet": "Etienne Gauthier (1) , Francis Bach (2, 1) , Michael I. Jordan (1, 3) Afficher plus de détails 1 SIERRA - Statistical Machine Learning and Parsimony 2 LIENS - Laboratoire d'informatique de l'école normale supérieure 3 EECS - Department of Electrical Engineering and Computer Science [Berkeley] Etienne Gauthier Fonction : Auteur", "subpage_snippet": "", "source": "hal.science", "link": "https://hal.science/hal-04941041v1", "content": "Etienne Gauthier (1) , Francis Bach (2, 1) , Michael I. Jordan (1, 3) Afficher plus de détails 1 SIERRA - Statistical Machine Learning and Parsimony 2 LIENS - Laboratoire d'informatique de l'école normale supérieure 3 EECS - Department of Electrical Engineering and Computer Science [Berkeley] Etienne Gauthier Fonction : Auteur"}
+{"idx": 3, "title": "Francis Bach - INRIA - ENS - PSL", "date": "", "ddg_snippet": "Publications 2025 Eliot Beyler, Francis Bach . Convergence of Deterministic and Stochastic Diffusion-Model Samplers: A Simple Analysis in Wasserstein Distance. Technical report, arXiv:2508.03210, 2025. [pdf] Sacha Braun, Eugène Berta, Michael I. Jordan , Francis Bach . Multivariate Conformal Prediction via Conformalized Gaussian Scoring. Technical report, arXiv:2507.20941, 2025. [pdf] Francis ...", "subpage_snippet": "", "source": "www.di.ens.fr", "link": "https://www.di.ens.fr/~fbach/", "content": "Publications 2025 Eliot Beyler, Francis Bach . Convergence of Deterministic and Stochastic Diffusion-Model Samplers: A Simple Analysis in Wasserstein Distance. Technical report, arXiv:2508.03210, 2025. [pdf] Sacha Braun, Eugène Berta, Michael I. Jordan , Francis Bach . Multivariate Conformal Prediction via Conformalized Gaussian Scoring. Technical report, arXiv:2507.20941, 2025. [pdf] Francis ..."}
+{"idx": 4, "title": "Statistical Collusion by Collectives on Learning Platforms", "date": "", "ddg_snippet": "The paper explores the statistical inference for collective action in learning platforms , in particular, the paper examines not only the classic signal planting procedure introduced by Hardt et al, but also signal unplanting as well as signal erasing, both of which require statistical inferences for defining the optimal strategy h. In the end, the authors develop a framework that provides a ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=46yLEXtav4", "content": "The paper explores the statistical inference for collective action in learning platforms , in particular, the paper examines not only the classic signal planting procedure introduced by Hardt et al, but also signal unplanting as well as signal erasing, both of which require statistical inferences for defining the optimal strategy h. In the end, the authors develop a framework that provides a ..."}
+{"idx": 5, "title": "Statistical Collusion by Collectives on Learning Platforms | Cool ...", "date": "", "ddg_snippet": "#1 Statistical Collusion by Collectives on Learning Platforms [PDF] [Copy] [Kimi 1] [REL] Authors: Etienne Gauthier , Francis Bach , Michael Jordan", "subpage_snippet": "", "source": "papers.cool", "link": "https://papers.cool/venue/46yLEXtav4@OpenReview", "content": "#1 Statistical Collusion by Collectives on Learning Platforms [PDF] [Copy] [Kimi 1] [REL] Authors: Etienne Gauthier , Francis Bach , Michael Jordan"}
+{"idx": 6, "title": "PDF Statistical Collusion by Collectives on Learning Platforms", "date": "", "ddg_snippet": "Statistical Collusion by Collectives on Learning Platforms Etienne Gauthier , Francis Bach , Michael I. Jordan (INRIA, Ecole Normale Supérieure) Numerous examples of collectives emerging to strategically influence platforms Uber drivers deactivate the app to create a supply shortage and drive up prices", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/media/icml-2025/Slides/47263.pdf", "content": "Statistical Collusion by Collectives on Learning Platforms Etienne Gauthier , Francis Bach , Michael I. Jordan (INRIA, Ecole Normale Supérieure) Numerous examples of collectives emerging to strategically influence platforms Uber drivers deactivate the app to create a supply shortage and drive up prices"}
+{"idx": 7, "title": "Statistical Collusion by Collectives on Learning Platforms", "date": "", "ddg_snippet": "Statistical Collusion by Collectives on Learning Platforms Etienne Gauthier 1 Francis Bach 1 Michael I. Jordan 1 2", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2502.04879", "content": "Statistical Collusion by Collectives on Learning Platforms Etienne Gauthier 1 Francis Bach 1 Michael I. Jordan 1 2"}
+{"idx": 8, "title": "[论文审查] Statistical Collusion by Collectives on Learning Platforms", "date": "", "ddg_snippet": "这篇论文由 Etienne Gauthier、Francis Bach和Michael I. Jordan 撰写，主要探讨了在学习平台上，集体如何通过协调行为影响系统以符合其自身利益的问题。随着各种平台越来越依赖于学习算法，可能会形成由多个个体组成的集体，寻求共同的策略来影响这些平台，该过程包括修改数据以实现他们的目标 ...", "subpage_snippet": "", "source": "www.themoonlight.io", "link": "https://www.themoonlight.io/zh/review/statistical-collusion-by-collectives-on-learning-platforms", "content": "这篇论文由 Etienne Gauthier、Francis Bach和Michael I. Jordan 撰写，主要探讨了在学习平台上，集体如何通过协调行为影响系统以符合其自身利益的问题。随着各种平台越来越依赖于学习算法，可能会形成由多个个体组成的集体，寻求共同的策略来影响这些平台，该过程包括修改数据以实现他们的目标 ..."}
+{"idx": 9, "title": "Publications - University of California, Berkeley", "date": "", "ddg_snippet": "Statistical collusion by collectives on learning platforms . E. Gauthier , F. Bach , and M. I. Jordan . International Conference on Machine Learning (ICML), 2025. Prediction-aware learning in multi-agent systems. A. Capitaine, E. Boursier, E. Moulines, M. I. Jordan , and A. Durmus. International Conference on Machine Learning (ICML), 2025.", "subpage_snippet": "", "source": "people.eecs.berkeley.edu", "link": "https://people.eecs.berkeley.edu/~jordan/publications.html", "content": "Statistical collusion by collectives on learning platforms . E. Gauthier , F. Bach , and M. I. Jordan . International Conference on Machine Learning (ICML), 2025. Prediction-aware learning in multi-agent systems. A. Capitaine, E. Boursier, E. Moulines, M. I. Jordan , and A. Durmus. International Conference on Machine Learning (ICML), 2025."}
diff --git a/data/sampled_jsons/Evaluating_Generative_AI_Systems_Is_a_Social_Science_Measurement_Challenge_Section_6_criticism_frame.jsonl b/data/sampled_jsons/Evaluating_Generative_AI_Systems_Is_a_Social_Science_Measurement_Challenge_Section_6_criticism_frame.jsonl
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index 0000000000000000000000000000000000000000..c0303c9432989f17ca981ad1317c2c72a278b75d
--- /dev/null
+++ b/data/sampled_jsons/Evaluating_Generative_AI_Systems_Is_a_Social_Science_Measurement_Challenge_Section_6_criticism_frame.jsonl
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+{"idx": 0, "title": "Evaluating Generative AI Systems is a Social Science Measurement Challenge", "date": "", "ddg_snippet": "Across academia, industry, and government, there is an increasing awareness that the measurement tasks involved in evaluating generative AI (GenAI) systems are especially difficult. We argue that these measurement tasks are highly reminiscent of measurement tasks found throughout the social sciences . With this in mind, we present a framework , grounded in measurement theory from the social ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2411.10939", "content": "Across academia, industry, and government, there is an increasing awareness that the measurement tasks involved in evaluating generative AI (GenAI) systems are especially difficult. We argue that these measurement tasks are highly reminiscent of measurement tasks found throughout the social sciences . With this in mind, we present a framework , grounded in measurement theory from the social ..."}
+{"idx": 1, "title": "Evaluating Generative AI Systems is a So...", "date": "", "ddg_snippet": "The valid measurement of generative AI (GenAI) systems' capabilities, risks, and impacts forms the bedrock of our ability to evaluate these systems . We introduce a shared standard for valid measurement that helps place many of the disparate-seeming evaluation practices in use today on a common footing. Our framework , grounded in measurement theory from the social sciences , extends the work of ...", "subpage_snippet": "", "source": "axi.lims.ac.uk", "link": "https://axi.lims.ac.uk/paper/2411.10939", "content": "The valid measurement of generative AI (GenAI) systems' capabilities, risks, and impacts forms the bedrock of our ability to evaluate these systems . We introduce a shared standard for valid measurement that helps place many of the disparate-seeming evaluation practices in use today on a common footing. Our framework , grounded in measurement theory from the social sciences , extends the work of ..."}
+{"idx": 2, "title": "Evaluating Generative AI Systems is a Social Science Measurement Challenge", "date": "", "ddg_snippet": "Across academia, industry, and government, there is an increasing awareness that the measurement tasks involved in evaluating generative AI (GenAI) systems are especially difficult. We argue that these measurement tasks are highly reminiscent of measurement tasks found throughout the social sciences . With this in mind, we present a framework , grounded in measurement theory from the […]", "subpage_snippet": "", "source": "www.microsoft.com", "link": "https://www.microsoft.com/en-us/research/publication/evaluating-generative-ai-systems-is-a-social-science-measurement-challenge/", "content": "Across academia, industry, and government, there is an increasing awareness that the measurement tasks involved in evaluating generative AI (GenAI) systems are especially difficult. We argue that these measurement tasks are highly reminiscent of measurement tasks found throughout the social sciences . With this in mind, we present a framework , grounded in measurement theory from the […]"}
+{"idx": 3, "title": "Toward an Evaluation Science for Generative AI Systems", "date": "", "ddg_snippet": "While generative AI creates unique challenges for system safety engineering and measurement science , the field can draw valuable insights from the development of safety evaluation practices in other fields, including transportation, aerospace, and pharmaceutical engineering.", "subpage_snippet": "", "source": "www.nae.edu", "link": "https://www.nae.edu/19579/19582/21020/337862/338231/Toward-an-Evaluation-Science-for-Generative-AI-Systems", "content": "While generative AI creates unique challenges for system safety engineering and measurement science , the field can draw valuable insights from the development of safety evaluation practices in other fields, including transportation, aerospace, and pharmaceutical engineering."}
+{"idx": 4, "title": "Evaluating the Social Impact of Generative AI Systems in Systems and ...", "date": "", "ddg_snippet": "Generative AI systems across modalities, ranging from text (including code), image, audio, and video, have broad social impacts, but there is no official standard for means of evaluating those ...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/381796100_Evaluating_the_Social_Impact_of_Generative_AI_Systems_in_Systems_and_Society", "content": "Generative AI systems across modalities, ranging from text (including code), image, audio, and video, have broad social impacts, but there is no official standard for means of evaluating those ..."}
+{"idx": 5, "title": "Evaluating social and ethical risks from generative AI", "date": "", "ddg_snippet": "Introducing a context-based framework for comprehensively evaluating the social and ethical risks of AI systems Generative AI systems are already being used to write books, create graphic designs, assist medical practitioners, and are becoming increasingly capable. Ensuring these systems are developed and deployed responsibly requires carefully evaluating the potential ethical and social risks ...", "subpage_snippet": "", "source": "deepmind.google", "link": "https://deepmind.google/discover/blog/evaluating-social-and-ethical-risks-from-generative-ai/", "content": "Introducing a context-based framework for comprehensively evaluating the social and ethical risks of AI systems Generative AI systems are already being used to write books, create graphic designs, assist medical practitioners, and are becoming increasingly capable. Ensuring these systems are developed and deployed responsibly requires carefully evaluating the potential ethical and social risks ..."}
+{"idx": 6, "title": "Position: Evaluating Generative AI Systems Is a Social Science ...", "date": "", "ddg_snippet": "Specifically, our position is that evaluating GenAI systems is a social science measurement challenge . We present a four-level framework , grounded in measurement theory from the social sciences , for measuring concepts related to the capabilities, behaviors, and impacts of GenAI systems .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2502.00561", "content": "Specifically, our position is that evaluating GenAI systems is a social science measurement challenge . We present a four-level framework , grounded in measurement theory from the social sciences , for measuring concepts related to the capabilities, behaviors, and impacts of GenAI systems ."}
+{"idx": 7, "title": "A Shared Standard for Valid Measurement of Generative AI Systems ...", "date": "", "ddg_snippet": "The valid measurement of generative AI (GenAI) systems' capabilities, risks, and impacts forms the bedrock of our ability to evaluate these systems . We introduce a shared standard for valid measurement that helps place many of the disparate-seeming evaluation practices in use today on a common footing. Our framework , grounded in measurement theory from the social […]", "subpage_snippet": "", "source": "www.microsoft.com", "link": "https://www.microsoft.com/en-us/research/publication/a-shared-standard-for-valid-measurement-of-generative-ai-systems-capabilities-risks-and-impacts/", "content": "The valid measurement of generative AI (GenAI) systems' capabilities, risks, and impacts forms the bedrock of our ability to evaluate these systems . We introduce a shared standard for valid measurement that helps place many of the disparate-seeming evaluation practices in use today on a common footing. Our framework , grounded in measurement theory from the social […]"}
+{"idx": 8, "title": "Position: Evaluating Generative AI Systems Is a Social Science ...", "date": "", "ddg_snippet": "In this position paper, we argue that the ML community would benefit from learning from and drawing on the social sciences when developing and using mea-surement instruments for evaluating GenAI sys-tems . Specifically, our position is that evaluating GenAI systems is a social science measurement challenge .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2502.00561", "content": "In this position paper, we argue that the ML community would benefit from learning from and drawing on the social sciences when developing and using mea-surement instruments for evaluating GenAI sys-tems . Specifically, our position is that evaluating GenAI systems is a social science measurement challenge ."}
+{"idx": 9, "title": "Evaluating Generative AI Systems is a Social Science Measurement Challenge", "date": "", "ddg_snippet": "Abstract Across academia, industry, and government, there is an increasing awareness that the measurement tasks involved in evaluating generative AI (GenAI) systems are especially dificult. We argue that these measurement tasks are highly reminiscent of measurement tasks found throughout the social sciences . With this in mind, we present a framework , grounded in measurement theory from the ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2411.10939", "content": "Abstract Across academia, industry, and government, there is an increasing awareness that the measurement tasks involved in evaluating generative AI (GenAI) systems are especially dificult. We argue that these measurement tasks are highly reminiscent of measurement tasks found throughout the social sciences . With this in mind, we present a framework , grounded in measurement theory from the ..."}
diff --git a/data/sampled_jsons/Evaluating_Generative_AI_Systems_social_science_measurement_Section_6_lot_of_work_computer_science_h.jsonl b/data/sampled_jsons/Evaluating_Generative_AI_Systems_social_science_measurement_Section_6_lot_of_work_computer_science_h.jsonl
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index 0000000000000000000000000000000000000000..24c3944322e17e3c4a13b6655bcba65628972962
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+{"idx": 0, "title": "Evaluating Generative AI Systems is a Social Science", "date": "", "ddg_snippet": "... academia, industry, and government, there is an increasing awareness that the measurement tasks involved in evaluating generative AI (GenAI) systems ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2411.10939v1", "content": "... academia, industry, and government, there is an increasing awareness that the measurement tasks involved in evaluating generative AI (GenAI) systems ..."}
+{"idx": 1, "title": "Position: Evaluating Generative AI Systems is a Social Science", "date": "", "ddg_snippet": "Position: Evaluating Generative AI Systems is a Social Science Measurement Challenge ... measurement tasks involved in evaluating generative AI ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2502.00561v1", "content": "Position: Evaluating Generative AI Systems is a Social Science Measurement Challenge ... measurement tasks involved in evaluating generative AI ..."}
+{"idx": 2, "title": "Generative AI and Jobs: A global analysis of potential effects", "date": "", "ddg_snippet": "While it is impossible to predict how generative AI will further develop, the current capabilities and future potential of this technology are ...", "subpage_snippet": "", "source": "webapps.ilo.org", "link": "https://webapps.ilo.org/static/english/intserv/working-papers/wp096/index.html", "content": "While it is impossible to predict how generative AI will further develop, the current capabilities and future potential of this technology are ..."}
+{"idx": 3, "title": "Report of the 1st Workshop on Generative AI and Law", "date": "", "ddg_snippet": "A concrete research agenda to promote collaboration and progress on emerging issues at the intersection of Generative AI and law ( Section 6 ).", "subpage_snippet": "", "source": "blog.genlaw.org", "link": "https://blog.genlaw.org/2023-full-report.html", "content": "A concrete research agenda to promote collaboration and progress on emerging issues at the intersection of Generative AI and law ( Section 6 )."}
+{"idx": 4, "title": "Guide on the use of generative artificial intelligence -", "date": "", "ddg_snippet": "When deciding whether to use generative AI tools, public servants should refer to the guide to ethical decision-making ( section 6", "subpage_snippet": "", "source": "www.canada.ca", "link": "https://www.canada.ca/en/government/system/digital-government/digital-government-innovations/responsible-use-ai/guide-use-generative-ai.html", "content": "When deciding whether to use generative AI tools, public servants should refer to the guide to ethical decision-making ( section 6"}
+{"idx": 5, "title": "Correlated Errors in Large Language Models", "date": "", "ddg_snippet": "Generative diversity and mode collapse is a common worry regarding large language models (Zhang et al., 2025 ; Senthilkumar et al., 2024 ; Xu et ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2506.07962v1", "content": "Generative diversity and mode collapse is a common worry regarding large language models (Zhang et al., 2025 ; Senthilkumar et al., 2024 ; Xu et ..."}
+{"idx": 6, "title": "HumanAgencyBench: Scalable Evaluation of Human Agency Support", "date": "", "ddg_snippet": "An open-source pipeline to generate and conduct evaluations of LLMs in terms of complex social behaviors that are intractable with conventional ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2509.08494v1", "content": "An open-source pipeline to generate and conduct evaluations of LLMs in terms of complex social behaviors that are intractable with conventional ..."}
+{"idx": 7, "title": "Understanding, Protecting, and Augmenting Human Cognition with", "date": "", "ddg_snippet": "... to AI -generated output that can discourage a reflective state, students can also lack a developed skill set for prompting and critically evaluating ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2508.21036v1", "content": "... to AI -generated output that can discourage a reflective state, students can also lack a developed skill set for prompting and critically evaluating ..."}
+{"idx": 8, "title": "Towards Interactive Evaluations for Interaction Harms in", "date": "", "ddg_snippet": "Concurrently, social science research is increasingly conducting large-scale experiments with AI systems to understand their impact on human behavior ...", "subpage_snippet": "", "source": "knightcolumbia.org", "link": "https://knightcolumbia.org/content/towards-interactive-evaluations-for-interaction-harms-in-human-ai-systems", "content": "Concurrently, social science research is increasingly conducting large-scale experiments with AI systems to understand their impact on human behavior ..."}
+{"idx": 9, "title": "(PDF) A synergistic future for AI and ecology", "date": "", "ddg_snippet": "Research in both ecology and AI strives for predictive understanding of complex systems , where nonlinearities arise from multidimensional ...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/373835337_A_synergistic_future_for_AI_and_ecology", "content": "Research in both ecology and AI strives for predictive understanding of complex systems , where nonlinearities arise from multidimensional ..."}
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+{"idx": 1, "title": "Download the Opera browser for computer, phone and tablet | Opera", "date": "", "ddg_snippet": "Download the Opera browser for computer, phone and tablet. Opera for Mac, Windows, Linux, Android and iOS. Free VPN, Ad Blocker, built-in messengers.", "subpage_snippet": "", "source": "www.opera.com", "link": "https://www.opera.com/download", "content": "Download the Opera browser for computer, phone and tablet. Opera for Mac, Windows, Linux, Android and iOS. Free VPN, Ad Blocker, built-in messengers."}
+{"idx": 2, "title": "Navigateur Opera | Plus rapide, plus sûr, plus intelligent |...", "date": "", "ddg_snippet": "Plus rapide, plus sûr, plus intelligent que les navigateurs par défaut. Plein de fonctionnalités pour la vie privée, la sécurité et tellement plus encore. Obtenez gratuitement le navigateur Opera rapide et efficace.", "subpage_snippet": "", "source": "www.opera.com", "link": "https://www.opera.com/fr", "content": "Plus rapide, plus sûr, plus intelligent que les navigateurs par défaut. Plein de fonctionnalités pour la vie privée, la sécurité et tellement plus encore. Obtenez gratuitement le navigateur Opera rapide et efficace."}
+{"idx": 3, "title": "Opera Web Browser | Faster, Safer, Smarter | Opera", "date": "", "ddg_snippet": "The latest version of Opera Browser includes browser AI, Tab Islands, smooth animations and a clean modular design, delivering the most forward-thinking browsing experience to date.", "subpage_snippet": "", "source": "www.opera.com", "link": "https://www.opera.com/", "content": "The latest version of Opera Browser includes browser AI, Tab Islands, smooth animations and a clean modular design, delivering the most forward-thinking browsing experience to date."}
+{"idx": 4, "title": "Браузер Opera | Быстрее, безопаснее, умнее | Opera", "date": "", "ddg_snippet": "Быстрее, безопаснее и умнее браузеров по умолчанию. Полнофункциональный, обеспечивающий конфиденциальность, безопасность и многое другое. Получите браузер Opera бесплатно.", "subpage_snippet": "", "source": "www.opera.com", "link": "https://www.opera.com/ru", "content": "Быстрее, безопаснее и умнее браузеров по умолчанию. Полнофункциональный, обеспечивающий конфиденциальность, безопасность и многое другое. Получите браузер Opera бесплатно."}
+{"idx": 5, "title": "Opera Web Tarayıcısı | Daha Hızlı, Daha Güvenli, Daha Akıllı | Op...", "date": "", "ddg_snippet": "Varsayılan tarayıcılardan daha hızlı, daha güvenli ve daha akıllı. Gizlilik, güvenlik ve çok daha fazlası için tam özellikli. Daha hızlı, daha iyi Opera tarayıcısını ücretsiz edinin.", "subpage_snippet": "", "source": "www.opera.com", "link": "https://www.opera.com/tr", "content": "Varsayılan tarayıcılardan daha hızlı, daha güvenli ve daha akıllı. Gizlilik, güvenlik ve çok daha fazlası için tam özellikli. Daha hızlı, daha iyi Opera tarayıcısını ücretsiz edinin."}
+{"idx": 6, "title": "Navegador web Opera | Más rápido, seguro e inteligente | Opera", "date": "", "ddg_snippet": "Mereces un navegador mejor VPN gratuito, bloqueador de anuncios e intercambio de archivos Flow de Opera. Son solo algunas de las funciones imprescindibles integradas en Opera para una navegación más rápida, fluida y sin distracciones diseñadas para mejorar tu experiencia online.", "subpage_snippet": "", "source": "www.opera.com", "link": "https://www.opera.com/es", "content": "Mereces un navegador mejor VPN gratuito, bloqueador de anuncios e intercambio de archivos Flow de Opera. Son solo algunas de las funciones imprescindibles integradas en Opera para una navegación más rápida, fluida y sin distracciones diseñadas para mejorar tu experiencia online."}
+{"idx": 7, "title": "Navigateur Opera | Windows, Mac, Linux, Android, iOS | Opera", "date": "", "ddg_snippet": "Obtiens le navigateur Opera pour Windows, Mac, Linux, iOS, Android et Chromebook. Ton compte Opera gratuit te permet de synchroniser les données entre les appareils, et la fonction Flow facilite l'envoi de données entre les appareils, où que tu sois.", "subpage_snippet": "", "source": "www.opera.com", "link": "https://www.opera.com/fr/opera", "content": "Obtiens le navigateur Opera pour Windows, Mac, Linux, iOS, Android et Chromebook. Ton compte Opera gratuit te permet de synchroniser les données entre les appareils, et la fonction Flow facilite l'envoi de données entre les appareils, où que tu sois."}
+{"idx": 8, "title": "Browser Web Opera | Più veloce, più sicuro, più intelligente |...", "date": "", "ddg_snippet": "Più veloce, più sicuro e più intelligente di tutti gli altri browser predefiniti. Funzionalità complete per privacy, sicurezza e molto altro ancora. Ottieni gratuitamente Opera, il browser migliore e più veloce.", "subpage_snippet": "", "source": "www.opera.com", "link": "https://www.opera.com/it", "content": "Più veloce, più sicuro e più intelligente di tutti gli altri browser predefiniti. Funzionalità complete per privacy, sicurezza e molto altro ancora. Ottieni gratuitamente Opera, il browser migliore e più veloce."}
+{"idx": 9, "title": "Opera Air | Ton navigateur pleine conscience | Opera", "date": "", "ddg_snippet": "Navigation consciente avec Opera Air. Prenez soin de votre bien-être et restez concentré dans un navigateur puissant et sans distractions.", "subpage_snippet": "", "source": "www.opera.com", "link": "https://www.opera.com/fr/air", "content": "Navigation consciente avec Opera Air. Prenez soin de votre bien-être et restez concentré dans un navigateur puissant et sans distractions."}
diff --git a/data/sampled_jsons/Executing_your_commands_via_motion_diffusion_in_latent_space_Chen_et_al_2023_MLD_year_2023.jsonl b/data/sampled_jsons/Executing_your_commands_via_motion_diffusion_in_latent_space_Chen_et_al_2023_MLD_year_2023.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..05a84edfa4505061d08bc200cffc0007be3c91d5
--- /dev/null
+++ b/data/sampled_jsons/Executing_your_commands_via_motion_diffusion_in_latent_space_Chen_et_al_2023_MLD_year_2023.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Executing your Commands via Motion Diffusion in Latent Space", "date": "", "ddg_snippet": "Then, instead of using a diffusion model to establish the connections between the raw motion sequences and the conditional inputs, we perform a diffusion process on the motion latent space .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2212.04048", "content": "Then, instead of using a diffusion model to establish the connections between the raw motion sequences and the conditional inputs, we perform a diffusion process on the motion latent space ."}
+{"idx": 1, "title": "PDF Executing your Commands via Motion Diffusion in Latent Space", "date": "", "ddg_snippet": "Then, instead of using a diffusion model to es-tablish the connections between the raw motion sequences and the conditional inputs, we perform a diffusion process on the motion latent space .", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2023/papers/Chen_Executing_Your_Commands_via_Motion_Diffusion_in_Latent_Space_CVPR_2023_paper.pdf", "content": "Then, instead of using a diffusion model to es-tablish the connections between the raw motion sequences and the conditional inputs, we perform a diffusion process on the motion latent space ."}
+{"idx": 2, "title": "ChenFengYe/motion-latent-diffusion - GitHub", "date": "", "ddg_snippet": "Motion Latent Diffusion ( MLD ) is a text-to- motion and action-to- motion diffusion model. Our work achieves state-of-the-art motion quality and two orders of magnitude faster than previous diffusion models on raw motion data.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/ChenFengYe/motion-latent-diffusion", "content": "Motion Latent Diffusion ( MLD ) is a text-to- motion and action-to- motion diffusion model. Our work achieves state-of-the-art motion quality and two orders of magnitude faster than previous diffusion models on raw motion data."}
+{"idx": 3, "title": "Executing your Commands via Motion Diffusion in Latent Space", "date": "", "ddg_snippet": "陈欣 | Research Scientist My research interests include generative AI, human agents, 3D and human motion generation.", "subpage_snippet": "", "source": "chenxin.tech", "link": "https://chenxin.tech/publication/mld/", "content": "陈欣 | Research Scientist My research interests include generative AI, human agents, 3D and human motion generation."}
+{"idx": 4, "title": "Executing your Commands via Motion Diffusion in Latent Space", "date": "", "ddg_snippet": "Executing your Commands via Motion Diffusion in Latent Space We study a challenging task, conditional human motion generation, which produces plausible human motion sequences according to various conditional inputs, such as action classes or textual descriptors.", "subpage_snippet": "", "source": "ieeexplore.ieee.org", "link": "https://ieeexplore.ieee.org/document/10204431", "content": "Executing your Commands via Motion Diffusion in Latent Space We study a challenging task, conditional human motion generation, which produces plausible human motion sequences according to various conditional inputs, such as action classes or textual descriptors."}
+{"idx": 5, "title": "Executing your Commands via Motion Diffusion in Latent Space", "date": "", "ddg_snippet": "To efficiently synthesize plausible and diverse human motion sequences according to the conditional inputs, in -spired by the success of the diffusion model on latent space in text-to-image synthesis [56], we combine the advantages of the latent space -based and the conditional diffusion -based methods and propose a motion latent -based diffusion model ( MLD ) for human motion generation ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2212.04048v1", "content": "To efficiently synthesize plausible and diverse human motion sequences according to the conditional inputs, in -spired by the success of the diffusion model on latent space in text-to-image synthesis [56], we combine the advantages of the latent space -based and the conditional diffusion -based methods and propose a motion latent -based diffusion model ( MLD ) for human motion generation ..."}
+{"idx": 6, "title": "motion-latent-diffusion/README.md at main - GitHub", "date": "", "ddg_snippet": "[CVPR 2023 ] Executing your Commands via Motion Diffusion in Latent Space , a fast and high-quality motion diffusion model - ChenFengYe/ motion - latent - diffusion", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/ChenFengYe/motion-latent-diffusion/blob/main/README.md", "content": "[CVPR 2023 ] Executing your Commands via Motion Diffusion in Latent Space , a fast and high-quality motion diffusion model - ChenFengYe/ motion - latent - diffusion"}
+{"idx": 7, "title": "Executing your Commands via Motion Diffusion in Latent Space", "date": "", "ddg_snippet": "Figure 12. Network architecture of our conditional MLD . We explain each component in its right bottom part and the loss terms in Sec. H.1. - \" Executing your Commands via Motion Diffusion in Latent Space \"", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/Executing-your-Commands-via-Motion-Diffusion-in-Chen-Jiang/7694f004c67840d7f098b3612d4b3dabd915c116/figure/23", "content": "Figure 12. Network architecture of our conditional MLD . We explain each component in its right bottom part and the loss terms in Sec. H.1. - \" Executing your Commands via Motion Diffusion in Latent Space \""}
+{"idx": 8, "title": "Executing your Commands via Motion Diffusion in Latent Space", "date": "", "ddg_snippet": "Our proposed Motion Latent Diffusion model ( MLD ) could produce vivid motion sequences (left) conforming to the given conditional inputs and substantially reduce the computational overhead (right) in both the training and inference stages. Extensive experiments on various human motion generation tasks demonstrate that our MLD achieves significant improvements over other SOTAs among extensive ...", "subpage_snippet": "", "source": "chenxin.tech", "link": "https://chenxin.tech/mld/", "content": "Our proposed Motion Latent Diffusion model ( MLD ) could produce vivid motion sequences (left) conforming to the given conditional inputs and substantially reduce the computational overhead (right) in both the training and inference stages. Extensive experiments on various human motion generation tasks demonstrate that our MLD achieves significant improvements over other SOTAs among extensive ..."}
+{"idx": 9, "title": "ChenFengYe/motion-latent-diffusion: [CVPR 2023] Executing your Commands ...", "date": "", "ddg_snippet": "Executing your Commands via Motion Diffusion in Latent Space Project Page | Arxiv - CVPR 2023 Motion Latent Diffusion ( MLD ) is a text-to- motion and action-to- motion diffusion model. Our work achieves state-of-the-art motion quality and two orders of magnitude faster than previous diffusion models on raw motion data.", "subpage_snippet": "", "source": "gitmemories.com", "link": "https://gitmemories.com/ChenFengYe/motion-latent-diffusion", "content": "Executing your Commands via Motion Diffusion in Latent Space Project Page | Arxiv - CVPR 2023 Motion Latent Diffusion ( MLD ) is a text-to- motion and action-to- motion diffusion model. Our work achieves state-of-the-art motion quality and two orders of magnitude faster than previous diffusion models on raw motion data."}
diff --git a/data/sampled_jsons/Executing_your_commands_via_motion_diffusion_in_latent_space_filetypepdf.jsonl b/data/sampled_jsons/Executing_your_commands_via_motion_diffusion_in_latent_space_filetypepdf.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..67eb45bfbf9b5dc2a37cb5d8ec850025425b0c0c
--- /dev/null
+++ b/data/sampled_jsons/Executing_your_commands_via_motion_diffusion_in_latent_space_filetypepdf.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Executing your Commands via Motion Diffusion in Latent Space", "date": "", "ddg_snippet": "Our proposed Motion Latent - based Diffusion model (MLD) could produce vivid motion sequences conforming to the given conditional inputs and substantially reduce the computational overhead in both the training and inference stages.", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2023/papers/Chen_Executing_Your_Commands_via_Motion_Diffusion_in_Latent_Space_CVPR_2023_paper.pdf", "content": "Our proposed Motion Latent - based Diffusion model (MLD) could produce vivid motion sequences conforming to the given conditional inputs and substantially reduce the computational overhead in both the training and inference stages."}
+{"idx": 1, "title": "MotionStreamer: Streaming Motion Generation via Diffusion ...", "date": "", "ddg_snippet": "Our core innovation is incorporating a diffusion head into an autoregressive model to predict the next motion latent , while introducing a causal motion compressor to en-able online decoding in a streaming manner.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2503.15451", "content": "Our core innovation is incorporating a diffusion head into an autoregressive model to predict the next motion latent , while introducing a causal motion compressor to en-able online decoding in a streaming manner."}
+{"idx": 2, "title": "DREAMoR: Diffusion-based REconstruction And Motion prioR", "date": "", "ddg_snippet": "Our key insight is to learn a latent space of motion transitions, capturing how motion evolves over time, and train a con-ditional latent diffusion model directly in that latent space .", "subpage_snippet": "", "source": "jingfeng0705.github.io", "link": "https://jingfeng0705.github.io/DREAMoR/static/assets/DREAMoR.pdf", "content": "Our key insight is to learn a latent space of motion transitions, capturing how motion evolves over time, and train a con-ditional latent diffusion model directly in that latent space ."}
+{"idx": 3, "title": "Robot Motion Diffusion Model: Motion Generation for Robotic ...", "date": "", "ddg_snippet": "Our method enables the seamless integration of kinematic motion generators with physics-based character control and can be deployed on robots. The example shows a robot performing the prompt \"a person who performed a right-handed uppercut.\"", "subpage_snippet": "", "source": "la.disneyresearch.com", "link": "https://la.disneyresearch.com/wp-content/uploads/RobotMDM_2.pdf", "content": "Our method enables the seamless integration of kinematic motion generators with physics-based character control and can be deployed on robots. The example shows a robot performing the prompt \"a person who performed a right-handed uppercut.\""}
+{"idx": 4, "title": "Rethinking Diffusion for Text-Driven Human Motion Generation ...", "date": "", "ddg_snippet": "To further optimize the motion representations, we then project those essential features into a compact and fine-grained latent space using a motion AutoEncoder (AE).", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2025/papers/Meng_Rethinking_Diffusion_for_Text-Driven_Human_Motion_Generation_Redundant_Representations_Evaluation_CVPR_2025_paper.pdf", "content": "To further optimize the motion representations, we then project those essential features into a compact and fine-grained latent space using a motion AutoEncoder (AE)."}
+{"idx": 5, "title": "Executing your Commands via Motion Diffusion in Latent Space", "date": "", "ddg_snippet": "We propose a motion latent -based diffusion model to generate plausible human motion sequences conforming to the action classes or natural language descriptions. Com-pared to the compatible cross-modal latent space -based method, our MLD could produce more diverse and plau-sible...", "subpage_snippet": "", "source": "scispace.com", "link": "https://scispace.com/pdf/executing-your-commands-via-motion-diffusion-in-latent-space-w2yvt5un.pdf", "content": "We propose a motion latent -based diffusion model to generate plausible human motion sequences conforming to the action classes or natural language descriptions. Com-pared to the compatible cross-modal latent space -based method, our MLD could produce more diverse and plau-sible..."}
+{"idx": 6, "title": "Executing your Commands via Motion Diffusion in Latent Space", "date": "", "ddg_snippet": "Our proposed Motion Latent -based Diffusion model (MLD) could produce vivid motion sequences conforming to the given conditional inputs and substantially reduce the computational overhead in both the training and inference stages.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2212.04048", "content": "Our proposed Motion Latent -based Diffusion model (MLD) could produce vivid motion sequences conforming to the given conditional inputs and substantially reduce the computational overhead in both the training and inference stages."}
+{"idx": 7, "title": "Executing your Commands via Motion Diffusion in Latent Space", "date": "", "ddg_snippet": "Our proposed Motion Latent Diffusion model (MLD) could produce vivid motion sequences (left) conforming to the given conditional inputs and substantially reduce the computational overhead (right) in both the training and inference stages.", "subpage_snippet": "", "source": "chenxin.tech", "link": "https://chenxin.tech/mld/", "content": "Our proposed Motion Latent Diffusion model (MLD) could produce vivid motion sequences (left) conforming to the given conditional inputs and substantially reduce the computational overhead (right) in both the training and inference stages."}
+{"idx": 8, "title": "ChenFengYe/ motion - latent - diffusion : [CVPR 2023] Executing your ...", "date": "", "ddg_snippet": "[CVPR 2023] Executing your Commands via Motion Diffusion in Latent Space , a fast and high-quality motion diffusion model.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/ChenFengYe/motion-latent-diffusion", "content": "[CVPR 2023] Executing your Commands via Motion Diffusion in Latent Space , a fast and high-quality motion diffusion model."}
+{"idx": 9, "title": "motion - latent - diffusion | Ecosystem Directory | market.dev", "date": "", "ddg_snippet": "motion - latent - diffusion . Compare To View Code on GitHub. [CVPR 2023] Executing your Commands via Motion Diffusion in Latent Space , a fast and high-quality motion diffusion model. MIT License Active.", "subpage_snippet": "", "source": "explore.market.dev", "link": "https://explore.market.dev/ecosystems/framer-motion/projects/chenfengye-motion-latent-diffusion", "content": "motion - latent - diffusion . Compare To View Code on GitHub. [CVPR 2023] Executing your Commands via Motion Diffusion in Latent Space , a fast and high-quality motion diffusion model. MIT License Active."}
diff --git a/data/sampled_jsons/FBox_score_equation_box_embeddings.jsonl b/data/sampled_jsons/FBox_score_equation_box_embeddings.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..2034fcfe076e1e5dcdca09301428fabef66bf514
--- /dev/null
+++ b/data/sampled_jsons/FBox_score_equation_box_embeddings.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "analysis - how to solve the system of differential equations", "date": "", "ddg_snippet": "... fbox {$m\\ddot\\rho-m\\rho\\dot \\varphi^2=2\\rho\\lambda$}$$ $$\\ fbox {$m\\ddot z+mg=-a\\lambda $}$$ $$\\ fbox {$ \\frac{d}{dt}\\left(\\rho^2\\dot\\varphi\\right)=0$}$$ ...", "subpage_snippet": "", "source": "math.stackexchange.com", "link": "https://math.stackexchange.com/questions/958377/how-to-solve-the-system-of-differential-equations-for-this-particle", "content": "... fbox {$m\\ddot\\rho-m\\rho\\dot \\varphi^2=2\\rho\\lambda$}$$ $$\\ fbox {$m\\ddot z+mg=-a\\lambda $}$$ $$\\ fbox {$ \\frac{d}{dt}\\left(\\rho^2\\dot\\varphi\\right)=0$}$$ ..."}
+{"idx": 1, "title": "Newest 'framed' Questions - TeX - LaTeX Stack Exchange", "date": "", "ddg_snippet": "I've been looking around for coding to put a box around a paragraph in LaTeX but I can't seem to get any of them to work.", "subpage_snippet": "", "source": "tex.stackexchange.com", "link": "https://tex.stackexchange.com/questions/tagged/framed", "content": "I've been looking around for coding to put a box around a paragraph in LaTeX but I can't seem to get any of them to work."}
+{"idx": 2, "title": "Newest 'nesting' Questions - TeX - LaTeX Stack Exchange", "date": "", "ddg_snippet": "... Score ... Highest score ... Nesting multiple equations", "subpage_snippet": "", "source": "tex.stackexchange.com", "link": "https://tex.stackexchange.com/questions/tagged/nesting", "content": "... Score ... Highest score ... Nesting multiple equations"}
+{"idx": 3, "title": "lstlisting - Can't put Python listing in LaTeX - Stack", "date": "", "ddg_snippet": "The \\ fbox \\parbox works well with text and equations in it, so the issue does not come from there. ... avoid the problem with manually breaking boxes ...", "subpage_snippet": "", "source": "stackoverflow.com", "link": "https://stackoverflow.com/questions/63662805/cant-put-python-listing-in-latex", "content": "The \\ fbox \\parbox works well with text and equations in it, so the issue does not come from there. ... avoid the problem with manually breaking boxes ..."}
+{"idx": 4, "title": "Newest 'mathjax' Questions - Mathematics Meta Stack", "date": "", "ddg_snippet": "... markdown editor to write questions or answers, I see this problem with Mathjax's display math mode (in preview mode only): In multi-line equations ...", "subpage_snippet": "", "source": "math.meta.stackexchange.com", "link": "https://math.meta.stackexchange.com/questions/tagged/mathjax", "content": "... markdown editor to write questions or answers, I see this problem with Mathjax's display math mode (in preview mode only): In multi-line equations ..."}
+{"idx": 5, "title": "Putnam-AXIOM: A Functional & Static Benchmark for Measuring", "date": "", "ddg_snippet": "We complement (”boxed”) accuracy with Teacher-Forced Accuracy (TFA), a lightweight metric that directly scores reasoning traces and automates ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2508.08292v1", "content": "We complement (”boxed”) accuracy with Teacher-Forced Accuracy (TFA), a lightweight metric that directly scores reasoning traces and automates ..."}
+{"idx": 6, "title": "US10460320B1 - Fraud detection in heterogeneous information", "date": "", "ddg_snippet": "9A, 9B, 9C, and 9D illustrates embodiments of graphs of accuracy, recall, precision, and F- score results, respectively, for a variety of fraud ...", "subpage_snippet": "", "source": "patents.google.com", "link": "https://patents.google.com/patent/US10460320B1/en", "content": "9A, 9B, 9C, and 9D illustrates embodiments of graphs of accuracy, recall, precision, and F- score results, respectively, for a variety of fraud ..."}
+{"idx": 7, "title": "US11190534B1 - Level of network suspicion detection - Google", "date": "", "ddg_snippet": "... tablet, ultrabook, netbook, laptop, multi-processor system, microprocessor-based or programmable consumer electronic, game console, set- top box ...", "subpage_snippet": "", "source": "patents.google.com", "link": "https://patents.google.com/patent/US11190534B1/en", "content": "... tablet, ultrabook, netbook, laptop, multi-processor system, microprocessor-based or programmable consumer electronic, game console, set- top box ..."}
+{"idx": 8, "title": "macros - Define a command (possibly recursively) to wrap text", "date": "", "ddg_snippet": "I'm trying to define a new command \\multiboxed in LaTeX so that I can put n boxes around an equation without having to manually nest \\boxed commands.", "subpage_snippet": "", "source": "tex.stackexchange.com", "link": "https://tex.stackexchange.com/questions/83157/define-a-command-possibly-recursively-to-wrap-text-in-a-command-n-times", "content": "I'm trying to define a new command \\multiboxed in LaTeX so that I can put n boxes around an equation without having to manually nest \\boxed commands."}
+{"idx": 9, "title": "theory - Why do quartal progressions typically not utilize", "date": "", "ddg_snippet": "I think it s also important not to equate Parallelism with Quartal vocabulary. ... a) is embedding a 2nd inversion E Major chord; (b) is embedding a ...", "subpage_snippet": "", "source": "music.stackexchange.com", "link": "https://music.stackexchange.com/questions/30050/why-do-quartal-progressions-typically-not-utilize-common-tones", "content": "I think it s also important not to equate Parallelism with Quartal vocabulary. ... a) is embedding a 2nd inversion E Major chord; (b) is embedding a ..."}
diff --git a/data/sampled_jsons/Face_X-ray_for_More_General_Face_Forgery_Detection.jsonl b/data/sampled_jsons/Face_X-ray_for_More_General_Face_Forgery_Detection.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..ce674f2dc71ab0c0105fd57b22b9eb7ace84f431
--- /dev/null
+++ b/data/sampled_jsons/Face_X-ray_for_More_General_Face_Forgery_Detection.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Face X-ray for More General Face Forgery Detection", "date": "", "ddg_snippet": "In this paper we propose a novel image representation called face X-ray for detecting forgery in face images. The face X-ray of an input face image is a greyscale image that reveals whether the input image can be decomposed into the blending of two images from different sources. It does so by showing the blending boundary for a forged image and the absence of blending for a real image. We ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/1912.13458", "content": "In this paper we propose a novel image representation called face X-ray for detecting forgery in face images. The face X-ray of an input face image is a greyscale image that reveals whether the input image can be decomposed into the blending of two images from different sources. It does so by showing the blending boundary for a forged image and the absence of blending for a real image. We ..."}
+{"idx": 1, "title": "PDF Face X-Ray for More General Face Forgery Detection", "date": "", "ddg_snippet": "Face X-ray reveals the blending boundaries in forged face images and returns a blank image for real images. It is a general and effective method for detecting face manipulation by most existing algorithms, without relying on specific artifacts or fake images.", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content_CVPR_2020/papers/Li_Face_X-Ray_for_More_General_Face_Forgery_Detection_CVPR_2020_paper.pdf", "content": "Face X-ray reveals the blending boundaries in forged face images and returns a blank image for real images. It is a general and effective method for detecting face manipulation by most existing algorithms, without relying on specific artifacts or fake images."}
+{"idx": 2, "title": "GitHub - nftport/face-x-ray: This repo contains code for [Face X-ray ...", "date": "", "ddg_snippet": "Face-X-ray This repo contains code for Face X-ray for More General Face Forgery Detection . Modifications - The authors have not explicitly defined the image segmentation network, here we have used DeepLabV3Plus with efficientnet-b6 backbone. The model achieves excellent accuracy for faceswapped deepfakes.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/nftport/face-x-ray", "content": "Face-X-ray This repo contains code for Face X-ray for More General Face Forgery Detection . Modifications - The authors have not explicitly defined the image segmentation network, here we have used DeepLabV3Plus with efficientnet-b6 backbone. The model achieves excellent accuracy for faceswapped deepfakes."}
+{"idx": 3, "title": "Face X-Ray for More General Face Forgery Detection - IEEE Xplore", "date": "", "ddg_snippet": "In this paper we propose a novel image representation called face X-ray for detecting forgery in face images. The face X-ray of an input face image is a greyscale image that reveals whether the input image can be decomposed into the blending of two images from different sources. It does so by showing the blending boundary for a forged image and the absence of blending for a real image. We ...", "subpage_snippet": "", "source": "ieeexplore.ieee.org", "link": "https://ieeexplore.ieee.org/document/9157215", "content": "In this paper we propose a novel image representation called face X-ray for detecting forgery in face images. The face X-ray of an input face image is a greyscale image that reveals whether the input image can be decomposed into the blending of two images from different sources. It does so by showing the blending boundary for a forged image and the absence of blending for a real image. We ..."}
+{"idx": 4, "title": "Face X-Ray for More General Face Forgery Detection", "date": "", "ddg_snippet": "Face X-Ray reveals whether a face image can be decomposed into the blending of two images from different sources. It is general and effective for detecting forgery generated by most existing face manipulation algorithms, without relying on any specific artifacts.", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content_CVPR_2020/html/Li_Face_X-Ray_for_More_General_Face_Forgery_Detection_CVPR_2020_paper.html", "content": "Face X-Ray reveals whether a face image can be decomposed into the blending of two images from different sources. It is general and effective for detecting forgery generated by most existing face manipulation algorithms, without relying on any specific artifacts."}
+{"idx": 5, "title": "Daisy-Zhang/Face-X-ray - GitHub", "date": "", "ddg_snippet": "Face_X-ray This is an unofficial implementation of Lingzhi Li, Jianmin Bao, Ting Zhang, Hao Yang, Dong Chen, Fang Wen, Baining Guo: Face X-Ray for More General Face Forgery Detection . CVPR 2020: 5000-5009.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/Daisy-Zhang/Face-X-ray", "content": "Face_X-ray This is an unofficial implementation of Lingzhi Li, Jianmin Bao, Ting Zhang, Hao Yang, Dong Chen, Fang Wen, Baining Guo: Face X-Ray for More General Face Forgery Detection . CVPR 2020: 5000-5009."}
+{"idx": 6, "title": "Face X-ray for More General Face Forgery Detection - arXiv.org", "date": "", "ddg_snippet": "rithm for computing face X-ray can be trained images generated by any of the state-of-the-art ulation methods. Extensive experiments show that face X-ray remains effective when applied to forgery generated by unseen face manipulation techniques, while most existing face forgery detection algorithms experience a significant performance drop.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/1912.13458v1", "content": "rithm for computing face X-ray can be trained images generated by any of the state-of-the-art ulation methods. Extensive experiments show that face X-ray remains effective when applied to forgery generated by unseen face manipulation techniques, while most existing face forgery detection algorithms experience a significant performance drop."}
+{"idx": 7, "title": "The author's unofficial PyTorch re-implementation of Face Xray", "date": "", "ddg_snippet": "Face-X-ray The author's unofficial PyTorch re-implementation of Face Xray This repo contains code for the BI data generation pipeline from Face X-ray for More General Face Forgery Detection by Lingzhi Li, Jianmin Bao, Ting Zhang, Hao Yang, Dong Chen, Fang Wen, Baining Guo.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/AlgoHunt/Face-Xray", "content": "Face-X-ray The author's unofficial PyTorch re-implementation of Face Xray This repo contains code for the BI data generation pipeline from Face X-ray for More General Face Forgery Detection by Lingzhi Li, Jianmin Bao, Ting Zhang, Hao Yang, Dong Chen, Fang Wen, Baining Guo."}
+{"idx": 8, "title": "GitHub - wkq-wukaiqi/Face-X-Ray: An unofficial implementation of ...", "date": "", "ddg_snippet": "Face-X-Ray An unofficial implementation of Lingzhi Li, Jianmin Bao, Ting Zhang, Hao Yang, Dong Chen, Fang Wen, Baining Guo: Face X-Ray for More General Face Forgery Detection . CVPR 2020.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/wkq-wukaiqi/Face-X-Ray", "content": "Face-X-Ray An unofficial implementation of Lingzhi Li, Jianmin Bao, Ting Zhang, Hao Yang, Dong Chen, Fang Wen, Baining Guo: Face X-Ray for More General Face Forgery Detection . CVPR 2020."}
+{"idx": 9, "title": "Facial Recognition Algorithms: A Systematic Literature Review", "date": "", "ddg_snippet": "This systematic literature review aims to understand new developments and challenges in facial recognition technology. This will provide an understanding of the system principles, performance metrics, and applications of facial recognition ...", "subpage_snippet": "", "source": "pmc.ncbi.nlm.nih.gov", "link": "https://pmc.ncbi.nlm.nih.gov/articles/PMC11856072/", "content": "This systematic literature review aims to understand new developments and challenges in facial recognition technology. This will provide an understanding of the system principles, performance metrics, and applications of facial recognition ..."}
diff --git a/data/sampled_jsons/Fake_it_till_you_make_it_ImageNet-100_6.5M.jsonl b/data/sampled_jsons/Fake_it_till_you_make_it_ImageNet-100_6.5M.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..df4d75ded4ab7ad0a3c372f83d3be662920d3ef3
--- /dev/null
+++ b/data/sampled_jsons/Fake_it_till_you_make_it_ImageNet-100_6.5M.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Fake It Until We Make It | TikTok", "date": "", "ddg_snippet": "\" Fake it till you make it \" (or \" Fake it until you make it \") is an aphorism that suggests that by imitating confidence, competence, and an optimistic mindset, a person can realize those qualities in their real life and achieve the results they seek.[1][2][3].", "subpage_snippet": "", "source": "www.tiktok.com", "link": "https://www.tiktok.com/discover/fake-it-until-we-make-it", "content": "\" Fake it till you make it \" (or \" Fake it until you make it \") is an aphorism that suggests that by imitating confidence, competence, and an optimistic mindset, a person can realize those qualities in their real life and achieve the results they seek.[1][2][3]."}
+{"idx": 1, "title": "Fake It Till You Make It Drama Chinese | TikTok", "date": "", "ddg_snippet": "Final Fake It Till We Make It Complete. Genuine or Fake Chinese Drama.Episode 80 | #FakeItTillYouMakeIt \"Pretending is also a kind of strength.” Fake It Till You Make It is not pretending anymore as it comes to an end.", "subpage_snippet": "", "source": "www.tiktok.com", "link": "https://www.tiktok.com/discover/fake-it-till-you-make-it-drama-chinese", "content": "Final Fake It Till We Make It Complete. Genuine or Fake Chinese Drama.Episode 80 | #FakeItTillYouMakeIt \"Pretending is also a kind of strength.” Fake It Till You Make It is not pretending anymore as it comes to an end."}
+{"idx": 2, "title": "hannn (@hannahgausepohl) / Twitter", "date": "", "ddg_snippet": "it’s so annoying when you’re sleeping and you wake up.@hannahgausepohl. · Dec 31, 2019. fake it till you make it . hannn. @hannahgausepohl.", "subpage_snippet": "", "source": "twitter.com", "link": "https://twitter.com/hannahgausepohl", "content": "it’s so annoying when you’re sleeping and you wake up.@hannahgausepohl. · Dec 31, 2019. fake it till you make it . hannn. @hannahgausepohl."}
+{"idx": 3, "title": "TinySubNets: An efficient and low capacity continual learning ...", "date": "", "ddg_snippet": "It can be observed that the performance varies significantly depending on the task (e.g., on Imagenet-100 , the perfor-mance varies from 68.9% in task 5 to 83.8% in task 7).", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2412.10869v3", "content": "It can be observed that the performance varies significantly depending on the task (e.g., on Imagenet-100 , the perfor-mance varies from 68.9% in task 5 to 83.8% in task 7)."}
+{"idx": 4, "title": "Daily Papers - Hugging Face", "date": "", "ddg_snippet": "All our datasets and a collection of over 100 human-written prompts are integrated into LM Evaluation Harness, ensuring flexible and reproducible evaluation. We describe the NorEval design and present the results of benchmarking 19 open-source pre-trained and instruction-tuned LMs for Norwegian in various scenarios.", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/papers?q=evaluation+suites", "content": "All our datasets and a collection of over 100 human-written prompts are integrated into LM Evaluation Harness, ensuring flexible and reproducible evaluation. We describe the NorEval design and present the results of benchmarking 19 open-source pre-trained and instruction-tuned LMs for Norwegian in various scenarios."}
+{"idx": 5, "title": "Fugu-MT 論文翻訳 (概要): A Comparative Study of YOLOv8 to YOLOv11...", "date": "", "ddg_snippet": "バニラ/階層設計 (5.7M$/ 6.5M $)による純軽量ViTの蒸留による事前トレーニングは、 ImageNet -1で79.4%$/78.9%の精度で達成できる。", "subpage_snippet": "", "source": "fugumt.com", "link": "https://fugumt.com/fugumt/paper_check/2509.12682v1", "content": "バニラ/階層設計 (5.7M$/ 6.5M $)による純軽量ViTの蒸留による事前トレーニングは、 ImageNet -1で79.4%$/78.9%の精度で達成できる。"}
+{"idx": 6, "title": "Schedule: All sessions: O'Reilly Artificial Intelligence", "date": "", "ddg_snippet": "Its easy-to-use API and seamless use of GPUs make it a sought-after tool for deep learning. ... Garrett Hoffman walks you through deep learning ...", "subpage_snippet": "", "source": "conferences.oreilly.com", "link": "https://conferences.oreilly.com/artificial-intelligence/ai-ny-2019/public/schedule/full/public.html", "content": "Its easy-to-use API and seamless use of GPUs make it a sought-after tool for deep learning. ... Garrett Hoffman walks you through deep learning ..."}
+{"idx": 7, "title": "DDAE++: Enhancing Diffusion Models Towards Unified Generative", "date": "", "ddg_snippet": "This makes our study, namely DDAE++, as broadly applicable as the original DDAE [ 68 ] for regular diffusion models.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2505.10999v1", "content": "This makes our study, namely DDAE++, as broadly applicable as the original DDAE [ 68 ] for regular diffusion models."}
+{"idx": 8, "title": "FastVLM: Efficient Vision Encoding for Vision Language Models", "date": "", "ddg_snippet": "Abstract Scaling the input image resolution is essential for enhancing the performance of Vision Language Models (VLMs), particularly in text-rich image understanding tasks. However, popular visual encoders such as ViTs become inefficient at high resolutions due to the large number of tokens and high encoding latency caused by stacked self-attention layers. At different operational resolutions ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2412.13303v1", "content": "Abstract Scaling the input image resolution is essential for enhancing the performance of Vision Language Models (VLMs), particularly in text-rich image understanding tasks. However, popular visual encoders such as ViTs become inefficient at high resolutions due to the large number of tokens and high encoding latency caused by stacked self-attention layers. At different operational resolutions ..."}
+{"idx": 9, "title": "QNet: Mumbai police register fraud case against the MLM", "date": "", "ddg_snippet": "According to a report in Times of India, the economic offenses wing (EoW) of Mumbai police has registered against QNet, for allegedly duping thousands of investors by selling them plastic and glass products terming them miraculous objects for treating severe diseases like Cancer.", "subpage_snippet": "", "source": "www.moneylife.in", "link": "https://www.moneylife.in/article/qnet-mumbai-police-register-fraud-case-against-the-mlm/34105/44129.html", "content": "According to a report in Times of India, the economic offenses wing (EoW) of Mumbai police has registered against QNet, for allegedly duping thousands of investors by selling them plastic and glass products terming them miraculous objects for treating severe diseases like Cancer."}
diff --git a/data/sampled_jsons/Fan_et_al.,_2024_machine_learning.jsonl b/data/sampled_jsons/Fan_et_al.,_2024_machine_learning.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..7a335c9b4133e2a38eec4f6e99ae2b34c03905cf
--- /dev/null
+++ b/data/sampled_jsons/Fan_et_al.,_2024_machine_learning.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Harnessing Machine Learning and Deep Learning for...", "date": "", "ddg_snippet": "Chandan RR et al ( 2024 ) Reviewing the impact of machine learning on disease diagnosis and prognosis: a comprehensive analysis.", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/article/10.1007/s11831-025-10367-9", "content": "Chandan RR et al ( 2024 ) Reviewing the impact of machine learning on disease diagnosis and prognosis: a comprehensive analysis."}
+{"idx": 1, "title": "Amun: a dversarial M achine un learning", "date": "", "ddg_snippet": "Early works in machine unlearning focused on exact solutions Cao and Yang [2015], Bourtoule et al .[ 2024 ], Fan et al . [ 2024 ], we performed 10 epochs for each of the unlearning methods, and searched for best learning rate and number of steps for a learning rate scheduler.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2503.00917", "content": "Early works in machine unlearning focused on exact solutions Cao and Yang [2015], Bourtoule et al .[ 2024 ], Fan et al . [ 2024 ], we performed 10 epochs for each of the unlearning methods, and searched for best learning rate and number of steps for a learning rate scheduler."}
+{"idx": 2, "title": "New machine learning model quickly and accurately predicts dielectric...", "date": "", "ddg_snippet": "More information: Tomohito Amano et al , Chemical bond based machine learning model for dipole moment: Application to dielectric properties of liquid methanol and ethanol, Physical Review B ( 2024 ).", "subpage_snippet": "", "source": "phys.org", "link": "https://phys.org/news/2024-10-machine-quickly-accurately-dielectric-function.html", "content": "More information: Tomohito Amano et al , Chemical bond based machine learning model for dipole moment: Application to dielectric properties of liquid methanol and ethanol, Physical Review B ( 2024 )."}
+{"idx": 3, "title": "Behave Differently when Clustering : A Semi-asynchronous", "date": "", "ddg_snippet": "Fan , Boyu. Association for Computing Machinery 2024 -05.ACM Reference Format: Boyu Fan , Xiang Su, Sasu Tarkoma, and Pan Hui. 2024 . Behave Differently when Clustering: A Semi-asynchronous Federated Learning Approach for IoT.", "subpage_snippet": "", "source": "helda.helsinki.fi", "link": "https://helda.helsinki.fi/server/api/core/bitstreams/ddd67f72-4a6f-454c-87f2-6443524bf733/content", "content": "Fan , Boyu. Association for Computing Machinery 2024 -05.ACM Reference Format: Boyu Fan , Xiang Su, Sasu Tarkoma, and Pan Hui. 2024 . Behave Differently when Clustering: A Semi-asynchronous Federated Learning Approach for IoT."}
+{"idx": 4, "title": "Towards the Causal Complete Cause of... | Read Paper on Bytez", "date": "", "ddg_snippet": "...Wang et al ., 2023c; Fan et al ., 2024 ) have been proposed to solve MML tasks through modality consistency, e.g., tokenize diverse modalities into sequences and utilize Transformers for joint learning (Bao et al ., 2022; Wang et al ., 2022), whereas CLIP (Radford et al ., 2021; Fan et al .", "subpage_snippet": "", "source": "bytez.com", "link": "https://bytez.com/docs/icml/46215/paper", "content": "...Wang et al ., 2023c; Fan et al ., 2024 ) have been proposed to solve MML tasks through modality consistency, e.g., tokenize diverse modalities into sequences and utilize Transformers for joint learning (Bao et al ., 2022; Wang et al ., 2022), whereas CLIP (Radford et al ., 2021; Fan et al ."}
+{"idx": 5, "title": "(PDF) A critical appraisal of water table depth estimation: Challenges...", "date": "", "ddg_snippet": "Keywords: Machine learning , physically-based models, Groundwater, Water Table Depth, North AmerIn this regard, V1 resembles Ma et al . ( 2024 )’s machine . learning simulations of US WTD, which used random forests trained on only well-based observations of.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/380299520_A_critical_appraisal_of_water_table_depth_estimation_Challenges_and_opportunities_within_machine_learning", "content": "Keywords: Machine learning , physically-based models, Groundwater, Water Table Depth, North AmerIn this regard, V1 resembles Ma et al . ( 2024 )’s machine . learning simulations of US WTD, which used random forests trained on only well-based observations of."}
+{"idx": 6, "title": "Integrating environmental and LULC drivers of groundwater droughts in...", "date": "", "ddg_snippet": "2024 ). Secci et al . (2021) found that SPEI outperforms SPI in capturing the effects of meteorological variability on groundwater.These drivers were identified and prioritized through machine learning -based feature importance analysis.", "subpage_snippet": "", "source": "ecologicalprocesses.springeropen.com", "link": "https://ecologicalprocesses.springeropen.com/articles/10.1186/s13717-025-00633-w", "content": "2024 ). Secci et al . (2021) found that SPEI outperforms SPI in capturing the effects of meteorological variability on groundwater.These drivers were identified and prioritized through machine learning -based feature importance analysis."}
+{"idx": 7, "title": "Advancing Regular Language Reasoning in Linear... - ACL Anthology", "date": "", "ddg_snippet": "Bibkey: fan - etal - 2024 -advancing. Cite (ACL): Ting-Han Fan , Ta-Chung Chi, and Alexander Rudnicky.", "subpage_snippet": "", "source": "aclanthology.org", "link": "https://aclanthology.org/2024.naacl-short.4/", "content": "Bibkey: fan - etal - 2024 -advancing. Cite (ACL): Ting-Han Fan , Ta-Chung Chi, and Alexander Rudnicky."}
+{"idx": 8, "title": "Frontiers | Estimation of the air conditioning energy consumption of...", "date": "", "ddg_snippet": "An example is using machine learning and simulation to explain air conditioning in buildings (Duhirwe et al ., 2024 ). Vergés et al . ( 2024 ) used neural networks to evaluate the energy implications of HVAC systems in nursing homes during the cooling season.", "subpage_snippet": "", "source": "www.frontiersin.org", "link": "https://www.frontiersin.org/journals/big-data/articles/10.3389/fdata.2025.1520574/full", "content": "An example is using machine learning and simulation to explain air conditioning in buildings (Duhirwe et al ., 2024 ). Vergés et al . ( 2024 ) used neural networks to evaluate the energy implications of HVAC systems in nursing homes during the cooling season."}
+{"idx": 9, "title": "Epigenetic Impacts of Trauma and Environmental... | OxJournal", "date": "", "ddg_snippet": "Blood-based methylation panels have been developed to predict anxiety disorders and treatment response, with Kwon et al . (2025) identifying seventeen novel biomarkers validated by machine - learning approaches.", "subpage_snippet": "", "source": "www.oxjournal.org", "link": "https://www.oxjournal.org/epigenetic-impacts-of-trauma-and-environmental-stressors-on-mental-health/", "content": "Blood-based methylation panels have been developed to predict anxiety disorders and treatment response, with Kwon et al . (2025) identifying seventeen novel biomarkers validated by machine - learning approaches."}
diff --git a/data/sampled_jsons/Figure_5_SRE-Agent_tool_usage_ITBench_year_2024.jsonl b/data/sampled_jsons/Figure_5_SRE-Agent_tool_usage_ITBench_year_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..3202a0801898b17a69d27180b8e0a2b2ae117a3f
--- /dev/null
+++ b/data/sampled_jsons/Figure_5_SRE-Agent_tool_usage_ITBench_year_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "ITBench: Evaluating AI Agents across Diverse Real-World ...", "date": "", "ddg_snippet": "Figure 5 : SRE - Agent Tool Usage Distribution. flaws. Based on this insight, we introduce two evaluation metrics: (i) Detoured Services: |Vvisited \\ Vgt| , the ...", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/poster/44303", "content": "Figure 5 : SRE - Agent Tool Usage Distribution. flaws. Based on this insight, we introduce two evaluation metrics: (i) Detoured Services: |Vvisited \\ Vgt| , the ..."}
+{"idx": 1, "title": "ITBench: Evaluating AI Agents across Diverse Real-World IT ...", "date": "", "ddg_snippet": "This paper presents the ITBench framework and reports on experiments evaluating the performance of different LLM-based agents on 94 real-world IT scenarios ...", "subpage_snippet": "", "source": "www.alphaxiv.org", "link": "https://www.alphaxiv.org/overview/2502.05352v1", "content": "This paper presents the ITBench framework and reports on experiments evaluating the performance of different LLM-based agents on 94 real-world IT scenarios ..."}
+{"idx": 2, "title": "itbench-hub/ITBench-Scenarios: Code repository for ...", "date": "", "ddg_snippet": "SRE Agent : SRE (Site Reliability Engineering) agents designed to diagnose and remediate problems in Kubernetes-based environments. Leverage logs, metrics ...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/itbench-hub/ITBench-Scenarios", "content": "SRE Agent : SRE (Site Reliability Engineering) agents designed to diagnose and remediate problems in Kubernetes-based environments. Leverage logs, metrics ..."}
+{"idx": 3, "title": "STRATUS: A Multi-agent System for Autonomous ...", "date": "", "ddg_snippet": "by Y Chen · 2025 — STRATUS aims to realize autonomous SRE which we define as an automated system on detecting failures, localizing failing components, analyzing ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2506.02009", "content": "by Y Chen · 2025 — STRATUS aims to realize autonomous SRE which we define as an automated system on detecting failures, localizing failing components, analyzing ..."}
+{"idx": 4, "title": "ITBench: Evaluating AI Agents across Diverse Real-World IT ...", "date": "", "ddg_snippet": "We developed baseline agents SRE-Agent for SRE ... Figure 5: SRE-Agent Tool Usage Distribution flaws ... Figure 9: Architecture of ITBench responsible for ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf/d2122a3a706c4c7cd0d382ee9b13da641bd1fe5a.pdf", "content": "We developed baseline agents SRE-Agent for SRE ... Figure 5: SRE-Agent Tool Usage Distribution flaws ... Figure 9: Architecture of ITBench responsible for ..."}
+{"idx": 5, "title": "STRATUS: A Multi-agent System for Autonomous Reliability ...", "date": "", "ddg_snippet": "STRATUS was evaluated on two established SRE benchmarks: AIOpsLab (13 problems) and ITBench (18 problems), using multiple LLM backends including GPT-4o, GPT-4o- ...", "subpage_snippet": "", "source": "www.alphaxiv.org", "link": "https://www.alphaxiv.org/overview/2506.02009v1", "content": "STRATUS was evaluated on two established SRE benchmarks: AIOpsLab (13 problems) and ITBench (18 problems), using multiple LLM backends including GPT-4o, GPT-4o- ..."}
+{"idx": 6, "title": "AI agents: Opportunities, risks, and mitigations", "date": "", "ddg_snippet": "AI agents can be connected with multiple external resources, tools , and other agents to help enhance their decision-making and quality of their responses. They ... 15 pages", "subpage_snippet": "", "source": "www.ibm.com", "link": "https://www.ibm.com/granite/docs/resources/ai-agents-opportunities-risks-and-mitigations.pdf", "content": "AI agents can be connected with multiple external resources, tools , and other agents to help enhance their decision-making and quality of their responses. They ... 15 pages"}
+{"idx": 7, "title": "Evaluation Methodologies for LLM-Based Agents in Real- ...", "date": "", "ddg_snippet": "Agentic evaluation focuses on assessing how well an LLM-based agent performs as an autonomous agent , examining not just final answers but also the agent 's ...", "subpage_snippet": "", "source": "medium.com", "link": "https://medium.com/@adnanmasood/evaluation-methodologies-for-llm-based-agents-in-real-world-applications-83bf87c2d37c", "content": "Agentic evaluation focuses on assessing how well an LLM-based agent performs as an autonomous agent , examining not just final answers but also the agent 's ..."}
+{"idx": 8, "title": "arXiv:2502.05352v1 [cs.AI] 7 Feb 2025", "date": "", "ddg_snippet": "by S Jha · 2025 · Cited by 3 — The goal of ITBench is to evaluate AI agents on a broad range of real-world IT automation tasks that are otherwise performed by SREs, FinOps, ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2502.05352?", "content": "by S Jha · 2025 · Cited by 3 — The goal of ITBench is to evaluate AI agents on a broad range of real-world IT automation tasks that are otherwise performed by SREs, FinOps, ..."}
+{"idx": 9, "title": "Revisiting the End-to-End Design Principle for An Internet-of ...", "date": "", "ddg_snippet": "by J Kim — Figure 5 : Stateful Monitoring of Agent System. 2.0 Flash [33]—and is implemented using the Smolagents [47] framework, generating Python code to orchestrate ...", "subpage_snippet": "", "source": "www.conviva.com", "link": "https://www.conviva.com/wp-content/uploads/2025/08/Hotnets25_Agentic_Reliability.pdf", "content": "by J Kim — Figure 5 : Stateful Monitoring of Agent System. 2.0 Flash [33]—and is implemented using the Smolagents [47] framework, generating Python code to orchestrate ..."}
diff --git a/data/sampled_jsons/FlowDec_Table_8_FAD_score_7.5_kbits_3.4.jsonl b/data/sampled_jsons/FlowDec_Table_8_FAD_score_7.5_kbits_3.4.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..aa56f090894f5e41e7d190501edc9b065b76e9f7
--- /dev/null
+++ b/data/sampled_jsons/FlowDec_Table_8_FAD_score_7.5_kbits_3.4.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "FlowDec: A flow-based full-band general audio codec with high ...", "date": "", "ddg_snippet": "Abstract We propose FlowDec , a neural full-band audio codec for general audio sampled at 48 kHz that combines non-adversarial codec training with a stochastic postfilter based on a novel conditional flow matching method. Compared to the prior work ScoreDec which is based on score matching, we generalize from speech to general audio and move from 24 kbit/s to as low as 4 kbit/s , while improving ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2503.01485v1", "content": "Abstract We propose FlowDec , a neural full-band audio codec for general audio sampled at 48 kHz that combines non-adversarial codec training with a stochastic postfilter based on a novel conditional flow matching method. Compared to the prior work ScoreDec which is based on score matching, we generalize from speech to general audio and move from 24 kbit/s to as low as 4 kbit/s , while improving ..."}
+{"idx": 1, "title": "GitHub - facebookresearch/FlowDec: An neural full-band audio codec for ...", "date": "", "ddg_snippet": "An neural full-band audio codec for general audio sampled at 48 kHz with 7.5 kps or 4.5 kbps. - facebookresearch/ FlowDec", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/facebookresearch/FlowDec", "content": "An neural full-band audio codec for general audio sampled at 48 kHz with 7.5 kps or 4.5 kbps. - facebookresearch/ FlowDec"}
+{"idx": 2, "title": "FlowDec: A flow-based full-band general audio codec with high ...", "date": "", "ddg_snippet": "We show that FlowDec is a competitive alternative to the recent GAN-dominated stream of neural codecs, achieving FAD scores better than those of the established GAN-based codec DAC and listening test scores that are on par, and producing qualitatively more natural reconstructions for speech and harmonic structures in music.", "subpage_snippet": "", "source": "sp-uhh.github.io", "link": "https://sp-uhh.github.io/FlowDec/", "content": "We show that FlowDec is a competitive alternative to the recent GAN-dominated stream of neural codecs, achieving FAD scores better than those of the established GAN-based codec DAC and listening test scores that are on par, and producing qualitatively more natural reconstructions for speech and harmonic structures in music."}
+{"idx": 3, "title": "KBIT Test Scores Explained - TestPrep-Online", "date": "", "ddg_snippet": "Understand your child's KBIT-2 Test scores and what it means for your child. Learn how to interpret KBIT scores , and how to improve them in the future.", "subpage_snippet": "", "source": "www.testprep-online.com", "link": "https://www.testprep-online.com/kbit-test-scores", "content": "Understand your child's KBIT-2 Test scores and what it means for your child. Learn how to interpret KBIT scores , and how to improve them in the future."}
+{"idx": 4, "title": "FlowDec: A flow-based full-band general audio codec with high...", "date": "", "ddg_snippet": "We propose FlowDec , a neural full-band audio codec for general audio sampled at 48 kHz that combines non-adversarial codec training with a stochastic postfilter based on a novel conditional flow matching method. Compared to the prior work ScoreDec which is based on score matching, we generalize from speech to general audio and move from 24 kbit/s to as low as 4 kbit/s , while improving output ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=uxDFlPGRLX", "content": "We propose FlowDec , a neural full-band audio codec for general audio sampled at 48 kHz that combines non-adversarial codec training with a stochastic postfilter based on a novel conditional flow matching method. Compared to the prior work ScoreDec which is based on score matching, we generalize from speech to general audio and move from 24 kbit/s to as low as 4 kbit/s , while improving output ..."}
+{"idx": 5, "title": "A arXiv:2503.01485v1 [cs.SD] 3 Mar 2025", "date": "", "ddg_snippet": "We showed that FlowDec achieves state-of-the-art FAD scores for the coding task and, in a listening test, performs on par with the current state-of-the-art GAN-based codec DAC (Kumar et al., 2024) at bitrates between 4.5 and 7.5kbit/s .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2503.01485v1", "content": "We showed that FlowDec achieves state-of-the-art FAD scores for the coding task and, in a listening test, performs on par with the current state-of-the-art GAN-based codec DAC (Kumar et al., 2024) at bitrates between 4.5 and 7.5kbit/s ."}
+{"idx": 6, "title": "Fréchet Audio Distance: A Reference-Free Metric for Evaluating Music ...", "date": "", "ddg_snippet": "We apply the FAD [19] score as main evaluation metric. In detail, FAD calculates the Fréchet distance F between a group of target sound audio clip t and a group of generated sound audio clip r ...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/335830031_Frechet_Audio_Distance_A_Reference-Free_Metric_for_Evaluating_Music_Enhancement_Algorithms", "content": "We apply the FAD [19] score as main evaluation metric. In detail, FAD calculates the Fréchet distance F between a group of target sound audio clip t and a group of generated sound audio clip r ..."}
+{"idx": 7, "title": "Appendix A: Codec Standards - Wiley Online Library", "date": "", "ddg_snippet": "s=8kHz Bit rate:B=16kbit/s Quality: - for speech comparable to (or partly better than) 32 kbit/s ADPCM (ITU-T/G.726) - modem signals (fax) up to 4.8 kbit/s Applications: - video telephony, voice over IP - \"digital circuit multiplication\" Algorithm: - backwards adaptation of an LPC predictor of ordern=50 (blockwise, Levinson ...", "subpage_snippet": "", "source": "onlinelibrary.wiley.com", "link": "https://onlinelibrary.wiley.com/doi/pdf/10.1002/0470031743.app1", "content": "s=8kHz Bit rate:B=16kbit/s Quality: - for speech comparable to (or partly better than) 32 kbit/s ADPCM (ITU-T/G.726) - modem signals (fax) up to 4.8 kbit/s Applications: - video telephony, voice over IP - \"digital circuit multiplication\" Algorithm: - backwards adaptation of an LPC predictor of ordern=50 (blockwise, Levinson ..."}
+{"idx": 8, "title": "ITU-T Rec. G.975.1 (02/2004) Forward error correction for high bit-rate ...", "date": "", "ddg_snippet": "Table 1 is a summary table format for correction ability of a super FEC scheme of the above parameters. In order to clarify correction ability, all values should be put into the table . Summary tables for correction ability are described in Appendix I.", "subpage_snippet": "", "source": "www.itu.int", "link": "https://www.itu.int/rec/dologin_pub.asp?lang=s&id=T-REC-G.975.1-200402-I!!PDF-E&type=items", "content": "Table 1 is a summary table format for correction ability of a super FEC scheme of the above parameters. In order to clarify correction ability, all values should be put into the table . Summary tables for correction ability are described in Appendix I."}
+{"idx": 9, "title": "Bandwidth Calculator", "date": "", "ddg_snippet": "A byte is a unit that consists of 8 bits. A byte can represent values from 0 to 255. The bit is the unit of data transfer, meaning that a communications device or system with a bandwidth of 8 MB has a transfer rate of 8 Mega bits per second, which is equivalent to 1 Mega byte per second. In relation, the unit of information storage is the byte.", "subpage_snippet": "", "source": "www.calculator.net", "link": "https://www.calculator.net/bandwidth-calculator.html", "content": "A byte is a unit that consists of 8 bits. A byte can represent values from 0 to 255. The bit is the unit of data transfer, meaning that a communications device or system with a bandwidth of 8 MB has a transfer rate of 8 Mega bits per second, which is equivalent to 1 Mega byte per second. In relation, the unit of information storage is the byte."}
diff --git a/data/sampled_jsons/FlowDec_paper_Section_5.1_ScoreDec_NFE_requirements_year_2023-2024.jsonl b/data/sampled_jsons/FlowDec_paper_Section_5.1_ScoreDec_NFE_requirements_year_2023-2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..e5176698fcc8c1fad87f0f3d45a1d93babc55559
--- /dev/null
+++ b/data/sampled_jsons/FlowDec_paper_Section_5.1_ScoreDec_NFE_requirements_year_2023-2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "FlowDec: A flow-based full-band general audio codec with high", "date": "", "ddg_snippet": "... ScoreDec which is based on score matching, we generalize from speech to general audio and move from 24 kbit/s to as low as 4 kbit/s, while ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2503.01485v1", "content": "... ScoreDec which is based on score matching, we generalize from speech to general audio and move from 24 kbit/s to as low as 4 kbit/s, while ..."}
+{"idx": 1, "title": "FlowDec: A flow-based full-band general audio codec with high ...", "date": "", "ddg_snippet": "We propose FlowDec , a neural full-band audio codec for general audio sampled at 48 kHz that combines non-adversarial codec training with a stochastic postfilter based on a novel conditional flow matching method. Compared to the prior work ScoreDec which is based on score matching, we generalize from speech to general audio and move from 24 kbit/s to as low as 4 kbit/s, while improving output ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2503.01485", "content": "We propose FlowDec , a neural full-band audio codec for general audio sampled at 48 kHz that combines non-adversarial codec training with a stochastic postfilter based on a novel conditional flow matching method. Compared to the prior work ScoreDec which is based on score matching, we generalize from speech to general audio and move from 24 kbit/s to as low as 4 kbit/s, while improving output ..."}
+{"idx": 2, "title": "GitHub - facebookresearch/FlowDec: An neural full-band audio codec for ...", "date": "", "ddg_snippet": "For automatically determining the frequency-dependent sigma_y (see Section 3.5 in our paper ), you can use the helper script scripts/estimate_flowdec_params.py. This script also implements the heuristic for a global sigma_y discussed in our Appendix A.1.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/facebookresearch/FlowDec", "content": "For automatically determining the frequency-dependent sigma_y (see Section 3.5 in our paper ), you can use the helper script scripts/estimate_flowdec_params.py. This script also implements the heuristic for a global sigma_y discussed in our Appendix A.1."}
+{"idx": 3, "title": "FlowDec/requirements.txt at main · facebookresearch/FlowDec", "date": "", "ddg_snippet": "An neural full-band audio codec for general audio sampled at 48 kHz with 7.5 kps or 4.5 kbps. - FlowDec / requirements .txt at main · facebookresearch/ FlowDec", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/facebookresearch/FlowDec/blob/main/requirements.txt", "content": "An neural full-band audio codec for general audio sampled at 48 kHz with 7.5 kps or 4.5 kbps. - FlowDec / requirements .txt at main · facebookresearch/ FlowDec"}
+{"idx": 4, "title": "FlowDec: A flow-based full-band general audio codec with high ...", "date": "", "ddg_snippet": "FlowDec achieves the following metrics at 7.5 kbps with 6 NFE (neural network evaluations), compared to ScoreDec and other baselines. There is almost no drop in performance for FlowDec at 6 NFE compared to 50 NFE , while ScoreDec shows a significant drop. We have used FlowDec at 6 NFE for all other evaluations and the audio examples shown above.", "subpage_snippet": "", "source": "sp-uhh.github.io", "link": "https://sp-uhh.github.io/FlowDec/", "content": "FlowDec achieves the following metrics at 7.5 kbps with 6 NFE (neural network evaluations), compared to ScoreDec and other baselines. There is almost no drop in performance for FlowDec at 6 NFE compared to 50 NFE , while ScoreDec shows a significant drop. We have used FlowDec at 6 NFE for all other evaluations and the audio examples shown above."}
+{"idx": 5, "title": "FlowDec: A flow-based full-band general audio codec with high...", "date": "", "ddg_snippet": "The paper introduces FlowDec , a neural audio codec that employs a two-stage approach: (1) an autoencoder with residual vector quantization, trained without adversarial loss; and (2) a postfilter that mitigates coding artifacts and enhances perceptual quality. FlowDec leverages conditional flow matching for signal enhancement, achieving notable improvements over previous score-based and flow ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=uxDFlPGRLX", "content": "The paper introduces FlowDec , a neural audio codec that employs a two-stage approach: (1) an autoencoder with residual vector quantization, trained without adversarial loss; and (2) a postfilter that mitigates coding artifacts and enhances perceptual quality. FlowDec leverages conditional flow matching for signal enhancement, achieving notable improvements over previous score-based and flow ..."}
+{"idx": 6, "title": "A arXiv:2503.01485v1 [cs.SD] 3 Mar 2025", "date": "", "ddg_snippet": "At NFE = 50, ScoreDec and FlowDec achieve similar SI-SDR, but FlowDec performs significantly better in FAD. A full metric comparison table can be found in Appendix A.7.1.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2503.01485v1", "content": "At NFE = 50, ScoreDec and FlowDec achieve similar SI-SDR, but FlowDec performs significantly better in FAD. A full metric comparison table can be found in Appendix A.7.1."}
+{"idx": 7, "title": "PDF New York State Stormwater Managment Design Manual", "date": "", "ddg_snippet": "This section presents a series of green infrastructure principles and practices that can be incorporated in the site design to allow for micro management of runoff, promote groundwater recharge, increase losses through evapotranspiration and emulate the preconstruction hydrology, resulting in reduced water-quality-treatment volume.", "subpage_snippet": "", "source": "extapps.dec.ny.gov", "link": "https://extapps.dec.ny.gov/docs/water_pdf/swdm2015chptr05.pdf", "content": "This section presents a series of green infrastructure principles and practices that can be incorporated in the site design to allow for micro management of runoff, promote groundwater recharge, increase losses through evapotranspiration and emulate the preconstruction hydrology, resulting in reduced water-quality-treatment volume."}
+{"idx": 8, "title": "Study Data Standards Resources | FDA", "date": "", "ddg_snippet": "Study Data Standards ResourcesThis page provides quick links to key guidances to support the submission of study data to FDA's Center for Biologics Evaluation and Research (CBER), Center for ...", "subpage_snippet": "", "source": "www.fda.gov", "link": "https://www.fda.gov/industry/fda-data-standards-advisory-board/study-data-standards-resources", "content": "Study Data Standards ResourcesThis page provides quick links to key guidances to support the submission of study data to FDA's Center for Biologics Evaluation and Research (CBER), Center for ..."}
+{"idx": 9, "title": "NFPA 80 - Fire Door", "date": "", "ddg_snippet": "Order NFPA 80-2007 Order NFPA 80-2010 The following text is reproduced with permission from NFPA 80, Fire Doors and Other Opening Protectives, Copyright © 2007 ...", "subpage_snippet": "", "source": "firedoorguide.com", "link": "http://firedoorguide.com/nfpa-80/", "content": "Order NFPA 80-2007 Order NFPA 80-2010 The following text is reproduced with permission from NFPA 80, Fire Doors and Other Opening Protectives, Copyright © 2007 ..."}
diff --git a/data/sampled_jsons/Foret_et_al._(2021)_Sharpness-aware_minimization_for_efficiently_improving_generalization_abstract.jsonl b/data/sampled_jsons/Foret_et_al._(2021)_Sharpness-aware_minimization_for_efficiently_improving_generalization_abstract.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..5a03b2cec4789ec1dfd113d25b81d2b237265304
--- /dev/null
+++ b/data/sampled_jsons/Foret_et_al._(2021)_Sharpness-aware_minimization_for_efficiently_improving_generalization_abstract.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Sharpness-Aware Minimization for Efficiently Improving Generalization", "date": "", "ddg_snippet": "View a PDF of the paper titled Sharpness - Aware Minimization for Efficiently Improving Generalization , by Pierre Foret and 3 other authors", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2010.01412", "content": "View a PDF of the paper titled Sharpness - Aware Minimization for Efficiently Improving Generalization , by Pierre Foret and 3 other authors"}
+{"idx": 1, "title": "ICLR 2021 Sharpness-aware Minimization for Efficiently Improving ...", "date": "", "ddg_snippet": "Spotlight Sharpness - aware Minimization for Efficiently Improving Generalization Pierre Foret · Ariel Kleiner · Hossein Mobahi · Behnam Neyshabur [ Abstract ] [ Visit Oral Session 2 ] [ Paper ] [ Paper ]", "subpage_snippet": "", "source": "iclr.cc", "link": "https://iclr.cc/virtual/2021/spotlight/3497", "content": "Spotlight Sharpness - aware Minimization for Efficiently Improving Generalization Pierre Foret · Ariel Kleiner · Hossein Mobahi · Behnam Neyshabur [ Abstract ] [ Visit Oral Session 2 ] [ Paper ] [ Paper ]"}
+{"idx": 2, "title": "Sharpness-Aware Minimization for Efficiently Improving Generalization", "date": "", "ddg_snippet": "This work introduces a novel, effective procedure for simultaneously minimizing loss value and loss sharpness , Sharpness - Aware Minimization (SAM), which improves model generalization across a variety of benchmark datasets and models, yielding novel state-of-the-art performance for several. In today's heavily overparameterized models, the value of the training loss provides few guarantees on ...", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/Sharpness-Aware-Minimization-for-Efficiently-Foret-Kleiner/a2cd073b57be744533152202989228cb4122270a", "content": "This work introduces a novel, effective procedure for simultaneously minimizing loss value and loss sharpness , Sharpness - Aware Minimization (SAM), which improves model generalization across a variety of benchmark datasets and models, yielding novel state-of-the-art performance for several. In today's heavily overparameterized models, the value of the training loss provides few guarantees on ..."}
+{"idx": 3, "title": "[Paper Review] Sharpness-Aware Minimization for Efficiently Improving ...", "date": "", "ddg_snippet": "Outlines References 1. Weak Generalization Power of Sharp Minima 2. Sharpness - Aware Minimization (SAM) 2.1. PAC Bayesian Generalization Bound 2.2. SAM Objective 3. Empirical Evaluation References Sharpness - Aware Minimization for Efficiently Improving Generalization , Foret et al , 2021 Proving that the dual of the lp norm is the lq norm An Introduction to PAC-Bayes 1. Weak Generalization Power ...", "subpage_snippet": "", "source": "suminizz.github.io", "link": "https://suminizz.github.io/sam/", "content": "Outlines References 1. Weak Generalization Power of Sharp Minima 2. Sharpness - Aware Minimization (SAM) 2.1. PAC Bayesian Generalization Bound 2.2. SAM Objective 3. Empirical Evaluation References Sharpness - Aware Minimization for Efficiently Improving Generalization , Foret et al , 2021 Proving that the dual of the lp norm is the lq norm An Introduction to PAC-Bayes 1. Weak Generalization Power ..."}
+{"idx": 4, "title": "dblp: Sharpness-aware Minimization for Efficiently Improving ...", "date": "", "ddg_snippet": "Bibliographic details on Sharpness - aware Minimization for Efficiently Improving Generalization .", "subpage_snippet": "", "source": "dblp.org", "link": "https://dblp.org/rec/conf/iclr/ForetKMN21", "content": "Bibliographic details on Sharpness - aware Minimization for Efficiently Improving Generalization ."}
+{"idx": 5, "title": "A P E SHARPNESS-AWARE MINIMIZATION - OpenReview", "date": "", "ddg_snippet": "ABSTRACT Sharpness - aware minimization (SAM), which searches for flat minima by min-max optimization, has been shown to be useful in improving model generalization . However, since each SAM update requires computing two gradients, its compu-tational cost and training time are both doubled compared to standard empirical risk minimization (ERM). Recent state-of-the-arts reduce the fraction of SAM ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=6Wl7-M2BC-", "content": "ABSTRACT Sharpness - aware minimization (SAM), which searches for flat minima by min-max optimization, has been shown to be useful in improving model generalization . However, since each SAM update requires computing two gradients, its compu-tational cost and training time are both doubled compared to standard empirical risk minimization (ERM). Recent state-of-the-arts reduce the fraction of SAM ..."}
+{"idx": 6, "title": "Sharpness-Aware Minimization for Efficiently Improving Generalization", "date": "", "ddg_snippet": "In particular, our procedure, Sharpness - Aware Minimization (SAM), seeks parameters that lie in neighborhoods having uniformly low loss; this formulation results in a min-max optimization problem on which gradient descent can be performed efficiently .", "subpage_snippet": "", "source": "ui.adsabs.harvard.edu", "link": "https://ui.adsabs.harvard.edu/abs/2020arXiv201001412F/abstract", "content": "In particular, our procedure, Sharpness - Aware Minimization (SAM), seeks parameters that lie in neighborhoods having uniformly low loss; this formulation results in a min-max optimization problem on which gradient descent can be performed efficiently ."}
+{"idx": 7, "title": "arXiv:2010.01412v3 [cs.LG] 29 Apr 2021", "date": "", "ddg_snippet": "(Shirish Keskar et al ., 2016; Dziugaite & Roy, 2017; Jiang et al ., 2019). While this connection has held the promise of enabling new approaches to model training that yield better generalization , practical efficient algorithms that specifically seek out flatter minima and furthermore effectively improve generalization on a range of state-of-the-art models have thus far been elusive (e.g., see ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2010.01412", "content": "(Shirish Keskar et al ., 2016; Dziugaite & Roy, 2017; Jiang et al ., 2019). While this connection has held the promise of enabling new approaches to model training that yield better generalization , practical efficient algorithms that specifically seek out flatter minima and furthermore effectively improve generalization on a range of state-of-the-art models have thus far been elusive (e.g., see ..."}
+{"idx": 8, "title": "PDF Sharpness-Aware Minimization Efficiently Selects Flatter Minima Late In ...", "date": "", "ddg_snippet": "at iteration . denotes the learning rate. [1] Pierre Foret , Ariel Kleiner, Hossein Mobahi, and Behnam Neyshabur. Sharpness - aware minimization for efficiently improving generalization . The effectiveness of gradient-based optimization methods can be attributed to their implicit bias toward solutions with favorable properties [2].", "subpage_snippet": "", "source": "iclr.cc", "link": "https://iclr.cc/media/iclr-2025/Slides/29182.pdf", "content": "at iteration . denotes the learning rate. [1] Pierre Foret , Ariel Kleiner, Hossein Mobahi, and Behnam Neyshabur. Sharpness - aware minimization for efficiently improving generalization . The effectiveness of gradient-based optimization methods can be attributed to their implicit bias toward solutions with favorable properties [2]."}
+{"idx": 9, "title": "Fundamental Convergence Analysis of Sharpness-Aware Minimization", "date": "", "ddg_snippet": "The paper investigates the fundamental convergence properties of Sharpness - Aware Minimization (SAM), a recently proposed gradient-based optimization method ( Foret et al ., 2021 ) that significantly improves the generalization of deep neural networks.", "subpage_snippet": "", "source": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper_files/paper/2024/hash/17b08a9de93e2accf13429643e7eafdc-Abstract-Conference.html", "content": "The paper investigates the fundamental convergence properties of Sharpness - Aware Minimization (SAM), a recently proposed gradient-based optimization method ( Foret et al ., 2021 ) that significantly improves the generalization of deep neural networks."}
diff --git a/data/sampled_jsons/FourCastNet_experimental_results_metrics_RMSE_ACC_anomaly_correlation.jsonl b/data/sampled_jsons/FourCastNet_experimental_results_metrics_RMSE_ACC_anomaly_correlation.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..1a132f07ff6a3c68e0772243da81320c4c5f3f96
--- /dev/null
+++ b/data/sampled_jsons/FourCastNet_experimental_results_metrics_RMSE_ACC_anomaly_correlation.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Section 6.2.2 Anomaly Correlation Coefficient - Forecast User Guide ...", "date": "", "ddg_snippet": "Note non-uniform scale for ACC values. Fig6.2.2-4: The plot shows the forecast lead-time (in days) at which Anomaly Correlation Coefficient ( ACC ) of the HRES forecast dropped to 80% for: the month mean (blue line, with blue spots at each month), twelve-month mean centred on that month (red line).", "subpage_snippet": "", "source": "confluence.ecmwf.int", "link": "https://confluence.ecmwf.int/display/FUG/Section+6.2.2+Anomaly+Correlation+Coefficient", "content": "Note non-uniform scale for ACC values. Fig6.2.2-4: The plot shows the forecast lead-time (in days) at which Anomaly Correlation Coefficient ( ACC ) of the HRES forecast dropped to 80% for: the month mean (blue line, with blue spots at each month), twelve-month mean centred on that month (red line)."}
+{"idx": 1, "title": "GitHub - NVlabs/FourCastNet: Initial public release of code, data, and ...", "date": "", "ddg_snippet": "This is so that you can analyze the skill of FourCastNet by comparing with the ERA5 ground truth via the RMSE and ACC metrics . The example scripts show you how to download pl and sl variables in an interval from 19 October 2021 to 31 October 2021.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/NVlabs/FourCastNet", "content": "This is so that you can analyze the skill of FourCastNet by comparing with the ERA5 ground truth via the RMSE and ACC metrics . The example scripts show you how to download pl and sl variables in an interval from 19 October 2021 to 31 October 2021."}
+{"idx": 2, "title": "Comparison of acc and rmse metrics between the (downsampled)", "date": "", "ddg_snippet": "Figure 10 Comparison of ACC and RMSE metrics between the (downsampled) FourCastNet predictions, (downsam- pled) IFS, and baseline state-of-the-art DLWP model [Weyn et al., 2020] for (a) Z509 and (b) T2,. We observe that the FourCastNet predictions show significant improvement over the baseline model. We also note that the FourCastNet generates predictions that have a higher resolution by a ...", "subpage_snippet": "", "source": "www.academia.edu", "link": "https://www.academia.edu/figures/48862467/figure-10-comparison-of-acc-and-rmse-metrics-between-the", "content": "Figure 10 Comparison of ACC and RMSE metrics between the (downsampled) FourCastNet predictions, (downsam- pled) IFS, and baseline state-of-the-art DLWP model [Weyn et al., 2020] for (a) Z509 and (b) T2,. We observe that the FourCastNet predictions show significant improvement over the baseline model. We also note that the FourCastNet generates predictions that have a higher resolution by a ..."}
+{"idx": 3, "title": "FourcastNET Analyzing Inference Outputs - Customer Stories - NVIDIA ...", "date": "", "ddg_snippet": "3) Ground Truth Data for RMSE and ACC Evaluation In the inference script, RMSE and ACC metrics are calculated. But I am unsure about the ground truth data used for validation. If I run a forecast for February 28, 2025, at 12:00 UTC, predicting for the next 36 hours (prediction length: 6), how should I obtain the ground truth data for comparison?", "subpage_snippet": "", "source": "forums.developer.nvidia.com", "link": "https://forums.developer.nvidia.com/t/fourcastnet-analyzing-inference-outputs/325545", "content": "3) Ground Truth Data for RMSE and ACC Evaluation In the inference script, RMSE and ACC metrics are calculated. But I am unsure about the ground truth data used for validation. If I run a forecast for February 28, 2025, at 12:00 UTC, predicting for the next 36 hours (prediction length: 6), how should I obtain the ground truth data for comparison?"}
+{"idx": 4, "title": "FourCastNet/utils/weighted_acc_rmse.py at master - GitHub", "date": "", "ddg_snippet": "Initial public release of code, data, and model weights for FourCastNet - NVlabs/ FourCastNet", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/NVlabs/FourCastNet/blob/master/utils/weighted_acc_rmse.py", "content": "Initial public release of code, data, and model weights for FourCastNet - NVlabs/ FourCastNet"}
+{"idx": 5, "title": "Frontiers | Optimizing data-driven arctic marine forecasting: a ...", "date": "", "ddg_snippet": "Through the utilization of metrics such as RMSE , Bias, and Correlation , we illustrate the areas in which our model outperforms well-known prediction models. Results : Our model demonstrates enhanced accuracy in forecasting ocean dynamics when compared to FourCastNet and PhyDNet.", "subpage_snippet": "", "source": "www.frontiersin.org", "link": "https://www.frontiersin.org/journals/marine-science/articles/10.3389/fmars.2024.1456480/full", "content": "Through the utilization of metrics such as RMSE , Bias, and Correlation , we illustrate the areas in which our model outperforms well-known prediction models. Results : Our model demonstrates enhanced accuracy in forecasting ocean dynamics when compared to FourCastNet and PhyDNet."}
+{"idx": 6, "title": "PDF Data Assimilation with Machine Learning Surrogate Models: A Case Study ...", "date": "", "ddg_snippet": "These results show that our 3DVar analyses yield lower RMSE and higher ACC metrics across a year compared to interpolatingrawobservations. Furthermore,our3DVaranalysesusinglow-resolutionobservations achieve stable metrics up to a 5 resolution.", "subpage_snippet": "", "source": "journals.ametsoc.org", "link": "https://journals.ametsoc.org/view/journals/aies/aop/AIES-D-24-0050.1/AIES-D-24-0050.1.pdf", "content": "These results show that our 3DVar analyses yield lower RMSE and higher ACC metrics across a year compared to interpolatingrawobservations. Furthermore,our3DVaranalysesusinglow-resolutionobservations achieve stable metrics up to a 5 resolution."}
+{"idx": 7, "title": "PDF Advancing Ocean Forecasting in the Russian Arctic: A ... - Authorea", "date": "", "ddg_snippet": "This paper presents an analysis and comparison of three data-driven models: our newly developed convLSTM-based MariNet, FourCastNet and the PhydNet, a physics-informed model for video prediction. Using metrics such as RMSE , Bias and Corre-lation , we demonstrate the areas where our model surpasses the performance of the prominent prediction models.", "subpage_snippet": "", "source": "d197for5662m48.cloudfront.net", "link": "https://d197for5662m48.cloudfront.net/documents/publicationstatus/183316/preprint_pdf/08f6504e25c86db4f4fa513731c806f2.pdf", "content": "This paper presents an analysis and comparison of three data-driven models: our newly developed convLSTM-based MariNet, FourCastNet and the PhydNet, a physics-informed model for video prediction. Using metrics such as RMSE , Bias and Corre-lation , we demonstrate the areas where our model surpasses the performance of the prominent prediction models."}
+{"idx": 8, "title": "High-resolution-global-weather-model-afno - arXiv.org", "date": "", "ddg_snippet": "FourCastNet's predictions are comparable to the IFS model on metrics of Root Mean Squared Error ( RMSE ) and Anomaly Correlation Coefficient ( ACC ) at lead times of up to three days.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2202.11214", "content": "FourCastNet's predictions are comparable to the IFS model on metrics of Root Mean Squared Error ( RMSE ) and Anomaly Correlation Coefficient ( ACC ) at lead times of up to three days."}
+{"idx": 9, "title": "Optimizing data-driven arctic marine forecasting: a comparative ...", "date": "", "ddg_snippet": "Experimental results show that ConvLSTMP3 achieves a good prediction skill with a mean RMSE of 0.057 m and accuracy of 93.4% averaged over a 15-d prediction period.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/385982637_Optimizing_data-driven_arctic_marine_forecasting_a_comparative_analysis_of_MariNet_FourCastNet_and_PhyDNet", "content": "Experimental results show that ConvLSTMP3 achieves a good prediction skill with a mean RMSE of 0.057 m and accuracy of 93.4% averaged over a 15-d prediction period."}
diff --git a/data/sampled_jsons/FuGps5Zyia_Ad-Hoc_Human-AI_Coordination_Challenge_Appendix_A.8_regularization_lambda_0.00_coordinati.jsonl b/data/sampled_jsons/FuGps5Zyia_Ad-Hoc_Human-AI_Coordination_Challenge_Appendix_A.8_regularization_lambda_0.00_coordinati.jsonl
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index 0000000000000000000000000000000000000000..ddec16f0e07557a55bf0bdbb4e5837e0987068bc
--- /dev/null
+++ b/data/sampled_jsons/FuGps5Zyia_Ad-Hoc_Human-AI_Coordination_Challenge_Appendix_A.8_regularization_lambda_0.00_coordinati.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Ad-Hoc Human-AI Coordination Challenge - arXiv.org", "date": "", "ddg_snippet": "To address these problems, we introduce the Ad-Hoc Human-AI Coordina-tion Challenge (AH2AC2) as a standardised way to evaluate human-AI coordination in Hanabi. Specifically, we develop human proxy agents through a combination of behavioural cloning (BC) and regularised reinforcement learning (RL).", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2506.21490", "content": "To address these problems, we introduce the Ad-Hoc Human-AI Coordina-tion Challenge (AH2AC2) as a standardised way to evaluate human-AI coordination in Hanabi. Specifically, we develop human proxy agents through a combination of behavioural cloning (BC) and regularised reinforcement learning (RL)."}
+{"idx": 1, "title": "Ad-Hoc Human-AI Coordination Challenge (AH2AC2) Docs", "date": "", "ddg_snippet": "The AH2AC2 Challenge The Ad-Hoc Human-AI Coordination Challenge (AH2AC2) provides a standardized environment for evaluating AI agents on their ability to coordinate with human -like counterparts in Hanabi. The challenge emphasizes data-efficient methods and uses human proxy agents for robust and reproducible evaluation.", "subpage_snippet": "", "source": "docs.ah2ac2.com", "link": "https://docs.ah2ac2.com/", "content": "The AH2AC2 Challenge The Ad-Hoc Human-AI Coordination Challenge (AH2AC2) provides a standardized environment for evaluating AI agents on their ability to coordinate with human -like counterparts in Hanabi. The challenge emphasizes data-efficient methods and uses human proxy agents for robust and reproducible evaluation."}
+{"idx": 2, "title": "Ad-Hoc Human-AI Coordination Challenge (AH2AC2) - GitHub", "date": "", "ddg_snippet": "Welcome to the Ad-Hoc Human-AI Coordination Challenge (AH2AC2)! The objective of AH2AC2 is to facilitate the development of AI agents capable of effective collaboration with human -like partners, especially in scenarios with limited prior interaction data.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/FLAIROx/ah2ac2", "content": "Welcome to the Ad-Hoc Human-AI Coordination Challenge (AH2AC2)! The objective of AH2AC2 is to facilitate the development of AI agents capable of effective collaboration with human -like partners, especially in scenarios with limited prior interaction data."}
+{"idx": 3, "title": "Ad-Hoc Human-AI Coordination Challenge - Science Cast", "date": "", "ddg_snippet": "Jun 25, 2025 · In this work, we introduce the Ad-Hoc Human-AI Coordination Challenge (AH2AC2) to overcome the constraints of costly and difficult-to-reproduce human evaluations. We develop \\textit { human proxy agents} on a large-scale human dataset that serve as robust, cheap, and reproducible human -like evaluation partners in AH2AC2.", "subpage_snippet": "", "source": "www.sciencecast.org", "link": "https://www.sciencecast.org/casts/q62pr180dj73", "content": "Jun 25, 2025 · In this work, we introduce the Ad-Hoc Human-AI Coordination Challenge (AH2AC2) to overcome the constraints of costly and difficult-to-reproduce human evaluations. We develop \\textit { human proxy agents} on a large-scale human dataset that serve as robust, cheap, and reproducible human -like evaluation partners in AH2AC2."}
+{"idx": 4, "title": "Ad-Hoc Human-AI Coordination Challenge | OpenReview", "date": "", "ddg_snippet": "May 1, 2025 · Achieving seamless coordination between AI agents and humans is crucial for real-world applications, yet it remains a significant open challenge . Hanabi is a cooperative card game featuring imperfect information, constrained communication, theory of mind requirements, and coordinated action -- making it an ideal testbed for human-AI coordination .", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=FuGps5Zyia", "content": "May 1, 2025 · Achieving seamless coordination between AI agents and humans is crucial for real-world applications, yet it remains a significant open challenge . Hanabi is a cooperative card game featuring imperfect information, constrained communication, theory of mind requirements, and coordinated action -- making it an ideal testbed for human-AI coordination ."}
+{"idx": 5, "title": "Ad-Hoc Human-AI Coordination Challenge - Semantic Scholar", "date": "", "ddg_snippet": "Table 17. Hyperparameters used for training all IPPO and BR-BC baseline agents. Here, we use feed-forward architecture. BC agents used for BR-BC are the same as shown in Table 15 and 16, depending on the challenge variety. - \" Ad-Hoc Human-AI Coordination Challenge \"", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/Ad-Hoc-Human-AI-Coordination-Challenge-Dizdarevic-Hammond/76e21098d2925daeb769a647c9af4886d00b05fd/figure/23", "content": "Table 17. Hyperparameters used for training all IPPO and BR-BC baseline agents. Here, we use feed-forward architecture. BC agents used for BR-BC are the same as shown in Table 15 and 16, depending on the challenge variety. - \" Ad-Hoc Human-AI Coordination Challenge \""}
+{"idx": 6, "title": "ICML Poster Ad-Hoc Human-AI Coordination Challenge", "date": "", "ddg_snippet": "Ad-Hoc Human-AI Coordination Challenge Tin Dizdarevic · Ravi Hammond · Tobias Gessler · Anisoara Calinescu · Jonathan Cook · Matteo Gallici · Andrei Lupu · Jakob Foerster", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/poster/45867", "content": "Ad-Hoc Human-AI Coordination Challenge Tin Dizdarevic · Ravi Hammond · Tobias Gessler · Anisoara Calinescu · Jonathan Cook · Matteo Gallici · Andrei Lupu · Jakob Foerster"}
+{"idx": 7, "title": "Regularization in Machine Learning - GeeksforGeeks", "date": "", "ddg_snippet": "\\ lambda = Regularization parameter that controls the strength of regularization . Lets see how to implement this using pythonAllows Fine-Tuning: Hyperparameters like alpha and lambda control regularization strength helps in balancing bias and variance.", "subpage_snippet": "", "source": "www.geeksforgeeks.org", "link": "https://www.geeksforgeeks.org/machine-learning/regularization-in-machine-learning/", "content": "\\ lambda = Regularization parameter that controls the strength of regularization . Lets see how to implement this using pythonAllows Fine-Tuning: Hyperparameters like alpha and lambda control regularization strength helps in balancing bias and variance."}
+{"idx": 8, "title": "towardsdatascience.com/understanding-l1-and-l2- regularization ...", "date": "", "ddg_snippet": "L1 and L2 Regularization Methods 6K 28 Anuja Nagpal.", "subpage_snippet": "", "source": "towardsdatascience.com", "link": "https://towardsdatascience.com/understanding-l1-and-l2-regularization-93918a5ac8d0/", "content": "L1 and L2 Regularization Methods 6K 28 Anuja Nagpal."}
+{"idx": 9, "title": "RPG: A Repository Planning Graph for Unified and Scalable Codebase...", "date": "", "ddg_snippet": "Distributed planning leverages multi-agent coordination [7, 8 ], where specialized roles (e.g., manager, architect, engineer) negotiate between high-level requirements and low-level implementation under predefined Standard Operating Procedures (SOPs).", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2509.16198", "content": "Distributed planning leverages multi-agent coordination [7, 8 ], where specialized roles (e.g., manager, architect, engineer) negotiate between high-level requirements and low-level implementation under predefined Standard Operating Procedures (SOPs)."}
diff --git a/data/sampled_jsons/GELU_ReLU_replacement_sparsity_neuromorphic_Loihi_2_activation_function_edge_deployment_year_2024.jsonl b/data/sampled_jsons/GELU_ReLU_replacement_sparsity_neuromorphic_Loihi_2_activation_function_edge_deployment_year_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..6ea761bf2a9d5979ff31d5d1f1784719ad67eb6d
--- /dev/null
+++ b/data/sampled_jsons/GELU_ReLU_replacement_sparsity_neuromorphic_Loihi_2_activation_function_edge_deployment_year_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Activation function - Wikipedia", "date": "", "ddg_snippet": "Machine learningand data mining. v. t. e. Logistic activation function . In artificial neural networks, the activation function of a node is a function that calculates the output of the node based on its individual inputs and their weights.", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/Activation_function", "content": "Machine learningand data mining. v. t. e. Logistic activation function . In artificial neural networks, the activation function of a node is a function that calculates the output of the node based on its individual inputs and their weights."}
+{"idx": 1, "title": "How Intel's bleeding- edge Loihi 2 chips help robots perceive the world", "date": "", "ddg_snippet": "Loihi 2 is Intel's second -generation neuromorphic research chip. Loihi 2 enables ultra-efficient spiking neural networks to replace resource-hungry deep neural networks in certain applications.", "subpage_snippet": "", "source": "newatlas.com", "link": "https://newatlas.com/computers/loihi-2-intel/", "content": "Loihi 2 is Intel's second -generation neuromorphic research chip. Loihi 2 enables ultra-efficient spiking neural networks to replace resource-hungry deep neural networks in certain applications."}
+{"idx": 2, "title": "A Look at Loihi 2 - Intel - Neuromorphic Chip - Open Neuromorphic", "date": "", "ddg_snippet": "Loihi 2 is Intel's latest neuromorphic research chip, implementing spiking neural networks with programmable dynamics, modular connectivity, and optimizations for scale, speed, and efficiency.", "subpage_snippet": "", "source": "open-neuromorphic.org", "link": "https://open-neuromorphic.org/neuromorphic-computing/hardware/loihi-2-intel/", "content": "Loihi 2 is Intel's latest neuromorphic research chip, implementing spiking neural networks with programmable dynamics, modular connectivity, and optimizations for scale, speed, and efficiency."}
+{"idx": 3, "title": "Layer activation functions", "date": "", "ddg_snippet": "keras. activations . gelu (x, approximate=False). Gaussian error linear unit ( GELU ) activation function .", "subpage_snippet": "", "source": "keras.io", "link": "https://keras.io/api/layers/activations/", "content": "keras. activations . gelu (x, approximate=False). Gaussian error linear unit ( GELU ) activation function ."}
+{"idx": 4, "title": "Intel announces Loihi 2 – A Neuromorphic Chip", "date": "", "ddg_snippet": "Intel announces its latest neuromorphic chip, the Loihi 2 .Instead of the entire chip operating on a per-cycle status, the neuromorphic design uses a spiking method whereby neurons respond on spike edges meaning that the design operates very similarly to neurons in the human brain.", "subpage_snippet": "", "source": "www.electropages.com", "link": "https://www.electropages.com/blog/2021/10/intel-announces-loihi-2-neuromorphic-chip", "content": "Intel announces its latest neuromorphic chip, the Loihi 2 .Instead of the entire chip operating on a per-cycle status, the neuromorphic design uses a spiking method whereby neurons respond on spike edges meaning that the design operates very similarly to neurons in the human brain."}
+{"idx": 5, "title": "Activation Functions in Neural Networks [12 Types & Use Cases]", "date": "", "ddg_snippet": "A neural network activation function is a function that is applied to the output of a neuron.Small negative values were zeroed out in ReLU activation function . However, those negative values may still be relevant for capturing patterns underlying the data.", "subpage_snippet": "", "source": "www.v7labs.com", "link": "https://www.v7labs.com/blog/neural-networks-activation-functions", "content": "A neural network activation function is a function that is applied to the output of a neuron.Small negative values were zeroed out in ReLU activation function . However, those negative values may still be relevant for capturing patterns underlying the data."}
+{"idx": 6, "title": "Efficient Neuromorphic Signal Processing with Loihi 2 | Request PDF", "date": "", "ddg_snippet": "This study introduces the Neuromorphic Sparse Sorter (NSS), a compact two -layer spiking neural network optimized for efficient spike sorting.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/356156931_Efficient_Neuromorphic_Signal_Processing_with_Loihi_2", "content": "This study introduces the Neuromorphic Sparse Sorter (NSS), a compact two -layer spiking neural network optimized for efficient spike sorting."}
+{"idx": 7, "title": "Intel Introduces 2 nd Gen Neuromorphic Research Chip: Loihi 2 on...", "date": "", "ddg_snippet": "Intel introduces Loihi 2 , its second -generation neuromorphic research chip. First chip fabricated on their Intel 4 EUV process, Loihi 2 brings faster processing and synaptic operations, new algorithm support, and increases the neurons count to 1 million.", "subpage_snippet": "", "source": "fuse.wikichip.org", "link": "https://fuse.wikichip.org/news/6383/intel-introduces-2nd-gen-neuromorphic-research-chip-loihi-2-on-intel-4-euv-process/", "content": "Intel introduces Loihi 2 , its second -generation neuromorphic research chip. First chip fabricated on their Intel 4 EUV process, Loihi 2 brings faster processing and synaptic operations, new algorithm support, and increases the neurons count to 1 million."}
+{"idx": 8, "title": "Intel Loihi 2 Neuromorphic Processor : Architecture & Its Working", "date": "", "ddg_snippet": "Discover Intel Loihi 2 , a Cutting Edge Neuromorphic Processor that Mimics the Brain's Structure. Learn How it Works, Specs, Comparison, etc.", "subpage_snippet": "", "source": "www.elprocus.com", "link": "https://www.elprocus.com/intel-loihi2-neuromorphic-processor/", "content": "Discover Intel Loihi 2 , a Cutting Edge Neuromorphic Processor that Mimics the Brain's Structure. Learn How it Works, Specs, Comparison, etc."}
+{"idx": 9, "title": "Intel's Loihi 2 Processor Gets Even Faster At... | HotHardware", "date": "", "ddg_snippet": "Loihi 2 improves upon the first-gen neuromorphic chip in every way, and will be the first commercial chip to be fabbed on Intel 4.intel loihi 2 fingertip hero. With the proliferation of artificial intelligence in recent years, the term \" neuromorphic \" is being used much more often in the tech sector.", "subpage_snippet": "", "source": "hothardware.com", "link": "https://hothardware.com/news/intel-launches-loihi-2-its-second-generation-neuromorphic-processor", "content": "Loihi 2 improves upon the first-gen neuromorphic chip in every way, and will be the first commercial chip to be fabbed on Intel 4.intel loihi 2 fingertip hero. With the proliferation of artificial intelligence in recent years, the term \" neuromorphic \" is being used much more often in the tech sector."}
diff --git a/data/sampled_jsons/GTA_Greedy_Task_Allocation_total_worker_time_inefficiency_number_of_workers_number_of_tasks.jsonl b/data/sampled_jsons/GTA_Greedy_Task_Allocation_total_worker_time_inefficiency_number_of_workers_number_of_tasks.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..23d5410dd21cc6126c512e623d4c4e185186d9af
--- /dev/null
+++ b/data/sampled_jsons/GTA_Greedy_Task_Allocation_total_worker_time_inefficiency_number_of_workers_number_of_tasks.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "ATA: Adaptive Task Allocation for Efficient Resource ...", "date": "", "ddg_snippet": "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": "icml.cc", "link": "https://icml.cc/virtual/2025/poster/46650", "content": "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": "ATA: Adaptive Task Allocation for Efficient Resource ...", "date": "", "ddg_snippet": "2 Feb 2025 — The total worker time ratio increases because GTA becomes less efficient, using more resources than necessary. The runtime ratio grows for ATA ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2502.00775v1", "content": "2 Feb 2025 — The total worker time ratio increases because GTA becomes less efficient, using more resources than necessary. The runtime ratio grows for ATA ..."}
+{"idx": 2, "title": "ATA: Adaptive Task Allocation for Efficient Resource ...", "date": "", "ddg_snippet": "19 Jun 2025 — Key metrics include runtime efficiency, total worker time , average iteration time, and cumulative regret. Theoretical Claims: n/a.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=1BaC3AdG1i&noteId=On98HLkOqs", "content": "19 Jun 2025 — Key metrics include runtime efficiency, total worker time , average iteration time, and cumulative regret. Theoretical Claims: n/a."}
+{"idx": 3, "title": "Profit-driven Task Assignment in Spatial Crowdsourcing", "date": "", "ddg_snippet": "by J Xia · Cited by 63 — For the sake of efficiency , we pro- pose a Greedy Task Assignment ( GTA ) algorithm that tries to give priority to the tasks with the highest possible reward per ...", "subpage_snippet": "", "source": "www.ijcai.org", "link": "https://www.ijcai.org/proceedings/2019/0265.pdf", "content": "by J Xia · Cited by 63 — For the sake of efficiency , we pro- pose a Greedy Task Assignment ( GTA ) algorithm that tries to give priority to the tasks with the highest possible reward per ..."}
+{"idx": 4, "title": "ATA: Adaptive Task Allocation for Efficient Resource ...", "date": "", "ddg_snippet": "The paper demonstrates how existing \" greedy \" task allocation strategies achieve fast runtimes but waste computational resources when many workers compete for ...", "subpage_snippet": "", "source": "www.alphaxiv.org", "link": "https://www.alphaxiv.org/overview/2502.00775v2", "content": "The paper demonstrates how existing \" greedy \" task allocation strategies achieve fast runtimes but waste computational resources when many workers compete for ..."}
+{"idx": 5, "title": "Coalition-based task assignment with priority-aware ...", "date": "", "ddg_snippet": "by Y Zhao · 2024 · Cited by 23 — Specifically, the greedy algorithm is a non-reducing reward allocation strategy that incentivizes workers to enlarge a coalition to achieve a ...", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/article/10.1007/s00778-023-00802-3", "content": "by Y Zhao · 2024 · Cited by 23 — Specifically, the greedy algorithm is a non-reducing reward allocation strategy that incentivizes workers to enlarge a coalition to achieve a ..."}
+{"idx": 6, "title": "Task allocation for maximum cooperation in complex ...", "date": "", "ddg_snippet": "by J Wang · 2024 · Cited by 1 — Based on them, we present two approaches, one greedy -based and the other A*-based, to assign actors for maximum cooperation in the complex ...", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S0950705124006233", "content": "by J Wang · 2024 · Cited by 1 — Based on them, we present two approaches, one greedy -based and the other A*-based, to assign actors for maximum cooperation in the complex ..."}
+{"idx": 7, "title": "Multiattribute E-CARGO Task Assignment Model Based on ...", "date": "", "ddg_snippet": "by Z Liu · Cited by 8 — 3) This article simulates the demand problems between tasks and workers in a large number of real-world scenarios, proving the high performance and excel- lent ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=dhQ3OKfzRu", "content": "by Z Liu · Cited by 8 — 3) This article simulates the demand problems between tasks and workers in a large number of real-world scenarios, proving the high performance and excel- lent ..."}
+{"idx": 8, "title": "Online Program Events Archive", "date": "", "ddg_snippet": "We document that time spent traveling has been dropping consistently during these years, and posit that the ultimate cause is a dramatic and ongoing ...", "subpage_snippet": "", "source": "annualmeeting.mytrb.org", "link": "https://annualmeeting.mytrb.org/OnlineProgramArchive/Details/19065", "content": "We document that time spent traveling has been dropping consistently during these years, and posit that the ultimate cause is a dramatic and ongoing ..."}
+{"idx": 9, "title": "Risk on — Greater Fool – Authored by Garth Turner", "date": "", "ddg_snippet": "The recent case in Alta where the Premier resigned due to excessive spending on personal matters, many people are totally disgusted how people with ...", "subpage_snippet": "", "source": "www.greaterfool.ca", "link": "https://www.greaterfool.ca/2014/03/30/risk-on/", "content": "The recent case in Alta where the Premier resigned due to excessive spending on personal matters, many people are totally disgusted how people with ..."}
diff --git a/data/sampled_jsons/GUI-Xplore_Table_4_rule-based_vs_GPT-based_clustering_comparison_sitearxiv.org.jsonl b/data/sampled_jsons/GUI-Xplore_Table_4_rule-based_vs_GPT-based_clustering_comparison_sitearxiv.org.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..05e195267055c3c1337eeab3b4fb76cf3720b114
--- /dev/null
+++ b/data/sampled_jsons/GUI-Xplore_Table_4_rule-based_vs_GPT-based_clustering_comparison_sitearxiv.org.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "GUI-Xplore: Empowering Generalizable GUI Agents with One ...", "date": "", "ddg_snippet": "Mar 22, 2025 · Based on this, we introduce Xplore -Agent, a baseline framework utilizing a GUI Transition Graph to model the exploration environment. Experiments show that this approach significantly improves cross-app generalization and multi-task capabilities.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2503.17709v1", "content": "Mar 22, 2025 · Based on this, we introduce Xplore -Agent, a baseline framework utilizing a GUI Transition Graph to model the exploration environment. Experiments show that this approach significantly improves cross-app generalization and multi-task capabilities."}
+{"idx": 1, "title": "[2503.17709] GUI-Xplore: Empowering Generalizable GUI Agents ... GPT4Table: Can Large Language Models Understand Structured ... GUI Agents with Foundation Models: A Comprehensive Survey A Survey on (M)LLM-Based GUI Agents - arXiv.org API Agents vs. GUI Agents: Divergence and Convergence GUI -Xplore: Empowering Generalizable GUI Agents with One Explorat… API Agents vs. GUI Agents : Divergence and Convergence - arXiv.org GUI -World: A Dataset for GUI -oriented Multimodal LLM- based Agents GUI Agents with Foundation Models: A Comprehensive Survey - arXi… GPT4Table: Can Large Language Models Understand Structured Tabl… API Agents vs. GUI Agents : Divergence and Convergence - arXiv.org GUI-World: A Dataset for GUI-oriented Multimodal LLM-based Agents", "date": "", "ddg_snippet": "Mar 22, 2025 · GUI agents hold significant potential to enhance the experience and efficiency of human-device interaction. However, current methods face challenges in generalizing across applications (apps) and tasks, primarily due to two fundamental limitations in existing datasets. First, these datasets overlook developer-induced structural variations among apps, limiting the transferability of knowledge ... The benchmark we create includes seven tasks, each with its own unique challenges, e.g., cell lookup, row retrieval, and size detection. We conduct a series of evaluations on GPT -3.5 and GPT - 4 . We find that the performance varied depending on several input choices, including table input format, content order, role prompting, and partition marks. Feb 13, 2025 · However, traditional rule-based and reinforcement learning- based methods struggle with tasks requiring human-like interactions (Liu et al., 2018), limiting their applicability. Figure 1: The foundational aspects and goals of GUI agents. Figure 2: Illustration of the growth trend in the field of GUI agents with foundation models. Abstract Graphical User Interface ( GUI ) Agents have emerged as a transformative paradigm in human-computer interaction, evolving from rule-based automation scripts to sophisticated AI-driven systems capable of understanding and executing complex interface operations. This survey provides a comprehensive examination of the rapidly advancing field of LLM- based GUI Agents, systematically ... In summary, the strategic considerations for deploying API- based versus GUI - based agents depend on the nature of the target software, the level of integration or validation required, and long-term sustainability concerns. What is GUI Xplore? GUI-Xplore integrates pre-recorded exploration videos providing contextual insights, alongside five hierarchically structured downstream tasks designed to comprehensively evaluate GUI agent capabilities. What is the difference between gpt-4o and O1? Compared to GUI -only execu-tion, the GUI with API setup achieves a 6.5% reduction in steps for GPT -4o and a substantial 58.5% reduction for o1. This improvement for o1 is due to its ability to strategi-cally use APIs to bypass multiple GUI interactions. Is gpt-4o better than open-source videollms? Commercial ImageLLMs, notably GPT-4V and GPT-4o, consistently outperform open-source VideoLLMs in zero-shot settings. As detailed in Table 4, GPT-4o exhibits superior performance across all GUI scenarios in complex tasks, reflected in its high scores in both multiple-choice and free-form queries, with an average of 84.8% and 3.573. Is UI perception a problem in GUI agent research? UI perception is also an important problem in GUI agent research, some work (You et al., 2024; Zhang et al., 2021; Lu et al., 2024b) focuses on understanding and processing UIs, rather than building the agent. What is the difference between change order and gpt-4? Change order refers to put external text (like questions, statement) ahead of tables. Noted that \" GPT - 4 \" refers to the evaluation outcomes utilizing the GPT - 4 model. Given the resource-intensive nature of GPT - 4 calls, we only conducting the GPT - 4 inference test on a subset of 300 samples (randomly sampled) from each task set. Does integrating APIs improve SR for gpt-4o and O1? The results show that incorporating APIs improves SR for both GPT-4o (an increase of 6.1%) and o1 (an increase of 8.2%), demonstrating the benefit of combining GUI and API actions. GPT-4o benefits primarily by avoiding control detection failures, which often arise from unannotated in-terface elements. As detailed in Table 4 , GPT -4o exhibits superior performance across all GUI scenarios in complex tasks, reflected in its high scores in both multiple-choice and free-form queries, with an average of 84.8% and 3.573.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2503.17709", "content": "Mar 22, 2025 · GUI agents hold significant potential to enhance the experience and efficiency of human-device interaction. However, current methods face challenges in generalizing across applications (apps) and tasks, primarily due to two fundamental limitations in existing datasets. First, these datasets overlook developer-induced structural variations among apps, limiting the transferability of knowledge ... The benchmark we create includes seven tasks, each with its own unique challenges, e.g., cell lookup, row retrieval, and size detection. We conduct a series of evaluations on GPT -3.5 and GPT - 4 . We find that the performance varied depending on several input choices, including table input format, content order, role prompting, and partition marks. Feb 13, 2025 · However, traditional rule-based and reinforcement learning- based methods struggle with tasks requiring human-like interactions (Liu et al., 2018), limiting their applicability. Figure 1: The foundational aspects and goals of GUI agents. Figure 2: Illustration of the growth trend in the field of GUI agents with foundation models. Abstract Graphical User Interface ( GUI ) Agents have emerged as a transformative paradigm in human-computer interaction, evolving from rule-based automation scripts to sophisticated AI-driven systems capable of understanding and executing complex interface operations. This survey provides a comprehensive examination of the rapidly advancing field of LLM- based GUI Agents, systematically ... In summary, the strategic considerations for deploying API- based versus GUI - based agents depend on the nature of the target software, the level of integration or validation required, and long-term sustainability concerns. What is GUI Xplore? GUI-Xplore integrates pre-recorded exploration videos providing contextual insights, alongside five hierarchically structured downstream tasks designed to comprehensively evaluate GUI agent capabilities. What is the difference between gpt-4o and O1? Compared to GUI -only execu-tion, the GUI with API setup achieves a 6.5% reduction in steps for GPT -4o and a substantial 58.5% reduction for o1. This improvement for o1 is due to its ability to strategi-cally use APIs to bypass multiple GUI interactions. Is gpt-4o better than open-source videollms? Commercial ImageLLMs, notably GPT-4V and GPT-4o, consistently outperform open-source VideoLLMs in zero-shot settings. As detailed in Table 4, GPT-4o exhibits superior performance across all GUI scenarios in complex tasks, reflected in its high scores in both multiple-choice and free-form queries, with an average of 84.8% and 3.573. Is UI perception a problem in GUI agent research? UI perception is also an important problem in GUI agent research, some work (You et al., 2024; Zhang et al., 2021; Lu et al., 2024b) focuses on understanding and processing UIs, rather than building the agent. What is the difference between change order and gpt-4? Change order refers to put external text (like questions, statement) ahead of tables. Noted that \" GPT - 4 \" refers to the evaluation outcomes utilizing the GPT - 4 model. Given the resource-intensive nature of GPT - 4 calls, we only conducting the GPT - 4 inference test on a subset of 300 samples (randomly sampled) from each task set. Does integrating APIs improve SR for gpt-4o and O1? The results show that incorporating APIs improves SR for both GPT-4o (an increase of 6.1%) and o1 (an increase of 8.2%), demonstrating the benefit of combining GUI and API actions. GPT-4o benefits primarily by avoiding control detection failures, which often arise from unannotated in-terface elements. As detailed in Table 4 , GPT -4o exhibits superior performance across all GUI scenarios in complex tasks, reflected in its high scores in both multiple-choice and free-form queries, with an average of 84.8% and 3.573."}
+{"idx": 2, "title": "GPT4Table: Can Large Language Models Understand Structured ...", "date": "", "ddg_snippet": "The benchmark we create includes seven tasks, each with its own unique challenges, e.g., cell lookup, row retrieval, and size detection. We conduct a series of evaluations on GPT -3.5 and GPT - 4 . We find that the performance varied depending on several input choices, including table input format, content order, role prompting, and partition marks.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2305.13062v3", "content": "The benchmark we create includes seven tasks, each with its own unique challenges, e.g., cell lookup, row retrieval, and size detection. We conduct a series of evaluations on GPT -3.5 and GPT - 4 . We find that the performance varied depending on several input choices, including table input format, content order, role prompting, and partition marks."}
+{"idx": 3, "title": "GUI Agents with Foundation Models: A Comprehensive Survey", "date": "", "ddg_snippet": "Feb 13, 2025 · However, traditional rule-based and reinforcement learning- based methods struggle with tasks requiring human-like interactions (Liu et al., 2018), limiting their applicability. Figure 1: The foundational aspects and goals of GUI agents. Figure 2: Illustration of the growth trend in the field of GUI agents with foundation models.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2411.04890v2", "content": "Feb 13, 2025 · However, traditional rule-based and reinforcement learning- based methods struggle with tasks requiring human-like interactions (Liu et al., 2018), limiting their applicability. Figure 1: The foundational aspects and goals of GUI agents. Figure 2: Illustration of the growth trend in the field of GUI agents with foundation models."}
+{"idx": 4, "title": "A Survey on (M)LLM-Based GUI Agents - arXiv.org", "date": "", "ddg_snippet": "Abstract Graphical User Interface ( GUI ) Agents have emerged as a transformative paradigm in human-computer interaction, evolving from rule-based automation scripts to sophisticated AI-driven systems capable of understanding and executing complex interface operations. This survey provides a comprehensive examination of the rapidly advancing field of LLM- based GUI Agents, systematically ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2504.13865v1", "content": "Abstract Graphical User Interface ( GUI ) Agents have emerged as a transformative paradigm in human-computer interaction, evolving from rule-based automation scripts to sophisticated AI-driven systems capable of understanding and executing complex interface operations. This survey provides a comprehensive examination of the rapidly advancing field of LLM- based GUI Agents, systematically ..."}
+{"idx": 5, "title": "API Agents vs. GUI Agents: Divergence and Convergence", "date": "", "ddg_snippet": "In summary, the strategic considerations for deploying API- based versus GUI - based agents depend on the nature of the target software, the level of integration or validation required, and long-term sustainability concerns.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2503.11069", "content": "In summary, the strategic considerations for deploying API- based versus GUI - based agents depend on the nature of the target software, the level of integration or validation required, and long-term sustainability concerns."}
+{"idx": 6, "title": "GUI-World: A Dataset for GUI-oriented Multimodal LLM-based Agents", "date": "", "ddg_snippet": "As detailed in Table 4 , GPT -4o exhibits superior performance across all GUI scenarios in complex tasks, reflected in its high scores in both multiple-choice and free-form queries, with an average of 84.8% and 3.573.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2406.10819", "content": "As detailed in Table 4 , GPT -4o exhibits superior performance across all GUI scenarios in complex tasks, reflected in its high scores in both multiple-choice and free-form queries, with an average of 84.8% and 3.573."}
+{"idx": 7, "title": "Advancing Mobile GUI Agents: A Verifier-Driven Approach ...", "date": "", "ddg_snippet": "5 Sept 2025 — A rule-based extractor is applied to detect UI elements, such as button, checkbox, and textbox, that are clickable, long-clickable, scrollable, ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2503.15937v3", "content": "5 Sept 2025 — A rule-based extractor is applied to detect UI elements, such as button, checkbox, and textbox, that are clickable, long-clickable, scrollable, ..."}
+{"idx": 8, "title": "Towards Efficient and Affordable LLM-based Exploration ...", "date": "", "ddg_snippet": "15 May 2025 — We attempted two methods to match the abstract UI states: the rule-based method and the LLM-based method.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2505.10593v1", "content": "15 May 2025 — We attempted two methods to match the abstract UI states: the rule-based method and the LLM-based method."}
+{"idx": 9, "title": "MLLM-Based UI2Code Automation Guided by UI Layout ...", "date": "", "ddg_snippet": "12 Jun 2025 — UI element grouping methods can be categorized into three main types: heuristic rules , clustering , and deep learning techniques. Screen ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2506.10376v1", "content": "12 Jun 2025 — UI element grouping methods can be categorized into three main types: heuristic rules , clustering , and deep learning techniques. Screen ..."}
diff --git a/data/sampled_jsons/Gao_et_al._2024_sparse_autoencoders_dead_latents_modifications.jsonl b/data/sampled_jsons/Gao_et_al._2024_sparse_autoencoders_dead_latents_modifications.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..ca685a398e15a24782ab1c1db7a504ea92a425e1
--- /dev/null
+++ b/data/sampled_jsons/Gao_et_al._2024_sparse_autoencoders_dead_latents_modifications.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "[2406.04093] Scaling and evaluating sparse autoencoders", "date": "", "ddg_snippet": "... the properties of autoencoder scaling is difficult due to the need to balance reconstruction and sparsity objectives and the presence of dead latents ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2406.04093", "content": "... the properties of autoencoder scaling is difficult due to the need to balance reconstruction and sparsity objectives and the presence of dead latents ..."}
+{"idx": 1, "title": "Scaling and evaluating sparse autoencoders", "date": "", "ddg_snippet": "Sparse autoencoders (SAEs) have shown great promise for finding features (Cunningham et al ., 2023 ; Bricken et al ., 2023 ; Templeton et al ., 2024 ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2406.04093v1", "content": "Sparse autoencoders (SAEs) have shown great promise for finding features (Cunningham et al ., 2023 ; Bricken et al ., 2023 ; Templeton et al ., 2024 ..."}
+{"idx": 2, "title": "Beyond Matryoshka: Revisiting Sparse Coding for Adaptive", "date": "", "ddg_snippet": "Leveraging recent advances in training sparse autoencoders (SAEs) (Cunningham et al ., 2023 ; Gao et al ., 2024 ) , we can train a lightweight 2 ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2503.01776v5", "content": "Leveraging recent advances in training sparse autoencoders (SAEs) (Cunningham et al ., 2023 ; Gao et al ., 2024 ) , we can train a lightweight 2 ..."}
+{"idx": 3, "title": "Research Report: Alternative sparsity methods for sparse", "date": "", "ddg_snippet": "Motivated by this question, Robert trained sparse autoencoders on a version of OthelloGPT (based on the work of Li et al ), a language model trained ...", "subpage_snippet": "", "source": "www.greaterwrong.com", "link": "https://www.greaterwrong.com/posts/ignCBxbqWWPYCdCCx/research-report-alternative-sparsity-methods-for-sparse", "content": "Motivated by this question, Robert trained sparse autoencoders on a version of OthelloGPT (based on the work of Li et al ), a language model trained ..."}
+{"idx": 4, "title": "Research Report: Alternative sparsity methods for sparse", "date": "", "ddg_snippet": "Motivated by this question, Robert trained sparse autoencoders on a version of OthelloGPT (based on the work of Li et al ), a language model trained ...", "subpage_snippet": "", "source": "www.lesswrong.com", "link": "https://www.lesswrong.com/posts/ignCBxbqWWPYCdCCx/research-report-alternative-sparsity-methods-for-sparse", "content": "Motivated by this question, Robert trained sparse autoencoders on a version of OthelloGPT (based on the work of Li et al ), a language model trained ..."}
+{"idx": 5, "title": "Shallow review of technical AI safety, 2024 - LessWrong 2.0", "date": "", "ddg_snippet": "Alignment evals with public test sets will probably be pretrained on, and as such will probably quickly stop meaning anything.", "subpage_snippet": "", "source": "www.greaterwrong.com", "link": "https://www.greaterwrong.com/posts/fAW6RXLKTLHC3WXkS/shallow-review-of-technical-ai-safety-2024", "content": "Alignment evals with public test sets will probably be pretrained on, and as such will probably quickly stop meaning anything."}
+{"idx": 6, "title": "Most Influential EMNLP Papers (2024-05 Version) –", "date": "", "ddg_snippet": "G-Eval: NLG Evaluation Using Gpt-4 with Better Human Alignment IF:6 Related Papers Related Patents Related Grants Related Venues Related Experts View ...", "subpage_snippet": "", "source": "resources.paperdigest.org", "link": "https://resources.paperdigest.org/2024/05/most-influential-emnlp-papers-2024-05/", "content": "G-Eval: NLG Evaluation Using Gpt-4 with Better Human Alignment IF:6 Related Papers Related Patents Related Grants Related Venues Related Experts View ..."}
+{"idx": 7, "title": "All-in-One Slider for Attribute Manipulation in Diffusion Models", "date": "", "ddg_snippet": "Inspired by the recent studies of sparse autoencoder (SAE) in LLMs (Karvonen et al . ... sparsely activated latent space of attributes, our ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2508.19195v1", "content": "Inspired by the recent studies of sparse autoencoder (SAE) in LLMs (Karvonen et al . ... sparsely activated latent space of attributes, our ..."}
+{"idx": 8, "title": "Enabling Precise Topic Alignment in Large Language Models via", "date": "", "ddg_snippet": "Although recent works have hypothesized that SAEs can be used for general topic alignment, ( Gao et al ., 2024 ; Templeton, 2024 ) , and in doing so ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2506.12576v2", "content": "Although recent works have hypothesized that SAEs can be used for general topic alignment, ( Gao et al ., 2024 ; Templeton, 2024 ) , and in doing so ..."}
+{"idx": 9, "title": "US20090055139A1 - Predictive discrete latent factor models for", "date": "", "ddg_snippet": "... data is an important data mining problem encountered in several domains such as social networks, recommendation systems, internet advertising, etc ...", "subpage_snippet": "", "source": "patents.google.com", "link": "https://patents.google.com/patent/US20090055139A1/en", "content": "... data is an important data mining problem encountered in several domains such as social networks, recommendation systems, internet advertising, etc ..."}
diff --git a/data/sampled_jsons/Gaussian_Splatting_anchor_interpolation_inverse_distance_weighted.jsonl b/data/sampled_jsons/Gaussian_Splatting_anchor_interpolation_inverse_distance_weighted.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..e31d57532285a96c3e17948d20b1a89b6a70c54d
--- /dev/null
+++ b/data/sampled_jsons/Gaussian_Splatting_anchor_interpolation_inverse_distance_weighted.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Inverse distance weighting - Wikipedia", "date": "", "ddg_snippet": "Inverse distance weighting (IDW) is a type of deterministic method for multivariate interpolation with a known homogeneously scattered set of points. The assigned values to unknown points are calculated with a weighted average of the values available at the known points.", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/Inverse_distance_weighting", "content": "Inverse distance weighting (IDW) is a type of deterministic method for multivariate interpolation with a known homogeneously scattered set of points. The assigned values to unknown points are calculated with a weighted average of the values available at the known points."}
+{"idx": 1, "title": "GS-IR: 3D Gaussian Splatting for Inverse Rendering", "date": "", "ddg_snippet": "We propose GS-IR, a novel inverse rendering approach based on 3D Gaussian Splatting (3DGS) that leverages for-ward mapping volume rendering to achieve photorealistic novel view synthesis and relighting results.", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2024/papers/Liang_GS-IR_3D_Gaussian_Splatting_for_Inverse_Rendering_CVPR_2024_paper.pdf", "content": "We propose GS-IR, a novel inverse rendering approach based on 3D Gaussian Splatting (3DGS) that leverages for-ward mapping volume rendering to achieve photorealistic novel view synthesis and relighting results."}
+{"idx": 2, "title": "ADC-GS: Anchor-Driven Deformable and Compressed Gaussian ...", "date": "", "ddg_snippet": "We propose anchor -driven deformable and compressed Gaussian Splatting (ADC-GS) for dynamic scene recon-struction. By leveraging compact anchors to eficiently model 4D scenes, our approach achieves an extraor-dinary storage reduction of up to 200× over existing 4DGS methods.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2505.08196", "content": "We propose anchor -driven deformable and compressed Gaussian Splatting (ADC-GS) for dynamic scene recon-struction. By leveraging compact anchors to eficiently model 4D scenes, our approach achieves an extraor-dinary storage reduction of up to 200× over existing 4DGS methods."}
+{"idx": 3, "title": "How inverse distance weighted interpolation works - Esri", "date": "", "ddg_snippet": "Inverse distance weighted (IDW) interpolation explicitly makes the assumption that things that are close to one another are more alike than those that are farther apart. To predict a value for any unmeasured location, IDW uses the measured values surrounding the prediction location.", "subpage_snippet": "", "source": "pro.arcgis.com", "link": "https://pro.arcgis.com/en/pro-app/latest/help/analysis/geostatistical-analyst/how-inverse-distance-weighted-interpolation-works.htm", "content": "Inverse distance weighted (IDW) interpolation explicitly makes the assumption that things that are close to one another are more alike than those that are farther apart. To predict a value for any unmeasured location, IDW uses the measured values surrounding the prediction location."}
+{"idx": 4, "title": "An adaptive inverse-distance weighting spatial interpolation ...", "date": "", "ddg_snippet": "Sep 1, 2008 · In this paper, we propose an adaptive inverse - distance weighting (AIDW) method to take advantage of the computational simplicity of IDW, but to provide the additional flexibility to accommodate variability in the distance-decay relationship over the study area.", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S0098300408000721", "content": "Sep 1, 2008 · In this paper, we propose an adaptive inverse - distance weighting (AIDW) method to take advantage of the computational simplicity of IDW, but to provide the additional flexibility to accommodate variability in the distance-decay relationship over the study area."}
+{"idx": 5, "title": "Inverse Distance Weighting (IDW) Interpolation - GIS Geography", "date": "", "ddg_snippet": "Whether you want to estimate the amount of rainfall or elevation in specific areas, you will probably want to learn about the different interpolation methods like inverse distance weighted .", "subpage_snippet": "", "source": "gisgeography.com", "link": "https://gisgeography.com/inverse-distance-weighting-idw-interpolation/", "content": "Whether you want to estimate the amount of rainfall or elevation in specific areas, you will probably want to learn about the different interpolation methods like inverse distance weighted ."}
+{"idx": 6, "title": "Interpolation, Kriging, Gaussian Processes - Duke University", "date": "", "ddg_snippet": "Nov 7, 2021 · In general, the closer xi is to xo, the more yi should influence the interpolated estimate yo. ˆ wi( xo) xi xi So the weights should be smaller for points that are close to xo, and small for values far from xo. For example, the weights wi( xo) can be specified to decrease with the doi xo (Euclidean) distance between and xi,", "subpage_snippet": "", "source": "people.duke.edu", "link": "https://people.duke.edu/~hpgavin/risk/interpolation.pdf", "content": "Nov 7, 2021 · In general, the closer xi is to xo, the more yi should influence the interpolated estimate yo. ˆ wi( xo) xi xi So the weights should be smaller for points that are close to xo, and small for values far from xo. For example, the weights wi( xo) can be specified to decrease with the doi xo (Euclidean) distance between and xi,"}
+{"idx": 7, "title": "Awesome 3D Gaussian Splatting Paper List", "date": "", "ddg_snippet": "The motion of a graph's links is propagated to individual Gaussians via dual quaternion skinning, with learnable weight painting functions that ...", "subpage_snippet": "", "source": "mrnerf.github.io", "link": "https://mrnerf.github.io/awesome-3D-gaussian-splatting/", "content": "The motion of a graph's links is propagated to individual Gaussians via dual quaternion skinning, with learnable weight painting functions that ..."}
+{"idx": 8, "title": "Gaussian Variation Field Diffusion for High-fidelity", "date": "", "ddg_snippet": "Our VAE framework encodes the canonical 3D Gaussian Splatting (3DGS) of objects and compresses each Gaussian ’s attribute variations ( i.e ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2507.23785v1", "content": "Our VAE framework encodes the canonical 3D Gaussian Splatting (3DGS) of objects and compresses each Gaussian ’s attribute variations ( i.e ..."}
+{"idx": 9, "title": "CVPR 2025 Papers", "date": "", "ddg_snippet": "SplatAD: Real-Time Lidar and Camera Rendering with 3D Gaussian Splatting for Autonomous Driving ... Epipolar Depth Priors in 3D Gaussian Splatting", "subpage_snippet": "", "source": "cvpr.thecvf.com", "link": "https://cvpr.thecvf.com/virtual/2025/papers.html", "content": "SplatAD: Real-Time Lidar and Camera Rendering with 3D Gaussian Splatting for Autonomous Driving ... Epipolar Depth Priors in 3D Gaussian Splatting"}
diff --git a/data/sampled_jsons/GenAI_Arena-_An_Open_Evaluation_Platform_for_Generative_Models_paper.jsonl b/data/sampled_jsons/GenAI_Arena-_An_Open_Evaluation_Platform_for_Generative_Models_paper.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..ebc4072d48c05680f7d61265cc99467270df890d
--- /dev/null
+++ b/data/sampled_jsons/GenAI_Arena-_An_Open_Evaluation_Platform_for_Generative_Models_paper.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "GenAI Arena : An Open Evaluation Platform for Generative Models", "date": "", "ddg_snippet": "This paper proposes an open platform GenAI - Arena to evaluate different image and video generative models , where users can actively participate in evaluating these models .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2406.04485", "content": "This paper proposes an open platform GenAI - Arena to evaluate different image and video generative models , where users can actively participate in evaluating these models ."}
+{"idx": 1, "title": "GenAI Arena : An Open Evaluation Platform for Generative Models", "date": "", "ddg_snippet": "This paper proposes an open platform GenAI - Arena to evaluate different image and video generative models , where users can actively participate in evaluating these models .", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=0Gmi8TkUC7", "content": "This paper proposes an open platform GenAI - Arena to evaluate different image and video generative models , where users can actively participate in evaluating these models ."}
+{"idx": 2, "title": "GenAI Arena : An Open Evaluation Platform for", "date": "", "ddg_snippet": "This paper proposes an open platform GENAI - ARENA to evaluate different image and video generative models , where users can actively participate in evaluating these models .", "subpage_snippet": "", "source": "papers.nips.cc", "link": "https://papers.nips.cc/paper_files/paper/2024/file/92249f9233286e437f808fa535d88b26-Paper-Datasets_and_Benchmarks_Track.pdf", "content": "This paper proposes an open platform GENAI - ARENA to evaluate different image and video generative models , where users can actively participate in evaluating these models ."}
+{"idx": 3, "title": "GenAI - Arena : An Open Platform for Community-Based Evaluation ...", "date": "", "ddg_snippet": "In this study, GenAI - Arena , an open platform driven by community voting is introduced to rank generative models across text-to-image, image editing, and text-to-video tasks based on user preferences for transparency.", "subpage_snippet": "", "source": "www.marktechpost.com", "link": "https://www.marktechpost.com/2024/06/12/genai-arena-an-open-platform-for-community-based-evaluation-of-generative-ai-models/", "content": "In this study, GenAI - Arena , an open platform driven by community voting is introduced to rank generative models across text-to-image, image editing, and text-to-video tasks based on user preferences for transparency."}
+{"idx": 4, "title": "3D Arena : An Open Platform for Generative 3D Evaluation - Paper ...", "date": "", "ddg_snippet": "To address this gap, we present 3D Arena , an open platform for evaluating image-to-3D generation models through large-scale human preference collection using pairwise comparisons.", "subpage_snippet": "", "source": "www.chatpaper.ai", "link": "https://www.chatpaper.ai/dashboard/paper/c7050477-41a9-4749-92bc-0dd2909069d1", "content": "To address this gap, we present 3D Arena , an open platform for evaluating image-to-3D generation models through large-scale human preference collection using pairwise comparisons."}
+{"idx": 5, "title": "3D Arena : An Open Platform for Generative 3D Evaluation", "date": "", "ddg_snippet": "To address this gap, we present 3D Arena , an open platform for evaluating image-to-3D generation models through large-scale human preference collection using pairwise comparisons.", "subpage_snippet": "", "source": "papers.cool", "link": "https://papers.cool/arxiv/2506.18787", "content": "To address this gap, we present 3D Arena , an open platform for evaluating image-to-3D generation models through large-scale human preference collection using pairwise comparisons."}
+{"idx": 6, "title": "(PDF) 3D Arena : An Open Platform for Generative 3D Evaluation", "date": "", "ddg_snippet": "Genai arena : An open evaluation platform for generative models . Advances in Neural. Information Processing Systems, 37:79889–79908, 2024.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/392942057_3D_Arena_An_Open_Platform_for_Generative_3D_Evaluation", "content": "Genai arena : An open evaluation platform for generative models . Advances in Neural. Information Processing Systems, 37:79889–79908, 2024."}
+{"idx": 7, "title": "AIGC Weekly | AIGC Top Papers and AI news of the week", "date": "", "ddg_snippet": "9.) GenAI Arena : An Open Evaluation Platform for Generative Models ( paper | space ). Generative AI has made remarkable strides to revolutionize fields such as image and video generation .", "subpage_snippet": "", "source": "newsletter.aigc.news", "link": "https://newsletter.aigc.news/p/aigc-weekly-72", "content": "9.) GenAI Arena : An Open Evaluation Platform for Generative Models ( paper | space ). Generative AI has made remarkable strides to revolutionize fields such as image and video generation ."}
+{"idx": 8, "title": "Collections - Hugging Face", "date": "", "ddg_snippet": "GenAI Arena : An Open Evaluation Platform for Generative Models .World Model on Million-Length Video And Language With RingAttention. Paper • 2402.08268 • Published Feb 13, 2024 •. 38.", "subpage_snippet": "", "source": "hf.global-rail.com", "link": "https://hf.global-rail.com/collections?paper=2406.04485", "content": "GenAI Arena : An Open Evaluation Platform for Generative Models .World Model on Million-Length Video And Language With RingAttention. Paper • 2402.08268 • Published Feb 13, 2024 •. 38."}
+{"idx": 9, "title": "GitHub - AlbaraaQ/ggbaraa", "date": "", "ddg_snippet": "2024 Jun 10: GenAI - Arena Paper is out. It is featured on Huggingface Daily Papers .title={ GenAI Arena : An Open Evaluation Platform for Generative Models }", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/AlbaraaQ/ggbaraa", "content": "2024 Jun 10: GenAI - Arena Paper is out. It is featured on Huggingface Daily Papers .title={ GenAI Arena : An Open Evaluation Platform for Generative Models }"}
diff --git a/data/sampled_jsons/GenAI_Arena_Elo_rating_formula.jsonl b/data/sampled_jsons/GenAI_Arena_Elo_rating_formula.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..9bf23aa01e5f9b7f532cdfe915afe270806db80d
--- /dev/null
+++ b/data/sampled_jsons/GenAI_Arena_Elo_rating_formula.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "PDF GenAI Arena: An Open Evaluation Platform for Generative Models", "date": "", "ddg_snippet": "Figure 1: GenAI Arena contains threecomponents: (1) text-to-image, text-to-video and image editing arena , which accept community voting to obtain the preference pairs. (2) The leaderboard utilizes the preference pairs to calculate elo ranking for all the evaluated models. (3) We further release GenAI -Bench to judge different multimodal LLM judges.", "subpage_snippet": "", "source": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper_files/paper/2024/file/92249f9233286e437f808fa535d88b26-Paper-Datasets_and_Benchmarks_Track.pdf", "content": "Figure 1: GenAI Arena contains threecomponents: (1) text-to-image, text-to-video and image editing arena , which accept community voting to obtain the preference pairs. (2) The leaderboard utilizes the preference pairs to calculate elo ranking for all the evaluated models. (3) We further release GenAI -Bench to judge different multimodal LLM judges."}
+{"idx": 1, "title": "GenAI-Arena arXiv:2406.04485v1 [cs.AI] 6 Jun 2024", "date": "", "ddg_snippet": "GenAI-Arena , the first open platform to rank multi-modal generative AI based on user preferences. Discussion and case studies of collected user votes, showing the reliability of GenAI-Arena . GenAI -Bench, a public benchmark for judging MLLM's evaluation ability for generative tasks.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2406.04485v1", "content": "GenAI-Arena , the first open platform to rank multi-modal generative AI based on user preferences. Discussion and case studies of collected user votes, showing the reliability of GenAI-Arena . GenAI -Bench, a public benchmark for judging MLLM's evaluation ability for generative tasks."}
+{"idx": 2, "title": "GitHub - TIGER-AI-Lab/GenAI-Bench: Code and Data for \"GenAI Arena: An ...", "date": "", "ddg_snippet": "GenAI -Bench is a benchmark designed to benchmark MLLMs's ability in judging the quality of AI generative contents by comparing with human preferences collected through our 🤗 GenAI -Arnea. In other words, we are evaluting the capabilities of existing MLLMs as a multimodal reward model, and in this view, GenAI -Bench is a reward-bench for multimodal generative models. We filter existing votes ...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/TIGER-AI-Lab/GenAI-Bench", "content": "GenAI -Bench is a benchmark designed to benchmark MLLMs's ability in judging the quality of AI generative contents by comparing with human preferences collected through our 🤗 GenAI -Arnea. In other words, we are evaluting the capabilities of existing MLLMs as a multimodal reward model, and in this view, GenAI -Bench is a reward-bench for multimodal generative models. We filter existing votes ..."}
+{"idx": 3, "title": "arena_elo/README.md · TIGER-Lab/GenAI-Arena at main", "date": "", "ddg_snippet": "Computing the Elo Ratings apt-get -y install pkg-config pip install -r requirements.txt", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/spaces/TIGER-Lab/GenAI-Arena/blob/main/arena_elo/README.md", "content": "Computing the Elo Ratings apt-get -y install pkg-config pip install -r requirements.txt"}
+{"idx": 4, "title": "Arena Statistics - openlm.ai", "date": "", "ddg_snippet": "Statistics for Chatbot Arena We added some additional figures to show more statistics. The code for generating them is included in this notebook. Please note that you may see different orders from different ranking methods. This is expected for models that perform similarly, as demonstrated by the confidence interval in the bootstrap figure. Going forward, we prefer the classical Elo ...", "subpage_snippet": "", "source": "openlm.ai", "link": "https://openlm.ai/arena-stats/", "content": "Statistics for Chatbot Arena We added some additional figures to show more statistics. The code for generating them is included in this notebook. Please note that you may see different orders from different ranking methods. This is expected for models that perform similarly, as demonstrated by the confidence interval in the bootstrap figure. Going forward, we prefer the classical Elo ..."}
+{"idx": 5, "title": "How to Read Elo Ratings and Arena Scores for LLMs - Statology", "date": "", "ddg_snippet": "Elo ratings and Arena scores provide a dynamic, tournament-style way to rank large language models (LLMs) based on head-to-head comparisons, similar to how chess players are ranked through competitive matches. Unlike static benchmarks that test specific skills, these systems measure which models users actually prefer in conversation through millions of pairwise votes.", "subpage_snippet": "", "source": "www.statology.org", "link": "https://www.statology.org/how-to-read-elo-ratings-and-arena-scores-for-llms/", "content": "Elo ratings and Arena scores provide a dynamic, tournament-style way to rank large language models (LLMs) based on head-to-head comparisons, similar to how chess players are ranked through competitive matches. Unlike static benchmarks that test specific skills, these systems measure which models users actually prefer in conversation through millions of pairwise votes."}
+{"idx": 6, "title": "GenAI-Arena: An Open Platform for Community-Based Evaluation of ...", "date": "", "ddg_snippet": "In this study, GenAI-Arena , an open platform driven by community voting is introduced to rank generative models across text-to-image, image editing, and text-to-video tasks based on user preferences for transparency. Over 6000 votes collected from February to June 2024 were used to compile Elo leaderboards, identifying state-of-the-art models while analysis revealed potential biases. The high ...", "subpage_snippet": "", "source": "metaailabs.com", "link": "https://metaailabs.com/genai-arena-an-open-platform-for-community-based-evaluation-of-generative-ai-models/", "content": "In this study, GenAI-Arena , an open platform driven by community voting is introduced to rank generative models across text-to-image, image editing, and text-to-video tasks based on user preferences for transparency. Over 6000 votes collected from February to June 2024 were used to compile Elo leaderboards, identifying state-of-the-art models while analysis revealed potential biases. The high ..."}
+{"idx": 7, "title": "GenAI Arena: An Open Evaluation Platform for Generative Models", "date": "", "ddg_snippet": "This paper proposes an open platform GenAI-Arena to evaluate different image and video generative models, where users can actively participate in evaluating these models. By leveraging collective user feedback and votes, GenAI-Arena aims to provide a more democratic and accurate measure of model performance.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2406.04485", "content": "This paper proposes an open platform GenAI-Arena to evaluate different image and video generative models, where users can actively participate in evaluating these models. By leveraging collective user feedback and votes, GenAI-Arena aims to provide a more democratic and accurate measure of model performance."}
+{"idx": 8, "title": "GenAI-Arena] New Platform To Evaluate Generated Models By User Votes", "date": "", "ddg_snippet": "Summary In this paper, we propose an open platform called GenAI-Arena . The platform aims to rank generative models for three main tasks: text-to-image generation, image editing, and video generation based on user preferences. unlike other platforms, GenAI-Arena is run by community voting, which allows for transparent and and sustainable operation.", "subpage_snippet": "", "source": "ai-scholar.tech", "link": "https://ai-scholar.tech/en/articles/large-language-models/genai-arena", "content": "Summary In this paper, we propose an open platform called GenAI-Arena . The platform aims to rank generative models for three main tasks: text-to-image generation, image editing, and video generation based on user preferences. unlike other platforms, GenAI-Arena is run by community voting, which allows for transparent and and sustainable operation."}
+{"idx": 9, "title": "arena_elo/elo_rating/basic_stats.py · TIGER-Lab/GenAI-Arena at main", "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/spaces/TIGER-Lab/GenAI-Arena/blob/main/arena_elo/elo_rating/basic_stats.py", "content": "We're on a journey to advance and democratize artificial intelligence through open source and open science."}
diff --git a/data/sampled_jsons/GeoLLaVA-8K_largest_image_size_remote_sensing_8192_pixels_year_2025.jsonl b/data/sampled_jsons/GeoLLaVA-8K_largest_image_size_remote_sensing_8192_pixels_year_2025.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..b5b064c412b70bbd9e37e5304f6ce598361ced8f
--- /dev/null
+++ b/data/sampled_jsons/GeoLLaVA-8K_largest_image_size_remote_sensing_8192_pixels_year_2025.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "GeoLLaVA-8K: Scaling Remote-Sensing Multimodal Large Language ... GeoLLaVA-8K: Scaling Remote-Sensing Multimodal Large Language ... Spatial Resolution, Pixel Size, and Scale - Canada GeoPix : A multimodal large language model for pixel-level image GeoLLaVA -8K: Scaling Remote -Sensing Multimodal Large Language … GeoPix : A multimodal large language model for pixel-level image GeoPix: A multimodal large language model for pixel-level ...", "date": "", "ddg_snippet": "Abstract Ultra-high- resolution (UHR) remote sensing (RS) imagery offers valuable data for Earth observation but pose challenges for existing multimodal foundation models due to two key bottlenecks: (1) limited availability of UHR training data, and (2) token explosion caused by the large image size . To address data scarcity, we introduce SuperRS-VQA (avg. 8,376 × 8,376) and HighRS-VQA (avg ... Ultra-high- resolution (UHR) remote sensing (RS) imagery offers valuable data for Earth observation but pose challenges for existing multimodal foundation models due to two key bottlenecks: (1) limited availability of UHR training data, and (2) to- ken explosion caused by the large image size . To address data scarcity, we introduce SuperRS-VQA (avg. 8,376×8,376) and HighRS-VQA (avg. 2,000× ... As we mentioned in Chapter 1, most remote sensing images are composed of a matrix of picture elements, or pixels , which are the smallest units of an image . Image pixels are normally square and represent a certain area on an image . It is important to distinguish between pixel size and spatial resolution - they are not interchangeable. Are multimodal large language models effective in image- and region-level remote sensing? Abstract: Multimodal (MM) large language models (MLLMs) have achieved remarkable success in image- and region-level remote sensing (RS) image understanding tasks, such as image captioning (IC), visual question answering (VQA), and visual grounding. Does geollava-8k perform well on XLRs-bench? GeoLLaVA-8K achieved state-of-the-art performance on the XLRS-Bench benchmark, outperforming both open- and closed-source MLLMs and demonstrating the effectiveness of our token-efficient approach. These results underscore the value of domain-adapted, token-efficient modeling and provide a new foundation for high-fidelity image understanding in RS. How does geopix extend image understanding capabilities to the pixel level? In this article, we propose GeoPix, an RS MLLM that extends image understanding capabilities to the pixel level. This is achieved by equipping the MLLM with a mask predictor , which transforms visual features from the vision encoder into masks conditioned on the LLM’s segmentation token embeddings. Multimodal (MM) large language models (MLLMs) have achieved remarkable success in image - and region-level remote sensing (RS) image understanding tasks, such as image captioning (IC), visual question answering (VQA), and visual grounding. However, existing RS MLLMs lack pixel -level dialogue capability, which involves responding to user instructions with segmentation masks for specific ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2505.21375v1", "content": "Abstract Ultra-high- resolution (UHR) remote sensing (RS) imagery offers valuable data for Earth observation but pose challenges for existing multimodal foundation models due to two key bottlenecks: (1) limited availability of UHR training data, and (2) token explosion caused by the large image size . To address data scarcity, we introduce SuperRS-VQA (avg. 8,376 × 8,376) and HighRS-VQA (avg ... Ultra-high- resolution (UHR) remote sensing (RS) imagery offers valuable data for Earth observation but pose challenges for existing multimodal foundation models due to two key bottlenecks: (1) limited availability of UHR training data, and (2) to- ken explosion caused by the large image size . To address data scarcity, we introduce SuperRS-VQA (avg. 8,376×8,376) and HighRS-VQA (avg. 2,000× ... As we mentioned in Chapter 1, most remote sensing images are composed of a matrix of picture elements, or pixels , which are the smallest units of an image . Image pixels are normally square and represent a certain area on an image . It is important to distinguish between pixel size and spatial resolution - they are not interchangeable. Are multimodal large language models effective in image- and region-level remote sensing? Abstract: Multimodal (MM) large language models (MLLMs) have achieved remarkable success in image- and region-level remote sensing (RS) image understanding tasks, such as image captioning (IC), visual question answering (VQA), and visual grounding. Does geollava-8k perform well on XLRs-bench? GeoLLaVA-8K achieved state-of-the-art performance on the XLRS-Bench benchmark, outperforming both open- and closed-source MLLMs and demonstrating the effectiveness of our token-efficient approach. These results underscore the value of domain-adapted, token-efficient modeling and provide a new foundation for high-fidelity image understanding in RS. How does geopix extend image understanding capabilities to the pixel level? In this article, we propose GeoPix, an RS MLLM that extends image understanding capabilities to the pixel level. This is achieved by equipping the MLLM with a mask predictor , which transforms visual features from the vision encoder into masks conditioned on the LLM’s segmentation token embeddings. Multimodal (MM) large language models (MLLMs) have achieved remarkable success in image - and region-level remote sensing (RS) image understanding tasks, such as image captioning (IC), visual question answering (VQA), and visual grounding. However, existing RS MLLMs lack pixel -level dialogue capability, which involves responding to user instructions with segmentation masks for specific ..."}
+{"idx": 1, "title": "GeoLLaVA-8K: Scaling Remote-Sensing Multimodal Large Language ...", "date": "", "ddg_snippet": "Ultra-high- resolution (UHR) remote sensing (RS) imagery offers valuable data for Earth observation but pose challenges for existing multimodal foundation models due to two key bottlenecks: (1) limited availability of UHR training data, and (2) to- ken explosion caused by the large image size . To address data scarcity, we introduce SuperRS-VQA (avg. 8,376×8,376) and HighRS-VQA (avg. 2,000× ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2505.21375", "content": "Ultra-high- resolution (UHR) remote sensing (RS) imagery offers valuable data for Earth observation but pose challenges for existing multimodal foundation models due to two key bottlenecks: (1) limited availability of UHR training data, and (2) to- ken explosion caused by the large image size . To address data scarcity, we introduce SuperRS-VQA (avg. 8,376×8,376) and HighRS-VQA (avg. 2,000× ..."}
+{"idx": 2, "title": "GeoLLaVA-8K: Scaling Remote-Sensing Multimodal Large Language ... GeoLLaVA-8K: Scaling Remote-Sensing Multimodal Large ... - GitHub GeoLLaVA-8K/README.md at main · MiliLab/GeoLLaVA-8K · GitHub GeoLLaVA-8K: Scaling Remote-Sensing Multimodal Large Language ... GeoLLaVA-8K: Scaling Remote-Sensing Multimodal Large Language ... Spatial Resolution, Pixel Size, and Scale - Canada GeoPix : A multimodal large language model for pixel-level image GeoLLaVA -8K: Scaling Remote -Sensing Multimodal Large Language … GeoPix : A multimodal large language model for pixel-level image GeoPix: A multimodal large language model for pixel-level ...", "date": "", "ddg_snippet": "May 27, 2025 · Ultra-high- resolution (UHR) remote sensing (RS) imagery offers valuable data for Earth observation but pose challenges for existing multimodal foundation models due to two key bottlenecks: (1) limited availability of UHR training data, and (2) token explosion caused by the large image size . To address data scarcity, we introduce SuperRS-VQA (avg. 8,376$\\\\times$8,376) and HighRS-VQA (avg. 2,000 ... May 28, 2025 · Official repo for \" GeoLLaVA-8K : Scaling Remote - Sensing Multimodal Large Language Models to 8K Resolution \" - MiliLab/ GeoLLaVA-8K May 28, 2025 · [NeurlPS 2025 Spotlight] \" GeoLLaVA-8K : Scaling Remote - Sensing Multimodal Large Language Models to 8K Resolution \" - MiliLab/ GeoLLaVA-8K Abstract Ultra-high- resolution (UHR) remote sensing (RS) imagery offers valuable data for Earth observation but pose challenges for existing multimodal foundation models due to two key bottlenecks: (1) limited availability of UHR training data, and (2) token explosion caused by the large image size . To address data scarcity, we introduce SuperRS-VQA (avg. 8,376 × 8,376) and HighRS-VQA (avg ... Ultra-high- resolution (UHR) remote sensing (RS) imagery offers valuable data for Earth observation but pose challenges for existing multimodal foundation models due to two key bottlenecks: (1) limited availability of UHR training data, and (2) to- ken explosion caused by the large image size . To address data scarcity, we introduce SuperRS-VQA (avg. 8,376×8,376) and HighRS-VQA (avg. 2,000× ... As we mentioned in Chapter 1, most remote sensing images are composed of a matrix of picture elements, or pixels , which are the smallest units of an image . Image pixels are normally square and represent a certain area on an image . It is important to distinguish between pixel size and spatial resolution - they are not interchangeable. Are multimodal large language models effective in image- and region-level remote sensing? Abstract: Multimodal (MM) large language models (MLLMs) have achieved remarkable success in image- and region-level remote sensing (RS) image understanding tasks, such as image captioning (IC), visual question answering (VQA), and visual grounding. Does geollava-8k perform well on XLRs-bench? GeoLLaVA-8K achieved state-of-the-art performance on the XLRS-Bench benchmark, outperforming both open- and closed-source MLLMs and demonstrating the effectiveness of our token-efficient approach. These results underscore the value of domain-adapted, token-efficient modeling and provide a new foundation for high-fidelity image understanding in RS. How does geopix extend image understanding capabilities to the pixel level? In this article, we propose GeoPix, an RS MLLM that extends image understanding capabilities to the pixel level. This is achieved by equipping the MLLM with a mask predictor , which transforms visual features from the vision encoder into masks conditioned on the LLM’s segmentation token embeddings. Multimodal (MM) large language models (MLLMs) have achieved remarkable success in image - and region-level remote sensing (RS) image understanding tasks, such as image captioning (IC), visual question answering (VQA), and visual grounding. However, existing RS MLLMs lack pixel -level dialogue capability, which involves responding to user instructions with segmentation masks for specific ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2505.21375", "content": "May 27, 2025 · Ultra-high- resolution (UHR) remote sensing (RS) imagery offers valuable data for Earth observation but pose challenges for existing multimodal foundation models due to two key bottlenecks: (1) limited availability of UHR training data, and (2) token explosion caused by the large image size . To address data scarcity, we introduce SuperRS-VQA (avg. 8,376$\\\\times$8,376) and HighRS-VQA (avg. 2,000 ... May 28, 2025 · Official repo for \" GeoLLaVA-8K : Scaling Remote - Sensing Multimodal Large Language Models to 8K Resolution \" - MiliLab/ GeoLLaVA-8K May 28, 2025 · [NeurlPS 2025 Spotlight] \" GeoLLaVA-8K : Scaling Remote - Sensing Multimodal Large Language Models to 8K Resolution \" - MiliLab/ GeoLLaVA-8K Abstract Ultra-high- resolution (UHR) remote sensing (RS) imagery offers valuable data for Earth observation but pose challenges for existing multimodal foundation models due to two key bottlenecks: (1) limited availability of UHR training data, and (2) token explosion caused by the large image size . To address data scarcity, we introduce SuperRS-VQA (avg. 8,376 × 8,376) and HighRS-VQA (avg ... Ultra-high- resolution (UHR) remote sensing (RS) imagery offers valuable data for Earth observation but pose challenges for existing multimodal foundation models due to two key bottlenecks: (1) limited availability of UHR training data, and (2) to- ken explosion caused by the large image size . To address data scarcity, we introduce SuperRS-VQA (avg. 8,376×8,376) and HighRS-VQA (avg. 2,000× ... As we mentioned in Chapter 1, most remote sensing images are composed of a matrix of picture elements, or pixels , which are the smallest units of an image . Image pixels are normally square and represent a certain area on an image . It is important to distinguish between pixel size and spatial resolution - they are not interchangeable. Are multimodal large language models effective in image- and region-level remote sensing? Abstract: Multimodal (MM) large language models (MLLMs) have achieved remarkable success in image- and region-level remote sensing (RS) image understanding tasks, such as image captioning (IC), visual question answering (VQA), and visual grounding. Does geollava-8k perform well on XLRs-bench? GeoLLaVA-8K achieved state-of-the-art performance on the XLRS-Bench benchmark, outperforming both open- and closed-source MLLMs and demonstrating the effectiveness of our token-efficient approach. These results underscore the value of domain-adapted, token-efficient modeling and provide a new foundation for high-fidelity image understanding in RS. How does geopix extend image understanding capabilities to the pixel level? In this article, we propose GeoPix, an RS MLLM that extends image understanding capabilities to the pixel level. This is achieved by equipping the MLLM with a mask predictor , which transforms visual features from the vision encoder into masks conditioned on the LLM’s segmentation token embeddings. Multimodal (MM) large language models (MLLMs) have achieved remarkable success in image - and region-level remote sensing (RS) image understanding tasks, such as image captioning (IC), visual question answering (VQA), and visual grounding. However, existing RS MLLMs lack pixel -level dialogue capability, which involves responding to user instructions with segmentation masks for specific ..."}
+{"idx": 3, "title": "GeoLLaVA-8K: Scaling Remote-Sensing Multimodal Large ... - GitHub", "date": "", "ddg_snippet": "May 28, 2025 · Official repo for \" GeoLLaVA-8K : Scaling Remote - Sensing Multimodal Large Language Models to 8K Resolution \" - MiliLab/ GeoLLaVA-8K", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/MiliLab/GeoLLaVA-8K", "content": "May 28, 2025 · Official repo for \" GeoLLaVA-8K : Scaling Remote - Sensing Multimodal Large Language Models to 8K Resolution \" - MiliLab/ GeoLLaVA-8K"}
+{"idx": 4, "title": "GeoLLaVA-8K/README.md at main · MiliLab/GeoLLaVA-8K · GitHub", "date": "", "ddg_snippet": "May 28, 2025 · [NeurlPS 2025 Spotlight] \" GeoLLaVA-8K : Scaling Remote - Sensing Multimodal Large Language Models to 8K Resolution \" - MiliLab/ GeoLLaVA-8K", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/MiliLab/GeoLLaVA-8K/blob/main/README.md", "content": "May 28, 2025 · [NeurlPS 2025 Spotlight] \" GeoLLaVA-8K : Scaling Remote - Sensing Multimodal Large Language Models to 8K Resolution \" - MiliLab/ GeoLLaVA-8K"}
+{"idx": 5, "title": "Spatial Resolution, Pixel Size, and Scale - Canada", "date": "", "ddg_snippet": "As we mentioned in Chapter 1, most remote sensing images are composed of a matrix of picture elements, or pixels , which are the smallest units of an image . Image pixels are normally square and represent a certain area on an image . It is important to distinguish between pixel size and spatial resolution - they are not interchangeable.", "subpage_snippet": "", "source": "natural-resources.canada.ca", "link": "https://natural-resources.canada.ca/maps-tools-publications/satellite-elevation-air-photos/spatial-resolution-pixel-size-scale", "content": "As we mentioned in Chapter 1, most remote sensing images are composed of a matrix of picture elements, or pixels , which are the smallest units of an image . Image pixels are normally square and represent a certain area on an image . It is important to distinguish between pixel size and spatial resolution - they are not interchangeable."}
+{"idx": 6, "title": "[Literature Review] GeoLLaVA-8K: Scaling Remote- ...", "date": "", "ddg_snippet": "26 May 2025 — The paper addresses the challenge of adapting Multimodal Large Language Models (MLLMs) to Ultra-High- Resolution (UHR) Remote Sensing (RS) imagery (eg, 8Kx8K)", "subpage_snippet": "", "source": "www.themoonlight.io", "link": "https://www.themoonlight.io/en/review/geollava-8k-scaling-remote-sensing-multimodal-large-language-models-to-8k-resolution", "content": "26 May 2025 — The paper addresses the challenge of adapting Multimodal Large Language Models (MLLMs) to Ultra-High- Resolution (UHR) Remote Sensing (RS) imagery (eg, 8Kx8K)"}
+{"idx": 7, "title": "Upscale and enhance your image resolution up to 4x using AI | i2IMG", "date": "", "ddg_snippet": "While they can increase the size of an image , they often introduce blurring, artifacts, and a general loss of detail. The resulting images may be larger , but they lack the sharpness and clarity of a truly high-resolution image .", "subpage_snippet": "", "source": "www.i2img.com", "link": "https://www.i2img.com/upscale-image", "content": "While they can increase the size of an image , they often introduce blurring, artifacts, and a general loss of detail. The resulting images may be larger , but they lack the sharpness and clarity of a truly high-resolution image ."}
+{"idx": 8, "title": "TV Size To Distance Calculator (And The Science...) - RTINGS.com", "date": "", "ddg_snippet": "Compare Size to Distance Table Review List Review Index Graph Recommendation Custom Ratings.", "subpage_snippet": "", "source": "www.rtings.com", "link": "https://www.rtings.com/tv/reviews/by-size/size-to-distance-relationship", "content": "Compare Size to Distance Table Review List Review Index Graph Recommendation Custom Ratings."}
+{"idx": 9, "title": "Maxar Satellite Imagery : Worldview, GeoEye and... - GIS Geography", "date": "", "ddg_snippet": "Home » Remote Sensing » Maxar Satellite Imagery : Worldview, GeoEye and IKONOS.Each pixel in a Worldview-3 image is about the size of the home plate on a baseball diamond. That’s about 31 cm.", "subpage_snippet": "", "source": "gisgeography.com", "link": "https://gisgeography.com/maxar-satellite-imagery/", "content": "Home » Remote Sensing » Maxar Satellite Imagery : Worldview, GeoEye and IKONOS.Each pixel in a Worldview-3 image is about the size of the home plate on a baseball diamond. That’s about 31 cm."}
diff --git a/data/sampled_jsons/Germain_MADE_Masked_Autoencoder_for_Distribution_Estimation_ICML_2015.jsonl b/data/sampled_jsons/Germain_MADE_Masked_Autoencoder_for_Distribution_Estimation_ICML_2015.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..946f55a3cacc52ad17272e303240aa2a20807a2e
--- /dev/null
+++ b/data/sampled_jsons/Germain_MADE_Masked_Autoencoder_for_Distribution_Estimation_ICML_2015.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "From Autoencoders to Autoregressive Models (Masked Autoencoders", "date": "", "ddg_snippet": "Mathieu Germain , Karol Gregor, Iain Murray, Hugo Larochelle ( 2015 ): MADE : Masked Autoencoder for Distribution Estimation", "subpage_snippet": "", "source": "www.inference.vc", "link": "https://www.inference.vc/masked-autoencoders-icml-paper-highlight/", "content": "Mathieu Germain , Karol Gregor, Iain Murray, Hugo Larochelle ( 2015 ): MADE : Masked Autoencoder for Distribution Estimation"}
+{"idx": 1, "title": "Machine Learning Feb 2015", "date": "", "ddg_snippet": "Comments: This technical report is superseded by a paper entitled \" Character-level Convolutional Networks for Text Classification \" , arXiv ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/list/cs.LG/2015-02", "content": "Comments: This technical report is superseded by a paper entitled \" Character-level Convolutional Networks for Text Classification \" , arXiv ..."}
+{"idx": 2, "title": "32nd International Conference on Machine Learning (ICML), Lille", "date": "", "ddg_snippet": "ICML 2015 is the leading international machine learning conference and is supported by the International Machine Learning Society (IMLS) .", "subpage_snippet": "", "source": "videolectures.net", "link": "https://videolectures.net/icml2015_lille/", "content": "ICML 2015 is the leading international machine learning conference and is supported by the International Machine Learning Society (IMLS) ."}
+{"idx": 3, "title": "Reparameterization methods for MC gradient estimation — The", "date": "", "ddg_snippet": "Now we suppose that we know some function \\(g\\) such that for some easy distribution ... MADE : Masked Autoencoder for Distribution Estimation .”", "subpage_snippet": "", "source": "danmackinlay.name", "link": "https://danmackinlay.name/notebook/reparameterization_trick", "content": "Now we suppose that we know some function \\(g\\) such that for some easy distribution ... MADE : Masked Autoencoder for Distribution Estimation .”"}
+{"idx": 4, "title": "Reparameterization methods for MC gradient estimation — The", "date": "", "ddg_snippet": "Now we suppose that we know some function \\(g\\) such that for some easy distribution ... MADE : Masked Autoencoder for Distribution Estimation .”", "subpage_snippet": "", "source": "danmackinlay.name", "link": "https://danmackinlay.name/notebook/reparameterization_trick.html", "content": "Now we suppose that we know some function \\(g\\) such that for some easy distribution ... MADE : Masked Autoencoder for Distribution Estimation .”"}
+{"idx": 5, "title": "Autoregressive Flow", "date": "", "ddg_snippet": "... via an efficient parallelism, ( Germain et al., 2015 ) abandoned the RNN encoder and proposed a mind-blowing idea by using an masked autoencoder .", "subpage_snippet": "", "source": "www.weideng.org", "link": "https://www.weideng.org/posts/autoregressive_flow/", "content": "... via an efficient parallelism, ( Germain et al., 2015 ) abandoned the RNN encoder and proposed a mind-blowing idea by using an masked autoencoder ."}
+{"idx": 6, "title": "Karol Gregor - researchr alias", "date": "", "ddg_snippet": "MADE : Masked Autoencoder for Distribution Estimation Mathieu Germain , Karol Gregor , Iain Murray , Hugo Larochelle .", "subpage_snippet": "", "source": "researchr.org", "link": "https://researchr.org/alias/karol-gregor", "content": "MADE : Masked Autoencoder for Distribution Estimation Mathieu Germain , Karol Gregor , Iain Murray , Hugo Larochelle ."}
+{"idx": 7, "title": "Proceedings of the 32nd International Conference on Machine", "date": "", "ddg_snippet": "Coresets for Nonparametric Estimation - the Case of DP-Means Olivier Bachem , Mario Lucic , Andreas Krause . ... Distributed Estimation of ...", "subpage_snippet": "", "source": "researchr.org", "link": "https://researchr.org/publication/icml-2015", "content": "Coresets for Nonparametric Estimation - the Case of DP-Means Olivier Bachem , Mario Lucic , Andreas Krause . ... Distributed Estimation of ..."}
+{"idx": 8, "title": "Flow-based Deep Generative Models | Lil'Log", "date": "", "ddg_snippet": "... we repeatedly substitute the variable for the new one according to the change of variables theorem and eventually obtain a probability distribution ...", "subpage_snippet": "", "source": "lilianweng.github.io", "link": "https://lilianweng.github.io/posts/2018-10-13-flow-models/", "content": "... we repeatedly substitute the variable for the new one according to the change of variables theorem and eventually obtain a probability distribution ..."}
+{"idx": 9, "title": "Publications by Iain Murray", "date": "", "ddg_snippet": "MADE : Masked Autoencoder for Distribution Estimation Mathieu Germain , Karol Gregor, Iain Murray and Hugo Larochelle .", "subpage_snippet": "", "source": "homepages.inf.ed.ac.uk", "link": "https://homepages.inf.ed.ac.uk/imurray2/pub/", "content": "MADE : Masked Autoencoder for Distribution Estimation Mathieu Germain , Karol Gregor, Iain Murray and Hugo Larochelle ."}
diff --git a/data/sampled_jsons/Glossy_sphere_MAE_EventPS-FCN_13.66_Ours_8.12.jsonl b/data/sampled_jsons/Glossy_sphere_MAE_EventPS-FCN_13.66_Ours_8.12.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..7ba8197d2c634a6d6c919ca0f40b163e03d1e9e3
--- /dev/null
+++ b/data/sampled_jsons/Glossy_sphere_MAE_EventPS-FCN_13.66_Ours_8.12.jsonl
@@ -0,0 +1,3 @@
+{"idx": 0, "title": "PS-EIP: Robust Photometric Stereo Based on Event Interval Profile", "date": "", "ddg_snippet": "A glossy sphere , Glossy in Fig. 7, is measured, and the reconstructed prole at a high-light point is shown in red in Fig. 3b. The average MAEs for all 3D-printed objects was 8 . 12 for EIP-PS, in contract to EventPS , which resulted in 13 . 66 .", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2025/papers/Kitazawa_PS-EIP_Robust_Photometric_Stereo_Based_on_Event_Interval_Profile_CVPR_2025_paper.pdf", "content": "A glossy sphere , Glossy in Fig. 7, is measured, and the reconstructed prole at a high-light point is shown in red in Fig. 3b. The average MAEs for all 3D-printed objects was 8 . 12 for EIP-PS, in contract to EventPS , which resulted in 13 . 66 ."}
+{"idx": 1, "title": "Robust Photometric Stereo Based on Event Interval Profile", "date": "", "ddg_snippet": "... EventPS-FCN [67] with its angular error map, and those by ours . Glossy , and Pole), two additional spheres are used: Colors, which has two colors, and ...", "subpage_snippet": "", "source": "cvpr.thecvf.com", "link": "https://cvpr.thecvf.com/virtual/2025/poster/33500", "content": "... EventPS-FCN [67] with its angular error map, and those by ours . Glossy , and Pole), two additional spheres are used: Colors, which has two colors, and ..."}
+{"idx": 2, "title": "", "date": "", "ddg_snippet": "", "subpage_snippet": "", "source": "", "link": "", "content": ""}
diff --git a/data/sampled_jsons/Gumiho_speculative_decoding_Table_3_ablation_study_wall_time_year_2024.jsonl b/data/sampled_jsons/Gumiho_speculative_decoding_Table_3_ablation_study_wall_time_year_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..2e03f82cd46de767210c9a1147deb4a05980d0ff
--- /dev/null
+++ b/data/sampled_jsons/Gumiho_speculative_decoding_Table_3_ablation_study_wall_time_year_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Gumiho: A Hybrid Architecture to Prioritize Early Tokens in Speculative ...", "date": "", "ddg_snippet": "Speculative Decoding Auto-regressive text generation in LLMs is time -consuming. Speculative decoding addresses this limitation by employing a smaller, faster draft model MS to generate candidate tokens ahead of time .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2503.10135", "content": "Speculative Decoding Auto-regressive text generation in LLMs is time -consuming. Speculative decoding addresses this limitation by employing a smaller, faster draft model MS to generate candidate tokens ahead of time ."}
+{"idx": 1, "title": "Gumiho: A Hybrid Architecture to Prioritize Early Tokens in...", "date": "", "ddg_snippet": "This paper proposes Gumiho , a hybrid architecture for speculative decoding in Large Language Models. The core idea is motivated by the insight that early tokens in a draft sequence are more critical for the overall acceptance rate than later ones.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=0ObGn4e1IS", "content": "This paper proposes Gumiho , a hybrid architecture for speculative decoding in Large Language Models. The core idea is motivated by the insight that early tokens in a draft sequence are more critical for the overall acceptance rate than later ones."}
+{"idx": 2, "title": "Gumiho: A Hybrid Architecture to Prioritize Early Tokens in ...", "date": "", "ddg_snippet": "Speculative decoding (SPD) aims to accelerate the auto-regressive token generation process of a target Large Language Model (LLM). Some approaches employ a ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2503.10135v2", "content": "Speculative decoding (SPD) aims to accelerate the auto-regressive token generation process of a target Large Language Model (LLM). Some approaches employ a ..."}
+{"idx": 3, "title": "LogitSpec: Accelerating Retrieval-based Speculative Decoding", "date": "", "ddg_snippet": "Notably, LogitSpec achieves up to 2.61 × \\ times × speedup and 3 .28 mean accepted tokens per decoding step without the need for an extra draft model.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2507.01449v1", "content": "Notably, LogitSpec achieves up to 2.61 × \\ times × speedup and 3 .28 mean accepted tokens per decoding step without the need for an extra draft model."}
+{"idx": 4, "title": "Gumiho: A Hybrid Architecture to Prioritize Early Tokens in Speculative ...", "date": "", "ddg_snippet": "This project implements Gumiho , a novel hybrid architecture designed to accelerate the auto-regressive token generation process of Large Language Models (LLMs) using speculative decoding . Unlike existing methods that treat all tokens within a generated sequence as equally important, Gumiho is based ...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/AMD-AGI/Gumiho", "content": "This project implements Gumiho , a novel hybrid architecture designed to accelerate the auto-regressive token generation process of Large Language Models (LLMs) using speculative decoding . Unlike existing methods that treat all tokens within a generated sequence as equally important, Gumiho is based ..."}
+{"idx": 5, "title": "Gumiho - a amd Collection - Hugging Face", "date": "", "ddg_snippet": "Gumiho : A Hybrid Architecture to Prioritize Early Tokens in Speculative Decoding Paper • 2503.10135 •Published Mar 13 Upvote - Share collection View history Collection guide Browse collections", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/collections/amd/gumiho-684a3b7cbbe86ab23b393e9f", "content": "Gumiho : A Hybrid Architecture to Prioritize Early Tokens in Speculative Decoding Paper • 2503.10135 •Published Mar 13 Upvote - Share collection View history Collection guide Browse collections"}
+{"idx": 6, "title": "Gumiho: A Hybrid Architecture to Prioritize Early Tokens in Speculative ...", "date": "", "ddg_snippet": "Speculative decoding (SPD) aims to accelerate the auto-regressive token generation process of a target Large Language Model (LLM). Some approaches employ a draft model with multiple heads to predict a sequence of future tokens, where each head handles a token in the sequence. The target LLM verifies the predicted sequence and accepts aligned tokens, enabling efficient multi-token generation ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2503.10135", "content": "Speculative decoding (SPD) aims to accelerate the auto-regressive token generation process of a target Large Language Model (LLM). Some approaches employ a draft model with multiple heads to predict a sequence of future tokens, where each head handles a token in the sequence. The target LLM verifies the predicted sequence and accepts aligned tokens, enabling efficient multi-token generation ..."}
+{"idx": 7, "title": "Gumiho: A Hybrid Architecture to Prioritize Early Tokens in Speculative ...", "date": "", "ddg_snippet": "PDF | Speculative decoding (SPD) aims to accelerate the auto-regressive token generation process of a target Large Language Model (LLM).", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/389821466_Gumiho_A_Hybrid_Architecture_to_Prioritize_Early_Tokens_in_Speculative_Decoding", "content": "PDF | Speculative decoding (SPD) aims to accelerate the auto-regressive token generation process of a target Large Language Model (LLM)."}
+{"idx": 8, "title": "Geralt-Targaryen/Awesome-Speculative-Decoding - GitHub", "date": "", "ddg_snippet": "Reading list on speculative decoding . Table of Contents History & Origin Draft Models Lookahead Padding Layer Skipping & Early Exiting Draft Model Based on Target Hidden States Others Retrieval-based Speculative Decoding Draft Tree Construction Verification Strategies Draft Length Control Speculative Decoding + Other Technologies Citation Other ...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/Geralt-Targaryen/Awesome-Speculative-Decoding", "content": "Reading list on speculative decoding . Table of Contents History & Origin Draft Models Lookahead Padding Layer Skipping & Early Exiting Draft Model Based on Target Hidden States Others Retrieval-based Speculative Decoding Draft Tree Construction Verification Strategies Draft Length Control Speculative Decoding + Other Technologies Citation Other ..."}
+{"idx": 9, "title": "Gumiho: A Hybrid Architecture to Prioritize Early Tokens in Speculative ...", "date": "", "ddg_snippet": "View recent discussion. Abstract: Speculative decoding (SPD) aims to accelerate the auto-regressive token generation process of a target Large Language Model (LLM). Some approaches employ a draft model with multiple heads to predict a sequence of future tokens, where each head handles a token in the sequence. The target LLM verifies the predicted sequence and accepts aligned tokens, enabling ...", "subpage_snippet": "", "source": "www.alphaxiv.org", "link": "https://www.alphaxiv.org/overview/2503.10135v2", "content": "View recent discussion. Abstract: Speculative decoding (SPD) aims to accelerate the auto-regressive token generation process of a target Large Language Model (LLM). Some approaches employ a draft model with multiple heads to predict a sequence of future tokens, where each head handles a token in the sequence. The target LLM verifies the predicted sequence and accepts aligned tokens, enabling ..."}
diff --git a/data/sampled_jsons/HDT_framework_history-driven_target_MCMC_LRU_cache_memory_efficiency.jsonl b/data/sampled_jsons/HDT_framework_history-driven_target_MCMC_LRU_cache_memory_efficiency.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..3e367f29b5d2045824afd84fd86537df6cd1dc7e
--- /dev/null
+++ b/data/sampled_jsons/HDT_framework_history-driven_target_MCMC_LRU_cache_memory_efficiency.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Beyond Self-Repellent Kernels: History-Driven Target Towards Efficient ...", "date": "", "ddg_snippet": "We propose a history-driven target ( HDT)framework in Markov Chain Monte Carlo ( MCMC ) to improve any random walk algorithm on discrete state spaces, such as general undirected graphs, for efficient sampling from target distribution 𝝁𝝁{\\bm{\\mu}}bold_italic_μ. With broad applications in network science and distributed optimization, recent innovations like the self-repellent random walk ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2505.18300v1", "content": "We propose a history-driven target ( HDT)framework in Markov Chain Monte Carlo ( MCMC ) to improve any random walk algorithm on discrete state spaces, such as general undirected graphs, for efficient sampling from target distribution 𝝁𝝁{\\bm{\\mu}}bold_italic_μ. With broad applications in network science and distributed optimization, recent innovations like the self-repellent random walk ..."}
+{"idx": 1, "title": "Our paper on efficient MCMC on graphs accepted at ICML 2025", "date": "", "ddg_snippet": "Excited to announce that our paper, \"Beyond Self-Repellent Kernels: History-Driven Target Towards Efficient Non-linear MCMC on General Graphs,\" has been chosen for an 𝗢𝗿𝗮𝗹 ...", "subpage_snippet": "", "source": "www.linkedin.com", "link": "https://www.linkedin.com/posts/jie-hu-ncsu_icml2025-mcmc-activity-7337284173516210176-FGlQ", "content": "Excited to announce that our paper, \"Beyond Self-Repellent Kernels: History-Driven Target Towards Efficient Non-linear MCMC on General Graphs,\" has been chosen for an 𝗢𝗿𝗮𝗹 ..."}
+{"idx": 2, "title": "Least Recently Used (LRU) Revealed: Cracking the Cache Code ... - Medium", "date": "", "ddg_snippet": "The Least Recently Used ( LRU ) algorithm is a powerful tool that strikes a balance between memory efficiency and performance optimisation in caching and memory management scenarios.", "subpage_snippet": "", "source": "medium.com", "link": "https://medium.com/@mohith.j/least-recently-used-lru-revealed-cracking-the-cache-code-for-optimal-performance-c007d74110aa", "content": "The Least Recently Used ( LRU ) algorithm is a powerful tool that strikes a balance between memory efficiency and performance optimisation in caching and memory management scenarios."}
+{"idx": 3, "title": "Beyond Self-Repellent Kernels: History-Driven Target Towards Efficient ...", "date": "", "ddg_snippet": "Abstract We propose a history-driven target ( HDT ) framework in Markov Chain Monte Carlo ( MCMC ) to improve any random walk algorithm on discrete state spaces, such as general undirected graphs, for efficient sampling from target distribution 𝝁 {\\bm {\\mu}} bold_italic_μ. With broad applications in network science and distributed optimization, recent innovations like the self-repellent random ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2505.18300v3", "content": "Abstract We propose a history-driven target ( HDT ) framework in Markov Chain Monte Carlo ( MCMC ) to improve any random walk algorithm on discrete state spaces, such as general undirected graphs, for efficient sampling from target distribution 𝝁 {\\bm {\\mu}} bold_italic_μ. With broad applications in network science and distributed optimization, recent innovations like the self-repellent random ..."}
+{"idx": 4, "title": "[2505.18300v3] Beyond Self-Repellent Kernels: History-Driven Target ...", "date": "", "ddg_snippet": "We propose a history-driven target ( HDT ) framework in Markov Chain Monte Carlo ( MCMC ) to improve any random walk algorithm on discrete state spaces, such as general undirected graphs, for efficient sampling from target distribution $\\\\boldsymbolμ$. With broad applications in network science and distributed optimization, recent innovations like the self-repellent random walk (SRRW) achieve ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2505.18300v3", "content": "We propose a history-driven target ( HDT ) framework in Markov Chain Monte Carlo ( MCMC ) to improve any random walk algorithm on discrete state spaces, such as general undirected graphs, for efficient sampling from target distribution $\\\\boldsymbolμ$. With broad applications in network science and distributed optimization, recent innovations like the self-repellent random walk (SRRW) achieve ..."}
+{"idx": 5, "title": "GitHub - GauravDhak/LRU-Cache-Memory: An LRU (Least Recently Used ...", "date": "", "ddg_snippet": "An LRU (Least Recently Used) cache memory in Verilog is designed to store and manage frequently accessed data by implementing a replacement policy that evicts the least recently used entries, ensuring efficient utilization of cache space and improved access times.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/GauravDhak/LRU-Cache-Memory", "content": "An LRU (Least Recently Used) cache memory in Verilog is designed to store and manage frequently accessed data by implementing a replacement policy that evicts the least recently used entries, ensuring efficient utilization of cache space and improved access times."}
+{"idx": 6, "title": "HBPB, applying reuse distance to improve cache efficiency proactively", "date": "", "ddg_snippet": "However, increasing the number of cores may intensify the competition for shared cache memory space and bandwidth, adversely affecting memory performance and energy efficiency due to over-utilization, cache pollution, and trash. Similarly, by increasing the size of caches , the energy consumption and latency per access also tend to grow.", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S0743731524000832", "content": "However, increasing the number of cores may intensify the competition for shared cache memory space and bandwidth, adversely affecting memory performance and energy efficiency due to over-utilization, cache pollution, and trash. Similarly, by increasing the size of caches , the energy consumption and latency per access also tend to grow."}
+{"idx": 7, "title": "Beyond Self-Repellent Kernels: History-Driven Target Towards...", "date": "", "ddg_snippet": "We propose a * history-driven target ( HDT )* framework in Markov Chain Monte Carlo ( MCMC ) to improve any random walk algorithm on discrete state spaces, such as general undirected graphs, for efficient sampling from target distribution $\\\\boldsymbol {\\\\mu}$. With broad applications in network science and distributed optimization, recent innovations like the self-repellent random walk (SRRW ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=0yzOEMbShU", "content": "We propose a * history-driven target ( HDT )* framework in Markov Chain Monte Carlo ( MCMC ) to improve any random walk algorithm on discrete state spaces, such as general undirected graphs, for efficient sampling from target distribution $\\\\boldsymbol {\\\\mu}$. With broad applications in network science and distributed optimization, recent innovations like the self-repellent random walk (SRRW ..."}
+{"idx": 8, "title": "PDF Beyond Self-Repellent Kernels: History-Driven Target Towards Efficient ...", "date": "", "ddg_snippet": "History-Driven • E.g., X (Twitter) platform Target with ( HDT ) 586 million MCMC : users ⇒ Requires >500 GB of RAM just to store the visit Tackle counters first of two every issues user. of SRRW --- computational costs & time-reversibility", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/media/icml-2025/Slides/47270.pdf", "content": "History-Driven • E.g., X (Twitter) platform Target with ( HDT ) 586 million MCMC : users ⇒ Requires >500 GB of RAM just to store the visit Tackle counters first of two every issues user. of SRRW --- computational costs & time-reversibility"}
+{"idx": 9, "title": "Jie Hu - catalyzex.com", "date": "", "ddg_snippet": "We propose a history-driven target ( HDT ) framework in Markov Chain Monte Carlo ( MCMC ) to improve any random walk algorithm on discrete state spaces, such as general undirected graphs, for efficient sampling from target distribution $\\boldsymbol {\\mu}$.", "subpage_snippet": "", "source": "www.catalyzex.com", "link": "https://www.catalyzex.com/author/Jie+Hu", "content": "We propose a history-driven target ( HDT ) framework in Markov Chain Monte Carlo ( MCMC ) to improve any random walk algorithm on discrete state spaces, such as general undirected graphs, for efficient sampling from target distribution $\\boldsymbol {\\mu}$."}
diff --git a/data/sampled_jsons/Habib_A_First_Look_at_Public_Service_Websites_from_the_Affordability_Lens.jsonl b/data/sampled_jsons/Habib_A_First_Look_at_Public_Service_Websites_from_the_Affordability_Lens.jsonl
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index 0000000000000000000000000000000000000000..c410587a0994554a5d9d2dda4364d193209df6f8
--- /dev/null
+++ b/data/sampled_jsons/Habib_A_First_Look_at_Public_Service_Websites_from_the_Affordability_Lens.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Some of the most insightful and memorable quotes from 2023 –", "date": "", "ddg_snippet": "Australian Centre for Health Services Innovation Forum 2013 ... Australian Health Promotion Association Conference 2013", "subpage_snippet": "", "source": "www.croakey.org", "link": "https://www.croakey.org/some-of-the-most-insightful-and-memorable-quotes-from-2023-you-read-them-here/", "content": "Australian Centre for Health Services Innovation Forum 2013 ... Australian Health Promotion Association Conference 2013"}
+{"idx": 1, "title": "Haq's Musings: Pakistan is the Second Biggest Source of Foreign", "date": "", "ddg_snippet": "When US Ambassador Richard Holbrooke suffered a massive heart attack in 2010, the doctors who responded to this emergency were both foreign: one from ...", "subpage_snippet": "", "source": "www.riazhaq.com", "link": "https://www.riazhaq.com/2023/04/pakistan-is-second-biggest-source-of.html", "content": "When US Ambassador Richard Holbrooke suffered a massive heart attack in 2010, the doctors who responded to this emergency were both foreign: one from ..."}
+{"idx": 2, "title": "When The Personal Is Political: Dr. Samar Habib | Sarahaji", "date": "", "ddg_snippet": "In the U.S., Daniel Pipes- and David Horowitz-types attempt to exert control over the lenses through which university courses permit students to see ...", "subpage_snippet": "", "source": "www.patheos.com", "link": "https://www.patheos.com/blogs/mmw/2010/06/when-the-personal-is-political-dr-samar-habib/", "content": "In the U.S., Daniel Pipes- and David Horowitz-types attempt to exert control over the lenses through which university courses permit students to see ..."}
+{"idx": 3, "title": "The Armory Square Prize for South Asian Literature in", "date": "", "ddg_snippet": "... the COVID-19 lockdown with its lens turned to an oft-forgotten resident of urban India — the street dog — this novel by Nasera Sharma ...", "subpage_snippet": "", "source": "indianexpress.com", "link": "https://indianexpress.com/article/books-and-literature/the-armory-square-prize-for-south-asian-literature-in-translation-releases-its-shortlist-8549301/", "content": "... the COVID-19 lockdown with its lens turned to an oft-forgotten resident of urban India — the street dog — this novel by Nasera Sharma ..."}
+{"idx": 4, "title": "Saudi Arabia sacks preachers for Brotherhood links – by", "date": "", "ddg_snippet": "Manama: Saudi Arabia has sacked “for life” three Friday preachers, banning them from delivering sermons or speeches at mosques or in religious ...", "subpage_snippet": "", "source": "lubpak.com", "link": "https://lubpak.com/archives/313455", "content": "Manama: Saudi Arabia has sacked “for life” three Friday preachers, banning them from delivering sermons or speeches at mosques or in religious ..."}
+{"idx": 5, "title": "Rumi", "date": "", "ddg_snippet": "A First Look at Public Service Websites from the Affordability Lens ... the first large-scale analysis of the afordability of public service websites ...", "subpage_snippet": "", "source": "rumaisahabib.com", "link": "https://rumaisahabib.com/", "content": "A First Look at Public Service Websites from the Affordability Lens ... the first large-scale analysis of the afordability of public service websites ..."}
+{"idx": 6, "title": "Christopher Schroeder – Chapter 7: Startup Rising | Genius", "date": "", "ddg_snippet": "Every woman should be celebrated no matter the shape and size.” I met Alex first at Omar Christidis’ ArabNet in Beirut in 2012, which she helped ...", "subpage_snippet": "", "source": "genius.com", "link": "https://genius.com/Christopher-schroeder-chapter-7-startup-rising-annotated", "content": "Every woman should be celebrated no matter the shape and size.” I met Alex first at Omar Christidis’ ArabNet in Beirut in 2012, which she helped ..."}
+{"idx": 7, "title": "A New School That Thinks Like a Kid | Getting Smart", "date": "", "ddg_snippet": "Split between classrooms and collaborative spaces, students will receive personalized support from all stakeholders, including Portal teachers and ...", "subpage_snippet": "", "source": "www.gettingsmart.com", "link": "https://www.gettingsmart.com/2020/12/12/a-new-school-that-thinks-like-a-kid/", "content": "Split between classrooms and collaborative spaces, students will receive personalized support from all stakeholders, including Portal teachers and ..."}
+{"idx": 8, "title": "International Journal of Accounting Information Systems,", "date": "", "ddg_snippet": "S1467089523000532 AIS research opportunities utilizing Machine Learning: From a Meta-Theory of accounting literature by Booker, Adam & Chiu, Victoria ...", "subpage_snippet": "", "source": "ideas.repec.org", "link": "https://ideas.repec.org/s/eee/ijoais.html", "content": "S1467089523000532 AIS research opportunities utilizing Machine Learning: From a Meta-Theory of accounting literature by Booker, Adam & Chiu, Victoria ..."}
+{"idx": 9, "title": "Building Better with BehSci: Collaborating Toward Sustainable", "date": "", "ddg_snippet": "... already been met with an all-hands approach from different stakeholders in the housing sector—buyers, builders, financiers, and regulators—each ...", "subpage_snippet": "", "source": "thedecisionlab.com", "link": "https://thedecisionlab.com/insights/society/building-better-with-behsci", "content": "... already been met with an all-hands approach from different stakeholders in the housing sector—buyers, builders, financiers, and regulators—each ..."}
diff --git a/data/sampled_jsons/Herbort_et_al_citation_photometric_stereo.jsonl b/data/sampled_jsons/Herbort_et_al_citation_photometric_stereo.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..b8d2479f9b1610808103bfceadeadc8ae6da4b07
--- /dev/null
+++ b/data/sampled_jsons/Herbort_et_al_citation_photometric_stereo.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Photometric stereo - Wikipedia", "date": "", "ddg_snippet": "Photometric stereo analyzes multiple images of an object under different lighting conditions to estimate a normal direction at each pixel. Photometric stereo is a technique in computer vision for estimating the surface normals of objects by observing...", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/Photometric_stereo", "content": "Photometric stereo analyzes multiple images of an object under different lighting conditions to estimate a normal direction at each pixel. Photometric stereo is a technique in computer vision for estimating the surface normals of objects by observing..."}
+{"idx": 1, "title": "Object surface recovery using a multi-light photometric stereo ...", "date": "", "ddg_snippet": "Download citation .Smith et al . used a six light photometric stereo system to eliminate pixels with shadows and specular reflections [26]. The system obtained good results for the visualization of a copper coin and a precision ball, but it was not tested beyond these objects. ...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/223793565_Object_surface_recovery_using_a_multi-light_photometric_stereo_technique_for_non-Lambertian_surfaces_subject_to_shadows_and_specularities", "content": "Download citation .Smith et al . used a six light photometric stereo system to eliminate pixels with shadows and specular reflections [26]. The system obtained good results for the visualization of a copper coin and a precision ball, but it was not tested beyond these objects. ..."}
+{"idx": 2, "title": "Median Photometric Stereo as Applied to the Segonko Tumulus and...", "date": "", "ddg_snippet": "The 4-source photometric stereo technique for three-dimensional surfaces in the presence of highlights and shadows. IEEE Transactions on Pattern Analysis and Machine Intelligence, 25(10), 1239–1252.Press, W. H. et al .", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/article/10.1007/s11263-009-0262-9", "content": "The 4-source photometric stereo technique for three-dimensional surfaces in the presence of highlights and shadows. IEEE Transactions on Pattern Analysis and Machine Intelligence, 25(10), 1239–1252.Press, W. H. et al ."}
+{"idx": 3, "title": "Robust Multiview Photometric Stereo using", "date": "", "ddg_snippet": "Wu et al . [37] use the spherical harmonics representation to es-timate global illumination and rene a preliminary mesh using photometric stereo by minimizing 1 penalties.", "subpage_snippet": "", "source": "jaesik.info", "link": "https://jaesik.info/publications/multiviewps/PAMI2016.pdf", "content": "Wu et al . [37] use the spherical harmonics representation to es-timate global illumination and rene a preliminary mesh using photometric stereo by minimizing 1 penalties."}
+{"idx": 4, "title": "Photometric Stereo Research Papers - Academia.edu", "date": "", "ddg_snippet": "View Photometric Stereo Research Papers on Academia.edu for free.", "subpage_snippet": "", "source": "www.academia.edu", "link": "https://www.academia.edu/Documents/in/Photometric_Stereo", "content": "View Photometric Stereo Research Papers on Academia.edu for free."}
+{"idx": 5, "title": "Uncertainty-Aware Deep Multi-View Photometric Stereo", "date": "", "ddg_snippet": "Kaya et al . [31] recently proposed a neural radiance fields-based ap-proach to solve MVPS. It uses a pre-trained deep-PS model to predict the surface normal.Uncertainty-Aware Deep Photometric Stereo Network Sample.", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2022/papers/Kaya_Uncertainty-Aware_Deep_Multi-View_Photometric_Stereo_CVPR_2022_paper.pdf", "content": "Kaya et al . [31] recently proposed a neural radiance fields-based ap-proach to solve MVPS. It uses a pre-trained deep-PS model to predict the surface normal.Uncertainty-Aware Deep Photometric Stereo Network Sample."}
+{"idx": 6, "title": "GitHub - Robin-WZQ/ Photometric - Stereo : Term project. A python...", "date": "", "ddg_snippet": "Term project. A python implementation of the Basic Photometric Stereo Algorithm.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/Robin-WZQ/Photometric-Stereo", "content": "Term project. A python implementation of the Basic Photometric Stereo Algorithm."}
+{"idx": 7, "title": "Photometric Stereo Technique | 2018-09-04 | Quality Magazine", "date": "", "ddg_snippet": "Instead, photometric stereo systems use a single camera with multiple sources of illumination. The photometric stereo technique for machine vision estimates the surface of an object by observing that object under different lighting conditions.", "subpage_snippet": "", "source": "www.qualitymag.com", "link": "https://www.qualitymag.com/articles/94943-photometric-stereo-technique", "content": "Instead, photometric stereo systems use a single camera with multiple sources of illumination. The photometric stereo technique for machine vision estimates the surface of an object by observing that object under different lighting conditions."}
+{"idx": 8, "title": "Non-Lambertian Photometric Stereo Network Based on... | IEEE Xplore", "date": "", "ddg_snippet": "Current non-Lambertian photometric stereo methods generally require a large number of images to ensure accurate surface normal estimation.", "subpage_snippet": "", "source": "ieeexplore.ieee.org", "link": "https://ieeexplore.ieee.org/document/9069410", "content": "Current non-Lambertian photometric stereo methods generally require a large number of images to ensure accurate surface normal estimation."}
+{"idx": 9, "title": "Detail-aware uncalibrated photometric stereo", "date": "", "ddg_snippet": "Photometric stereo is the problem of jointly inferring the 3D reconstruction, reflectance, lighting and specularities of an object from a set of visual signals.Index Terms— Uncalibrated Photometric Stereo , Mini-mal Surfaces, Unsupervised Vision, Specular Materials.", "subpage_snippet": "", "source": "upcommons.upc.edu", "link": "https://upcommons.upc.edu/bitstream/handle/2117/407954/2730-Detail-aware-Uncalibrated-Photometric-Stereo.pdf?sequence=3&isAllowed=y", "content": "Photometric stereo is the problem of jointly inferring the 3D reconstruction, reflectance, lighting and specularities of an object from a set of visual signals.Index Terms— Uncalibrated Photometric Stereo , Mini-mal Surfaces, Unsupervised Vision, Specular Materials."}
diff --git a/data/sampled_jsons/Hierarchical_Overlapping_Clustering_Algorithm_2_k-HOC_time_complexity_O(n).jsonl b/data/sampled_jsons/Hierarchical_Overlapping_Clustering_Algorithm_2_k-HOC_time_complexity_O(n).jsonl
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index 0000000000000000000000000000000000000000..8ec41650910092febd53838a072b2e7c07a6ab6c
--- /dev/null
+++ b/data/sampled_jsons/Hierarchical_Overlapping_Clustering_Algorithm_2_k-HOC_time_complexity_O(n).jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Hierarchical overlapping clustering: cost function, algorithm ...", "date": "", "ddg_snippet": "by Y Pan — Summary: The paper presents the problem of hierarchical overlapping clustering (HOC) for graph data, where clusters may overlap and form hierarchies. The paper ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=oHSXRy29tj", "content": "by Y Pan — Summary: The paper presents the problem of hierarchical overlapping clustering (HOC) for graph data, where clusters may overlap and form hierarchies. The paper ..."}
+{"idx": 1, "title": "Hierarchical Overlapping Clustering on Graphs: Cost ...", "date": "", "ddg_snippet": "by Y Pan — 2 -OC can be considered as a key ingredient of HOC with multiple clusters since it could be a nice way to construct a k - HOC graph by recursively calling 2 -OC ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=51x0dfsD8A", "content": "by Y Pan — 2 -OC can be considered as a key ingredient of HOC with multiple clusters since it could be a nice way to construct a k - HOC graph by recursively calling 2 -OC ..."}
+{"idx": 2, "title": "An Efficient Hierarchical Clustering Method for Multivariate ...", "date": "", "ddg_snippet": "by J Zhou · 2023 · Cited by 18 — For a dataset of size N , agglomerative clustering algorithms typically have a time complexity of O ( N 2 ) and require Ω( N 2 ) memory (Nielsen, 2016); these ...", "subpage_snippet": "", "source": "pmc.ncbi.nlm.nih.gov", "link": "https://pmc.ncbi.nlm.nih.gov/articles/PMC10584088/", "content": "by J Zhou · 2023 · Cited by 18 — For a dataset of size N , agglomerative clustering algorithms typically have a time complexity of O ( N 2 ) and require Ω( N 2 ) memory (Nielsen, 2016); these ..."}
+{"idx": 3, "title": "A Rapid Review of Clustering Algorithms", "date": "", "ddg_snippet": "14 Jan 2024 — In this work, we analyzed existing clustering algorithms and classify mainstream algorithms across five different dimensions.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2401.07389v1", "content": "14 Jan 2024 — In this work, we analyzed existing clustering algorithms and classify mainstream algorithms across five different dimensions."}
+{"idx": 4, "title": "The Computational Complexity of Hierarchical Clustering ...", "date": "", "ddg_snippet": "by VH Bui · 2023 · Cited by 7 — The time complexity of the LPAf is O ( kn +mlog n ) O ( k n + m log n ) in which k is the maximum degree of vertices in the network. The effectiveness of LPAf was ...", "subpage_snippet": "", "source": "www.worldscientific.com", "link": "https://www.worldscientific.com/doi/full/10.1142/S2196888823300016?srsltid=AfmBOoq7EfjlN6cqdHEw3_n0A-x4tEOOJTl_yUwYEQVCtvxmDzyS3x_-", "content": "by VH Bui · 2023 · Cited by 7 — The time complexity of the LPAf is O ( kn +mlog n ) O ( k n + m log n ) in which k is the maximum degree of vertices in the network. The effectiveness of LPAf was ..."}
+{"idx": 5, "title": "Cluster validity indices for automatic clustering", "date": "", "ddg_snippet": "by AM Ikotun · 2025 · Cited by 8 — The Cluster Validity Index is an integral part of clustering algorithms . It evaluates inter- cluster separation and intra- cluster cohesion of candidate clusters.", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S2405844025003330", "content": "by AM Ikotun · 2025 · Cited by 8 — The Cluster Validity Index is an integral part of clustering algorithms . It evaluates inter- cluster separation and intra- cluster cohesion of candidate clusters."}
+{"idx": 6, "title": "Streaming Hierarchical Clustering Based on Point-Set Kernel", "date": "", "ddg_snippet": "by X Han · Cited by 16 — StreaKHC is the fastest algorithm . Although both StreaKHC and. PERCH have time complexity of O ( n ) when the tree size is bounded,. PERCH has ...", "subpage_snippet": "", "source": "www.researchsquare.com", "link": "https://www.researchsquare.com/article/rs-1711503/latest.pdf", "content": "by X Han · Cited by 16 — StreaKHC is the fastest algorithm . Although both StreaKHC and. PERCH have time complexity of O ( n ) when the tree size is bounded,. PERCH has ..."}
+{"idx": 7, "title": "Data clustering: a fundamental method in data science and ...", "date": "", "ddg_snippet": "by T Dinh · 2025 · Cited by 2 — This study investigates the pivotal role of data clustering in both data science and management, focusing on core methodologies, tools, ...", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S2666764925000360", "content": "by T Dinh · 2025 · Cited by 2 — This study investigates the pivotal role of data clustering in both data science and management, focusing on core methodologies, tools, ..."}
+{"idx": 8, "title": "Distribution-Oriented Approach to Divisive Hierarchical Clustering", "date": "", "ddg_snippet": "This paper contributes to creating a new distribution-oriented linear - time approach to Divisive Hi- erarchical Clustering (DHC). Unlike existing clustering ...", "subpage_snippet": "", "source": "papers.ssrn.com", "link": "https://papers.ssrn.com/sol3/Delivery.cfm/a4d6ed79-4cfd-4893-a4e4-839a097f9aba-MECA.pdf?abstractid=4935787&mirid=1", "content": "This paper contributes to creating a new distribution-oriented linear - time approach to Divisive Hi- erarchical Clustering (DHC). Unlike existing clustering ..."}
+{"idx": 9, "title": "Improved space breakdown method – A robust clustering ...", "date": "", "ddg_snippet": "by ER Ardelean · 2023 — With regard to the complexity, it has a time complexity of O ( n 2 ), where n is the number of samples. Within spike sorting, where overlapping clusters is common, ...", "subpage_snippet": "", "source": "www.frontiersin.org", "link": "https://www.frontiersin.org/journals/computational-neuroscience/articles/10.3389/fncom.2023.1019637/full", "content": "by ER Ardelean · 2023 — With regard to the complexity, it has a time complexity of O ( n 2 ), where n is the number of samples. Within spike sorting, where overlapping clusters is common, ..."}
diff --git a/data/sampled_jsons/Human-in-the-loop_Provably_Efficient_Preference-based_Reinforcement_Learning_Chen_theorem_4.5_regret_year_2022.jsonl b/data/sampled_jsons/Human-in-the-loop_Provably_Efficient_Preference-based_Reinforcement_Learning_Chen_theorem_4.5_regret_year_2022.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..b45f6dff09893cc9f0918efdc4ebad3cd2d69278
--- /dev/null
+++ b/data/sampled_jsons/Human-in-the-loop_Provably_Efficient_Preference-based_Reinforcement_Learning_Chen_theorem_4.5_regret_year_2022.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Human-in-the-loop: Provably Efficient Preference-based ... Module 7: Human-in-the-loop autonomy - Preference Based ... A survey of Preference Reinforcement Learning - GitHub ICML 2022 Human-in-the-loop: Provably Efficient Preference ... Human-in-the-loop: Provably Efficient Preference-based ... Human - in - the - loop : Provably Efficient Preference - based Reinforcem… GitHub - Kavka1/Preference- RL : A survey of Preference Reinforceme… [2212.03363] Few-Shot Preference Learning for Human - in - the - Loop … [2212.03363] Few-Shot Preference Learning for Human - in - the - Loop … Human - in - the - loop : Provably Efficient Preference - based Reinforcem… Human - in - the - loop : Provably Efficient Preference - based Reinforcem… Few-Shot Preference Learning for Human-in-the-Loop RL", "date": "", "ddg_snippet": "We study human - in - the - loop reinforcement learn - ing (RL) with trajectory preferences , where in-stead of receiving a numeric reward at each step, the RL agent only receives preferences over tra-jectory pairs from a human overseer. The goal of the RL agent is to learn the optimal policy which is most preferred by the human overseer. Despite the empiri... See full list on proceedings.mlr.press In this section, we present the main results for preference - based RL. We first propose a novel algorithm called Preference - based Optimistic Planning (PbOP) and estab-lish the regret upper bound for it. To show the sharpness of our result, we also prove an information-theoretic lower bound in the linear case. See full list on proceedings.mlr.press In this subsection, we establish the lower bound for PbRL in the linear setting, which is derived using the reduction from the problem of RL with once-per-episode feedback. Firstly, we show the reduction from the problem of RL with once-per-episode feedback setting to the PbRL setting. Specifically, suppose we have an algorithm ALG for PbRL problem... See full list on proceedings.mlr.press In the previous section, we propose a sample-eficient al-gorithm with near-optimal regret for the problem of PbRL with trajectory feedback. However, this setting cannot cover some other RL situations with preference feedback. For example, in robotics, sampling new trajectories can be ex-pensive and time-consuming compared with labeling prefer-ences... See full list on proceedings.mlr.press Jun 7, 2022 · In this survey, we talk about the evolution of human perference based reinforcement learning , with a bunch of examples. * Regret analysis of the preference-based algorithm under assumption of trajectory embedding and preferences Human-in-the-loop : Provably Efficient Preference-based Reinforcement Learning with General Function Approximation. Abstract: We study human-in-the-loop reinforcement learning (RL) with trajectory preferences , where instead of receiving a numeric reward at each step, the RL agent only receives preferences over trajectory pairs from a human overseer. The goal of the RL agent is to learn the optimal policy which is most preferred by the human overseer. Dive into the research topics of ' Human-in-the-loop : Provably Efficient Preference-based Reinforcement Learning with General Function Approximation'. Together they form a unique fingerprint. How does human-in-the-loop reinforcement learn-ing work? We study human - in - the - loop reinforcement learn-ing (RL) with trajectory preferences, where in -stead of receiving a numeric reward at each step, the RL agent only receives preferences over tra-jectory pairs from a human overseer. The goal of the RL agent is to learn the optimal policy which is most preferred by the human overseer. What is preference-based learning (pbrl)? Similar to traditional Inverse Reinforcement Learning (IRL) learning a reward function based on expert demonstrations, Preference - based RL (PBRL) aims to learn a reward function given limited preference signals of paired trajectories / sub-trajectories from human / rule- based systems. Categorized by the research problems then methods. What are preference based RL algorithms? Preference based RL algorithms seek to overcome these challenges by directly learning reward functions from human feedback . Why is reinforcement learning a problem in robotics? While reinforcement learning (RL) has become a more popular approach for robotics, designing sufficiently informative reward functions for complex tasks has proven to be extremely difficult due their inability to capture human intent and policy exploitation . What is reinforcement learning? Introduction Reinforcement learning (RL) is concerned with sequential decision-making problems in which the agent interacts with the environment to maximize its cumulative rewards. Which algorithm is used to learn transition dynamics and preference function? Algorithm The algorithm is formally defined in Algorithm 1. Overall, we employ the standard least-squares regression to learn the transition dynamics and the preference function. In each episode, we first update the model estimation based on the history samples till episode k − 1. Dec 6, 2022 · While reinforcement learning (RL) has become a more popular approach for robotics, designing sufficiently informative reward functions for complex tasks has proven to be extremely difficult due their inability to capture human intent and policy exploitation. Preference based RL algorithms seek to overcome these challenges by directly learning reward functions from human feedback. Unfortunately ...", "subpage_snippet": "", "source": "proceedings.mlr.press", "link": "https://proceedings.mlr.press/v162/chen22ag/chen22ag.pdf", "content": "We study human - in - the - loop reinforcement learn - ing (RL) with trajectory preferences , where in-stead of receiving a numeric reward at each step, the RL agent only receives preferences over tra-jectory pairs from a human overseer. The goal of the RL agent is to learn the optimal policy which is most preferred by the human overseer. Despite the empiri... See full list on proceedings.mlr.press In this section, we present the main results for preference - based RL. We first propose a novel algorithm called Preference - based Optimistic Planning (PbOP) and estab-lish the regret upper bound for it. To show the sharpness of our result, we also prove an information-theoretic lower bound in the linear case. See full list on proceedings.mlr.press In this subsection, we establish the lower bound for PbRL in the linear setting, which is derived using the reduction from the problem of RL with once-per-episode feedback. Firstly, we show the reduction from the problem of RL with once-per-episode feedback setting to the PbRL setting. Specifically, suppose we have an algorithm ALG for PbRL problem... See full list on proceedings.mlr.press In the previous section, we propose a sample-eficient al-gorithm with near-optimal regret for the problem of PbRL with trajectory feedback. However, this setting cannot cover some other RL situations with preference feedback. For example, in robotics, sampling new trajectories can be ex-pensive and time-consuming compared with labeling prefer-ences... See full list on proceedings.mlr.press Jun 7, 2022 · In this survey, we talk about the evolution of human perference based reinforcement learning , with a bunch of examples. * Regret analysis of the preference-based algorithm under assumption of trajectory embedding and preferences Human-in-the-loop : Provably Efficient Preference-based Reinforcement Learning with General Function Approximation. Abstract: We study human-in-the-loop reinforcement learning (RL) with trajectory preferences , where instead of receiving a numeric reward at each step, the RL agent only receives preferences over trajectory pairs from a human overseer. The goal of the RL agent is to learn the optimal policy which is most preferred by the human overseer. Dive into the research topics of ' Human-in-the-loop : Provably Efficient Preference-based Reinforcement Learning with General Function Approximation'. Together they form a unique fingerprint. How does human-in-the-loop reinforcement learn-ing work? We study human - in - the - loop reinforcement learn-ing (RL) with trajectory preferences, where in -stead of receiving a numeric reward at each step, the RL agent only receives preferences over tra-jectory pairs from a human overseer. The goal of the RL agent is to learn the optimal policy which is most preferred by the human overseer. What is preference-based learning (pbrl)? Similar to traditional Inverse Reinforcement Learning (IRL) learning a reward function based on expert demonstrations, Preference - based RL (PBRL) aims to learn a reward function given limited preference signals of paired trajectories / sub-trajectories from human / rule- based systems. Categorized by the research problems then methods. What are preference based RL algorithms? Preference based RL algorithms seek to overcome these challenges by directly learning reward functions from human feedback . Why is reinforcement learning a problem in robotics? While reinforcement learning (RL) has become a more popular approach for robotics, designing sufficiently informative reward functions for complex tasks has proven to be extremely difficult due their inability to capture human intent and policy exploitation . What is reinforcement learning? Introduction Reinforcement learning (RL) is concerned with sequential decision-making problems in which the agent interacts with the environment to maximize its cumulative rewards. Which algorithm is used to learn transition dynamics and preference function? Algorithm The algorithm is formally defined in Algorithm 1. Overall, we employ the standard least-squares regression to learn the transition dynamics and the preference function. In each episode, we first update the model estimation based on the history samples till episode k − 1. Dec 6, 2022 · While reinforcement learning (RL) has become a more popular approach for robotics, designing sufficiently informative reward functions for complex tasks has proven to be extremely difficult due their inability to capture human intent and policy exploitation. Preference based RL algorithms seek to overcome these challenges by directly learning reward functions from human feedback. Unfortunately ..."}
+{"idx": 1, "title": "Human-in-the-loop: Provably Efficient Preference-based ...", "date": "", "ddg_snippet": "May 23, 2022 · We study human-in-the-loop reinforcement learning (RL) with trajectory preferences , where instead of receiving a numeric reward at each step, the agent only receives preferences over trajectory pairs from a human overseer. The goal of the agent is to learn the optimal policy which is most preferred by the human overseer. Despite the empirical successes, the theoretical understanding of ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2205.11140", "content": "May 23, 2022 · We study human-in-the-loop reinforcement learning (RL) with trajectory preferences , where instead of receiving a numeric reward at each step, the agent only receives preferences over trajectory pairs from a human overseer. The goal of the agent is to learn the optimal policy which is most preferred by the human overseer. Despite the empirical successes, the theoretical understanding of ..."}
+{"idx": 2, "title": "Module 7: Human-in-the-loop autonomy - Preference Based ...", "date": "", "ddg_snippet": "Jun 7, 2022 · In this survey, we talk about the evolution of human perference based reinforcement learning , with a bunch of examples.", "subpage_snippet": "", "source": "ucladeepvision.github.io", "link": "https://ucladeepvision.github.io/CS269-surveys-2022spring/2022/06/07/module07-Preference-Based-Learning.html", "content": "Jun 7, 2022 · In this survey, we talk about the evolution of human perference based reinforcement learning , with a bunch of examples."}
+{"idx": 3, "title": "A survey of Preference Reinforcement Learning - GitHub", "date": "", "ddg_snippet": "* Regret analysis of the preference-based algorithm under assumption of trajectory embedding and preferences Human-in-the-loop : Provably Efficient Preference-based Reinforcement Learning with General Function Approximation.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/Kavka1/Preference-RL", "content": "* Regret analysis of the preference-based algorithm under assumption of trajectory embedding and preferences Human-in-the-loop : Provably Efficient Preference-based Reinforcement Learning with General Function Approximation."}
+{"idx": 4, "title": "ICML 2022 Human-in-the-loop: Provably Efficient Preference ...", "date": "", "ddg_snippet": "Abstract: We study human-in-the-loop reinforcement learning (RL) with trajectory preferences , where instead of receiving a numeric reward at each step, the RL agent only receives preferences over trajectory pairs from a human overseer. The goal of the RL agent is to learn the optimal policy which is most preferred by the human overseer.", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2022/spotlight/18372", "content": "Abstract: We study human-in-the-loop reinforcement learning (RL) with trajectory preferences , where instead of receiving a numeric reward at each step, the RL agent only receives preferences over trajectory pairs from a human overseer. The goal of the RL agent is to learn the optimal policy which is most preferred by the human overseer."}
+{"idx": 5, "title": "Human-in-the-loop: Provably Efficient Preference-based ...", "date": "", "ddg_snippet": "Dive into the research topics of ' Human-in-the-loop : Provably Efficient Preference-based Reinforcement Learning with General Function Approximation'. Together they form a unique fingerprint.", "subpage_snippet": "", "source": "www.scholars.northwestern.edu", "link": "https://www.scholars.northwestern.edu/en/publications/human-in-the-loop-provably-efficient-preference-based-reinforceme", "content": "Dive into the research topics of ' Human-in-the-loop : Provably Efficient Preference-based Reinforcement Learning with General Function Approximation'. Together they form a unique fingerprint."}
+{"idx": 6, "title": "Few-Shot Preference Learning for Human-in-the-Loop RL", "date": "", "ddg_snippet": "Dec 6, 2022 · While reinforcement learning (RL) has become a more popular approach for robotics, designing sufficiently informative reward functions for complex tasks has proven to be extremely difficult due their inability to capture human intent and policy exploitation. Preference based RL algorithms seek to overcome these challenges by directly learning reward functions from human feedback. Unfortunately ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2212.03363", "content": "Dec 6, 2022 · While reinforcement learning (RL) has become a more popular approach for robotics, designing sufficiently informative reward functions for complex tasks has proven to be extremely difficult due their inability to capture human intent and policy exploitation. Preference based RL algorithms seek to overcome these challenges by directly learning reward functions from human feedback. Unfortunately ..."}
+{"idx": 7, "title": "RA-PbRL: Provably Efficient Risk-Aware Preference-Based ...", "date": "", "ddg_snippet": "9 Dec 2024 — Preference - based Reinforcement Learning (PbRL) studies the problem where agents receive only preferences over pairs of trajectories in each ...", "subpage_snippet": "", "source": "neurips.cc", "link": "https://neurips.cc/virtual/2024/poster/95716", "content": "9 Dec 2024 — Preference - based Reinforcement Learning (PbRL) studies the problem where agents receive only preferences over pairs of trajectories in each ..."}
+{"idx": 8, "title": "Can RLHF be More Efficient with Imperfect Reward Models ...", "date": "", "ddg_snippet": "by J Huang · 2025 · Cited by 2 — Human-in-the-loop: Provably efficient preference-based reinforcement learning with general function approxima- tion. In International Conference on Machine ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2502.19255", "content": "by J Huang · 2025 · Cited by 2 — Human-in-the-loop: Provably efficient preference-based reinforcement learning with general function approxima- tion. In International Conference on Machine ..."}
+{"idx": 9, "title": "RA-PbRL: Provably Efficient Risk-Aware Preference-Based ...", "date": "", "ddg_snippet": "by Y Zhao · 2024 · Cited by 4 — Human-in-the-loop: Provably efficient preference-based reinforcement learning with general function approximation. In International Conference on Machine ... 37 pages", "subpage_snippet": "", "source": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper_files/paper/2024/file/7016d7b7b6e3c05b2128ac5b3aae492d-Paper-Conference.pdf", "content": "by Y Zhao · 2024 · Cited by 4 — Human-in-the-loop: Provably efficient preference-based reinforcement learning with general function approximation. In International Conference on Machine ... 37 pages"}
diff --git a/data/sampled_jsons/ICML_2025_Medium_paper_1_initialization_year_2025.jsonl b/data/sampled_jsons/ICML_2025_Medium_paper_1_initialization_year_2025.jsonl
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+{"idx": 0, "title": "Initialization (programming) - Wikipedia", "date": "", "ddg_snippet": "In computer programming, initialization or initialisation is the assignment of an initial value for a data object or variable. The manner in which initialization is performed depends on the programming language, as well as the type, storage class, et...", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/Initialization_(programming)", "content": "In computer programming, initialization or initialisation is the assignment of an initial value for a data object or variable. The manner in which initialization is performed depends on the programming language, as well as the type, storage class, et..."}
+{"idx": 1, "title": "2025 Conference", "date": "", "ddg_snippet": "Forty-Second International Conference on Machine Learning .View ICML 2025 exhibitors » Become an 2025 Exhibitor (not currently taking applications) Exhibitor Info ». Important Dates. Registration Open. Oct 19 '24 01:00 PM PDT *. Early pricing before this date.", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/", "content": "Forty-Second International Conference on Machine Learning .View ICML 2025 exhibitors » Become an 2025 Exhibitor (not currently taking applications) Exhibitor Info ». Important Dates. Registration Open. Oct 19 '24 01:00 PM PDT *. Early pricing before this date."}
+{"idx": 2, "title": "A quote from ICML 2025 | Simon Willison’s Weblog", "date": "", "ddg_snippet": "Although ICML 2025 reviewers are forbidden from using LLMs to produce their reviews of paper submissions, this fact does not excuse the attempted subversion.— ICML 2025 , Statement about subversive hidden LLM prompts.", "subpage_snippet": "", "source": "simonwillison.net", "link": "https://simonwillison.net/2025/jul/23/icml-2025/", "content": "Although ICML 2025 reviewers are forbidden from using LLMs to produce their reviews of paper submissions, this fact does not excuse the attempted subversion.— ICML 2025 , Statement about subversive hidden LLM prompts."}
+{"idx": 3, "title": "Lakera's paper on AI security accepted to ICML 2025 | LinkedIn", "date": "", "ddg_snippet": "Excited to share that our paper \"Gandalf the Red: Adaptive Security for LLMs\" has been accepted to ICML 2025 ! This marks a big milestone for us at Lakera, and a testament to the incredible work behind Gandalf—our AI security game that has g...", "subpage_snippet": "", "source": "www.linkedin.com", "link": "https://www.linkedin.com/posts/mateor_icml2025-aisecurity-llms-activity-7328690131379363840-CZlO", "content": "Excited to share that our paper \"Gandalf the Red: Adaptive Security for LLMs\" has been accepted to ICML 2025 ! This marks a big milestone for us at Lakera, and a testament to the incredible work behind Gandalf—our AI security game that has g..."}
+{"idx": 4, "title": "Explore our scientific papers on fundamental problems in machine ...", "date": "", "ddg_snippet": "ICML , 2025 . Abstract. This paper addresses the problem of quantifying diversity for a set of objects. First , we conduct a systematic review of existing diversity measures and explore their undesirable behavior in certain cases.", "subpage_snippet": "", "source": "research.yandex.com", "link": "https://research.yandex.com/publications", "content": "ICML , 2025 . Abstract. This paper addresses the problem of quantifying diversity for a set of objects. First , we conduct a systematic review of existing diversity measures and explore their undesirable behavior in certain cases."}
+{"idx": 5, "title": "Kaiming Initialization in Deep Learning - GeeksforGeeks", "date": "", "ddg_snippet": "The Kaiming initialization method, also known as Kaiming He initialization or He normal initialization , is a technique for initializing the weights of artificial neural networks. This method was introduced in the paper titled \"Delving Deep into Rectifiers: Surpassing Human-Level...", "subpage_snippet": "", "source": "www.geeksforgeeks.org", "link": "https://www.geeksforgeeks.org/deep-learning/kaiming-initialization-in-deep-learning/", "content": "The Kaiming initialization method, also known as Kaiming He initialization or He normal initialization , is a technique for initializing the weights of artificial neural networks. This method was introduced in the paper titled \"Delving Deep into Rectifiers: Surpassing Human-Level..."}
+{"idx": 6, "title": "Kaiming He initialization . We will derive Kaiming initialization | Medium", "date": "", "ddg_snippet": "This paper introduced both the PReLU activation and Kaiming initialization . We will discuss Kaiming initialization in this post. Deep NN models have difficulties in converging when the weights are initialized using Normal Distribution with fixed standard deviation.", "subpage_snippet": "", "source": "medium.com", "link": "https://medium.com/@shauryagoel/kaiming-he-initialization-a8d9ed0b5899", "content": "This paper introduced both the PReLU activation and Kaiming initialization . We will discuss Kaiming initialization in this post. Deep NN models have difficulties in converging when the weights are initialized using Normal Distribution with fixed standard deviation."}
+{"idx": 7, "title": "Position: Future Research and Challenges Remain... | OpenReview", "date": "", "ddg_snippet": "In this position paper , our goal is threefold. First , we provide a taxonomy of measures and tasks to categorize work towards AI software engineering.Published: 01 May 2025 , Last Modified: 23 Jul 2025 ICML 2025 Position Paper Track posterEveryoneRevisionsBibTeXCC BY 4.0.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=RuLsq4LSZK", "content": "In this position paper , our goal is threefold. First , we provide a taxonomy of measures and tasks to categorize work towards AI software engineering.Published: 01 May 2025 , Last Modified: 23 Jul 2025 ICML 2025 Position Paper Track posterEveryoneRevisionsBibTeXCC BY 4.0."}
+{"idx": 8, "title": "An open platform for evaluating AI through human preference", "date": "", "ddg_snippet": "Learn more about our open datasets and research papers . LMArena has open-sourced the largest repository of organic human preferences on generative models in the world.", "subpage_snippet": "", "source": "lmarena.ai", "link": "https://lmarena.ai/how-it-works", "content": "Learn more about our open datasets and research papers . LMArena has open-sourced the largest repository of organic human preferences on generative models in the world."}
+{"idx": 9, "title": "Boyang Li's Personal Website", "date": "", "ddg_snippet": "Initialization Matters: Regularizing Manifold-informed Initialization for Neural Recommendation Systems. ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD).What's New. May 2025 : 1 paper accepted to ICML 2025 .", "subpage_snippet": "", "source": "boyangli.org", "link": "http://boyangli.org/", "content": "Initialization Matters: Regularizing Manifold-informed Initialization for Neural Recommendation Systems. ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD).What's New. May 2025 : 1 paper accepted to ICML 2025 ."}
diff --git a/data/sampled_jsons/ICML_2025_accepted_papers_collective_action_year_2025.jsonl b/data/sampled_jsons/ICML_2025_accepted_papers_collective_action_year_2025.jsonl
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index 0000000000000000000000000000000000000000..3cca039e392a8d6908601f5c6eeaa513433fdc77
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@@ -0,0 +1,10 @@
+{"idx": 0, "title": "ICML 2025 Accepted Papers | 南京大学大模型研究协同创新中心", "date": "", "ddg_snippet": "3 days ago · ICML is one of the most prestigious and influential conferences in machine learning. It is among the longest-running and largest venues in the field and a CCF Class-A conference. Four papers from the Large Model Center of the Department of Computer Science and Technology, Nanjing University, were accepted to ICML 2025 .", "subpage_snippet": "", "source": "cs.nju.edu.cn", "link": "https://cs.nju.edu.cn/lm/en/post/2025-09-19-icml2025-accepted-papers/index.html", "content": "3 days ago · ICML is one of the most prestigious and influential conferences in machine learning. It is among the longest-running and largest venues in the field and a CCF Class-A conference. Four papers from the Large Model Center of the Department of Computer Science and Technology, Nanjing University, were accepted to ICML 2025 ."}
+{"idx": 1, "title": "ICML 2024 Papers", "date": "", "ddg_snippet": "The Role of Learning Algorithms in Collective Action CoLoRA: Continuous low-rank adaptation for reduced implicit neural modeling of parameterized partial differential equations By Tying Embeddings You Are Assuming the Distributional Hypothesis Controlling Behavioral Diversity in Multi-Agent Reinforcement Learning", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2024/papers.html", "content": "The Role of Learning Algorithms in Collective Action CoLoRA: Continuous low-rank adaptation for reduced implicit neural modeling of parameterized partial differential equations By Tying Embeddings You Are Assuming the Distributional Hypothesis Controlling Behavioral Diversity in Multi-Agent Reinforcement Learning"}
+{"idx": 2, "title": "The Role of Learning Algorithms in Collective Action Volume 235: International Conference on Machine Learning, 21 ... ICML 2025 Review Controversies Spark Academic Debate ICML 2025 Paper Accepted - ELSALAB Algorithmic Collective Action in Machine Learning | Social ...", "date": "", "ddg_snippet": "May 10, 2024 · Collective action in machine learning is the study of the control that a coordinated group can have over machine learning algorithms. While previous research has concentrated on assessing the impact of collectives against Bayes (sub-)optimal classifiers, this perspective is limited in that it does not account for the choice of learning algorithm. Since classifiers seldom behave like Bayes ... Proceedings of the 41st International Conference on Machine Learning Held in Vienna, Austria on 21-27 July 2024 Published as Volume 235 by the Proceedings of Machine Learning Research on 08 July 2024. May 2, 2025 · The ICML 2025 acceptance results have recently been announced, marking a historic high with 12,107 valid submissions, resulting in 3,260 accepted papers —an acceptance rate of 26.9%. Despite the impressive volume, numerous serious issues in the review process have emerged, sparking extensive discussions within the academic community. ICML 2025 Paper Accepted 🎉 One paper from Elsa Lab members and our collaborators has been accepted for presentation at ICML 2025 . 💪 \"Retraining-Free Merging of Sparse MoE via Hierarchical Clustering\". We are excited to share our work on HC-SMoE, a novel approach for merging experts in Sparse Mixture-of-Experts models without retraining! Social Foundations of Computation Algorithms and Society Conference Paper Algorithmic Collective Action in Machine Learning Hardt, M., Mazumdar, E., Mendler-Dünner, C., Zrnic, T. In Proceedings of the 40th International Conference on Machine Learning ( ICML 2023), PMLR, The Forty International Conference on Machine Learning ( ICML ), July 2023 (Published) URL BibTeX", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2405.06582v3", "content": "May 10, 2024 · Collective action in machine learning is the study of the control that a coordinated group can have over machine learning algorithms. While previous research has concentrated on assessing the impact of collectives against Bayes (sub-)optimal classifiers, this perspective is limited in that it does not account for the choice of learning algorithm. Since classifiers seldom behave like Bayes ... Proceedings of the 41st International Conference on Machine Learning Held in Vienna, Austria on 21-27 July 2024 Published as Volume 235 by the Proceedings of Machine Learning Research on 08 July 2024. May 2, 2025 · The ICML 2025 acceptance results have recently been announced, marking a historic high with 12,107 valid submissions, resulting in 3,260 accepted papers —an acceptance rate of 26.9%. Despite the impressive volume, numerous serious issues in the review process have emerged, sparking extensive discussions within the academic community. ICML 2025 Paper Accepted 🎉 One paper from Elsa Lab members and our collaborators has been accepted for presentation at ICML 2025 . 💪 \"Retraining-Free Merging of Sparse MoE via Hierarchical Clustering\". We are excited to share our work on HC-SMoE, a novel approach for merging experts in Sparse Mixture-of-Experts models without retraining! Social Foundations of Computation Algorithms and Society Conference Paper Algorithmic Collective Action in Machine Learning Hardt, M., Mazumdar, E., Mendler-Dünner, C., Zrnic, T. In Proceedings of the 40th International Conference on Machine Learning ( ICML 2023), PMLR, The Forty International Conference on Machine Learning ( ICML ), July 2023 (Published) URL BibTeX"}
+{"idx": 3, "title": "ICML 2025 Review Controversies Spark Academic Debate", "date": "", "ddg_snippet": "May 2, 2025 · The ICML 2025 acceptance results have recently been announced, marking a historic high with 12,107 valid submissions, resulting in 3,260 accepted papers —an acceptance rate of 26.9%. Despite the impressive volume, numerous serious issues in the review process have emerged, sparking extensive discussions within the academic community.", "subpage_snippet": "", "source": "forum.cspaper.org", "link": "https://forum.cspaper.org/topic/62/icml-2025-review-controversies-spark-academic-debate", "content": "May 2, 2025 · The ICML 2025 acceptance results have recently been announced, marking a historic high with 12,107 valid submissions, resulting in 3,260 accepted papers —an acceptance rate of 26.9%. Despite the impressive volume, numerous serious issues in the review process have emerged, sparking extensive discussions within the academic community."}
+{"idx": 4, "title": "ICML 2025 Paper Accepted - ELSALAB", "date": "", "ddg_snippet": "ICML 2025 Paper Accepted 🎉 One paper from Elsa Lab members and our collaborators has been accepted for presentation at ICML 2025 . 💪 \"Retraining-Free Merging of Sparse MoE via Hierarchical Clustering\". We are excited to share our work on HC-SMoE, a novel approach for merging experts in Sparse Mixture-of-Experts models without retraining!", "subpage_snippet": "", "source": "www.elsalab.ai", "link": "https://www.elsalab.ai/news/250502-icml-2025", "content": "ICML 2025 Paper Accepted 🎉 One paper from Elsa Lab members and our collaborators has been accepted for presentation at ICML 2025 . 💪 \"Retraining-Free Merging of Sparse MoE via Hierarchical Clustering\". We are excited to share our work on HC-SMoE, a novel approach for merging experts in Sparse Mixture-of-Experts models without retraining!"}
+{"idx": 5, "title": "Algorithmic Collective Action in Machine Learning | Social ...", "date": "", "ddg_snippet": "Social Foundations of Computation Algorithms and Society Conference Paper Algorithmic Collective Action in Machine Learning Hardt, M., Mazumdar, E., Mendler-Dünner, C., Zrnic, T. In Proceedings of the 40th International Conference on Machine Learning ( ICML 2023), PMLR, The Forty International Conference on Machine Learning ( ICML ), July 2023 (Published) URL BibTeX", "subpage_snippet": "", "source": "is.mpg.de", "link": "https://is.mpg.de/hml/en/projects/algorithmic-collective-action-in-machine-learning", "content": "Social Foundations of Computation Algorithms and Society Conference Paper Algorithmic Collective Action in Machine Learning Hardt, M., Mazumdar, E., Mendler-Dünner, C., Zrnic, T. In Proceedings of the 40th International Conference on Machine Learning ( ICML 2023), PMLR, The Forty International Conference on Machine Learning ( ICML ), July 2023 (Published) URL BibTeX"}
+{"idx": 6, "title": "How AI can help us harness our ' collective intelligence'", "date": "", "ddg_snippet": "However, recent advances in artificial intelligence (AI) could make harnessing this collective wisdom much easier, making us more effective at our jobs and better able to solve pressing social challenges.", "subpage_snippet": "", "source": "www.bbc.com", "link": "https://www.bbc.com/worklife/article/20200513-how-ai-can-help-us-harness-our-collective-intelligence", "content": "However, recent advances in artificial intelligence (AI) could make harnessing this collective wisdom much easier, making us more effective at our jobs and better able to solve pressing social challenges."}
+{"idx": 7, "title": "Accepted papers for the 2015 Conference on Empirical Methods on...", "date": "", "ddg_snippet": "List of accepted short papers . TACL papers to be presented at EMNLP. Long papers . Document Modeling with Convolutional-Gated Recurrent Neural Network for Sentiment Classification Duyu Tang, Bing Qin and Ting Liu.", "subpage_snippet": "", "source": "www.emnlp2015.org", "link": "https://www.emnlp2015.org/accepted-papers.html", "content": "List of accepted short papers . TACL papers to be presented at EMNLP. Long papers . Document Modeling with Convolutional-Gated Recurrent Neural Network for Sentiment Classification Duyu Tang, Bing Qin and Ting Liu."}
+{"idx": 8, "title": "Summary of “If Anyone Builds It, Everyone Dies” | AI Frontiers", "date": "", "ddg_snippet": "Laura Hiscott, Sep 16, 2025 — An overview of the core arguments in Yudkowsky and Soares’s new book.", "subpage_snippet": "", "source": "ai-frontiers.org", "link": "https://ai-frontiers.org/articles/summary-of-if-anyone-builds-it-everyone-dies", "content": "Laura Hiscott, Sep 16, 2025 — An overview of the core arguments in Yudkowsky and Soares’s new book."}
+{"idx": 9, "title": "Speech by the President of Ukraine at the 58th Munich Security...", "date": "", "ddg_snippet": "5 September 2025 - 18:43. Statement by the President of Ukraine during a Joint Press Conference with the Prime Minister of Slovakia.Only all of us, together.\" The audience gave a standing ovation. But, unfortunately, the collective applause did not grow into collective action .", "subpage_snippet": "", "source": "president.gov.ua", "link": "https://president.gov.ua/en/news/vistup-prezidenta-ukrayini-na-58-j-myunhenskij-konferenciyi-72997", "content": "5 September 2025 - 18:43. Statement by the President of Ukraine during a Joint Press Conference with the Prime Minister of Slovakia.Only all of us, together.\" The audience gave a standing ovation. But, unfortunately, the collective applause did not grow into collective action ."}
diff --git a/data/sampled_jsons/ITBench_gpt-4o_pass@1_13.81%_9.52%_year_2024.jsonl b/data/sampled_jsons/ITBench_gpt-4o_pass@1_13.81%_9.52%_year_2024.jsonl
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@@ -0,0 +1,10 @@
+{"idx": 0, "title": "ITBench : Evaluating AI Agents across", "date": "", "ddg_snippet": "For instance, GPT - 4 o ’s pass @ 1 in diagnosis falls from 13 . 81 % with traces to 9 . 52 % without them, and mitigation plummets to 2.86%.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2502.05352", "content": "For instance, GPT - 4 o ’s pass @ 1 in diagnosis falls from 13 . 81 % with traces to 9 . 52 % without them, and mitigation plummets to 2.86%."}
+{"idx": 1, "title": "Paper tables with annotated results for ITBench ... | Papers With Code", "date": "", "ddg_snippet": "ITBench includes an initial set of 94 real-world scenarios, which can be easily extended by community contributions. Our results show that agents powered by state-of-the-art models resolve only 13.8% of SRE scenarios, 25.2% of CISO scenarios, and 0% of FinOps scenarios.", "subpage_snippet": "", "source": "paperswithcode.com", "link": "https://paperswithcode.com/paper/itbench-evaluating-ai-agents-across-diverse/review/", "content": "ITBench includes an initial set of 94 real-world scenarios, which can be easily extended by community contributions. Our results show that agents powered by state-of-the-art models resolve only 13.8% of SRE scenarios, 25.2% of CISO scenarios, and 0% of FinOps scenarios."}
+{"idx": 2, "title": "(PDF) ITBench : Evaluating AI Agents across Diverse Real-World IT...", "date": "", "ddg_snippet": "(see Table 19 and Table 20 in Appendix). For instance, GPT -. 4 o ’s pass @ 1 in diagnosis falls from 13 . 81 % with traces to. 9 . 52 % without them, and mitigation plummets to 2.86%.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/388882803_ITBench_Evaluating_AI_Agents_across_Diverse_Real-World_IT_Automation_Tasks", "content": "(see Table 19 and Table 20 in Appendix). For instance, GPT -. 4 o ’s pass @ 1 in diagnosis falls from 13 . 81 % with traces to. 9 . 52 % without them, and mitigation plummets to 2.86%."}
+{"idx": 3, "title": "Introducing GPT - 4 . 1 in the API | OpenAI", "date": "", "ddg_snippet": "Introducing GPT - 4 . 1 in the API—a new family of models with across-the-board improvements, including major gains in coding, instruction following, and long-context understanding. We’re also releasing our first nano model. Available to developers worldwide starting today.", "subpage_snippet": "", "source": "openai.com", "link": "https://openai.com/index/gpt-4-1/", "content": "Introducing GPT - 4 . 1 in the API—a new family of models with across-the-board improvements, including major gains in coding, instruction following, and long-context understanding. We’re also releasing our first nano model. Available to developers worldwide starting today."}
+{"idx": 4, "title": "GPT - 4 o vs. GPT - 4 : How do they compare? | TechTarget", "date": "", "ddg_snippet": "GPT - 4 and GPT - 4 o -- that's the letter o, for omni -- are advanced generative AI models that OpenAI developed for use within the ChatGPT interface.", "subpage_snippet": "", "source": "www.techtarget.com", "link": "https://www.techtarget.com/searchenterpriseai/feature/GPT-4o-vs-GPT-4-How-do-they-compare", "content": "GPT - 4 and GPT - 4 o -- that's the letter o, for omni -- are advanced generative AI models that OpenAI developed for use within the ChatGPT interface."}
+{"idx": 5, "title": "Купите доступ к ChatGPT в России по низкой цене от 99₽ на ggsel", "date": "", "ddg_snippet": "ChatGPT Plus ( GPT 5 + SORA) | 1 МЕСЯЦ. Покупка на ваш аккаунт.", "subpage_snippet": "", "source": "ggsel.net", "link": "https://ggsel.net/catalog/programs-open", "content": "ChatGPT Plus ( GPT 5 + SORA) | 1 МЕСЯЦ. Покупка на ваш аккаунт."}
+{"idx": 6, "title": "No Need ChatGPT Plus, Accessing Unlimited GPT - 4 o Conversation...", "date": "", "ddg_snippet": "On May 13, OpenAI unveiled its latest flagship AI model: GPT - 4 o , marking a significant milestone in the industry of AI! The abbreviation \"o\" in GPT - 4 o stands for \"Omni\", indicating an all-capable system.", "subpage_snippet": "", "source": "anakin.ai", "link": "https://anakin.ai/blog/unlimited-gpt-4o/", "content": "On May 13, OpenAI unveiled its latest flagship AI model: GPT - 4 o , marking a significant milestone in the industry of AI! The abbreviation \"o\" in GPT - 4 o stands for \"Omni\", indicating an all-capable system."}
+{"idx": 7, "title": "How to run a ChatGPT model locally and offline with GPT 4 All and train...", "date": "", "ddg_snippet": "GPT 4 All is optimized to run 7-13B parameter large language models on the CPUs of any computer running OSX/Windows/Linux. It has a very simple user interface much like Open AI’s ChatGPT.", "subpage_snippet": "", "source": "codeornocode.com", "link": "https://codeornocode.com/tutorial/how-to-run-chatgpt-local-gpt4all/", "content": "GPT 4 All is optimized to run 7-13B parameter large language models on the CPUs of any computer running OSX/Windows/Linux. It has a very simple user interface much like Open AI’s ChatGPT."}
+{"idx": 8, "title": "Benchmarks OpenAI o 1 vs. GPT - 4 o - Was... — DEINKIKOMPASS.de", "date": "", "ddg_snippet": "Quelle: OpenAI. Überlegene Leistung in Evals. Um die Verbesserungen von o 1 gegenüber GPT - 4 o hervorzuheben, wurde das Modell auf verschiedenen menschlichen Prüfungen und ML-Benchmarks getestet", "subpage_snippet": "", "source": "deinkikompass.de", "link": "https://deinkikompass.de/blog/benchmarks-openai-o1-vs-gpt-4o-was-steckt-hinter-dem-modell", "content": "Quelle: OpenAI. Überlegene Leistung in Evals. Um die Verbesserungen von o 1 gegenüber GPT - 4 o hervorzuheben, wurde das Modell auf verschiedenen menschlichen Prüfungen und ML-Benchmarks getestet"}
+{"idx": 9, "title": "Как создать загрузочный EFI-раздел Windows на GPT -диске вручную", "date": "", "ddg_snippet": "Windows на подключённый к компьютеру второй GPT -диск не устанавливается со своей EFI-структурой. При обычной установке с установочного носителя загрузчик второй системы прописывается на уже существующий EFI-раздел – тот, что находится на первом...", "subpage_snippet": "", "source": "www.white-windows.ru", "link": "https://www.white-windows.ru/kak-sozdat-zagruzochnyj-efi-razdel-windows-na-gpt-diske-vruchnuyu/", "content": "Windows на подключённый к компьютеру второй GPT -диск не устанавливается со своей EFI-структурой. При обычной установке с установочного носителя загрузчик второй системы прописывается на уже существующий EFI-раздел – тот, что находится на первом..."}
diff --git a/data/sampled_jsons/Identifying_and_characterizing_Sybils_in_the_Tor_network_abstract_Winter_Ensafi_Loesing_Feamster.jsonl b/data/sampled_jsons/Identifying_and_characterizing_Sybils_in_the_Tor_network_abstract_Winter_Ensafi_Loesing_Feamster.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..2ddc59f77338b4e5df1babde4f11729dd517162c
--- /dev/null
+++ b/data/sampled_jsons/Identifying_and_characterizing_Sybils_in_the_Tor_network_abstract_Winter_Ensafi_Loesing_Feamster.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Identifying and Characterizing Sybils in the Tor Network", "date": "", "ddg_snippet": "Our findings include diverse Sybils , ranging from botnets, to academic research, and relays that hijacked Bitcoin transactions. Our work shows that existing Sybil defenses do not apply to Tor , it delivers insights into realworld attacks, and provides practical tools to uncover and characterize Sybils , making the network safer for its users.", "subpage_snippet": "", "source": "www.usenix.org", "link": "https://www.usenix.org/conference/usenixsecurity16/technical-sessions/presentation/winter", "content": "Our findings include diverse Sybils , ranging from botnets, to academic research, and relays that hijacked Bitcoin transactions. Our work shows that existing Sybil defenses do not apply to Tor , it delivers insights into realworld attacks, and provides practical tools to uncover and characterize Sybils , making the network safer for its users."}
+{"idx": 1, "title": "Spatial Path Selection and Network Topology Optimisation in P2P", "date": "", "ddg_snippet": "In this paper, we show the potential use of geo-sharding in decentralized routing ... Identifying and characterizing sybils in the tor network .", "subpage_snippet": "", "source": "journals.riverpublishers.com", "link": "https://journals.riverpublishers.com/index.php/JWE/article/view/12519", "content": "In this paper, we show the potential use of geo-sharding in decentralized routing ... Identifying and characterizing sybils in the tor network ."}
+{"idx": 2, "title": "Cryptography and Security Feb 2016", "date": "", "ddg_snippet": "We gratefully acknowledge support from the Simons Foundation, member institutions , and all contributors. ... International journal of security, ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/list/cs.CR/2016-02", "content": "We gratefully acknowledge support from the Simons Foundation, member institutions , and all contributors. ... International journal of security, ..."}
+{"idx": 3, "title": "Go Wiki: Research Papers - The Go Programming Language", "date": "", "ddg_snippet": "In : Proceedings of the 2018 Workshop on Advanced Tools, Programming Languages, and PLatforms for Implementing and Evaluating Algorithms for ...", "subpage_snippet": "", "source": "go.dev", "link": "https://go.dev/wiki/ResearchPapers", "content": "In : Proceedings of the 2018 Workshop on Advanced Tools, Programming Languages, and PLatforms for Implementing and Evaluating Algorithms for ..."}
+{"idx": 4, "title": "PDF Identifying and Characterizing Sybils in the Tor Network - USENIX", "date": "", "ddg_snippet": "Abstract Being a volunteer-run, distributed anonymity network , Tor is vulnerable to Sybil attacks. Little is known about real-world Sybils in the Tor network , and we lack practi-cal tools and methods to expose Sybil attacks. In this work, we develop sybilhunter, a system for detecting Sybil relays based on their appearance, such as config-uration; and behavior, such as uptime sequences. We ...", "subpage_snippet": "", "source": "www.usenix.org", "link": "https://www.usenix.org/system/files/conference/usenixsecurity16/sec16_paper_winter.pdf", "content": "Abstract Being a volunteer-run, distributed anonymity network , Tor is vulnerable to Sybil attacks. Little is known about real-world Sybils in the Tor network , and we lack practi-cal tools and methods to expose Sybil attacks. In this work, we develop sybilhunter, a system for detecting Sybil relays based on their appearance, such as config-uration; and behavior, such as uptime sequences. We ..."}
+{"idx": 5, "title": "Identifying and characterizing Sybils in the Tor network", "date": "", "ddg_snippet": "View a PDF of the paper titled Identifying and characterizing Sybils in the Tor network , by Philipp Winter and Roya Ensafi and Karsten Loesing and Nick Feamster", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/1602.07787", "content": "View a PDF of the paper titled Identifying and characterizing Sybils in the Tor network , by Philipp Winter and Roya Ensafi and Karsten Loesing and Nick Feamster"}
+{"idx": 6, "title": "Identifying and characterizing sybils in the tor network", "date": "", "ddg_snippet": "Abstract Being a volunteer-run, distributed anonymity network , Tor is vulnerable to Sybil attacks. Little is known about real-world Sybils in the Tor network , and we lack practical tools and methods to expose Sybil attacks.", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/abs/10.5555/3241094.3241185", "content": "Abstract Being a volunteer-run, distributed anonymity network , Tor is vulnerable to Sybil attacks. Little is known about real-world Sybils in the Tor network , and we lack practical tools and methods to expose Sybil attacks."}
+{"idx": 7, "title": "PDF Identifying and characterizing Sybils in the Tor network", "date": "", "ddg_snippet": "Identifying and characterizing Sybils in the Tor network August 12, 2016 USENIX Security Symposium Philipp Winter Princeton University and Karlstad University", "subpage_snippet": "", "source": "ensa.fi", "link": "https://ensa.fi/slides/security16_slides_winter.pdf", "content": "Identifying and characterizing Sybils in the Tor network August 12, 2016 USENIX Security Symposium Philipp Winter Princeton University and Karlstad University"}
+{"idx": 8, "title": "Protecting Tor from Sybil attacks - nymity.ch", "date": "", "ddg_snippet": "Writing Identifying and characterizing Sybils in the Tor network Philipp Winter , Roya Ensafi , Karsten Loesing , and Nick Feamster In Proc. of USENIX Security, August 2016 Slides • Bibliography • LaTeX source • BibTeX • Recording Protecting the Tor network from Sybil attacks Philipp Winter , Roya Ensafi , Karsten Loesing , and Nick Feamster", "subpage_snippet": "", "source": "nymity.ch", "link": "https://nymity.ch/sybilhunting/", "content": "Writing Identifying and characterizing Sybils in the Tor network Philipp Winter , Roya Ensafi , Karsten Loesing , and Nick Feamster In Proc. of USENIX Security, August 2016 Slides • Bibliography • LaTeX source • BibTeX • Recording Protecting the Tor network from Sybil attacks Philipp Winter , Roya Ensafi , Karsten Loesing , and Nick Feamster"}
+{"idx": 9, "title": "LIBRIS - Identifying and characterizin...", "date": "", "ddg_snippet": "Little is known about real-world Sybils in the Tor network , and we lack practical tools and methods to expose Sybil attacks. In this work, we develop sybilhunter, a system for detecting Sybil relays based on their appearance, such as configuration; and behavior, such as uptime sequences.", "subpage_snippet": "", "source": "libris.kb.se", "link": "https://libris.kb.se/bib/19659695", "content": "Little is known about real-world Sybils in the Tor network , and we lack practical tools and methods to expose Sybil attacks. In this work, we develop sybilhunter, a system for detecting Sybil relays based on their appearance, such as configuration; and behavior, such as uptime sequences."}
diff --git a/data/sampled_jsons/ImagineFSL_Flowers_DISEF_performance_table_sitepeihuali.orgImagineFSL.jsonl b/data/sampled_jsons/ImagineFSL_Flowers_DISEF_performance_table_sitepeihuali.orgImagineFSL.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..4698582c9c02bcd86af27d03681a7696b2defe77
--- /dev/null
+++ b/data/sampled_jsons/ImagineFSL_Flowers_DISEF_performance_table_sitepeihuali.orgImagineFSL.jsonl
@@ -0,0 +1 @@
+{"idx": 0, "title": "ImagineFSL: Self-Supervised Pretraining Matters on Imagined ...", "date": "", "ddg_snippet": "We find that, compared to no pretraining, both supervised and self-supervised pretraining are beneficial, with the latter providing better performance . Based on on this finding, we propose an improved self-supervised method tailored for few-shot scenarios, enhancing the transferability of representations from synthetic to real image domains.", "subpage_snippet": "", "source": "www.peihuali.org", "link": "https://www.peihuali.org/ImagineFSL/", "content": "We find that, compared to no pretraining, both supervised and self-supervised pretraining are beneficial, with the latter providing better performance . Based on on this finding, we propose an improved self-supervised method tailored for few-shot scenarios, enhancing the transferability of representations from synthetic to real image domains."}
diff --git a/data/sampled_jsons/Improving_the_Scaling_Laws_of_Synthetic_Data_with_Deliberate_Practice_Fan_et_al.,_2024.jsonl b/data/sampled_jsons/Improving_the_Scaling_Laws_of_Synthetic_Data_with_Deliberate_Practice_Fan_et_al.,_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..56c148eaf60617a6084b06a5ae52823c47857a34
--- /dev/null
+++ b/data/sampled_jsons/Improving_the_Scaling_Laws_of_Synthetic_Data_with_Deliberate_Practice_Fan_et_al.,_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Improving the Scaling Laws of Synthetic Data with Deliberate Practice", "date": "", "ddg_snippet": "Inspired by the principle of deliberate practice in human learning, we propose Deliberate Practice for Synthetic Data Generation (DP), a novel framework that improves sample efficiency through dynamic synthetic data generation. Prior work has shown that scaling synthetic data is inherently challenging, as naively adding new data leads to diminishing returns. To address this, pruning has been ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2502.15588", "content": "Inspired by the principle of deliberate practice in human learning, we propose Deliberate Practice for Synthetic Data Generation (DP), a novel framework that improves sample efficiency through dynamic synthetic data generation. Prior work has shown that scaling synthetic data is inherently challenging, as naively adding new data leads to diminishing returns. To address this, pruning has been ..."}
+{"idx": 1, "title": "Improving the Scaling Laws of Synthetic Data with Deliberate Practice", "date": "", "ddg_snippet": "A key principle underlying learning in human is deliberate practice (DP)—progress is made not by repeating what is already known but by continuously engaging with tasks that stretch the limits of one's abilities (Ericsson et al.,1993). For example, when learning to play the guitar, simply prac- ticing songs that one has mastered does little to improve skill. Instead, targeted practice on ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=0LZRtvK871", "content": "A key principle underlying learning in human is deliberate practice (DP)—progress is made not by repeating what is already known but by continuously engaging with tasks that stretch the limits of one's abilities (Ericsson et al.,1993). For example, when learning to play the guitar, simply prac- ticing songs that one has mastered does little to improve skill. Instead, targeted practice on ..."}
+{"idx": 2, "title": "dblp: Improving the Scaling Laws of Synthetic Data with Deliberate ...", "date": "", "ddg_snippet": "Bibliographic details on Improving the Scaling Laws of Synthetic Data with Deliberate Practice .", "subpage_snippet": "", "source": "dblp.org", "link": "https://dblp.org/rec/journals/corr/abs-2502-15588", "content": "Bibliographic details on Improving the Scaling Laws of Synthetic Data with Deliberate Practice ."}
+{"idx": 3, "title": "Improving the Scaling Laws of Synthetic Data with Deliberate Practice ...", "date": "", "ddg_snippet": "Improving the Scaling Laws of Synthetic Data with Deliberate Practice Arxiv Papers 9.49K subscribers Subscribed", "subpage_snippet": "", "source": "www.youtube.com", "link": "https://www.youtube.com/watch?v=CqBHIcfOFjc", "content": "Improving the Scaling Laws of Synthetic Data with Deliberate Practice Arxiv Papers 9.49K subscribers Subscribed"}
+{"idx": 4, "title": "Improving the Scaling Laws of Synthetic Data with Deliberate Practice", "date": "", "ddg_snippet": "Join the discussion on this paper pageImproving the Scaling Laws of Synthetic Data with Deliberate Practice", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/papers/2502.15588", "content": "Join the discussion on this paper pageImproving the Scaling Laws of Synthetic Data with Deliberate Practice"}
+{"idx": 5, "title": "Improving the Scaling Laws of Synthetic Data with Deliberate Practice", "date": "", "ddg_snippet": "Poster presentation: Improving the Scaling Laws of Synthetic Data with Deliberate Practice Wed 16 Jul 11 a.m. PDT — 1:30 p.m. PDT", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/oral/47271", "content": "Poster presentation: Improving the Scaling Laws of Synthetic Data with Deliberate Practice Wed 16 Jul 11 a.m. PDT — 1:30 p.m. PDT"}
+{"idx": 6, "title": "NeurIPS Deliberate Practice with Synthetic Data", "date": "", "ddg_snippet": "Deliberate Practice with Synthetic Data Reyhane Askari Hemmat · Mohammad Pezeshki · Pietro Astolfi · Melissa Hall · Florian Bordes · Jakob Verbeek · Michal Drozdzal · Adriana Romero", "subpage_snippet": "", "source": "neurips.cc", "link": "https://neurips.cc/virtual/2024/104920", "content": "Deliberate Practice with Synthetic Data Reyhane Askari Hemmat · Mohammad Pezeshki · Pietro Astolfi · Melissa Hall · Florian Bordes · Jakob Verbeek · Michal Drozdzal · Adriana Romero"}
+{"idx": 7, "title": "Improving the Scaling Laws of Synthetic Data with Deliberate Practice", "date": "", "ddg_snippet": "Abstract Inspired by the principle of deliberate practice in human learning, we propose Deliberate Practice for Synthetic Data Generation (DP), a novel framework that improves sample efficiency through dynamic synthetic data generation.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2502.15588", "content": "Abstract Inspired by the principle of deliberate practice in human learning, we propose Deliberate Practice for Synthetic Data Generation (DP), a novel framework that improves sample efficiency through dynamic synthetic data generation."}
+{"idx": 8, "title": "Improving the Scaling Laws of Synthetic Data with Deliberate Practice", "date": "", "ddg_snippet": "Unlike static approaches that generateall synthetic training data upfront ( Fan et al., 2024 ; Shin et al., 2023; Hemmat et al., 2023), our frameworkincorporates a dynamic loop between a dif f usion model and a downstream learner throughout the training.More concretely, rather than generating an entire dataset at once and irrespective of the ...", "subpage_snippet": "", "source": "www.doc88.com", "link": "https://www.doc88.com/p-18443812103034.html", "content": "Unlike static approaches that generateall synthetic training data upfront ( Fan et al., 2024 ; Shin et al., 2023; Hemmat et al., 2023), our frameworkincorporates a dynamic loop between a dif f usion model and a downstream learner throughout the training.More concretely, rather than generating an entire dataset at once and irrespective of the ..."}
+{"idx": 9, "title": "Improving the Scaling Laws of Synthetic Data with Deliberate Practice ...", "date": "", "ddg_snippet": "Conclusion The deliberate practice methodology represents a significant advance in synthetic data generation. By focusing on generating high-quality, targeted training examples, this approach could substantially reduce the data requirements for training AI systems.", "subpage_snippet": "", "source": "www.aimodels.fyi", "link": "https://www.aimodels.fyi/papers/arxiv/improving-scaling-laws-synthetic-data-deliberate-practice", "content": "Conclusion The deliberate practice methodology represents a significant advance in synthetic data generation. By focusing on generating high-quality, targeted training examples, this approach could substantially reduce the data requirements for training AI systems."}
diff --git a/data/sampled_jsons/Improving_the_Throughput_of_Sharding_Blockchain_via_Deep_Reinforcement_Learning_Based_State_Placemen.jsonl b/data/sampled_jsons/Improving_the_Throughput_of_Sharding_Blockchain_via_Deep_Reinforcement_Learning_Based_State_Placemen.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..d3419cd0fd5d3cc412646201e729c12d0e537429
--- /dev/null
+++ b/data/sampled_jsons/Improving_the_Throughput_of_Sharding_Blockchain_via_Deep_Reinforcement_Learning_Based_State_Placemen.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "SPRING: Improving the Throughput of Sharding Blockchain via Deep ...", "date": "", "ddg_snippet": "In this paper, we present SPRING, the first deep-reinforcement-learning (DRL)-based sharding framework for state placement . SPRING formulates the state placement as a Markov Decision Process, which considers the cross-shard transaction ratio and workload balancing and employs DRL to learn the effective state placement policy.", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.1145/3589334.3645386", "content": "In this paper, we present SPRING, the first deep-reinforcement-learning (DRL)-based sharding framework for state placement . SPRING formulates the state placement as a Markov Decision Process, which considers the cross-shard transaction ratio and workload balancing and employs DRL to learn the effective state placement policy."}
+{"idx": 1, "title": "PDF SPRING: Improving the Throughput of Sharding Blockchain via Deep ...", "date": "", "ddg_snippet": "Existing sharding solutions place states based on heuristic algorithms or redistribute states via graph-partitioning- based methods, which are either less efective or costly. In this paper, we present Spring, the first deep - reinforcement -learning(DRL)- based sharding framework for state placement .", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/attachment?id=8oczaP1YKD&name=pdf", "content": "Existing sharding solutions place states based on heuristic algorithms or redistribute states via graph-partitioning- based methods, which are either less efective or costly. In this paper, we present Spring, the first deep - reinforcement -learning(DRL)- based sharding framework for state placement ."}
+{"idx": 2, "title": "Optimal Sharding for Dynamic Throughput Optimization in Blockchain ...", "date": "", "ddg_snippet": "The rapid advancement in blockchain technology has enabled its applications across wide spectrum of fields. The blockchain throughput , which is usually measured by Transactions Per Second (TPS), is one of the key metrics to reflect the performance of the blockchain systems. However, current blockchain systems have low TPS rates that makes them unsuitable for latency critical applications like ...", "subpage_snippet": "", "source": "ieeexplore.ieee.org", "link": "https://ieeexplore.ieee.org/document/10394337", "content": "The rapid advancement in blockchain technology has enabled its applications across wide spectrum of fields. The blockchain throughput , which is usually measured by Transactions Per Second (TPS), is one of the key metrics to reflect the performance of the blockchain systems. However, current blockchain systems have low TPS rates that makes them unsuitable for latency critical applications like ..."}
+{"idx": 3, "title": "Cross-shard transaction optimization based on community detection in ...", "date": "", "ddg_snippet": "SPRING proposes a sharding framework based on deep reinforcement learning , which constructs the placement problem as a Markov Decision Process (MDP) while considering the proportion of cross-shard transactions and workload balancing.", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S1568494624012250", "content": "SPRING proposes a sharding framework based on deep reinforcement learning , which constructs the placement problem as a Markov Decision Process (MDP) while considering the proportion of cross-shard transactions and workload balancing."}
+{"idx": 4, "title": "PDF SPRING: Improving the Throughput of Sharding Blockchain via Deep ...", "date": "", "ddg_snippet": "In this paper, we present SPRING, the first deep - reinforcement -learning(DRL)- based shard-ing framework for state placement . SPRING formulates the state placement as a Markov Decision Process, which considers the cross-shard transaction ratio and workload balancing and employs DRL to learn the efective state placement policy.", "subpage_snippet": "", "source": "zhenxiao.com", "link": "http://zhenxiao.com/papers/WWW_Spring_camera_ready.pdf", "content": "In this paper, we present SPRING, the first deep - reinforcement -learning(DRL)- based shard-ing framework for state placement . SPRING formulates the state placement as a Markov Decision Process, which considers the cross-shard transaction ratio and workload balancing and employs DRL to learn the efective state placement policy."}
+{"idx": 5, "title": "AERO: Enhancing Sharding Blockchain via Deep Reinforcement Learning for ...", "date": "", "ddg_snippet": "SPRING: Improving the Throughput of Sharding Blockchain via Deep Reinforcement Learning Based State Placement . In Proceedings of the ACM on Web Conference 2024 . 2836--2846.", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.1145/3696410.3714926", "content": "SPRING: Improving the Throughput of Sharding Blockchain via Deep Reinforcement Learning Based State Placement . In Proceedings of the ACM on Web Conference 2024 . 2836--2846."}
+{"idx": 6, "title": "Deep Learning Approaches for Blockchain Scalability Through Sharding ...", "date": "", "ddg_snippet": "Sharding technology creates new difficulties even as it helps traditional blockchain networks overcome performance issues. The distribution of malicious nodes may be unequal because of random node allocation, resulting in performance variations and security threats. Current reputation- based sharding techniques frequently ignore node performance features and don't take care of leader election ...", "subpage_snippet": "", "source": "ieeexplore.ieee.org", "link": "https://ieeexplore.ieee.org/document/10830433", "content": "Sharding technology creates new difficulties even as it helps traditional blockchain networks overcome performance issues. The distribution of malicious nodes may be unequal because of random node allocation, resulting in performance variations and security threats. Current reputation- based sharding techniques frequently ignore node performance features and don't take care of leader election ..."}
+{"idx": 7, "title": "dblp: SPRING: Improving the Throughput of Sharding Blockchain via Deep ...", "date": "", "ddg_snippet": "Bibliographic details on SPRING: Improving the Throughput of Sharding Blockchain via Deep Reinforcement Learning Based State Placement .", "subpage_snippet": "", "source": "dblp.org", "link": "https://dblp.org/rec/conf/www/LiSXXDGL24", "content": "Bibliographic details on SPRING: Improving the Throughput of Sharding Blockchain via Deep Reinforcement Learning Based State Placement ."}
+{"idx": 8, "title": "Blockchain sharding scheme based on generative AI and DRL: Applied to ...", "date": "", "ddg_snippet": "Secondly, the system integrates a reinforcement learning module, DL-AI, specifically tailored for configuring parameters of the blockchain sharding system, such as the number of shards, block size, and block interval, to automatically optimize them, aiming to further enhance the system's throughput .", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S2667345224000117", "content": "Secondly, the system integrates a reinforcement learning module, DL-AI, specifically tailored for configuring parameters of the blockchain sharding system, such as the number of shards, block size, and block interval, to automatically optimize them, aiming to further enhance the system's throughput ."}
+{"idx": 9, "title": "PDF A dynamic state sharding blockchain architecture for scalable and ...", "date": "", "ddg_snippet": "Considering that blockchain based crowdsourcing systems rely on the underlying blockchain , we aim to optimize the scalability and decentralization of blockchain while ensuring security by proposing a blockchain architecture based on state sharding using deep rein-forcement learning , thereby improving the performance and security of blockchain ...", "subpage_snippet": "", "source": "xiaodingwang.github.io", "link": "https://xiaodingwang.github.io/WangXiaoding.github.io/assets/files/59.pdf", "content": "Considering that blockchain based crowdsourcing systems rely on the underlying blockchain , we aim to optimize the scalability and decentralization of blockchain while ensuring security by proposing a blockchain architecture based on state sharding using deep rein-forcement learning , thereby improving the performance and security of blockchain ..."}
diff --git a/data/sampled_jsons/Incorrect_Synthetic_Data_Scales_the_Efficiency_of_LLM_Math_Reasoning_by_Eight-Fold_Figure_2_performa_year_2024.jsonl b/data/sampled_jsons/Incorrect_Synthetic_Data_Scales_the_Efficiency_of_LLM_Math_Reasoning_by_Eight-Fold_Figure_2_performa_year_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..3e6c90255dd87a712ead6fc86628f8a1939d103a
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+++ b/data/sampled_jsons/Incorrect_Synthetic_Data_Scales_the_Efficiency_of_LLM_Math_Reasoning_by_Eight-Fold_Figure_2_performa_year_2024.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.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2406.14532v1", "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."}
+{"idx": 1, "title": "RL on Incorrect Synthetic Data Scales", "date": "", "ddg_snippet": "Negative Data Identifies Spurious Steps with Advantage Estimates. RL on Incorrect Synthetic Data Scales the Efficiency of LLM Math Reasoning by Eight - Fold .", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=9m87e9Keq1", "content": "Negative Data Identifies Spurious Steps with Advantage Estimates. RL on Incorrect Synthetic Data Scales the Efficiency of LLM Math Reasoning by Eight - Fold ."}
+{"idx": 2, "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.", "subpage_snippet": "", "source": "synthical.com", "link": "https://synthical.com/article/RL-on-Incorrect-Synthetic-Data-Scales-the-Efficiency-of-LLM-Math-Reasoning-by-Eight-Fold-17552ae8-02cf-4931-9351-04c6f7b243c5", "content": "Training on model-generated synthetic data is a promising approach for finetuning LLMs, but it remains unclear when it helps or hurts."}
+{"idx": 3, "title": "RL on Incorrect Synthetic Data Scales the Efficiency of LLM Math ...", "date": "", "ddg_snippet": "The study investigates the impact of synthetic data , both correct and incorrect , on the fine-tuning of LLMs for enhanced math reasoning using supervised fine-tuning ( SFT ) and reinforcement learning (RL).", "subpage_snippet": "", "source": "www.emergentmind.com", "link": "https://www.emergentmind.com/papers/2406.14532", "content": "The study investigates the impact of synthetic data , both correct and incorrect , on the fine-tuning of LLMs for enhanced math reasoning using supervised fine-tuning ( SFT ) and reinforcement learning (RL)."}
+{"idx": 4, "title": "(PDF) RL on Incorrect Synthetic Data Scales the Efficiency of LLM ...", "date": "", "ddg_snippet": "SFT on synthetic problems and res ponses by 2 x, whereas using step-level RL with negativ e data scales the efficiency by 8 x.", "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": "SFT on synthetic problems and res ponses by 2 x, whereas using step-level RL with negativ e data scales the efficiency by 8 x."}
+{"idx": 5, "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.", "subpage_snippet": "", "source": "paperswithcode.com", "link": "https://paperswithcode.com/paper/rl-on-incorrect-synthetic-data-scales-the", "content": "Training on model-generated synthetic data is a promising approach for finetuning LLMs, but it remains unclear when it helps or hurts."}
+{"idx": 6, "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.", "subpage_snippet": "", "source": "papers.nips.cc", "link": "https://papers.nips.cc/paper_files/paper/2024/hash/4b77d5b896c321a29277524a98a50215-Abstract-Conference.html", "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."}
+{"idx": 7, "title": "Bayesian beagle - RL on Incorrect Synthetic Data Scales the ...", "date": "", "ddg_snippet": "Finetuning LLMs with model-generated data can improve math reasoning , especially with self-generated correct solutions and per-step negative responses. This approach can double efficiency and reduce spurious correlations.", "subpage_snippet": "", "source": "bayesian-beagle.netlify.app", "link": "https://bayesian-beagle.netlify.app/posts/rl_on_incorrect_synthetic_data_scales_the_efficiency_of_llm_math_reasoning_by_eight_fold/2024-06-20-rl_on_incorrect_synthetic_data_scales_the_efficiency_of_llm_math_reasoning_by_eight_fold", "content": "Finetuning LLMs with model-generated data can improve math reasoning , especially with self-generated correct solutions and per-step negative responses. This approach can double efficiency and reduce spurious correlations."}
+{"idx": 8, "title": "AI-Powered Paper Summarization about the arXiv paper 2406.14532v1", "date": "", "ddg_snippet": "RL on Incorrect Synthetic Data Scales the Efficiency of LLM Math Reasoning by Eight - Fold .And indeed, they find that fine-tuning again with this self-generated positive data leads to a doubling of efficiency when solving the same synthetic problems.", "subpage_snippet": "", "source": "summarizepaper.com", "link": "https://summarizepaper.com/en/arxiv-id/2406.14532v1/", "content": "RL on Incorrect Synthetic Data Scales the Efficiency of LLM Math Reasoning by Eight - Fold .And indeed, they find that fine-tuning again with this self-generated positive data leads to a doubling of efficiency when solving the same synthetic problems."}
+{"idx": 9, "title": "RL on Incorrect Synthetic Data Scales the Efficiency of LLM Math ...", "date": "", "ddg_snippet": "Figure 1: Positive and negative synthetic data : Pictorial representation of positive/negative synthetic data definitions we use and how they are fed to SFT , RFT and DPO. The figure compares the performance of training LLMs on two types of synthetic d...", "subpage_snippet": "", "source": "deep-diver.github.io", "link": "https://deep-diver.github.io/neurips2024/posters/9m87e9keq1/", "content": "Figure 1: Positive and negative synthetic data : Pictorial representation of positive/negative synthetic data definitions we use and how they are fed to SFT , RFT and DPO. The figure compares the performance of training LLMs on two types of synthetic d..."}
diff --git a/data/sampled_jsons/Incorrect_Synthetic_Data_Scales_the_Efficiency_of_LLM_Math_Reasoning_by_Eight-Fold_Section_5_spuriou_year_2023-2024.jsonl b/data/sampled_jsons/Incorrect_Synthetic_Data_Scales_the_Efficiency_of_LLM_Math_Reasoning_by_Eight-Fold_Section_5_spuriou_year_2023-2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..5e564389e3b032d2ecc10c145f4af8e79bffd57e
--- /dev/null
+++ b/data/sampled_jsons/Incorrect_Synthetic_Data_Scales_the_Efficiency_of_LLM_Math_Reasoning_by_Eight-Fold_Section_5_spuriou_year_2023-2024.jsonl
@@ -0,0 +1,10 @@
+{"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": "At the same time, training on model-generated positives can amplify various spurious correlations, resulting in flat or even inverse scaling trends as the amount of data increases.", "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": "At the same time, training on model-generated positives can amplify various spurious correlations, resulting in flat or even inverse scaling trends as the amount of data increases."}
+{"idx": 2, "title": "RL on Incorrect Synthetic Data · MinWoo Park", "date": "", "ddg_snippet": "At the same time, training on model-generated positives can amplify various spurious correlations, resulting in flat or even inverse scaling trends as the amount of data increases.", "subpage_snippet": "", "source": "dsdanielpark.github.io", "link": "https://dsdanielpark.github.io/llm/2024-06-25-RLonIncorrectSyntheticData.html", "content": "At the same time, training on model-generated positives can amplify various spurious correlations, resulting in flat or even inverse scaling trends as the amount of data increases."}
+{"idx": 3, "title": "RL on Incorrect Synthetic Data Scales the Efficiency of LLM Math ...", "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": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper_files/paper/2024/hash/4b77d5b896c321a29277524a98a50215-Abstract-Conference.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": 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. 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": 5, "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": "arxiv.org", "link": "https://arxiv.org/html/2406.14532v1", "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": 6, "title": "PDF RLonIncorrectSyntheticDataScalesthe EfficiencyofLLMMathReasoningbyEight ...", "date": "", "ddg_snippet": "RLonIncorrectSyntheticDataScalesthe EfficiencyofLLMMathReasoningbyEight-Fold RL on Incorrect Synthetic Data Scales the Eficiency of LLM Math Reasoning by Eight-Fold", "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/fulltext/6675a4158408575b837d3b71/RL-on-Incorrect-Synthetic-Data-Scales-the-Efficiency-of-LLM-Math-Reasoning-by-Eight-Fold.pdf", "content": "RLonIncorrectSyntheticDataScalesthe EfficiencyofLLMMathReasoningbyEight-Fold RL on Incorrect Synthetic Data Scales the Eficiency of LLM Math Reasoning by Eight-Fold"}
+{"idx": 7, "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": 8, "title": "What Do Learning Dynamics Reveal About Generalization in LLM Reasoning?", "date": "", "ddg_snippet": "Rl on incorrect synthetic data scales the efficiency of llm math reasoning by eight-fold . arXiv preprint arXiv:2406.14532, 2024. Shao et al. [2024] Z. Shao, P. Wang, Q. Zhu, R. Xu, J. Song, M. Zhang, Y. Li, Y. Wu, and D. Guo. Deepseekmath: Pushing the limits of mathematical reasoning in open language models. arXiv preprint arXiv:2402.03300, 2024.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2411.07681v1", "content": "Rl on incorrect synthetic data scales the efficiency of llm math reasoning by eight-fold . arXiv preprint arXiv:2406.14532, 2024. Shao et al. [2024] Z. Shao, P. Wang, Q. Zhu, R. Xu, J. Song, M. Zhang, Y. Li, Y. Wu, and D. Guo. Deepseekmath: Pushing the limits of mathematical reasoning in open language models. arXiv preprint arXiv:2402.03300, 2024."}
+{"idx": 9, "title": "Learning to Reason by Failing: Offline RL on Sub-optimal Rollouts ...", "date": "", "ddg_snippet": "This paper investigates the helps/hurts of training models on model-generated synthetic data for math reasoning . They first conduct an empirical study and then propose to construct negative samples to address spurious correlations in SFT/RFT policy. The reviewers agree that the research problem is significant. The observations are insightful and are well-motivated and grounded with theorems ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=v2PV1yCFJk", "content": "This paper investigates the helps/hurts of training models on model-generated synthetic data for math reasoning . They first conduct an empirical study and then propose to construct negative samples to address spurious correlations in SFT/RFT policy. The reviewers agree that the research problem is significant. The observations are insightful and are well-motivated and grounded with theorems ..."}
diff --git a/data/sampled_jsons/Indyk_Motwani_1998_fundamental_principle_of_Locality_Sensitive_Hashing.jsonl b/data/sampled_jsons/Indyk_Motwani_1998_fundamental_principle_of_Locality_Sensitive_Hashing.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..1e6520e168ade485612c4fa67c94434274d85d19
--- /dev/null
+++ b/data/sampled_jsons/Indyk_Motwani_1998_fundamental_principle_of_Locality_Sensitive_Hashing.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Local Sensitivity Hashing (L.S.H.): A Comprehensive Guide -", "date": "", "ddg_snippet": "Locality - Sensitive Hashing (LSH) , introduced by Indyk - Motwani in 1998 , is a technique used in approximate nearest neighbor ( ANN ) searches that ...", "subpage_snippet": "", "source": "zilliz.com", "link": "https://zilliz.com/learn/Local-Sensitivity-Hashing-A-Comprehensive-Guide", "content": "Locality - Sensitive Hashing (LSH) , introduced by Indyk - Motwani in 1998 , is a technique used in approximate nearest neighbor ( ANN ) searches that ..."}
+{"idx": 1, "title": "Thomas Dybdahl Ahle", "date": "", "ddg_snippet": "These functions form the basis of the successful Indyk - Motwani algorithm (STOC 1998 ) for nearest neighbour problems.", "subpage_snippet": "", "source": "thomasahle.com", "link": "https://thomasahle.com/", "content": "These functions form the basis of the successful Indyk - Motwani algorithm (STOC 1998 ) for nearest neighbour problems."}
+{"idx": 2, "title": "Maximum Inner Product is Query-Scaled Nearest Neighbor", "date": "", "ddg_snippet": "... fundamental question: Can we leverage the efficient computation reduction properties without altering the vector space? We begin with an intriguing ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2503.06882v2", "content": "... fundamental question: Can we leverage the efficient computation reduction properties without altering the vector space? We begin with an intriguing ..."}
+{"idx": 3, "title": "AH-UGC: Adaptive and Heterogeneous-Universal Graph Coarsening", "date": "", "ddg_snippet": "To overcome these limitations, we introduce a novel framework that combines Locality - Sensitive Hashing (LSH) with Consistent Hashing to enable ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2505.15842v1", "content": "To overcome these limitations, we introduce a novel framework that combines Locality - Sensitive Hashing (LSH) with Consistent Hashing to enable ..."}
+{"idx": 4, "title": "Maintaining Stream Statistics over Sliding Windows | SIAM", "date": "", "ddg_snippet": "Indyk , Stable distributions, pseudorandom generators, embeddings and data stream computation , in Proceedings of the 41st IEEE Symposium on ...", "subpage_snippet": "", "source": "epubs.siam.org", "link": "https://epubs.siam.org/doi/abs/10.1137/S0097539701398363", "content": "Indyk , Stable distributions, pseudorandom generators, embeddings and data stream computation , in Proceedings of the 41st IEEE Symposium on ..."}
+{"idx": 5, "title": "MUVERA: Transforming Multi-Vector Information Retrieval Through", "date": "", "ddg_snippet": "Neural embedding models have become a fundamental component of modern information retrieval (IR) pipelines [Abstract].", "subpage_snippet": "", "source": "keyurramoliya.com", "link": "https://keyurramoliya.com/posts/Muvera/", "content": "Neural embedding models have become a fundamental component of modern information retrieval (IR) pipelines [Abstract]."}
+{"idx": 6, "title": "US20060229878A1 - Waveform recognition method and apparatus -", "date": "", "ddg_snippet": "They collect the positions of the most prominent spectral peaks in the signal under a variety of spectral shifts (to cope with the speed change) and ...", "subpage_snippet": "", "source": "patents.google.com", "link": "https://patents.google.com/patent/US20060229878A1/en", "content": "They collect the positions of the most prominent spectral peaks in the signal under a variety of spectral shifts (to cope with the speed change) and ..."}
+{"idx": 7, "title": "US6941303B2 - System and method for organizing, compressing and", "date": "", "ddg_snippet": "Data mining is the use of automated data analysis techniques to uncover previously undetected relationships among data items.", "subpage_snippet": "", "source": "patents.google.com", "link": "https://patents.google.com/patent/US6941303B2/en", "content": "Data mining is the use of automated data analysis techniques to uncover previously undetected relationships among data items."}
+{"idx": 8, "title": "Large-scale similarity data management with distributed Metric", "date": "", "ddg_snippet": "The great success of visual features learned from deep neural networks has led to a significant effort to develop efficient and scalable technologies ...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/232275492_Large-scale_similarity_data_management_with_distributed_Metric_Index", "content": "The great success of visual features learned from deep neural networks has led to a significant effort to develop efficient and scalable technologies ..."}
+{"idx": 9, "title": "Accurate Estimation of Neural Population Dynamics without Spike", "date": "", "ddg_snippet": "A growing number of studies spanning systems neuroscience seek to relate the dynamical evolution of neural population states to an organism’s ...", "subpage_snippet": "", "source": "www.cell.com", "link": "https://www.cell.com/neuron/fulltext/S0896-6273(19)30428-3", "content": "A growing number of studies spanning systems neuroscience seek to relate the dynamical evolution of neural population states to an organism’s ..."}
diff --git a/data/sampled_jsons/Instant_Gaussian_Stream_N3DV_dataset_test_sequences_coffee_room_meeting_room.jsonl b/data/sampled_jsons/Instant_Gaussian_Stream_N3DV_dataset_test_sequences_coffee_room_meeting_room.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..5df403db6f9b1b8c8806035edeb3b65f64d05671
--- /dev/null
+++ b/data/sampled_jsons/Instant_Gaussian_Stream_N3DV_dataset_test_sequences_coffee_room_meeting_room.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Instant Gaussian Stream: Fast and Generalizable Streaming of ...", "date": "", "ddg_snippet": "Train AGM-Net Datasets Preparation Our Training Dataset Download our processed data from 4 sequences of N3DV , which can be directly used for training. It contains 1,200 optimized Gaussian points and requires 150GB of storage space. After extraction, the directory structure is as follows:", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/yjb6/IGS", "content": "Train AGM-Net Datasets Preparation Our Training Dataset Download our processed data from 4 sequences of N3DV , which can be directly used for training. It contains 1,200 optimized Gaussian points and requires 150GB of storage space. After extraction, the directory structure is as follows:"}
+{"idx": 1, "title": "(PDF) Instant Gaussian Stream : Fast and Generalizable Streaming ...", "date": "", "ddg_snippet": "Meeting Room Datasets [29]includes3dynamicscenes.ation on two test sequences from the N 3 DV dataset , with. results shown in T ab. 1 . For a fair comparison of per-. formance, we tested 3DGStream using the same Gaussians .", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/390114414_Instant_Gaussian_Stream_Fast_and_Generalizable_Streaming_of_Dynamic_Scene_Reconstruction_via_Gaussian_Splatting", "content": "Meeting Room Datasets [29]includes3dynamicscenes.ation on two test sequences from the N 3 DV dataset , with. results shown in T ab. 1 . For a fair comparison of per-. formance, we tested 3DGStream using the same Gaussians ."}
+{"idx": 2, "title": "4DGC: Rate-Aware 4D Gaussian Compression for Efficient ...", "date": "", "ddg_snippet": "We present a qualitative comparison with ReRF, TeTriRF, and 3DGStream on the coffee martini sequence from the N3DV dataset and the trimming sequence from the MeetRoom dataset , as shown in the figure. Our approach achieves comparable reconstruction quality to 3DGStream at a substantially lower bitrate, achieving a compression rate exceeding 16×.", "subpage_snippet": "", "source": "waveviewer.github.io", "link": "https://waveviewer.github.io/4dgc/", "content": "We present a qualitative comparison with ReRF, TeTriRF, and 3DGStream on the coffee martini sequence from the N3DV dataset and the trimming sequence from the MeetRoom dataset , as shown in the figure. Our approach achieves comparable reconstruction quality to 3DGStream at a substantially lower bitrate, achieving a compression rate exceeding 16×."}
+{"idx": 3, "title": "Instant Gaussian Stream: Fast and Generalizable Streaming of ...", "date": "", "ddg_snippet": "In-domain evaluation: We present our in-domain evalu-ation on two test sequences from the N3DV dataset , with results shown in Tab. 1. For a fair comparison of per-formance, we tested 3DGStream using the same Gaussians from the 0th frame and applied the same variant of Gaus-sian Splatting Rasterization as used in our approach (de-noted with ...", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2025/papers/Yan_Instant_Gaussian_Stream_Fast_and_Generalizable_Streaming_of_Dynamic_Scene_CVPR_2025_paper.pdf", "content": "In-domain evaluation: We present our in-domain evalu-ation on two test sequences from the N3DV dataset , with results shown in Tab. 1. For a fair comparison of per-formance, we tested 3DGStream using the same Gaussians from the 0th frame and applied the same variant of Gaus-sian Splatting Rasterization as used in our approach (de-noted with ..."}
+{"idx": 4, "title": "Instant Gaussian Stream: Fast and Generalizable Streaming of ...", "date": "", "ddg_snippet": "Apr 5, 2025 · In-domain evaluation: We present our in-domain evaluation on two test sequences from the N3DV dataset , with results shown in Tab. 1. For a fair comparison of performance, we tested 3DGStream using the same Gaussians from the 0th frame and applied the same variant of Gaussian Splatting Rasterization as used in our approach (denoted with ...", "subpage_snippet": "", "source": "ar5iv.labs.arxiv.org", "link": "https://ar5iv.labs.arxiv.org/html/2503.16979", "content": "Apr 5, 2025 · In-domain evaluation: We present our in-domain evaluation on two test sequences from the N3DV dataset , with results shown in Tab. 1. For a fair comparison of performance, we tested 3DGStream using the same Gaussians from the 0th frame and applied the same variant of Gaussian Splatting Rasterization as used in our approach (denoted with ..."}
+{"idx": 5, "title": "Instant Gaussian Stream : Fast and Generalizable... | alphaXiv", "date": "", "ddg_snippet": "Instant Gaussian Stream (IGS) is a novel framework that addresses these challenges by providing fast and generalizable streaming reconstruction of dynamic scenes.", "subpage_snippet": "", "source": "www.alphaxiv.org", "link": "https://www.alphaxiv.org/overview/2503.16979", "content": "Instant Gaussian Stream (IGS) is a novel framework that addresses these challenges by providing fast and generalizable streaming reconstruction of dynamic scenes."}
+{"idx": 6, "title": "How to get FPS 140 and Storage 200MB with N 3 DV dataset ?", "date": "", "ddg_snippet": "I am trying to cite your paper and run comparative experiments on the N 3 DV dataset using an RTX 4090. To this end, I measured the FPS using the provided code at a resolution of 1352x1014 with the following commands", "subpage_snippet": "", "source": "githubissues.com", "link": "https://githubissues.com/oppo-us-research/SpacetimeGaussians/108", "content": "I am trying to cite your paper and run comparative experiments on the N 3 DV dataset using an RTX 4090. To this end, I measured the FPS using the provided code at a resolution of 1352x1014 with the following commands"}
+{"idx": 7, "title": "Streaming Radiance Fields for 3D Video Synthesis", "date": "", "ddg_snippet": "Top: Our meet room dataset ; Bottom: N 3 DV dataset .We validate the performance of diff-based compression in Meet Room dataset , with the following configurations, i.e., training on the entire sequence , saving the models with and without diff-based compression.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=oMhmv3hLOF2", "content": "Top: Our meet room dataset ; Bottom: N 3 DV dataset .We validate the performance of diff-based compression in Meet Room dataset , with the following configurations, i.e., training on the entire sequence , saving the models with and without diff-based compression."}
+{"idx": 8, "title": "3DGStream: On-the-Fly Training of 3D Gaussians for Efficient...", "date": "", "ddg_snippet": "This approach allows for meticulous manipulation of 3DGs to accommodate scene alterations with minimal performance overhead. In the implementation, the training of initial 3DGs involves fine-tuning learning rates on the N 3 DV dataset and applying them to the Meet Room dataset .", "subpage_snippet": "", "source": "dsin.ai", "link": "https://dsin.ai/news/article/c2iQJoG/3dgstream_on_the_fly_training_of_3d_gaussians_for_efficient_streaming_of_photo_realistic_free_viewpoint_videos", "content": "This approach allows for meticulous manipulation of 3DGs to accommodate scene alterations with minimal performance overhead. In the implementation, the training of initial 3DGs involves fine-tuning learning rates on the N 3 DV dataset and applying them to the Meet Room dataset ."}
+{"idx": 9, "title": "3DGStream", "date": "", "ddg_snippet": "3DGStream: On-the-Fly Training of 3D Gaussians for Efficient Streaming of Photo-Realistic Free-Viewpoint Videos.", "subpage_snippet": "", "source": "sjojok.top", "link": "https://sjojok.top/3dgstream/", "content": "3DGStream: On-the-Fly Training of 3D Gaussians for Efficient Streaming of Photo-Realistic Free-Viewpoint Videos."}
diff --git a/data/sampled_jsons/Intervention_and_Conditioning_in_Causal_Bayesian_Networks.jsonl b/data/sampled_jsons/Intervention_and_Conditioning_in_Causal_Bayesian_Networks.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..9648636574c0e113b92a6ebc52cc1e8cf66cf033
--- /dev/null
+++ b/data/sampled_jsons/Intervention_and_Conditioning_in_Causal_Bayesian_Networks.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Intervention and Conditioning in Causal Bayesian Networks", "date": "", "ddg_snippet": "by S Galhotra · 2024 · Cited by 3 — We show that by making simple yet often realistic independence assumptions, it is possible to uniquely estimate the probability of an interventional formula.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2405.14728", "content": "by S Galhotra · 2024 · Cited by 3 — We show that by making simple yet often realistic independence assumptions, it is possible to uniquely estimate the probability of an interventional formula."}
+{"idx": 1, "title": "Intervention and Conditioning in Causal Bayesian Networks", "date": "", "ddg_snippet": "by S Galhotra · 2024 · Cited by 3 — We show that by making simple yetoften realistic independence assumptions, it is possible to uniquely estimate the probability of an interventional formula.", "subpage_snippet": "", "source": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper_files/paper/2024/hash/a2118322165fffb648d1e341ff5a5b05-Abstract-Conference.html", "content": "by S Galhotra · 2024 · Cited by 3 — We show that by making simple yetoften realistic independence assumptions, it is possible to uniquely estimate the probability of an interventional formula."}
+{"idx": 2, "title": "Intervention and Conditioning in Causal Bayesian Networks", "date": "", "ddg_snippet": "by J Halpern — The paper introduces a condition under which the counterfactual probabilities can be computed from a Causal Bayesian network (CBN). This is not ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=DC28Fpk76s", "content": "by J Halpern — The paper introduces a condition under which the counterfactual probabilities can be computed from a Causal Bayesian network (CBN). This is not ..."}
+{"idx": 3, "title": "Intervention and Conditioning in Causal Bayesian Networks", "date": "", "ddg_snippet": "by J Halpern — Causal models are crucial for understanding complex systems and identifying causal relationships among variables. Even though causal models are extremely.", "subpage_snippet": "", "source": "papers.nips.cc", "link": "https://papers.nips.cc/paper_files/paper/2024/file/a2118322165fffb648d1e341ff5a5b05-Paper-Conference.pdf", "content": "by J Halpern — Causal models are crucial for understanding complex systems and identifying causal relationships among variables. Even though causal models are extremely."}
+{"idx": 4, "title": "Intervention and conditioning in causal Bayesian networks", "date": "", "ddg_snippet": "5 Jun 2025 — Causal models are crucial for understanding complex systems and identifying causal relationships among variables. Even though causal models ...", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.5555/3737916.3740740", "content": "5 Jun 2025 — Causal models are crucial for understanding complex systems and identifying causal relationships among variables. Even though causal models ..."}
+{"idx": 5, "title": "Intervention and Conditioning in Causal Bayesian Networks", "date": "", "ddg_snippet": "23 May 2024 — Interventions and conditioning are the most fundamental procedures in the application of causal models, useful to examine and analyze causal ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2405.14728v1", "content": "23 May 2024 — Interventions and conditioning are the most fundamental procedures in the application of causal models, useful to examine and analyze causal ..."}
+{"idx": 6, "title": "Interventions and Causal Inference", "date": "", "ddg_snippet": "by F Eberhardt · Cited by 386 — First, determining the causal structure from parametric interventions requires more conditional independence tests with larger conditioning sets. This implies. 15 pages", "subpage_snippet": "", "source": "www.cmu.edu", "link": "https://www.cmu.edu/dietrich/philosophy/docs/scheines/PSA2006.pdf", "content": "by F Eberhardt · Cited by 386 — First, determining the causal structure from parametric interventions requires more conditional independence tests with larger conditioning sets. This implies. 15 pages"}
+{"idx": 7, "title": "Intervention and Conditioning in Causal Bayesian Networks", "date": "", "ddg_snippet": "Intervention and Conditioning in Causal Bayesian Networks . Sainyam Galhotra, Joseph Y. Halpern. January, 2024. Cite DOI URL. Type. Journal article. Publication.", "subpage_snippet": "", "source": "sainyamgalhotra.com", "link": "https://sainyamgalhotra.com/publication/dblp-neurips24/", "content": "Intervention and Conditioning in Causal Bayesian Networks . Sainyam Galhotra, Joseph Y. Halpern. January, 2024. Cite DOI URL. Type. Journal article. Publication."}
+{"idx": 8, "title": "Causal Bayes Nets (CBNs): A Concise Overview", "date": "", "ddg_snippet": "8 Sept 2025 — CBNs are Bayesian networks that encode direct cause-effect relations , enabling intervention analysis and advanced causal inference in ...", "subpage_snippet": "", "source": "www.emergentmind.com", "link": "https://www.emergentmind.com/topics/causal-bayes-nets-cbns", "content": "8 Sept 2025 — CBNs are Bayesian networks that encode direct cause-effect relations , enabling intervention analysis and advanced causal inference in ..."}
+{"idx": 9, "title": "A New Characterization of the Experimental Implications of ...", "date": "", "ddg_snippet": "by J Tian · 2002 · Cited by 14 — We offer a complete characterization of the set of distribu- tions that could be induced by local interventions on variables governed by a causal Bayesian ...", "subpage_snippet": "", "source": "ftp.cs.ucla.edu", "link": "https://ftp.cs.ucla.edu/pub/stat_ser/R298.pdf", "content": "by J Tian · 2002 · Cited by 14 — We offer a complete characterization of the set of distribu- tions that could be induced by local interventions on variables governed by a causal Bayesian ..."}
diff --git a/data/sampled_jsons/Izacard_Grave_2021_retrieval_k_passages_performance_improvement.jsonl b/data/sampled_jsons/Izacard_Grave_2021_retrieval_k_passages_performance_improvement.jsonl
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index 0000000000000000000000000000000000000000..0584be3e320ae0f800e1c1ab71a0cde7b399fe42
--- /dev/null
+++ b/data/sampled_jsons/Izacard_Grave_2021_retrieval_k_passages_performance_improvement.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Frustratingly Simple Retrieval Improves Challenging,", "date": "", "ddg_snippet": "... improve performance on challenging, reasoning-intensive LM benchmarks using minimal retrieval —dense retrieval followed by generation ? To this end, ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2507.01297v1", "content": "... improve performance on challenging, reasoning-intensive LM benchmarks using minimal retrieval —dense retrieval followed by generation ? To this end, ..."}
+{"idx": 1, "title": "Unleashing the Power of LLMs in Dense Retrieval with Query", "date": "", "ddg_snippet": "As we know, a potent encoder for dense retrieval should possess the ability to 1) effectively condense the semantics of documents or passages to a ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2504.05216v3", "content": "As we know, a potent encoder for dense retrieval should possess the ability to 1) effectively condense the semantics of documents or passages to a ..."}
+{"idx": 2, "title": "(PDF) Achieving State-of-the-Art Open-Domain QA Performance", "date": "", "ddg_snippet": "... in-Decoder, retrieves informative passages ... passage retrieval and generativ e models while efficiently improving the performance of open-domain", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/374031063_Achieving_State-of-the-Art_Open-Domain_QA_Performance_through_Fusion-in-Decoder_Method", "content": "... in-Decoder, retrieves informative passages ... passage retrieval and generativ e models while efficiently improving the performance of open-domain"}
+{"idx": 3, "title": "‘retrieval AI’ directory · Gwern.net", "date": "", "ddg_snippet": "... Retrieved Knowledge in RAG Systems ”, Tan et ... RNNs Are Not Transformers (Yet): The Key Bottleneck on In-Context Retrieval ”, Wen et al 2024", "subpage_snippet": "", "source": "gwern.net", "link": "https://gwern.net/doc/ai/nn/retrieval/index", "content": "... Retrieved Knowledge in RAG Systems ”, Tan et ... RNNs Are Not Transformers (Yet): The Key Bottleneck on In-Context Retrieval ”, Wen et al 2024"}
+{"idx": 4, "title": "MIRACL: A Multilingual Retrieval Dataset Covering 18 Diverse", "date": "", "ddg_snippet": "For retrieval models in English, the MS MARCO datasets (Bajaj et al., 2018 ; Craswell et al., 2021 ; Lin et al., 2022 ) have had a transformative ...", "subpage_snippet": "", "source": "direct.mit.edu", "link": "https://direct.mit.edu/tacl/article/doi/10.1162/tacl_a_00595/117438/MIRACL-A-Multilingual-Retrieval-Dataset-Covering", "content": "For retrieval models in English, the MS MARCO datasets (Bajaj et al., 2018 ; Craswell et al., 2021 ; Lin et al., 2022 ) have had a transformative ..."}
+{"idx": 5, "title": "GitHub - princeton-nlp/DensePhrases: [ACL 2021] Learning Dense", "date": "", "ddg_snippet": "... for details on how to learn dense representations of phrases and the EMNLP paper ( Phrase Retrieval Learns Passage Retrieval , Too ) on how to perform ...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/princeton-nlp/DensePhrases", "content": "... for details on how to learn dense representations of phrases and the EMNLP paper ( Phrase Retrieval Learns Passage Retrieval , Too ) on how to perform ..."}
+{"idx": 6, "title": "eSapiens: A Platform for Secure and Auditable", "date": "", "ddg_snippet": "To evaluate the system, we conduct two experiments: (1) A retrieval benchmark on legal corpora reveals that chunk size of 512 tokens yields the ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2507.09588v1", "content": "To evaluate the system, we conduct two experiments: (1) A retrieval benchmark on legal corpora reveals that chunk size of 512 tokens yields the ..."}
+{"idx": 7, "title": "Reformulating Domain Adaptation of Large Language Models as", "date": "", "ddg_snippet": "... 2021 ; Ma et al., 2023 ) introduces retrieval -based methods that first use the given query to retrieve relevant evidence candidates from the external ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2310.03328v3", "content": "... 2021 ; Ma et al., 2023 ) introduces retrieval -based methods that first use the given query to retrieve relevant evidence candidates from the external ..."}
+{"idx": 8, "title": "Towards Adaptive Memory-Based Optimization for Enhanced", "date": "", "ddg_snippet": "Retrieval -Augmented Generation (RAG), by integrating non-parametric knowledge from external knowledge bases into models, has emerged as a promising ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2504.05312v4", "content": "Retrieval -Augmented Generation (RAG), by integrating non-parametric knowledge from external knowledge bases into models, has emerged as a promising ..."}
+{"idx": 9, "title": "How to Build an Open-Domain Question Answering System? |", "date": "", "ddg_snippet": "... Phrase Index ” ; Seo et al., 2019 ), is to encode all the text in the knowledge corpus at the phrase level and then only rely on the retriever ...", "subpage_snippet": "", "source": "lilianweng.github.io", "link": "https://lilianweng.github.io/posts/2020-10-29-odqa/", "content": "... Phrase Index ” ; Seo et al., 2019 ), is to encode all the text in the knowledge corpus at the phrase level and then only rely on the retriever ..."}
diff --git a/data/sampled_jsons/Kerner_2024_CrowdStrike_outage_financial_cost.jsonl b/data/sampled_jsons/Kerner_2024_CrowdStrike_outage_financial_cost.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..f28fecc012359e2ddf9d2af770a67b7190dab839
--- /dev/null
+++ b/data/sampled_jsons/Kerner_2024_CrowdStrike_outage_financial_cost.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "CrowdStrike outage explained: What caused it and what’s next Counting the cost of CrowdStrike: the bug that bit billions CrowdStrike outage will cost Fortune 500 companies $5.4 ... CrowdStrike’s IT outage costs companies over $5B | Semafor How much did the Crowdstrike IT outage cost ? - Silicon Republic CrowdStrike outage explained: What caused it and what’s next How much did the Crowdstrike IT outage cost ? - Silicon Republic CrowdStrike outage explained: What caused it and what’s next CrowdStrike outage explained: What caused it and what’s next CrowdStrike outage explained: What caused it and what’s next How much did the Crowdstrike IT outage cost? - Silicon Republic", "date": "", "ddg_snippet": "Oct 29, 2024 · What might be considered the largest IT outage in history was triggered by a botched software update from security vendor CrowdStrike , affecting millions of Windows systems around the world. Insurers estimate the outage will cost U.S. Fortune 500 companies $5.4 billion. The outage occurred July 19, 2024 , with millions of Windows systems failing and showing the infamous blue screen of death ... Jul 26, 2024 · As eye-popping estimates emerge for the cost to enterprises of dealing with aftermath of last week’s CrowdStrike -induced outages , it’s crucial to break down the sources of these expenses and ... Aug 3, 2024 · The days-long cyberincident — which grounded planes, shuttered businesses and stopped markets — cost Fortune 500 companies about $5.4 billion in damages, according to insurance company Parametrix. Jul 25, 2024 · The outage ’s global financial cost could be $15 billion, insurer Parametrix told Reuters, and it could well be “ the biggest accumulation event ” in cyber insurance history. CrowdStrike on Wednesday vowed to improve its internal testing and introduce staggered releases of future updates. Who will suffer the biggest financial loss from a power outage? CEO and president George Kurtz apologised for the outage and noted the “gravity and impact of the situation”. According to the Parametrix report, the largest direct financial loss will be suffered by Fortune 500 companies in the healthcare sector followed by banking. What happened to CrowdStrike? On Sept. 23, 2024 , CrowdStrike testified in a U.S. House of Representatives hearing held by the House Subcommittee on Cybersecurity and Infrastructure Protection. During the hearing, Adam Meyers, senior vice president of counter adversary operations at CrowdStrike , apologized to Congress for the outage . Was CrowdStrike a systemic Cyber Loss Event? “Our analysis of the CrowdStrike outage shows not only the possible extent of a systemic cyber loss event , but also its boundaries,” said Jonathan Hatzor, co-founder and CEO of Parametrix. Why did CrowdStrike issue a post incident review? On July 24, 2024, the company issued a preliminary Post Incident Review (PIR). According to the PIR, there was a flaw in CrowdStrike's Content Validator component , used to check the integrity of rapid response content update. That flaw enabled the faulty version of channel file 291 to pass validation, even though it had an error. When did CrowdStrike come into production? Bounds checking came into the system on July 25, 2024 , while a patch that validates the number of actual inputs went into production July 27, 2024 . On Sept. 23, 2024 , CrowdStrike testified in a U.S. House of Representatives hearing held by the House Subcommittee on Cybersecurity and Infrastructure Protection. What's wrong with CrowdStrike Falcon? The flaw in CrowdStrike Falcon was inside of a sensor configuration update . The sensor is regularly updated -- sometimes multiple times daily -- to provide users with mitigation and threat protection. Jul 25, 2024 · One in four Fortune 500 companies was impacted by the Crowdstrike IT outage last week, which cost an estimated $5.4bn according to a report.", "subpage_snippet": "", "source": "www.techtarget.com", "link": "https://www.techtarget.com/WhatIs/feature/Explaining-the-largest-IT-outage-in-history-and-whats-next", "content": "Oct 29, 2024 · What might be considered the largest IT outage in history was triggered by a botched software update from security vendor CrowdStrike , affecting millions of Windows systems around the world. Insurers estimate the outage will cost U.S. Fortune 500 companies $5.4 billion. The outage occurred July 19, 2024 , with millions of Windows systems failing and showing the infamous blue screen of death ... Jul 26, 2024 · As eye-popping estimates emerge for the cost to enterprises of dealing with aftermath of last week’s CrowdStrike -induced outages , it’s crucial to break down the sources of these expenses and ... Aug 3, 2024 · The days-long cyberincident — which grounded planes, shuttered businesses and stopped markets — cost Fortune 500 companies about $5.4 billion in damages, according to insurance company Parametrix. Jul 25, 2024 · The outage ’s global financial cost could be $15 billion, insurer Parametrix told Reuters, and it could well be “ the biggest accumulation event ” in cyber insurance history. CrowdStrike on Wednesday vowed to improve its internal testing and introduce staggered releases of future updates. Who will suffer the biggest financial loss from a power outage? CEO and president George Kurtz apologised for the outage and noted the “gravity and impact of the situation”. According to the Parametrix report, the largest direct financial loss will be suffered by Fortune 500 companies in the healthcare sector followed by banking. What happened to CrowdStrike? On Sept. 23, 2024 , CrowdStrike testified in a U.S. House of Representatives hearing held by the House Subcommittee on Cybersecurity and Infrastructure Protection. During the hearing, Adam Meyers, senior vice president of counter adversary operations at CrowdStrike , apologized to Congress for the outage . Was CrowdStrike a systemic Cyber Loss Event? “Our analysis of the CrowdStrike outage shows not only the possible extent of a systemic cyber loss event , but also its boundaries,” said Jonathan Hatzor, co-founder and CEO of Parametrix. Why did CrowdStrike issue a post incident review? On July 24, 2024, the company issued a preliminary Post Incident Review (PIR). According to the PIR, there was a flaw in CrowdStrike's Content Validator component , used to check the integrity of rapid response content update. That flaw enabled the faulty version of channel file 291 to pass validation, even though it had an error. When did CrowdStrike come into production? Bounds checking came into the system on July 25, 2024 , while a patch that validates the number of actual inputs went into production July 27, 2024 . On Sept. 23, 2024 , CrowdStrike testified in a U.S. House of Representatives hearing held by the House Subcommittee on Cybersecurity and Infrastructure Protection. What's wrong with CrowdStrike Falcon? The flaw in CrowdStrike Falcon was inside of a sensor configuration update . The sensor is regularly updated -- sometimes multiple times daily -- to provide users with mitigation and threat protection. Jul 25, 2024 · One in four Fortune 500 companies was impacted by the Crowdstrike IT outage last week, which cost an estimated $5.4bn according to a report."}
+{"idx": 1, "title": "CrowdStrike-Microsoft Outage To Cost $44M Per Fortune 500 ...", "date": "", "ddg_snippet": "Jul 25, 2024 · The massive CrowdStrike outage that affected millions of Microsoft devices is predicted to cost U.S. Fortune 500 companies $5.4 billion in total direct financial loss, with an average loss of $44 ...", "subpage_snippet": "", "source": "www.crn.com", "link": "https://www.crn.com/news/security/2024/crowdstrike-outage-costs-44-million-per-fortune-500-company", "content": "Jul 25, 2024 · The massive CrowdStrike outage that affected millions of Microsoft devices is predicted to cost U.S. Fortune 500 companies $5.4 billion in total direct financial loss, with an average loss of $44 ..."}
+{"idx": 2, "title": "CrowdStrike Outage Leads to Estimated Financial Loss of $5.4 ...", "date": "", "ddg_snippet": "Jul 29, 2024 · A recent global IT outage linked to CrowdStrike , a leading cybersecurity company, has resulted in an estimated $5.4 billion in direct financial losses for Fortune 500 companies, according to a report released by cloud insurance firm Parametrix.", "subpage_snippet": "", "source": "cybersecuritynews.com", "link": "https://cybersecuritynews.com/crowdstrike-outage-loss-5-4-billion/", "content": "Jul 29, 2024 · A recent global IT outage linked to CrowdStrike , a leading cybersecurity company, has resulted in an estimated $5.4 billion in direct financial losses for Fortune 500 companies, according to a report released by cloud insurance firm Parametrix."}
+{"idx": 3, "title": "Counting the cost of CrowdStrike: the bug that bit billions", "date": "", "ddg_snippet": "Jul 26, 2024 · As eye-popping estimates emerge for the cost to enterprises of dealing with aftermath of last week’s CrowdStrike -induced outages , it’s crucial to break down the sources of these expenses and ...", "subpage_snippet": "", "source": "www.cio.com", "link": "https://www.cio.com/article/3478068/counting-the-cost-of-crowdstrike-the-bug-that-bit-billions.html", "content": "Jul 26, 2024 · As eye-popping estimates emerge for the cost to enterprises of dealing with aftermath of last week’s CrowdStrike -induced outages , it’s crucial to break down the sources of these expenses and ..."}
+{"idx": 4, "title": "CrowdStrike outage will cost Fortune 500 companies $5.4 ...", "date": "", "ddg_snippet": "Aug 3, 2024 · The days-long cyberincident — which grounded planes, shuttered businesses and stopped markets — cost Fortune 500 companies about $5.4 billion in damages, according to insurance company Parametrix.", "subpage_snippet": "", "source": "fortune.com", "link": "https://fortune.com/2024/08/03/crowdstrike-outage-fortune-500-companies-5-4-billion-damages-uninsured-losses/", "content": "Aug 3, 2024 · The days-long cyberincident — which grounded planes, shuttered businesses and stopped markets — cost Fortune 500 companies about $5.4 billion in damages, according to insurance company Parametrix."}
+{"idx": 5, "title": "CrowdStrike’s IT outage costs companies over $5B | Semafor", "date": "", "ddg_snippet": "Jul 25, 2024 · The outage ’s global financial cost could be $15 billion, insurer Parametrix told Reuters, and it could well be “ the biggest accumulation event ” in cyber insurance history. CrowdStrike on Wednesday vowed to improve its internal testing and introduce staggered releases of future updates.", "subpage_snippet": "", "source": "www.semafor.com", "link": "https://www.semafor.com/article/07/25/2024/crowdstrikes-outage-costs-companies-billions", "content": "Jul 25, 2024 · The outage ’s global financial cost could be $15 billion, insurer Parametrix told Reuters, and it could well be “ the biggest accumulation event ” in cyber insurance history. CrowdStrike on Wednesday vowed to improve its internal testing and introduce staggered releases of future updates."}
+{"idx": 6, "title": "How much did the Crowdstrike IT outage cost? - Silicon Republic", "date": "", "ddg_snippet": "Jul 25, 2024 · One in four Fortune 500 companies was impacted by the Crowdstrike IT outage last week, which cost an estimated $5.4bn according to a report.", "subpage_snippet": "", "source": "www.siliconrepublic.com", "link": "https://www.siliconrepublic.com/enterprise/crowdstrike-it-outage-cost-loss-fortune-500-companies-parametrix", "content": "Jul 25, 2024 · One in four Fortune 500 companies was impacted by the Crowdstrike IT outage last week, which cost an estimated $5.4bn according to a report."}
+{"idx": 7, "title": "Microsoft, SecOps pros weigh kernel access post-CrowdStrike |", "date": "", "ddg_snippet": "The outage began July 19 when a bug in an update to CrowdStrike 's Falcon software on Windows systems failed to load properly.", "subpage_snippet": "", "source": "www.techtarget.com", "link": "https://www.techtarget.com/searchitoperations/news/366599066/Microsoft-SecOps-pros-weigh-kernel-access-post-CrowdStrike", "content": "The outage began July 19 when a bug in an update to CrowdStrike 's Falcon software on Windows systems failed to load properly."}
+{"idx": 8, "title": "InfoSec community sounds off on CrowdStrike outage, next steps", "date": "", "ddg_snippet": "While infosec experts agree the recent global IT outage caused by a defective CrowdStrike channel file update highlights inherent problems with the ...", "subpage_snippet": "", "source": "www.techtarget.com", "link": "https://www.techtarget.com/searchsecurity/news/366599607/InfoSec-community-sounds-off-on-CrowdStrike-outage-next-steps", "content": "While infosec experts agree the recent global IT outage caused by a defective CrowdStrike channel file update highlights inherent problems with the ..."}
+{"idx": 9, "title": "8 largest IT outages in history", "date": "", "ddg_snippet": "Just after midnight Eastern Standard Time on July 19, 2024 , cybersecurity company CrowdStrike rolled out an update for its Falcon Sensors to ...", "subpage_snippet": "", "source": "www.techtarget.com", "link": "https://www.techtarget.com/whatis/feature/8-largest-IT-outages-in-history", "content": "Just after midnight Eastern Standard Time on July 19, 2024 , cybersecurity company CrowdStrike rolled out an update for its Falcon Sensors to ..."}
diff --git a/data/sampled_jsons/Kevin_Roose_New_York_Times_2024_AI_evaluation_mess.jsonl b/data/sampled_jsons/Kevin_Roose_New_York_Times_2024_AI_evaluation_mess.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..b63c1f7fa85b89c03991e5fc1dfb4f980378696d
--- /dev/null
+++ b/data/sampled_jsons/Kevin_Roose_New_York_Times_2024_AI_evaluation_mess.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "A.I. Has a Measurement Problem - The New York Times", "date": "", "ddg_snippet": "Which A.I . system writes the best computer code or generates the most realistic image? Right now, there's no easy way to answer those questions. Credit...Davide Comai Supported by By Kevin Roose ...", "subpage_snippet": "", "source": "www.nytimes.com", "link": "https://www.nytimes.com/2024/04/15/technology/ai-models-measurement.html", "content": "Which A.I . system writes the best computer code or generates the most realistic image? Right now, there's no easy way to answer those questions. Credit...Davide Comai Supported by By Kevin Roose ..."}
+{"idx": 1, "title": "Kevin Roose", "date": "", "ddg_snippet": "Personal website of Kevin Roose , a tech columnist for the New York Times and the author of three books.", "subpage_snippet": "", "source": "www.kevinroose.com", "link": "https://www.kevinroose.com/", "content": "Personal website of Kevin Roose , a tech columnist for the New York Times and the author of three books."}
+{"idx": 2, "title": "Powerful A.I. Is Coming. We're Not Ready. - The New York Times", "date": "", "ddg_snippet": "Kevin Roose is a technology columnist and a co-host of the New York Times tech podcast \" Hard Fork.\"", "subpage_snippet": "", "source": "www.nytimes.com", "link": "https://www.nytimes.com/2025/03/14/technology/why-im-feeling-the-agi.html", "content": "Kevin Roose is a technology columnist and a co-host of the New York Times tech podcast \" Hard Fork.\""}
+{"idx": 3, "title": "Kevin Roose - The New York Times", "date": "", "ddg_snippet": "Kevin Roose I'm a technology columnist for The New York Times , based in the San Francisco Bay Area, and a co-host of the Times tech podcast, \" Hard Fork.\"", "subpage_snippet": "", "source": "www.nytimes.com", "link": "https://www.nytimes.com/by/kevin-roose", "content": "Kevin Roose I'm a technology columnist for The New York Times , based in the San Francisco Bay Area, and a co-host of the Times tech podcast, \" Hard Fork.\""}
+{"idx": 4, "title": "The Shift - The New York Times", "date": "", "ddg_snippet": "Kevin Roose examines the intersection of technology, business, and culture.", "subpage_snippet": "", "source": "www.nytimes.com", "link": "https://www.nytimes.com/column/the-shift", "content": "Kevin Roose examines the intersection of technology, business, and culture."}
+{"idx": 5, "title": "Op-Ed: Your AI reputation — Another fine mess, and extremely dangerous", "date": "", "ddg_snippet": "There's a quite funny but seriously alarming article by a guy called Kevin Roose in The New York Times about how he got a bad reputation with chatbots.", "subpage_snippet": "", "source": "www.digitaljournal.com", "link": "https://www.digitaljournal.com/tech-science/op-ed-your-ai-reputation-another-fine-mess-and-extremely-dangerous/article", "content": "There's a quite funny but seriously alarming article by a guy called Kevin Roose in The New York Times about how he got a bad reputation with chatbots."}
+{"idx": 6, "title": "AI has a measurement problem - eKathimerini.com", "date": "", "ddg_snippet": "In short, AI measurement is a mess - a tangle of sloppy tests, apples-to-oranges comparisons and self-serving hype that has left users, regulators and AI developers themselves grasping in the dark.", "subpage_snippet": "", "source": "www.ekathimerini.com", "link": "https://www.ekathimerini.com/nytimes/1236644/ai-has-a-measurement-problem/", "content": "In short, AI measurement is a mess - a tangle of sloppy tests, apples-to-oranges comparisons and self-serving hype that has left users, regulators and AI developers themselves grasping in the dark."}
+{"idx": 7, "title": "Measuring AI models needs an overhaul. - The Verge", "date": "", "ddg_snippet": "I often mention AI model benchmarks in posts, but Kevin Roose at The New York Times said the quiet part out loud: AI benchmark tests don't help in comparing models, and these need to change.", "subpage_snippet": "", "source": "www.theverge.com", "link": "https://www.theverge.com/2024/4/15/24131097/measuring-ai-models-needs-an-overhaul", "content": "I often mention AI model benchmarks in posts, but Kevin Roose at The New York Times said the quiet part out loud: AI benchmark tests don't help in comparing models, and these need to change."}
+{"idx": 8, "title": "Investigative Issues: Powerful AI Is Coming. We're Not Ready.", "date": "", "ddg_snippet": "Investigative Issues: Powerful AI Is Coming. We're Not Ready. By Kevin Roose , New York Times March 14, 2025 Google DeepMind", "subpage_snippet": "", "source": "www.realclearinvestigations.com", "link": "https://www.realclearinvestigations.com/articles/2025/03/14/investigative_issues_powerful_ai_is_coming_were_not_ready_1097761.html", "content": "Investigative Issues: Powerful AI Is Coming. We're Not Ready. By Kevin Roose , New York Times March 14, 2025 Google DeepMind"}
+{"idx": 9, "title": "Abstract Dimensions of Generative AI Evaluati - arXiv.org", "date": "", "ddg_snippet": "1 Introduction Evaluating the capabilities and risks of generative AI (GenAI) models and systems is crucial for their successful development, deployment, and adoption. Despite this, many would likely agree with New York Times columnist Kevin Roose's recent characterization of the current state of GenAI evaluation as \"a mess—a tangle of sloppy tests, apples-to-oranges comparisons and self ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2411.12709", "content": "1 Introduction Evaluating the capabilities and risks of generative AI (GenAI) models and systems is crucial for their successful development, deployment, and adoption. Despite this, many would likely agree with New York Times columnist Kevin Roose's recent characterization of the current state of GenAI evaluation as \"a mess—a tangle of sloppy tests, apples-to-oranges comparisons and self ..."}
diff --git a/data/sampled_jsons/KijslFbfOL_Simple_yet_Effective_Incomplete_Multi-view_Clustering_Algorithm_2_termination_condition.jsonl b/data/sampled_jsons/KijslFbfOL_Simple_yet_Effective_Incomplete_Multi-view_Clustering_Algorithm_2_termination_condition.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..f378b21cc6a3a36aa7ea61c23ec3388fd5f35b7f
--- /dev/null
+++ b/data/sampled_jsons/KijslFbfOL_Simple_yet_Effective_Incomplete_Multi-view_Clustering_Algorithm_2_termination_condition.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Simple yet Effective Incomplete Multi - view Clustering :...", "date": "", "ddg_snippet": "Most of incomplete multi - view clustering (IMVC) methods typically choose to ignore the missing samples and only utilize observed unpaired samples to construct bipartite similarity.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=KijslFbfOL", "content": "Most of incomplete multi - view clustering (IMVC) methods typically choose to ignore the missing samples and only utilize observed unpaired samples to construct bipartite similarity."}
+{"idx": 1, "title": "Prototype Matching Learning for Incomplete Multi - View Clustering", "date": "", "ddg_snippet": "Incomplete Multi ‐ View Clustering aims to enhance clustering performance by using data from multiple modalities.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/388207724_Prototype_Matching_Learning_for_Incomplete_Multi-view_Clustering", "content": "Incomplete Multi ‐ View Clustering aims to enhance clustering performance by using data from multiple modalities."}
+{"idx": 2, "title": "How SLIC ( Simple Linear Iterative Clustering ) algorithm ... - YouTube", "date": "", "ddg_snippet": "Based on the publication from Achanta et al. (2010) I created this video, to represent visually the application of the SLIC algorithms in the context of supe...", "subpage_snippet": "", "source": "www.youtube.com", "link": "https://www.youtube.com/watch?v=-hmUbB-Y8R0", "content": "Based on the publication from Achanta et al. (2010) I created this video, to represent visually the application of the SLIC algorithms in the context of supe..."}
+{"idx": 3, "title": "ICLR Poster Simple yet Effective Incomplete Multi - view Clustering ...", "date": "", "ddg_snippet": "To eliminate these drawbacks, we present a simple yet effective IMVC approach, SIIHPC, in this work.Finally, the clustering results are obtained by implementing spectral grouping action on the eigenvectors of stacked multi -scale consensus similarity.", "subpage_snippet": "", "source": "iclr.cc", "link": "https://iclr.cc/virtual/2025/poster/30038", "content": "To eliminate these drawbacks, we present a simple yet effective IMVC approach, SIIHPC, in this work.Finally, the clustering results are obtained by implementing spectral grouping action on the eigenvectors of stacked multi -scale consensus similarity."}
+{"idx": 4, "title": "Efficient and Effective Incomplete Multi - view Clustering", "date": "", "ddg_snippet": "Incomplete multi - view clustering (IMVC) optimally fuses multiple pre-specied incomplete views to improve cluster -ing performance.", "subpage_snippet": "", "source": "ojs.aaai.org", "link": "https://ojs.aaai.org/index.php/AAAI/article/download/4350/4228", "content": "Incomplete multi - view clustering (IMVC) optimally fuses multiple pre-specied incomplete views to improve cluster -ing performance."}
+{"idx": 5, "title": "(Open Access) One-Pass Incomplete Multi - view Clustering (2019)", "date": "", "ddg_snippet": "Clustering on such multi - view datasets is called incomplete multi - view clustering (IMC) and quite challenging. To date, though many approaches have been developed, most of them are offline and have high computational and memory costs especially for large scale datasets.", "subpage_snippet": "", "source": "scispace.com", "link": "https://scispace.com/papers/one-pass-incomplete-multi-view-clustering-4gxvpbge7b", "content": "Clustering on such multi - view datasets is called incomplete multi - view clustering (IMC) and quite challenging. To date, though many approaches have been developed, most of them are offline and have high computational and memory costs especially for large scale datasets."}
+{"idx": 6, "title": "Late Fusion Multiview Clustering via Min-Max Optimization-Bohrium", "date": "", "ddg_snippet": "Multiview clustering (MVC) sufficiently exploits the diverse and complementary information among different views to improve the clustering performance.", "subpage_snippet": "", "source": "www.bohrium.com", "link": "https://www.bohrium.com/paper-details/late-fusion-multiview-clustering-via-min-max-optimization/865006048774193314-2434", "content": "Multiview clustering (MVC) sufficiently exploits the diverse and complementary information among different views to improve the clustering performance."}
+{"idx": 7, "title": "Incomplete Multi - View Clustering via Auto-Weighted Fusion in...", "date": "", "ddg_snippet": "As a class of effective methods for incomplete multi - view clustering , graph-based algorithms have recently drawn wide attention.", "subpage_snippet": "", "source": "www.sciopen.com", "link": "https://www.sciopen.com/article/10.26599/TST.2022.9010025?issn=1007-0214", "content": "As a class of effective methods for incomplete multi - view clustering , graph-based algorithms have recently drawn wide attention."}
+{"idx": 8, "title": "Confidence Graph Learning for Incomplete Multi - View Clustering", "date": "", "ddg_snippet": "To achieve incomplete multi - view clustering , this paper proposes an effective confidence graph learning method, namely CGL-IMVC.", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.1145/3653804.3654722", "content": "To achieve incomplete multi - view clustering , this paper proposes an effective confidence graph learning method, namely CGL-IMVC."}
+{"idx": 9, "title": "Efficient and Effective Regularized Incomplete Multi - View Clustering", "date": "", "ddg_snippet": "Incomplete multi - view clustering (IMVC) optimally combines multiple pre-specified incomplete views to improve clustering performance.In this paper, we first propose an Efficient and Effective Incomplete Multi - view Clustering (EE-IMVC) algorithm to address these issues.", "subpage_snippet": "", "source": "www.computer.org", "link": "https://www.computer.org/csdl/journal/tp/2021/08/09001210/1hwt4GmzDoc", "content": "Incomplete multi - view clustering (IMVC) optimally combines multiple pre-specified incomplete views to improve clustering performance.In this paper, we first propose an Efficient and Effective Incomplete Multi - view Clustering (EE-IMVC) algorithm to address these issues."}
diff --git a/data/sampled_jsons/Kirilenko_2015_climate_change_Twitter_single_causal_pathway_abstract.jsonl b/data/sampled_jsons/Kirilenko_2015_climate_change_Twitter_single_causal_pathway_abstract.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..47ccec0ece12088acf11c6e9099a37b009a0f273
--- /dev/null
+++ b/data/sampled_jsons/Kirilenko_2015_climate_change_Twitter_single_causal_pathway_abstract.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Paris Agreement - Wikipedia", "date": "", "ddg_snippet": "The Paris Agreement is an international treaty on climate change that was signed in 2016. The treaty covers climate change mitigation, adaptation, and finance. The Paris Agreement was negotiated by 196 parties at the 2015 United Nations Climate Chang...", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/Paris_Agreement", "content": "The Paris Agreement is an international treaty on climate change that was signed in 2016. The treaty covers climate change mitigation, adaptation, and finance. The Paris Agreement was negotiated by 196 parties at the 2015 United Nations Climate Chang..."}
+{"idx": 1, "title": "Attention, sentiments and emotions towards emerging climate ...", "date": "", "ddg_snippet": "The climate change Twitter dataset. Expert Syst. Kirilenko , A.P., Molodtsova, T., Stepchenkova, S.O., 2015 . People as sensors: Mass media and local temperature influence climate change discussion on Twitter .", "subpage_snippet": "", "source": "pure.iiasa.ac.at", "link": "https://pure.iiasa.ac.at/id/eprint/19384/1/1-s2.0-S0959378023001310-main+(1).pdf", "content": "The climate change Twitter dataset. Expert Syst. Kirilenko , A.P., Molodtsova, T., Stepchenkova, S.O., 2015 . People as sensors: Mass media and local temperature influence climate change discussion on Twitter ."}
+{"idx": 2, "title": "Climate change : global temperature | NOAA Climate .gov", "date": "", "ddg_snippet": "Earth's surface temperature has risen about 2 degrees Fahrenheit since the start of the NOAA record in 1850. It may seem like a small change , but it's a tremendous increase in stored heat.", "subpage_snippet": "", "source": "www.climate.gov", "link": "https://www.climate.gov/news-features/understanding-climate/climate-change-global-temperature", "content": "Earth's surface temperature has risen about 2 degrees Fahrenheit since the start of the NOAA record in 1850. It may seem like a small change , but it's a tremendous increase in stored heat."}
+{"idx": 3, "title": "Has Climate Math Been Rigged This Whole Time?", "date": "", "ddg_snippet": "The figure shows how national climate pledges (NDCs) compare with global pathways that would limit warming to between 1.5°C and 4°C. Under the approach as proposed in the research, global emissions are divided up in a way that reflects fairness and equity.", "subpage_snippet": "", "source": "scitechdaily.com", "link": "https://scitechdaily.com/has-climate-math-been-rigged-this-whole-time/", "content": "The figure shows how national climate pledges (NDCs) compare with global pathways that would limit warming to between 1.5°C and 4°C. Under the approach as proposed in the research, global emissions are divided up in a way that reflects fairness and equity."}
+{"idx": 4, "title": "How Is Climate Change Affecting the... | The Climate Reality Project", "date": "", "ddg_snippet": "Twitter .The Global Climate Risk Index 2015 listed the Philippines as the number one most affected country by climate change , using 2013’s data.", "subpage_snippet": "", "source": "www.climaterealityproject.org", "link": "https://www.climaterealityproject.org/blog/how-climate-change-affecting-philippines", "content": "Twitter .The Global Climate Risk Index 2015 listed the Philippines as the number one most affected country by climate change , using 2013’s data."}
+{"idx": 5, "title": "Climate Change Is Already Driving Mass Migration Around the Globe", "date": "", "ddg_snippet": "According to Steve Trent of the Environmental Justice Foundation, an organization based in the United Kingdom that advocates for environmental causes through a human rights lens, climate change “is the unpredictable ingredient that, when added to existing social, economic...", "subpage_snippet": "", "source": "www.nrdc.org", "link": "https://www.nrdc.org/stories/climate-change-already-driving-mass-migration-around-globe", "content": "According to Steve Trent of the Environmental Justice Foundation, an organization based in the United Kingdom that advocates for environmental causes through a human rights lens, climate change “is the unpredictable ingredient that, when added to existing social, economic..."}
+{"idx": 6, "title": "‘Something is working’: UN climate chief optimistic... | The Guardian", "date": "", "ddg_snippet": "Simon Stiell, executive secretary of the UN framework convention on climate change .The pathway matters: if the EU lands on the lower end of the range, like 66.3%, the final stretch to 90% just five years later will be a steep and uphill climb.", "subpage_snippet": "", "source": "www.theguardian.com", "link": "https://www.theguardian.com/environment/2025/sep/20/simon-stiell-un-climate-chief-climate-progress-green-transition", "content": "Simon Stiell, executive secretary of the UN framework convention on climate change .The pathway matters: if the EU lands on the lower end of the range, like 66.3%, the final stretch to 90% just five years later will be a steep and uphill climb."}
+{"idx": 7, "title": "They didn't change the name from 'global warming' to ' climat...", "date": "", "ddg_snippet": "A long-term warming background climate does not cause La Nina or El Nino (which are largely responsible for this natural variation) to disappear. The following images should make this clearer - the variation is unrealistically smooth, but it's just for illustrative purposes.", "subpage_snippet": "", "source": "skepticalscience.com", "link": "https://skepticalscience.com/climate-change-global-warming-basic.html", "content": "A long-term warming background climate does not cause La Nina or El Nino (which are largely responsible for this natural variation) to disappear. The following images should make this clearer - the variation is unrealistically smooth, but it's just for illustrative purposes."}
+{"idx": 8, "title": "The moral element of climate change | Stanford Report", "date": "", "ddg_snippet": "Blake Francis, a doctoral candidate in philosophy at Stanford and a Geballe Dissertation Prize Fellow at the Stanford Humanities Center, hopes to help guide those decisions by identifying the harms of climate change and assessing their moral significance.", "subpage_snippet": "", "source": "news.stanford.edu", "link": "https://news.stanford.edu/stories/2017/02/moral-element-climate-change", "content": "Blake Francis, a doctoral candidate in philosophy at Stanford and a Geballe Dissertation Prize Fellow at the Stanford Humanities Center, hopes to help guide those decisions by identifying the harms of climate change and assessing their moral significance."}
+{"idx": 9, "title": "Browse Articles | Nature Climate Change", "date": "", "ddg_snippet": "Climate change will raise the severity and frequency of forest disturbance, damaging the economic value of timber. Researchers show Europe’s timber-based forestry could lose up to €247 billion, yet in some regions the increase in forest productivity could offset these shocks.", "subpage_snippet": "", "source": "www.nature.com", "link": "https://www.nature.com/nclimate/articles?error=cookies_not_supported&code=3a284073-ec6c-47ca-b93e-6839a668dc59", "content": "Climate change will raise the severity and frequency of forest disturbance, damaging the economic value of timber. Researchers show Europe’s timber-based forestry could lose up to €247 billion, yet in some regions the increase in forest productivity could offset these shocks."}
diff --git a/data/sampled_jsons/Kirilenko_Molodtsova_Stepchenkova_2015_abstract_regression_models_single_variable_analysis.jsonl b/data/sampled_jsons/Kirilenko_Molodtsova_Stepchenkova_2015_abstract_regression_models_single_variable_analysis.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..94b939e11b3d83e08930b017c7e67d5038758d50
--- /dev/null
+++ b/data/sampled_jsons/Kirilenko_Molodtsova_Stepchenkova_2015_abstract_regression_models_single_variable_analysis.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Kirilenko-et-al.--2015.--Mass-Media--Temperatures-Affect- ...", "date": "", "ddg_snippet": "by AP Kirilenko · 2015 · Cited by 241 — The analysis conducted at the two levels of aggregation, national and local, confirmed the high significance of the mass media and temperature variables in the ... 9 pages", "subpage_snippet": "", "source": "research.fit.edu", "link": "https://research.fit.edu/media/site-specific/researchfitedu/coast-climate-adaptation-library/climate-communications/youth-climate-amp-social-media/Kirilenko-et-al.--2015.--Mass-Media--Temperatures-Affect-CC-Discussion-On-Twitter.pdf", "content": "by AP Kirilenko · 2015 · Cited by 241 — The analysis conducted at the two levels of aggregation, national and local, confirmed the high significance of the mass media and temperature variables in the ... 9 pages"}
+{"idx": 1, "title": "Mass media and local temperature influence climate ...", "date": "", "ddg_snippet": "by AP Kirilenko · 2015 · Cited by 242 — This study examined whether people living in the US connect their sensory experiences with local temperature to climate change and whether mass media ...", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/abs/pii/S0959378014001952", "content": "by AP Kirilenko · 2015 · Cited by 242 — This study examined whether people living in the US connect their sensory experiences with local temperature to climate change and whether mass media ..."}
+{"idx": 2, "title": "Tourists' Perceptions of Climate: Application of Machine ...", "date": "", "ddg_snippet": "by YG Tao · 2021 · Cited by 8 — This paper constructs a sentiment analysis framework for tourists' perceptions of climate using not only continuous climate data but also short-term weather ...", "subpage_snippet": "", "source": "journals.ametsoc.org", "link": "https://journals.ametsoc.org/view/journals/wcas/13/4/WCAS-D-21-0039.1.xml", "content": "by YG Tao · 2021 · Cited by 8 — This paper constructs a sentiment analysis framework for tourists' perceptions of climate using not only continuous climate data but also short-term weather ..."}
+{"idx": 3, "title": "Social influence and economic intervention policies to save ...", "date": "", "ddg_snippet": "by C Spandagos · 2021 · Cited by 33 — One major finding from the regression analysis shown in Table 3 is that the dependent variables are explained adequately neither by income, age, education ...", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S1364032121002082", "content": "by C Spandagos · 2021 · Cited by 33 — One major finding from the regression analysis shown in Table 3 is that the dependent variables are explained adequately neither by income, age, education ..."}
+{"idx": 4, "title": "Climate Change Communication in an Online Q&A ...", "date": "", "ddg_snippet": "10 May 2018 — Similar to the Poisson regression model , Equation (2) is used to link explanatory variables to the negative binomial distribution of the ...", "subpage_snippet": "", "source": "scispace.com", "link": "https://scispace.com/pdf/climate-change-communication-in-an-online-q-a-community-a-s91q6pm9wh.pdf", "content": "10 May 2018 — Similar to the Poisson regression model , Equation (2) is used to link explanatory variables to the negative binomial distribution of the ..."}
+{"idx": 5, "title": "Mimicking a Green Image: Selective Disclosure of Corporate ...", "date": "", "ddg_snippet": "by Y Shi — Our baseline regression analysis estimates a linear probability model in which the supplier ... Kirilenko , A. P., Molodtsova , T., Stepchenkova , S. O., 2015 .", "subpage_snippet": "", "source": "www.yu-zhang-econ.com", "link": "http://www.yu-zhang-econ.com/s/nonblind-mimicking-a-green-image.pdf", "content": "by Y Shi — Our baseline regression analysis estimates a linear probability model in which the supplier ... Kirilenko , A. P., Molodtsova , T., Stepchenkova , S. O., 2015 ."}
+{"idx": 6, "title": "Emotional framing in online environmental activism", "date": "", "ddg_snippet": "by M Sanford · 2023 · Cited by 21 — Next, two regression models are used to model the relationships between the variables of interests. ... Kirilenko A. P., Molodtsova T., Stepchenkova S. O. ( 2015 ).", "subpage_snippet": "", "source": "pmc.ncbi.nlm.nih.gov", "link": "https://pmc.ncbi.nlm.nih.gov/articles/PMC9928158/", "content": "by M Sanford · 2023 · Cited by 21 — Next, two regression models are used to model the relationships between the variables of interests. ... Kirilenko A. P., Molodtsova T., Stepchenkova S. O. ( 2015 )."}
+{"idx": 7, "title": "How has the COVID-19 pandemic affected the climate ...", "date": "", "ddg_snippet": "by ML Loureiro · 2021 · Cited by 39 — The COVID-19 pandemic has significantly decreased the overall number of messages written about climate change, postponing the climate debate worldwide.", "subpage_snippet": "", "source": "pmc.ncbi.nlm.nih.gov", "link": "https://pmc.ncbi.nlm.nih.gov/articles/PMC9760398/", "content": "by ML Loureiro · 2021 · Cited by 39 — The COVID-19 pandemic has significantly decreased the overall number of messages written about climate change, postponing the climate debate worldwide."}
+{"idx": 8, "title": "Guiding Environmental Messaging by ... - AMS Journals", "date": "", "ddg_snippet": "by A Noviello · 2023 · Cited by 11 — The study hypothesized that specific major natural disasters would lead to increases in the number of climate change–related Twitter posts and news articles, as.", "subpage_snippet": "", "source": "journals.ametsoc.org", "link": "https://journals.ametsoc.org/view/journals/wcas/15/1/WCAS-D-22-0053.1.pdf", "content": "by A Noviello · 2023 · Cited by 11 — The study hypothesized that specific major natural disasters would lead to increases in the number of climate change–related Twitter posts and news articles, as."}
+{"idx": 9, "title": "Modelling the Climate Change Debate in Italy through ...", "date": "", "ddg_snippet": "21 Mar 2025 — In this context, we propose a model that analyses the Supply and Demand of information to better understand information circulation and ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2503.17026v1", "content": "21 Mar 2025 — In this context, we propose a model that analyses the Supply and Demand of information to better understand information circulation and ..."}
diff --git a/data/sampled_jsons/LLaVA-NeXT_dynamic_high_resolution_architecture_technical_details.jsonl b/data/sampled_jsons/LLaVA-NeXT_dynamic_high_resolution_architecture_technical_details.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..bf276f5330cc9f63e8ef14789a7cff39c2b17e97
--- /dev/null
+++ b/data/sampled_jsons/LLaVA-NeXT_dynamic_high_resolution_architecture_technical_details.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "LLaVA-NeXT: Improved reasoning, OCR, and world knowledge", "date": "", "ddg_snippet": "30 Jan 2024 — It supports three aspect ratios, up to 672x672, 336x1344, 1344x336 resolution . Better visual reasoning and OCR capability with an improved ...", "subpage_snippet": "", "source": "llava-vl.github.io", "link": "https://llava-vl.github.io/blog/2024-01-30-llava-next/", "content": "30 Jan 2024 — It supports three aspect ratios, up to 672x672, 336x1344, 1344x336 resolution . Better visual reasoning and OCR capability with an improved ..."}
+{"idx": 1, "title": "Papers Explained 107: LLaVA 1.6 - Ritvik Rastogi - Medium", "date": "", "ddg_snippet": "A significant upgrade in LLaVA-1.6 is the introduction of the Dynamic High Resolution feature . By increasing the input image resolution to four ...", "subpage_snippet": "", "source": "ritvik19.medium.com", "link": "https://ritvik19.medium.com/papers-explained-107-llava-1-6-a312efd496c5", "content": "A significant upgrade in LLaVA-1.6 is the introduction of the Dynamic High Resolution feature . By increasing the input image resolution to four ..."}
+{"idx": 2, "title": "LLaVA-NeXT-Interleave: Tackling Multi-image, Video, and ...", "date": "", "ddg_snippet": "10 Jul 2024 — To this end, we introduce LLaVA - NeXT -Interleave, which simultaneously tackles Multi-image, Multi-frame (video), Multi-view (3D), and Multi-patch ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2407.07895v1", "content": "10 Jul 2024 — To this end, we introduce LLaVA - NeXT -Interleave, which simultaneously tackles Multi-image, Multi-frame (video), Multi-view (3D), and Multi-patch ..."}
+{"idx": 3, "title": "Feast Your Eyes: Mixture-of-Resolution Adaptation for ...", "date": "", "ddg_snippet": "by G Luo · Cited by 88 — Moreover, LLaVA-HR can be seamlessly combined with the dynamic high-resolution strategy of LLaVA-NeXT to further boost performance on OCR-related tasks, i.e., ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=1EnpStvBU8", "content": "by G Luo · Cited by 88 — Moreover, LLaVA-HR can be seamlessly combined with the dynamic high-resolution strategy of LLaVA-NeXT to further boost performance on OCR-related tasks, i.e., ..."}
+{"idx": 4, "title": "gokayfem/awesome-vlm-architectures: Famous Vision ...", "date": "", "ddg_snippet": "... data -efficient design of its predecessor. The architecture of LLaVA - NeXT is optimized for high performance, supporting input image resolutions up to 672x672 ...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/gokayfem/awesome-vlm-architectures", "content": "... data -efficient design of its predecessor. The architecture of LLaVA - NeXT is optimized for high performance, supporting input image resolutions up to 672x672 ..."}
+{"idx": 5, "title": "LLaVA: A Revolution in Multimodal AI", "date": "", "ddg_snippet": "LLaVA 1.6, also known as LLaVA-NeXT , improves upon LLaVA 1.5 by using more diverse and high-quality data mixtures, dynamic high resolution, and ...", "subpage_snippet": "", "source": "medium.com", "link": "https://medium.com/thedeephub/llava-a-revolution-in-multimodal-ai-7d771ab50f40", "content": "LLaVA 1.6, also known as LLaVA-NeXT , improves upon LLaVA 1.5 by using more diverse and high-quality data mixtures, dynamic high resolution, and ..."}
+{"idx": 6, "title": "LLaVA-NeXT: What Else Influences Visual Instruction Tuning ...", "date": "", "ddg_snippet": "25 May 2024 — Architectures: The LLaVA architecture consists of a pre-trained LLM and a pre-trained vision encoder . The model size scaling of LLM is more ...", "subpage_snippet": "", "source": "llava-vl.github.io", "link": "https://llava-vl.github.io/blog/2024-05-25-llava-next-ablations/", "content": "25 May 2024 — Architectures: The LLaVA architecture consists of a pre-trained LLM and a pre-trained vision encoder . The model size scaling of LLM is more ..."}
+{"idx": 7, "title": "LLaVA-NeXT: A Strong Zero-shot Video Understanding Model", "date": "", "ddg_snippet": "30 Apr 2024 — It segments the image into a grid of sub-images with various configurations, such as {2x2, 1x{2,3,4}, {2,3,4}x1}.", "subpage_snippet": "", "source": "llava-vl.github.io", "link": "https://llava-vl.github.io/blog/2024-04-30-llava-next-video/", "content": "30 Apr 2024 — It segments the image into a grid of sub-images with various configurations, such as {2x2, 1x{2,3,4}, {2,3,4}x1}."}
+{"idx": 8, "title": "lmms-lab/LLaVA-NeXT-Data · 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/lmms-lab/LLaVA-NeXT-Data", "content": "We're on a journey to advance and democratize artificial intelligence through open source and open science."}
+{"idx": 9, "title": "Improving Your Vision-language Model with Affordable Strategies", "date": "", "ddg_snippet": "To address image distortion issues inherent in Dynamic High Resolution , we propose a novel image splitting strategy called Consistent Aspect Ratio Dynamic High ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2409.04828v3", "content": "To address image distortion issues inherent in Dynamic High Resolution , we propose a novel image splitting strategy called Consistent Aspect Ratio Dynamic High ..."}
diff --git a/data/sampled_jsons/LSIF_loss_function_equation_21_On_a_Connection_Between_Imitation_Learning_and_RLHF.jsonl b/data/sampled_jsons/LSIF_loss_function_equation_21_On_a_Connection_Between_Imitation_Learning_and_RLHF.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..7128851f239fd391502681b2d96496f4df57c070
--- /dev/null
+++ b/data/sampled_jsons/LSIF_loss_function_equation_21_On_a_Connection_Between_Imitation_Learning_and_RLHF.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "ON A CONNECTION BETWEEN IMITATION LEARNING AND RLHF", "date": "", "ddg_snippet": "ABSTRACT This work studies the alignment of large language models with preference data from an imitation learning perspective. We establish a close theoretical connection be-tween reinforcement learning from human feedback ( RLHF ) and imitation learning (IL), revealing that RLHF implicitly performs imitation learning on the preference data distribution. Building on this connection , we propose ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=2QdsjiNXgj", "content": "ABSTRACT This work studies the alignment of large language models with preference data from an imitation learning perspective. We establish a close theoretical connection be-tween reinforcement learning from human feedback ( RLHF ) and imitation learning (IL), revealing that RLHF implicitly performs imitation learning on the preference data distribution. Building on this connection , we propose ..."}
+{"idx": 1, "title": "On a Connection Between Imitation Learning and RLHF", "date": "", "ddg_snippet": "Abstract This work studies the alignment of large language models with preference data from an imitation learning perspective. We establish a close theoretical connection between reinforcement learning from human feedback ( RLHF ) and imitation learning (IL), revealing that RLHF implicitly performs imitation learning on the preference data distribution. Building on this connection , we propose ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2503.05079v1", "content": "Abstract This work studies the alignment of large language models with preference data from an imitation learning perspective. We establish a close theoretical connection between reinforcement learning from human feedback ( RLHF ) and imitation learning (IL), revealing that RLHF implicitly performs imitation learning on the preference data distribution. Building on this connection , we propose ..."}
+{"idx": 2, "title": "On a Connection Between Imitation Learning and RLHF", "date": "", "ddg_snippet": "Jan 22, 2025 · Equation (13) (also shown in DPO) shows that imitation learning between optimizing policy response and energy-based policy is exactly the same as the RL loss . Thus, RLHF with two steps (reward learning and policy learning) can be viewed as conducting imitation learning between optimizing policy and the distribution of chosen response.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=2QdsjiNXgj", "content": "Jan 22, 2025 · Equation (13) (also shown in DPO) shows that imitation learning between optimizing policy response and energy-based policy is exactly the same as the RL loss . Thus, RLHF with two steps (reward learning and policy learning) can be viewed as conducting imitation learning between optimizing policy and the distribution of chosen response."}
+{"idx": 3, "title": "On a Connection Between Imitation Learningand RLHF", "date": "", "ddg_snippet": "Mar 12, 2025 · Imitation Learning and RLHF Teng Xiao (PSU), Yige Yuan (CAS), Mingxiao Li (Tecent), Zhengyu Chen (Meituan), Vasant G Honavar (PSU)", "subpage_snippet": "", "source": "aitime-lundao.oss-cn-beijing.aliyuncs.com", "link": "https://aitime-lundao.oss-cn-beijing.aliyuncs.com/AitimeReport/20250312/1741781794566", "content": "Mar 12, 2025 · Imitation Learning and RLHF Teng Xiao (PSU), Yige Yuan (CAS), Mingxiao Li (Tecent), Zhengyu Chen (Meituan), Vasant G Honavar (PSU)"}
+{"idx": 4, "title": "Lecture 8: Imitation Learning and RLHF - Stanford University", "date": "", "ddg_snippet": "Imitation Learning Summary Imitation learning can greatly reduce the amount of data need to learn a good policy Challenges remain and one exciting area is combining inverse RL / learning from demonstration and online reinforcement learning For a look into some of the theory between imitation learning and RL, see Sun, Venkatraman, Gordon, Boots, Bagnell (ICML 2017)", "subpage_snippet": "", "source": "web.stanford.edu", "link": "https://web.stanford.edu/class/cs234/slides/lecture8post.pdf", "content": "Imitation Learning Summary Imitation learning can greatly reduce the amount of data need to learn a good policy Challenges remain and one exciting area is combining inverse RL / learning from demonstration and online reinforcement learning For a look into some of the theory between imitation learning and RL, see Sun, Venkatraman, Gordon, Boots, Bagnell (ICML 2017)"}
+{"idx": 5, "title": "On a Connection Between Imitation Learning and RLHF", "date": "", "ddg_snippet": "DIL is proposed, a principled framework that directly optimizes the imitation learning objective and provides a unified imitation learning perspective on alignment, encompassing existing alignment algorithms as special cases while naturally introducing new variants. This work studies the alignment of large language models with preference data from an imitation learning perspective. We ...", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/On-a-Connection-Between-Imitation-Learning-and-RLHF-Xiao-Yuan/9b4ecc297389ca6753817ce3d3dfb3057c34ae76", "content": "DIL is proposed, a principled framework that directly optimizes the imitation learning objective and provides a unified imitation learning perspective on alignment, encompassing existing alignment algorithms as special cases while naturally introducing new variants. This work studies the alignment of large language models with preference data from an imitation learning perspective. We ..."}
+{"idx": 6, "title": "Imitation Learning - Stanford University", "date": "", "ddg_snippet": "Imitation Learning The formulation of the imitation learning problem is quite similar to the RL problem formulation from the previous chapter. The main difference is that in-stead of leveraging an explicit reward function rt = R(xt, ut) it will be assumed that a set of demonstrations from an expert are provided.", "subpage_snippet": "", "source": "web.stanford.edu", "link": "https://web.stanford.edu/class/cs237b/pdfs/lecture/lecture_10111213.pdf", "content": "Imitation Learning The formulation of the imitation learning problem is quite similar to the RL problem formulation from the previous chapter. The main difference is that in-stead of leveraging an explicit reward function rt = R(xt, ut) it will be assumed that a set of demonstrations from an expert are provided."}
+{"idx": 7, "title": "learning and alignment with human preferences and values", "date": "", "ddg_snippet": "by T Xiao · 2025 — ... On a Connection Between Imitation Learning and RLHF . 34. 4.1 Introduction ... Our proposed loss in Equation (2.10) is an empirical estimate of this ...", "subpage_snippet": "", "source": "etda.libraries.psu.edu", "link": "https://etda.libraries.psu.edu/files/final_submissions/32968", "content": "by T Xiao · 2025 — ... On a Connection Between Imitation Learning and RLHF . 34. 4.1 Introduction ... Our proposed loss in Equation (2.10) is an empirical estimate of this ..."}
+{"idx": 8, "title": "On a Connection Between Imitation Learning and RLHF | alphaXiv", "date": "", "ddg_snippet": "alphaXiv. Go Home. On a Connection Between Imitation Learning and RLHF .This formula reveals that RLHF is effectively performing imitation learning on the preference data distribution, with the reward function serving as a correction term to transform the reference distribution.", "subpage_snippet": "", "source": "www.alphaxiv.org", "link": "https://www.alphaxiv.org/overview/2503.05079v1", "content": "alphaXiv. Go Home. On a Connection Between Imitation Learning and RLHF .This formula reveals that RLHF is effectively performing imitation learning on the preference data distribution, with the reward function serving as a correction term to transform the reference distribution."}
+{"idx": 9, "title": "[Literature Review] On a Connection Between Imitation Learning ...", "date": "", "ddg_snippet": "The paper titled \" On a Connection Between Imitation Learning and RLHF \" presents a novel perspective on aligning large language models (LLMs) with human preferences through imitation learning .", "subpage_snippet": "", "source": "www.themoonlight.io", "link": "https://www.themoonlight.io/en/review/on-a-connection-between-imitation-learning-and-rlhf", "content": "The paper titled \" On a Connection Between Imitation Learning and RLHF \" presents a novel perspective on aligning large language models (LLMs) with human preferences through imitation learning ."}
diff --git a/data/sampled_jsons/LWeVVPuIx0_Last-Iterate_Convergence_Properties_of_Regret-Matching_Algorithms_in_Games_equation_2.jsonl b/data/sampled_jsons/LWeVVPuIx0_Last-Iterate_Convergence_Properties_of_Regret-Matching_Algorithms_in_Games_equation_2.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..f374a310a8281a8e75b86f62ed6e4b0a187b8ca4
--- /dev/null
+++ b/data/sampled_jsons/LWeVVPuIx0_Last-Iterate_Convergence_Properties_of_Regret-Matching_Algorithms_in_Games_equation_2.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "[2311.00676] Last - Iterate Convergence Properties of ...", "date": "", "ddg_snippet": "We study last - iterate convergence properties of algorithms for solving two-player zero-sum games based on Regret Matching $^+$ (RM$^+$). Despite their widespread use for solving real games , virtually nothing is known about their last - iterate convergence .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2311.00676", "content": "We study last - iterate convergence properties of algorithms for solving two-player zero-sum games based on Regret Matching $^+$ (RM$^+$). Despite their widespread use for solving real games , virtually nothing is known about their last - iterate convergence ."}
+{"idx": 1, "title": "Last - Iterate Convergence Properties of Regret - Matching ...", "date": "", "ddg_snippet": "We study last - iterate convergence properties of algorithms for solving two-player zero-sum games based on Regret Matching $^+$ (RM$^+$). Despite their widespread use for solving real games , virtually nothing is known about their last - iterate convergence .", "subpage_snippet": "", "source": "proceedings.iclr.cc", "link": "https://proceedings.iclr.cc/paper_files/paper/2025/hash/7c20cba1f6aeeb219f9f9cbdde5c7382-Abstract-Conference.html", "content": "We study last - iterate convergence properties of algorithms for solving two-player zero-sum games based on Regret Matching $^+$ (RM$^+$). Despite their widespread use for solving real games , virtually nothing is known about their last - iterate convergence ."}
+{"idx": 2, "title": "Last - Iterate Convergence Properties of Regret - Matching ...", "date": "", "ddg_snippet": "We study last - iterate convergence properties of algorithms for solving two-player zero-sum games based on Regret Matching . + (RM. + ). Despite their widespread use for solving real games , virtually nothing is known about their last - iterate convergence .", "subpage_snippet": "", "source": "paperswithcode.com", "link": "https://paperswithcode.com/paper/last-iterate-convergence-properties-of-regret", "content": "We study last - iterate convergence properties of algorithms for solving two-player zero-sum games based on Regret Matching . + (RM. + ). Despite their widespread use for solving real games , virtually nothing is known about their last - iterate convergence ."}
+{"idx": 3, "title": "Last - Iterate Convergence Properties of Regret - Matching ...", "date": "", "ddg_snippet": "Last - Iterate Convergence Properties of Regret - Matching Algorithms in Games . Yang Cai, Gabriele Farina, Julien Grand-Clément, Christian Kroer, Chung-Wei Lee, Haipeng Luo, Weiqiang Zheng.", "subpage_snippet": "", "source": "www.columbia.edu", "link": "https://www.columbia.edu/~ck2945/publication/cai-2025-last/", "content": "Last - Iterate Convergence Properties of Regret - Matching Algorithms in Games . Yang Cai, Gabriele Farina, Julien Grand-Clément, Christian Kroer, Chung-Wei Lee, Haipeng Luo, Weiqiang Zheng."}
+{"idx": 4, "title": "Regret Matching +: - Instability, average- and last - iterate convergence ...", "date": "", "ddg_snippet": "Last - iterate convergence of regret matching -based algorithms in games , Cai, Farina, G.-C., Kroer, Lee, Luo, Zheng, under review.", "subpage_snippet": "", "source": "people.hec.edu", "link": "https://people.hec.edu/grand-clement/wp-content/uploads/sites/51/2023/12/slides_jgc_cirm.pdf", "content": "Last - iterate convergence of regret matching -based algorithms in games , Cai, Farina, G.-C., Kroer, Lee, Luo, Zheng, under review."}
+{"idx": 5, "title": "Games played by Exponential Weights Algorithms", "date": "", "ddg_snippet": "This paper studies the last - iterate convergence properties of the exponential weights algo - rithm with constant learning rates.Keywords: Repeated Games , Exponential Weights Algorithms , Nash Equilibrium with Equalizing Payos.", "subpage_snippet": "", "source": "hal.science", "link": "https://hal.science/hal-04638802/document", "content": "This paper studies the last - iterate convergence properties of the exponential weights algo - rithm with constant learning rates.Keywords: Repeated Games , Exponential Weights Algorithms , Nash Equilibrium with Equalizing Payos."}
+{"idx": 6, "title": "Kernelized Multiplicative Weights for 0/1-Polyhedral Games : Bridging...", "date": "", "ddg_snippet": "Table 1. Properties of various no- regret algorithms for EFGs. Last - iterate convergence results are for two-player zero-sum games , and some results rely on the assumption of a unique Nash equilibrium—see Section 5.3 for details. ∗Lee et al.", "subpage_snippet": "", "source": "proceedings.mlr.press", "link": "https://proceedings.mlr.press/v162/farina22a/farina22a.pdf", "content": "Table 1. Properties of various no- regret algorithms for EFGs. Last - iterate convergence results are for two-player zero-sum games , and some results rely on the assumption of a unique Nash equilibrium—see Section 5.3 for details. ∗Lee et al."}
+{"idx": 7, "title": "Fast Last - Iterate Convergence of Learning in Games", "date": "", "ddg_snippet": "iterate convergence properties in zero-sum games .In this paper, we study last - iterate convergence rates of OFTRL algorithms with various popular regularizers, including the popular OMWU algorithm .", "subpage_snippet": "", "source": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper_files/paper/2024/file/29c861b02308a57aec18990f0dfe3777-Paper-Conference.pdf", "content": "iterate convergence properties in zero-sum games .In this paper, we study last - iterate convergence rates of OFTRL algorithms with various popular regularizers, including the popular OMWU algorithm ."}
+{"idx": 8, "title": "Combining No- regret and Q-learning", "date": "", "ddg_snippet": "Furthermore, by leveraging last iterate converging no- regret algorithms (one of which we introduce), we show empirical last iterate convergence in all domains tested with LONR.", "subpage_snippet": "", "source": "ifaamas.org", "link": "https://ifaamas.org/Proceedings/aamas2020/pdfs/p593.pdf", "content": "Furthermore, by leveraging last iterate converging no- regret algorithms (one of which we introduce), we show empirical last iterate convergence in all domains tested with LONR."}
+{"idx": 9, "title": "Last - Iterate Convergence Properties of Regret - Matching ...", "date": "", "ddg_snippet": "Computer Science - Computer Science and Game Theory Computer Science - Machine Learning.", "subpage_snippet": "", "source": "weiqiang-zheng.com", "link": "https://weiqiang-zheng.com/publication/cai-last-2025/", "content": "Computer Science - Computer Science and Game Theory Computer Science - Machine Learning."}
diff --git a/data/sampled_jsons/Learned_Augmented_Residual_Layer_LAUREL-40_Table_7_year_2023.jsonl b/data/sampled_jsons/Learned_Augmented_Residual_Layer_LAUREL-40_Table_7_year_2023.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..368af6da4993b71519dc0759f6a4704bb263c129
--- /dev/null
+++ b/data/sampled_jsons/Learned_Augmented_Residual_Layer_LAUREL-40_Table_7_year_2023.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "LAuReL : Learned Augmented Residual Layer", "date": "", "ddg_snippet": "2 Learned Augmented Residual Layer . 2.1 Residual Weights Version ( LAuReL -RW).In this paper we introduce learned augmented residual layer , LAuReL , which generalizes the canonical residual connection.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2411.07501v4", "content": "2 Learned Augmented Residual Layer . 2.1 Residual Weights Version ( LAuReL -RW).In this paper we introduce learned augmented residual layer , LAuReL , which generalizes the canonical residual connection."}
+{"idx": 1, "title": "Google AI Introduces LAuReL ( Learned Augmented Residual Layer )...", "date": "", "ddg_snippet": "LAUREL ’s implementation is tested in both vision and language domains, focusing on the ResNet-50 model for ImageNet-1K classification and a 3B parameter decoder-only transformer for language tasks. The architecture seamlessly integrates with existing residual connections, requiring...", "subpage_snippet": "", "source": "www.marktechpost.com", "link": "https://www.marktechpost.com/2024/11/16/google-ai-introduces-laurel-learned-augmented-residual-layer-revolutionizing-neural-networks-with-enhanced-residual-connections-for-efficient-model-performance/", "content": "LAUREL ’s implementation is tested in both vision and language domains, focusing on the ResNet-50 model for ImageNet-1K classification and a 3B parameter decoder-only transformer for language tasks. The architecture seamlessly integrates with existing residual connections, requiring..."}
+{"idx": 2, "title": "LAuReL : Learned Augmented Residual Layer", "date": "", "ddg_snippet": "The paper introduces the LAUREL ( Learned Augmented Residual Layer ) framework, which represents a significant architectural innovation aimed at enhancing model quality while maintaining efficiency in terms of model size and latency.", "subpage_snippet": "", "source": "powerdrill.ai", "link": "https://powerdrill.ai/discover/discover-LAuReL-Learned-Augmented-cm3hsv976bjks016fio3mu37r", "content": "The paper introduces the LAUREL ( Learned Augmented Residual Layer ) framework, which represents a significant architectural innovation aimed at enhancing model quality while maintaining efficiency in terms of model size and latency."}
+{"idx": 3, "title": "LAuReL : Learned Augmented Residual Layer | Papers With Code", "date": "", "ddg_snippet": "In this paper we introduce \\emph{ Learned Augmented Residual Layer } ( LAuReL ) -- a novel generalization of the canonical residual connection -- with the goal to be an in-situ replacement of the latter while outperforming on both model quality and footprint metrics.", "subpage_snippet": "", "source": "paperswithcode.com", "link": "https://paperswithcode.com/paper/laurel-learned-augmented-residual-layer", "content": "In this paper we introduce \\emph{ Learned Augmented Residual Layer } ( LAuReL ) -- a novel generalization of the canonical residual connection -- with the goal to be an in-situ replacement of the latter while outperforming on both model quality and footprint metrics."}
+{"idx": 4, "title": "\" LAuReL : Learned Augmented Residual Layer \" - by Rohan Paul", "date": "", "ddg_snippet": "Solution in this Paper: → LAUREL ( Learned Augmented Residual Layer ) introduces learnable parameters to the residual connection, making it dynamic and context-aware.", "subpage_snippet": "", "source": "www.rohan-paul.com", "link": "https://www.rohan-paul.com/p/laurel-learned-augmented-residual", "content": "Solution in this Paper: → LAUREL ( Learned Augmented Residual Layer ) introduces learnable parameters to the residual connection, making it dynamic and context-aware."}
+{"idx": 5, "title": "Vidhyanand (Vick) Mahase PharmD, PhD. on LinkedIn: LAuReL ...", "date": "", "ddg_snippet": "Google researchers have introduced a groundbreaking method called Learned Augmented Residual Layer ( LAUREL ). This replaces conventional residual connections, enhancing both model quality and efficiency.", "subpage_snippet": "", "source": "www.linkedin.com", "link": "https://www.linkedin.com/posts/vick-mahase-pharmd-phd_laurel-learned-augmented-residual-layer-activity-7264001619392970752-_BOd", "content": "Google researchers have introduced a groundbreaking method called Learned Augmented Residual Layer ( LAUREL ). This replaces conventional residual connections, enhancing both model quality and efficiency."}
+{"idx": 6, "title": "AI Innovations: From LAuReL ’s Neural Network Revolution to...", "date": "", "ddg_snippet": "Explore the latest in AI advancements with Google's LAuReL enhancing neural network efficiency and NVIDIA's LLaMA-Mesh unifying 3D mesh generation with language models.", "subpage_snippet": "", "source": "simply-ai.podbean.com", "link": "https://simply-ai.podbean.com/e/ai-innovations-from-laurel-s-neural-network-revolution-to-llama-mesh-s-3d-breakthrough/", "content": "Explore the latest in AI advancements with Google's LAuReL enhancing neural network efficiency and NVIDIA's LLaMA-Mesh unifying 3D mesh generation with language models."}
+{"idx": 7, "title": "Residual Connection Learning by Contextual... | Preprints.org", "date": "", "ddg_snippet": "Along with performance improvements, we provide clear evidence that the learned modulators effectively manipulate layer -wise scaling. These findings demonstrate the effectiveness of CoMT as a general mechanism for context-sensitive residual connection modulation.", "subpage_snippet": "", "source": "www.preprints.org", "link": "https://www.preprints.org/manuscript/202506.0120/v1", "content": "Along with performance improvements, we provide clear evidence that the learned modulators effectively manipulate layer -wise scaling. These findings demonstrate the effectiveness of CoMT as a general mechanism for context-sensitive residual connection modulation."}
+{"idx": 8, "title": "Aman's AI Journal • Primers • Skip Connections", "date": "", "ddg_snippet": "Deep Residual Learning for Image Recognition. LAUREL : Learned Augmented Residual Layer . LAUREL -RW+LR+PA: 75.25% (+2. 40 % params), outperforming naive scaling with fewer parameters.", "subpage_snippet": "", "source": "aman.ai", "link": "https://aman.ai/primers/ai/skip-connections/", "content": "Deep Residual Learning for Image Recognition. LAUREL : Learned Augmented Residual Layer . LAUREL -RW+LR+PA: 75.25% (+2. 40 % params), outperforming naive scaling with fewer parameters."}
+{"idx": 9, "title": "Gaurav Menghani - Google Akademik", "date": "", "ddg_snippet": "LAUREL : Learned Augmented Residual Layer .Efficient Embedding Table Storage and Lookup. G Menghani. US Patent App. 18/390,524, 2024.", "subpage_snippet": "", "source": "scholar.google.fi", "link": "https://scholar.google.fi/citations?user=XvncD4IAAAAJ&hl=tr", "content": "LAUREL : Learned Augmented Residual Layer .Efficient Embedding Table Storage and Lookup. G Menghani. US Patent App. 18/390,524, 2024."}
diff --git a/data/sampled_jsons/Learning_stochastic_dynamics_snapshots_regularized_unbalanced_optimal_transport_LEnergy_loss.jsonl b/data/sampled_jsons/Learning_stochastic_dynamics_snapshots_regularized_unbalanced_optimal_transport_LEnergy_loss.jsonl
new file mode 100644
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--- /dev/null
+++ b/data/sampled_jsons/Learning_stochastic_dynamics_snapshots_regularized_unbalanced_optimal_transport_LEnergy_loss.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Learning stochasticdynamics from snapshots through ...", "date": "", "ddg_snippet": "Learning stochastic dynamics from snapshots through regularized unbalanced optimal transport ZhenyiZhang TiejunLi* Peking University PeijieZhou* *Joint corresponding authors", "subpage_snippet": "", "source": "iclr.cc", "link": "https://iclr.cc/media/iclr-2025/Slides/31800_i2NXVyQ.pdf", "content": "Learning stochastic dynamics from snapshots through regularized unbalanced optimal transport ZhenyiZhang TiejunLi* Peking University PeijieZhou* *Joint corresponding authors"}
+{"idx": 1, "title": "Learning stochastic dynamics from snapshots through ...", "date": "", "ddg_snippet": "Here, we introduce a new deep learning approach for solving regularized unbalanced optimal transport (RUOT) and inferring continuous unbalanced stochastic dynamics from observed snapshots .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2410.00844v5", "content": "Here, we introduce a new deep learning approach for solving regularized unbalanced optimal transport (RUOT) and inferring continuous unbalanced stochastic dynamics from observed snapshots ."}
+{"idx": 2, "title": "LEARNING STOCHASTIC DYNAMICS FROM SNAP SHOTS THROUGH ...", "date": "", "ddg_snippet": "ABSTRACT Reconstructing dynamics using samples from sparsely time-resolved snapshots is an important problem in both natural sciences and machine learning . Here, we introduce a new deep learning approach for solving regularized unbalanced optimal transport (RUOT) and inferring continuous unbalanced stochastic dy- namics from observed snapshots .", "subpage_snippet": "", "source": "proceedings.iclr.cc", "link": "https://proceedings.iclr.cc/paper_files/paper/2025/file/32b8a612105de5c22db337b774ce7b61-Paper-Conference.pdf", "content": "ABSTRACT Reconstructing dynamics using samples from sparsely time-resolved snapshots is an important problem in both natural sciences and machine learning . Here, we introduce a new deep learning approach for solving regularized unbalanced optimal transport (RUOT) and inferring continuous unbalanced stochastic dy- namics from observed snapshots ."}
+{"idx": 3, "title": "LEARNING STOCHASTIC DYNAMICS FROM SNAP SHOTS THROUGH ...", "date": "", "ddg_snippet": "ABSTRACT Reconstructing dynamics using samples from sparsely time-resolved snapshots is an important problem in both natural sciences and machine learning . Here, we introduce a new deep learning approach for solving regularized unbalanced opti- mal transport (RUOT) and inferring continuous unbalanced stochastic dynamics from observed snapshots .", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/notes/edits/attachment?id=4oXLrbMaV4&name=pdf", "content": "ABSTRACT Reconstructing dynamics using samples from sparsely time-resolved snapshots is an important problem in both natural sciences and machine learning . Here, we introduce a new deep learning approach for solving regularized unbalanced opti- mal transport (RUOT) and inferring continuous unbalanced stochastic dynamics from observed snapshots ."}
+{"idx": 4, "title": "[2410.00844] Learning stochastic dynamics from snapshots ...", "date": "", "ddg_snippet": "Oct 1, 2024 · Reconstructing dynamics using samples from sparsely time-resolved snapshots is an important problem in both natural sciences and machine learning . Here, we introduce a new deep learning approach for solving regularized unbalanced optimal transport (RUOT) and inferring continuous unbalanced stochastic dynamics from observed snapshots .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2410.00844", "content": "Oct 1, 2024 · Reconstructing dynamics using samples from sparsely time-resolved snapshots is an important problem in both natural sciences and machine learning . Here, we introduce a new deep learning approach for solving regularized unbalanced optimal transport (RUOT) and inferring continuous unbalanced stochastic dynamics from observed snapshots ."}
+{"idx": 5, "title": "Learning stochastic dynamics from snapshots through ...", "date": "", "ddg_snippet": "Here, we introduce a new deep learning approach for solving regularized unbalanced optimal transport (RUOT) and inferring continuous unbalanced stochastic ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2410.00844v4", "content": "Here, we introduce a new deep learning approach for solving regularized unbalanced optimal transport (RUOT) and inferring continuous unbalanced stochastic ..."}
+{"idx": 6, "title": "Learning stochastic dynamics from snapshots through ...", "date": "", "ddg_snippet": "1 Oct 2024 — Here, we introduce a new deep learning approach for solving regularized unbalanced optimal transport (RUOT) and inferring continuous unbalanced ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2410.00844v1", "content": "1 Oct 2024 — Here, we introduce a new deep learning approach for solving regularized unbalanced optimal transport (RUOT) and inferring continuous unbalanced ..."}
+{"idx": 7, "title": "Action Matching: Learning Stochastic Dynamics from Samples", "date": "", "ddg_snippet": "by K Neklyudov · 2023 · Cited by 83 — Existing methods for trajectory inference often optimize an entropy- regularized ( unbalanced ) optimal transport loss be-.", "subpage_snippet": "", "source": "proceedings.mlr.press", "link": "https://proceedings.mlr.press/v202/neklyudov23a/neklyudov23a.pdf", "content": "by K Neklyudov · 2023 · Cited by 83 — Existing methods for trajectory inference often optimize an entropy- regularized ( unbalanced ) optimal transport loss be-."}
+{"idx": 8, "title": "stVCR: Spatiotemporal dynamics of single cells", "date": "", "ddg_snippet": "by Q Peng · 2024 · Cited by 4 — & Zhou, P. Learning stochastic dynamics from snapshots through regularized unbalanced optimal transport . arXiv preprint arXiv:2410.00844 (2024). 29 pages", "subpage_snippet": "", "source": "www.biorxiv.org", "link": "https://www.biorxiv.org/content/10.1101/2024.06.02.596937v2.full.pdf", "content": "by Q Peng · 2024 · Cited by 4 — & Zhou, P. Learning stochastic dynamics from snapshots through regularized unbalanced optimal transport . arXiv preprint arXiv:2410.00844 (2024). 29 pages"}
+{"idx": 9, "title": "Publications - Zhenyi Zhang", "date": "", "ddg_snippet": "Reconstructing dynamics using samples from sparsely time-resolved snapshots is an important problem in both natural sciences and machine learning . Here, we introduce a new deep learning approach for solving regularized unbalanced optimal transport (RUOT) and inferring continuous unbalanced stochastic dynamics from observed snapshots .", "subpage_snippet": "", "source": "zhenyiizhang.github.io", "link": "https://zhenyiizhang.github.io/publications/", "content": "Reconstructing dynamics using samples from sparsely time-resolved snapshots is an important problem in both natural sciences and machine learning . Here, we introduce a new deep learning approach for solving regularized unbalanced optimal transport (RUOT) and inferring continuous unbalanced stochastic dynamics from observed snapshots ."}
diff --git a/data/sampled_jsons/Learning_stochastic_dynamics_snapshots_regularized_unbalanced_optimal_transport_equation_10_LEnergy.jsonl b/data/sampled_jsons/Learning_stochastic_dynamics_snapshots_regularized_unbalanced_optimal_transport_equation_10_LEnergy.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..13d9a3a2c8b0ee0d389d56839018f631ecd86a74
--- /dev/null
+++ b/data/sampled_jsons/Learning_stochastic_dynamics_snapshots_regularized_unbalanced_optimal_transport_equation_10_LEnergy.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Learning stochastic dynamics from snapshots through ...", "date": "", "ddg_snippet": "Introduction Reconstructing dynamics using samples from sparsely time-resolved snapshots is an important problem in both natural sciences and machine learning . Here, we introduce a new deep learning approach for solving regularized unbalanced optimal transport (RUOT) and inferring continuous unbalanced stochastic dynamics from observed snapshots .", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/zhenyiizhang/DeepRUOT", "content": "Introduction Reconstructing dynamics using samples from sparsely time-resolved snapshots is an important problem in both natural sciences and machine learning . Here, we introduce a new deep learning approach for solving regularized unbalanced optimal transport (RUOT) and inferring continuous unbalanced stochastic dynamics from observed snapshots ."}
+{"idx": 1, "title": "[2410.00844] Learning stochastic dynamics from snapshots ...", "date": "", "ddg_snippet": "Oct 1, 2024 · Reconstructing dynamics using samples from sparsely time-resolved snapshots is an important problem in both natural sciences and machine learning . Here, we introduce a new deep learning approach for solving regularized unbalanced optimal transport (RUOT) and inferring continuous unbalanced stochastic dynamics from observed snapshots .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2410.00844", "content": "Oct 1, 2024 · Reconstructing dynamics using samples from sparsely time-resolved snapshots is an important problem in both natural sciences and machine learning . Here, we introduce a new deep learning approach for solving regularized unbalanced optimal transport (RUOT) and inferring continuous unbalanced stochastic dynamics from observed snapshots ."}
+{"idx": 2, "title": "LEARNING STOCHASTIC DYNAMICS FROM SNAP SHOTS THROUGH ...", "date": "", "ddg_snippet": "ABSTRACT Reconstructing dynamics using samples from sparsely time-resolved snapshots is an important problem in both natural sciences and machine learning . Here, we introduce a new deep learning approach for solving regularized unbalanced optimal transport (RUOT) and inferring continuous unbalanced stochastic dy- namics from observed snapshots .", "subpage_snippet": "", "source": "proceedings.iclr.cc", "link": "https://proceedings.iclr.cc/paper_files/paper/2025/file/32b8a612105de5c22db337b774ce7b61-Paper-Conference.pdf", "content": "ABSTRACT Reconstructing dynamics using samples from sparsely time-resolved snapshots is an important problem in both natural sciences and machine learning . Here, we introduce a new deep learning approach for solving regularized unbalanced optimal transport (RUOT) and inferring continuous unbalanced stochastic dy- namics from observed snapshots ."}
+{"idx": 3, "title": "LEARNING STOCHASTIC DYNAMICS FROM SNAP SHOTS THROUGH ...", "date": "", "ddg_snippet": "ABSTRACT Reconstructing dynamics using samples from sparsely time-resolved snapshots is an important problem in both natural sciences and machine learning . Here, we introduce a new deep learning approach for solving regularized unbalanced opti- mal transport (RUOT) and inferring continuous unbalanced stochastic dynamics from observed snapshots . Based on the RUOT form, our method models these ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/notes/edits/attachment?id=4oXLrbMaV4&name=pdf", "content": "ABSTRACT Reconstructing dynamics using samples from sparsely time-resolved snapshots is an important problem in both natural sciences and machine learning . Here, we introduce a new deep learning approach for solving regularized unbalanced opti- mal transport (RUOT) and inferring continuous unbalanced stochastic dynamics from observed snapshots . Based on the RUOT form, our method models these ..."}
+{"idx": 4, "title": "Learning stochastic dynamics from snapshots through ...", "date": "", "ddg_snippet": "Abstract Reconstructing dynamics using samples from sparsely time-resolved snapshots is an important problem in both natural sciences and machine learning . Here, we introduce a new deep learning approach for solving regularized unbalanced optimal transport (RUOT) and inferring continuous unbalanced stochastic dynamics from observed snapshots .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2410.00844v1", "content": "Abstract Reconstructing dynamics using samples from sparsely time-resolved snapshots is an important problem in both natural sciences and machine learning . Here, we introduce a new deep learning approach for solving regularized unbalanced optimal transport (RUOT) and inferring continuous unbalanced stochastic dynamics from observed snapshots ."}
+{"idx": 5, "title": "Learning stochastic dynamics from snapshots through regularized ...", "date": "", "ddg_snippet": "We have introduced DeepRUOT for learning regularized unbalanced optimal transport (RUOT) and continuous unbalanced stochastic dynamics from time-series snapshot data. By leveraging Fisher regularization , our method transforms an SDE problem into an ODE constraint.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2410.00844v4", "content": "We have introduced DeepRUOT for learning regularized unbalanced optimal transport (RUOT) and continuous unbalanced stochastic dynamics from time-series snapshot data. By leveraging Fisher regularization , our method transforms an SDE problem into an ODE constraint."}
+{"idx": 6, "title": "(PDF) Learning Stochastic Dynamics from Snapshots through...", "date": "", "ddg_snippet": "Through regularized unbalanced optimal transport . Zhenyi zhang†, tiejun LI‡,and peijie zhou§. Abstract. Reconstructing dynamics using samples from sparsely time-resolved snapshots is an im-. portant problem in both natural sciences and machine...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/384563451_Learning_Stochastic_Dynamics_from_Snapshots_through_Regularized_Unbalanced_Optimal_Transport", "content": "Through regularized unbalanced optimal transport . Zhenyi zhang†, tiejun LI‡,and peijie zhou§. Abstract. Reconstructing dynamics using samples from sparsely time-resolved snapshots is an im-. portant problem in both natural sciences and machine..."}
+{"idx": 7, "title": "Learning stochastic dynamics from snapshots through regularized ...", "date": "", "ddg_snippet": "Reconstructing dynamics using samples from sparsely time-resolved snapshots is an important problem in both natural sciences and machine learning . Here, we introduce a new deep learning approach for solving regularized unbalanced optimal transport (RUOT)...", "subpage_snippet": "", "source": "synthical.com", "link": "https://synthical.com/article/Learning-Stochastic-Dynamics-from-Snapshots-through-Regularized-Unbalanced-Optimal-Transport-39225f33-522f-44ce-b836-70679229a4ab", "content": "Reconstructing dynamics using samples from sparsely time-resolved snapshots is an important problem in both natural sciences and machine learning . Here, we introduce a new deep learning approach for solving regularized unbalanced optimal transport (RUOT)..."}
+{"idx": 8, "title": "Learning Stochastic Dynamics from Snapshots through...", "date": "", "ddg_snippet": "This paper presents a method for learning stochastic dynamics from snapshot data using regularized unbalanced optimal transport .", "subpage_snippet": "", "source": "www.aimodels.fyi", "link": "https://www.aimodels.fyi/papers/arxiv/learning-stochastic-dynamics-from-snapshots-through-regularized", "content": "This paper presents a method for learning stochastic dynamics from snapshot data using regularized unbalanced optimal transport ."}
+{"idx": 9, "title": "Learning stochastic dynamics from snapshots through regularized ...", "date": "", "ddg_snippet": "Reconstructing dynamics using samples from sparsely time-resolved snapshots is an important problem in both natural sciences and machine learning .", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=gQlxd3Mtru", "content": "Reconstructing dynamics using samples from sparsely time-resolved snapshots is an important problem in both natural sciences and machine learning ."}
diff --git a/data/sampled_jsons/Learning_without_Forgetting_Li_Hoiem_2016_distillation_knowledge_preserving_original_capabilities_ca_year_2016.jsonl b/data/sampled_jsons/Learning_without_Forgetting_Li_Hoiem_2016_distillation_knowledge_preserving_original_capabilities_ca_year_2016.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..a2bffbca11cb83faaa0fe313e8b6dbf1590c4713
--- /dev/null
+++ b/data/sampled_jsons/Learning_without_Forgetting_Li_Hoiem_2016_distillation_knowledge_preserving_original_capabilities_ca_year_2016.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "[1606.09282] Learning without Forgetting - arXiv.org", "date": "", "ddg_snippet": "A new problem arises where we add new capabilities to a Convolutional Neural Network (CNN), but the training data for its existing capabilities are unavailable. We propose our Learning without Forgetting method, which uses only new task data to train the network while preserving the original capabilities .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/1606.09282", "content": "A new problem arises where we add new capabilities to a Convolutional Neural Network (CNN), but the training data for its existing capabilities are unavailable. We propose our Learning without Forgetting method, which uses only new task data to train the network while preserving the original capabilities ."}
+{"idx": 1, "title": "[PDF] Learning without Forgetting | Semantic Scholar", "date": "", "ddg_snippet": "This work proposes the Learning without Forgetting method, which uses only new task data to train the network while preserving the original capabilities , and performs favorably compared to commonly used feature extraction and fine-tuning adaption techniques. When building a unified vision system or gradually adding new apabilities to a system, the usual assumption is that training data for all ...", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/Learning-without-Forgetting-Li-Hoiem/8f3b80ddc0dd62e6c3369fabb1715990c29e9b9a", "content": "This work proposes the Learning without Forgetting method, which uses only new task data to train the network while preserving the original capabilities , and performs favorably compared to commonly used feature extraction and fine-tuning adaption techniques. When building a unified vision system or gradually adding new apabilities to a system, the usual assumption is that training data for all ..."}
+{"idx": 2, "title": "Learning Without Forgetting | SpringerLink", "date": "", "ddg_snippet": "A new problem arises where we add new capabilities to a Convolutional Neural Network (CNN), but the training data for its existing capabilities are unavailable. We propose our Learning without Forgetting method, which uses only new task data to train the network while preserving the original capabilities .", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/chapter/10.1007/978-3-319-46493-0_37", "content": "A new problem arises where we add new capabilities to a Convolutional Neural Network (CNN), but the training data for its existing capabilities are unavailable. We propose our Learning without Forgetting method, which uses only new task data to train the network while preserving the original capabilities ."}
+{"idx": 3, "title": "Learning without Forgetting | IEEE Journals & Magazine | IEEE Xplore", "date": "", "ddg_snippet": "A new problem arises where we add new capabilities to a Convolutional Neural Network (CNN), but the training data for its existing capabilities are unavailable. We propose our Learning without Forgetting method, which uses only new task data to train the network while preserving the original capabilities .", "subpage_snippet": "", "source": "ieeexplore.ieee.org", "link": "https://ieeexplore.ieee.org/abstract/document/8107520", "content": "A new problem arises where we add new capabilities to a Convolutional Neural Network (CNN), but the training data for its existing capabilities are unavailable. We propose our Learning without Forgetting method, which uses only new task data to train the network while preserving the original capabilities ."}
+{"idx": 4, "title": "Learning without Forgetting | IEEE Transactions on Pattern Analysis and ...", "date": "", "ddg_snippet": "A new problem arises where we add new capabilities to a Convolutional Neural Network (CNN), but the training data for its existing capabilities are unavailable. We propose our Learning without Forgetting method, which uses only new task data to train the network while preserving the original capabilities .", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.1109/TPAMI.2017.2773081", "content": "A new problem arises where we add new capabilities to a Convolutional Neural Network (CNN), but the training data for its existing capabilities are unavailable. We propose our Learning without Forgetting method, which uses only new task data to train the network while preserving the original capabilities ."}
+{"idx": 5, "title": "Paper page - Learning without Forgetting - Hugging Face", "date": "", "ddg_snippet": "A new problem arises where we add new capabilities to a Convolutional Neural Network (CNN), but the training data for its existing capabilities are unavailable. We propose our Learning without Forgetting method, which uses only new task data to train the network while preserving the original capabilities .", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/papers/1606.09282", "content": "A new problem arises where we add new capabilities to a Convolutional Neural Network (CNN), but the training data for its existing capabilities are unavailable. We propose our Learning without Forgetting method, which uses only new task data to train the network while preserving the original capabilities ."}
+{"idx": 6, "title": "1 Learning without Forgetting - arXiv.org", "date": "", "ddg_snippet": "data for the original tasks. We propose the Learning without Forgetting method for convo-lutional neural networks, which can be seen as a hybrid of knowledge distillation and fine-tuning, learning parameters that are discriminative for the new task while preserving outputs for the origin", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/1606.09282", "content": "data for the original tasks. We propose the Learning without Forgetting method for convo-lutional neural networks, which can be seen as a hybrid of knowledge distillation and fine-tuning, learning parameters that are discriminative for the new task while preserving outputs for the origin"}
+{"idx": 7, "title": "Learning without Forgetting - GitHub", "date": "", "ddg_snippet": "Introduction Learning without Forgetting aims at adding new capabilities (new tasks) to an existing Convolutional Neural Network, sharing representation with the original capabilities (old tasks), while allowing for adjusting the shared representation to adapt for both tasks without using the original training data.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/lizhitwo/LearningWithoutForgetting", "content": "Introduction Learning without Forgetting aims at adding new capabilities (new tasks) to an existing Convolutional Neural Network, sharing representation with the original capabilities (old tasks), while allowing for adjusting the shared representation to adapt for both tasks without using the original training data."}
+{"idx": 8, "title": "Learning without Forgetting - NASA/ADS", "date": "", "ddg_snippet": "A new problem arises where we add new capabilities to a Convolutional Neural Network (CNN), but the training data for its existing capabilities are unavailable. We propose our Learning without Forgetting method, which uses only new task data to train the network while preserving the original capabilities .", "subpage_snippet": "", "source": "ui.adsabs.harvard.edu", "link": "https://ui.adsabs.harvard.edu/abs/2016arXiv160609282L/abstract", "content": "A new problem arises where we add new capabilities to a Convolutional Neural Network (CNN), but the training data for its existing capabilities are unavailable. We propose our Learning without Forgetting method, which uses only new task data to train the network while preserving the original capabilities ."}
+{"idx": 9, "title": "PDF tpami-2773081-pp - ha", "date": "", "ddg_snippet": "A new problem arises where we add new capabilities to a Convolutional Neural Network (CNN), but the training data for its existing capabilities are unavailable. We propose our Learning without Forgetting method, which uses only new task data to train the network while preserving the original capabilities .", "subpage_snippet": "", "source": "miai.ha.edu.cn", "link": "http://miai.ha.edu.cn/Essays/2Deep+learning/Learning+without+Forgetting.pdf", "content": "A new problem arises where we add new capabilities to a Convolutional Neural Network (CNN), but the training data for its existing capabilities are unavailable. We propose our Learning without Forgetting method, which uses only new task data to train the network while preserving the original capabilities ."}
diff --git a/data/sampled_jsons/Lemma_3.6_average_neighborhood_size_HDT_SRRW_cost_advantage_theoretical.jsonl b/data/sampled_jsons/Lemma_3.6_average_neighborhood_size_HDT_SRRW_cost_advantage_theoretical.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..cd923e2d081f076485c006bbc546206aea60532b
--- /dev/null
+++ b/data/sampled_jsons/Lemma_3.6_average_neighborhood_size_HDT_SRRW_cost_advantage_theoretical.jsonl
@@ -0,0 +1,9 @@
+{"idx": 0, "title": "Examples with the Pumping Lemma - GitHub Pages", "date": "", "ddg_snippet": "The contradiction step typically requires detailed case analysis of scenarios. There are three possible cases: 5.1 y is all 0s: Pumped strings, e.g., xyyz, are not in B because they have more 0s than 1s, breaking condition 1 of the pumping lemma . So we have a contradiction. 5.2 y is all 1s: Same as above. 5. 3 y has both 0s and 1s: Pumped strings preserve equal counts, but is out of order and ...", "subpage_snippet": "", "source": "stchang.github.io", "link": "https://stchang.github.io/cs420/s21/lectures/lecture09.pdf", "content": "The contradiction step typically requires detailed case analysis of scenarios. There are three possible cases: 5.1 y is all 0s: Pumped strings, e.g., xyyz, are not in B because they have more 0s than 1s, breaking condition 1 of the pumping lemma . So we have a contradiction. 5.2 y is all 1s: Same as above. 5. 3 y has both 0s and 1s: Pumped strings preserve equal counts, but is out of order and ..."}
+{"idx": 1, "title": "6.045J Lecture 5: Non-regular languages and the pumping lemma", "date": "", "ddg_snippet": "6.045: Automata, Computability, and Complexity Or, Great Ideas in Theoretical Computer Science Spring, 2010 Class 5 Nancy Lynch", "subpage_snippet": "", "source": "ocw.mit.edu", "link": "https://ocw.mit.edu/courses/6-045j-automata-computability-and-complexity-spring-2011/92a359d37025e9b216256bba46a4eee1_MIT6_045JS11_lec05.pdf", "content": "6.045: Automata, Computability, and Complexity Or, Great Ideas in Theoretical Computer Science Spring, 2010 Class 5 Nancy Lynch"}
+{"idx": 2, "title": "Neighborhood Size - an overview | ScienceDirect Topics", "date": "", "ddg_snippet": "Theoretical models incorporating neighborhood effects involve two types of basic questions. First, how do the characteristics of a neighborhood affect the decision making of its members, and in the aggregate, the behaviors in the neighborhood as a whole?", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/topics/social-sciences/neighborhood-size", "content": "Theoretical models incorporating neighborhood effects involve two types of basic questions. First, how do the characteristics of a neighborhood affect the decision making of its members, and in the aggregate, the behaviors in the neighborhood as a whole?"}
+{"idx": 3, "title": "What's the deal with HDT SMP? (Frame Rate Smasher ... - Reddit", "date": "", "ddg_snippet": "What i did if I really like the look of the armor was to delete said xml files so I can use it but without any physics at all, the same with HDT hair mods and wigs for followers.", "subpage_snippet": "", "source": "www.reddit.com", "link": "https://www.reddit.com/r/skyrimmods/comments/mb3iwm/whats_the_deal_with_hdt_smp_frame_rate_smasher/", "content": "What i did if I really like the look of the armor was to delete said xml files so I can use it but without any physics at all, the same with HDT hair mods and wigs for followers."}
+{"idx": 4, "title": "Beyond Self-Repellent Kernels: History-Driven Target Towards Efficient...", "date": "", "ddg_snippet": "The cost -based covariances between SRRW and HDT -MCMC in (11) and (12) are ordered as followsα\\alphaitalic_α. , suggesting a universal advantage . This factor becomes more pronounced in dense or nearly complete graphs, where the average neighborhood size is.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2505.18300v3", "content": "The cost -based covariances between SRRW and HDT -MCMC in (11) and (12) are ordered as followsα\\alphaitalic_α. , suggesting a universal advantage . This factor becomes more pronounced in dense or nearly complete graphs, where the average neighborhood size is."}
+{"idx": 5, "title": "Beyond Self-Repellent Kernels: History-Driven Target Towards ...", "date": "", "ddg_snippet": "Figure 1: Flowchart comparison between (a) SRRW (Doshi et al., 2023)and (b) our History-Driven Target ( HDT ) framework. 1.1 Recent Advances in Graph Sampling In social networks, e-commerce, recommendation systems and other domains, practitioners need to estimate graph size, node degree distribution, and label distribution (Xie et al., 2021).", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2505.18300v1", "content": "Figure 1: Flowchart comparison between (a) SRRW (Doshi et al., 2023)and (b) our History-Driven Target ( HDT ) framework. 1.1 Recent Advances in Graph Sampling In social networks, e-commerce, recommendation systems and other domains, practitioners need to estimate graph size, node degree distribution, and label distribution (Xie et al., 2021)."}
+{"idx": 6, "title": "History-Driven Target Towards Efficient Nonlinear MCMC ...", "date": "", "ddg_snippet": "ing an additional O(1/E[|N(i)|]) variance reduction relative to SRRW by the average neighborhood size . ... Lemma 3.6 implies that the cost -based covariance of HDT ...", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/poster/46659", "content": "ing an additional O(1/E[|N(i)|]) variance reduction relative to SRRW by the average neighborhood size . ... Lemma 3.6 implies that the cost -based covariance of HDT ..."}
+{"idx": 7, "title": "Abstract 1. Introduction - arXiv.org", "date": "", "ddg_snippet": "By embedding self-repellency in the target rather than the tran- sition kernel, HDT maintains unbiased sampling with a lightweight design and provides an O(1/α) variance reduc- tion without high computational cost or time-reversibility constraints from SRRW .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2505.18300v3", "content": "By embedding self-repellency in the target rather than the tran- sition kernel, HDT maintains unbiased sampling with a lightweight design and provides an O(1/α) variance reduc- tion without high computational cost or time-reversibility constraints from SRRW ."}
+{"idx": 8, "title": "Hunter WOLF Selected for S-MET Program Increment II Award", "date": "", "ddg_snippet": "Oct 28, 2024 · HDT Global announced today the selection of the Hunter WOLFTM by the U.S. Army for the Small Multipurpose Equipment Transport (S-MET) Increment II program. HDT is one of two proposed solutions selected through the National Advanced Mobility Consortium’s (NAMC) Other Transaction Agreement. This contract is valued at $11.55M and the Hunter WOLF will be delivered to the U.S. Army for government ...", "subpage_snippet": "", "source": "www.hdtglobal.com", "link": "https://www.hdtglobal.com/2024/10/28/hunter-wolf-selected-for-s-met-program-increment-ii-award/", "content": "Oct 28, 2024 · HDT Global announced today the selection of the Hunter WOLFTM by the U.S. Army for the Small Multipurpose Equipment Transport (S-MET) Increment II program. HDT is one of two proposed solutions selected through the National Advanced Mobility Consortium’s (NAMC) Other Transaction Agreement. This contract is valued at $11.55M and the Hunter WOLF will be delivered to the U.S. Army for government ..."}
diff --git a/data/sampled_jsons/Leutgeb_2004_Distinct_ensemble_codes_OR_Independent_codes_hippocampus_year_2004.jsonl b/data/sampled_jsons/Leutgeb_2004_Distinct_ensemble_codes_OR_Independent_codes_hippocampus_year_2004.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..723881b6b9bdda9ae2652ce11ccb32586e551996
--- /dev/null
+++ b/data/sampled_jsons/Leutgeb_2004_Distinct_ensemble_codes_OR_Independent_codes_hippocampus_year_2004.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Distinct ensemble codes in hippocampal areas CA3 and CA1", "date": "", "ddg_snippet": "by S Leutgeb · 2004 · Cited by 998 — Distinct ensemble codes in hippocampal areas CA3 and CA1. Science. 2004 Aug 27 ... Jill K Leutgeb , Alessandro Treves, May-Britt Moser, Edvard I Moser ...", "subpage_snippet": "", "source": "pubmed.ncbi.nlm.nih.gov", "link": "https://pubmed.ncbi.nlm.nih.gov/15272123/", "content": "by S Leutgeb · 2004 · Cited by 998 — Distinct ensemble codes in hippocampal areas CA3 and CA1. Science. 2004 Aug 27 ... Jill K Leutgeb , Alessandro Treves, May-Britt Moser, Edvard I Moser ..."}
+{"idx": 1, "title": "Distinct Ensemble Codes in Hippocampal Areas CA3 and ...", "date": "", "ddg_snippet": "by S Leutgeb · 2004 · Cited by 998 — The hippocampus has differentiated into an extensively connected ... Distinct Ensemble Codes in Hippocampal Areas CA3 and CA1. Stefan Leutgeb , Jill K.", "subpage_snippet": "", "source": "www.science.org", "link": "https://www.science.org/doi/10.1126/science.1100265", "content": "by S Leutgeb · 2004 · Cited by 998 — The hippocampus has differentiated into an extensively connected ... Distinct Ensemble Codes in Hippocampal Areas CA3 and CA1. Stefan Leutgeb , Jill K."}
+{"idx": 2, "title": "Independent codes for spatial and episodic memory in ...", "date": "", "ddg_snippet": "by S Leutgeb · 2005 · Cited by 1119 — Independent codes for spatial and episodic memory in hippocampal neuronal ensembles ... Stefan Leutgeb , Jill K Leutgeb , Carol A Barnes, Edvard I Moser, Bruce L ...", "subpage_snippet": "", "source": "pubmed.ncbi.nlm.nih.gov", "link": "https://pubmed.ncbi.nlm.nih.gov/16040709/", "content": "by S Leutgeb · 2005 · Cited by 1119 — Independent codes for spatial and episodic memory in hippocampal neuronal ensembles ... Stefan Leutgeb , Jill K Leutgeb , Carol A Barnes, Edvard I Moser, Bruce L ..."}
+{"idx": 3, "title": "Distinct Ensemble Codes in Hippocampal Areas CA3 and ...", "date": "", "ddg_snippet": "by S Leutgeb · 2004 · Cited by 998 — Distinct Ensemble Codes in Hippocampal Areas CA3 and CA1. Stefan Leutgeb , Jill K. Leutgeb , Alessandro Treves , May-Britt Moser , Edvard I. Moser · Science ...", "subpage_snippet": "", "source": "www.jstor.org", "link": "https://www.jstor.org/stable/3837670", "content": "by S Leutgeb · 2004 · Cited by 998 — Distinct Ensemble Codes in Hippocampal Areas CA3 and CA1. Stefan Leutgeb , Jill K. Leutgeb , Alessandro Treves , May-Britt Moser , Edvard I. Moser · Science ..."}
+{"idx": 4, "title": "[PDF] Independent Codes for Spatial and Episodic Memory ...", "date": "", "ddg_snippet": "Independent Codes for Spatial and Episodic Memory ... Distinct Ensemble Codes in Hippocampal Areas CA3 and CA1 · S. Leutgeb J. Leutgeb A. TrevesM. MoserE. Moser.", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/Independent-Codes-for-Spatial-and-Episodic-Memory-Leutgeb-Leutgeb/3156ca1332d2cbb54b3a6baec0dc2c35c1887970", "content": "Independent Codes for Spatial and Episodic Memory ... Distinct Ensemble Codes in Hippocampal Areas CA3 and CA1 · S. Leutgeb J. Leutgeb A. TrevesM. MoserE. Moser."}
+{"idx": 5, "title": "Distinct Ensemble Codes in Hippocampal Areas CA3 and CA1", "date": "", "ddg_snippet": "The hippocampus has differentiated into an ... Distinct Ensemble Codes in Hippocampal Areas CA3 and CA1. SLStefan Leutgeb . Stefan Leutgeb ... 26 August 2004 .", "subpage_snippet": "", "source": "www.scilit.com", "link": "https://www.scilit.com/publications/df29460526dafece2c0d90d24fca16bb", "content": "The hippocampus has differentiated into an ... Distinct Ensemble Codes in Hippocampal Areas CA3 and CA1. SLStefan Leutgeb . Stefan Leutgeb ... 26 August 2004 ."}
+{"idx": 6, "title": "Independent codes for spatial and - H1 Connect", "date": "", "ddg_snippet": "Independent codes for spatial and episodic memory in hippocampal neuronal ensembles. Leutgeb S et al. Science. 2005 Jul 22; 309(5734):619-623. https://doi ...", "subpage_snippet": "", "source": "connect.h1.co", "link": "https://connect.h1.co/article/1027043", "content": "Independent codes for spatial and episodic memory in hippocampal neuronal ensembles. Leutgeb S et al. Science. 2005 Jul 22; 309(5734):619-623. https://doi ..."}
+{"idx": 7, "title": "Distinct ensemble codes in hippocampal areas CA3 and CA1", "date": "", "ddg_snippet": "Independent Codes for Spatial and Episodic Memory in Hippocampal Neuronal Ensembles ... Distinct Ensemble Codes in Hippocampal Areas CA3 and CA1 Stefan Leutgeb ,1 ...", "subpage_snippet": "", "source": "www.academia.edu", "link": "https://www.academia.edu/1800266/Distinct_ensemble_codes_in_hippocampal_areas_CA3_and_CA1", "content": "Independent Codes for Spatial and Episodic Memory in Hippocampal Neuronal Ensembles ... Distinct Ensemble Codes in Hippocampal Areas CA3 and CA1 Stefan Leutgeb ,1 ..."}
+{"idx": 8, "title": "References", "date": "", "ddg_snippet": "Leutgeb , S., Leutgeb , J.K., Treves, A., Moser, M.B. and Moser. E.I. ( 2004 ) Distinct Ensemble Codes in Hippocampal Areas CA3 and CA1.", "subpage_snippet": "", "source": "www.scirp.org", "link": "https://www.scirp.org/reference/referencespapers?referenceid=1430879", "content": "Leutgeb , S., Leutgeb , J.K., Treves, A., Moser, M.B. and Moser. E.I. ( 2004 ) Distinct Ensemble Codes in Hippocampal Areas CA3 and CA1."}
+{"idx": 9, "title": "Publications", "date": "", "ddg_snippet": "Leutgeb S, Leutgeb JK, Treves A, Moser M-B, Moser EI ( 2004 ) Distinct Ensemble Codes in Hippocampal Areas CA3 and CA1. Science 305, 1295-1298. [PDF].", "subpage_snippet": "", "source": "www.leutgeblabs.com", "link": "https://www.leutgeblabs.com/new-page", "content": "Leutgeb S, Leutgeb JK, Treves A, Moser M-B, Moser EI ( 2004 ) Distinct Ensemble Codes in Hippocampal Areas CA3 and CA1. Science 305, 1295-1298. [PDF]."}
diff --git a/data/sampled_jsons/Leveraging_Per-Instance_Privacy_for_Machine_Unlearning_Section_4.3_group_unlearning_analysis_limitat.jsonl b/data/sampled_jsons/Leveraging_Per-Instance_Privacy_for_Machine_Unlearning_Section_4.3_group_unlearning_analysis_limitat.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..b9f3273c6d5b1612310e5fbb450028d5a46d2cf2
--- /dev/null
+++ b/data/sampled_jsons/Leveraging_Per-Instance_Privacy_for_Machine_Unlearning_Section_4.3_group_unlearning_analysis_limitat.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Towards Reliable Forgetting: A Survey on Machine Unlearning", "date": "", "ddg_snippet": "... for machine unlearning offer mechanisms to erase specific training data from models, concerns about trust and transparency persist—particularly due ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2506.15115v1", "content": "... for machine unlearning offer mechanisms to erase specific training data from models, concerns about trust and transparency persist—particularly due ..."}
+{"idx": 1, "title": "ToFU: Transforming How Federated Learning Systems Forget User", "date": "", "ddg_snippet": "The analysis on the influence of transformations on unlearning performance leads us to propose a learning-to- unlearn T ransf o rmation-guided F ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2509.15861v1", "content": "The analysis on the influence of transformations on unlearning performance leads us to propose a learning-to- unlearn T ransf o rmation-guided F ..."}
+{"idx": 2, "title": "Towards Mitigating Excessive Forgetting in LLM Unlearning via", "date": "", "ddg_snippet": "Machine unlearning [ 5 , 4 ] has emerged as a practical alternative that directly updates model parameters to remove the influence of targeted data, ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2508.20443v1", "content": "Machine unlearning [ 5 , 4 ] has emerged as a practical alternative that directly updates model parameters to remove the influence of targeted data, ..."}
+{"idx": 3, "title": "A Neuro-inspired Interpretation of Unlearning in Large Language", "date": "", "ddg_snippet": "For instance , the superior performance of certain unlearning algorithms might be attributed merely to the inherent ease of unlearning the selected ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2504.06658v1", "content": "For instance , the superior performance of certain unlearning algorithms might be attributed merely to the inherent ease of unlearning the selected ..."}
+{"idx": 4, "title": "CRISP: Persistent Concept Unlearning via Sparse Autoencoders", "date": "", "ddg_snippet": "... unlearning of target concepts, retention of benign concepts, and the fluency of model generations, outperforming previous methods by 5 5 - 34 34 ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2508.13650v1", "content": "... unlearning of target concepts, retention of benign concepts, and the fluency of model generations, outperforming previous methods by 5 5 - 34 34 ..."}
+{"idx": 5, "title": "Machine Unlearning: A Survey | ACM Computing Surveys", "date": "", "ddg_snippet": "... between online learning and machine unlearning is that the former requires a merge operation to incorporate updates, while machine unlearning is an ...", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.1145/3603620", "content": "... between online learning and machine unlearning is that the former requires a merge operation to incorporate updates, while machine unlearning is an ..."}
+{"idx": 6, "title": "To be forgotten or to be fair: unveiling fairness implications", "date": "", "ddg_snippet": "... recognized machine unlearning methods on three AI fairness datasets and used four evaluation metrics to measure the fairness on machine unlearning ...", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/article/10.1007/s43681-023-00398-y", "content": "... recognized machine unlearning methods on three AI fairness datasets and used four evaluation metrics to measure the fairness on machine unlearning ..."}
+{"idx": 7, "title": "Privacy-Preserving Techniques in Generative AI and Large", "date": "", "ddg_snippet": "For articles published under an open access Creative Common CC BY license, any part of the article may be reused without permission provided that the ...", "subpage_snippet": "", "source": "www.mdpi.com", "link": "https://www.mdpi.com/2078-2489/15/11/697", "content": "For articles published under an open access Creative Common CC BY license, any part of the article may be reused without permission provided that the ..."}
+{"idx": 8, "title": "CVPR 2023 Papers", "date": "", "ddg_snippet": "X3KD: Knowledge Distillation Across Modalities, Tasks and Stages for Multi-Camera 3D Object Detection ... for 3D Object Segmentation Without Scene ...", "subpage_snippet": "", "source": "cvpr.thecvf.com", "link": "https://cvpr.thecvf.com/virtual/2023/papers.html", "content": "X3KD: Knowledge Distillation Across Modalities, Tasks and Stages for Multi-Camera 3D Object Detection ... for 3D Object Segmentation Without Scene ..."}
+{"idx": 9, "title": "Layer 9: August 2016", "date": "", "ddg_snippet": "... groups similar sessions based on the observation that ... It performs 3 .2% better than harmonic mean and 14% to buffer based solution, for instance .", "subpage_snippet": "", "source": "www.layer9.org", "link": "http://www.layer9.org/2016/08/", "content": "... groups similar sessions based on the observation that ... It performs 3 .2% better than harmonic mean and 14% to buffer based solution, for instance ."}
diff --git a/data/sampled_jsons/Llama-2-7b-chat_16x_Attack_Success_Rate_safety_head_ablation_Zhou_year_2024.jsonl b/data/sampled_jsons/Llama-2-7b-chat_16x_Attack_Success_Rate_safety_head_ablation_Zhou_year_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..e97bfea5528128e703594ae6725f5cd07f653e6c
--- /dev/null
+++ b/data/sampled_jsons/Llama-2-7b-chat_16x_Attack_Success_Rate_safety_head_ablation_Zhou_year_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "On the Role of Attention Heads in Large Language Model Safety", "date": "", "ddg_snippet": "We analyze the overlap in safety heads when attributing to Llama-2-7b-chat and Vicuna- 7b -v1.5111Both of which are fine-tuned versions on top of Llama - 2 - 7b , having undergone identical pre-training. using two ablation methods on the Malicious Instruct dataset.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2410.13708v1", "content": "We analyze the overlap in safety heads when attributing to Llama-2-7b-chat and Vicuna- 7b -v1.5111Both of which are fine-tuned versions on top of Llama - 2 - 7b , having undergone identical pre-training. using two ablation methods on the Malicious Instruct dataset."}
+{"idx": 1, "title": "meta-llama/Llama-2-7b-chat-hf · Hugging Face", "date": "", "ddg_snippet": "Model Developers Meta Variations Llama 2 comes in a range of parameter sizes — 7B , 13B, and 70B — as well as pretrained and fine-tuned variations. Input Models input text only. Output Models generate text only. Model Architecture Llama 2 is an auto-regressive language model that uses an optimized transformer architecture.", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/meta-llama/Llama-2-7b-chat-hf", "content": "Model Developers Meta Variations Llama 2 comes in a range of parameter sizes — 7B , 13B, and 70B — as well as pretrained and fine-tuned variations. Input Models input text only. Output Models generate text only. Model Architecture Llama 2 is an auto-regressive language model that uses an optimized transformer architecture."}
+{"idx": 2, "title": "Finetune LLaMA-2-7b-chat to perform safety evaluation of user ... Research Assistant - Bohrium | AI for Science with Global ... Safety performance of Llama2-7B-Chat under Fine-Tuning ... On the Role of Attention Heads in Large Language Model Safety On the Role of Attention Heads in Large Language Model Safety On the Role of Attention Heads in Large Language Model Safety On the Role of Attention Heads in Large Language Model Safety On the Role of Attention Heads in Large Language Model Safety GitHub - llm-attacks/llm-attacks: Universal and Transferable ...", "date": "", "ddg_snippet": "The recent advance in Generative Pre-trained Transformer (GPT) technology (e.g. ChatGPT, LLaMA , Claude, Dolly, etc.) has been inspiring numerious tech companies and AI enthusiasts to utilize it to expore, experiment and deploy novel applications in search engine, recommendation system, work productivity tools, healthcare, advertising, and so on. GPT, or more broadly AI, is a double-edged sword. On one hand, it can tremendously boost the efficacy and efficiency of information retrieval and dissemination among humans. On the other hand, it might endager the safety and integrity of human beings if it is abused. In the US, the federal government has been enacting laws and policies to regulate AI (reference1). In Europe, an AI Act was proposed to regulate the development and use of AI (reference2) See full list on github.com Generally, we follow the same framework in Constitutional AI: Harmlessness from AI Feedback to develop Safety LLaMA . The basic idea of Safety LLaMA to use an independent GPT model to monitor and evaluate the safety and integrity of an AI chatbot's response to a user's prompt. It serves as an alternative to OpenAI's moderation endpoint tool, which can only be used by OpenAI users. As human beings, we provide Safety LLaMA with a set of fundamental principles that an AI chatbot must comply with, which is called AI Constitution. Here is an example showing how to prompt Safety LLaMA (see prompt for scripts): Here is an example of Safety LLaMA 's evaluation: In the example above, we instruct Safety LLaMA model to apply these principles to evaluate another AI chatbot's response and flag it if it violates the safety guidelines above. There are several advantages to train a GPT model to perform the evaluation and detection job: 1.Flexibility: Safety guidelines can be updated and configed to satisfy different end customer's needs 2 .Few Train Labels: Very few training labels are required to achieve solid performance since the base model is pre-trained based on an enormous amount of text data, which has decent zero-shot and few-shot prompting performance without fintuning. See full list on github.com The harmless dataset from Anthropics is a list of sensitive questions (or prompts) asked by red teams, to which an AI chatbot is inclined to give inapproriate or dangerous answers. The dataset has about 15000 train prompts and 2200 test prompts. See full list on github.com LLaMA-2-70B-chat model was used to generate responses to prompts in the harmless dataset (5000 train prompts and 2200 test prompts). This step was done on a cloud server with 8xA100(80GB) GPUs. The total computation time is about 5-6 hours . See llama_gen_response_to_prompt. See full list on github.com In step 1, we use LLaMA - 2 -70B- chat model to generate answers to prompts. In this step, we make the model to do self critique. Bascially, we use the same model to evaluate its answers according to the safety guileines. See llama_gen_safety_evaluation. For all 5000 (prompt, answer) pairs from the train dataset, only 1 is flagged as unsafe. All 2200 (prompt, answer) pairs from the test dataset are evaluated as safe responses. In order to cross validate the accuracy of LLaMA - 2 -70B- chat model's evaluation, ChatGPT 3.5 turbo was used to evaluate all (prompt, answer) pairs. See chatgpt_gen_safety_evaluation. It turns out that ChatGPT 3.5 is mostly aligned with LLaMA - 2 -70B- chat , which thinks all responses are safe. This also verifies that LLaMA - 2 -70B- chat is a pretty mature and safe model to use. See full list on github.com It requires 8xA100 GPUs to run LLaMA - 2 -70B- chat to generate safety evaluation, which is very costly and time-consuming. In this step, we use the evaluation dataset of LLaMA - 2 -70B- chat from step 2 to finetune a LLaMA - 2 - 7B - chat model using int8 quantization and Low-Rank Adaptation (LoRA). This finetuning step was done on a single A40 GPU and the total computation time is about 2 -3 hours. See finetune for instructions and scripts. See safetyllama_finetune_using_huggingface for calling the finetuned model. The finetuned model (aka adapter model) is hosted in huggingface. On the other hand, we treat the evaluation decision from LLaMA - 2 -70B- chat and ChatGPT 3.5 as the correct decisions and use them to guage the performance of smaller models. The result is given in the table below: When asked to evaluate (prompt, answer) pairs from the test dataset, the original LLaMA - 2 - 7B - chat model shows a few undesired behaviors: 1.No Decision: model dodges the question and refuses to give safety evaluation based on the given safety guidelines 2 .Wrong Decision: model gives wrong safety evaluations However, the fintuned LLaMA - 2 - 7B - chat model does not have these problems and its evaluations are aligned with LLaMA - 2 -70B- chat and ChatGPT 3.5. This demonstrates that a small LLaMA model can achieve similar performance on specific tasks as the large models with very low-cost finetuning. See full list on github.com Selectively ablating identified safety heads increases the attack success rate (ASR) for models like Llama - 2 - 7b - chat and Vicuna - 7b - v1.5. The Safety Attention Head Attribution Algorithm (Sahara) is presented to identify groups of heads whose ablation weakens safety capabilities. Safety performance of Llama2-7B-Chat under Fine-Tuning attacks (Alpaca and Dolly) across various parameter-efficient fine-tuning setups. Results with * are from Qi et al. (2023). We analyze the overlap in safety heads when attributing to Llama-2-7b-chat and Vicuna- 7b -v1.5111Both of which are fine-tuned versions on top of Llama - 2 - 7b , having undergone identical pre-training. using two ablation methods on the Malicious Instruct dataset. Can ships improve attack success rate of llama-2-7b-chat? Experimental results show that on three harmful query datasets, using Ships to identify safe heads and using undifferentiated attention ablation (only modifying ∼ similar-to \\sim ∼ 0.006% of the parameters) can improve the attack success rate (ASR) of Llama-2-7b-chat from 0.04 to 0.64 \\uparrow and Vicuna-7b-v1.5 from 0.27 to 0.55 \\uparrow. Does llama-2-7b-chat weaken safety capability? For Llama-2-7b-chat, head 2-26 emerges as the most crucial safety attention head. When ablated individually with the input template from Appendix B.1, it significantly weakens safety capability . Table 1: Safety capability degradation and parameter attribution granularity. Tested model is Llama-2-7b-chat. Does ablating the attention head weaken the safety capability collaboratively? We name this heuristic method Safety Attention Head AttRibution Algorithm (Sahara). Experimental results show that ablating the attention head group can further weaken the safety capability collaboratively . Based on the Ships and Sahara, we interpret the safety head of attention on several popular LLMs, such as Llama-2-7b-chat and Vicuna-7b-v1.5. What happens if a safety attention head is ablated? Figure 1: Upper. Ablation of the safety attention head through undifferentiated attention causes the attention weight to degenerate to the mean ; Bottom. After ablating the attention head according to the upper, the safety capability is weakened, and it responds to both harmful and benign queries. Aug 1, 2024 · They might also include workaround for solving a similar issue in your situation. Prompting Llama-2-7B-Chat -GGML Possible Naming Issue for Running Experiments on Windows Currently the codebase only supports training with LLaMA or Pythia based models. Running the scripts with other models (with different tokenizers) will likely result in silent ...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/chaoluond/safetyllama", "content": "The recent advance in Generative Pre-trained Transformer (GPT) technology (e.g. ChatGPT, LLaMA , Claude, Dolly, etc.) has been inspiring numerious tech companies and AI enthusiasts to utilize it to expore, experiment and deploy novel applications in search engine, recommendation system, work productivity tools, healthcare, advertising, and so on. GPT, or more broadly AI, is a double-edged sword. On one hand, it can tremendously boost the efficacy and efficiency of information retrieval and dissemination among humans. On the other hand, it might endager the safety and integrity of human beings if it is abused. In the US, the federal government has been enacting laws and policies to regulate AI (reference1). In Europe, an AI Act was proposed to regulate the development and use of AI (reference2) See full list on github.com Generally, we follow the same framework in Constitutional AI: Harmlessness from AI Feedback to develop Safety LLaMA . The basic idea of Safety LLaMA to use an independent GPT model to monitor and evaluate the safety and integrity of an AI chatbot's response to a user's prompt. It serves as an alternative to OpenAI's moderation endpoint tool, which can only be used by OpenAI users. As human beings, we provide Safety LLaMA with a set of fundamental principles that an AI chatbot must comply with, which is called AI Constitution. Here is an example showing how to prompt Safety LLaMA (see prompt for scripts): Here is an example of Safety LLaMA 's evaluation: In the example above, we instruct Safety LLaMA model to apply these principles to evaluate another AI chatbot's response and flag it if it violates the safety guidelines above. There are several advantages to train a GPT model to perform the evaluation and detection job: 1.Flexibility: Safety guidelines can be updated and configed to satisfy different end customer's needs 2 .Few Train Labels: Very few training labels are required to achieve solid performance since the base model is pre-trained based on an enormous amount of text data, which has decent zero-shot and few-shot prompting performance without fintuning. See full list on github.com The harmless dataset from Anthropics is a list of sensitive questions (or prompts) asked by red teams, to which an AI chatbot is inclined to give inapproriate or dangerous answers. The dataset has about 15000 train prompts and 2200 test prompts. See full list on github.com LLaMA-2-70B-chat model was used to generate responses to prompts in the harmless dataset (5000 train prompts and 2200 test prompts). This step was done on a cloud server with 8xA100(80GB) GPUs. The total computation time is about 5-6 hours . See llama_gen_response_to_prompt. See full list on github.com In step 1, we use LLaMA - 2 -70B- chat model to generate answers to prompts. In this step, we make the model to do self critique. Bascially, we use the same model to evaluate its answers according to the safety guileines. See llama_gen_safety_evaluation. For all 5000 (prompt, answer) pairs from the train dataset, only 1 is flagged as unsafe. All 2200 (prompt, answer) pairs from the test dataset are evaluated as safe responses. In order to cross validate the accuracy of LLaMA - 2 -70B- chat model's evaluation, ChatGPT 3.5 turbo was used to evaluate all (prompt, answer) pairs. See chatgpt_gen_safety_evaluation. It turns out that ChatGPT 3.5 is mostly aligned with LLaMA - 2 -70B- chat , which thinks all responses are safe. This also verifies that LLaMA - 2 -70B- chat is a pretty mature and safe model to use. See full list on github.com It requires 8xA100 GPUs to run LLaMA - 2 -70B- chat to generate safety evaluation, which is very costly and time-consuming. In this step, we use the evaluation dataset of LLaMA - 2 -70B- chat from step 2 to finetune a LLaMA - 2 - 7B - chat model using int8 quantization and Low-Rank Adaptation (LoRA). This finetuning step was done on a single A40 GPU and the total computation time is about 2 -3 hours. See finetune for instructions and scripts. See safetyllama_finetune_using_huggingface for calling the finetuned model. The finetuned model (aka adapter model) is hosted in huggingface. On the other hand, we treat the evaluation decision from LLaMA - 2 -70B- chat and ChatGPT 3.5 as the correct decisions and use them to guage the performance of smaller models. The result is given in the table below: When asked to evaluate (prompt, answer) pairs from the test dataset, the original LLaMA - 2 - 7B - chat model shows a few undesired behaviors: 1.No Decision: model dodges the question and refuses to give safety evaluation based on the given safety guidelines 2 .Wrong Decision: model gives wrong safety evaluations However, the fintuned LLaMA - 2 - 7B - chat model does not have these problems and its evaluations are aligned with LLaMA - 2 -70B- chat and ChatGPT 3.5. This demonstrates that a small LLaMA model can achieve similar performance on specific tasks as the large models with very low-cost finetuning. See full list on github.com Selectively ablating identified safety heads increases the attack success rate (ASR) for models like Llama - 2 - 7b - chat and Vicuna - 7b - v1.5. The Safety Attention Head Attribution Algorithm (Sahara) is presented to identify groups of heads whose ablation weakens safety capabilities. Safety performance of Llama2-7B-Chat under Fine-Tuning attacks (Alpaca and Dolly) across various parameter-efficient fine-tuning setups. Results with * are from Qi et al. (2023). We analyze the overlap in safety heads when attributing to Llama-2-7b-chat and Vicuna- 7b -v1.5111Both of which are fine-tuned versions on top of Llama - 2 - 7b , having undergone identical pre-training. using two ablation methods on the Malicious Instruct dataset. Can ships improve attack success rate of llama-2-7b-chat? Experimental results show that on three harmful query datasets, using Ships to identify safe heads and using undifferentiated attention ablation (only modifying ∼ similar-to \\sim ∼ 0.006% of the parameters) can improve the attack success rate (ASR) of Llama-2-7b-chat from 0.04 to 0.64 \\uparrow and Vicuna-7b-v1.5 from 0.27 to 0.55 \\uparrow. Does llama-2-7b-chat weaken safety capability? For Llama-2-7b-chat, head 2-26 emerges as the most crucial safety attention head. When ablated individually with the input template from Appendix B.1, it significantly weakens safety capability . Table 1: Safety capability degradation and parameter attribution granularity. Tested model is Llama-2-7b-chat. Does ablating the attention head weaken the safety capability collaboratively? We name this heuristic method Safety Attention Head AttRibution Algorithm (Sahara). Experimental results show that ablating the attention head group can further weaken the safety capability collaboratively . Based on the Ships and Sahara, we interpret the safety head of attention on several popular LLMs, such as Llama-2-7b-chat and Vicuna-7b-v1.5. What happens if a safety attention head is ablated? Figure 1: Upper. Ablation of the safety attention head through undifferentiated attention causes the attention weight to degenerate to the mean ; Bottom. After ablating the attention head according to the upper, the safety capability is weakened, and it responds to both harmful and benign queries. Aug 1, 2024 · They might also include workaround for solving a similar issue in your situation. Prompting Llama-2-7B-Chat -GGML Possible Naming Issue for Running Experiments on Windows Currently the codebase only supports training with LLaMA or Pythia based models. Running the scripts with other models (with different tokenizers) will likely result in silent ..."}
+{"idx": 3, "title": "Safety performance of Llama2-7B-Chat under Fine-Tuning ...", "date": "", "ddg_snippet": "Safety performance of Llama2-7B-Chat under Fine-Tuning attacks (Alpaca and Dolly) across various parameter-efficient fine-tuning setups. Results with * are from Qi et al. (2023).", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/figure/Safety-performance-of-Llama2-7B-Chat-under-Fine-Tuning-attacks-Alpaca-and-Dolly-across_tbl1_384938913", "content": "Safety performance of Llama2-7B-Chat under Fine-Tuning attacks (Alpaca and Dolly) across various parameter-efficient fine-tuning setups. Results with * are from Qi et al. (2023)."}
+{"idx": 4, "title": "GitHub - llm-attacks/llm-attacks: Universal and Transferable ...", "date": "", "ddg_snippet": "Aug 1, 2024 · They might also include workaround for solving a similar issue in your situation. Prompting Llama-2-7B-Chat -GGML Possible Naming Issue for Running Experiments on Windows Currently the codebase only supports training with LLaMA or Pythia based models. Running the scripts with other models (with different tokenizers) will likely result in silent ...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/llm-attacks/llm-attacks", "content": "Aug 1, 2024 · They might also include workaround for solving a similar issue in your situation. Prompting Llama-2-7B-Chat -GGML Possible Naming Issue for Running Experiments on Windows Currently the codebase only supports training with LLaMA or Pythia based models. Running the scripts with other models (with different tokenizers) will likely result in silent ..."}
+{"idx": 5, "title": "ON THE ROLE OF ATTENTION HEADS IN LARGE ...", "date": "", "ddg_snippet": "by Z Zhou · Cited by 19 — Ablating a single safety head allows the aligned model (e.g.,. Llama - 2 - 7b - chat ) to respond to 16× ↑ more harmful queries, while only modifying 0.006% ↓ of the ... 30 pages", "subpage_snippet": "", "source": "proceedings.iclr.cc", "link": "https://proceedings.iclr.cc/paper_files/paper/2025/file/d0bcff6425bbf850ec87d5327a965db9-Paper-Conference.pdf", "content": "by Z Zhou · Cited by 19 — Ablating a single safety head allows the aligned model (e.g.,. Llama - 2 - 7b - chat ) to respond to 16× ↑ more harmful queries, while only modifying 0.006% ↓ of the ... 30 pages"}
+{"idx": 6, "title": "Special Characters Attack: Toward Scalable Training Data ...", "date": "", "ddg_snippet": "20 May 2024 — Model Parameter Count We conduct experiments on three Llama - 2 - Chat models with 7B , 13B, and 70B parameters, respectively. The result in Table 4 ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2405.05990v2", "content": "20 May 2024 — Model Parameter Count We conduct experiments on three Llama - 2 - Chat models with 7B , 13B, and 70B parameters, respectively. The result in Table 4 ..."}
+{"idx": 7, "title": "Main track accepted papers (Guangzhou)", "date": "", "ddg_snippet": "Experimental results indicate that MiniMal achieves an attack success rate ... LLaMA - 2 - 7B model, which is quantized with W4A4. 3217: Exploring the Over ...", "subpage_snippet": "", "source": "2025.ijcai.org", "link": "https://2025.ijcai.org/guangzhou-main-track-accepted-papers/", "content": "Experimental results indicate that MiniMal achieves an attack success rate ... LLaMA - 2 - 7B model, which is quantized with W4A4. 3217: Exploring the Over ..."}
+{"idx": 8, "title": "arXiv:2410.13708v2 [cs.CL] 24 Feb 2025", "date": "", "ddg_snippet": "for attributing safety attention head . Experimental results show that on three harmful query datasets, using Ships to identify safe heads and using undifferentiated attention ablation (only modifying ∼ 0.006% of the parameters) can improve the attack success rate (ASR) of Llama-2-7b-chat from 0.04 to 0.64 ↑ an", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2410.13708", "content": "for attributing safety attention head . Experimental results show that on three harmful query datasets, using Ships to identify safe heads and using undifferentiated attention ablation (only modifying ∼ 0.006% of the parameters) can improve the attack success rate (ASR) of Llama-2-7b-chat from 0.04 to 0.64 ↑ an"}
+{"idx": 9, "title": "Research Assistant - Bohrium | AI for Science with Global ...", "date": "", "ddg_snippet": "Selectively ablating identified safety heads increases the attack success rate (ASR) for models like Llama - 2 - 7b - chat and Vicuna - 7b - v1.5. The Safety Attention Head Attribution Algorithm (Sahara) is presented to identify groups of heads whose ablation weakens safety capabilities.", "subpage_snippet": "", "source": "www.bohrium.com", "link": "https://www.bohrium.com/paper/arxiv/2410.13708", "content": "Selectively ablating identified safety heads increases the attack success rate (ASR) for models like Llama - 2 - 7b - chat and Vicuna - 7b - v1.5. The Safety Attention Head Attribution Algorithm (Sahara) is presented to identify groups of heads whose ablation weakens safety capabilities."}
diff --git a/data/sampled_jsons/Llama2_license_Llama3_license_incompatible_clause_restriction.jsonl b/data/sampled_jsons/Llama2_license_Llama3_license_incompatible_clause_restriction.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..c33b3c1f541c1a25378dd4e455f3cdb3f67fbae8
--- /dev/null
+++ b/data/sampled_jsons/Llama2_license_Llama3_license_incompatible_clause_restriction.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "LLaMA - 维基百科，自由的百科全书", "date": "", "ddg_snippet": "LLaMA（英语：Large Language Model Meta AI）是Meta于2023年2月发布的大型语言模型。它训练了各种模型，这些模型的参数从70亿到650亿不等。", "subpage_snippet": "", "source": "zh.wikipedia.org", "link": "https://zh.wikipedia.org/zh-cn/LLaMA", "content": "LLaMA（英语：Large Language Model Meta AI）是Meta于2023年2月发布的大型语言模型。它训练了各种模型，这些模型的参数从70亿到650亿不等。"}
+{"idx": 1, "title": "You're Probably Breaking the Llama Community License - Notes", "date": "", "ddg_snippet": "Only applies for Llama 3 License and later. The Llama 2 License did not have this requirement at all. A summary of the changes across Llama License versions can be found here.", "subpage_snippet": "", "source": "notes.victor.earth", "link": "https://notes.victor.earth/youre-probably-breaking-the-llama-community-license/", "content": "Only applies for Llama 3 License and later. The Llama 2 License did not have this requirement at all. A summary of the changes across Llama License versions can be found here."}
+{"idx": 2, "title": "Llama/ license at main · meta-llama/llama · github", "date": "", "ddg_snippet": "llama 3 / LICENSE at main · meta-llama/ llama 3 · GitHub. If logprobs is True, token log probabilities are computed for each generated token.Brett Douglas Viral: Llama 2 License Meta.", "subpage_snippet": "", "source": "6kij7.freedomladder.org", "link": "https://6kij7.freedomladder.org/", "content": "llama 3 / LICENSE at main · meta-llama/ llama 3 · GitHub. If logprobs is True, token log probabilities are computed for each generated token.Brett Douglas Viral: Llama 2 License Meta."}
+{"idx": 3, "title": "They've Stolen My GPL-Licensed Model!", "date": "", "ddg_snippet": "16 Dec 2024 — For example, the terms in Llama 2 license is specifically drafted to govern the Llama 2 model and its derivatives 7 7 7For example, Clause 1.v.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2412.11483v1", "content": "16 Dec 2024 — For example, the terms in Llama 2 license is specifically drafted to govern the Llama 2 model and its derivatives 7 7 7For example, Clause 1.v."}
+{"idx": 4, "title": "[License-review] [Resubmission] ModelGo Attribution ...", "date": "", "ddg_snippet": "23 Mar 2025 — ... License Version 2.0 (MG-BY-2.0) is a new license designed for publishing models (typically neural networks like Llama2 , DeepSeek). It is one ...", "subpage_snippet": "", "source": "lists.opensource.org", "link": "https://lists.opensource.org/pipermail/license-review_lists.opensource.org/2025-March/005741.html", "content": "23 Mar 2025 — ... License Version 2.0 (MG-BY-2.0) is a new license designed for publishing models (typically neural networks like Llama2 , DeepSeek). It is one ..."}
+{"idx": 5, "title": "Carolyn Duby - What is open source software license?", "date": "", "ddg_snippet": "When using open source software, it is important to understand the restrictions of the license .", "subpage_snippet": "", "source": "www.linkedin.com", "link": "https://www.linkedin.com/posts/carolynduby_when-using-open-source-software-it-is-important-activity-7214290490429423617-vOTW", "content": "When using open source software, it is important to understand the restrictions of the license ."}
+{"idx": 6, "title": "[License-review] ModelGo Zero License, Version 2.0", "date": "", "ddg_snippet": "15 Feb 2025 — ... restriction . Another point of concern is the expression “non-transferable, sublicensable.” While in many instances the “sublicensable ...", "subpage_snippet": "", "source": "lists.opensource.org", "link": "https://lists.opensource.org/pipermail/license-review_lists.opensource.org/2025-February/005669.html", "content": "15 Feb 2025 — ... restriction . Another point of concern is the expression “non-transferable, sublicensable.” While in many instances the “sublicensable ..."}
+{"idx": 7, "title": "The Mirage of Artificial Intelligence Terms of Use Restrictions", "date": "", "ddg_snippet": "by P Henderson · 2025 · Cited by 4 — Artificial intelligence (AI) model creators commonly attach restrictive terms of use to both their models and their outputs. These terms typically prohibit ...", "subpage_snippet": "", "source": "www.repository.law.indiana.edu", "link": "https://www.repository.law.indiana.edu/cgi/viewcontent.cgi?article=11576&context=ilj", "content": "by P Henderson · 2025 · Cited by 4 — Artificial intelligence (AI) model creators commonly attach restrictive terms of use to both their models and their outputs. These terms typically prohibit ..."}
+{"idx": 8, "title": "Welcome Llama 3 - Meta's new open LLM", "date": "", "ddg_snippet": "The Llama 3 release introduces 4 new open LLM models by Meta based on the Llama 2 architecture.Regarding the licensing terms, Llama 3 comes with a permissive license that allows redistribution, fine-tuning, and derivative works.", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/blog/llama3", "content": "The Llama 3 release introduces 4 new open LLM models by Meta based on the Llama 2 architecture.Regarding the licensing terms, Llama 3 comes with a permissive license that allows redistribution, fine-tuning, and derivative works."}
+{"idx": 9, "title": "Current Model Licensing Practices are Dragging Us into a ...", "date": "", "ddg_snippet": "Similarly, merging two models licensed under Llama2 and Llama3 , respectively, is prohibited, as Clause 1.v of both licenses restricts using licensed models ...", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/poster/40180", "content": "Similarly, merging two models licensed under Llama2 and Llama3 , respectively, is prohibited, as Clause 1.v of both licenses restricts using licensed models ..."}
diff --git a/data/sampled_jsons/Long-Form_Speech_Generation_with_Spoken_Language_Models_'Initialization'_section_'LM_initialization'_year_2023.jsonl b/data/sampled_jsons/Long-Form_Speech_Generation_with_Spoken_Language_Models_'Initialization'_section_'LM_initialization'_year_2023.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..86c740e019770c34bc82c8bc5045efec382bbdb8
--- /dev/null
+++ b/data/sampled_jsons/Long-Form_Speech_Generation_with_Spoken_Language_Models_'Initialization'_section_'LM_initialization'_year_2023.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Long-Form Speech Generation with Spoken Language ...", "date": "", "ddg_snippet": "24 Dec 2024 — We propose SpeechSSM , the first speech language model to learn from and sample long - form spoken audio (eg, 16 minutes of read or extemporaneous speech) in a ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2412.18603v1", "content": "24 Dec 2024 — We propose SpeechSSM , the first speech language model to learn from and sample long - form spoken audio (eg, 16 minutes of read or extemporaneous speech) in a ..."}
+{"idx": 1, "title": "Long-Form Speech Generation with Spoken Language ...", "date": "", "ddg_snippet": "Abstract. We consider the generative modeling of speech over multiple minutes, a requirement for long - form multimedia generation and audio-native.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/attachment?id=4AmFA0qNQ2&name=pdf", "content": "Abstract. We consider the generative modeling of speech over multiple minutes, a requirement for long - form multimedia generation and audio-native."}
+{"idx": 2, "title": "Long-Form Speech Generation with Spoken Language ...", "date": "", "ddg_snippet": "We consider the generative modeling of speech over multiple minutes, a requirement for long - form multimedia generation and audio-native voice assistants.", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/poster/46499", "content": "We consider the generative modeling of speech over multiple minutes, a requirement for long - form multimedia generation and audio-native voice assistants."}
+{"idx": 3, "title": "Long-Form Speech Generation with Spoken Language ...", "date": "", "ddg_snippet": "10 Jul 2025 — We derive SpeechSSM , the first speech language model family to learn from and sample long - form spoken audio (eg, 16 minutes of read or extemporaneous speech)", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2412.18603v2", "content": "10 Jul 2025 — We derive SpeechSSM , the first speech language model family to learn from and sample long - form spoken audio (eg, 16 minutes of read or extemporaneous speech)"}
+{"idx": 4, "title": "Recent Advances in Speech Language Models: A Survey", "date": "", "ddg_snippet": "by W Cui · 2025 · Cited by 43 — Text-based Large Language Models (LLMs) have demonstrated remarkable capabilities in generating text and performing a wide array of natural lan-. 28 pages", "subpage_snippet": "", "source": "aclanthology.org", "link": "https://aclanthology.org/2025.acl-long.682.pdf", "content": "by W Cui · 2025 · Cited by 43 — Text-based Large Language Models (LLMs) have demonstrated remarkable capabilities in generating text and performing a wide array of natural lan-. 28 pages"}
+{"idx": 5, "title": "On The Landscape of Spoken Language Models", "date": "", "ddg_snippet": "Some sequence generation models use text tokens as the first-layer tokens, followed by speech tokens (either phonetic or audio codec tokens). This approach ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf/753d38eadd6444e571f1171872e932fee7dca205.pdf", "content": "Some sequence generation models use text tokens as the first-layer tokens, followed by speech tokens (either phonetic or audio codec tokens). This approach ..."}
+{"idx": 6, "title": "Optimizing Hidden Markov Language Models: An Empirical ...", "date": "", "ddg_snippet": "by I Lee · 2025 — We pro- vide a comprehensive empirical analysis of two approaches to enhance HMM optimization: reparameterization and initialization of HMM. 12 pages", "subpage_snippet": "", "source": "aclanthology.org", "link": "https://aclanthology.org/2025.findings-naacl.429.pdf", "content": "by I Lee · 2025 — We pro- vide a comprehensive empirical analysis of two approaches to enhance HMM optimization: reparameterization and initialization of HMM. 12 pages"}
+{"idx": 7, "title": "Daily Papers", "date": "", "ddg_snippet": "Generative Spoken Language Modeling research focuses on optimizing speech Language Models (LMs) using raw audio recordings without accessing any textual ...", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/papers?q=Generative+Spoken+Language+Modeling", "content": "Generative Spoken Language Modeling research focuses on optimizing speech Language Models (LMs) using raw audio recordings without accessing any textual ..."}
+{"idx": 8, "title": "Daily Papers", "date": "", "ddg_snippet": "Long - Form Speech Generation with Spoken Language Models · We consider the generative modeling of speech over multiple minutes, a requirement for long - form ...", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/papers?q=LongAudioBench", "content": "Long - Form Speech Generation with Spoken Language Models · We consider the generative modeling of speech over multiple minutes, a requirement for long - form ..."}
+{"idx": 9, "title": "ICML 2025 Papers", "date": "", "ddg_snippet": "Long - Form Speech Generation with Spoken Language Models · Sampling Binary Data ... An Analysis for Reasoning Bias of Language Models with Small Initialization ...", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/papers.html", "content": "Long - Form Speech Generation with Spoken Language Models · Sampling Binary Data ... An Analysis for Reasoning Bias of Language Models with Small Initialization ..."}
diff --git a/data/sampled_jsons/LongLLMLingua_paper_abstract_year_2023.jsonl b/data/sampled_jsons/LongLLMLingua_paper_abstract_year_2023.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..b297ee587e5bd00c50f2e7f625d7ff9f32cb9342
--- /dev/null
+++ b/data/sampled_jsons/LongLLMLingua_paper_abstract_year_2023.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "LongLLMLingua: Accelerating and Enhancing LLMs in Long Context ...", "date": "", "ddg_snippet": "Abstract page for arXiv paper 2310.06839: LongLLMLingua : Accelerating and Enhancing LLMs in Long Context Scenarios via Prompt Compression", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2310.06839", "content": "Abstract page for arXiv paper 2310.06839: LongLLMLingua : Accelerating and Enhancing LLMs in Long Context Scenarios via Prompt Compression"}
+{"idx": 1, "title": "LongLLMLingua: Accelerating and Enhancing LLMs in Long Context ...", "date": "", "ddg_snippet": "Abstract In long context scenarios, large language models (LLMs) face three main challenges: higher computational cost, performance reduction, and position bias. Research indicates that LLM performance hinges on the density and position of key information in the input prompt. Inspired by these findings, we propose LongLLMLingua for prompt compression towards improving LLMs' perception of the ...", "subpage_snippet": "", "source": "aclanthology.org", "link": "https://aclanthology.org/2024.acl-long.91/", "content": "Abstract In long context scenarios, large language models (LLMs) face three main challenges: higher computational cost, performance reduction, and position bias. Research indicates that LLM performance hinges on the density and position of key information in the input prompt. Inspired by these findings, we propose LongLLMLingua for prompt compression towards improving LLMs' perception of the ..."}
+{"idx": 2, "title": "Paper page - LongLLMLingua: Accelerating and Enhancing LLMs in Long ...", "date": "", "ddg_snippet": "Abstract In long context scenarios, large language models (LLMs) face three main challenges: higher computational/financial cost, longer latency, and inferior performance. Some studies reveal that the performance of LLMs depends on both the density and the position of the key information (question relevant) in the input prompt. Inspired by these findings, we propose LongLLMLingua for prompt ...", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/papers/2310.06839", "content": "Abstract In long context scenarios, large language models (LLMs) face three main challenges: higher computational/financial cost, longer latency, and inferior performance. Some studies reveal that the performance of LLMs depends on both the density and the position of the key information (question relevant) in the input prompt. Inspired by these findings, we propose LongLLMLingua for prompt ..."}
+{"idx": 3, "title": "LongLLMLingua Model: A Solution for LLMs in Long Context Scenarios ...", "date": "", "ddg_snippet": "LongLLMLingua Model: A Solution for LLMs in Long Context Scenarios Core Challenges 1. Question-Context Relevance Problem Traditional prompt compression methods face several critical issues when dealing with long contexts:", "subpage_snippet": "", "source": "danghoangnhan.github.io", "link": "https://danghoangnhan.github.io/longlingua/", "content": "LongLLMLingua Model: A Solution for LLMs in Long Context Scenarios Core Challenges 1. Question-Context Relevance Problem Traditional prompt compression methods face several critical issues when dealing with long contexts:"}
+{"idx": 4, "title": "PDF LongLLMLingua: ACCELERATING AND ENHANCING LLM L CONTEXT SCENARIOS VIA ...", "date": "", "ddg_snippet": "ABSTRACT In long context scenarios, large language models (LLMs) face three main chal-lenges: higher computational cost, performance reduction, and position bias. Re-search indicates that LLM performance hinges on the density and position of key information in the input prompt. Addressing this, we introduce LongLLMLingua ,", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/attachment?id=9YvfRrpmyw&name=pdf", "content": "ABSTRACT In long context scenarios, large language models (LLMs) face three main chal-lenges: higher computational cost, performance reduction, and position bias. Re-search indicates that LLM performance hinges on the density and position of key information in the input prompt. Addressing this, we introduce LongLLMLingua ,"}
+{"idx": 5, "title": "LongLLMLingua: Accelerating and Enhancing LLMs in Long Context ...", "date": "", "ddg_snippet": "Conclusion We propose LongLLMLingua to address the three challenges, i.e., higher computational cost, performance reduction, and position bias for LLMs in long context scenarios. We develop LongLLMLingua from the perspective of efficient prompt compression, thus reducing computational cost.", "subpage_snippet": "", "source": "llmlingua.com", "link": "https://llmlingua.com/longllmlingua.html", "content": "Conclusion We propose LongLLMLingua to address the three challenges, i.e., higher computational cost, performance reduction, and position bias for LLMs in long context scenarios. We develop LongLLMLingua from the perspective of efficient prompt compression, thus reducing computational cost."}
+{"idx": 6, "title": "LongLLMLingua: Accelerating and Enhancing LLMs in Long Context ...", "date": "", "ddg_snippet": "Abstract In long context scenarios, large language mod-els (LLMs) face three main challenges: higher computational cost, performance reduction, and position bias. Research indicates that LLM performance hinges on the density and posi-tion of key information in the input prompt. In-spired by these findings, we propose LongLLM-Lingua for prompt compression towards im-proving LLMs' perception ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2310.06839", "content": "Abstract In long context scenarios, large language mod-els (LLMs) face three main challenges: higher computational cost, performance reduction, and position bias. Research indicates that LLM performance hinges on the density and posi-tion of key information in the input prompt. In-spired by these findings, we propose LongLLM-Lingua for prompt compression towards im-proving LLMs' perception ..."}
+{"idx": 7, "title": "LongLLMLingua: Accelerating and Enhancing LLMs in Long Context ...", "date": "", "ddg_snippet": "This work proposes LongLLMLingua for prompt compression towards improving LLMs' perception of the key information to simultaneously address the three challenges of higher computational cost, performance reduction, and position bias. In long context scenarios, large language models (LLMs) face three main challenges: higher computational cost, performance reduction, and position bias. Research ...", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/LongLLMLingua:-Accelerating-and-Enhancing-LLMs-in-Jiang-Wu/4c0428917aeee6aa7bd434f337d039f35996b736", "content": "This work proposes LongLLMLingua for prompt compression towards improving LLMs' perception of the key information to simultaneously address the three challenges of higher computational cost, performance reduction, and position bias. In long context scenarios, large language models (LLMs) face three main challenges: higher computational cost, performance reduction, and position bias. Research ..."}
+{"idx": 8, "title": "LongLLMLingua: Accelerating and Enhancing LLMs in Long Context ...", "date": "", "ddg_snippet": "View recent discussion. Abstract : In long context scenarios, large language models (LLMs) face three main challenges: higher computational cost, performance reduction, and position bias. Research indicates that LLM performance hinges on the density and position of key information in the input prompt. Inspired by these findings, we propose LongLLMLingua for prompt compression towards improving ...", "subpage_snippet": "", "source": "www.alphaxiv.org", "link": "https://www.alphaxiv.org/overview/2310.06839v2", "content": "View recent discussion. Abstract : In long context scenarios, large language models (LLMs) face three main challenges: higher computational cost, performance reduction, and position bias. Research indicates that LLM performance hinges on the density and position of key information in the input prompt. Inspired by these findings, we propose LongLLMLingua for prompt compression towards improving ..."}
+{"idx": 9, "title": "LongLLMLingua: Accelerating and Enhancing LLMs in Long Context ...", "date": "", "ddg_snippet": "In long context scenarios, large language models (LLMs) face three main challenges: higher computational/financial cost, longer latency, and inferior performance. Some studies reveal that the performance of LLMs depends on both the density and the position of the key information (question relevant) in the input prompt. Inspired by these findings, we propose LongLLMLingua for prompt compression ...", "subpage_snippet": "", "source": "www.x-mol.com", "link": "https://www.x-mol.com/paper/1712324582935646208", "content": "In long context scenarios, large language models (LLMs) face three main challenges: higher computational/financial cost, longer latency, and inferior performance. Some studies reveal that the performance of LLMs depends on both the density and the position of the key information (question relevant) in the input prompt. Inspired by these findings, we propose LongLLMLingua for prompt compression ..."}
diff --git a/data/sampled_jsons/Longpre_et_al._2021_NQ-Swap_dataset_abstract.jsonl b/data/sampled_jsons/Longpre_et_al._2021_NQ-Swap_dataset_abstract.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..2f7e399fb732681cc471c83f34a95fe365083e2b
--- /dev/null
+++ b/data/sampled_jsons/Longpre_et_al._2021_NQ-Swap_dataset_abstract.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "arXiv:2305.14739v1 [cs.CL] 24 May 2023", "date": "", "ddg_snippet": "24 May 2023 — NQ - Swap is based on a QA dataset , natural questions (NQ) (Kwiatkowski et al ., 2019), where the objective is to answer ques- tions based on a ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2305.14739", "content": "24 May 2023 — NQ - Swap is based on a QA dataset , natural questions (NQ) (Kwiatkowski et al ., 2019), where the objective is to answer ques- tions based on a ..."}
+{"idx": 1, "title": "Entity-Based Knowledge Conflicts in Question Answering", "date": "", "ddg_snippet": "by S Longpre · 2021 · Cited by 290 — This substitution framework extends partially- automated dataset creation techniques introduced by Chen et al . ( 2021 ) for Ambiguous Entity Re-.", "subpage_snippet": "", "source": "aclanthology.org", "link": "https://aclanthology.org/2021.emnlp-main.565.pdf", "content": "by S Longpre · 2021 · Cited by 290 — This substitution framework extends partially- automated dataset creation techniques introduced by Chen et al . ( 2021 ) for Ambiguous Entity Re-."}
+{"idx": 2, "title": "Exploiting Contextual Knowledge in LLMs through V- ...", "date": "", "ddg_snippet": "the NQ dataset (Kwiatkowski et al ., 2019) and its. 488 variant, NQ - Swap ( Longpre et al ., 2021 ). The NQ-. 489. Swap dataset , derived from the original NQ, exclu-.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=B3i76OuS4d", "content": "the NQ dataset (Kwiatkowski et al ., 2019) and its. 488 variant, NQ - Swap ( Longpre et al ., 2021 ). The NQ-. 489. Swap dataset , derived from the original NQ, exclu-."}
+{"idx": 3, "title": "FastMem: Fast Memorization of Prompt Improves Context ...", "date": "", "ddg_snippet": "by J Zhu · 2024 · Cited by 7 — NQ -. SWAP is constructed from NQ using the method of. Longpre et al . ( 2021 ), where we swap the answer strings, which are entities, with random ... 19 pages", "subpage_snippet": "", "source": "aclanthology.org", "link": "https://aclanthology.org/2024.findings-emnlp.687.pdf", "content": "by J Zhu · 2024 · Cited by 7 — NQ -. SWAP is constructed from NQ using the method of. Longpre et al . ( 2021 ), where we swap the answer strings, which are entities, with random ... 19 pages"}
+{"idx": 4, "title": "Aligning Large Language Models towards Contextual Faithfulness", "date": "", "ddg_snippet": "these behaviours. 240. We use the NQ - SWAP dataset ( Longpre et al .,. 241. 2022), which contains questions where the re-. 242 trieved context contradicts the ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf/0b91a7d4208031f6b180e794396c6ca0d2bab510.pdf", "content": "these behaviours. 240. We use the NQ - SWAP dataset ( Longpre et al .,. 241. 2022), which contains questions where the re-. 242 trieved context contradicts the ..."}
+{"idx": 5, "title": "Fewer Truncations Improve Language Modeling", "date": "", "ddg_snippet": "by H Ding · 2024 · Cited by 33 — Specifically, we evaluate with 5-shot on NQ - Swap ( Longpre et al ., 2021 ), a perturbed version of. Natural Questions by replacing both the answer and answer.", "subpage_snippet": "", "source": "assets.amazon.science", "link": "https://assets.amazon.science/be/1e/215dde844d49a13136e70d683d8b/fewer-truncations-improve-language-modeling.pdf", "content": "by H Ding · 2024 · Cited by 33 — Specifically, we evaluate with 5-shot on NQ - Swap ( Longpre et al ., 2021 ), a perturbed version of. Natural Questions by replacing both the answer and answer."}
+{"idx": 6, "title": "General Examination Document for Your Name", "date": "", "ddg_snippet": "by X Han · 2025 — ... et al ., 2022). Knowledge conflicts We evaluate performance on two knowledge conflict datasets : MemoTrap (Liu and Liu, 2023) and NQ - Swap ( Longpre et al ., 2021 ).", "subpage_snippet": "", "source": "digital.lib.washington.edu", "link": "https://digital.lib.washington.edu/server/api/core/bitstreams/a750f2b4-ca2b-408a-98b9-40d2cdbae77d/content", "content": "by X Han · 2025 — ... et al ., 2022). Knowledge conflicts We evaluate performance on two knowledge conflict datasets : MemoTrap (Liu and Liu, 2023) and NQ - Swap ( Longpre et al ., 2021 )."}
+{"idx": 7, "title": "BIVLC: Extending Vision-Language Compositionality ...", "date": "", "ddg_snippet": "by IMASE Agirre · Cited by 3 — Different datasets and benchmarks have been proposed following this approach: for example, [Ma et al ., 2023] introduced CREPE, a large dataset containing hard ...", "subpage_snippet": "", "source": "papers.nips.cc", "link": "https://papers.nips.cc/paper_files/paper/2024/file/b8b93c48f5bfa385d071342089d70422-Paper-Datasets_and_Benchmarks_Track.pdf", "content": "by IMASE Agirre · Cited by 3 — Different datasets and benchmarks have been proposed following this approach: for example, [Ma et al ., 2023] introduced CREPE, a large dataset containing hard ..."}
+{"idx": 8, "title": "Taming Knowledge Conflicts in Language Models", "date": "", "ddg_snippet": "14 Mar 2025 — Prior works have explored the behavior of LMs under knowledge conflicts, either by treating the model as an oracle to analyze how different ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2503.10996v1", "content": "14 Mar 2025 — Prior works have explored the behavior of LMs under knowledge conflicts, either by treating the model as an oracle to analyze how different ..."}
+{"idx": 9, "title": "Data augmentation approaches in natural language ...", "date": "", "ddg_snippet": "by B Li · 2022 · Cited by 442 — In this survey, we frame DA methods into three categories based on the diversity of augmented data , including paraphrasing, noising, and sampling.", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S2666651022000080", "content": "by B Li · 2022 · Cited by 442 — In this survey, we frame DA methods into three categories based on the diversity of augmented data , including paraphrasing, noising, and sampling."}
diff --git a/data/sampled_jsons/Luo_Tseng_1992_convergence_coordinate_descent_convex_differentiable_minimization.jsonl b/data/sampled_jsons/Luo_Tseng_1992_convergence_coordinate_descent_convex_differentiable_minimization.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..1ae5cf762fa3250fb593d9efb8da5dc7a3e502f7
--- /dev/null
+++ b/data/sampled_jsons/Luo_Tseng_1992_convergence_coordinate_descent_convex_differentiable_minimization.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Coordinate descent - Wikipedia", "date": "", "ddg_snippet": "Luo , Zhiquan; Tseng , P. ( 1992 ), \"On the convergence of the coordinate descent method for convex differentiable minimization \", Journal of Optimization Theory and Applications, vol. 72, no. 1, Kluwer Academic/Plenum Publishers, pp. 7–35, doi: 10.1007/BF00939948, hdl: 1721.1/3164, S2CID 121091844.", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/Coordinate_descent", "content": "Luo , Zhiquan; Tseng , P. ( 1992 ), \"On the convergence of the coordinate descent method for convex differentiable minimization \", Journal of Optimization Theory and Applications, vol. 72, no. 1, Kluwer Academic/Plenum Publishers, pp. 7–35, doi: 10.1007/BF00939948, hdl: 1721.1/3164, S2CID 121091844."}
+{"idx": 1, "title": "On the convergence of the coordinate descent method for ...", "date": "", "ddg_snippet": "On the convergence of the coordinate descent method for convex differentiable minimization Author (s) Luo , Zhi-Quan.; Tseng , Paul.; Massachusetts Institute of Technology. Laboratory for Information and Decision Systems.", "subpage_snippet": "", "source": "dspace.mit.edu", "link": "https://dspace.mit.edu/handle/1721.1/3164", "content": "On the convergence of the coordinate descent method for convex differentiable minimization Author (s) Luo , Zhi-Quan.; Tseng , Paul.; Massachusetts Institute of Technology. Laboratory for Information and Decision Systems."}
+{"idx": 2, "title": "On the Linear Convergence of Descent Methods for Convex ...", "date": "", "ddg_snippet": "Z.-Q. Luo , P. Tseng , On the convergence of the coordinate descent method for convex differentiable minimization , J. Optim. Theory Appl., 72 ( 1992 ), 7–35, Laboratory for Information and Decision Systems Report No. P–1924, Massachusetts Institute of Technology, Cambridge, MA (1989; revised 1990)", "subpage_snippet": "", "source": "epubs.siam.org", "link": "https://epubs.siam.org/doi/10.1137/0330025", "content": "Z.-Q. Luo , P. Tseng , On the convergence of the coordinate descent method for convex differentiable minimization , J. Optim. Theory Appl., 72 ( 1992 ), 7–35, Laboratory for Information and Decision Systems Report No. P–1924, Massachusetts Institute of Technology, Cambridge, MA (1989; revised 1990)"}
+{"idx": 3, "title": "On the convergence of the coordinate descent method for ...", "date": "", "ddg_snippet": "Jan 3, 1992 · article On the convergence of the coordinate descent method for convex differentiable minimization Authors: Z. Q. Luo , P. Tseng Authors Info & Claims Journal of Optimization Theory and Applications, Volume 72, Issue 1", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.1007/BF00939948", "content": "Jan 3, 1992 · article On the convergence of the coordinate descent method for convex differentiable minimization Authors: Z. Q. Luo , P. Tseng Authors Info & Claims Journal of Optimization Theory and Applications, Volume 72, Issue 1"}
+{"idx": 4, "title": "Onthe Convergence of the Coordinate Descent Method for Convex ...", "date": "", "ddg_snippet": "Abstract. The coordinate descent method enjoys along history incon-vex differentiable min mization. Surprisingly, very little isknown about the convergence of the iterates generated by his method. Convergence typically requires restrictive assumptions such asthat thecost function has bounded l vel sets and is in some sense strictly convex . In a recent work, Luo and Tseng showed that the ...", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/content/pdf/10.1007/bf00939948.pdf", "content": "Abstract. The coordinate descent method enjoys along history incon-vex differentiable min mization. Surprisingly, very little isknown about the convergence of the iterates generated by his method. Convergence typically requires restrictive assumptions such asthat thecost function has bounded l vel sets and is in some sense strictly convex . In a recent work, Luo and Tseng showed that the ..."}
+{"idx": 5, "title": "Sci-Hub | On the convergence of the coordinate descent method ...", "date": "", "ddg_snippet": "Sci-Hub | On the convergence of the coordinate descent method for convex differentiable minimization . Journal of Optimization Theory and Applications, 72 (1), 7–35 | 10.1007/BF00939948", "subpage_snippet": "", "source": "sci-hub.se", "link": "https://sci-hub.se/10.1007/BF00939948", "content": "Sci-Hub | On the convergence of the coordinate descent method for convex differentiable minimization . Journal of Optimization Theory and Applications, 72 (1), 7–35 | 10.1007/BF00939948"}
+{"idx": 6, "title": "On the convergence of the coordinate descent method for ...", "date": "", "ddg_snippet": "On the convergence of the coordinate descent method for convex differentiable minimization release_3nayugjzv5bvnkssglxvkjn4e4", "subpage_snippet": "", "source": "scholar.archive.org", "link": "https://scholar.archive.org/fatcat/release/3nayugjzv5bvnkssglxvkjn4e4", "content": "On the convergence of the coordinate descent method for convex differentiable minimization release_3nayugjzv5bvnkssglxvkjn4e4"}
+{"idx": 7, "title": "On the convergence of the coordinate descent method for ...", "date": "", "ddg_snippet": "by ZQ Luo · 1992 · Cited by 739 — Luo, Z.Q., Tseng, P. On the convergence of the coordinate descent method for convex differentiable minimization . J Optim Theory Appl 72, 7–35 (1992). https ...", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/article/10.1007/BF00939948", "content": "by ZQ Luo · 1992 · Cited by 739 — Luo, Z.Q., Tseng, P. On the convergence of the coordinate descent method for convex differentiable minimization . J Optim Theory Appl 72, 7–35 (1992). https ..."}
+{"idx": 8, "title": "On the convergence of the coordinate descent method for ...", "date": "", "ddg_snippet": "by ZQ Luo · 1992 · Cited by 738 — The coordinate descent method enjoys a long history in convex differentiable minimization . Surprisingly, very little is known about the convergence of the ...", "subpage_snippet": "", "source": "experts.umn.edu", "link": "https://experts.umn.edu/en/publications/on-the-convergence-of-the-coordinate-descent-method-for-convex-di", "content": "by ZQ Luo · 1992 · Cited by 738 — The coordinate descent method enjoys a long history in convex differentiable minimization . Surprisingly, very little is known about the convergence of the ..."}
+{"idx": 9, "title": "On the convergence of the coordinate descent method for ...", "date": "", "ddg_snippet": "3 Jan 1992 — The coordinate descent method enjoys a long history in convex differentiable minimization . Surprisingly, very little is known about the ...", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/On-the-convergence-of-the-coordinate-descent-method-Luo-Tseng/f68cacb50482fd50991b0fbbf3e9b67a3607907e", "content": "3 Jan 1992 — The coordinate descent method enjoys a long history in convex differentiable minimization . Surprisingly, very little is known about the ..."}
diff --git a/data/sampled_jsons/MADE_Masked_Autoencoder_for_Distribution_Estimation_uses_MLP_neural_network.jsonl b/data/sampled_jsons/MADE_Masked_Autoencoder_for_Distribution_Estimation_uses_MLP_neural_network.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..6476afd46f8bccece1ac9a545a020861900ddc3c
--- /dev/null
+++ b/data/sampled_jsons/MADE_Masked_Autoencoder_for_Distribution_Estimation_uses_MLP_neural_network.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "MADE : Masked Autoencoder for Distribution Estimation", "date": "", "ddg_snippet": "We introduce a simple modification for autoencoder neural networks that yields powerful generative models. Our method masks the autoencoder 's parameters to respect autoregressive constraints: each input is reconstructed only from previous inputs in a given ordering.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/1502.03509", "content": "We introduce a simple modification for autoencoder neural networks that yields powerful generative models. Our method masks the autoencoder 's parameters to respect autoregressive constraints: each input is reconstructed only from previous inputs in a given ordering."}
+{"idx": 1, "title": "MADE : Masked Autoencoder for Distribution Estimation | DeepAI", "date": "", "ddg_snippet": "We introduce a simple modification for autoencoder neural networks that yields powerful generative models. Our method masks the autoencoder 's parameters to respect autoregressive constraints: each input is reconstructed only from previous inputs in a given ordering.", "subpage_snippet": "", "source": "deepai.org", "link": "https://deepai.org/publication/made-masked-autoencoder-for-distribution-estimation", "content": "We introduce a simple modification for autoencoder neural networks that yields powerful generative models. Our method masks the autoencoder 's parameters to respect autoregressive constraints: each input is reconstructed only from previous inputs in a given ordering."}
+{"idx": 2, "title": "MADE : Masked Autoencoder for Distribution Estimation", "date": "", "ddg_snippet": "The resulting Masked Autoencoder Distribution Estimator ( MADE ) preserves the efciency of a single pass through a regular autoencoder . Implementation on a GPU is straightforward, making the method scalable.", "subpage_snippet": "", "source": "proceedings.mlr.press", "link": "https://proceedings.mlr.press/v37/germain15.pdf", "content": "The resulting Masked Autoencoder Distribution Estimator ( MADE ) preserves the efciency of a single pass through a regular autoencoder . Implementation on a GPU is straightforward, making the method scalable."}
+{"idx": 3, "title": "(PDF) MADE : Masked Autoencoder for Distribution Estimation", "date": "", "ddg_snippet": "by the network . MADE : Masked Autoencoder for Distribution Estimation .We present Neural Autoregressive Distribution Estimation (NADE) models, which are neu- ral network architectures applied to the problem of unsupervised distribution and density esitmation.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/272194203_MADE_Masked_Autoencoder_for_Distribution_Estimation", "content": "by the network . MADE : Masked Autoencoder for Distribution Estimation .We present Neural Autoregressive Distribution Estimation (NADE) models, which are neu- ral network architectures applied to the problem of unsupervised distribution and density esitmation."}
+{"idx": 4, "title": "MADE — Masked Autoencoder for Distribution Estimation | Medium", "date": "", "ddg_snippet": "An in-depth explanation of Masked Autoencoder for distribution estimation ( MADE ).The modern formulation of Neural Networks enables vectorization and usage of accelerators and that helps in achieving the critical speed requirements during test time.", "subpage_snippet": "", "source": "medium.com", "link": "https://medium.com/data-science/made-masked-autoencoder-for-distribution-estimation-fc95aaca8467", "content": "An in-depth explanation of Masked Autoencoder for distribution estimation ( MADE ).The modern formulation of Neural Networks enables vectorization and usage of accelerators and that helps in achieving the critical speed requirements during test time."}
+{"idx": 5, "title": "MADE : Masked Autoencoder for Distribution Estimation : Mathieu...", "date": "", "ddg_snippet": "We introduce a simple modification for autoencoder neural networks that yields powerful generative models. Our method masks the autoencoder 's parameters to respect autoregressive constraints: each input is reconstructed only from previous inputs in a given ordering.", "subpage_snippet": "", "source": "archive.org", "link": "https://archive.org/details/arxiv-1502.03509", "content": "We introduce a simple modification for autoencoder neural networks that yields powerful generative models. Our method masks the autoencoder 's parameters to respect autoregressive constraints: each input is reconstructed only from previous inputs in a given ordering."}
+{"idx": 6, "title": "MADE : Masked Autoencoder for Distribution Estimation ...", "date": "", "ddg_snippet": "Distribution Estimation as Autoregression. Now we want to impose some property on autoencoders , such that its output can be used to obtain valid probabilities. MADE is a straightforward yet efficient approach to estimate probability distribution from a single pass through an autoencoder .", "subpage_snippet": "", "source": "cevgroup.org", "link": "https://cevgroup.org/made-masked-autoencoder-for-distribution-estimation/", "content": "Distribution Estimation as Autoregression. Now we want to impose some property on autoencoders , such that its output can be used to obtain valid probabilities. MADE is a straightforward yet efficient approach to estimate probability distribution from a single pass through an autoencoder ."}
+{"idx": 7, "title": "Deep Dive into MADE ( Masked Autoencoder for Distribution ...)", "date": "", "ddg_snippet": "Autoencoders are a neural network which is used to learn the data representation in an efficient and unsupervised manner. e.g. if we give a set of cat images to an autoencoder , it should then be able to generate new cat images i.e. synthetic images which look like cats.", "subpage_snippet": "", "source": "nsanghi.com", "link": "https://nsanghi.com/blog/made/", "content": "Autoencoders are a neural network which is used to learn the data representation in an efficient and unsupervised manner. e.g. if we give a set of cat images to an autoencoder , it should then be able to generate new cat images i.e. synthetic images which look like cats."}
+{"idx": 8, "title": "[PDF] MADE : Masked Autoencoder for Distribution Estimation", "date": "", "ddg_snippet": "We introduce a simple modification for autoencoder neural networks that yields powerful generative models. Our method masks the autoencoder 's parameters to respect autoregressive constraints: each input is reconstructed only from previous inputs in a given ordering.", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/MADE:-Masked-Autoencoder-for-Distribution-Germain-Gregor/90f72fbbe5f0a29e627db28999e01a30a9655bc6", "content": "We introduce a simple modification for autoencoder neural networks that yields powerful generative models. Our method masks the autoencoder 's parameters to respect autoregressive constraints: each input is reconstructed only from previous inputs in a given ordering."}
+{"idx": 9, "title": "MADE : Masked Autoencoder for Distribution Estimation", "date": "", "ddg_snippet": "- ANLGBOY/ MADE - Masked - Autoencoder - for - Distribution - Estimation - using -PyTorch.There has been a lot of recent interest in designing neural network models to estimate a distribution from a set of examples.", "subpage_snippet": "", "source": "paperswithcode.com", "link": "https://paperswithcode.com/paper/made-masked-autoencoder-for-distribution", "content": "- ANLGBOY/ MADE - Masked - Autoencoder - for - Distribution - Estimation - using -PyTorch.There has been a lot of recent interest in designing neural network models to estimate a distribution from a set of examples."}
diff --git a/data/sampled_jsons/MHaSq1LlTe_Signed_Laplacians_for_Constrained_Graph_Clustering_Algorithm_1.jsonl b/data/sampled_jsons/MHaSq1LlTe_Signed_Laplacians_for_Constrained_Graph_Clustering_Algorithm_1.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..fd214e538c702c02d3cb499e959b8209be2e69ea
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@@ -0,0 +1,10 @@
+{"idx": 0, "title": "ICML Poster Signed Laplacians for Constrained Graph Clustering", "date": "", "ddg_snippet": "relates the solution of the constrained clustering problem to the spectral properties of $ G$ and $H$. To reduce computational complexity, we utilise the signed Laplacian of $H$, streamlining calculations while maintaining accuracy.", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/poster/45552", "content": "relates the solution of the constrained clustering problem to the spectral properties of $ G$ and $H$. To reduce computational complexity, we utilise the signed Laplacian of $H$, streamlining calculations while maintaining accuracy."}
+{"idx": 1, "title": "The Constrained Laplacian Rank Algorithm for Graph -Based...", "date": "", "ddg_snippet": "In particular, our Constrained Laplacian Rank (CLR) method learns a graph with exactly k connected components (where k is the number of clusters ). We develop two versions of this method, based upon the L 1 -norm and the L2-norm, which yield two new graph -based clustering objectives.", "subpage_snippet": "", "source": "colab.ws", "link": "https://colab.ws/articles/10.1609/aaai.v30i1.10302", "content": "In particular, our Constrained Laplacian Rank (CLR) method learns a graph with exactly k connected components (where k is the number of clusters ). We develop two versions of this method, based upon the L 1 -norm and the L2-norm, which yield two new graph -based clustering objectives."}
+{"idx": 2, "title": "The Constrained Laplacian Rank Algorithm for Graph -Based...", "date": "", "ddg_snippet": "In partic-ular, our Constrained Laplacian Rank (CLR) method learns a graph with exactly k connected components (where k is the number of clusters ). We develop two versions of this method, based upon the L 1 -norm and the L2-norm, which yield two new graph -based clus - tering objectives.", "subpage_snippet": "", "source": "people.eecs.berkeley.edu", "link": "https://people.eecs.berkeley.edu/~jordan/papers/CLR_aaai16_ready.pdf", "content": "In partic-ular, our Constrained Laplacian Rank (CLR) method learns a graph with exactly k connected components (where k is the number of clusters ). We develop two versions of this method, based upon the L 1 -norm and the L2-norm, which yield two new graph -based clus - tering objectives."}
+{"idx": 3, "title": "Large-Scale Clustering With Anchor-Based Constrained Laplacian ...", "date": "", "ddg_snippet": "Laplace Equations, Bipartite Graph , Clustering Methods, Artificial Intelligence, Optimization, Vectors, Clustering Algorithms , Time Complexity, Optics, Electronic Mail, Constrained Laplacian Rank, Large Scale Clustering , Anchor, Bipartite Graph , Graph Connected Structure, Two Step...", "subpage_snippet": "", "source": "www.computer.org", "link": "https://www.computer.org/csdl/journal/tk/2025/07/10955201/25ICs1iU6Qg", "content": "Laplace Equations, Bipartite Graph , Clustering Methods, Artificial Intelligence, Optimization, Vectors, Clustering Algorithms , Time Complexity, Optics, Electronic Mail, Constrained Laplacian Rank, Large Scale Clustering , Anchor, Bipartite Graph , Graph Connected Structure, Two Step..."}
+{"idx": 4, "title": "iMGC: Interactive Multiple Graph Clustering With Constrained ...", "date": "", "ddg_snippet": "Then, a constrained Laplacian rank is applied on the unified graph matrix to generate the unified clustering result directly, which is able to preserve association features across multiple graphs .", "subpage_snippet": "", "source": "scispace.com", "link": "https://scispace.com/papers/imgc-interactive-multiple-graph-clustering-with-constrained-2xun6x2m", "content": "Then, a constrained Laplacian rank is applied on the unified graph matrix to generate the unified clustering result directly, which is able to preserve association features across multiple graphs ."}
+{"idx": 5, "title": "The Constrained Laplacian Rank Algorithm for Graph -Based...", "date": "", "ddg_snippet": "In particular, our Constrained Laplacian Rank (CLR) method learns a graph with exactly k connected components (where k is the number of clusters ). We develop two versions of this method, based upon the L 1 -norm and the L2-norm, which yield two new graph -based clustering objectives.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/361564229_The_Constrained_Laplacian_Rank_Algorithm_for_Graph-Based_Clustering", "content": "In particular, our Constrained Laplacian Rank (CLR) method learns a graph with exactly k connected components (where k is the number of clusters ). We develop two versions of this method, based upon the L 1 -norm and the L2-norm, which yield two new graph -based clustering objectives."}
+{"idx": 6, "title": "Parametric and nonparametric symmetries in graphical", "date": "", "ddg_snippet": "5. 1 . Signed Laplacian - constrained Gaussian graphical models under RCON constraints .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2306.00703", "content": "5. 1 . Signed Laplacian - constrained Gaussian graphical models under RCON constraints ."}
+{"idx": 7, "title": "Multi-view spectral clustering based on constrained Laplacian rank", "date": "", "ddg_snippet": "The graph -based approach is a representative clustering method among multi-view clustering algorithms .(2) To achieve the overall optimization we construct a graph learning method based on constrained Laplacian rank and combine it with spectral clustering .", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/article/10.1007/s00138-023-01497-w", "content": "The graph -based approach is a representative clustering method among multi-view clustering algorithms .(2) To achieve the overall optimization we construct a graph learning method based on constrained Laplacian rank and combine it with spectral clustering ."}
+{"idx": 8, "title": "MSGNN: A Spectral Graph Neural Network Based on a Novel...", "date": "", "ddg_snippet": "2. We propose an efficient spectral graph neural network architecture, MSGNN, based on this magnetic signed Laplacian , which attains leading performance on extensive node clustering and link prediction tasks, including novel tasks that consider edge sign and directionality jointly.", "subpage_snippet": "", "source": "proceedings.mlr.press", "link": "https://proceedings.mlr.press/v198/he22c/he22c.pdf", "content": "2. We propose an efficient spectral graph neural network architecture, MSGNN, based on this magnetic signed Laplacian , which attains leading performance on extensive node clustering and link prediction tasks, including novel tasks that consider edge sign and directionality jointly."}
+{"idx": 9, "title": "Learning an Optimal Bipartite Graph for Subspace Clustering via...", "date": "", "ddg_snippet": "Different from the traditional graph -based methods, co- clustering can utilize the bipartite graph to extract the duality relationship between samples and features. It means that the bipartite graph can obtain more information than other traditional graph methods.", "subpage_snippet": "", "source": "ieeexplore.ieee.org", "link": "https://ieeexplore.ieee.org/document/9568770", "content": "Different from the traditional graph -based methods, co- clustering can utilize the bipartite graph to extract the duality relationship between samples and features. It means that the bipartite graph can obtain more information than other traditional graph methods."}
diff --git a/data/sampled_jsons/MLD_HumanAct12_FID_Chen_et_al._2023_table_value_year_2023.jsonl b/data/sampled_jsons/MLD_HumanAct12_FID_Chen_et_al._2023_table_value_year_2023.jsonl
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index 0000000000000000000000000000000000000000..25aa34155d28d20e873b042ea0b1bb9133e7a9e3
--- /dev/null
+++ b/data/sampled_jsons/MLD_HumanAct12_FID_Chen_et_al._2023_table_value_year_2023.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "MOTION FLOW MATCHING FOR EFFICIENT HUMAN ...", "date": "", "ddg_snippet": "MLD (Chen et al., 2023) advances the latent diffusion model (Rombach et al., 2022) to generate motions based on different conditional inputs. 21 pages", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf/61a1228b68fe5cc477b223010e69dce5ad7aff34.pdf", "content": "MLD (Chen et al., 2023) advances the latent diffusion model (Rombach et al., 2022) to generate motions based on different conditional inputs. 21 pages"}
+{"idx": 1, "title": "Fitness Aware Human Motion Generation with Fine-Tuning", "date": "", "ddg_snippet": "MDM Tevet et al . ( 2023 ) processes motion diffusion by working with raw motion data to learn how it relates to the specified input conditions. MLD Chen et al .", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf/3394f71641f1c4a91eb613a43bed5c1a6737c7c3.pdf", "content": "MDM Tevet et al . ( 2023 ) processes motion diffusion by working with raw motion data to learn how it relates to the specified input conditions. MLD Chen et al ."}
+{"idx": 2, "title": "arXiv:2401.11115v3 [cs.CV] 24 Jan 2024", "date": "", "ddg_snippet": "24 Jan 2024 — 2022), MLD ( Chen et al . 2022), have demonstrated the ability of diffusion-based models to generate plausible human motion, guided by tex- tual ...", "subpage_snippet": "", "source": "scispace.com", "link": "https://scispace.com/pdf/motionmix-weakly-supervised-diffusion-for-controllable-1a4e690k44.pdf", "content": "24 Jan 2024 — 2022), MLD ( Chen et al . 2022), have demonstrated the ability of diffusion-based models to generate plausible human motion, guided by tex- tual ..."}
+{"idx": 3, "title": "Multi-Fusion Retrieval Augmented Generation for Human Motion", "date": "", "ddg_snippet": "Table 1. Comparision of text-to-motion retrieval approaches - Text. Robustness (ability to handle diverse language inputs), Generaliz- ability (adaptation ...", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/WACV2025/papers/Kalakonda_MoRAG_-_Multi-Fusion_Retrieval_Augmented_Generation_for_Human_Motion_WACV_2025_paper.pdf", "content": "Table 1. Comparision of text-to-motion retrieval approaches - Text. Robustness (ability to handle diverse language inputs), Generaliz- ability (adaptation ..."}
+{"idx": 4, "title": "UniMotion-DM: Uniform Text-Motion Generation and ...", "date": "", "ddg_snippet": "by S Lin · 2024 — Precision and FID metrics, as demonstrated in Table II and. Table III. Specifically, the increase in R-Precision by 59.69% over MDM indicates superior ...", "subpage_snippet": "", "source": "ieeexplore.ieee.org", "link": "https://ieeexplore.ieee.org/iel8/6287639/6514899/10802885.pdf", "content": "by S Lin · 2024 — Precision and FID metrics, as demonstrated in Table II and. Table III. Specifically, the increase in R-Precision by 59.69% over MDM indicates superior ..."}
+{"idx": 5, "title": "Robust Diffusion‐based Motion In‐betweening - Qin - 2024", "date": "", "ddg_snippet": "7 Nov 2024 — Table 3 illustrates the experimental results, showcasing the better performance of our method across nearly all metrics. In terms of K- FID , both ...", "subpage_snippet": "", "source": "onlinelibrary.wiley.com", "link": "https://onlinelibrary.wiley.com/doi/10.1111/cgf.15260?af=R", "content": "7 Nov 2024 — Table 3 illustrates the experimental results, showcasing the better performance of our method across nearly all metrics. In terms of K- FID , both ..."}
+{"idx": 6, "title": "Motion Flow Matching for Human Motion Synthesis and ...", "date": "", "ddg_snippet": "In this paper, we propose Motion Flow Matching, a novel generative model designed for human motion generation featuring efficient sampling and effectiveness in ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2312.08895v1", "content": "In this paper, we propose Motion Flow Matching, a novel generative model designed for human motion generation featuring efficient sampling and effectiveness in ..."}
+{"idx": 7, "title": "Move-in-2D: 2D-Conditioned Human Motion Generation", "date": "", "ddg_snippet": "17 Dec 2024 — We propose Move-in-2D, a novel approach to generate human motion sequences conditioned on a scene image, allowing for diverse motion that adapts to different ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2412.13185v1", "content": "17 Dec 2024 — We propose Move-in-2D, a novel approach to generate human motion sequences conditioned on a scene image, allowing for diverse motion that adapts to different ..."}
+{"idx": 8, "title": "MoMask: Generative Masked Modeling of 3D Human Motions", "date": "", "ddg_snippet": "by C Guo · 2024 · Cited by 213 — We introduce MoMask, a novel masked modeling frame- work for text-driven 3D human motion generation. In Mo-. Mask, a hierarchical quantization scheme is ...", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2024/papers/Guo_MoMask_Generative_Masked_Modeling_of_3D_Human_Motions_CVPR_2024_paper.pdf", "content": "by C Guo · 2024 · Cited by 213 — We introduce MoMask, a novel masked modeling frame- work for text-driven 3D human motion generation. In Mo-. Mask, a hierarchical quantization scheme is ..."}
+{"idx": 9, "title": "CoMo: Controllable Motion Generation through Language ...", "date": "", "ddg_snippet": "Abstract. Text-to-motion models excel at efficient human motion gen- eration, but existing approaches lack fine-grained controllability over the.", "subpage_snippet": "", "source": "yueyang1996.github.io", "link": "https://yueyang1996.github.io/papers/como.pdf", "content": "Abstract. Text-to-motion models excel at efficient human motion gen- eration, but existing approaches lack fine-grained controllability over the."}
diff --git a/data/sampled_jsons/MLD_motion_diffusion_HumanAct12_FID_score_table_results.jsonl b/data/sampled_jsons/MLD_motion_diffusion_HumanAct12_FID_score_table_results.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..2bf07c65e6d2fe499674e14e1d04f20deb0d06a8
--- /dev/null
+++ b/data/sampled_jsons/MLD_motion_diffusion_HumanAct12_FID_score_table_results.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "GitHub - ChenFengYe/motion-latent-diffusion: [CVPR 2023 ...", "date": "", "ddg_snippet": "Jun 20, 2023 · Motion Latent Diffusion ( MLD ) is a text-to- motion and action-to- motion diffusion model. Our work achieves state-of-the-art motion quality and two orders of magnitude faster than previous diffusion models on raw motion data.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/ChenFengYe/motion-latent-diffusion", "content": "Jun 20, 2023 · Motion Latent Diffusion ( MLD ) is a text-to- motion and action-to- motion diffusion model. Our work achieves state-of-the-art motion quality and two orders of magnitude faster than previous diffusion models on raw motion data."}
+{"idx": 1, "title": "Attention-guided multi-scale temporal interaction diffusion ...", "date": "", "ddg_snippet": "As shown in Table 1, Table 2, we compare our approach with state-of-the-art methods on two action-to- motion datasets: HumanAct12 [9] and UESTC [33]. On the HumanAct12 [9] test set, our AMTIDM achieves the highest accuracy, with its FID , Diversity, and MultiModality metrics being surpassed only by MotionDiffuse.", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S0925231225018776", "content": "As shown in Table 1, Table 2, we compare our approach with state-of-the-art methods on two action-to- motion datasets: HumanAct12 [9] and UESTC [33]. On the HumanAct12 [9] test set, our AMTIDM achieves the highest accuracy, with its FID , Diversity, and MultiModality metrics being surpassed only by MotionDiffuse."}
+{"idx": 2, "title": "GitHub - GuyTevet/motion-diffusion-model: The official ... Motion Flow Matching for Human Motion Synthesis and Editing Action Conditioned Attention Encoder-Decoder and ... Executing your Commands via Motion Diffusion in Latent Space GitHub - ChenFengYe/motion-latent-diffusion: [CVPR 2023 ... Executing your Commands via Motion Diffusion in Latent Space Executing your Commands via Motion Diffusion in Latent Space Executing your Commands via Motion Diffusion in Latent Space Action Conditioned Attention Encoder-Decoder and Discriminator for Executing your Commands via Motion Diffusion in Latent Space ChenFengYe/ motion -latent- diffusion - GitHub MOTION FLOW MATCHING FOR EFFICIENT HUMAN MOTION SYNTHESIS AND ...", "date": "", "ddg_snippet": "The official PyTorch implementation of the paper \"Human Motion Diffusion Model\". Please visit our webpage for more details. Bibtex If you find this code useful in your research, please cite: See full list on github.com 🐉 SinMDM - Learns single motion motifs - even for non-humanoid characters. 👯 PriorMDM - Uses MDM as a generative prior, enabling new generation tasks with few examples or even no data at all. See full list on github.com 📢 25/Jan/24 - Fixed bug in evalutation code (#182) - Please use the fixed results when citing MDM. 📢 1/Jun/23 - Fixed generation issue (#104) - Please pull to improve generation results . 📢 23/Nov/22 - Fixed evaluation issue (#42) - Please pull and run bash prepare/download_t2m_evaluators.sh from the top of the repo to adapt. 📢 4/Nov/22 - Added sampling, training and evaluation of unconstrained tasks. Note slight env changes adapting to the new code. If you already have an installed environment, run bash prepare/download_unconstrained_assets.sh; conda install -y -c anaconda scikit-learn to adapt. 📢 3/Nov/22 - Added in-between and upper-body editing. 📢 31/Oct/22 - Added sampling, training and evaluation of action-to- motion tasks. See full list on github.com 1. Setup environment Install ffmpeg (if not already installed):For windows use this instead.Setup conda env:Download dependencies: Text to Motion There are two paths to get the data:(a) Go the easy way if you just want to generate text-to- motion (excluding editing which does require motion capture data)(b) Get full data to train and evaluate the model. Action to Motion UESTC, HumanAct12 See full list on github.com See full list on github.com Unconditioned editing You may also define:•--num_samples (default is 10) / --num_repetitions (default is 3).•--device id.•--seed to sample different prompts.•--edit_mode upper_body For upper body editing (lower body is fixed).The output will look like this (blue frames are from the input motion ; orange were generated by the model):•As in Motion Synthesis, you may follow the Render SMPL mesh section to obtain meshes for your edited motions. Text conditioned editing Just add the text conditioning using --text_condition. For example:The output will look like this (blue joints are from the input motion ; orange were generated by the model): See full list on github.com Text to Motion HumanML3DKIT Unconstrained •Use --device to define GPU id.•Use --arch to choose one of the architectures reported in the paper {trans_enc, trans_dec, gru} (trans_enc is default).•Add --train_platform_type {ClearmlPlatform, TensorboardPlatform} to track results with either ClearML or Tensorboard.•Add --eval_during_training to run a short (90 minutes) evaluation for each saved checkpoint. This will slow down training but will give you better monitoring. See full list on github.com Text to Motion •Takes about 20 hours (on a single GPU)•The output of this script for the pre-trained models (as was reported in the paper) is provided in the checkpoints zip file.HumanML3DKIT Action to Motion •Takes about 7 hours for UESTC and 2 hours for HumanAct12 (on a single GPU)•The output of this script for the pre-trained models (as was reported in the paper) is provided in the checkpoints zip file.where path-to-model-ckpt can be a path to any of the pretrained action-to- motion models listed above, or to a checkpoint trained by the user. Unconstrained •Takes about 3 hours (on a single GPU)Precision and recall are not computed to save computing time. If you wish to compute them, edit the file eval/a2m/gru_eval.py and change the string fast=True to fast=False. See full list on github.com This code is standing on the shoulders of giants. We want to thank the following contributors that our code is based on: guided- diffusion , MotionCLIP, text-to- motion , actor, joints2smpl, MoDi. See full list on github.com This code is distributed under an MIT LICENSE. Note that our code depends on other libraries, including CLIP, SMPL, SMPL-X, PyTorch3D, and uses datasets that each have their own respective licenses that must also be followed. See full list on github.com We present our results on the action-to- motion dataset HumanAct12 in Table 4. Our method achieves results that are comparable to various baselines, all while significantly reducing the number of forward evaluations and utilizing fewer parameters. Aug 21, 2024 · The FID measures on the HumanAct12 dataset compared to the state-of-the-art are mentioned in Table 3, and Table 4 represents FID scores on the NTU 120 RGB+D dataset compared to the state-of-the-art. MLD achieves state-of-the-art ac-curacy and diversity on UESTC and competitive results on HumanAct12 , indicating that diffusion models in motion la-tent can also benefit action-conditioned generation task. Jun 20, 2023 · Motion Latent Diffusion ( MLD ) is a text-to- motion and action-to- motion diffusion model. Our work achieves state-of-the-art motion quality and two orders of magnitude faster than previous diffusion models on raw motion data. Does MLD improve human motion generation? Extensive experiments on various human motion generation tasks demonstrate that our MLD achieves significant improvements over the state-of-the-art methods among extensive human motion genera-tion tasks, with two orders of magnitude faster than previous diffusion models on raw motion sequences. 1. Introduction What is MLD in diffusion learning? MLD comprises a motion VAE model V and latent diffu-sion models , and both influence its effect. We first focus on V to evaluate its components with generation and recon-struction metrics. Based on these V, we evaluate MLDs in diffusion learning aiming at text-to- motion and uncondi-tional synthesis, and then report time costs on inference. Can diffusion models in motion latent benefit action-conditioned generation task? MLD achieves state-of-the-art ac-curacy and diversity on UESTC and competitive results on HumanAct12, indicating that diffusion models in motion la-tent can also benefit action-conditioned generation task . 4.5. Comparisons on Unconditional Generation What is human motion diffusion model (MDM)? A human motion diffusion model (MDM) introduced in , aims to generate natural and expressive human motion using a transformer-based architecture with text-to-motion and action-to-motion inputs. The main drawback of this work is the long inference time which is not well applicable in real-time applications. How many action-to-motion sequences does humanact12 provide? Thanks to , after the pro-cessing, HumanAct12 provides 1,191 raw motion se-quences and 12 action categories, and UESTC pro-vides 24K sequences and 40 action categories. We rely on these two datasets for action-to-motion evaluation. Evaluation Metrics summarize in four parts. Can I use MLD framework to train diffusion on raw motion like MDM? [2023/02/28] MLD got accepted by CVPR 2023! [2023/01/09] release no VAE config and pre-train model, you can use MLD framework to train diffusion on raw motion like MDM . We test our code on Python 3.9.12 and PyTorch 1.12.1. We present our results on the action-to- motion dataset HumanAct12 in Table 4. Our method achieves results that are comparable to various baselines, all while significantly reducing the number of forward evaluations and utilizing fewer parameters.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/GuyTevet/motion-diffusion-model", "content": "The official PyTorch implementation of the paper \"Human Motion Diffusion Model\". Please visit our webpage for more details. Bibtex If you find this code useful in your research, please cite: See full list on github.com 🐉 SinMDM - Learns single motion motifs - even for non-humanoid characters. 👯 PriorMDM - Uses MDM as a generative prior, enabling new generation tasks with few examples or even no data at all. See full list on github.com 📢 25/Jan/24 - Fixed bug in evalutation code (#182) - Please use the fixed results when citing MDM. 📢 1/Jun/23 - Fixed generation issue (#104) - Please pull to improve generation results . 📢 23/Nov/22 - Fixed evaluation issue (#42) - Please pull and run bash prepare/download_t2m_evaluators.sh from the top of the repo to adapt. 📢 4/Nov/22 - Added sampling, training and evaluation of unconstrained tasks. Note slight env changes adapting to the new code. If you already have an installed environment, run bash prepare/download_unconstrained_assets.sh; conda install -y -c anaconda scikit-learn to adapt. 📢 3/Nov/22 - Added in-between and upper-body editing. 📢 31/Oct/22 - Added sampling, training and evaluation of action-to- motion tasks. See full list on github.com 1. Setup environment Install ffmpeg (if not already installed):For windows use this instead.Setup conda env:Download dependencies: Text to Motion There are two paths to get the data:(a) Go the easy way if you just want to generate text-to- motion (excluding editing which does require motion capture data)(b) Get full data to train and evaluate the model. Action to Motion UESTC, HumanAct12 See full list on github.com See full list on github.com Unconditioned editing You may also define:•--num_samples (default is 10) / --num_repetitions (default is 3).•--device id.•--seed to sample different prompts.•--edit_mode upper_body For upper body editing (lower body is fixed).The output will look like this (blue frames are from the input motion ; orange were generated by the model):•As in Motion Synthesis, you may follow the Render SMPL mesh section to obtain meshes for your edited motions. Text conditioned editing Just add the text conditioning using --text_condition. For example:The output will look like this (blue joints are from the input motion ; orange were generated by the model): See full list on github.com Text to Motion HumanML3DKIT Unconstrained •Use --device to define GPU id.•Use --arch to choose one of the architectures reported in the paper {trans_enc, trans_dec, gru} (trans_enc is default).•Add --train_platform_type {ClearmlPlatform, TensorboardPlatform} to track results with either ClearML or Tensorboard.•Add --eval_during_training to run a short (90 minutes) evaluation for each saved checkpoint. This will slow down training but will give you better monitoring. See full list on github.com Text to Motion •Takes about 20 hours (on a single GPU)•The output of this script for the pre-trained models (as was reported in the paper) is provided in the checkpoints zip file.HumanML3DKIT Action to Motion •Takes about 7 hours for UESTC and 2 hours for HumanAct12 (on a single GPU)•The output of this script for the pre-trained models (as was reported in the paper) is provided in the checkpoints zip file.where path-to-model-ckpt can be a path to any of the pretrained action-to- motion models listed above, or to a checkpoint trained by the user. Unconstrained •Takes about 3 hours (on a single GPU)Precision and recall are not computed to save computing time. If you wish to compute them, edit the file eval/a2m/gru_eval.py and change the string fast=True to fast=False. See full list on github.com This code is standing on the shoulders of giants. We want to thank the following contributors that our code is based on: guided- diffusion , MotionCLIP, text-to- motion , actor, joints2smpl, MoDi. See full list on github.com This code is distributed under an MIT LICENSE. Note that our code depends on other libraries, including CLIP, SMPL, SMPL-X, PyTorch3D, and uses datasets that each have their own respective licenses that must also be followed. See full list on github.com We present our results on the action-to- motion dataset HumanAct12 in Table 4. Our method achieves results that are comparable to various baselines, all while significantly reducing the number of forward evaluations and utilizing fewer parameters. Aug 21, 2024 · The FID measures on the HumanAct12 dataset compared to the state-of-the-art are mentioned in Table 3, and Table 4 represents FID scores on the NTU 120 RGB+D dataset compared to the state-of-the-art. MLD achieves state-of-the-art ac-curacy and diversity on UESTC and competitive results on HumanAct12 , indicating that diffusion models in motion la-tent can also benefit action-conditioned generation task. Jun 20, 2023 · Motion Latent Diffusion ( MLD ) is a text-to- motion and action-to- motion diffusion model. Our work achieves state-of-the-art motion quality and two orders of magnitude faster than previous diffusion models on raw motion data. Does MLD improve human motion generation? Extensive experiments on various human motion generation tasks demonstrate that our MLD achieves significant improvements over the state-of-the-art methods among extensive human motion genera-tion tasks, with two orders of magnitude faster than previous diffusion models on raw motion sequences. 1. Introduction What is MLD in diffusion learning? MLD comprises a motion VAE model V and latent diffu-sion models , and both influence its effect. We first focus on V to evaluate its components with generation and recon-struction metrics. Based on these V, we evaluate MLDs in diffusion learning aiming at text-to- motion and uncondi-tional synthesis, and then report time costs on inference. Can diffusion models in motion latent benefit action-conditioned generation task? MLD achieves state-of-the-art ac-curacy and diversity on UESTC and competitive results on HumanAct12, indicating that diffusion models in motion la-tent can also benefit action-conditioned generation task . 4.5. Comparisons on Unconditional Generation What is human motion diffusion model (MDM)? A human motion diffusion model (MDM) introduced in , aims to generate natural and expressive human motion using a transformer-based architecture with text-to-motion and action-to-motion inputs. The main drawback of this work is the long inference time which is not well applicable in real-time applications. How many action-to-motion sequences does humanact12 provide? Thanks to , after the pro-cessing, HumanAct12 provides 1,191 raw motion se-quences and 12 action categories, and UESTC pro-vides 24K sequences and 40 action categories. We rely on these two datasets for action-to-motion evaluation. Evaluation Metrics summarize in four parts. Can I use MLD framework to train diffusion on raw motion like MDM? [2023/02/28] MLD got accepted by CVPR 2023! [2023/01/09] release no VAE config and pre-train model, you can use MLD framework to train diffusion on raw motion like MDM . We test our code on Python 3.9.12 and PyTorch 1.12.1. We present our results on the action-to- motion dataset HumanAct12 in Table 4. Our method achieves results that are comparable to various baselines, all while significantly reducing the number of forward evaluations and utilizing fewer parameters."}
+{"idx": 3, "title": "Motion Flow Matching for Human Motion Synthesis and Editing", "date": "", "ddg_snippet": "We present our results on the action-to- motion dataset HumanAct12 in Table 4. Our method achieves results that are comparable to various baselines, all while significantly reducing the number of forward evaluations and utilizing fewer parameters.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2312.08895", "content": "We present our results on the action-to- motion dataset HumanAct12 in Table 4. Our method achieves results that are comparable to various baselines, all while significantly reducing the number of forward evaluations and utilizing fewer parameters."}
+{"idx": 4, "title": "Executing your Commands via Motion Diffusion in Latent Space", "date": "", "ddg_snippet": "MLD achieves state-of-the-art ac-curacy and diversity on UESTC and competitive results on HumanAct12 , indicating that diffusion models in motion la-tent can also benefit action-conditioned generation task.", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2023/papers/Chen_Executing_Your_Commands_via_Motion_Diffusion_in_Latent_Space_CVPR_2023_paper.pdf", "content": "MLD achieves state-of-the-art ac-curacy and diversity on UESTC and competitive results on HumanAct12 , indicating that diffusion models in motion la-tent can also benefit action-conditioned generation task."}
+{"idx": 5, "title": "MOTION FLOW MATCHING FOR EFFICIENT HUMAN MOTION SYNTHESIS AND ...", "date": "", "ddg_snippet": "We present our results on the action-to- motion dataset HumanAct12 in Table 4. Our method achieves results that are comparable to various baselines, all while significantly reducing the number of forward evaluations and utilizing fewer parameters.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=ikdB0VXPlw", "content": "We present our results on the action-to- motion dataset HumanAct12 in Table 4. Our method achieves results that are comparable to various baselines, all while significantly reducing the number of forward evaluations and utilizing fewer parameters."}
+{"idx": 6, "title": "Executing your Commands via Motion Diffusion in Latent ...", "date": "", "ddg_snippet": "We provide the process of the training and evaluation of MLD models, the pre-trained model files, the demo script, and example results . A. Qualitative Results .", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2023/supplemental/Chen_Executing_Your_Commands_CVPR_2023_supplemental.pdf", "content": "We provide the process of the training and evaluation of MLD models, the pre-trained model files, the demo script, and example results . A. Qualitative Results ."}
+{"idx": 7, "title": "LS-GAN: Human Motion Synthesis with Latent-space GANs", "date": "", "ddg_snippet": "30 Dec 2024 — We perform experiments on the HumanML3D, HumanAct12 benchmarks and demonstrate that a remarkably simple GAN in the latent space achieves a FID ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2501.01449v1", "content": "30 Dec 2024 — We perform experiments on the HumanML3D, HumanAct12 benchmarks and demonstrate that a remarkably simple GAN in the latent space achieves a FID ..."}
+{"idx": 8, "title": "Executing your Commands via Motion Diffusion in Latent Space", "date": "", "ddg_snippet": "TL;DR: In this paper, a motion latent-based diffusion model ( MLD ) is proposed to generate human motion sequences conforming to the given conditional inputs ...", "subpage_snippet": "", "source": "scispace.com", "link": "https://scispace.com/papers/executing-your-commands-via-motion-diffusion-in-latent-space-w2yvt5un?citations_page=8", "content": "TL;DR: In this paper, a motion latent-based diffusion model ( MLD ) is proposed to generate human motion sequences conforming to the given conditional inputs ..."}
+{"idx": 9, "title": "MOTION FLOW MATCHING FOR EFFICIENT HUMAN ...", "date": "", "ddg_snippet": "We present our results on the action-to- motion dataset HumanAct12 in Table 4. ... This aligns with the results reported in MLD (Chen et al., 2023) (as seen ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf/61a1228b68fe5cc477b223010e69dce5ad7aff34.pdf", "content": "We present our results on the action-to- motion dataset HumanAct12 in Table 4. ... This aligns with the results reported in MLD (Chen et al., 2023) (as seen ..."}
diff --git a/data/sampled_jsons/MLOps_model_dependency_tree_verification_tools_2024.jsonl b/data/sampled_jsons/MLOps_model_dependency_tree_verification_tools_2024.jsonl
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@@ -0,0 +1,10 @@
+{"idx": 0, "title": "MLOps Tools You Need to Know in 2024 - by MLOps Guru", "date": "", "ddg_snippet": "Aug 7, 2024 · 7. Metaflow Metaflow is a powerful, battle-hardened workflow management tool for data science and machine learning projects. It was built for data scientists so they can focus on building models instead of worrying about MLOps engineering. With Metaflow, you can design workflow, run it on the scale, and deploy the model in production.", "subpage_snippet": "", "source": "mlopsguru.substack.com", "link": "https://mlopsguru.substack.com/p/mlops-tools-you-need-to-know-in-2024", "content": "Aug 7, 2024 · 7. Metaflow Metaflow is a powerful, battle-hardened workflow management tool for data science and machine learning projects. It was built for data scientists so they can focus on building models instead of worrying about MLOps engineering. With Metaflow, you can design workflow, run it on the scale, and deploy the model in production."}
+{"idx": 1, "title": "Top End-to-End Open-Source MLOps Tools for 2024 - Medium 25 Top MLOps Tools You Need to Know in 2025 - DataCamp Top 7 MLOps Tools for 2024: Innovations in Machine Learning ... 28. Exploring the Best MLOps Tools of 2024 – cognidots blog GitHub - marvelousmlops/mlops-roadmap-2024 25 Top MLOps Tools You Need to Know in 2025 - DataCamp GitHub - marvelousmlops/ mlops -roadmap- 2024 25 Top MLOps Tools You Need to Know in 2025 - DataCamp GitHub - marvelousmlops/ mlops -roadmap- 2024 Mastering MLOps Tools: The Ultimate Guide in 2024", "date": "", "ddg_snippet": "Sep 6, 2024 · MLOps is important because it helps automate workflows, improve model reliability, and scale machine learning solutions. 2. What are some top open-source MLOps tools for 2024 ? With the introduction of GPT-4 and later GPT-4o, the race has begun to produce large language models and realize the full potential of modern AI. LLMs require vector databases and integration frameworks for building intelligent AI applications. See full list on datacamp.com Feature stores are centralized repositories for storing, versioning, managing, and serving features (processed data attributes used for training machine learning models) for machine learning models in production as well as for training purposes. See full list on datacamp.com With these MLOps tools , you can test model quality and ensure machine learning models' reliability, robustness, and accuracy: See full list on datacamp.com Whether your ML model is in development, validation, or deployed to production, these tools can help you monitor a range of factors: See full list on datacamp.com The runtime engine is responsible for loading the model , preprocessing input data, running inference, and returning the results to the client application. See full list on datacamp.com If you’re looking for a comprehensive MLOps tool that can help during the entire process, here are some of the best: See full list on datacamp.com Here's a comparison table so you can evaluate these tools side-by-side and decide on the best ones for your projects: See full list on datacamp.com We’re at a time when there is a boom in the MLOps industry. Every week you see new developments, new startups, and new tools launching to solve the basic problem of converting notebooks into production-ready applications. Even existing tools are expanding the horizon and integrating new features to become super MLOps tools . In this blog, we have le... See full list on datacamp.com Explore the leading MLOps tools for 2024 , including AWS SageMaker, Google Cloud AI, Databricks, and more. Discover how these platforms streamline the machine learning lifecycle, from model development to deployment and monitoring. The world of Machine Learning Operations ( MLOps ) is constantly evolving, and with it, the tools that help data scientists and engineers streamline their workflows. As we step into 2024 , let’s take a look at some of the most popular MLOps tools that are making waves in the industry. MLOps Roadmap 2024 1. Programming Programming skills are crucial for an MLOps engineer. Python is the most commonly used language in machine learning, making it important for collaboration with machine learning engineers and data scientists. What are MLOps tools used for? MLOps tools help standardize, simplify, and streamline the ML ecosystem. These tools are used for experiment tracking, model metadata management, orchestration, model optimization, workflow versioning, model deployment and serving, and model monitoring in production. What skills are needed for an MLOps Engineer? Which cloud is best for MLOps ? How do I contribute to MLOps-Roadmap 2024 development? Contribute to marvelousmlops/mlops-roadmap-2024 development by creating an account on GitHub . Is there a boom in the MLOps industry? We’re at a time when there is a boom in the MLOps industry . Every week you see new developments, new startups, and new tools launching to solve the basic problem of converting notebooks into production-ready applications. Even existing tools are expanding the horizon and integrating new features to become super MLOps tools. What are MLOps platforms? MLOps platforms consist of various components, from version control to feature stores. The minimum set of must-haves for MLOps article. 5.1. Apr 22, 2024 · Discover the power of MLOps tools for optimizing machine learning processes in 2024 . Explore the ultimate guide to mastering MLOps tools for efficiency and success.", "subpage_snippet": "", "source": "medium.com", "link": "https://medium.com/@analyticsinsight/top-end-to-end-open-source-mlops-tools-for-2024-cb625921a3c9", "content": "Sep 6, 2024 · MLOps is important because it helps automate workflows, improve model reliability, and scale machine learning solutions. 2. What are some top open-source MLOps tools for 2024 ? With the introduction of GPT-4 and later GPT-4o, the race has begun to produce large language models and realize the full potential of modern AI. LLMs require vector databases and integration frameworks for building intelligent AI applications. See full list on datacamp.com Feature stores are centralized repositories for storing, versioning, managing, and serving features (processed data attributes used for training machine learning models) for machine learning models in production as well as for training purposes. See full list on datacamp.com With these MLOps tools , you can test model quality and ensure machine learning models' reliability, robustness, and accuracy: See full list on datacamp.com Whether your ML model is in development, validation, or deployed to production, these tools can help you monitor a range of factors: See full list on datacamp.com The runtime engine is responsible for loading the model , preprocessing input data, running inference, and returning the results to the client application. See full list on datacamp.com If you’re looking for a comprehensive MLOps tool that can help during the entire process, here are some of the best: See full list on datacamp.com Here's a comparison table so you can evaluate these tools side-by-side and decide on the best ones for your projects: See full list on datacamp.com We’re at a time when there is a boom in the MLOps industry. Every week you see new developments, new startups, and new tools launching to solve the basic problem of converting notebooks into production-ready applications. Even existing tools are expanding the horizon and integrating new features to become super MLOps tools . In this blog, we have le... See full list on datacamp.com Explore the leading MLOps tools for 2024 , including AWS SageMaker, Google Cloud AI, Databricks, and more. Discover how these platforms streamline the machine learning lifecycle, from model development to deployment and monitoring. The world of Machine Learning Operations ( MLOps ) is constantly evolving, and with it, the tools that help data scientists and engineers streamline their workflows. As we step into 2024 , let’s take a look at some of the most popular MLOps tools that are making waves in the industry. MLOps Roadmap 2024 1. Programming Programming skills are crucial for an MLOps engineer. Python is the most commonly used language in machine learning, making it important for collaboration with machine learning engineers and data scientists. What are MLOps tools used for? MLOps tools help standardize, simplify, and streamline the ML ecosystem. These tools are used for experiment tracking, model metadata management, orchestration, model optimization, workflow versioning, model deployment and serving, and model monitoring in production. What skills are needed for an MLOps Engineer? Which cloud is best for MLOps ? How do I contribute to MLOps-Roadmap 2024 development? Contribute to marvelousmlops/mlops-roadmap-2024 development by creating an account on GitHub . Is there a boom in the MLOps industry? We’re at a time when there is a boom in the MLOps industry . Every week you see new developments, new startups, and new tools launching to solve the basic problem of converting notebooks into production-ready applications. Even existing tools are expanding the horizon and integrating new features to become super MLOps tools. What are MLOps platforms? MLOps platforms consist of various components, from version control to feature stores. The minimum set of must-haves for MLOps article. 5.1. Apr 22, 2024 · Discover the power of MLOps tools for optimizing machine learning processes in 2024 . Explore the ultimate guide to mastering MLOps tools for efficiency and success."}
+{"idx": 2, "title": "25 Top MLOps Tools You Need to Know in 2025 - DataCamp Top 7 MLOps Tools for 2024: Innovations in Machine Learning ... 28. Exploring the Best MLOps Tools of 2024 – cognidots blog GitHub - marvelousmlops/mlops-roadmap-2024 25 Top MLOps Tools You Need to Know in 2025 - DataCamp GitHub - marvelousmlops/ mlops -roadmap- 2024 25 Top MLOps Tools You Need to Know in 2025 - DataCamp GitHub - marvelousmlops/ mlops -roadmap- 2024 Mastering MLOps Tools: The Ultimate Guide in 2024", "date": "", "ddg_snippet": "With the introduction of GPT-4 and later GPT-4o, the race has begun to produce large language models and realize the full potential of modern AI. LLMs require vector databases and integration frameworks for building intelligent AI applications. See full list on datacamp.com Feature stores are centralized repositories for storing, versioning, managing, and serving features (processed data attributes used for training machine learning models) for machine learning models in production as well as for training purposes. See full list on datacamp.com With these MLOps tools , you can test model quality and ensure machine learning models' reliability, robustness, and accuracy: See full list on datacamp.com Whether your ML model is in development, validation, or deployed to production, these tools can help you monitor a range of factors: See full list on datacamp.com The runtime engine is responsible for loading the model , preprocessing input data, running inference, and returning the results to the client application. See full list on datacamp.com If you’re looking for a comprehensive MLOps tool that can help during the entire process, here are some of the best: See full list on datacamp.com Here's a comparison table so you can evaluate these tools side-by-side and decide on the best ones for your projects: See full list on datacamp.com We’re at a time when there is a boom in the MLOps industry. Every week you see new developments, new startups, and new tools launching to solve the basic problem of converting notebooks into production-ready applications. Even existing tools are expanding the horizon and integrating new features to become super MLOps tools . In this blog, we have le... See full list on datacamp.com Explore the leading MLOps tools for 2024 , including AWS SageMaker, Google Cloud AI, Databricks, and more. Discover how these platforms streamline the machine learning lifecycle, from model development to deployment and monitoring. The world of Machine Learning Operations ( MLOps ) is constantly evolving, and with it, the tools that help data scientists and engineers streamline their workflows. As we step into 2024 , let’s take a look at some of the most popular MLOps tools that are making waves in the industry. MLOps Roadmap 2024 1. Programming Programming skills are crucial for an MLOps engineer. Python is the most commonly used language in machine learning, making it important for collaboration with machine learning engineers and data scientists. What are MLOps tools used for? MLOps tools help standardize, simplify, and streamline the ML ecosystem. These tools are used for experiment tracking, model metadata management, orchestration, model optimization, workflow versioning, model deployment and serving, and model monitoring in production. What skills are needed for an MLOps Engineer? Which cloud is best for MLOps ? How do I contribute to MLOps-Roadmap 2024 development? Contribute to marvelousmlops/mlops-roadmap-2024 development by creating an account on GitHub . Is there a boom in the MLOps industry? We’re at a time when there is a boom in the MLOps industry . Every week you see new developments, new startups, and new tools launching to solve the basic problem of converting notebooks into production-ready applications. Even existing tools are expanding the horizon and integrating new features to become super MLOps tools. What are MLOps platforms? MLOps platforms consist of various components, from version control to feature stores. The minimum set of must-haves for MLOps article. 5.1. Apr 22, 2024 · Discover the power of MLOps tools for optimizing machine learning processes in 2024 . Explore the ultimate guide to mastering MLOps tools for efficiency and success.", "subpage_snippet": "", "source": "www.datacamp.com", "link": "https://www.datacamp.com/blog/top-mlops-tools", "content": "With the introduction of GPT-4 and later GPT-4o, the race has begun to produce large language models and realize the full potential of modern AI. LLMs require vector databases and integration frameworks for building intelligent AI applications. See full list on datacamp.com Feature stores are centralized repositories for storing, versioning, managing, and serving features (processed data attributes used for training machine learning models) for machine learning models in production as well as for training purposes. See full list on datacamp.com With these MLOps tools , you can test model quality and ensure machine learning models' reliability, robustness, and accuracy: See full list on datacamp.com Whether your ML model is in development, validation, or deployed to production, these tools can help you monitor a range of factors: See full list on datacamp.com The runtime engine is responsible for loading the model , preprocessing input data, running inference, and returning the results to the client application. See full list on datacamp.com If you’re looking for a comprehensive MLOps tool that can help during the entire process, here are some of the best: See full list on datacamp.com Here's a comparison table so you can evaluate these tools side-by-side and decide on the best ones for your projects: See full list on datacamp.com We’re at a time when there is a boom in the MLOps industry. Every week you see new developments, new startups, and new tools launching to solve the basic problem of converting notebooks into production-ready applications. Even existing tools are expanding the horizon and integrating new features to become super MLOps tools . In this blog, we have le... See full list on datacamp.com Explore the leading MLOps tools for 2024 , including AWS SageMaker, Google Cloud AI, Databricks, and more. Discover how these platforms streamline the machine learning lifecycle, from model development to deployment and monitoring. The world of Machine Learning Operations ( MLOps ) is constantly evolving, and with it, the tools that help data scientists and engineers streamline their workflows. As we step into 2024 , let’s take a look at some of the most popular MLOps tools that are making waves in the industry. MLOps Roadmap 2024 1. Programming Programming skills are crucial for an MLOps engineer. Python is the most commonly used language in machine learning, making it important for collaboration with machine learning engineers and data scientists. What are MLOps tools used for? MLOps tools help standardize, simplify, and streamline the ML ecosystem. These tools are used for experiment tracking, model metadata management, orchestration, model optimization, workflow versioning, model deployment and serving, and model monitoring in production. What skills are needed for an MLOps Engineer? Which cloud is best for MLOps ? How do I contribute to MLOps-Roadmap 2024 development? Contribute to marvelousmlops/mlops-roadmap-2024 development by creating an account on GitHub . Is there a boom in the MLOps industry? We’re at a time when there is a boom in the MLOps industry . Every week you see new developments, new startups, and new tools launching to solve the basic problem of converting notebooks into production-ready applications. Even existing tools are expanding the horizon and integrating new features to become super MLOps tools. What are MLOps platforms? MLOps platforms consist of various components, from version control to feature stores. The minimum set of must-haves for MLOps article. 5.1. Apr 22, 2024 · Discover the power of MLOps tools for optimizing machine learning processes in 2024 . Explore the ultimate guide to mastering MLOps tools for efficiency and success."}
+{"idx": 3, "title": "Top 7 MLOps Tools for 2024: Innovations in Machine Learning ...", "date": "", "ddg_snippet": "Explore the leading MLOps tools for 2024 , including AWS SageMaker, Google Cloud AI, Databricks, and more. Discover how these platforms streamline the machine learning lifecycle, from model development to deployment and monitoring.", "subpage_snippet": "", "source": "www.reportsanddata.com", "link": "https://www.reportsanddata.com/blog/7-best-mlops-tools-for-2024", "content": "Explore the leading MLOps tools for 2024 , including AWS SageMaker, Google Cloud AI, Databricks, and more. Discover how these platforms streamline the machine learning lifecycle, from model development to deployment and monitoring."}
+{"idx": 4, "title": "28. Exploring the Best MLOps Tools of 2024 – cognidots blog", "date": "", "ddg_snippet": "The world of Machine Learning Operations ( MLOps ) is constantly evolving, and with it, the tools that help data scientists and engineers streamline their workflows. As we step into 2024 , let’s take a look at some of the most popular MLOps tools that are making waves in the industry.", "subpage_snippet": "", "source": "blog.cognitivedots.com", "link": "https://blog.cognitivedots.com/28-exploring-the-best-mlops-tools-of-2024/", "content": "The world of Machine Learning Operations ( MLOps ) is constantly evolving, and with it, the tools that help data scientists and engineers streamline their workflows. As we step into 2024 , let’s take a look at some of the most popular MLOps tools that are making waves in the industry."}
+{"idx": 5, "title": "GitHub - marvelousmlops/mlops-roadmap-2024", "date": "", "ddg_snippet": "MLOps Roadmap 2024 1. Programming Programming skills are crucial for an MLOps engineer. Python is the most commonly used language in machine learning, making it important for collaboration with machine learning engineers and data scientists.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/marvelousmlops/mlops-roadmap-2024", "content": "MLOps Roadmap 2024 1. Programming Programming skills are crucial for an MLOps engineer. Python is the most commonly used language in machine learning, making it important for collaboration with machine learning engineers and data scientists."}
+{"idx": 6, "title": "Mastering MLOps Tools: The Ultimate Guide in 2024", "date": "", "ddg_snippet": "Apr 22, 2024 · Discover the power of MLOps tools for optimizing machine learning processes in 2024 . Explore the ultimate guide to mastering MLOps tools for efficiency and success.", "subpage_snippet": "", "source": "www.myscale.com", "link": "https://www.myscale.com/blog/mastering-mlops-tools-ultimate-guide-2024/", "content": "Apr 22, 2024 · Discover the power of MLOps tools for optimizing machine learning processes in 2024 . Explore the ultimate guide to mastering MLOps tools for efficiency and success."}
+{"idx": 7, "title": "Learn how MLOps streamlines AI/ML workflows with... | Datumo", "date": "", "ddg_snippet": "The Azure MLOps ecosystem provides a comprehensive environment to tackle these issues, offering seamless integration with tools like MLflow for experiment tracking, Azure Blob Storage for scalable data management, and CI/CD pipelines for automating model training and deployment.", "subpage_snippet": "", "source": "www.datumo.io", "link": "https://www.datumo.io/blog/building-an-effective-mlops-platform-in-the-azure-cloud", "content": "The Azure MLOps ecosystem provides a comprehensive environment to tackle these issues, offering seamless integration with tools like MLflow for experiment tracking, Azure Blob Storage for scalable data management, and CI/CD pipelines for automating model training and deployment."}
+{"idx": 8, "title": "MLOps : Model Monitoring 101 - KDnuggets", "date": "", "ddg_snippet": "A proposed model monitoring metrics stack is given in Fig 3 below. It defines three broad types of metrics based on the dependency of the metric on data and/or ML model .", "subpage_snippet": "", "source": "www.kdnuggets.com", "link": "https://www.kdnuggets.com/2021/01/mlops-model-monitoring-101.html", "content": "A proposed model monitoring metrics stack is given in Fig 3 below. It defines three broad types of metrics based on the dependency of the metric on data and/or ML model ."}
+{"idx": 9, "title": "Marvelous MLOps #56: Streamlining ML Model Monitoring with...", "date": "", "ddg_snippet": "Ground Truth Dependency : Model monitoring relies on ground truth labels to calculate metrics like accuracy or error. This can be a limitation if real-world labels are delayed or unavailable, impacting the ability to monitor models in real time. Data Drift vs. Performance Impact: While...", "subpage_snippet": "", "source": "www.linkedin.com", "link": "https://www.linkedin.com/pulse/marvelous-mlops-56-streamlining-ml-model-monitoring-databricks-woiie", "content": "Ground Truth Dependency : Model monitoring relies on ground truth labels to calculate metrics like accuracy or error. This can be a limitation if real-world labels are delayed or unavailable, impacting the ability to monitor models in real time. Data Drift vs. Performance Impact: While..."}
diff --git a/data/sampled_jsons/Machine_Learning_meets_Algebraic_Combinatorics_Suite_of_Datasets.jsonl b/data/sampled_jsons/Machine_Learning_meets_Algebraic_Combinatorics_Suite_of_Datasets.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..49c2b8e9265f430a97d511bec7d09a2c831c9e8e
--- /dev/null
+++ b/data/sampled_jsons/Machine_Learning_meets_Algebraic_Combinatorics_Suite_of_Datasets.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Machine Learning meets Algebraic Combinatorics: A Suite ...", "date": "", "ddg_snippet": "by H Chau · 2025 · Cited by 3 — We introduce a new collection of datasets , the Algebraic Combinatorics Dataset Repository (ACD Repo), representing either foundational results or open problems.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2503.06366", "content": "by H Chau · 2025 · Cited by 3 — We introduce a new collection of datasets , the Algebraic Combinatorics Dataset Repository (ACD Repo), representing either foundational results or open problems."}
+{"idx": 1, "title": "Machine Learning meets Algebraic Combinatorics: A Suite ...", "date": "", "ddg_snippet": "To address this, we introduce a new collection of datasets , the Algebraic Combinatorics Dataset Repository (ACD Repo), representing either foundational results ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=tlniJJFUW2&noteId=kkpF1lRRMF", "content": "To address this, we introduce a new collection of datasets , the Algebraic Combinatorics Dataset Repository (ACD Repo), representing either foundational results ..."}
+{"idx": 2, "title": "Machine Learning meets Algebraic Combinatorics", "date": "", "ddg_snippet": "by AI Accelerate · 2024 — Machine Learning meets Algebraic . Combinatorics . A Suite of Benchmark Datasets to Accelerate AI for Mathematics Research. September 2024. Herman Chau. Helen ...", "subpage_snippet": "", "source": "www.pnnl.gov", "link": "https://www.pnnl.gov/main/publications/external/technical_reports/PNNL-36691.pdf", "content": "by AI Accelerate · 2024 — Machine Learning meets Algebraic . Combinatorics . A Suite of Benchmark Datasets to Accelerate AI for Mathematics Research. September 2024. Herman Chau. Helen ..."}
+{"idx": 3, "title": "Machine Learning meets Algebraic Combinatorics: A Suite ...", "date": "", "ddg_snippet": "by H Chau — Abstract: The use of benchmark datasets has become an important engine of progress in machine learning (ML) over the past 15 years.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=KQ1gI5qzAf", "content": "by H Chau — Abstract: The use of benchmark datasets has become an important engine of progress in machine learning (ML) over the past 15 years."}
+{"idx": 4, "title": "ML Benchmarks in Algebraic Combinatorics", "date": "", "ddg_snippet": "A collection of datasets drawn from the mathematical domain of algebraic combinatorics , an area of mathematics which studies discrete structures.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/pnnl/ML4AlgComb", "content": "A collection of datasets drawn from the mathematical domain of algebraic combinatorics , an area of mathematics which studies discrete structures."}
+{"idx": 5, "title": "A Suite of Benchmark Datasets to Accelerate AI for ...", "date": "", "ddg_snippet": "by HH Chau · 2024 — We introduce a new collection of benchmark datasets , Algebraic Combinatorics Benchmarks (ACBench), representing either classic or open problems in algebraic ...", "subpage_snippet": "", "source": "www.osti.gov", "link": "https://www.osti.gov/biblio/2476539", "content": "by HH Chau · 2024 — We introduce a new collection of benchmark datasets , Algebraic Combinatorics Benchmarks (ACBench), representing either classic or open problems in algebraic ..."}
+{"idx": 6, "title": "Machine Learning meets Algebraic Combinatorics: A Suite ...", "date": "", "ddg_snippet": "13 Jul 2025 — Machine Learning meets Algebraic Combinatorics : A Suite of Datasets Capturing Research-level Conjecturing Ability in Pure Mathematics.", "subpage_snippet": "", "source": "slideslive.com", "link": "https://slideslive.com/39039913/machine-learning-meets-algebraic-combinatorics-a-suite-of-datasets-capturing-researchlevel-conjecturing-ability-in-pure-mathematics", "content": "13 Jul 2025 — Machine Learning meets Algebraic Combinatorics : A Suite of Datasets Capturing Research-level Conjecturing Ability in Pure Mathematics."}
+{"idx": 7, "title": "[Literature Review] Machine Learning meets Algebraic ...", "date": "", "ddg_snippet": "9 Mar 2025 — It encompasses nine distinct datasets , each focused on research-level problems in algebraic combinatorics. Introduction. The authors highlight ...", "subpage_snippet": "", "source": "www.themoonlight.io", "link": "https://www.themoonlight.io/en/review/machine-learning-meets-algebraic-combinatorics-a-suite-of-datasets-capturing-research-level-conjecturing-ability-in-pure-mathematics", "content": "9 Mar 2025 — It encompasses nine distinct datasets , each focused on research-level problems in algebraic combinatorics. Introduction. The authors highlight ..."}
+{"idx": 8, "title": "Machine Learning meets Algebraic Combinatorics", "date": "", "ddg_snippet": "8 Mar 2025 — Each dataset includes an open-ended research-level question and a large collection of examples (up to 10M in some cases) from which conjectures ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2503.06366v1", "content": "8 Mar 2025 — Each dataset includes an open-ended research-level question and a large collection of examples (up to 10M in some cases) from which conjectures ..."}
+{"idx": 9, "title": "Henry Kvinge", "date": "", "ddg_snippet": "[Spotlight oral] Machine Learning meets Algebraic Combinatorics : A Suite of Datasets Capturing Research-level Conjecturing Ability in Pure Mathematics", "subpage_snippet": "", "source": "hkvinge.github.io", "link": "https://hkvinge.github.io/", "content": "[Spotlight oral] Machine Learning meets Algebraic Combinatorics : A Suite of Datasets Capturing Research-level Conjecturing Ability in Pure Mathematics"}
diff --git a/data/sampled_jsons/Machine_Learning_meets_Algebraic_Combinatorics_table_1_accuracy_values_Schubert.jsonl b/data/sampled_jsons/Machine_Learning_meets_Algebraic_Combinatorics_table_1_accuracy_values_Schubert.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..c4203c8e4a22f6658006018414586cadf0fd55aa
--- /dev/null
+++ b/data/sampled_jsons/Machine_Learning_meets_Algebraic_Combinatorics_table_1_accuracy_values_Schubert.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Machine Learning meets Algebraic Combinatorics: A Suite of ...", "date": "", "ddg_snippet": "To address this, we introduce a new collection of datasets, the Algebraic Combinatorics Dataset Repository (ACD Repo), representing either foundational results or open problems in algebraic combinatorics , a subfield of mathematics that studies discrete structures arising from abstract algebra .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2503.06366v1", "content": "To address this, we introduce a new collection of datasets, the Algebraic Combinatorics Dataset Repository (ACD Repo), representing either foundational results or open problems in algebraic combinatorics , a subfield of mathematics that studies discrete structures arising from abstract algebra ."}
+{"idx": 1, "title": "Machine Learning meets Algebraic Combinatorics", "date": "", "ddg_snippet": "by H Chau · 2025 · Cited by 3 — Both LLMs ( Table 2) and narrow models. ( Table 1 ) achieve fairly high- accuracy (though note that the dataset is heavily imbalanced for larger n) ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2503.06366", "content": "by H Chau · 2025 · Cited by 3 — Both LLMs ( Table 2) and narrow models. ( Table 1 ) achieve fairly high- accuracy (though note that the dataset is heavily imbalanced for larger n) ..."}
+{"idx": 2, "title": "Machine Learning meets Algebraic Combinatorics: A Suite ...", "date": "", "ddg_snippet": "How logistic regression was optimized? Most of the problems can be solved with the baseline methods with quite high accuracy , which makes one wonder how ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=tlniJJFUW2&noteId=kkpF1lRRMF", "content": "How logistic regression was optimized? Most of the problems can be solved with the baseline methods with quite high accuracy , which makes one wonder how ..."}
+{"idx": 3, "title": "[Literature Review] Machine Learning meets Algebraic ...", "date": "", "ddg_snippet": "9 Mar 2025 — This page provides the most accurate and concise summary worldwide for the paper titled Machine Learning meets Algebraic Combinatorics : A Suite ...", "subpage_snippet": "", "source": "www.themoonlight.io", "link": "https://www.themoonlight.io/en/review/machine-learning-meets-algebraic-combinatorics-a-suite-of-datasets-capturing-research-level-conjecturing-ability-in-pure-mathematics", "content": "9 Mar 2025 — This page provides the most accurate and concise summary worldwide for the paper titled Machine Learning meets Algebraic Combinatorics : A Suite ..."}
+{"idx": 4, "title": "Machine Learning meets Algebraic Combinatorics", "date": "", "ddg_snippet": "by H Chau — In this paper we introduced Algebraic Combinatorics Dataset Repository, a collection of datasets structured for machine learning and designed to facilitate the ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=KQ1gI5qzAf", "content": "by H Chau — In this paper we introduced Algebraic Combinatorics Dataset Repository, a collection of datasets structured for machine learning and designed to facilitate the ..."}
+{"idx": 5, "title": "Algebraic Combinatorics Dataset Repository - GitHub", "date": "", "ddg_snippet": "To lower the barrier of entry to the machine learning community, we include datasets centered around open problems in algebraic combinatorics . We hope that use of these by the AI-community will translate into progress in mathematics.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/pnnl/ML4AlgComb", "content": "To lower the barrier of entry to the machine learning community, we include datasets centered around open problems in algebraic combinatorics . We hope that use of these by the AI-community will translate into progress in mathematics."}
+{"idx": 6, "title": "Machine Learning meets Algebraic Combinatorics: A Suite of ...", "date": "", "ddg_snippet": "View recent discussion. Abstract: With recent dramatic increases in AI system capabilities, there has been growing interest in utilizing machine learning for reasoning-heavy, quantitative tasks, particularly mathematics. While there are many resources capturing mathematics at the high-school, undergraduate, and graduate level, there are far fewer resources available that align with the level ...", "subpage_snippet": "", "source": "www.alphaxiv.org", "link": "https://www.alphaxiv.org/overview/2503.06366v1", "content": "View recent discussion. Abstract: With recent dramatic increases in AI system capabilities, there has been growing interest in utilizing machine learning for reasoning-heavy, quantitative tasks, particularly mathematics. While there are many resources capturing mathematics at the high-school, undergraduate, and graduate level, there are far fewer resources available that align with the level ..."}
+{"idx": 7, "title": "Machine Learning Meets Algebraic Combinatorics: A Suite of ...", "date": "", "ddg_snippet": "Algebraic Combinatorics Dataset Repository (ACD Repo) 1 , a collection of 9 datasets consisting of many examples along with an associated question(s). Our collection includes both open problems (e.g., the combinatorial interpretation of Schubert polynomial structure constants) and classic prob-lems whose solution is a major result in the field (e.g., a combinatorial method of calculating the ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=tlniJJFUW2", "content": "Algebraic Combinatorics Dataset Repository (ACD Repo) 1 , a collection of 9 datasets consisting of many examples along with an associated question(s). Our collection includes both open problems (e.g., the combinatorial interpretation of Schubert polynomial structure constants) and classic prob-lems whose solution is a major result in the field (e.g., a combinatorial method of calculating the ..."}
+{"idx": 8, "title": "Machine Learning meets Algebraic Combinatorics: A Suite of ...", "date": "", "ddg_snippet": "Mar 9, 2025 · With recent dramatic increases in AI system capabilities, there has been growing interest in utilizing machine learning for reasoning-heavy, quantitative tasks, particularly mathematics. While there are many resources capturing mathematics at the high-school, undergraduate, and graduate level, there are far fewer resources available that align with the level of difficulty and open endedness ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2503.06366", "content": "Mar 9, 2025 · With recent dramatic increases in AI system capabilities, there has been growing interest in utilizing machine learning for reasoning-heavy, quantitative tasks, particularly mathematics. While there are many resources capturing mathematics at the high-school, undergraduate, and graduate level, there are far fewer resources available that align with the level of difficulty and open endedness ..."}
+{"idx": 9, "title": "[Literature Review] Signed combinatorial interpretations in ...", "date": "", "ddg_snippet": "The paper explores the existence of signed combinatorial interpretations for the structure constants of various algebraic objects, mainly focusing on ...", "subpage_snippet": "", "source": "www.themoonlight.io", "link": "https://www.themoonlight.io/en/review/signed-combinatorial-interpretations-in-algebraic-combinatorics", "content": "The paper explores the existence of signed combinatorial interpretations for the structure constants of various algebraic objects, mainly focusing on ..."}
diff --git a/data/sampled_jsons/Marvin_Li_Harvard_GitHub_blink-eye.jsonl b/data/sampled_jsons/Marvin_Li_Harvard_GitHub_blink-eye.jsonl
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index 0000000000000000000000000000000000000000..f7fd0df77ca85b67355e7f89f7dfc55de433ba85
--- /dev/null
+++ b/data/sampled_jsons/Marvin_Li_Harvard_GitHub_blink-eye.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Blink of an eye : a simple theory for feature localization in generative...", "date": "", "ddg_snippet": "Large language models (LLMs) can exhibit undesirable and unexpected behavior in the blink of an eye . In a recent Anthropic demo, Claude switched from coding to Googling pictures of Yellowstone...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2502.00921v1", "content": "Large language models (LLMs) can exhibit undesirable and unexpected behavior in the blink of an eye . In a recent Anthropic demo, Claude switched from coding to Googling pictures of Yellowstone..."}
+{"idx": 1, "title": "Marvin Li - Google Scholar", "date": "", "ddg_snippet": "Sahil Kuchlous Sahil KuchlousStudent, Harvard UniversityVerified email at college. harvard .edu. Blink of an eye : a simple theory for feature localization in generative models.", "subpage_snippet": "", "source": "scholar.google.com", "link": "https://scholar.google.com/citations?user=NhMTzpsAAAAJ&hl=en", "content": "Sahil Kuchlous Sahil KuchlousStudent, Harvard UniversityVerified email at college. harvard .edu. Blink of an eye : a simple theory for feature localization in generative models."}
+{"idx": 2, "title": "Marvin Li (@ marvin _ li 03) on X", "date": "", "ddg_snippet": "Marvin Li (@ marvin _ li 03) on X Harvard '25 | Building theory for generative models.", "subpage_snippet": "", "source": "twitter.com", "link": "https://twitter.com/marvin_li03", "content": "Marvin Li (@ marvin _ li 03) on X Harvard '25 | Building theory for generative models."}
+{"idx": 3, "title": "Marvin Li - Harvard John A. Paulson School of Engineering... | LinkedIn", "date": "", "ddg_snippet": "Outside of class, I… · Experience: Harvard John A. Paulson School of Engineering and Applied Sciences · Education: Harvard University · Location: Salisbury · 500+ connections on LinkedIn. View Marvin Li ’s profile on LinkedIn, a professional community of 1 billion members.", "subpage_snippet": "", "source": "www.linkedin.com", "link": "https://www.linkedin.com/in/marvin-li-2297a8124", "content": "Outside of class, I… · Experience: Harvard John A. Paulson School of Engineering and Applied Sciences · Education: Harvard University · Location: Salisbury · 500+ connections on LinkedIn. View Marvin Li ’s profile on LinkedIn, a professional community of 1 billion members."}
+{"idx": 4, "title": "eye - blink · GitHub Topics · GitHub", "date": "", "ddg_snippet": "GitHub is where people build software. More than 150 million people use GitHub to discover, fork, and contribute to over 420 million projects.Detects and counts eye blinks . opencv-python eye - blink . Updated Aug 8, 2019.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/topics/eye-blink", "content": "GitHub is where people build software. More than 150 million people use GitHub to discover, fork, and contribute to over 420 million projects.Detects and counts eye blinks . opencv-python eye - blink . Updated Aug 8, 2019."}
+{"idx": 5, "title": "How To Make Eye Blinking Effect In Premiere Pro - YouTube", "date": "", "ddg_snippet": "how to make eye blink effect in adobe premiere pro 2023 2024 2025 2026Ultimate 500+ Preset Pack: https://adobebasics.com/products/ultimate-premiere-pro-prese...", "subpage_snippet": "", "source": "www.youtube.com", "link": "https://www.youtube.com/watch?v=KBNeZ_8h0_M", "content": "how to make eye blink effect in adobe premiere pro 2023 2024 2025 2026Ultimate 500+ Preset Pack: https://adobebasics.com/products/ultimate-premiere-pro-prese..."}
+{"idx": 6, "title": "Why Your Eye Hurts When You Blink : 10 Possible Causes", "date": "", "ddg_snippet": "Common Causes of Eye Pain When Blinking . Eye pain while blinking can have many reasons. Some are mild and temporary, while others may need quick medical care. Find out the causes to decide if you can handle it at home or need medical help. 1. Dry Eyes .", "subpage_snippet": "", "source": "docus.ai", "link": "https://docus.ai/symptoms-guide/eye-hurt-when-blink", "content": "Common Causes of Eye Pain When Blinking . Eye pain while blinking can have many reasons. Some are mild and temporary, while others may need quick medical care. Find out the causes to decide if you can handle it at home or need medical help. 1. Dry Eyes ."}
+{"idx": 7, "title": "Deep Nostalgia AI - Turn old photos into videos, bring image to life with...", "date": "", "ddg_snippet": "Deep Nostalgia AI can generate a wide range of realistic animations, including facial expressions, head movements, and eye blinking . The animations are designed to be natural and true to life, bringing your old photos to life in a unique and touching way.", "subpage_snippet": "", "source": "deepnostalgia.ai", "link": "https://deepnostalgia.ai/?ref=devhunt", "content": "Deep Nostalgia AI can generate a wide range of realistic animations, including facial expressions, head movements, and eye blinking . The animations are designed to be natural and true to life, bringing your old photos to life in a unique and touching way."}
+{"idx": 8, "title": "CTL 14 – Florae Garden", "date": "", "ddg_snippet": "On the team Yoongeon led, Yebin was the manager. Yebin, unable to quickly follow the abrupt change in topic, blinked her eyes . “What you review and pass, I will also pass without complaint.", "subpage_snippet": "", "source": "floraegarden.com", "link": "https://floraegarden.com/story/cross-the-line/ctl-14/", "content": "On the team Yoongeon led, Yebin was the manager. Yebin, unable to quickly follow the abrupt change in topic, blinked her eyes . “What you review and pass, I will also pass without complaint."}
+{"idx": 9, "title": "Горящие туры из Москвы 2025– Египет, Турция, ОАЭ до -70...", "date": "", "ddg_snippet": "Горящие туры из Москвы, цены до -70% на \"все включено\" от всех туроператоров в Египет, Турцию, Таиланд, Сочи, ОАЭ. Вылет в ближайшие дни, перелёт и трансфер включены в стоимость.", "subpage_snippet": "", "source": "travelata.ru", "link": "https://travelata.ru/tury", "content": "Горящие туры из Москвы, цены до -70% на \"все включено\" от всех туроператоров в Египет, Турцию, Таиланд, Сочи, ОАЭ. Вылет в ближайшие дни, перелёт и трансфер включены в стоимость."}
diff --git a/data/sampled_jsons/Mimetic_Initialization_Self-Attention_Trockman_Kolter_2023_abstract.jsonl b/data/sampled_jsons/Mimetic_Initialization_Self-Attention_Trockman_Kolter_2023_abstract.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..4b3a0008c7601c8cd32c0b912b5b45fb73a0a475
--- /dev/null
+++ b/data/sampled_jsons/Mimetic_Initialization_Self-Attention_Trockman_Kolter_2023_abstract.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "'self-attention' directory", "date": "", "ddg_snippet": "“ Mimetic Initialization of Self - Attention Layers ”, Trockman & Kolter 2023 ... abstract : “Efficient Attention: Breaking The Quadratic Transformer ...", "subpage_snippet": "", "source": "gwern.net", "link": "https://gwern.net/doc/ai/nn/transformer/attention/index", "content": "“ Mimetic Initialization of Self - Attention Layers ”, Trockman & Kolter 2023 ... abstract : “Efficient Attention: Breaking The Quadratic Transformer ..."}
+{"idx": 1, "title": "J. Zico Kolter | DeepAI", "date": "", "ddg_snippet": "Mimetic Initialization of Self - Attention Layers ... Neural network weights are typically initialized at random from univaria...", "subpage_snippet": "", "source": "deepai.org", "link": "https://deepai.org/profile/j-zico-kolter", "content": "Mimetic Initialization of Self - Attention Layers ... Neural network weights are typically initialized at random from univaria..."}
+{"idx": 2, "title": "J. Zico Kolter", "date": "", "ddg_snippet": "Mimetic Initialization Helps ... Understanding Augmentation-based Self -Supervised Representation Learning via RKHS Approximation and Regression.", "subpage_snippet": "", "source": "www.csauthors.net", "link": "https://www.csauthors.net/j-zico-kolter/", "content": "Mimetic Initialization Helps ... Understanding Augmentation-based Self -Supervised Representation Learning via RKHS Approximation and Regression."}
+{"idx": 3, "title": "On the Surprising Effectiveness of Attention Transfer for", "date": "", "ddg_snippet": "Attention Copy (left): We simply “copy-and-paste” the attention maps from a pre-trained teacher model to a randomly initialized student one.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2411.09702v1", "content": "Attention Copy (left): We simply “copy-and-paste” the attention maps from a pre-trained teacher model to a randomly initialized student one."}
+{"idx": 4, "title": "[2305.09828] Mimetic Initialization of Self-Attention Layers", "date": "", "ddg_snippet": "by A Trockman · 2023 · Cited by 51 — We find that simply initializing the weights of self - attention layers so that they look more like their pre-trained counterparts allows us to train vanilla ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2305.09828", "content": "by A Trockman · 2023 · Cited by 51 — We find that simply initializing the weights of self - attention layers so that they look more like their pre-trained counterparts allows us to train vanilla ..."}
+{"idx": 5, "title": "Mimetic Initialization of Self-Attention Layers", "date": "", "ddg_snippet": "by A Trockman · Cited by 51 — Abstract . It is notoriously difficult to train Transformers on small datasets; typically, large pre-trained models are instead used as the starting point.", "subpage_snippet": "", "source": "proceedings.mlr.press", "link": "https://proceedings.mlr.press/v202/trockman23a/trockman23a.pdf?utm_source=ainews&utm_medium=email&utm_campaign=ainews-google-io-in-60-seconds", "content": "by A Trockman · Cited by 51 — Abstract . It is notoriously difficult to train Transformers on small datasets; typically, large pre-trained models are instead used as the starting point."}
+{"idx": 6, "title": "Mimetic Initialization of Self-Attention Layers", "date": "", "ddg_snippet": "by A Trockman · 2023 · Cited by 51 — Abstract . It is notoriously difficult to train Transformers on small datasets; typically, large pre-trained models.", "subpage_snippet": "", "source": "arxiv.org", "link": "http://arxiv.org/pdf/2305.09828", "content": "by A Trockman · 2023 · Cited by 51 — Abstract . It is notoriously difficult to train Transformers on small datasets; typically, large pre-trained models."}
+{"idx": 7, "title": "Mimetic Initialization Helps State Space Models Learn to ...", "date": "", "ddg_snippet": "by A Trockman · Cited by 5 — We propose a structured initialization technique that allows state space layers to more readily mimic attention .", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=iVy7aRMb0K", "content": "by A Trockman · Cited by 5 — We propose a structured initialization technique that allows state space layers to more readily mimic attention ."}
+{"idx": 8, "title": "Mimetic Initialization for Deep Neural Networks", "date": "", "ddg_snippet": "Mimetic initialization for self-attention layers Trockman and Kolter (2023 ) attempted to make self-attention more “con- volutional” (localized receptive ...", "subpage_snippet": "", "source": "csd.cmu.edu", "link": "https://csd.cmu.edu/sites/default/files/phd-thesis/CMU-CS-25-114.pdf", "content": "Mimetic initialization for self-attention layers Trockman and Kolter (2023 ) attempted to make self-attention more “con- volutional” (localized receptive ..."}
+{"idx": 9, "title": "MIMETIC INITIALIZATION HELPS STATE SPACE MODELS ...", "date": "", "ddg_snippet": "by A Trockman · Cited by 5 — Asher Trockman and J Zico Kolter. Mimetic initialization of self-attention layers . In International. Conference on Machine Learning, pp. 34456–34468. PMLR, 2023 ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=iVy7aRMb0K", "content": "by A Trockman · Cited by 5 — Asher Trockman and J Zico Kolter. Mimetic initialization of self-attention layers . In International. Conference on Machine Learning, pp. 34456–34468. PMLR, 2023 ..."}
diff --git a/data/sampled_jsons/Mind2Web_data_collection_methodology_human_annotation_crowdsourcing.jsonl b/data/sampled_jsons/Mind2Web_data_collection_methodology_human_annotation_crowdsourcing.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..eb91ddcd03a3ff7f7b9ac9683b537ffb34a6bb8d
--- /dev/null
+++ b/data/sampled_jsons/Mind2Web_data_collection_methodology_human_annotation_crowdsourcing.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Mind2Web: Towards a Generalist Agent for the Web - GitHub", "date": "", "ddg_snippet": "Mar 18, 2024 · Mind2Web is the first dataset for developing and evaluating generalist agents for the web that can follow language instructions to complete complex tasks on any website.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/OSU-NLP-Group/Mind2Web", "content": "Mar 18, 2024 · Mind2Web is the first dataset for developing and evaluating generalist agents for the web that can follow language instructions to complete complex tasks on any website."}
+{"idx": 1, "title": "All About Crowdsourcing Data Annotation: Leveraging the Power ...", "date": "", "ddg_snippet": "In data science, crowdsourcing data annotation is one of the newest and most effective methods for labeling data for machine learning and AI projects. By leveraging the collective power of diverse contributors, organizations can achieve high-quality, scalable, and cost-effective data annotation . This approach accelerates the data annotation process and brings new perspectives and domain ...", "subpage_snippet": "", "source": "www.sapien.io", "link": "https://www.sapien.io/blog/all-about-crowdsourcing-data-annotation-leveraging-the-power-of-the-crowd", "content": "In data science, crowdsourcing data annotation is one of the newest and most effective methods for labeling data for machine learning and AI projects. By leveraging the collective power of diverse contributors, organizations can achieve high-quality, scalable, and cost-effective data annotation . This approach accelerates the data annotation process and brings new perspectives and domain ..."}
+{"idx": 2, "title": "Mind2Web: Towards a Generalist Agent for the Web - arXiv.org", "date": "", "ddg_snippet": "Dec 9, 2023 · Our data collection process consists of four stages: website selection, task proposal, task demonstration, and task verification. Website selection and task verification are done by the authors. For task proposal and demonstration, we develop a sophisticated annotation tool using Playwright 1 and hire annotators through Amazon Mechanical Turk.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2306.06070v3", "content": "Dec 9, 2023 · Our data collection process consists of four stages: website selection, task proposal, task demonstration, and task verification. Website selection and task verification are done by the authors. For task proposal and demonstration, we develop a sophisticated annotation tool using Playwright 1 and hire annotators through Amazon Mechanical Turk."}
+{"idx": 3, "title": "Crowdsourcing Data Annotation: Benefits & Risks | Sama", "date": "", "ddg_snippet": "What is Crowdsourcing Data Annotation ? Crowdsourced data annotation is the process of obtaining labeled data by outsourcing the annotation task to a large group of contributors, usually through a crowdsourcing platform. The contributors are typically anonymous and can come from a wide range of backgrounds and expertise levels. Crowdsourcing platforms typically provide a user-friendly interface ...", "subpage_snippet": "", "source": "www.sama.com", "link": "https://www.sama.com/blog/crowdsourcing-data-annotation-benefits-risks", "content": "What is Crowdsourcing Data Annotation ? Crowdsourced data annotation is the process of obtaining labeled data by outsourcing the annotation task to a large group of contributors, usually through a crowdsourcing platform. The contributors are typically anonymous and can come from a wide range of backgrounds and expertise levels. Crowdsourcing platforms typically provide a user-friendly interface ..."}
+{"idx": 4, "title": "Crowdsourcing and Data Annotation | Jonathan K. Kummerfeld", "date": "", "ddg_snippet": "Crowdsourcing , collecting annotations of data from a distributed group of people online, is a major source of data for AI research. The original idea involved people doing it as volunteers (e.g. Folding@home) or as a byproduct of some other goal (e.g. reCAPTCHA), but most of the data collected in AI today is from paid workers. Methods Combining human and AI effort: If some errors are ...", "subpage_snippet": "", "source": "jkk.name", "link": "https://jkk.name/reading-notes/crowdsourcing/", "content": "Crowdsourcing , collecting annotations of data from a distributed group of people online, is a major source of data for AI research. The original idea involved people doing it as volunteers (e.g. Folding@home) or as a byproduct of some other goal (e.g. reCAPTCHA), but most of the data collected in AI today is from paid workers. Methods Combining human and AI effort: If some errors are ..."}
+{"idx": 5, "title": "Human-centred design on crowdsourcing annotation towards ...", "date": "", "ddg_snippet": "Oct 31, 2023 · This study aims to identify effective crowdsourcing interaction designs to promote the quality of human annotations and therefore the natural language processing (NLP)-based machine learning model performance. Specifically, the study experimented with four human -centred design techniques: highlight, guidelines, validation and text amount.", "subpage_snippet": "", "source": "journals.sagepub.com", "link": "https://journals.sagepub.com/doi/pdf/10.1177/01655515231204802", "content": "Oct 31, 2023 · This study aims to identify effective crowdsourcing interaction designs to promote the quality of human annotations and therefore the natural language processing (NLP)-based machine learning model performance. Specifically, the study experimented with four human -centred design techniques: highlight, guidelines, validation and text amount."}
+{"idx": 6, "title": "Crowdsourcing Annotation with CVAT and Human Protocol", "date": "", "ddg_snippet": "Introduction to Croudsourcing As dataset sizes grow, the demand for scalable and efficient data annotation methods increases. Crowdsourcing can be a solution, as it offers significant advantages like scalability and reduced costs but comes with challenges in management, communication, and technical requirements. To address this, recently, we’ve introduced a crowdsourcing solution ...", "subpage_snippet": "", "source": "www.cvat.ai", "link": "https://www.cvat.ai/resources/blog/crowdsource-annotations-with-cvat-and-human-protocol", "content": "Introduction to Croudsourcing As dataset sizes grow, the demand for scalable and efficient data annotation methods increases. Crowdsourcing can be a solution, as it offers significant advantages like scalability and reduced costs but comes with challenges in management, communication, and technical requirements. To address this, recently, we’ve introduced a crowdsourcing solution ..."}
+{"idx": 7, "title": "Crowdsourced data labeling: when to use it, and when not to | iMerit", "date": "", "ddg_snippet": "This annotation method is also easily accessible and rapidly deployable, making it a go-to source for annotation for all the busy data scientists that don’t have time to research other approaches to annotation .", "subpage_snippet": "", "source": "imerit.net", "link": "https://imerit.net/resources/blog/data-annotation-experts-all-pbm/", "content": "This annotation method is also easily accessible and rapidly deployable, making it a go-to source for annotation for all the busy data scientists that don’t have time to research other approaches to annotation ."}
+{"idx": 8, "title": "Human -LLM Hybrid Text Answer Aggregation for Crowd Annotations", "date": "", "ddg_snippet": "We apply data collection methodology of an existing crowdsourcing study (similar scale,prompts and seed data ) using ChatGPT and Falcon-40B.A typical crowd - sourcing application can be divided into three steps: data collection , data cura-tion, and learning.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/385140156_Human-LLM_Hybrid_Text_Answer_Aggregation_for_Crowd_Annotations", "content": "We apply data collection methodology of an existing crowdsourcing study (similar scale,prompts and seed data ) using ChatGPT and Falcon-40B.A typical crowd - sourcing application can be divided into three steps: data collection , data cura-tion, and learning."}
+{"idx": 9, "title": "[2109.02688] Rethinking Crowdsourcing Annotation : Partial...", "date": "", "ddg_snippet": "We conduct experiments on practical crowdsourcing data , the Open Street Map (OSM) dataset and benchmark dataset COCO 2014. When compared with state-of-the-art classification methods trained on fully annotated images, the proposed ATAM can achieve higher accuracy.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2109.02688", "content": "We conduct experiments on practical crowdsourcing data , the Open Street Map (OSM) dataset and benchmark dataset COCO 2014. When compared with state-of-the-art classification methods trained on fully annotated images, the proposed ATAM can achieve higher accuracy."}
diff --git a/data/sampled_jsons/Mind2Web_dataset_construction_method_year_2023.jsonl b/data/sampled_jsons/Mind2Web_dataset_construction_method_year_2023.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..4e20af977924b3bbb154e83a0c3ae88e0a315178
--- /dev/null
+++ b/data/sampled_jsons/Mind2Web_dataset_construction_method_year_2023.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Mind2Web: Towards a Generalist Agent for the Web", "date": "", "ddg_snippet": "We introduce Mind2Web , the first dataset for ... Unlike existing datasets predominantly constructed within simulated environments Shi et al.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2306.06070v3", "content": "We introduce Mind2Web , the first dataset for ... Unlike existing datasets predominantly constructed within simulated environments Shi et al."}
+{"idx": 1, "title": "Plancraft: an evaluation dataset for planning with LLM agents", "date": "", "ddg_snippet": "Minecraft has a unique advantage in that it is a real game, rather than a construction designed for agent evaluation, and therefore there exists a ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2412.21033v1", "content": "Minecraft has a unique advantage in that it is a real game, rather than a construction designed for agent evaluation, and therefore there exists a ..."}
+{"idx": 2, "title": "TGPO: Tree-Guided Preference Optimization for Robust Web Agent", "date": "", "ddg_snippet": "Experiments on Online- Mind2Web and our self-constructed C-WebShop datasets demonstrate that TGPO significantly outperforms existing methods ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2509.14172v1", "content": "Experiments on Online- Mind2Web and our self-constructed C-WebShop datasets demonstrate that TGPO significantly outperforms existing methods ..."}
+{"idx": 3, "title": "Small Models, Big Results: Achieving Superior Intent Extraction", "date": "", "ddg_snippet": "Mind2Web ’s data collection included a validation step where annotators verified the alignment between the completed trajectory steps and the intent, ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2509.12423v1", "content": "Mind2Web ’s data collection included a validation step where annotators verified the alignment between the completed trajectory steps and the intent, ..."}
+{"idx": 4, "title": "Paper Reading", "date": "", "ddg_snippet": "Our dataset comprises 3,119 multiple-choice question- answer pairs derived from 1,805 scenes across 200 diverse movies, spanning five novel fine ...", "subpage_snippet": "", "source": "www.paperreading.club", "link": "https://www.paperreading.club/", "content": "Our dataset comprises 3,119 multiple-choice question- answer pairs derived from 1,805 scenes across 200 diverse movies, spanning five novel fine ..."}
+{"idx": 5, "title": "OpenCodeInterpreter", "date": "", "ddg_snippet": "In subsequent sections, we delve into the specific methods employed in constructing the dataset , illustrating our commitment to creating a robust and ...", "subpage_snippet": "", "source": "opencodeinterpreter.github.io", "link": "https://opencodeinterpreter.github.io/", "content": "In subsequent sections, we delve into the specific methods employed in constructing the dataset , illustrating our commitment to creating a robust and ..."}
+{"idx": 6, "title": "ScribeAgent: Fine-Tuning Open-Source LLMs for Enhanced Web", "date": "", "ddg_snippet": "We introduce two key aspects to make fine-tuning successful: (1) Constructing a large-scale, high-quality dataset and (2) fine-tuning LLMs to ...", "subpage_snippet": "", "source": "blog.ml.cmu.edu", "link": "https://blog.ml.cmu.edu/2024/12/06/scribeagent-fine-tuning-open-source-llms-for-enhanced-web-navigation/", "content": "We introduce two key aspects to make fine-tuning successful: (1) Constructing a large-scale, high-quality dataset and (2) fine-tuning LLMs to ..."}
+{"idx": 7, "title": "GitHub - reworkd/bananalyzer: Open source AI Agent evaluation", "date": "", "ddg_snippet": "There exists valuable web task datasets and evaluations that we'd like to unify in a single repo ( Mind2Web , WebArena , etc).", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/reworkd/bananalyzer", "content": "There exists valuable web task datasets and evaluations that we'd like to unify in a single repo ( Mind2Web , WebArena , etc)."}
+{"idx": 8, "title": "Yang Deng - ACL Anthology", "date": "", "ddg_snippet": "Extensive experiments are conducted to benchmark the MT- Mind2Web dataset , and validate the effectiveness of the proposed method .", "subpage_snippet": "", "source": "aclanthology.org", "link": "https://aclanthology.org/people/y/yang-deng/", "content": "Extensive experiments are conducted to benchmark the MT- Mind2Web dataset , and validate the effectiveness of the proposed method ."}
+{"idx": 9, "title": "Agent-RewardBench: Towards a Unified Benchmark for Reward", "date": "", "ddg_snippet": "To construct the reward benchmark, we construct a set of pairs S r raw = { ( r + , r − ) } subscript 𝑆 r raw superscript 𝑟 superscript 𝑟 S ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2506.21252v1", "content": "To construct the reward benchmark, we construct a set of pairs S r raw = { ( r + , r − ) } subscript 𝑆 r raw superscript 𝑟 superscript 𝑟 S ..."}
diff --git a/data/sampled_jsons/Mind2Web_dataset_construction_methodology_manual_automatic_task_creation_year_2023.jsonl b/data/sampled_jsons/Mind2Web_dataset_construction_methodology_manual_automatic_task_creation_year_2023.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..3db1b869d1c23ead2e9a67eb389ccf59af0d5668
--- /dev/null
+++ b/data/sampled_jsons/Mind2Web_dataset_construction_methodology_manual_automatic_task_creation_year_2023.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Automatic Dataset Construction (ADC): Sample Collection, Data ...", "date": "", "ddg_snippet": "However, creating high-quality datasets quickly and accurately remains a challenge due to annotation errors, the substantial time and costs associated with human labor. To address these issues, we propose Automatic Dataset Construction (ADC), an innovative methodology that automates dataset creation with negligible cost and high efficiency.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2408.11338v1", "content": "However, creating high-quality datasets quickly and accurately remains a challenge due to annotation errors, the substantial time and costs associated with human labor. To address these issues, we propose Automatic Dataset Construction (ADC), an innovative methodology that automates dataset creation with negligible cost and high efficiency."}
+{"idx": 1, "title": "mind2web/README.md at main · benchflow-yaml/mind2web · GitHub", "date": "", "ddg_snippet": "Dataset Summary Mind2Web is the first dataset for developing and evaluating generalist agents for the web that can follow language instructions to complete complex tasks on any website. Existing datasets for web agents either use simulated websites or only cover a limited set of websites and tasks , thus not suitable for generalist web agents.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/benchflow-yaml/mind2web/blob/main/README.md", "content": "Dataset Summary Mind2Web is the first dataset for developing and evaluating generalist agents for the web that can follow language instructions to complete complex tasks on any website. Existing datasets for web agents either use simulated websites or only cover a limited set of websites and tasks , thus not suitable for generalist web agents."}
+{"idx": 2, "title": "README.md · osunlp/Mind2Web at main - Hugging Face", "date": "", "ddg_snippet": "Dataset Summary Mind2Web is a dataset for developing and evaluating generalist agents for the web that can follow language instructions to complete complex tasks on any website. Existing datasets for web agents either use simulated websites or only cover a limited set of websites and tasks , thus not suitable for generalist web agents.", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/datasets/osunlp/Mind2Web/blob/main/README.md", "content": "Dataset Summary Mind2Web is a dataset for developing and evaluating generalist agents for the web that can follow language instructions to complete complex tasks on any website. Existing datasets for web agents either use simulated websites or only cover a limited set of websites and tasks , thus not suitable for generalist web agents."}
+{"idx": 3, "title": "Mind2Web - GitHub Pages", "date": "", "ddg_snippet": "Mind2Web is a dataset for developing and evaluating generalist agents for the web that can follow language instructions to complete complex tasks on any website. Mind2Web contains 2,350 tasks from 137 websites spanning 31 domains that: Reflect diverse and practical use cases on the web. Provide challenging yet realistic environments with real-world websites. Test generalization ability across ...", "subpage_snippet": "", "source": "osu-nlp-group.github.io", "link": "https://osu-nlp-group.github.io/Mind2Web/", "content": "Mind2Web is a dataset for developing and evaluating generalist agents for the web that can follow language instructions to complete complex tasks on any website. Mind2Web contains 2,350 tasks from 137 websites spanning 31 domains that: Reflect diverse and practical use cases on the web. Provide challenging yet realistic environments with real-world websites. Test generalization ability across ..."}
+{"idx": 4, "title": "Automatic Dataset Construction: A New Approach to Data ...", "date": "", "ddg_snippet": "Jun 24, 2025 · However, creating high-quality datasets quickly and accurately remains a challenge due to annotation errors, the substantial time and costs associated with human labor. To address these issues, we propose Automatic Dataset Construction (ADC), an innovative methodology that automates dataset creation with negligible cost and high efficiency.", "subpage_snippet": "", "source": "scisimple.com", "link": "https://scisimple.com/en/articles/2025-06-24-automatic-dataset-construction-a-new-approach-to-data-collection--a3ow01j", "content": "Jun 24, 2025 · However, creating high-quality datasets quickly and accurately remains a challenge due to annotation errors, the substantial time and costs associated with human labor. To address these issues, we propose Automatic Dataset Construction (ADC), an innovative methodology that automates dataset creation with negligible cost and high efficiency."}
+{"idx": 5, "title": "Mind2Web/README.md at main · OSU-NLP-Group/Mind2Web", "date": "", "ddg_snippet": "Mind2Web is the first dataset for developing and evaluating generalist agents for the web that can follow language instructions to complete complex tasks on any website. Existing datasets for web agents either use simulated websites or only cover a limited set of websites and tasks , thus not suitable for generalist web agents. With over 2,000 open-ended tasks collected from 137 websites ...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/OSU-NLP-Group/Mind2Web/blob/main/README.md", "content": "Mind2Web is the first dataset for developing and evaluating generalist agents for the web that can follow language instructions to complete complex tasks on any website. Existing datasets for web agents either use simulated websites or only cover a limited set of websites and tasks , thus not suitable for generalist web agents. With over 2,000 open-ended tasks collected from 137 websites ..."}
+{"idx": 6, "title": "Automatic Dataset Construction (ADC): Sample Collection, Data...", "date": "", "ddg_snippet": "Dec 31, 2023 · However, creating high-quality datasets quickly and accurately remains a challenge due to annotation errors, the substantial time and costs associated with human labor. To address these issues, we propose Automatic Dataset Construction (ADC), an innovative methodology that automates dataset creation with negligible cost and high efficiency.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=lyAosx437b", "content": "Dec 31, 2023 · However, creating high-quality datasets quickly and accurately remains a challenge due to annotation errors, the substantial time and costs associated with human labor. To address these issues, we propose Automatic Dataset Construction (ADC), an innovative methodology that automates dataset creation with negligible cost and high efficiency."}
+{"idx": 7, "title": "Mind2Web: Towards a Generalist Agent for the Web", "date": "", "ddg_snippet": "We introduce Mind2Web , the first dataset for developing and evaluating generalist agents for the web that can follow language instructions to complete complex ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2306.06070v3", "content": "We introduce Mind2Web , the first dataset for developing and evaluating generalist agents for the web that can follow language instructions to complete complex ..."}
+{"idx": 8, "title": "Mind2Web: Towards a Generalist Agent for the Web", "date": "", "ddg_snippet": "by X Deng · Cited by 635 — This paper introduces MIND2WEB , a novel dataset for creating and assessing versatile web agents capable of executing complex tasks on any website using language ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=kiYqbO3wqw", "content": "by X Deng · Cited by 635 — This paper introduces MIND2WEB , a novel dataset for creating and assessing versatile web agents capable of executing complex tasks on any website using language ..."}
+{"idx": 9, "title": "MIND2WEB: towards a generalist agent for the web", "date": "", "ddg_snippet": "by X Deng · 2023 · Cited by 635 — We introduce MIND2WEB , the first dataset for developing and evaluating generalist agents for the web that can follow language instructions to complete complex ...", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.5555/3666122.3667342", "content": "by X Deng · 2023 · Cited by 635 — We introduce MIND2WEB , the first dataset for developing and evaluating generalist agents for the web that can follow language instructions to complete complex ..."}
diff --git a/data/sampled_jsons/Mistral-7B_MMLU_score_performance_vs_Pythia-7B_benchmark_results_year_2023.jsonl b/data/sampled_jsons/Mistral-7B_MMLU_score_performance_vs_Pythia-7B_benchmark_results_year_2023.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..9b8da4af3a5c077f1a64fac136fa0fd32fa00e4a
--- /dev/null
+++ b/data/sampled_jsons/Mistral-7B_MMLU_score_performance_vs_Pythia-7B_benchmark_results_year_2023.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "LLM Benchmarks", "date": "", "ddg_snippet": "19 Jul 2024 — LLM Benchmarks are standardized tests designed to evaluate the performance , capabilities, and limitations of Large Language Models (LLMs) ...", "subpage_snippet": "", "source": "klu.ai", "link": "https://klu.ai/glossary/llm-benchmarks", "content": "19 Jul 2024 — LLM Benchmarks are standardized tests designed to evaluate the performance , capabilities, and limitations of Large Language Models (LLMs) ..."}
+{"idx": 1, "title": "The Rise of Open-Source AI Models (2024 — 2025)", "date": "", "ddg_snippet": "For example, on the popular MMLU academic test , Mistral 7B's score (~57%) beat LLaMA 2's 13B (~53%) by several points. It was also “vastly ...", "subpage_snippet": "", "source": "medium.com", "link": "https://medium.com/@justjlee/the-rise-of-open-source-ai-models-2024-2025-11354a0e8e23", "content": "For example, on the popular MMLU academic test , Mistral 7B's score (~57%) beat LLaMA 2's 13B (~53%) by several points. It was also “vastly ..."}
+{"idx": 2, "title": "Should We Really Edit Language Models? On the ...", "date": "", "ddg_snippet": "by Q Li · 2024 · Cited by 10 — Figure 12: Performance trends of evaluating edited Mistral -Instruction- 7B based model across different benchmarks using 4 editing methods.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2410.18785?", "content": "by Q Li · 2024 · Cited by 10 — Figure 12: Performance trends of evaluating edited Mistral -Instruction- 7B based model across different benchmarks using 4 editing methods."}
+{"idx": 3, "title": "Best Open Source LLMs of 2025", "date": "", "ddg_snippet": "Mistral 7B is a 7 -billion-parameter LLM developed by Mistral AI. It outperforms Llama 2 13B on all benchmarks and uses Sliding Window Attention (SWA) to ...", "subpage_snippet": "", "source": "klu.ai", "link": "https://klu.ai/blog/open-source-llm-models", "content": "Mistral 7B is a 7 -billion-parameter LLM developed by Mistral AI. It outperforms Llama 2 13B on all benchmarks and uses Sliding Window Attention (SWA) to ..."}
+{"idx": 4, "title": "A Critical Analysis of the Variability in Large Language ...", "date": "", "ddg_snippet": "29 Jul 2024 — As examples, for MMLU , the performance of Mistral - 7B ranges from 61.4% and 65.8%; while for HellaSwag, the performance of Llama2-70B ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2407.21072v1", "content": "29 Jul 2024 — As examples, for MMLU , the performance of Mistral - 7B ranges from 61.4% and 65.8%; while for HellaSwag, the performance of Llama2-70B ..."}
+{"idx": 5, "title": "Compact Language Models via Pruning and Knowledge ...", "date": "", "ddg_snippet": "9 Dec 2024 — MINITRON models exhibit up to a 16% improvement in MMLU scores compared to training from scratch, perform comparably to other community models ...", "subpage_snippet": "", "source": "neurips.cc", "link": "https://neurips.cc/virtual/2024/poster/96308", "content": "9 Dec 2024 — MINITRON models exhibit up to a 16% improvement in MMLU scores compared to training from scratch, perform comparably to other community models ..."}
+{"idx": 6, "title": "OLMES: A Standard for Language Model Evaluations", "date": "", "ddg_snippet": "by Y Gu · 2025 · Cited by 29 — OpenAI (2024) showing superhuman performance on benchmarks like MMLU . ... better than smaller ones (see average scores of Pythia -6.7B outperforms ...", "subpage_snippet": "", "source": "aclanthology.org", "link": "https://aclanthology.org/2025.findings-naacl.282.pdf", "content": "by Y Gu · 2025 · Cited by 29 — OpenAI (2024) showing superhuman performance on benchmarks like MMLU . ... better than smaller ones (see average scores of Pythia -6.7B outperforms ..."}
+{"idx": 7, "title": "Google publishes open source 2B and 7B model", "date": "", "ddg_snippet": "We evaluated them on the Open LLM Leaderboard, here's the 7B (compared to other pretrained 7Bs)! It's main performance boost compared to Mistral is GSM8K, aka ...", "subpage_snippet": "", "source": "www.reddit.com", "link": "https://www.reddit.com/r/LocalLLaMA/comments/1awbo84/google_publishes_open_source_2b_and_7b_model/", "content": "We evaluated them on the Open LLM Leaderboard, here's the 7B (compared to other pretrained 7Bs)! It's main performance boost compared to Mistral is GSM8K, aka ..."}
+{"idx": 8, "title": "onejune2018/Awesome-LLM-Eval", "date": "", "ddg_snippet": "20 Aug 2025 — A study using the CommonGen-lite dataset to evaluate LLMs, employing GPT-4 for assessment and comparing the performance of different models, ...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/onejune2018/Awesome-LLM-Eval", "content": "20 Aug 2025 — A study using the CommonGen-lite dataset to evaluate LLMs, employing GPT-4 for assessment and comparing the performance of different models, ..."}
+{"idx": 9, "title": "Stress-Testing Capability Elicitation With Password-Locked ...", "date": "", "ddg_snippet": "by R Greenblatt · 2024 · Cited by 19 — We also show the results for a MMLU password-locked models trained on soft labels: it is trained to imitate Mistral - 7B fine-tuned on. MMLU instead of imitating ...", "subpage_snippet": "", "source": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper_files/paper/2024/file/7ff97417474268e6b5a38bcbfae04944-Paper-Conference.pdf", "content": "by R Greenblatt · 2024 · Cited by 19 — We also show the results for a MMLU password-locked models trained on soft labels: it is trained to imitate Mistral - 7B fine-tuned on. MMLU instead of imitating ..."}
diff --git a/data/sampled_jsons/MovieLens_100k_dataset_origin.jsonl b/data/sampled_jsons/MovieLens_100k_dataset_origin.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..34fab563418c53f62b27a6ed168755fd3cfcaf0c
--- /dev/null
+++ b/data/sampled_jsons/MovieLens_100k_dataset_origin.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "MovieLens | GroupLens", "date": "", "ddg_snippet": "Also consider using the MovieLens 20M or latest datasets , which also contain (more recent) tag genome data or the Tag Genome 2021 dataset .", "subpage_snippet": "", "source": "grouplens.org", "link": "https://grouplens.org/datasets/movielens/", "content": "Also consider using the MovieLens 20M or latest datasets , which also contain (more recent) tag genome data or the Tag Genome 2021 dataset ."}
+{"idx": 1, "title": "movie_lens | TensorFlow Datasets", "date": "", "ddg_snippet": "This dataset contains a set of movie ratings from the MovieLens website, a movie recommendation service. ... The 1m dataset and 100k dataset contain ...", "subpage_snippet": "", "source": "www.tensorflow.org", "link": "https://www.tensorflow.org/datasets/catalog/movie_lens", "content": "This dataset contains a set of movie ratings from the MovieLens website, a movie recommendation service. ... The 1m dataset and 100k dataset contain ..."}
+{"idx": 2, "title": "Dataset module — Recommenders documentation", "date": "", "ddg_snippet": "The MovieLens datasets , first released in 1998, describe people’s expressed ... https://files.grouplens.org/ datasets / movielens /ml- 100k /", "subpage_snippet": "", "source": "recommenders-team.github.io", "link": "https://recommenders-team.github.io/recommenders/datasets.html", "content": "The MovieLens datasets , first released in 1998, describe people’s expressed ... https://files.grouplens.org/ datasets / movielens /ml- 100k /"}
+{"idx": 3, "title": "Datasets — ML Glossary documentation", "date": "", "ddg_snippet": "Public datasets in vision, nlp and more forked from caesar0301’s awesome datasets wiki. ... Delve Datasets for classification and regression (Univ.", "subpage_snippet": "", "source": "ml-cheatsheet.readthedocs.io", "link": "https://ml-cheatsheet.readthedocs.io/en/latest/datasets.html", "content": "Public datasets in vision, nlp and more forked from caesar0301’s awesome datasets wiki. ... Delve Datasets for classification and regression (Univ."}
+{"idx": 4, "title": "10 Media Datasets to Use AI for Film, TV, and More", "date": "", "ddg_snippet": "A hybrid dataset combining MovieLens and TMDb data with search queries and relevance scores. ... to do something other than the Titanic dataset , the ...", "subpage_snippet": "", "source": "opendatascience.com", "link": "https://opendatascience.com/10-media-datasets-to-use-ai-for-film-tv-and-more/", "content": "A hybrid dataset combining MovieLens and TMDb data with search queries and relevance scores. ... to do something other than the Titanic dataset , the ..."}
+{"idx": 5, "title": "glaiveai/glaive-code-assistant-v2 · Datasets at Hugging Face", "date": "", "ddg_snippet": "The `LEFT JOIN` is then used to match these combinations with the original table (YearsTable) based on the condition that the years2 substring is ...", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/datasets/glaiveai/glaive-code-assistant-v2", "content": "The `LEFT JOIN` is then used to match these combinations with the original table (YearsTable) based on the condition that the years2 substring is ..."}
+{"idx": 6, "title": "List-wise ranking", "date": "", "ddg_snippet": "The MovieLens 100K dataset , in its original form, provides individual rating instances (one user, one movie, one rating per example), rather than ...", "subpage_snippet": "", "source": "keras.io", "link": "https://keras.io/keras_rs/examples/listwise_ranking/", "content": "The MovieLens 100K dataset , in its original form, provides individual rating instances (one user, one movie, one rating per example), rather than ..."}
+{"idx": 7, "title": "Lesson 5 Advanced Discussion ✅ - Page 2 - Advanced (Part 1", "date": "", "ddg_snippet": "I haven’t tested this codebase, but would that RMSE of 0.836 (MSE of 0.7) be possible to get with fastai on the movielens 100K dataset ?", "subpage_snippet": "", "source": "forums.fast.ai", "link": "https://forums.fast.ai/t/lesson-5-advanced-discussion/30865?page=2", "content": "I haven’t tested this codebase, but would that RMSE of 0.836 (MSE of 0.7) be possible to get with fastai on the movielens 100K dataset ?"}
+{"idx": 8, "title": "US11080776B2 - Methods, systems, and computer readable media", "date": "", "ddg_snippet": "... when rating new products or users), it typically requires less assumptions than content filtering and yields a superior performance in real datasets ...", "subpage_snippet": "", "source": "patents.google.com", "link": "https://patents.google.com/patent/US11080776B2/en", "content": "... when rating new products or users), it typically requires less assumptions than content filtering and yields a superior performance in real datasets ..."}
+{"idx": 9, "title": "Data Pruning in Recommender Systems Research: Best Practice or", "date": "", "ddg_snippet": "One author (3%) used the unpruned MovieLens Latest, Full dataset , which, according to ... MovieLens 100k is the third most popular dataset (23%).", "subpage_snippet": "", "source": "isg.beel.org", "link": "https://isg.beel.org/blog/2019/08/12/data-pruning-in-recommender-systems-research-best-practice-or-malpractice-extended-pre-print/", "content": "One author (3%) used the unpruned MovieLens Latest, Full dataset , which, according to ... MovieLens 100k is the third most popular dataset (23%)."}
diff --git a/data/sampled_jsons/Multi-resolution_partial_differential_equations_preserved_solver_for_speeding_up_simulations.jsonl b/data/sampled_jsons/Multi-resolution_partial_differential_equations_preserved_solver_for_speeding_up_simulations.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..9bdcce9c8c466846501723ea9660e5d07369365a
--- /dev/null
+++ b/data/sampled_jsons/Multi-resolution_partial_differential_equations_preserved_solver_for_speeding_up_simulations.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Multi-resolution partial differential equations preserved ...", "date": "", "ddg_snippet": "by XY Liu · 2024 · Cited by 65 — These physical phenomena are usually governed by partial differential equations (PDEs) and can be simulated by solving these PDEs numerically ...", "subpage_snippet": "", "source": "www.nature.com", "link": "https://www.nature.com/articles/s42005-024-01521-z", "content": "by XY Liu · 2024 · Cited by 65 — These physical phenomena are usually governed by partial differential equations (PDEs) and can be simulated by solving these PDEs numerically ..."}
+{"idx": 1, "title": "Multi-resolution partial differential equations preserved ...", "date": "", "ddg_snippet": "This method, embedding discretized PDEs through convolutional residual networks in a multi - resolution setting, largely improves the generalizability and long- ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2205.03990v3", "content": "This method, embedding discretized PDEs through convolutional residual networks in a multi - resolution setting, largely improves the generalizability and long- ..."}
+{"idx": 2, "title": "Multi-resolution partial differential equations preserved learning ...", "date": "", "ddg_snippet": "Multi-resolution partial differential equations preserved learning framework for spatiotemporal dynamics · References (57) · Similar Records · Related Subjects.", "subpage_snippet": "", "source": "www.osti.gov", "link": "https://www.osti.gov/biblio/2527398", "content": "Multi-resolution partial differential equations preserved learning framework for spatiotemporal dynamics · References (57) · Similar Records · Related Subjects."}
+{"idx": 3, "title": "Multi-resolution partial differential equations preserved ...", "date": "", "ddg_snippet": "This work proposes to leverage physics prior knowledge by “baking” the discretized governing equations into the neural network architecture via the ...", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/Multi-resolution-partial-differential-equations-for-Liu-Zhu/b2ea1eac752e48bb545bb3f7a9c6668769324018", "content": "This work proposes to leverage physics prior knowledge by “baking” the discretized governing equations into the neural network architecture via the ..."}
+{"idx": 4, "title": "Multi-scale time-stepping of Partial Differential Equations ...", "date": "", "ddg_snippet": "by AP Hemmasian · 2024 · Cited by 16 — We incorporate the idea of multi -scale hierarchical time-stepping to increase the prediction speed and decrease accumulated error over time.", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S0045782524002391", "content": "by AP Hemmasian · 2024 · Cited by 16 — We incorporate the idea of multi -scale hierarchical time-stepping to increase the prediction speed and decrease accumulated error over time."}
+{"idx": 5, "title": "Millionfold accelerated AI solver for 3D multi-physical ...", "date": "", "ddg_snippet": "by Y Liu · 2025 — The intelligent solver solves the 3D forward problems in seconds , which is approximately 10 5 -10 6 times faster than traditional finite-element based method.", "subpage_snippet": "", "source": "www.nature.com", "link": "https://www.nature.com/articles/s41545-025-00491-1", "content": "by Y Liu · 2025 — The intelligent solver solves the 3D forward problems in seconds , which is approximately 10 5 -10 6 times faster than traditional finite-element based method."}
+{"idx": 6, "title": "NeurIPS Poster P$^2$C$^2$Net: PDE-Preserved Coarse ...", "date": "", "ddg_snippet": "9 Dec 2024 — These approaches can operate on coarse grids, enabling faster simulations compared with traditional numerical solvers while retaining accuracy.", "subpage_snippet": "", "source": "neurips.cc", "link": "https://neurips.cc/virtual/2024/poster/93729", "content": "9 Dec 2024 — These approaches can operate on coarse grids, enabling faster simulations compared with traditional numerical solvers while retaining accuracy."}
+{"idx": 7, "title": "PDE-constrained Learning with Multi-time-stepping for ...", "date": "", "ddg_snippet": "by Q Wang — This paper introduces MultiPDENet , a neural network architecture designed to accelerate fluid dynamic simulations by combining classical numerical methods, such ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=stcN89QGfL", "content": "by Q Wang — This paper introduces MultiPDENet , a neural network architecture designed to accelerate fluid dynamic simulations by combining classical numerical methods, such ..."}
+{"idx": 8, "title": "MultiPDENet: PDE-embedded Learning with Multi-time ...", "date": "", "ddg_snippet": "15 Jul 2025 — Multi- resolution partial differential equations preserved learning framework for spatiotemporal dynamics. Communications Physics, 7(1):31 ...", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/poster/46029", "content": "15 Jul 2025 — Multi- resolution partial differential equations preserved learning framework for spatiotemporal dynamics. Communications Physics, 7(1):31 ..."}
+{"idx": 9, "title": "Accelerating multigrid solver with generative super-resolution", "date": "", "ddg_snippet": "7 Mar 2024 — The geometric multigrid algorithm is an efficient numerical method for solving a variety of elliptic partial differential equations (PDEs).", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2403.07936v1", "content": "7 Mar 2024 — The geometric multigrid algorithm is an efficient numerical method for solving a variety of elliptic partial differential equations (PDEs)."}
diff --git a/data/sampled_jsons/MultiPDENet_MaNN_Block_Section_3.2.4_function_description.jsonl b/data/sampled_jsons/MultiPDENet_MaNN_Block_Section_3.2.4_function_description.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..918dca01e8a71d348db82bb77d65c3301353da11
--- /dev/null
+++ b/data/sampled_jsons/MultiPDENet_MaNN_Block_Section_3.2.4_function_description.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "MultiPDENet: PDE-embedded Learning with Multi-time-stepping", "date": "", "ddg_snippet": "3.2 Model Architecture In this section , we introduce MultiPDENet and show how our model efficiently captures the underlying spatiotemporal dynamics. As illustrated in Figure 1 (a), predicting 𝐮 k + 1 from the input 𝐮 k involves two main components: the Physics Block and the M a NN Block .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2501.15987v1", "content": "3.2 Model Architecture In this section , we introduce MultiPDENet and show how our model efficiently captures the underlying spatiotemporal dynamics. As illustrated in Figure 1 (a), predicting 𝐮 k + 1 from the input 𝐮 k involves two main components: the Physics Block and the M a NN Block ."}
+{"idx": 1, "title": "Concept Function Block Manual PDF - Scribd", "date": "", "ddg_snippet": "Concept function block manual.pdf - Free download as PDF File (.pdf), Text File (.txt) or read online for free.", "subpage_snippet": "", "source": "www.scribd.com", "link": "https://www.scribd.com/document/401254730/Concept-function-block-manual-pdf", "content": "Concept function block manual.pdf - Free download as PDF File (.pdf), Text File (.txt) or read online for free."}
+{"idx": 2, "title": "PDF Standard function blocks - ABB", "date": "", "ddg_snippet": "Standard function blocks What this chapter contains This chapter describes the standard function blocks . The blocks are grouped according to the grouping in the DriveSPC tool. It depends about drive type which amount of blocks and what kind of blocks are available. The number in brackets in the standard block heading is the block number.", "subpage_snippet": "", "source": "library.e.abb.com", "link": "https://library.e.abb.com/public/04663f4640f1aa0ac1257674004539b2/DriveSPC_Standard_function_blocks.pdf", "content": "Standard function blocks What this chapter contains This chapter describes the standard function blocks . The blocks are grouped according to the grouping in the DriveSPC tool. It depends about drive type which amount of blocks and what kind of blocks are available. The number in brackets in the standard block heading is the block number."}
+{"idx": 3, "title": "PDF Concept IEC block library Part: AKFEFB - Alameda Electric", "date": "", "ddg_snippet": "A derived function block represents the Call up of a derived function block type. Details of the graphic form of call up can be found in the definition \" Function block (Item)\". Contrary to calling up EFB types, calling up DFB types is denoted by double vertical lines on the left and right side of the rectangular block symbol. The body of a derived function block type is designed using FBD ...", "subpage_snippet": "", "source": "igate.alamedaelectric.com", "link": "https://igate.alamedaelectric.com/Modicon+Documents/PLC+Concept+Software+2.6+IEC+Block+Library.pdf", "content": "A derived function block represents the Call up of a derived function block type. Details of the graphic form of call up can be found in the definition \" Function block (Item)\". Contrary to calling up EFB types, calling up DFB types is denoted by double vertical lines on the left and right side of the rectangular block symbol. The body of a derived function block type is designed using FBD ..."}
+{"idx": 4, "title": "PDF Function Block Programming Manual", "date": "", "ddg_snippet": "Manual Overview This manual attempts to accommodate users who are unfamiliar with the function block system as well as more experienced users. When read from front to back, this manual provides an increasing level of detail, with each chapter building upon information presented in the previous chapter.", "subpage_snippet": "", "source": "literature.rockwellautomation.com", "link": "https://literature.rockwellautomation.com/idc/groups/literature/documents/um/1336t-um007_-en-p.pdf", "content": "Manual Overview This manual attempts to accommodate users who are unfamiliar with the function block system as well as more experienced users. When read from front to back, this manual provides an increasing level of detail, with each chapter building upon information presented in the previous chapter."}
+{"idx": 5, "title": "PDF Function Blocks", "date": "", "ddg_snippet": "Additional information on this group of function blocks , e.g. symbol, mode of operation, I/O and technical data are provided in the online help for the particular block .", "subpage_snippet": "", "source": "support.industry.siemens.com", "link": "https://support.industry.siemens.com/cs/attachments/14943298/fm1_funktion_e.pdf", "content": "Additional information on this group of function blocks , e.g. symbol, mode of operation, I/O and technical data are provided in the online help for the particular block ."}
+{"idx": 6, "title": "PLC Programming With Function Block Diagrams - Control.com", "date": "", "ddg_snippet": "PLC Programming With Function Block Diagrams Function block diagram (FBD) programming is a common language for PLCs following the IEC 61131 standard. What is FBD, and how does it differ from the familiar ladder logic programs?", "subpage_snippet": "", "source": "control.com", "link": "https://control.com/technical-articles/plc-programming-with-function-block-diagrams/", "content": "PLC Programming With Function Block Diagrams Function block diagram (FBD) programming is a common language for PLCs following the IEC 61131 standard. What is FBD, and how does it differ from the familiar ladder logic programs?"}
+{"idx": 7, "title": "Function Block Diagram (FBD) PLC Programming Tutorial for Beginners", "date": "", "ddg_snippet": "Learn all about Function Block Diagram (FBD), the official PLC programming language described in IEC 61131-3. Start programming with Function Blocks and explore the world of standard and custom function blocks .", "subpage_snippet": "", "source": "www.plcacademy.com", "link": "https://www.plcacademy.com/function-block-diagram-programming/", "content": "Learn all about Function Block Diagram (FBD), the official PLC programming language described in IEC 61131-3. Start programming with Function Blocks and explore the world of standard and custom function blocks ."}
+{"idx": 8, "title": "MultiPDENet: PDE-embedded Learning with Multi-time-stepping for ...", "date": "", "ddg_snippet": "Model Architecture In this section , we introduce MultiPDENet and show how our model efficiently captures the underlying spatiotem- poral dynamics. As illustrated in Figure1(a), predicting uk+1from the input u involves two main components: the Physics Block and the M aNN Block . 3.2.1. MULTI-SCALE FORWARD TIME STEPPING SCHEME", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=D1gs8QT74m", "content": "Model Architecture In this section , we introduce MultiPDENet and show how our model efficiently captures the underlying spatiotem- poral dynamics. As illustrated in Figure1(a), predicting uk+1from the input u involves two main components: the Physics Block and the M aNN Block . 3.2.1. MULTI-SCALE FORWARD TIME STEPPING SCHEME"}
+{"idx": 9, "title": "PDE-constrained Learning with Multi-time-stepping for Accelerated...", "date": "", "ddg_snippet": "Added additional references to the related work section ( Section 2, Page 3). Provided a clearer description of the model architecture ( Section 3.2.1, Page 4). Refined the explanation of the Physics block ( Section 3.2.2, Page 4). Clarified the description of the NN block ( Section 3.2.4 , Page 5).", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=stcN89QGfL", "content": "Added additional references to the related work section ( Section 2, Page 3). Provided a clearer description of the model architecture ( Section 3.2.1, Page 4). Refined the explanation of the Physics block ( Section 3.2.2, Page 4). Clarified the description of the NN block ( Section 3.2.4 , Page 5)."}
diff --git a/data/sampled_jsons/MultiPDENet_PDE-embedded_Learning_with_Multi-time-stepping_for_Accelerated_Flow_Simulation_citations.jsonl b/data/sampled_jsons/MultiPDENet_PDE-embedded_Learning_with_Multi-time-stepping_for_Accelerated_Flow_Simulation_citations.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..24c88bf403ce3ca75ee9fee02505901436c0e235
--- /dev/null
+++ b/data/sampled_jsons/MultiPDENet_PDE-embedded_Learning_with_Multi-time-stepping_for_Accelerated_Flow_Simulation_citations.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "MultiPDENet: PDE-embedded Learning with Multi-time ...", "date": "", "ddg_snippet": "by Q Wang · 2025 — MultiPDENet: PDE-embedded Learning with Multi-time-stepping for Accelerated Flow Simulation . Authors:Qi Wang, Yuan Mi, Haoyun Wang, Yi Zhang, ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2501.15987", "content": "by Q Wang · 2025 — MultiPDENet: PDE-embedded Learning with Multi-time-stepping for Accelerated Flow Simulation . Authors:Qi Wang, Yuan Mi, Haoyun Wang, Yi Zhang, ..."}
+{"idx": 1, "title": "MultiPDENet: PDE-embedded Learning with Multi-time- ...", "date": "", "ddg_snippet": "by Q Wang — ... MultiPDENet: PDE-embedded Learning with Multi-time-stepping for Accelerated Flow Simulation . Download PDF. Qi Wang, Yuan Mi, Wang Haoyun, Yi Zhang, Ruizhi ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=D1gs8QT74m", "content": "by Q Wang — ... MultiPDENet: PDE-embedded Learning with Multi-time-stepping for Accelerated Flow Simulation . Download PDF. Qi Wang, Yuan Mi, Wang Haoyun, Yi Zhang, Ruizhi ..."}
+{"idx": 2, "title": "MultiPDENet: PDE-embedded Learning with Multi-time ...", "date": "", "ddg_snippet": "15 Jul 2025 — MultiPDENet: PDE-embedded Learning with Multi-time-stepping for Accelerated Flow Simulation . Qi Wang, Yuan Mi, Wang Haoyun, Yi Zhang, Ruizhi Chengze, Hongsheng ...", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/poster/46029", "content": "15 Jul 2025 — MultiPDENet: PDE-embedded Learning with Multi-time-stepping for Accelerated Flow Simulation . Qi Wang, Yuan Mi, Wang Haoyun, Yi Zhang, Ruizhi Chengze, Hongsheng ..."}
+{"idx": 3, "title": "MultiPDENet: PDE-embedded Learning with Multi-time ...", "date": "", "ddg_snippet": "27 Jan 2025 — We developed MultiPDENet, a PDE-embedded network with multiscale time-stepping, for accelerated flow simulations on spatiotemporal coarse grids.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2501.15987v1", "content": "27 Jan 2025 — We developed MultiPDENet, a PDE-embedded network with multiscale time-stepping, for accelerated flow simulations on spatiotemporal coarse grids."}
+{"idx": 4, "title": "MultiPDENet: PDE-embedded Learning with Multi-time- ...", "date": "", "ddg_snippet": "Solving partial differential equations (PDEs) by numerical methods meet computational cost chal- lenge for getting the accurate solution since fine.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf/046ccccd77df13df48f47ff1081b58969771121a.pdf", "content": "Solving partial differential equations (PDEs) by numerical methods meet computational cost chal- lenge for getting the accurate solution since fine."}
+{"idx": 5, "title": "[Literature Review] MultiPDENet: PDE-embedded Learning with ...", "date": "", "ddg_snippet": "The paper titled \" MultiPDENet: PDE-embedded Learning with Multi-time-stepping for Accelerated Flow Simulation \" introduces a novel approach integrating ...", "subpage_snippet": "", "source": "www.themoonlight.io", "link": "https://www.themoonlight.io/en/review/multipdenet-pde-embedded-learning-with-multi-time-stepping-for-accelerated-flow-simulation", "content": "The paper titled \" MultiPDENet: PDE-embedded Learning with Multi-time-stepping for Accelerated Flow Simulation \" introduces a novel approach integrating ..."}
+{"idx": 6, "title": "PDE-embedded Learning with Multi-time- ...", "date": "", "ddg_snippet": "MultiPDENet: PDE-embedded Learning with Multi-time-stepping for Accelerated Flow Simulation ... references . [1] A Hybrid Approach to Atmospheric Modeling ...", "subpage_snippet": "", "source": "bytez.com", "link": "https://bytez.com/docs/icml/46029/references?_c=eyJ2IjoxLCJyZWxhdGVkIjpbImNvZGUiLCJyZWZlcmVuY2VzIiwiY29uZmVyZW5jZSJdfQ==", "content": "MultiPDENet: PDE-embedded Learning with Multi-time-stepping for Accelerated Flow Simulation ... references . [1] A Hybrid Approach to Atmospheric Modeling ..."}
+{"idx": 7, "title": "Multi-resolution partial differential equations preserved ...", "date": "", "ddg_snippet": "... MultiPDENet: PDE-embedded Learning with Multi-time-stepping for Accelerated Flow Simulation · Qi WangYuan Mi +5 authors. Hao Sun. Computer Science, Engineering.", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/Multi-resolution-partial-differential-equations-for-Liu-Zhu/b2ea1eac752e48bb545bb3f7a9c6668769324018", "content": "... MultiPDENet: PDE-embedded Learning with Multi-time-stepping for Accelerated Flow Simulation · Qi WangYuan Mi +5 authors. Hao Sun. Computer Science, Engineering."}
+{"idx": 8, "title": "PDE-embedded Learning with Multi-time- ...", "date": "", "ddg_snippet": "... MultiPDENet: PDE-embedded Learning with Multi-time-stepping for Accelerated Flow Simulation . 2 months ago. ·. ICML. Paper · ICML 2025 · authors · audio.", "subpage_snippet": "", "source": "bytez.com", "link": "https://bytez.com/docs/icml/46029/audio?_c=eyJ2IjoxLCJyZWxhdGVkIjpbImNvZGUiLCJyZWZlcmVuY2VzIiwiY29uZmVyZW5jZSJdfQ==", "content": "... MultiPDENet: PDE-embedded Learning with Multi-time-stepping for Accelerated Flow Simulation . 2 months ago. ·. ICML. Paper · ICML 2025 · authors · audio."}
+{"idx": 9, "title": "Zeruizhi Cheng - Google 学术搜索", "date": "", "ddg_snippet": "... MultiPDENet: PDE-embedded Learning with Multi-time-stepping for Accelerated Flow Simulation . Q Wang, Y Mi, H Wang, Y Zhang, R Chengze, H Liu, JR Wen, H Sun.", "subpage_snippet": "", "source": "scholar.google.com", "link": "https://scholar.google.com/citations?user=2imLVnkAAAAJ&hl=zh-CN", "content": "... MultiPDENet: PDE-embedded Learning with Multi-time-stepping for Accelerated Flow Simulation . Q Wang, Y Mi, H Wang, Y Zhang, R Chengze, H Liu, JR Wen, H Sun."}
diff --git a/data/sampled_jsons/NeRF_Mildenhall_2020_Neural_Radiance_Fields_view_synthesis_abstract_year_2020.jsonl b/data/sampled_jsons/NeRF_Mildenhall_2020_Neural_Radiance_Fields_view_synthesis_abstract_year_2020.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..f0a49ee2214a8edb7d1c40ec06ff3c2acac3bc7b
--- /dev/null
+++ b/data/sampled_jsons/NeRF_Mildenhall_2020_Neural_Radiance_Fields_view_synthesis_abstract_year_2020.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "NeRF: Neural Radiance Fields", "date": "", "ddg_snippet": "inproceedings{mildenhall2020nerf, title={ NeRF : Representing Scenes as Neural Radiance Fields for View Synthesis }, author={Ben Mildenhall and ...", "subpage_snippet": "", "source": "www.matthewtancik.com", "link": "https://www.matthewtancik.com/nerf", "content": "inproceedings{mildenhall2020nerf, title={ NeRF : Representing Scenes as Neural Radiance Fields for View Synthesis }, author={Ben Mildenhall and ..."}
+{"idx": 1, "title": "Neural Fields: NeRF: Representing Scenes as Neural Radiance", "date": "", "ddg_snippet": "... NeRF : Representing Scenes as Neural Radiance Fields for View Synthesis }, year = { 2020 }, url = {http://arxiv.org/abs/2003.08934v2}, entrytype = ...", "subpage_snippet": "", "source": "neuralfields.cs.brown.edu", "link": "https://neuralfields.cs.brown.edu/paper_33.html", "content": "... NeRF : Representing Scenes as Neural Radiance Fields for View Synthesis }, year = { 2020 }, url = {http://arxiv.org/abs/2003.08934v2}, entrytype = ..."}
+{"idx": 2, "title": "[2003.08934] NeRF: Representing Scenes as Neural Radiance", "date": "", "ddg_snippet": "View a PDF of the paper titled NeRF : Representing Scenes as Neural Radiance Fields for View Synthesis , by Ben Mildenhall and 5 other authors", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2003.08934", "content": "View a PDF of the paper titled NeRF : Representing Scenes as Neural Radiance Fields for View Synthesis , by Ben Mildenhall and 5 other authors"}
+{"idx": 3, "title": "BRDF-NeRF: Neural Radiance Fields with Optical Satellite Images", "date": "", "ddg_snippet": "While vanilla NeRF excels in view synthesis , it cannot relight or edit materials, due to its inability to decompose outgoing radiance into incoming ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2409.12014v3", "content": "While vanilla NeRF excels in view synthesis , it cannot relight or edit materials, due to its inability to decompose outgoing radiance into incoming ..."}
+{"idx": 4, "title": "NeRFshop: Interactive Editing of Neural Radiance Fields", "date": "", "ddg_snippet": "Neural Radiance Fields ( NeRFs ) have revolutionized novel view synthesis for captured scenes, with recent methods allowing interactive free-viewpoint ...", "subpage_snippet": "", "source": "repo-sam.inria.fr", "link": "https://repo-sam.inria.fr/fungraph/nerfshop/", "content": "Neural Radiance Fields ( NeRFs ) have revolutionized novel view synthesis for captured scenes, with recent methods allowing interactive free-viewpoint ..."}
+{"idx": 5, "title": "NeRF at NeurIPS 2022 | Mark Boss", "date": "", "ddg_snippet": "Neural Reflectance Field from Shading and Shadow under a Fixed Viewpoint leverages a fixed viewpoint with a moving light source.", "subpage_snippet": "", "source": "markboss.me", "link": "https://markboss.me/post/nerf_at_neurips22/", "content": "Neural Reflectance Field from Shading and Shadow under a Fixed Viewpoint leverages a fixed viewpoint with a moving light source."}
+{"idx": 6, "title": "Neural Scene Flow Fields for Space-Time View Synthesis of", "date": "", "ddg_snippet": "... a method to perform novel view and time synthesis of ... A gallery of results comparing with Neural Radiance Fields ( NeRF ) [ Mildenhall et al 2020 ]", "subpage_snippet": "", "source": "www.cs.cornell.edu", "link": "https://www.cs.cornell.edu/~zl548/NSFF/", "content": "... a method to perform novel view and time synthesis of ... A gallery of results comparing with Neural Radiance Fields ( NeRF ) [ Mildenhall et al 2020 ]"}
+{"idx": 7, "title": "NeuS: Learning Neural Implicit Surfaces by Volume Rendering for", "date": "", "ddg_snippet": "Meanwhile, recent neural methods for novel view synthesis , such as NeRF [ Mildenhall et al., 2020 ] and its variants, use volume rendering to produce a ...", "subpage_snippet": "", "source": "lingjie0206.github.io", "link": "https://lingjie0206.github.io/papers/NeuS/", "content": "Meanwhile, recent neural methods for novel view synthesis , such as NeRF [ Mildenhall et al., 2020 ] and its variants, use volume rendering to produce a ..."}
+{"idx": 8, "title": "Computational Imaging DehazeNeRF: Haze Removal using Neural", "date": "", "ddg_snippet": "Neural radiance fields ( NeRFs ) have demonstrated state-of-the-art performance for 3D computer vision tasks, including novel view synthesis and 3D ...", "subpage_snippet": "", "source": "www.computationalimaging.org", "link": "https://www.computationalimaging.org/publications/dehazenerf/", "content": "Neural radiance fields ( NeRFs ) have demonstrated state-of-the-art performance for 3D computer vision tasks, including novel view synthesis and 3D ..."}
+{"idx": 9, "title": "Jon Barron", "date": "", "ddg_snippet": "... Radiance Fields for Real-time View Synthesis in Unbounded Scenes Christian Reiser , Richard Szeliski , Dor Verbin , Pratul Srinivasan , Ben Mildenhall ...", "subpage_snippet": "", "source": "jonbarron.info", "link": "https://jonbarron.info/", "content": "... Radiance Fields for Real-time View Synthesis in Unbounded Scenes Christian Reiser , Richard Szeliski , Dor Verbin , Pratul Srinivasan , Ben Mildenhall ..."}
diff --git a/data/sampled_jsons/NeRF_Representing_Scenes_as_Neural_Radiance_Fields_for_View_Synthesis_abstract_Mildenhall.jsonl b/data/sampled_jsons/NeRF_Representing_Scenes_as_Neural_Radiance_Fields_for_View_Synthesis_abstract_Mildenhall.jsonl
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index 0000000000000000000000000000000000000000..094b933c006deec968cc3e536477c385f6c751e9
--- /dev/null
+++ b/data/sampled_jsons/NeRF_Representing_Scenes_as_Neural_Radiance_Fields_for_View_Synthesis_abstract_Mildenhall.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "[2003.08934] NeRF : Representing Scenes as Neural Radiance ...", "date": "", "ddg_snippet": "View a PDF of the paper titled NeRF : Representing Scenes as Neural Radiance Fields for View Synthesis , by Ben Mildenhall and 5 other authors.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2003.08934", "content": "View a PDF of the paper titled NeRF : Representing Scenes as Neural Radiance Fields for View Synthesis , by Ben Mildenhall and 5 other authors."}
+{"idx": 1, "title": "Notes on NeRF : Representing Scenes as Neural Radiance Fields ...", "date": "", "ddg_snippet": "Mildenhall et al built a fully connected network without using convolutional layers. It uses a single 5D coordinate (x,y,z,θ, Φ) that compresses spatial and viewing/ray direction information to output a single volume density σ and RGB color radiance . It requires 3 steps to render a NeRF .", "subpage_snippet": "", "source": "medium.com", "link": "https://medium.com/@chengmu/notes-on-nerf-representing-scenes-as-neural-radiance-fields-for-view-synthesis-by-mildenhall-et-al-fd88f715fe77", "content": "Mildenhall et al built a fully connected network without using convolutional layers. It uses a single 5D coordinate (x,y,z,θ, Φ) that compresses spatial and viewing/ray direction information to output a single volume density σ and RGB color radiance . It requires 3 steps to render a NeRF ."}
+{"idx": 2, "title": "NeRF : Representing Scenes as", "date": "", "ddg_snippet": "NeRF : Representing Scenes as Neural Radiance Fields for View Synthesis . Ben Mildenhall 1 Pratul P. Srinivasan1 Matthew Tancik1 Jonathan T. Barron2 Ravi Ramamoorthi3 Ren Ng1. 1UC Berkeley 2Google Research 3UC San Diego. Abstract . We present a method that...", "subpage_snippet": "", "source": "papers.baulab.info", "link": "https://papers.baulab.info/papers/Mildenhall-2020.pdf", "content": "NeRF : Representing Scenes as Neural Radiance Fields for View Synthesis . Ben Mildenhall 1 Pratul P. Srinivasan1 Matthew Tancik1 Jonathan T. Barron2 Ravi Ramamoorthi3 Ren Ng1. 1UC Berkeley 2Google Research 3UC San Diego. Abstract . We present a method that..."}
+{"idx": 3, "title": "NeRF", "date": "", "ddg_snippet": "NeRF : Representing Scenes as Neural Radiance Fields for View Synthesis . By Ben Mildenhall , Pratul P. Srinivasan, Matthew Tancik, Jonathan T. Barron, Ravi Ramamoorthi, and Ren Ng. Abstract We present a method that achieves...", "subpage_snippet": "", "source": "www.cs.jhu.edu", "link": "https://www.cs.jhu.edu/~misha/ReadingSeminar/Papers/Mildenhall21.pdf", "content": "NeRF : Representing Scenes as Neural Radiance Fields for View Synthesis . By Ben Mildenhall , Pratul P. Srinivasan, Matthew Tancik, Jonathan T. Barron, Ravi Ramamoorthi, and Ren Ng. Abstract We present a method that achieves..."}
+{"idx": 4, "title": "NeRF : Representing Scenes as Neural Radiance Fields for View ...", "date": "", "ddg_snippet": "Abstract . We present a method that achieves state-of-the-art results for synthesizing novel views of complex scenes by optimizing an underlying continuous volumetric scene function using a sparse set of input views. Our algorithm represents a scene using a fully-connected...", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/chapter/10.1007/978-3-030-58452-8_24", "content": "Abstract . We present a method that achieves state-of-the-art results for synthesizing novel views of complex scenes by optimizing an underlying continuous volumetric scene function using a sparse set of input views. Our algorithm represents a scene using a fully-connected..."}
+{"idx": 5, "title": "NeRF : representing scenes as neural radiance fields for view ...", "date": "", "ddg_snippet": "Neural Radiance Fields ( NeRF ) [16] introduced a major shift by modeling scenes as volumetric radiance fields optimized through differentiable rendering.Recently, Neural Radiance Fields ( NeRF ) have been used for urban‐scale scenes with potentially infinite scales.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/357498745_NeRF_representing_scenes_as_neural_radiance_fields_for_view_synthesis", "content": "Neural Radiance Fields ( NeRF ) [16] introduced a major shift by modeling scenes as volumetric radiance fields optimized through differentiable rendering.Recently, Neural Radiance Fields ( NeRF ) have been used for urban‐scale scenes with potentially infinite scales."}
+{"idx": 6, "title": "NeRF : Neural Radiance Fields", "date": "", "ddg_snippet": "NeRF . Representing Scenes as Neural Radiance Fields for View Synthesis . ECCV 2020 Oral - Best Paper Honorable Mention.", "subpage_snippet": "", "source": "www.matthewtancik.com", "link": "https://www.matthewtancik.com/nerf", "content": "NeRF . Representing Scenes as Neural Radiance Fields for View Synthesis . ECCV 2020 Oral - Best Paper Honorable Mention."}
+{"idx": 7, "title": "NeRF : Representing Scenes as", "date": "", "ddg_snippet": "NeRF : Representing Scenes as Neural Radiance Fields for View Synthesis", "subpage_snippet": "", "source": "www.ecva.net", "link": "https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123460392-supp.pdf", "content": "NeRF : Representing Scenes as Neural Radiance Fields for View Synthesis"}
+{"idx": 8, "title": "NeRF : Representing Scenes as Neural Radiance Fields for View ...", "date": "", "ddg_snippet": "Thus, following Rahaman et al.2, they map the inputs to a higher dimensional space using high-frequency functions to fit data with high-frequency variations. The results on both real and synthetic images from NeRF significantly outperforms pervious papers.", "subpage_snippet": "", "source": "tinypapers.substack.com", "link": "https://tinypapers.substack.com/p/nerf-representing-scenes-as-neural", "content": "Thus, following Rahaman et al.2, they map the inputs to a higher dimensional space using high-frequency functions to fit data with high-frequency variations. The results on both real and synthetic images from NeRF significantly outperforms pervious papers."}
+{"idx": 9, "title": "NeRF : Representing Scenes as Neural Radiance Fields for View ...", "date": "", "ddg_snippet": "Ben Mildenhall 1. , Pratul P. Srinivasan 1. Abstract . We present a method that achieves state-of-the-art results for synthesizing novel views of complex scenes by optimizing an underlying continuous volumetric scene function using a sparse set of input views.", "subpage_snippet": "", "source": "colab.ws", "link": "https://colab.ws/articles/10.1007/978-3-030-58452-8_24", "content": "Ben Mildenhall 1. , Pratul P. Srinivasan 1. Abstract . We present a method that achieves state-of-the-art results for synthesizing novel views of complex scenes by optimizing an underlying continuous volumetric scene function using a sparse set of input views."}
diff --git a/data/sampled_jsons/Neural_Persistence_Dynamics_Section_2_crocker_plots_stacks_scalability_dimensionality_observation_se.jsonl b/data/sampled_jsons/Neural_Persistence_Dynamics_Section_2_crocker_plots_stacks_scalability_dimensionality_observation_se.jsonl
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index 0000000000000000000000000000000000000000..d06addf1e3065f06eee046f5b6ab299bfd09ab11
--- /dev/null
+++ b/data/sampled_jsons/Neural_Persistence_Dynamics_Section_2_crocker_plots_stacks_scalability_dimensionality_observation_se.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Neural Persistence Dynamics - arXiv.org", "date": "", "ddg_snippet": "Despite remarkable success in distinguishing different configurations of models for collective behav-ior, all approaches suffer scalability issues, either in terms of the dimensionality of the vectorized persistence diagrams (as with the PSK approach of [23]), or in terms of the number of observation sequences (as is the case for crocker plots ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2405.15732v1", "content": "Despite remarkable success in distinguishing different configurations of models for collective behav-ior, all approaches suffer scalability issues, either in terms of the dimensionality of the vectorized persistence diagrams (as with the PSK approach of [23]), or in terms of the number of observation sequences (as is the case for crocker plots ..."}
+{"idx": 1, "title": "plus-rkwitt/neural_persistence_dynamics - GitHub", "date": "", "ddg_snippet": "The Crocker stacks baseline comparison is implemented in crocker _ stacks .py. To execute this script, you must first prepare the data using compute_cs.py. Additionally, you need to install the teaspoon library with the appropriate version for computing the Crocker stacks .", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/plus-rkwitt/neural_persistence_dynamics", "content": "The Crocker stacks baseline comparison is implemented in crocker _ stacks .py. To execute this script, you must first prepare the data using compute_cs.py. Additionally, you need to install the teaspoon library with the appropriate version for computing the Crocker stacks ."}
+{"idx": 2, "title": "Neural Persistence Dynamics - proceedings.neurips.cc", "date": "", "ddg_snippet": "Various (ablation) experiments not only demonstrate the relevance of each model component but provide compelling empirical evidence that our proposed model -- Neural Persistence Dynamics -- substantially outperforms the state-of-the-art across a diverse set of parameter regression tasks.", "subpage_snippet": "", "source": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper_files/paper/2024/hash/3a509449a73fd0aab8c0cf5705827036-Abstract-Conference.html", "content": "Various (ablation) experiments not only demonstrate the relevance of each model component but provide compelling empirical evidence that our proposed model -- Neural Persistence Dynamics -- substantially outperforms the state-of-the-art across a diverse set of parameter regression tasks."}
+{"idx": 3, "title": "Neural Persistence Dynamics · NeurIPS 2024", "date": "", "ddg_snippet": "Sep 26, 2024 · This table compares the performance of the proposed Neural Persistence Dynamics model against two state-of-the-art methods (Path Signature Kernel and Crocker stacks ) for parameter regression tasks on four different datasets simulating collective behavior.", "subpage_snippet": "", "source": "deep-diver.github.io", "link": "https://deep-diver.github.io/neurips2024/posters/rcnzrfikx6/", "content": "Sep 26, 2024 · This table compares the performance of the proposed Neural Persistence Dynamics model against two state-of-the-art methods (Path Signature Kernel and Crocker stacks ) for parameter regression tasks on four different datasets simulating collective behavior."}
+{"idx": 4, "title": "Neural Persistence Dynamics - OpenReview", "date": "", "ddg_snippet": "Dec 31, 2023 · We consider the problem of learning the dynamics in the topology of time-evolving point clouds, the prevalent spatiotemporal model for systems exhibiting collective behavior, such as swarms of insects and birds or particles in physics. In such systems, patterns emerge from (local) interactions among self-propelled entities.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=163DugGjNn", "content": "Dec 31, 2023 · We consider the problem of learning the dynamics in the topology of time-evolving point clouds, the prevalent spatiotemporal model for systems exhibiting collective behavior, such as swarms of insects and birds or particles in physics. In such systems, patterns emerge from (local) interactions among self-propelled entities."}
+{"idx": 5, "title": "Neural Persistence Dynamics", "date": "", "ddg_snippet": "Crocker stacks [57], an extension of this concept, adds a smoothing step that gradually reduces the impact of points of low persistence and, upon discretization, yields a third dimension to crocker plots .", "subpage_snippet": "", "source": "papers.nips.cc", "link": "https://papers.nips.cc/paper_files/paper/2024/file/3a509449a73fd0aab8c0cf5705827036-Paper-Conference.pdf", "content": "Crocker stacks [57], an extension of this concept, adds a smoothing step that gradually reduces the impact of points of low persistence and, upon discretization, yields a third dimension to crocker plots ."}
+{"idx": 6, "title": "(PDF) Neural Persistence Dynamics - ResearchGate", "date": "", "ddg_snippet": "May 24, 2024 · In view of this observation , it is worth highlighting, that there is a clear distinction in terms of the source of point cloud dynamics when comparing collective behavior to problems in computer ...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/380895040_Neural_Persistence_Dynamics", "content": "May 24, 2024 · In view of this observation , it is worth highlighting, that there is a clear distinction in terms of the source of point cloud dynamics when comparing collective behavior to problems in computer ..."}
+{"idx": 7, "title": "Persistent Topological Features in Large Language Models", "date": "", "ddg_snippet": "... Neural Networks (CNN) used topological descriptors to explore the shape of activation functions [ 28 ] or their relations to performance [ 25 ] .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2410.11042v3", "content": "... Neural Networks (CNN) used topological descriptors to explore the shape of activation functions [ 28 ] or their relations to performance [ 25 ] ."}
+{"idx": 8, "title": "Perspective: An outlook on fluorescence tracking", "date": "", "ddg_snippet": "... dynamics within biomolecular condensates [ 17 , 18 ] and immune receptor dynamics [ 19 ] to mRNA transport and localization [ 13 , 14 ] , toward ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2508.13668v1", "content": "... dynamics within biomolecular condensates [ 17 , 18 ] and immune receptor dynamics [ 19 ] to mRNA transport and localization [ 13 , 14 ] , toward ..."}
+{"idx": 9, "title": "Persistent homology: An introduction and a new text", "date": "", "ddg_snippet": "... 2022)) to extract key topics, constructs a dynamic topic network representing the flow of these topics (Zhu, 2013) , and applies TDA, specifically ...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/262311097_Persistent_homology_An_introduction_and_a_new_text_representation_for_natural_language_processing", "content": "... 2022)) to extract key topics, constructs a dynamic topic network representing the flow of these topics (Zhu, 2013) , and applies TDA, specifically ..."}
diff --git a/data/sampled_jsons/Nichol_Dhariwal_2021_Improved_Denoising_Diffusion_Probabilistic_Models_arXiv2102.09672.jsonl b/data/sampled_jsons/Nichol_Dhariwal_2021_Improved_Denoising_Diffusion_Probabilistic_Models_arXiv2102.09672.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..7fc2995d5ed2facd82f6795d1ace228f4f1bc682
--- /dev/null
+++ b/data/sampled_jsons/Nichol_Dhariwal_2021_Improved_Denoising_Diffusion_Probabilistic_Models_arXiv2102.09672.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "[ 2102 . 09672 v1] Improved Denoising Diffusion Probabilistic Models", "date": "", "ddg_snippet": "arXiv : 2102 . 09672 v1 (cs). [Submitted on 18 Feb 2021 ]. Title: Improved Denoising Diffusion Probabilistic Models .View a PDF of the paper titled Improved Denoising Diffusion Probabilistic Models , by Alex Nichol and 1 other authors.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2102.09672v1", "content": "arXiv : 2102 . 09672 v1 (cs). [Submitted on 18 Feb 2021 ]. Title: Improved Denoising Diffusion Probabilistic Models .View a PDF of the paper titled Improved Denoising Diffusion Probabilistic Models , by Alex Nichol and 1 other authors."}
+{"idx": 1, "title": "Paper page - Improved Denoising Diffusion Probabilistic Models", "date": "", "ddg_snippet": "Papers. arxiv : 2102 . 09672 . Improved Denoising Diffusion Probabilistic Models . Published on Feb 18, 2021 . Denoising diffusion probabilistic models (DDPM) are a class of generative models which have recently been shown to produce excellent samples.", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/papers/2102.09672", "content": "Papers. arxiv : 2102 . 09672 . Improved Denoising Diffusion Probabilistic Models . Published on Feb 18, 2021 . Denoising diffusion probabilistic models (DDPM) are a class of generative models which have recently been shown to produce excellent samples."}
+{"idx": 2, "title": "[PDF] Improved Denoising Diffusion Probabilistic Models", "date": "", "ddg_snippet": "Denoising diffusion probabilistic models (DDPM) are a class of generative models which have recently been shown to produce excellent samples.Computer Science, Mathematics. ArXiv . 2021 . TLDR.", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/Improved-Denoising-Diffusion-Probabilistic-Models-Nichol-Dhariwal/de18baa4964804cf471d85a5a090498242d2e79f", "content": "Denoising diffusion probabilistic models (DDPM) are a class of generative models which have recently been shown to produce excellent samples.Computer Science, Mathematics. ArXiv . 2021 . TLDR."}
+{"idx": 3, "title": "Improved Denoising Diffusion Probabilistic Models : A Brief Summary", "date": "", "ddg_snippet": "Denoising diffusion probabilistic models (DDPMs) generate images similar to a given data set. It is one of the most popular generative models .References. Nichol , A., & Dhariwal , P. ( 2021 ). Improved Denoising Diffusion Probabilistic Models . ArXiv . /abs/ 2102 . 09672 .", "subpage_snippet": "", "source": "readmedium.com", "link": "https://readmedium.com/improved-denoising-diffusion-probabilistic-models-a-brief-summary-7624cbf7e0df", "content": "Denoising diffusion probabilistic models (DDPMs) generate images similar to a given data set. It is one of the most popular generative models .References. Nichol , A., & Dhariwal , P. ( 2021 ). Improved Denoising Diffusion Probabilistic Models . ArXiv . /abs/ 2102 . 09672 ."}
+{"idx": 4, "title": "Improved Denoising Diffusion Probabilistic Models", "date": "", "ddg_snippet": "DOI:10.48550/ arXiv . 2102 . 09672 . Authors: Alex Nichol . Denoising diffusion probabilistic models (DDPM) are a class of generative models which have recently been shown to produce excellent samples.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/349492049_Improved_Denoising_Diffusion_Probabilistic_Models", "content": "DOI:10.48550/ arXiv . 2102 . 09672 . Authors: Alex Nichol . Denoising diffusion probabilistic models (DDPM) are a class of generative models which have recently been shown to produce excellent samples."}
+{"idx": 5, "title": "Three-Dimensional Medical Image Synthesis with Denoising", "date": "", "ddg_snippet": "Denoising Diffusion Probabilistic Models (DDPMs) (Ho et al., 2020; Nichol and Dhariwal , 2021 ) have emerged as a powerful family of generative models that exhibit superior per-formance and have been extensively studied.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=Oz7lKWVh45H", "content": "Denoising Diffusion Probabilistic Models (DDPMs) (Ho et al., 2020; Nichol and Dhariwal , 2021 ) have emerged as a powerful family of generative models that exhibit superior per-formance and have been extensively studied."}
+{"idx": 6, "title": "vvvm23/ddpm: PyTorch implementation of \" Denoising Diffusion ...\"", "date": "", "ddg_snippet": "Denoising Diffusion Probabilistic Models (WIP).title={ Improved Denoising Diffusion Probabilistic Models }, author={Alex Nichol and Prafulla Dhariwal }, year={ 2021 }", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/vvvm23/ddpm", "content": "Denoising Diffusion Probabilistic Models (WIP).title={ Improved Denoising Diffusion Probabilistic Models }, author={Alex Nichol and Prafulla Dhariwal }, year={ 2021 }"}
+{"idx": 7, "title": "Improved Denoising Diffusion Probabilistic Models", "date": "", "ddg_snippet": "Full Citation: “ Nichol , Alexander Quinn, and Prafulla Dhariwal . “ Improved denoising diffusion probabilistic models .”", "subpage_snippet": "", "source": "gkalstn000.github.io", "link": "https://gkalstn000.github.io/2023/02/10/Improved-Denoising-Diffusion-Probabilistic-Models/", "content": "Full Citation: “ Nichol , Alexander Quinn, and Prafulla Dhariwal . “ Improved denoising diffusion probabilistic models .”"}
+{"idx": 8, "title": "Using Denoising Diffusion Probabilistic Models ... | Earthdoc", "date": "", "ddg_snippet": "Nichol , A. and Dhariwal , P. [ 2021 ] Improved Denoising Diffusion Probabilistic Model arXiv : 2102 . 09672 .", "subpage_snippet": "", "source": "www.earthdoc.org", "link": "https://www.earthdoc.org/content/papers/10.3997/2214-4609.202310958", "content": "Nichol , A. and Dhariwal , P. [ 2021 ] Improved Denoising Diffusion Probabilistic Model arXiv : 2102 . 09672 ."}
+{"idx": 9, "title": "How diffusion models work: the math from scratch | AI Summer", "date": "", "ddg_snippet": "Improved Denoising Diffusion Probabilistic Models . arXiv : 2102 . 09672 , arXiv , 18 Feb.", "subpage_snippet": "", "source": "theaisummer.com", "link": "https://theaisummer.com/diffusion-models/", "content": "Improved Denoising Diffusion Probabilistic Models . arXiv : 2102 . 09672 , arXiv , 18 Feb."}
diff --git a/data/sampled_jsons/Nichol_Dhariwal_2021_iDDPM_class-conditional_OR_conditional_ImageNet_64x64_FID_score_original_paper_year_2021.jsonl b/data/sampled_jsons/Nichol_Dhariwal_2021_iDDPM_class-conditional_OR_conditional_ImageNet_64x64_FID_score_original_paper_year_2021.jsonl
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index 0000000000000000000000000000000000000000..477e77263aec64cc1454f2fcb154b0e751aca399
--- /dev/null
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@@ -0,0 +1,10 @@
+{"idx": 0, "title": "GitHub - LeiMa0324/IDN: AAAI 2021: Beyond Class-Conditional ...", "date": "", "ddg_snippet": "Beyond Class-Conditional Assumption: A Primary Attempt to Combat Instance-Dependent Label Noise. This is the official repository for the paper Beyond Class-Conditional Assumption: A Primary Attempt to Combat Instance-Dependent Label Noise. (AAAI 2021 ). In this paper , one of the contributions is to provide rigorous motivations for studying instance-dependent label noise.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/LeiMa0324/IDN", "content": "Beyond Class-Conditional Assumption: A Primary Attempt to Combat Instance-Dependent Label Noise. This is the official repository for the paper Beyond Class-Conditional Assumption: A Primary Attempt to Combat Instance-Dependent Label Noise. (AAAI 2021 ). In this paper , one of the contributions is to provide rigorous motivations for studying instance-dependent label noise."}
+{"idx": 1, "title": "Paper tables with annotated results for Direct... | Papers With Code", "date": "", "ddg_snippet": "iDDPM ( nichol 2021 improved).Table 2: Results on class - conditional ImageNet - 64 . † Including diffusion distillation methods with auxiliary GAN loss. ‡ We find strict class balance crucial for FID on ImageNet and slightly modify the original sampling script to ensure this.", "subpage_snippet": "", "source": "paperswithcode.com", "link": "https://paperswithcode.com/paper/direct-discriminative-optimization-your-1/review/", "content": "iDDPM ( nichol 2021 improved).Table 2: Results on class - conditional ImageNet - 64 . † Including diffusion distillation methods with auxiliary GAN loss. ‡ We find strict class balance crucial for FID on ImageNet and slightly modify the original sampling script to ensure this."}
+{"idx": 2, "title": "(PDF) Synthetic Data from Diffusion Models Improves ImageNet ...", "date": "", "ddg_snippet": "class - conditional generative models on ImageNet .64×64→256×256 super-resolution model is fine-tuned for. 490K steps, on 256 TPU-v4 chips with a batch size of. 2048. As suggested in the original Imagen training pro", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/370070681_Synthetic_Data_from_Diffusion_Models_Improves_ImageNet_Classification", "content": "class - conditional generative models on ImageNet .64×64→256×256 super-resolution model is fine-tuned for. 490K steps, on 256 TPU-v4 chips with a batch size of. 2048. As suggested in the original Imagen training pro"}
+{"idx": 3, "title": "Elucidating the Design Space of... | Read Paper on Bytez", "date": "", "ddg_snippet": "For class - conditional ImageNet - 64 , we use the ADM architecture of Dhariwal and Nichol [9] with no changes. The model has a total of 296 million trainable parameters.• ImageNet - 64 model by Dhariwal and Nichol [9]: MIT license.", "subpage_snippet": "", "source": "bytez.com", "link": "https://bytez.com/docs/neurips/54947/paper", "content": "For class - conditional ImageNet - 64 , we use the ADM architecture of Dhariwal and Nichol [9] with no changes. The model has a total of 296 million trainable parameters.• ImageNet - 64 model by Dhariwal and Nichol [9]: MIT license."}
+{"idx": 4, "title": "Cascaded Diffusion Models for High Fidelity Image Generation", "date": "", "ddg_snippet": "Abstract. We show that cascaded diffusion models are capable of generating high fidelity images on the class - conditional ImageNet generation benchmark, without any assistance from auxiliary image classifiers to boost sample quality.", "subpage_snippet": "", "source": "jmlr.org", "link": "https://jmlr.org/papers/v23/21-0635.html", "content": "Abstract. We show that cascaded diffusion models are capable of generating high fidelity images on the class - conditional ImageNet generation benchmark, without any assistance from auxiliary image classifiers to boost sample quality."}
+{"idx": 5, "title": "[2106.03802] Learning to Efficiently Sample from Diffusion ...", "date": "", "ddg_snippet": "Table 2: Negative log likelihoods (bits/dim) in the few-step regime for a DDPM model trained with L hybrid on ImageNet 64x64 ( Nichol and Dhariwal , 2021 ), as well as state-of-the-art unconditional generative models in the same dataset.", "subpage_snippet": "", "source": "ar5iv.labs.arxiv.org", "link": "https://ar5iv.labs.arxiv.org/html/2106.03802", "content": "Table 2: Negative log likelihoods (bits/dim) in the few-step regime for a DDPM model trained with L hybrid on ImageNet 64x64 ( Nichol and Dhariwal , 2021 ), as well as state-of-the-art unconditional generative models in the same dataset."}
+{"idx": 6, "title": "(PDF) Is Noise Conditioning Necessary for Denoising ...", "date": "", "ddg_snippet": "Feb 18, 2025 · Changes of FID scores in the absence of noise conditioning, on class-unconditional ImageNet 32ˆ32 and FFHQ 64ˆ64, and class-conditional CIFAR-10.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/389130643_Is_Noise_Conditioning_Necessary_for_Denoising_Generative_Models", "content": "Feb 18, 2025 · Changes of FID scores in the absence of noise conditioning, on class-unconditional ImageNet 32ˆ32 and FFHQ 64ˆ64, and class-conditional CIFAR-10."}
+{"idx": 7, "title": "Denoising Diffusion Probabilistic Models (DDPM)", "date": "", "ddg_snippet": "This is a PyTorch implementation/tutorial of the paper Denoising Diffusion Probabilistic Models. In simple terms, we get an image from data and add noise step by step. Then We train a model to predict that noise at each step and use the model to generate images. The following definitions and derivations show how this works.", "subpage_snippet": "", "source": "nn.labml.ai", "link": "https://nn.labml.ai/diffusion/ddpm/index.html", "content": "This is a PyTorch implementation/tutorial of the paper Denoising Diffusion Probabilistic Models. In simple terms, we get an image from data and add noise step by step. Then We train a model to predict that noise at each step and use the model to generate images. The following definitions and derivations show how this works."}
+{"idx": 8, "title": "Yang Song", "date": "", "ddg_snippet": "For example, we achieve the new state-of-the-art FID of 3.55 on CIFAR-10 and 6.20 on ImageNet 64x64 for one-step generation. When trained as standalone generative models, consistency models also outperform single-step, non-adversarial generative models on standard benchmarks like CIFAR-10, ImageNet 64x64 and LSUN 256x256.", "subpage_snippet": "", "source": "yang-song.net", "link": "http://yang-song.net/", "content": "For example, we achieve the new state-of-the-art FID of 3.55 on CIFAR-10 and 6.20 on ImageNet 64x64 for one-step generation. When trained as standalone generative models, consistency models also outperform single-step, non-adversarial generative models on standard benchmarks like CIFAR-10, ImageNet 64x64 and LSUN 256x256."}
+{"idx": 9, "title": "arXiv:2207.12598v1 [cs.LG] 26 Jul 2022", "date": "", "ddg_snippet": "ineffective ( Dhariwal & Nichol , 2021 ). Classifier guidance instead mixes a diffusion model’s score estimate with the inp for a 64x64 ImageNet diffusion model. Left to right: increasing amounts of classifier-free guidance, starti", "subpage_snippet": "", "source": "papers.baulab.info", "link": "https://papers.baulab.info/papers/also/Ho-2022.pdf", "content": "ineffective ( Dhariwal & Nichol , 2021 ). Classifier guidance instead mixes a diffusion model’s score estimate with the inp for a 64x64 ImageNet diffusion model. Left to right: increasing amounts of classifier-free guidance, starti"}
diff --git a/data/sampled_jsons/Nikhil_Bansal_FOCS_2010_discrepancy_minimization_constructive_algorithm_year_2010.jsonl b/data/sampled_jsons/Nikhil_Bansal_FOCS_2010_discrepancy_minimization_constructive_algorithm_year_2010.jsonl
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index 0000000000000000000000000000000000000000..db6fb9f009e23d8d50f83036e14c54164fa2d29e
--- /dev/null
+++ b/data/sampled_jsons/Nikhil_Bansal_FOCS_2010_discrepancy_minimization_constructive_algorithm_year_2010.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Constructive Algorithms for Discrepancy Minimization", "date": "", "ddg_snippet": "by N Bansal · 2010 · Cited by 220 — In this paper we give the first polynomial time algorithms for discrepancy minimization that achieve bounds similar to those known existentially using the so- ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/1002.2259", "content": "by N Bansal · 2010 · Cited by 220 — In this paper we give the first polynomial time algorithms for discrepancy minimization that achieve bounds similar to those known existentially using the so- ..."}
+{"idx": 1, "title": "Constructive Algorithms for Discrepancy Minimization", "date": "", "ddg_snippet": "by N Bansal · 2010 · Cited by 220 — We also give a first approximation-like result for discrepancy . Specifically we give efficient randomized algorithms to: 1) Construct an O(n 1/2 ) discrepancy ...", "subpage_snippet": "", "source": "ieeexplore.ieee.org", "link": "http://ieeexplore.ieee.org/document/5670954", "content": "by N Bansal · 2010 · Cited by 220 — We also give a first approximation-like result for discrepancy . Specifically we give efficient randomized algorithms to: 1) Construct an O(n 1/2 ) discrepancy ..."}
+{"idx": 2, "title": "Constructive Algorithms for Discrepancy Minimization", "date": "", "ddg_snippet": "by N Bansal · Cited by 220 — In this paper we give the first polynomial time algorithms for discrepancy minimization that achieve bounds similar to those. 8 pages", "subpage_snippet": "", "source": "ieee-focs.org", "link": "https://ieee-focs.org/FOCS-2010-Papers/Constructive-Algorithms-for-Discrepancy-Minimization.pdf", "content": "by N Bansal · Cited by 220 — In this paper we give the first polynomial time algorithms for discrepancy minimization that achieve bounds similar to those. 8 pages"}
+{"idx": 3, "title": "Constructive Algorithms for Discrepancy Minimization", "date": "", "ddg_snippet": "by N Bansal · 2010 · Cited by 220 — In this paper we give the first polynomial time algorithms for discrepancy minimization that achieve bounds similar to those known existentially using the so- ...", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.1109/FOCS.2010.7", "content": "by N Bansal · 2010 · Cited by 220 — In this paper we give the first polynomial time algorithms for discrepancy minimization that achieve bounds similar to those known existentially using the so- ..."}
+{"idx": 4, "title": "Constructive Algorithms for Discrepancy Minimization", "date": "", "ddg_snippet": "by N Bansal · 2010 · Cited by 220 — In this paper we give the first polynomial time algorithms for discrepancy minimization that achieve bounds similar to those known existentially ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/1002.2259", "content": "by N Bansal · 2010 · Cited by 220 — In this paper we give the first polynomial time algorithms for discrepancy minimization that achieve bounds similar to those known existentially ..."}
+{"idx": 5, "title": "Constructive Discrepancy Minimization by Walking on the ...", "date": "", "ddg_snippet": "by S Lovett · Cited by 205 — Recently, a breakthrough work of Bansal ( FOCS 2010 ) gave an efficient algorithm which finds such a coloring. His algorithm was based on an SDP relaxation of the ...", "subpage_snippet": "", "source": "ieee-focs.org", "link": "https://ieee-focs.org/FOCS-2012-Papers/4874a061.pdf", "content": "by S Lovett · Cited by 205 — Recently, a breakthrough work of Bansal ( FOCS 2010 ) gave an efficient algorithm which finds such a coloring. His algorithm was based on an SDP relaxation of the ..."}
+{"idx": 6, "title": "FOCS 2010 Accepted Papers - Stanford CS Theory", "date": "", "ddg_snippet": "In this paper we give the first polynomial time algorithms for discrepancy minimization , that achieve bounds similar to those known existentially using the so- ...", "subpage_snippet": "", "source": "theory.stanford.edu", "link": "http://theory.stanford.edu/focs2010/accabs.html", "content": "In this paper we give the first polynomial time algorithms for discrepancy minimization , that achieve bounds similar to those known existentially using the so- ..."}
+{"idx": 7, "title": "Constructive Discrepancy Minimization by Walking on the ...", "date": "", "ddg_snippet": "by S Lovett · 2015 · Cited by 205 — N. Bansal, Constructive algorithms for discrepancy minimization , in Proceedings of FOCS, 2010, pp. 3--10. Google Scholar.", "subpage_snippet": "", "source": "epubs.siam.org", "link": "https://epubs.siam.org/doi/abs/10.1137/130929400?journalCode=smjcat", "content": "by S Lovett · 2015 · Cited by 205 — N. Bansal, Constructive algorithms for discrepancy minimization , in Proceedings of FOCS, 2010, pp. 3--10. Google Scholar."}
+{"idx": 8, "title": "Deterministic Discrepancy Minimization", "date": "", "ddg_snippet": "by N Bansal · 2011 · Cited by 30 — We derandomize a recent algorithmic approach due to Bansal [2] to efficiently compute low discrepancy colorings for several problems.", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/chapter/10.1007/978-3-642-23719-5_35", "content": "by N Bansal · 2011 · Cited by 30 — We derandomize a recent algorithmic approach due to Bansal [2] to efficiently compute low discrepancy colorings for several problems."}
+{"idx": 9, "title": "Algorithmic discrepancy beyond partial coloring", "date": "", "ddg_snippet": "{Ban10} Nikhil Bansal. Constructive algorithms for discrepancy minimization . In Foundations of Computer Science (FOCS), pages 3–10, 2010. Digital Library.", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.1145/3055399.3055490", "content": "{Ban10} Nikhil Bansal. Constructive algorithms for discrepancy minimization . In Foundations of Computer Science (FOCS), pages 3–10, 2010. Digital Library."}
diff --git a/data/sampled_jsons/No_Free_Delivery_Service_Epistemic_limits_passive_data_collection_complex_social_systems_train-test__year_2024.jsonl b/data/sampled_jsons/No_Free_Delivery_Service_Epistemic_limits_passive_data_collection_complex_social_systems_train-test__year_2024.jsonl
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index 0000000000000000000000000000000000000000..72e4fdc17513c9c985e97918fc203600cf5c974b
--- /dev/null
+++ b/data/sampled_jsons/No_Free_Delivery_Service_Epistemic_limits_passive_data_collection_complex_social_systems_train-test__year_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "No Free Delivery Service: Epistemic limits of passive data collection ...", "date": "", "ddg_snippet": "Rapid model validation via the train-test paradigm has been a key driver for the breathtaking progress in machine learning and AI. However, modern AI systems often depend on a combination of tasks and data collection practices that violate all assumptions ensuring test validity . Yet, without rigorous model validation we cannot ensure the intended outcomes of deployed AI systems , including ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2411.13653", "content": "Rapid model validation via the train-test paradigm has been a key driver for the breathtaking progress in machine learning and AI. However, modern AI systems often depend on a combination of tasks and data collection practices that violate all assumptions ensuring test validity . Yet, without rigorous model validation we cannot ensure the intended outcomes of deployed AI systems , including ..."}
+{"idx": 1, "title": "PDF No free delivery service", "date": "", "ddg_snippet": "For passively collected data in complex social systems the train-test paradigm cannot be valid under ontological parsimony for the vast majority of the system . This includes widely considered variants of recommender systems and question answering.", "subpage_snippet": "", "source": "proceedings.nips.cc", "link": "https://proceedings.nips.cc/paper_files/paper/2024/file/b97fc02c9e536d68300d82be05c23aa2-Paper-Conference.pdf", "content": "For passively collected data in complex social systems the train-test paradigm cannot be valid under ontological parsimony for the vast majority of the system . This includes widely considered variants of recommender systems and question answering."}
+{"idx": 2, "title": "No Free Delivery Service: Epistemic limits of passive data collection ...", "date": "", "ddg_snippet": "Authors Maximilian Nickel Abstract Rapid model validation via the train-test paradigm has been a key driver for the breathtaking progress in machine learning and AI. However, modern AI systems often depend on a combination of tasks and data collection practices that violate all assumptions ensuring test validity . Yet, without rigorous model validation we cannot ensure the intended outcomes of ...", "subpage_snippet": "", "source": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper_files/paper/2024/hash/b97fc02c9e536d68300d82be05c23aa2-Abstract-Conference.html", "content": "Authors Maximilian Nickel Abstract Rapid model validation via the train-test paradigm has been a key driver for the breathtaking progress in machine learning and AI. However, modern AI systems often depend on a combination of tasks and data collection practices that violate all assumptions ensuring test validity . Yet, without rigorous model validation we cannot ensure the intended outcomes of ..."}
+{"idx": 3, "title": "No Free Delivery Service: Epistemic limits of passive data...", "date": "", "ddg_snippet": "Rapid model validation via the train-test paradigm has been a key driver for the breathtaking progress in machine learning and AI. However, modern AI systems often depend on a combination of tasks and data collection practices that violate all assumptions ensuring test validity .", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=XZ0fpoAKEB", "content": "Rapid model validation via the train-test paradigm has been a key driver for the breathtaking progress in machine learning and AI. However, modern AI systems often depend on a combination of tasks and data collection practices that violate all assumptions ensuring test validity ."}
+{"idx": 4, "title": "No Free Delivery Service: Epistemic limits of passive data collection ...", "date": "", "ddg_snippet": "Addressing the epistemic limitations of passive data collection in complex social systems necessitates a paradigm shift. Participatory data curation emerges as a crucial remedy, empowering individuals and communities to actively shape data collection and validation processes.", "subpage_snippet": "", "source": "deep-diver.github.io", "link": "https://deep-diver.github.io/neurips2024/posters/xz0fpoakeb/", "content": "Addressing the epistemic limitations of passive data collection in complex social systems necessitates a paradigm shift. Participatory data curation emerges as a crucial remedy, empowering individuals and communities to actively shape data collection and validation processes."}
+{"idx": 5, "title": "No Free Delivery Service: Epistemic limits of passive data collection ...", "date": "", "ddg_snippet": "Rapid model validation via the train-test paradigm has been a key driver for the breathtaking progress in machine learning and AI. However, modern AI systems often depend on a combination of tasks and data collection practices that violate all assumptions ensuring test validity .", "subpage_snippet": "", "source": "www.deepnlp.org", "link": "http://www.deepnlp.org/content/articles/no-free-delivery-service:-epistemic-limits-of-passive-data-collection-in-complex-social-systems", "content": "Rapid model validation via the train-test paradigm has been a key driver for the breathtaking progress in machine learning and AI. However, modern AI systems often depend on a combination of tasks and data collection practices that violate all assumptions ensuring test validity ."}
+{"idx": 6, "title": "No Free Delivery Service: Epistemic limits of passive data collection ...", "date": "", "ddg_snippet": "This paper discusses problems with how we check if AI models work well, particularly when they are trained using data collected from social systems , like user interactions online. The usual method...", "subpage_snippet": "", "source": "neurips.cc", "link": "https://neurips.cc/virtual/2024/poster/94758", "content": "This paper discusses problems with how we check if AI models work well, particularly when they are trained using data collected from social systems , like user interactions online. The usual method..."}
+{"idx": 7, "title": "NoFreeDeliveryService No - arXiv.org", "date": "", "ddg_snippet": "Theorem 1 (Informal). For passively collected data in complex social systems the train-test paradigm cannot be valid under ontological parsimony for the vast m jority of the system . This includes widely considered variants of recommender systems a", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2411.13653", "content": "Theorem 1 (Informal). For passively collected data in complex social systems the train-test paradigm cannot be valid under ontological parsimony for the vast m jority of the system . This includes widely considered variants of recommender systems a"}
+{"idx": 8, "title": "No free delivery service Epistemic limits of passive data collection in ...", "date": "", "ddg_snippet": "Rapid model validation via the train-test paradigm has been a key driver for the breathtaking progress in machine learning and AI. However, modern AI systems often depend on a combination of tasks and data collection practices that violate all assumptions ensuring test validity .", "subpage_snippet": "", "source": "aitopics.org", "link": "https://aitopics.org/doc/conferences:804118BB", "content": "Rapid model validation via the train-test paradigm has been a key driver for the breathtaking progress in machine learning and AI. However, modern AI systems often depend on a combination of tasks and data collection practices that violate all assumptions ensuring test validity ."}
+{"idx": 9, "title": "No Free Delivery Service: Epistemic limits of passive data collection ...", "date": "", "ddg_snippet": "This paper critiques the inadequacy of the train-test paradigm for validating AI models in complex social systems due to biases in passive data collection , advocating for innovative validation approaches like participatory data curation to ensure meaningful AI outputs.", "subpage_snippet": "", "source": "chatpaper.com", "link": "https://chatpaper.com/chatpaper/paper/84424?from=search", "content": "This paper critiques the inadequacy of the train-test paradigm for validating AI models in complex social systems due to biases in passive data collection , advocating for innovative validation approaches like participatory data curation to ensure meaningful AI outputs."}
diff --git a/data/sampled_jsons/No_Free_Delivery_Service_epistemic_limits_Nickel_No_Free_Lunch_theorem.jsonl b/data/sampled_jsons/No_Free_Delivery_Service_epistemic_limits_Nickel_No_Free_Lunch_theorem.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..463fa6963819f636e343ac4edbcc5b284f499264
--- /dev/null
+++ b/data/sampled_jsons/No_Free_Delivery_Service_epistemic_limits_Nickel_No_Free_Lunch_theorem.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "No free lunch theorem - Wikipedia", "date": "", "ddg_snippet": "In mathematical folklore, the \" no free lunch \" theorem of David Wolpert and William Macready, alludes to the saying \"no such thing as a free lunch \", that is, there are no easy shortcuts to success. It appeared in the 1997 \" No Free ...", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/No_free_lunch_theorem", "content": "In mathematical folklore, the \" no free lunch \" theorem of David Wolpert and William Macready, alludes to the saying \"no such thing as a free lunch \", that is, there are no easy shortcuts to success. It appeared in the 1997 \" No Free ..."}
+{"idx": 1, "title": "[2411.13653] No Free Delivery Service : Epistemic limits of passive...", "date": "", "ddg_snippet": "Authors:Maximilian Nickel .Importantly, this includes variants of both recommender systems and reasoning via large language models, and neither naïve scaling nor limited benchmarks are suited to address this issue.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2411.13653", "content": "Authors:Maximilian Nickel .Importantly, this includes variants of both recommender systems and reasoning via large language models, and neither naïve scaling nor limited benchmarks are suited to address this issue."}
+{"idx": 2, "title": "No free delivery service", "date": "", "ddg_snippet": "No free delivery service Epistemic limits of passive data collection. in complex social systems.", "subpage_snippet": "", "source": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper_files/paper/2024/file/b97fc02c9e536d68300d82be05c23aa2-Paper-Conference.pdf", "content": "No free delivery service Epistemic limits of passive data collection. in complex social systems."}
+{"idx": 3, "title": "No Free Delivery Service : Epistemic limits of passive data collection...", "date": "", "ddg_snippet": "Maximilian Nickel .The no - free - lunch theorems promote a skeptical conclusion that all possible machine learning algorithms equally lack justification. But how could this leave room for a learning theory, that shows that some algorithms are better than others?", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/386021650_No_Free_Delivery_Service_Epistemic_limits_of_passive_data_collection_in_complex_social_systems", "content": "Maximilian Nickel .The no - free - lunch theorems promote a skeptical conclusion that all possible machine learning algorithms equally lack justification. But how could this leave room for a learning theory, that shows that some algorithms are better than others?"}
+{"idx": 4, "title": "No Free Delivery Service : Epistemic limits of passive... | OpenReview", "date": "", "ddg_snippet": "These formal impossibility results highlight a fundamental epistemic issue, i.e., that for key tasks in modern AI we cannot know whether models are valid under current data collection practices. Importantly, this includes variants of both recommender systems and reasoning via large...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=XZ0fpoAKEB", "content": "These formal impossibility results highlight a fundamental epistemic issue, i.e., that for key tasks in modern AI we cannot know whether models are valid under current data collection practices. Importantly, this includes variants of both recommender systems and reasoning via large..."}
+{"idx": 5, "title": "No Free Delivery Service : Epistemic limits of passive data collection...", "date": "", "ddg_snippet": "These formal impossibility results highlight a fundamental epistemic issue, i.e., that for key tasks in modern AI we cannot know whether models are valid under current data collection practices. Importantly, this includes variants of both recommender systems and reasoning via large...", "subpage_snippet": "", "source": "papers.cool", "link": "https://papers.cool/venue/XZ0fpoAKEB@OpenReview", "content": "These formal impossibility results highlight a fundamental epistemic issue, i.e., that for key tasks in modern AI we cannot know whether models are valid under current data collection practices. Importantly, this includes variants of both recommender systems and reasoning via large..."}
+{"idx": 6, "title": "machinelearningmastery.com/ no - free - lunch - theorem -for-machine...", "date": "", "ddg_snippet": "no - free - lunch -theoremNo Free Lunch Theorem for Machine Learning.", "subpage_snippet": "", "source": "machinelearningmastery.com", "link": "https://machinelearningmastery.com/no-free-lunch-theorem-for-machine-learning/", "content": "no - free - lunch -theoremNo Free Lunch Theorem for Machine Learning."}
+{"idx": 7, "title": "No Free Delivery Service : Epistemic limits of passive data collection...", "date": "", "ddg_snippet": "The paper argues that there is no \" free delivery service \" of data that guarantees test validity on a global scale, highlighting the necessity for careful consideration of sampling distributions and the structure of the data .", "subpage_snippet": "", "source": "powerdrill.ai", "link": "https://powerdrill.ai/discover/discover-No-Free-Delivery-cm3t89fot54t201a7hdglyl3v", "content": "The paper argues that there is no \" free delivery service \" of data that guarantees test validity on a global scale, highlighting the necessity for careful consideration of sampling distributions and the structure of the data ."}
+{"idx": 8, "title": "optimization - No free lunch theorems - Mathematics Stack Exchange", "date": "", "ddg_snippet": "No Free Lunch Theorem 1 Proof. 4. MPC trajectory optimization intuition.Bringing in a peanut butter sandwich to discourage lunch thief who has peanut allergy. Happy 2025! This math equation is finally true.", "subpage_snippet": "", "source": "math.stackexchange.com", "link": "https://math.stackexchange.com/questions/790293/no-free-lunch-theorems/904925", "content": "No Free Lunch Theorem 1 Proof. 4. MPC trajectory optimization intuition.Bringing in a peanut butter sandwich to discourage lunch thief who has peanut allergy. Happy 2025! This math equation is finally true."}
+{"idx": 9, "title": "What is important about the No Free Lunch theorems ?-Bohrium", "date": "", "ddg_snippet": "The No Free Lunch (NFL) theorems were first published in [1] and popularized in [2]. The initial theorems focused on supervised machine learning, and later ones on search.", "subpage_snippet": "", "source": "www.bohrium.com", "link": "https://www.bohrium.com/paper-details/what-is-important-about-the-no-free-lunch-theorems/867746722401485250-108614", "content": "The No Free Lunch (NFL) theorems were first published in [1] and popularized in [2]. The initial theorems focused on supervised machine learning, and later ones on search."}
diff --git a/data/sampled_jsons/Not_All_Diffusion_Model_Activations_SDXL_attention_query_key_feature_selection.jsonl b/data/sampled_jsons/Not_All_Diffusion_Model_Activations_SDXL_attention_query_key_feature_selection.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..ba517b9d2848763f0101e2af3dba1dec39b44252
--- /dev/null
+++ b/data/sampled_jsons/Not_All_Diffusion_Model_Activations_SDXL_attention_query_key_feature_selection.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Not All Diffusion Model Activations Have Been Evaluated as ...", "date": "", "ddg_snippet": "From SDXL , we select all the activations as described in the feature selection solution for SDXL and select one additional activation : the upsampler output activation from the 2nd resolution, to harness high-resolution information (up-level1-upsampler-out).", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2410.03558v3", "content": "From SDXL , we select all the activations as described in the feature selection solution for SDXL and select one additional activation : the upsampler output activation from the 2nd resolution, to harness high-resolution information (up-level1-upsampler-out)."}
+{"idx": 1, "title": "Not All Diffusion Model Activations Have Been Evaluated as...", "date": "", "ddg_snippet": "Sep 25, 2024 · However, we find that many potential activations have not been evaluated, such as the queries and keys used to compute attention scores. Moreover, recent advancements in diffusion architectures bring many new activations , such as those within embedded ViT modules. Both combined, activation selection remains unresolved but overlooked.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=7uqVfZW6Mo", "content": "Sep 25, 2024 · However, we find that many potential activations have not been evaluated, such as the queries and keys used to compute attention scores. Moreover, recent advancements in diffusion architectures bring many new activations , such as those within embedded ViT modules. Both combined, activation selection remains unresolved but overlooked."}
+{"idx": 2, "title": "GitHub - Darkbblue/generic-diffusion-feature: Official ...", "date": "", "ddg_snippet": "Generic Diffusion Feature We want this to be both the official implementation of NeurIPS'24 Spotlight paper Not All Diffusion Model Activations Have Been Evaluated as Discriminative Features and a generic codebase for all who are interested in diffusion feature .", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/Darkbblue/generic-diffusion-feature", "content": "Generic Diffusion Feature We want this to be both the official implementation of NeurIPS'24 Spotlight paper Not All Diffusion Model Activations Have Been Evaluated as Discriminative Features and a generic codebase for all who are interested in diffusion feature ."}
+{"idx": 3, "title": "This is what Stable Diffusion's attention looks like : r ... Rethinking attention modules of SDXL in a flow perspective Not All Diffusion Model Activations Have Been Evaluated as ... Not All Diffusion Model Activations Have Been Evaluated as ...", "date": "", "ddg_snippet": "The key , query , and value vector all come from linear layers in the neural network that run against each pixel. Because of this, the only extractable info we can look at is just the attention magnitude for each pixel. We reinterpreted the attention mechanism in the training-free methods of diffusion models using flow matching theory. Based on this, we proposed the FlowAttnXL training-free framework for text-to-image tasks. It introduces two modules: Flow Matching Cross- Attention (FMXA) and Flow Arithmetic Perturbation Guidance (FAPG). These modules operate plug-and-play, requiring no retraining and ... However, we find that many potential activations have not been evaluated, such as the queries and keys used to compute attention scores. Moreover, recent advancements in diffusion architectures bring many new activations , such as those within embedded ViT modules. Sep 26, 2024 · The selection of optimal activations from within a diffusion model is crucial for effective feature extraction. Early approaches focused on a limited set of readily available activations , overlooking potentially valuable signals within attention mechanisms and newer architectures.", "subpage_snippet": "", "source": "www.reddit.com", "link": "https://www.reddit.com/r/StableDiffusion/comments/18lgmn3/this_is_what_stable_diffusions_attention_looks/", "content": "The key , query , and value vector all come from linear layers in the neural network that run against each pixel. Because of this, the only extractable info we can look at is just the attention magnitude for each pixel. We reinterpreted the attention mechanism in the training-free methods of diffusion models using flow matching theory. Based on this, we proposed the FlowAttnXL training-free framework for text-to-image tasks. It introduces two modules: Flow Matching Cross- Attention (FMXA) and Flow Arithmetic Perturbation Guidance (FAPG). These modules operate plug-and-play, requiring no retraining and ... However, we find that many potential activations have not been evaluated, such as the queries and keys used to compute attention scores. Moreover, recent advancements in diffusion architectures bring many new activations , such as those within embedded ViT modules. Sep 26, 2024 · The selection of optimal activations from within a diffusion model is crucial for effective feature extraction. Early approaches focused on a limited set of readily available activations , overlooking potentially valuable signals within attention mechanisms and newer architectures."}
+{"idx": 4, "title": "Rethinking attention modules of SDXL in a flow perspective", "date": "", "ddg_snippet": "We reinterpreted the attention mechanism in the training-free methods of diffusion models using flow matching theory. Based on this, we proposed the FlowAttnXL training-free framework for text-to-image tasks. It introduces two modules: Flow Matching Cross- Attention (FMXA) and Flow Arithmetic Perturbation Guidance (FAPG). These modules operate plug-and-play, requiring no retraining and ...", "subpage_snippet": "", "source": "www.aimsciences.org", "link": "https://www.aimsciences.org/article/doi/10.3934/mfc.2025015", "content": "We reinterpreted the attention mechanism in the training-free methods of diffusion models using flow matching theory. Based on this, we proposed the FlowAttnXL training-free framework for text-to-image tasks. It introduces two modules: Flow Matching Cross- Attention (FMXA) and Flow Arithmetic Perturbation Guidance (FAPG). These modules operate plug-and-play, requiring no retraining and ..."}
+{"idx": 5, "title": "Not All Diffusion Model Activations Have Been Evaluated as ...", "date": "", "ddg_snippet": "However, we find that many potential activations have not been evaluated, such as the queries and keys used to compute attention scores. Moreover, recent advancements in diffusion architectures bring many new activations , such as those within embedded ViT modules.", "subpage_snippet": "", "source": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper_files/paper/2024/hash/633780c1344d0c95e4d2dd3431fe08d9-Abstract-Conference.html", "content": "However, we find that many potential activations have not been evaluated, such as the queries and keys used to compute attention scores. Moreover, recent advancements in diffusion architectures bring many new activations , such as those within embedded ViT modules."}
+{"idx": 6, "title": "Not All Diffusion Model Activations Have Been Evaluated as ...", "date": "", "ddg_snippet": "Sep 26, 2024 · The selection of optimal activations from within a diffusion model is crucial for effective feature extraction. Early approaches focused on a limited set of readily available activations , overlooking potentially valuable signals within attention mechanisms and newer architectures.", "subpage_snippet": "", "source": "deep-diver.github.io", "link": "https://deep-diver.github.io/neurips2024/spotlight-others/7uqvfzw6mo/", "content": "Sep 26, 2024 · The selection of optimal activations from within a diffusion model is crucial for effective feature extraction. Early approaches focused on a limited set of readily available activations , overlooking potentially valuable signals within attention mechanisms and newer architectures."}
+{"idx": 7, "title": "GitHub - apple/ml-stable-diffusion: Stable Diffusion with Core", "date": "", "ddg_snippet": "... not impact performance because the Core ML model is converted with a static shape that computes the forward pass for all of the 77 elements ( ...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/apple/ml-stable-diffusion", "content": "... not impact performance because the Core ML model is converted with a static shape that computes the forward pass for all of the 77 elements ( ..."}
+{"idx": 8, "title": "Installation on Apple Silicon ·", "date": "", "ddg_snippet": "... keyboard and clicking here , then rename it with the .yaml extension in the same way as mentioned above and put it in ...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Installation-on-Apple-Silicon", "content": "... keyboard and clicking here , then rename it with the .yaml extension in the same way as mentioned above and put it in ..."}
+{"idx": 9, "title": "Extensions · AUTOMATIC1111/stable-diffusion-webui Wiki ·", "date": "", "ddg_snippet": "An extension of the built-in Composable Diffusion , allows you to determine the region of the latent space that reflects your subprompts.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Extensions", "content": "An extension of the built-in Composable Diffusion , allows you to determine the region of the latent space that reflects your subprompts."}
diff --git a/data/sampled_jsons/OFUL_algorithm_computational_complexity_confidence_ellipsoid_optimization.jsonl b/data/sampled_jsons/OFUL_algorithm_computational_complexity_confidence_ellipsoid_optimization.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..c71cf66f871d5ab55bdbd7488e3b4affcd6a52bb
--- /dev/null
+++ b/data/sampled_jsons/OFUL_algorithm_computational_complexity_confidence_ellipsoid_optimization.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "(PDF) Linear Bandits on Ellipsoids : Minimax Optimal Algorithms", "date": "", "ddg_snippet": ". The confidence ellipsoids in. OFUL . and.difference.This clearly illustrates the lower computational complexity of. E2TC. with respect to optimistic algorithms .", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/389315749_Linear_Bandits_on_Ellipsoids_Minimax_Optimal_Algorithms", "content": ". The confidence ellipsoids in. OFUL . and.difference.This clearly illustrates the lower computational complexity of. E2TC. with respect to optimistic algorithms ."}
+{"idx": 1, "title": "Improved Algorithms for Linear Stochastic Bandits", "date": "", "ddg_snippet": "We call the resulting algorithm the OFUL ALGORITHM for “optimism in the face of uncertainty linear bandit algorithm ”.Just to mention a few examples, the new inequality could be used to improve the computational complexity of the HOO algorithm Bubeck et al.", "subpage_snippet": "", "source": "david.palenica.com", "link": "https://david.palenica.com/papers/linear-bandit/linear-bandits-NIPS2011-camera-ready.pdf", "content": "We call the resulting algorithm the OFUL ALGORITHM for “optimism in the face of uncertainty linear bandit algorithm ”.Just to mention a few examples, the new inequality could be used to improve the computational complexity of the HOO algorithm Bubeck et al."}
+{"idx": 2, "title": "[PDF] Stochastic Linear Optimization under Bandit... | Semantic Scholar", "date": "", "ddg_snippet": "A nearly complete characterization of the classical stochastic k-armed bandit problem in terms of both upper and lower bounds for the regret is given, and two variants of an algorithm based on the idea of “upper confidence bounds” are presented.", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/Stochastic-Linear-Optimization-under-Bandit-Dani-Hayes/551e19e5113cdff60a3c545d684fc4b9eb9a7306", "content": "A nearly complete characterization of the classical stochastic k-armed bandit problem in terms of both upper and lower bounds for the regret is given, and two variants of an algorithm based on the idea of “upper confidence bounds” are presented."}
+{"idx": 3, "title": "Stochastic Linear Bandits and UCB – Bandit Algorithms", "date": "", "ddg_snippet": "Computation .An algorithm that is based on a confidence ellipsoid is described in the paper by Varsha Dani, Thomas Hayes and Sham Kakade: V. Dani, T. Hayes and S. Kakade: Stochastic Linear Optimization under Bandit Feedback, COLT-2008.", "subpage_snippet": "", "source": "banditalgs.com", "link": "https://banditalgs.com/2016/10/19/stochastic-linear-bandits/", "content": "Computation .An algorithm that is based on a confidence ellipsoid is described in the paper by Varsha Dani, Thomas Hayes and Sham Kakade: V. Dani, T. Hayes and S. Kakade: Stochastic Linear Optimization under Bandit Feedback, COLT-2008."}
+{"idx": 4, "title": "Solved From the OFUL paper, Theorem 3 requires the | Chegg.com", "date": "", "ddg_snippet": "According to my point of view The “optimism in the face of uncertainty linear bandit\" ( OFUL ) algorithm was introduced in [1]. OFUL resorts to ridge regression, constructs a confidence ellipsoid for the parameter estimate, and chooses the action th…", "subpage_snippet": "", "source": "www.chegg.com", "link": "https://www.chegg.com/homework-help/questions-and-answers/oful-paper-theorem-3-requires-assumption-1-1--could-try-find-regret-bound-without-assumpti-q91295640", "content": "According to my point of view The “optimism in the face of uncertainty linear bandit\" ( OFUL ) algorithm was introduced in [1]. OFUL resorts to ridge regression, constructs a confidence ellipsoid for the parameter estimate, and chooses the action th…"}
+{"idx": 5, "title": "M Inimax o ptimal a lgorithms", "date": "", "ddg_snippet": "We use a commercially available optimization solver Gurobi Optimization , LLC [2024] to handle the bilinear maxi-mization problem max(x,θ) x⊤θ subject to (x, θ) ∈ X × Ct with Ct the confidence ellipsoid at time t, which must be solved at each step to implement OFUL and OLSOFUL.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2502.17175", "content": "We use a commercially available optimization solver Gurobi Optimization , LLC [2024] to handle the bilinear maxi-mization problem max(x,θ) x⊤θ subject to (x, θ) ∈ X × Ct with Ct the confidence ellipsoid at time t, which must be solved at each step to implement OFUL and OLSOFUL."}
+{"idx": 6, "title": "Paper reading: Near-Optimal Representation Learning for Linear...", "date": "", "ddg_snippet": "Linear stochastic bandit - confidence sets. Theorem ( Confidence Ellipsoid Cont’d).Recall the confidence set from OFUL algorithm : Ct =.", "subpage_snippet": "", "source": "zcc1307.github.io", "link": "https://zcc1307.github.io/courses/csc696fa21/notes/nov_11_yao.pdf", "content": "Linear stochastic bandit - confidence sets. Theorem ( Confidence Ellipsoid Cont’d).Recall the confidence set from OFUL algorithm : Ct =."}
+{"idx": 7, "title": "G eometry -a ware", "date": "", "ddg_snippet": "Regularized Least Square and Confidence Ellipsoid .2. We introduce a comprehensive family of algorithms , termed POFUL (encompassing OFUL , LinTS, TS-Freq, and Greedy as specific instances), and derive a general, data-driven frequentist regret bound for them.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=Oeb0I3JcVc", "content": "Regularized Least Square and Confidence Ellipsoid .2. We introduce a comprehensive family of algorithms , termed POFUL (encompassing OFUL , LinTS, TS-Freq, and Greedy as specific instances), and derive a general, data-driven frequentist regret bound for them."}
+{"idx": 8, "title": "createdbydvipdf", "date": "", "ddg_snippet": "Key words: Convex Optimization ; Ellipsoid Algorithm ; Computational Complexity . Complexity of convex optimization using geometry-based measures and a reference point. Mathematical Programming, 99:197–221, 2004.", "subpage_snippet": "", "source": "dspace.mit.edu", "link": "https://dspace.mit.edu/bitstream/handle/1721.1/55961/Freund_Equivalent+of+Convex.pdf?sequence=1&isAllowed=y", "content": "Key words: Convex Optimization ; Ellipsoid Algorithm ; Computational Complexity . Complexity of convex optimization using geometry-based measures and a reference point. Mathematical Programming, 99:197–221, 2004."}
+{"idx": 9, "title": "Online Instrumental Variable Regression: Regret Analysis and Bandit...", "date": "", "ddg_snippet": "OFUL builds a confidence ellipsoid centered at βRidge,t to concentrate around β, while OFUL -IV uses O2SLS to build an accurate estimate. We deploy the experiments in. Python3 on a single Intel(R) Core(TM) i7-8665U CPU@1.90GHz.", "subpage_snippet": "", "source": "hal.science", "link": "https://hal.science/hal-03831210v2/document", "content": "OFUL builds a confidence ellipsoid centered at βRidge,t to concentrate around β, while OFUL -IV uses O2SLS to build an accurate estimate. We deploy the experiments in. Python3 on a single Intel(R) Core(TM) i7-8665U CPU@1.90GHz."}
diff --git a/data/sampled_jsons/OFUL_algorithm_confidence_set_construction_computational_complexity_implementation.jsonl b/data/sampled_jsons/OFUL_algorithm_confidence_set_construction_computational_complexity_implementation.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..cb495396c14fbebbcfd9cbcd308b11c5aa7c27e5
--- /dev/null
+++ b/data/sampled_jsons/OFUL_algorithm_confidence_set_construction_computational_complexity_implementation.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Algorithm - Wikipedia", "date": "", "ddg_snippet": "However, algorithms are also implemented by other means, such as in a biological neural network (for example, the human brain performing arithmetic or an insect looking for food), in an electrical circuit, or a mechanical device.", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/Algorithm", "content": "However, algorithms are also implemented by other means, such as in a biological neural network (for example, the human brain performing arithmetic or an insect looking for food), in an electrical circuit, or a mechanical device."}
+{"idx": 1, "title": "Noise-Adaptive Confidence Sets for Linear Bandits and Application to...", "date": "", "ddg_snippet": "proved confidence set , we construct an OFUL -style alFor efficient implementation , one needs to maintain suffi-cient statistics for the estimators θˆt,ℓ and θ¯t,ℓ, and update the inverse of the covariance matrices Σ−t,ℓ1 and Σt−,ℓ1 using the matrix inversion lemma.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2402.07341", "content": "proved confidence set , we construct an OFUL -style alFor efficient implementation , one needs to maintain suffi-cient statistics for the estimators θˆt,ℓ and θ¯t,ℓ, and update the inverse of the covariance matrices Σ−t,ℓ1 and Σt−,ℓ1 using the matrix inversion lemma."}
+{"idx": 2, "title": "Improved Algorithms for Linear Stochastic Bandits", "date": "", "ddg_snippet": "We call the resulting algorithm the OFUL ALGORITHM for “optimism in the face of uncertainty linear bandit algorithm ”. Pseudo-code of the algorithm is given in Figure 1. The crux of the problem is the construction of the condence sets Ct.", "subpage_snippet": "", "source": "david.palenica.com", "link": "https://david.palenica.com/papers/linear-bandit/linear-bandits-NIPS2011-camera-ready.pdf", "content": "We call the resulting algorithm the OFUL ALGORITHM for “optimism in the face of uncertainty linear bandit algorithm ”. Pseudo-code of the algorithm is given in Figure 1. The crux of the problem is the construction of the condence sets Ct."}
+{"idx": 3, "title": "Online-to-Condence- Set Conversions and Application to Sparse", "date": "", "ddg_snippet": "Consider the OFUL algorithm from Figure 1 that uses the condence set Cn constructed in Corollary 2 from an online linear prediction algorithm .", "subpage_snippet": "", "source": "proceedings.mlr.press", "link": "https://proceedings.mlr.press/v22/abbasi-yadkori12/abbasi-yadkori12.pdf", "content": "Consider the OFUL algorithm from Figure 1 that uses the condence set Cn constructed in Corollary 2 from an online linear prediction algorithm ."}
+{"idx": 4, "title": "Boosting and Hard-Core Set Construction | Machine Learning", "date": "", "ddg_snippet": "This paper connects hard-core set construction , a type of hardness amplification from computational complexity , and boosting, a technique from computationa.", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/article/10.1023/A:1022949332276", "content": "This paper connects hard-core set construction , a type of hardness amplification from computational complexity , and boosting, a technique from computationa."}
+{"idx": 5, "title": "Computational Complexity : What is an explicit Construction ?", "date": "", "ddg_snippet": "The whole lower bounds stuff in computational complexity is also \"sick\" about finding a \" constructive \" or \"explicit\" or \"specific\" hard to compute function. And nobody in fact cares about what these terms actually mean.", "subpage_snippet": "", "source": "blog.computationalcomplexity.org", "link": "https://blog.computationalcomplexity.org/2009/12/what-is-explicit-construction.html", "content": "The whole lower bounds stuff in computational complexity is also \"sick\" about finding a \" constructive \" or \"explicit\" or \"specific\" hard to compute function. And nobody in fact cares about what these terms actually mean."}
+{"idx": 6, "title": "Bandit Algorithms for Recommender Systems", "date": "", "ddg_snippet": "Confidence Sets The LinUCB algorithm relies on using previously-observed rewards to construct carefully tuned confidence sets on 𝜃˚. The following construction is based on that of [1]: Let 𝜆 ą 0 be an arbitrary constant (in practice, this can be treated as a tuning parameter).", "subpage_snippet": "", "source": "www.cmu.edu", "link": "https://www.cmu.edu/tepper/programs/phd/program/assets/dissertations/2022-operations-research-korkut-dissertation.pdf", "content": "Confidence Sets The LinUCB algorithm relies on using previously-observed rewards to construct carefully tuned confidence sets on 𝜃˚. The following construction is based on that of [1]: Let 𝜆 ą 0 be an arbitrary constant (in practice, this can be treated as a tuning parameter)."}
+{"idx": 7, "title": "A C++ Implementation of Otsu's Image Segmentation Method", "date": "", "ddg_snippet": "Implementation . Computational Complexity . Case Study.The proposed implementation for Otsu’s algorithm is described in Section 3. Section 4 presents a study of the application of the proposed method to a set of images.", "subpage_snippet": "", "source": "www.ipol.im", "link": "https://www.ipol.im/pub/art/2016/158/article.pdf", "content": "Implementation . Computational Complexity . Case Study.The proposed implementation for Otsu’s algorithm is described in Section 3. Section 4 presents a study of the application of the proposed method to a set of images."}
+{"idx": 8, "title": "(PDF) Efficient Kernel UCB for Contextual Bandits", "date": "", "ddg_snippet": "exploration algorithms typically rely on constructing . confidence sets for the parameter vector and exploring. using upper confidence bound (UCB) rules (Li et al., 2010). The extensions to infinite-dimensional feature.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/358578701_Efficient_Kernel_UCB_for_Contextual_Bandits", "content": "exploration algorithms typically rely on constructing . confidence sets for the parameter vector and exploring. using upper confidence bound (UCB) rules (Li et al., 2010). The extensions to infinite-dimensional feature."}
+{"idx": 9, "title": "Read this arXiv paper as a responsive web page with clickable citations.", "date": "", "ddg_snippet": "We then construct a confidence set with the constraint above to choose the optimistic arm as in Eq. (5). Such a confidence set does not involve S∗ and thus is norm-agnostic like NAOFUL, but OFUL 0 does not have a known regret bound.111 In fact, the proof of Eq.", "subpage_snippet": "", "source": "www.arxiv-vanity.com", "link": "https://www.arxiv-vanity.com/papers/2205.01257/", "content": "We then construct a confidence set with the constraint above to choose the optimistic arm as in Eq. (5). Such a confidence set does not involve S∗ and thus is norm-agnostic like NAOFUL, but OFUL 0 does not have a known regret bound.111 In fact, the proof of Eq."}
diff --git a/data/sampled_jsons/OS-Atlas_paper_GPU_experimental_setup_UGround-7B-V1.jsonl b/data/sampled_jsons/OS-Atlas_paper_GPU_experimental_setup_UGround-7B-V1.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..06149a09d5f6eaf8449ab1d676c6fdbcadb3b477
--- /dev/null
+++ b/data/sampled_jsons/OS-Atlas_paper_GPU_experimental_setup_UGround-7B-V1.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "OS-ATLAS: A Foundation Action Model for Generalist GUI ...", "date": "", "ddg_snippet": "30 Oct 2024 — To facilitate future research in this area, we developed OS-Atlas —a foundational GUI action model that excels at GUI grounding and OOD agentic ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2410.23218v1", "content": "30 Oct 2024 — To facilitate future research in this area, we developed OS-Atlas —a foundational GUI action model that excels at GUI grounding and OOD agentic ..."}
+{"idx": 1, "title": "Daily Papers", "date": "", "ddg_snippet": "Empirical results on six benchmarks spanning three categories (grounding, offline agent, and online agent) show that 1) UGround substantially outperforms ...", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/papers?q=LVLM-based+Graphical+User+Interface", "content": "Empirical results on six benchmarks spanning three categories (grounding, offline agent, and online agent) show that 1) UGround substantially outperforms ..."}
+{"idx": 2, "title": "What Limits Virtual Agent Application? OmniBench", "date": "", "ddg_snippet": "els, we conduct a comparative experiment using two separate datasets to fine-tune OS - Atlas -Base-4B and UGround - V1 - 7B . One dataset includes task intent ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf/d5d3691187b9f32f92e8e287e5005d40aa429f80.pdf", "content": "els, we conduct a comparative experiment using two separate datasets to fine-tune OS - Atlas -Base-4B and UGround - V1 - 7B . One dataset includes task intent ..."}
+{"idx": 3, "title": "Generalist Virtual Agents: A Survey on Autonomous ...", "date": "", "ddg_snippet": "This repository includes all the resources, code, and references related to the paper . Our objective is to provide a comprehensive overview of Generalist ...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/wendell0218/GVA-Survey", "content": "This repository includes all the resources, code, and references related to the paper . Our objective is to provide a comprehensive overview of Generalist ..."}
+{"idx": 4, "title": "DiMo-GUI: Advancing Test-time Scaling in GUI Grounding via ...", "date": "", "ddg_snippet": "When integrated with OS - Atlas - 7B and UGround - V1 - 7B , we observe that in the early iterations, the models often fail to return accurate coordinates—primarily due ...", "subpage_snippet": "", "source": "bytez.com", "link": "https://bytez.com/docs/arxiv/2507.00008/paper", "content": "When integrated with OS - Atlas - 7B and UGround - V1 - 7B , we observe that in the early iterations, the models often fail to return accurate coordinates—primarily due ..."}
+{"idx": 5, "title": "ScienceBoard: Evaluating Multimodal Autonomous Agents ...", "date": "", "ddg_snippet": "In our experiments , this model is deployed using a single A100 80GB GPU with. vLLM (Kwon et al., 2023). • UGround - V1 - 7B (Gou et al., 2025): A universal visual ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf/a83358cde98349182a43c8964a90ca12f13c6dc3.pdf", "content": "In our experiments , this model is deployed using a single A100 80GB GPU with. vLLM (Kwon et al., 2023). • UGround - V1 - 7B (Gou et al., 2025): A universal visual ..."}
+{"idx": 6, "title": "Qwen-GUI-3B: A Lightweight Vision-Language Model for Cross- ...", "date": "", "ddg_snippet": "Experimental Setup : Single GPU Training. All experiments were conducted using a single NVIDIA RTX 4090 GPU (24GB VRAM). Model training followed a resource ...", "subpage_snippet": "", "source": "www.aimodels.fyi", "link": "https://www.aimodels.fyi/papers/arxiv/qwen-gui-3b-lightweight-vision-language-model", "content": "Experimental Setup : Single GPU Training. All experiments were conducted using a single NVIDIA RTX 4090 GPU (24GB VRAM). Model training followed a resource ..."}
+{"idx": 7, "title": "Enhancing Visual Grounding for GUI Agents via Self ...", "date": "", "ddg_snippet": "18 May 2025 — With only 3k training samples, our 7B -parameter model achieves state-of-the-art results among similarly sized models on three grounding ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2505.12370v1", "content": "18 May 2025 — With only 3k training samples, our 7B -parameter model achieves state-of-the-art results among similarly sized models on three grounding ..."}
+{"idx": 8, "title": "UI-R1: Enhancing Action Prediction of GUI Agents ...", "date": "", "ddg_snippet": "The document presents UI-R1, a novel approach that enhances the reasoning capabilities of multimodal large language models (MLLMs) for GUI action prediction ...", "subpage_snippet": "", "source": "www.scribd.com", "link": "https://www.scribd.com/document/844556727/2503-21620v1", "content": "The document presents UI-R1, a novel approach that enhances the reasoning capabilities of multimodal large language models (MLLMs) for GUI action prediction ..."}
+{"idx": 9, "title": "UI-AGILE: Advancing GUI Agents With Effective ...", "date": "", "ddg_snippet": "Experiments demonstrate that UI-AGILE achieves state-of-the-art performance, improving grounding accuracy by 23% over existing baselines on benchmarks ...", "subpage_snippet": "", "source": "www.scribd.com", "link": "https://www.scribd.com/document/895046807/2507-22025v1", "content": "Experiments demonstrate that UI-AGILE achieves state-of-the-art performance, improving grounding accuracy by 23% over existing baselines on benchmarks ..."}
diff --git a/data/sampled_jsons/OSU-NLP-Group_UGround_GitHub_implementation_details_year_2024.jsonl b/data/sampled_jsons/OSU-NLP-Group_UGround_GitHub_implementation_details_year_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..8058cf5ebfb8326027a4ac50e967421165f16b94
--- /dev/null
+++ b/data/sampled_jsons/OSU-NLP-Group_UGround_GitHub_implementation_details_year_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "GitHub - OSU - NLP - Group / UGround : [ICLR'25 Oral] UGround ...", "date": "", "ddg_snippet": "OSU - NLP - Group / UGround Public. Notifications You must be signed in to change notification settings.This is the official code repository for the project: Navigating the Digital World as Humans Do: Universal Visual Grounding for GUI Agents [ICLR'25 Oral].", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/OSU-NLP-Group/UGround", "content": "OSU - NLP - Group / UGround Public. Notifications You must be signed in to change notification settings.This is the official code repository for the project: Navigating the Digital World as Humans Do: Universal Visual Grounding for GUI Agents [ICLR'25 Oral]."}
+{"idx": 1, "title": "UGround /README.md at main · OSU - NLP - Group / UGround · GitHub", "date": "", "ddg_snippet": "Contribute to OSU - NLP - Group / UGround development by creating an account on GitHub . UGround (Work In Progress). This is the official code repository for the project: Navigating the Digital World as Humans Do: Universal Visual Grounding for GUI Agents.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/OSU-NLP-Group/UGround/blob/main/README.md", "content": "Contribute to OSU - NLP - Group / UGround development by creating an account on GitHub . UGround (Work In Progress). This is the official code repository for the project: Navigating the Digital World as Humans Do: Universal Visual Grounding for GUI Agents."}
+{"idx": 2, "title": "osunlp/ UGround · Hugging Face", "date": "", "ddg_snippet": "UGround is a strong GUI visual grounding model trained with a simple recipe. Check our homepage and paper for more details . This work is a collaboration between OSU NLP Group and Orby AI. radar.", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/osunlp/UGround", "content": "UGround is a strong GUI visual grounding model trained with a simple recipe. Check our homepage and paper for more details . This work is a collaboration between OSU NLP Group and Orby AI. radar."}
+{"idx": 3, "title": "UGround Homepage", "date": "", "ddg_snippet": "UGround is a universal visual grounding model for locating the element of an action by pixel coordinates on GUIs.", "subpage_snippet": "", "source": "osu-nlp-group.github.io", "link": "https://osu-nlp-group.github.io/UGround/", "content": "UGround is a universal visual grounding model for locating the element of an action by pixel coordinates on GUIs."}
+{"idx": 4, "title": "UGround -V1-7B huggingface.co api & osunlp UGround -V1-7B github ...", "date": "", "ddg_snippet": "Model Details of UGround -V1-7B. UGround -V1-7B （Qwen2-VL-Based). UGround -V1-7B is an open source model from GitHub that offers a free installation service, and any user can find UGround -V1-7B on GitHub to install.", "subpage_snippet": "", "source": "www.toolify.ai", "link": "https://www.toolify.ai/ai-model/osunlp-uground-v1-7b", "content": "Model Details of UGround -V1-7B. UGround -V1-7B （Qwen2-VL-Based). UGround -V1-7B is an open source model from GitHub that offers a free installation service, and any user can find UGround -V1-7B on GitHub to install."}
+{"idx": 5, "title": "GitHub topics: gui-agents | Ecosyste.ms: Repos", "date": "", "ddg_snippet": "OSU - NLP - Group / UGround . UGround : Universal GUI Visual Grounding for GUI Agents. Language : Python - Size: 53.3 MB - Last synced at: about 11 hours ago - Pushed at: 1 day ago - Stars: 128 - Forks: 6.", "subpage_snippet": "", "source": "repos.ecosyste.ms", "link": "https://repos.ecosyste.ms/hosts/GitHub/topics/gui-agents", "content": "OSU - NLP - Group / UGround . UGround : Universal GUI Visual Grounding for GUI Agents. Language : Python - Size: 53.3 MB - Last synced at: about 11 hours ago - Pushed at: 1 day ago - Stars: 128 - Forks: 6."}
+{"idx": 6, "title": "gui-agents · GitHub Topics", "date": "", "ddg_snippet": "OSU - NLP - Group / UGround . [ICLR'25 Oral] UGround : Universal GUI Visual Grounding for GUI Agents.", "subpage_snippet": "", "source": "www.github-zh.com", "link": "https://www.github-zh.com/topics/gui-agents", "content": "OSU - NLP - Group / UGround . [ICLR'25 Oral] UGround : Universal GUI Visual Grounding for GUI Agents."}
+{"idx": 7, "title": "OSU Natural Language Processing · GitHub", "date": "", "ddg_snippet": "OSU Natural Language Processing has 58 repositories available. Follow their code on GitHub .", "subpage_snippet": "", "source": "www.hubp.de", "link": "https://www.hubp.de/OSU-NLP-Group", "content": "OSU Natural Language Processing has 58 repositories available. Follow their code on GitHub ."}
+{"idx": 8, "title": "osunlp ( OSU NLP Group )", "date": "", "ddg_snippet": "AI & ML interests. Natural language processing , language models, language agents.osunlp/ UGround -V1-72B:I want to know, what format of data is the model better at returning?", "subpage_snippet": "", "source": "zhoutlhf.pages.dev", "link": "https://zhoutlhf.pages.dev/osunlp", "content": "AI & ML interests. Natural language processing , language models, language agents.osunlp/ UGround -V1-72B:I want to know, what format of data is the model better at returning?"}
+{"idx": 9, "title": "OSU Natural Language Processing | Ecosystem Directory | market.dev", "date": "", "ddg_snippet": "OSU Natural Language Processing . United States of America. OSU - NLP - Group avatar. Mind2Web. Owner.", "subpage_snippet": "", "source": "explore.market.dev", "link": "https://explore.market.dev/experts/OSU-NLP-Group", "content": "OSU Natural Language Processing . United States of America. OSU - NLP - Group avatar. Mind2Web. Owner."}
diff --git a/data/sampled_jsons/Olah_et_al._2020_Zoom_In_Introduction_to_Circuits_neural_networks.jsonl b/data/sampled_jsons/Olah_et_al._2020_Zoom_In_Introduction_to_Circuits_neural_networks.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..5e8d5425d58d4658edc9b68b7a1117cd942ebfb2
--- /dev/null
+++ b/data/sampled_jsons/Olah_et_al._2020_Zoom_In_Introduction_to_Circuits_neural_networks.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Zoom In: An Introduction to Circuits - Distill", "date": "", "ddg_snippet": "Zoom In : An Introduction to Circuits By studying the connections between neurons, we can find meaningful algorithms in the weights of neural networks .", "subpage_snippet": "", "source": "distill.pub", "link": "https://distill.pub/2020/circuits/zoom-in/", "content": "Zoom In : An Introduction to Circuits By studying the connections between neurons, we can find meaningful algorithms in the weights of neural networks ."}
+{"idx": 1, "title": "Zoom In: An Introduction to Circuits - ResearchGate", "date": "", "ddg_snippet": "A multitude of studies have later focused on interpreting weights and intermediate representations in neural networks ( Olah et al ., 2017 ( Olah et al ., , 2018 ( Olah et al ., , 2020 Voss et al ., 2021 ...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/339841165_Zoom_In_An_Introduction_to_Circuits", "content": "A multitude of studies have later focused on interpreting weights and intermediate representations in neural networks ( Olah et al ., 2017 ( Olah et al ., , 2018 ( Olah et al ., , 2020 Voss et al ., 2021 ..."}
+{"idx": 2, "title": "Zoom In: An Introduction to Circuits — AI Alignment Forum", "date": "", "ddg_snippet": "Once it's established that neural networks have meaningful features and circuits in them, discovering new such circuits becomes a legitimate scientific endeavor—especially if, as the third claim suggests, those features and circuits are universal across many different networks . From \" Zoom In :\"", "subpage_snippet": "", "source": "www.alignmentforum.org", "link": "https://www.alignmentforum.org/posts/MG4ZjWQDrdpgeu8wG/zoom-in-an-introduction-to-circuits", "content": "Once it's established that neural networks have meaningful features and circuits in them, discovering new such circuits becomes a legitimate scientific endeavor—especially if, as the third claim suggests, those features and circuits are universal across many different networks . From \" Zoom In :\""}
+{"idx": 3, "title": "Zoom In: An Introduction to Circuits. Published by OpenAI. March 10, 2020", "date": "", "ddg_snippet": "Zoom In : An Introduction to Circuits By studying the connections between neurons, we can find meaningful algorithms in the weights of neural networks - OpenAI. March 10, 2020 Zoom In : An Introduction to Circuits By studying the connections between neurons, we can find meaningful algorithms in the weights of neural networks .", "subpage_snippet": "", "source": "blog.biocomm.ai", "link": "https://blog.biocomm.ai/2020/03/10/zoom-in-an-introduction-to-circuits-published-by-openai-march-10-2020/", "content": "Zoom In : An Introduction to Circuits By studying the connections between neurons, we can find meaningful algorithms in the weights of neural networks - OpenAI. March 10, 2020 Zoom In : An Introduction to Circuits By studying the connections between neurons, we can find meaningful algorithms in the weights of neural networks ."}
+{"idx": 4, "title": "Distill: Zoom in on Circuits - Dynamically Typed", "date": "", "ddg_snippet": "From DT #35: \"By studying the connections between neurons, we can find meaningful algorithms in the weights of neural networks .\" Chris Olah et al. wrote a fascinating new Distill article about \" circuits \" in convolutional neural networks . The authors aim to reposition the field of AI interpretability as a natural science, like biology and chemistry: There are two common proposals for ...", "subpage_snippet": "", "source": "dynamicallytyped.com", "link": "https://dynamicallytyped.com/stories/2020/distill-zoom-in-on-circuits/", "content": "From DT #35: \"By studying the connections between neurons, we can find meaningful algorithms in the weights of neural networks .\" Chris Olah et al. wrote a fascinating new Distill article about \" circuits \" in convolutional neural networks . The authors aim to reposition the field of AI interpretability as a natural science, like biology and chemistry: There are two common proposals for ..."}
+{"idx": 5, "title": "PDF Chris-Olahs-Research/Zoom In An Introduction to Circuits.pdf ... - GitHub", "date": "", "ddg_snippet": "An educational collection of Chris Olahs fundamental papers for learning, research, and interpretive analysis. - Chris- Olahs -Research/ Zoom In An Introduction to Circuits .pdf at main · davidkimai/Chris- Olahs -Research", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/davidkimai/Chris-Olahs-Research/blob/main/Zoom+In+An+Introduction+to+Circuits.pdf", "content": "An educational collection of Chris Olahs fundamental papers for learning, research, and interpretive analysis. - Chris- Olahs -Research/ Zoom In An Introduction to Circuits .pdf at main · davidkimai/Chris- Olahs -Research"}
+{"idx": 6, "title": "Zoom In: An Introduction to Circuits | Nick's Notes", "date": "", "ddg_snippet": "Makes three speculative claims about the understandability of neural networks : Features - Features are the fundamental unit of neural networks (e.g. curve detectors). Circuits - Features are connected by weights, forming circuits . A \" circuit \" is a computational subgraph of a neural network . It consists of a set of features, and the weighted edges that go between them in the original ...", "subpage_snippet": "", "source": "www.nickjalbert.com", "link": "http://www.nickjalbert.com/reading/2020/03/27/zoom-in-an-introduction-to-circuits.html", "content": "Makes three speculative claims about the understandability of neural networks : Features - Features are the fundamental unit of neural networks (e.g. curve detectors). Circuits - Features are connected by weights, forming circuits . A \" circuit \" is a computational subgraph of a neural network . It consists of a set of features, and the weighted edges that go between them in the original ..."}
+{"idx": 7, "title": "Notes: \"Zoom In - An Introduction to Circuits\" - Tanay Biradar", "date": "", "ddg_snippet": "🌳 Notes: \" Zoom In - An Introduction to Circuits \" Introduction There's an uncanny overlap between the Effective Altruism and machine learning communities. Even more so in mechanistic interpretability, the subfield of ML that focuses on reverse-engineering and understanding the weights inside neural networks . I don't consider myself to have gone that deep in to the EA realm, but I've read a ...", "subpage_snippet": "", "source": "tanaybiradar.com", "link": "https://tanaybiradar.com/blog/notes-on-zoom-in-circuits/", "content": "🌳 Notes: \" Zoom In - An Introduction to Circuits \" Introduction There's an uncanny overlap between the Effective Altruism and machine learning communities. Even more so in mechanistic interpretability, the subfield of ML that focuses on reverse-engineering and understanding the weights inside neural networks . I don't consider myself to have gone that deep in to the EA realm, but I've read a ..."}
+{"idx": 8, "title": "PDF An Introduction to Circuits in CNNs - GitHub Pages", "date": "", "ddg_snippet": "To what extent are there abstract recurring patterns -- \" circuit motifs\" -- in neural networks ? Traditional study of circuit motifs relies on highly sparse graphs to systematically analyze them.", "subpage_snippet": "", "source": "interpretablevision.github.io", "link": "https://interpretablevision.github.io/slide/cvpr20_chris.pdf", "content": "To what extent are there abstract recurring patterns -- \" circuit motifs\" -- in neural networks ? Traditional study of circuit motifs relies on highly sparse graphs to systematically analyze them."}
+{"idx": 9, "title": "Zoom In: An Introduction to Circuits - Semantic Scholar", "date": "", "ddg_snippet": "Semantic Scholar extracted view of \" Zoom In : An Introduction to Circuits \" by Christopher Olah et al.", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/Zoom-In:-An-Introduction-to-Circuits-Olah-Cammarata/a0cfd36e6c7abf070f492ae52a35af895a1c5592", "content": "Semantic Scholar extracted view of \" Zoom In : An Introduction to Circuits \" by Christopher Olah et al."}
diff --git a/data/sampled_jsons/OmniBench_Section_2_composable_task_complexity_graphs_unique.jsonl b/data/sampled_jsons/OmniBench_Section_2_composable_task_complexity_graphs_unique.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..a75cba7abd7eb164370bb5668f52ce78762e5418
--- /dev/null
+++ b/data/sampled_jsons/OmniBench_Section_2_composable_task_complexity_graphs_unique.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "OmniBench", "date": "", "ddg_snippet": "OmniBench Overview To cost-effectively construct diverse task scenarios with complexity at multiple granularities for comprehensive agent evaluation, we propose a novel self-generating, graph -based benchmark, OmniBench . It dynamically synthesizes tasks with controllable complexity based on a bottom-up pipeline.", "subpage_snippet": "", "source": "omni-bench.github.io", "link": "https://omni-bench.github.io/", "content": "OmniBench Overview To cost-effectively construct diverse task scenarios with complexity at multiple granularities for comprehensive agent evaluation, we propose a novel self-generating, graph -based benchmark, OmniBench . It dynamically synthesizes tasks with controllable complexity based on a bottom-up pipeline."}
+{"idx": 1, "title": "What Limits Virtual Agent Application? OmniBench: A Scalable ...", "date": "", "ddg_snippet": "Notably, to the best of our knowledge, OmniBench is the only scalable benchmark for virtual agents that de-fines composable task complexity using graphs to evaluate multiple essential capabilities.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2506.08933", "content": "Notably, to the best of our knowledge, OmniBench is the only scalable benchmark for virtual agents that de-fines composable task complexity using graphs to evaluate multiple essential capabilities."}
+{"idx": 2, "title": "GitHub - antgroup/OmniBench: [ICML 2025 Oral] This is the ...", "date": "", "ddg_snippet": "Overview of OmniBench , a systematic benchmark with five-dimensional task complexity and bottom-up automatic task synthesis for generating structured task graphs . It evaluates ten virtual agent capabilities using high-quality graph -based data, ensuring scalable and realistic task assessments.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/antgroup/OmniBench", "content": "Overview of OmniBench , a systematic benchmark with five-dimensional task complexity and bottom-up automatic task synthesis for generating structured task graphs . It evaluates ten virtual agent capabilities using high-quality graph -based data, ensuring scalable and realistic task assessments."}
+{"idx": 3, "title": "What Limits Virtual Agent Application? OmniBench: A Scalable...", "date": "", "ddg_snippet": "May 1, 2025 · The paper introduces OmniBench , a scalable, graph -based benchmark designed to evaluate multimodal virtual agents by systematically synthesizing diverse tasks of controllable complexity through automatic task composition.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=4tFSKOY2mT", "content": "May 1, 2025 · The paper introduces OmniBench , a scalable, graph -based benchmark designed to evaluate multimodal virtual agents by systematically synthesizing diverse tasks of controllable complexity through automatic task composition."}
+{"idx": 4, "title": "OmniBench - m-a-p.ai", "date": "", "ddg_snippet": "The design logic and statistics of the dataset and the annotation protocols are introduced in this section . We propose a novel task type categorization in OmniBench that assesses a broad spectrum of reasoning and cognitive abilities.", "subpage_snippet": "", "source": "m-a-p.ai", "link": "https://m-a-p.ai/OmniBench/", "content": "The design logic and statistics of the dataset and the annotation protocols are introduced in this section . We propose a novel task type categorization in OmniBench that assesses a broad spectrum of reasoning and cognitive abilities."}
+{"idx": 5, "title": "README.md · m-a-p/OmniBench at main - Hugging Face", "date": "", "ddg_snippet": "The project introduces OmniBench , a novel benchmark designed to rigorously evaluate models' ability to recognize, interpret, and reason across visual, acoustic, and textual inputs simultaneously. We define models capable of such tri-modal processing as omni-language models (OLMs).", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/datasets/m-a-p/OmniBench/blob/main/README.md", "content": "The project introduces OmniBench , a novel benchmark designed to rigorously evaluate models' ability to recognize, interpret, and reason across visual, acoustic, and textual inputs simultaneously. We define models capable of such tri-modal processing as omni-language models (OLMs)."}
+{"idx": 6, "title": "[2506.08933] What Limits Virtual Agent Application? OmniBench ...", "date": "", "ddg_snippet": "Jun 10, 2025 · In response to these challenges, we introduce OmniBench , a self-generating, cross-platform, graph -based benchmark with an automated pipeline for synthesizing tasks of controllable complexity through subtask composition.", "subpage_snippet": "", "source": "export.arxiv.org", "link": "http://export.arxiv.org/abs/2506.08933", "content": "Jun 10, 2025 · In response to these challenges, we introduce OmniBench , a self-generating, cross-platform, graph -based benchmark with an automated pipeline for synthesizing tasks of controllable complexity through subtask composition."}
+{"idx": 7, "title": "What Limits Virtual Agent Application? OmniBench", "date": "", "ddg_snippet": "fines composable task complexity using graphs to evaluate multiple essential capabilities. 3. OmniBench . OmniBench consists of 36k high-quality graph ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf/d5d3691187b9f32f92e8e287e5005d40aa429f80.pdf", "content": "fines composable task complexity using graphs to evaluate multiple essential capabilities. 3. OmniBench . OmniBench consists of 36k high-quality graph ..."}
+{"idx": 8, "title": "What Limits Virtual Agent Application? OmniBench", "date": "", "ddg_snippet": "Unlike previous benchmarks, OmniBench features automatic task composition, five-dimensional task complexity , and a 10-capability evaluation framework. ... vides ...", "subpage_snippet": "", "source": "bytez.com", "link": "https://bytez.com/docs/icml/46463/paper", "content": "Unlike previous benchmarks, OmniBench features automatic task composition, five-dimensional task complexity , and a 10-capability evaluation framework. ... vides ..."}
+{"idx": 9, "title": "Reinforcement Learning Foundations for Deep Research ...", "date": "", "ddg_snippet": "8 Sept 2025 — Deep research systems, agentic AI that solve complex , multi-step tasks by coordinating reasoning, search across the open web and user files, and ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2509.06733v1", "content": "8 Sept 2025 — Deep research systems, agentic AI that solve complex , multi-step tasks by coordinating reasoning, search across the open web and user files, and ..."}
diff --git a/data/sampled_jsons/OmniBench_paper_2506.08933_Section_5.1_experimental_setup_GPU_used.jsonl b/data/sampled_jsons/OmniBench_paper_2506.08933_Section_5.1_experimental_setup_GPU_used.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..fa28cf9bb8494390191ad530a7747269d7bae77c
--- /dev/null
+++ b/data/sampled_jsons/OmniBench_paper_2506.08933_Section_5.1_experimental_setup_GPU_used.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "What Limits Virtual Agent Application? OmniBench: A Scalable Multi ...", "date": "", "ddg_snippet": "In this section , we first introduce the experimental setup ( Sec-tion 5.1 ). Then, we comprehensively compare the differences in capabilities across various models on OmniBench , along with several key findings ( Section 5.2).", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2506.08933", "content": "In this section , we first introduce the experimental setup ( Sec-tion 5.1 ). Then, we comprehensively compare the differences in capabilities across various models on OmniBench , along with several key findings ( Section 5.2)."}
+{"idx": 1, "title": "PDF Estimating GPU Memory Consumption of Deep Learning Models", "date": "", "ddg_snippet": "This paper presents DNNMem, an accurate estimation tool for GPU memory consumption of DL models. Our key observation is that the algorithmic execution of a DL model can be represented as iterative forward and backward propagation on its computa-tion graph.", "subpage_snippet": "", "source": "www.microsoft.com", "link": "https://www.microsoft.com/en-us/research/wp-content/uploads/2020/09/dnnmem.pdf", "content": "This paper presents DNNMem, an accurate estimation tool for GPU memory consumption of DL models. Our key observation is that the algorithmic execution of a DL model can be represented as iterative forward and backward propagation on its computa-tion graph."}
+{"idx": 2, "title": "How to benchmark GPU on Cinebench: A step-by-step guide", "date": "", "ddg_snippet": "The latest version of Cinebench now supports GPU testing. Here's a step-by-step guide detailing how to use it.", "subpage_snippet": "", "source": "www.xda-developers.com", "link": "https://www.xda-developers.com/how-benchmark-gpu-cinebench/", "content": "The latest version of Cinebench now supports GPU testing. Here's a step-by-step guide detailing how to use it."}
+{"idx": 3, "title": "Best GPU Benchmarking Software [Sept. 2025 ] - GPU Mag", "date": "", "ddg_snippet": "In the everchanging world of GPUs , you need to know how to test them. Here's a guide to the best GPU benchmarking software available.", "subpage_snippet": "", "source": "www.gpumag.com", "link": "https://www.gpumag.com/best-gpu-benchmarking-software/", "content": "In the everchanging world of GPUs , you need to know how to test them. Here's a guide to the best GPU benchmarking software available."}
+{"idx": 4, "title": "How to benchmark your GPU | Tom's Guide", "date": "", "ddg_snippet": "Knowing how to benchmark your GPU can help you gauge its capabilities, identify potential issues, and optimize settings for enhanced gaming and graphic-intensive tasks.", "subpage_snippet": "", "source": "www.tomsguide.com", "link": "https://www.tomsguide.com/how-to/how-to-benchmark-your-gpu-on-windows-macos-or-linux", "content": "Knowing how to benchmark your GPU can help you gauge its capabilities, identify potential issues, and optimize settings for enhanced gaming and graphic-intensive tasks."}
+{"idx": 5, "title": "MAXON Cinebench 2024 2024.1.0 Download Free | TechSpot", "date": "", "ddg_snippet": "Download Cinebench 2024 - Cinebench 2024 utilizes the power of Redshift, Cinema 4D's default rendering engine, to evaluate your computer's CPU and GPU capabilities.", "subpage_snippet": "", "source": "www.techspot.com", "link": "https://www.techspot.com/downloads/6709-cinebench.html", "content": "Download Cinebench 2024 - Cinebench 2024 utilizes the power of Redshift, Cinema 4D's default rendering engine, to evaluate your computer's CPU and GPU capabilities."}
+{"idx": 6, "title": "GitHub - stepjam/RLBench: A large-scale benchmark and learning environment.", "date": "", "ddg_snippet": "A large-scale benchmark and learning environment. Contribute to stepjam/RLBench development by creating an account on GitHub.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/stepjam/RLBench", "content": "A large-scale benchmark and learning environment. Contribute to stepjam/RLBench development by creating an account on GitHub."}
+{"idx": 7, "title": "The cross-platform performance site - GFXBench - Unified cross-platform ...", "date": "", "ddg_snippet": "The first unified cross-platform 3D graphics benchmark database for comparing Android, iOS, Windows 8, Windows Phone 8 and Windows RT capable devices based on graphics processing power.", "subpage_snippet": "", "source": "gfxbench.com", "link": "https://gfxbench.com/", "content": "The first unified cross-platform 3D graphics benchmark database for comparing Android, iOS, Windows 8, Windows Phone 8 and Windows RT capable devices based on graphics processing power."}
+{"idx": 8, "title": "Free MAXON Cinebench R23 Download - R23.2 | TechSpot", "date": "", "ddg_snippet": "Cinebench R23 is a real-world cross-platform test suite that evaluates your computer's hardware capabilities. Improvements to Cinebench R23 reflect the overall advancements to CPU and rendering ...", "subpage_snippet": "", "source": "www.techspot.com", "link": "https://www.techspot.com/downloads/7579-cinebench-r23.html", "content": "Cinebench R23 is a real-world cross-platform test suite that evaluates your computer's hardware capabilities. Improvements to Cinebench R23 reflect the overall advancements to CPU and rendering ..."}
+{"idx": 9, "title": "Geekbench 6 - Cross-Platform Benchmark", "date": "", "ddg_snippet": "Introducing Geekbench 6 Geekbench 6 is a cross-platform benchmark that measures your system's performance with the press of a button. How will your mobile device or desktop computer perform when push comes to crunch? How will it compare to the newest devices on the market? Find out today with Geekbench 6.", "subpage_snippet": "", "source": "www.geekbench.com", "link": "https://www.geekbench.com/", "content": "Introducing Geekbench 6 Geekbench 6 is a cross-platform benchmark that measures your system's performance with the press of a button. How will your mobile device or desktop computer perform when push comes to crunch? How will it compare to the newest devices on the market? Find out today with Geekbench 6."}
diff --git a/data/sampled_jsons/On_Differential_Privacy_for_Adaptively_Solving_Search_Problems_via_Sketching_coordinate-wise_private.jsonl b/data/sampled_jsons/On_Differential_Privacy_for_Adaptively_Solving_Search_Problems_via_Sketching_coordinate-wise_private.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..0d2133832ae8fa356a47b7726a0b77317ec77bfd
--- /dev/null
+++ b/data/sampled_jsons/On_Differential_Privacy_for_Adaptively_Solving_Search_Problems_via_Sketching_coordinate-wise_private.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "On Differential Privacy for Adaptively Solving Search ...", "date": "", "ddg_snippet": "To offset the blowup in condition number, we scale down α𝛼\\alphaitalic_αby a factor of κ𝜅\\kappaitalic_κ, and thus establish the utility of the coordinate - wise private median mechanism.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2506.05503v1", "content": "To offset the blowup in condition number, we scale down α𝛼\\alphaitalic_αby a factor of κ𝜅\\kappaitalic_κ, and thus establish the utility of the coordinate - wise private median mechanism."}
+{"idx": 1, "title": "On Differential Privacy for Adaptively Solving Search ...", "date": "", "ddg_snippet": "In this paper, we investigate the use of diferential privacy for adaptive queries to search problems , which are significantly more challenging since the responses to queries can reveal much more about the internal randomness than a single numerical query.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2506.05503", "content": "In this paper, we investigate the use of diferential privacy for adaptive queries to search problems , which are significantly more challenging since the responses to queries can reveal much more about the internal randomness than a single numerical query."}
+{"idx": 2, "title": "On Differential Privacy for Adaptively Solving Search ...", "date": "", "ddg_snippet": "In this paper, we investigate the use of differential privacy for adaptive queries to search problems , which are significantly more challenging since the responses to queries can reveal much more about the internal randomness than a single numerical query.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2506.05503v1", "content": "In this paper, we investigate the use of differential privacy for adaptive queries to search problems , which are significantly more challenging since the responses to queries can reveal much more about the internal randomness than a single numerical query."}
+{"idx": 3, "title": "On Differential Privacy for Adaptively Solving Search ...", "date": "", "ddg_snippet": "We focus on two classical search problems : nearest neighbor queries and regression with arbitrary turnstile updates.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=kEn7Wt6Yj2", "content": "We focus on two classical search problems : nearest neighbor queries and regression with arbitrary turnstile updates."}
+{"idx": 4, "title": "David P. Woodruff", "date": "", "ddg_snippet": "ICML, On Differential Privacy for Adaptively Solving Search Problems via Sketching with Shiyuan Feng, Ying Feng, George Li, Zhao Song, and Lichen ...", "subpage_snippet": "", "source": "www.cs.cmu.edu", "link": "https://www.cs.cmu.edu/~dwoodruf/", "content": "ICML, On Differential Privacy for Adaptively Solving Search Problems via Sketching with Shiyuan Feng, Ying Feng, George Li, Zhao Song, and Lichen ..."}
+{"idx": 5, "title": "David P. Woodruff", "date": "", "ddg_snippet": "ICML, On Differential Privacy for Adaptively Solving Search Problems via Sketching with Shiyuan Feng, Ying Feng, George Li, Zhao Song, and Lichen ...", "subpage_snippet": "", "source": "www.cs.cmu.edu", "link": "http://www.cs.cmu.edu/~dwoodruf/", "content": "ICML, On Differential Privacy for Adaptively Solving Search Problems via Sketching with Shiyuan Feng, Ying Feng, George Li, Zhao Song, and Lichen ..."}
+{"idx": 6, "title": "ICML 2025 Papers", "date": "", "ddg_snippet": "Online Differentially Private ... Online Curvature-Aware Replay: Leveraging $\\mathbf{2^{nd}}$ Order Information for Online Continual Learning", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/papers.html", "content": "Online Differentially Private ... Online Curvature-Aware Replay: Leveraging $\\mathbf{2^{nd}}$ Order Information for Online Continual Learning"}
+{"idx": 7, "title": "ICML 2020 Papers", "date": "", "ddg_snippet": "Robust One -Bit Recovery via ReLU ... On the (In)tractability of Computing Normalizing Constants for the Product of Determinantal Point Processes", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2020/papers.html?filter=keywords", "content": "Robust One -Bit Recovery via ReLU ... On the (In)tractability of Computing Normalizing Constants for the Product of Determinantal Point Processes"}
+{"idx": 8, "title": "Downloads", "date": "", "ddg_snippet": "Bridging the Gap: Providing Post-Hoc Symbolic Explanations for Sequential Decision-Making Problems with Inscrutable Representations", "subpage_snippet": "", "source": "iclr.cc", "link": "https://iclr.cc/Downloads/2022", "content": "Bridging the Gap: Providing Post-Hoc Symbolic Explanations for Sequential Decision-Making Problems with Inscrutable Representations"}
+{"idx": 9, "title": "ICLR 2024 Schedule", "date": "", "ddg_snippet": "Rethinking Channel Dimensions to Isolate Outliers for Low-bit Weight Quantization of Large Language Models ... Component Fourier Neural Operators for ...", "subpage_snippet": "", "source": "iclr.cc", "link": "https://iclr.cc/virtual/2024/calendar", "content": "Rethinking Channel Dimensions to Isolate Outliers for Low-bit Weight Quantization of Large Language Models ... Component Fourier Neural Operators for ..."}
diff --git a/data/sampled_jsons/On_a_Connection_Between_Imitation_Learning_and_RLHF_LSIF_loss.jsonl b/data/sampled_jsons/On_a_Connection_Between_Imitation_Learning_and_RLHF_LSIF_loss.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..6b64c6209e74f51f594a647dde9cc7da78aaa5bc
--- /dev/null
+++ b/data/sampled_jsons/On_a_Connection_Between_Imitation_Learning_and_RLHF_LSIF_loss.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "On a Connection Between Imitation Learning and RLHF On a Connection Between Imitation Learning and RLHF On a Connection Between Imitation Learningand RLHF ON A CONNECTION BETWEEN IMITATION LEARNING AND RLHF 【深度论文解读】On a Connection Between Imitation Learning and RL... Direct Imitation Learning: RLHF Secretly Performs Imitation ... On a Connection Between Imitation Learning and RLHF | AI ...", "date": "", "ddg_snippet": "Mar 7, 2025 · Building on this connection , we propose DIL, a principled framework that directly optimizes the imitation learning objective. DIL provides a unified imitation learning perspective on alignment, encompassing existing alignment algorithms as special cases while naturally introducing new variants. Jan 22, 2025 · Although the choice of the loss function and optimization procedure differs, the central aim of our work is to emphasize that the optimal policies of RLHF and DPO are theoretically the same as the imitation learning process. Mar 12, 2025 · t SimPO and DPO in various benchmarks DIL: Results DIL aligns better with human preferences than baselines, achieving a win. gainst the chosen responses. DIL: Training Dynamic DIL exhibits the least decline in chosen likelihoods and demo. nimizes Rev. In this paper, we revisit RLHF from the perspective of imitation learning . In particular, we show that RLHF is a special case of a general imitation learning problem expressed exclusively in terms of pairwise preferences. Mar 7, 2025 · 这篇文章做了一篇类似 DPO (Direct Preference Optimization) 却高于它的工作 (通过理论分析将 reward 融进 los s 中，再付诸实验)，将 IL ( Im itation Learning ) 与 R LH F (Reinforcement Learning for Human Feedback) 归一为 DIL (Direct Im itation Learning )，其中涉及大量概念和数学推导以及丰富的实验 ... We address this problem from a novel imitation learning (IL) perspective. We establish a close connection between alignment and imitation learning , which shows that existing alignment objectives implicitly align model and preference data distributions. RLHF is like having someone taste your food repeatedly and tell you what they like and don't like, then you adjust your cooking accordingly. The authors show that if you have access to excellent recipes (high-quality examples), you can learn to cook just as well by simply following those recipes.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2503.05079", "content": "Mar 7, 2025 · Building on this connection , we propose DIL, a principled framework that directly optimizes the imitation learning objective. DIL provides a unified imitation learning perspective on alignment, encompassing existing alignment algorithms as special cases while naturally introducing new variants. Jan 22, 2025 · Although the choice of the loss function and optimization procedure differs, the central aim of our work is to emphasize that the optimal policies of RLHF and DPO are theoretically the same as the imitation learning process. Mar 12, 2025 · t SimPO and DPO in various benchmarks DIL: Results DIL aligns better with human preferences than baselines, achieving a win. gainst the chosen responses. DIL: Training Dynamic DIL exhibits the least decline in chosen likelihoods and demo. nimizes Rev. In this paper, we revisit RLHF from the perspective of imitation learning . In particular, we show that RLHF is a special case of a general imitation learning problem expressed exclusively in terms of pairwise preferences. Mar 7, 2025 · 这篇文章做了一篇类似 DPO (Direct Preference Optimization) 却高于它的工作 (通过理论分析将 reward 融进 los s 中，再付诸实验)，将 IL ( Im itation Learning ) 与 R LH F (Reinforcement Learning for Human Feedback) 归一为 DIL (Direct Im itation Learning )，其中涉及大量概念和数学推导以及丰富的实验 ... We address this problem from a novel imitation learning (IL) perspective. We establish a close connection between alignment and imitation learning , which shows that existing alignment objectives implicitly align model and preference data distributions. RLHF is like having someone taste your food repeatedly and tell you what they like and don't like, then you adjust your cooking accordingly. The authors show that if you have access to excellent recipes (high-quality examples), you can learn to cook just as well by simply following those recipes."}
+{"idx": 1, "title": "On a Connection Between Imitation Learning and RLHF", "date": "", "ddg_snippet": "Jan 22, 2025 · Although the choice of the loss function and optimization procedure differs, the central aim of our work is to emphasize that the optimal policies of RLHF and DPO are theoretically the same as the imitation learning process.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=2QdsjiNXgj", "content": "Jan 22, 2025 · Although the choice of the loss function and optimization procedure differs, the central aim of our work is to emphasize that the optimal policies of RLHF and DPO are theoretically the same as the imitation learning process."}
+{"idx": 2, "title": "On a Connection Between Imitation Learningand RLHF", "date": "", "ddg_snippet": "Mar 12, 2025 · t SimPO and DPO in various benchmarks DIL: Results DIL aligns better with human preferences than baselines, achieving a win. gainst the chosen responses. DIL: Training Dynamic DIL exhibits the least decline in chosen likelihoods and demo. nimizes Rev.", "subpage_snippet": "", "source": "aitime-lundao.oss-cn-beijing.aliyuncs.com", "link": "https://aitime-lundao.oss-cn-beijing.aliyuncs.com/AitimeReport/20250312/1741781794566", "content": "Mar 12, 2025 · t SimPO and DPO in various benchmarks DIL: Results DIL aligns better with human preferences than baselines, achieving a win. gainst the chosen responses. DIL: Training Dynamic DIL exhibits the least decline in chosen likelihoods and demo. nimizes Rev."}
+{"idx": 3, "title": "ON A CONNECTION BETWEEN IMITATION LEARNING AND RLHF", "date": "", "ddg_snippet": "In this paper, we revisit RLHF from the perspective of imitation learning . In particular, we show that RLHF is a special case of a general imitation learning problem expressed exclusively in terms of pairwise preferences.", "subpage_snippet": "", "source": "proceedings.iclr.cc", "link": "https://proceedings.iclr.cc/paper_files/paper/2025/file/acf4a08f67724e9d2de34099f57a9c25-Paper-Conference.pdf", "content": "In this paper, we revisit RLHF from the perspective of imitation learning . In particular, we show that RLHF is a special case of a general imitation learning problem expressed exclusively in terms of pairwise preferences."}
+{"idx": 4, "title": "【深度论文解读】On a Connection Between Imitation Learning and RL...", "date": "", "ddg_snippet": "Mar 7, 2025 · 这篇文章做了一篇类似 DPO (Direct Preference Optimization) 却高于它的工作 (通过理论分析将 reward 融进 los s 中，再付诸实验)，将 IL ( Im itation Learning ) 与 R LH F (Reinforcement Learning for Human Feedback) 归一为 DIL (Direct Im itation Learning )，其中涉及大量概念和数学推导以及丰富的实验 ...", "subpage_snippet": "", "source": "zhuanlan.zhihu.com", "link": "https://zhuanlan.zhihu.com/p/1910382777079165403", "content": "Mar 7, 2025 · 这篇文章做了一篇类似 DPO (Direct Preference Optimization) 却高于它的工作 (通过理论分析将 reward 融进 los s 中，再付诸实验)，将 IL ( Im itation Learning ) 与 R LH F (Reinforcement Learning for Human Feedback) 归一为 DIL (Direct Im itation Learning )，其中涉及大量概念和数学推导以及丰富的实验 ..."}
+{"idx": 5, "title": "Direct Imitation Learning: RLHF Secretly Performs Imitation ...", "date": "", "ddg_snippet": "We address this problem from a novel imitation learning (IL) perspective. We establish a close connection between alignment and imitation learning , which shows that existing alignment objectives implicitly align model and preference data distributions.", "subpage_snippet": "", "source": "papers.cool", "link": "https://papers.cool/venue/2QdsjiNXgj@OpenReview", "content": "We address this problem from a novel imitation learning (IL) perspective. We establish a close connection between alignment and imitation learning , which shows that existing alignment objectives implicitly align model and preference data distributions."}
+{"idx": 6, "title": "On a Connection Between Imitation Learning and RLHF | AI ...", "date": "", "ddg_snippet": "RLHF is like having someone taste your food repeatedly and tell you what they like and don't like, then you adjust your cooking accordingly. The authors show that if you have access to excellent recipes (high-quality examples), you can learn to cook just as well by simply following those recipes.", "subpage_snippet": "", "source": "www.aimodels.fyi", "link": "https://www.aimodels.fyi/papers/arxiv/connection-between-imitation-learning-rlhf", "content": "RLHF is like having someone taste your food repeatedly and tell you what they like and don't like, then you adjust your cooking accordingly. The authors show that if you have access to excellent recipes (high-quality examples), you can learn to cook just as well by simply following those recipes."}
+{"idx": 7, "title": "On a Connection Between Imitation Learning and RLHF", "date": "", "ddg_snippet": "We establish a close theoretical connection between reinforcement learning from human feedback ( RLHF ) and imitation learning (IL), revealing that RLHF implicitly performs imitation learning on the preference data distribution.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2503.05079v1", "content": "We establish a close theoretical connection between reinforcement learning from human feedback ( RLHF ) and imitation learning (IL), revealing that RLHF implicitly performs imitation learning on the preference data distribution."}
+{"idx": 8, "title": "On a Connection Between Imitation Learning and RLHF | alphaXiv", "date": "", "ddg_snippet": "alphaXiv. Go Home. On a Connection Between Imitation Learning and RLHF .This formula reveals that RLHF is effectively performing imitation learning on the preference data distribution, with the reward function serving as a correction term to transform the reference distribution.", "subpage_snippet": "", "source": "www.alphaxiv.org", "link": "https://www.alphaxiv.org/overview/2503.05079v1", "content": "alphaXiv. Go Home. On a Connection Between Imitation Learning and RLHF .This formula reveals that RLHF is effectively performing imitation learning on the preference data distribution, with the reward function serving as a correction term to transform the reference distribution."}
+{"idx": 9, "title": "[Literature Review] On a Connection Between Imitation Learning ...", "date": "", "ddg_snippet": "The paper titled \" On a Connection Between Imitation Learning and RLHF \" presents a novel perspective on aligning large language models (LLMs) with human preferences through imitation learning .", "subpage_snippet": "", "source": "www.themoonlight.io", "link": "https://www.themoonlight.io/en/review/on-a-connection-between-imitation-learning-and-rlhf", "content": "The paper titled \" On a Connection Between Imitation Learning and RLHF \" presents a novel perspective on aligning large language models (LLMs) with human preferences through imitation learning ."}
diff --git a/data/sampled_jsons/On_a_Connection_Between_Imitation_Learning_and_RLHF_filetypepdf.jsonl b/data/sampled_jsons/On_a_Connection_Between_Imitation_Learning_and_RLHF_filetypepdf.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..bd51ed5e03c4c15384b6473c118fe7f2d040dc3d
--- /dev/null
+++ b/data/sampled_jsons/On_a_Connection_Between_Imitation_Learning_and_RLHF_filetypepdf.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "ON A CONNECTION BETWEEN IMITATION LEARNING AND RLHF", "date": "", "ddg_snippet": "We establish a close theoretical connection be-tween reinforcement learning from human feedback ( RLHF ) and imitation learning (IL), revealing that RLHF implicitly performs imitation learning on the preference data distribution.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=2QdsjiNXgj", "content": "We establish a close theoretical connection be-tween reinforcement learning from human feedback ( RLHF ) and imitation learning (IL), revealing that RLHF implicitly performs imitation learning on the preference data distribution."}
+{"idx": 1, "title": "On a Connection Between Imitation Learningand RLHF", "date": "", "ddg_snippet": "Mar 12, 2025 · t SimPO and DPO in various benchmarks DIL: Results DIL aligns better with human preferences than baselines, achieving a win. gainst the chosen responses. DIL: Training Dynamic DIL exhibits the least decline in chosen likelihoods and demo. nimizes Rev.", "subpage_snippet": "", "source": "aitime-lundao.oss-cn-beijing.aliyuncs.com", "link": "https://aitime-lundao.oss-cn-beijing.aliyuncs.com/AitimeReport/20250312/1741781794566", "content": "Mar 12, 2025 · t SimPO and DPO in various benchmarks DIL: Results DIL aligns better with human preferences than baselines, achieving a win. gainst the chosen responses. DIL: Training Dynamic DIL exhibits the least decline in chosen likelihoods and demo. nimizes Rev."}
+{"idx": 2, "title": "Imitating Language via Scalable Inverse Reinforcement Learning", "date": "", "ddg_snippet": "We focus on investigating the inverse reinforcement learning (IRL) perspective to imi -tation, extracting rewards and directly optimizing sequences instead of individual token likelihoods and evaluate its benefits for fine-tuning large language models.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2409.01369", "content": "We focus on investigating the inverse reinforcement learning (IRL) perspective to imi -tation, extracting rewards and directly optimizing sequences instead of individual token likelihoods and evaluate its benefits for fine-tuning large language models."}
+{"idx": 3, "title": "On a Connection Between Imitation Learning and RLHF", "date": "", "ddg_snippet": "On a Connection Between Imitation Learning and RLHF HF is Barely RL, and Secretly Performs Imitation Lear Key Finding:RLHF performs imitation learning, not RL!", "subpage_snippet": "", "source": "yuanyige.github.io", "link": "https://yuanyige.github.io/resources/iclr2025-dil-poster.pdf", "content": "On a Connection Between Imitation Learning and RLHF HF is Barely RL, and Secretly Performs Imitation Lear Key Finding:RLHF performs imitation learning, not RL!"}
+{"idx": 4, "title": "Lecture 8: Imitation Learning and RLHF", "date": "", "ddg_snippet": "Note: Much of this presentation follows the slides from Katerina Fragkiadaki’s Deep Reinforcement Learning and Control Lecture on Maximum Entropy Inverse RL.", "subpage_snippet": "", "source": "web.stanford.edu", "link": "https://web.stanford.edu/class/cs234/CS234Spr2024/slides/lecture8post.pdf", "content": "Note: Much of this presentation follows the slides from Katerina Fragkiadaki’s Deep Reinforcement Learning and Control Lecture on Maximum Entropy Inverse RL."}
+{"idx": 5, "title": "Reinforcement Learning in the Era Of", "date": "", "ddg_snippet": "Control Essential Ideas • What is RL ? An agent learns from trial a. d error, to maximize a cumulative rewar. . agent: can be human or neural networks. It has a pol. y (clinical guidelines, public poli. ies) trial and error: Online o. cumulative reward: the . eward Hypothesis Reward Can. e Sparse... “Win the game”, b. h.", "subpage_snippet": "", "source": "holarissun.github.io", "link": "https://holarissun.github.io/files/RLHF_Nov.pdf", "content": "Control Essential Ideas • What is RL ? An agent learns from trial a. d error, to maximize a cumulative rewar. . agent: can be human or neural networks. It has a pol. y (clinical guidelines, public poli. ies) trial and error: Online o. cumulative reward: the . eward Hypothesis Reward Can. e Sparse... “Win the game”, b. h."}
+{"idx": 6, "title": "learning and alignment with human preferences and values", "date": "", "ddg_snippet": "by T Xiao · 2025 — ... On a Connection Between Imitation Learning and RLHF . 34. 4.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34. 4.2 ...", "subpage_snippet": "", "source": "etda.libraries.psu.edu", "link": "https://etda.libraries.psu.edu/files/final_submissions/32968", "content": "by T Xiao · 2025 — ... On a Connection Between Imitation Learning and RLHF . 34. 4.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34. 4.2 ..."}
+{"idx": 7, "title": "Fact-Level Calibration and Correction for Long-Form ...", "date": "", "ddg_snippet": "by Y Yuan · 2025 — On a Connection Between Imitation Learning and RLHF . In The Thirteenth. International Conference on Learning Representations. https ...", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/pdf/10.1145/3726302.3730195", "content": "by Y Yuan · 2025 — On a Connection Between Imitation Learning and RLHF . In The Thirteenth. International Conference on Learning Representations. https ..."}
+{"idx": 8, "title": "[2503.05079] On a Connection Between Imitation Learning and RLHF", "date": "", "ddg_snippet": "View a PDF of the paper titled On a Connection Between Imitation Learning and RLHF , by Teng Xiao and 4 other authors. View PDF HTML (experimental). Abstract:This work studies the alignment of large language models with preference data from an imitation learning perspective.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2503.05079", "content": "View a PDF of the paper titled On a Connection Between Imitation Learning and RLHF , by Teng Xiao and 4 other authors. View PDF HTML (experimental). Abstract:This work studies the alignment of large language models with preference data from an imitation learning perspective."}
+{"idx": 9, "title": "On a Connection Between Imitation Learning and RLHF", "date": "", "ddg_snippet": "We establish a close theoretical connection between reinforcement learning from human feedback ( RLHF ) and imitation learning (IL), revealing that RLHF implicitly performs imitation learning on the preference data distribution.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2503.05079v1", "content": "We establish a close theoretical connection between reinforcement learning from human feedback ( RLHF ) and imitation learning (IL), revealing that RLHF implicitly performs imitation learning on the preference data distribution."}
diff --git a/data/sampled_jsons/On_the_Role_of_Attention_Heads_in_Large_Language_Model_Safety_ASR_4.5%_72%_Llama-2-7b-chat.jsonl b/data/sampled_jsons/On_the_Role_of_Attention_Heads_in_Large_Language_Model_Safety_ASR_4.5%_72%_Llama-2-7b-chat.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..e1c0a2f64f02dfef1c20c05395d471bb1d5da775
--- /dev/null
+++ b/data/sampled_jsons/On_the_Role_of_Attention_Heads_in_Large_Language_Model_Safety_ASR_4.5%_72%_Llama-2-7b-chat.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Self-Aware Safety Augmentation: Leveraging Internal Semantic", "date": "", "ddg_snippet": "... only the top- 5 safety -related heads increases the attack success rate by an average of 47%, demonstrating that the model ’s safety is largely ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2507.21637v1", "content": "... only the top- 5 safety -related heads increases the attack success rate by an average of 47%, demonstrating that the model ’s safety is largely ..."}
+{"idx": 1, "title": "Beyond I’m Sorry, I Can’t: Dissecting Large-Language-Model", "date": "", "ddg_snippet": "Refusal on harmful prompts is a key safety behaviour in instruction‑tuned large language models (LLMs), yet the internal causes of this behaviour ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2509.09708v1", "content": "Refusal on harmful prompts is a key safety behaviour in instruction‑tuned large language models (LLMs), yet the internal causes of this behaviour ..."}
+{"idx": 2, "title": "Hierarchical Safety Realignment: Lightweight Restoration of", "date": "", "ddg_snippet": "... safety realignment of the pruned model in two steps: The first step identifies the top-h most important attention heads for safety , while the second ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2505.16104v1", "content": "... safety realignment of the pruned model in two steps: The first step identifies the top-h most important attention heads for safety , while the second ..."}
+{"idx": 3, "title": "Exploring Large-Scale Language Models to Evaluate EEG-Based", "date": "", "ddg_snippet": "Similarly, the Video-SME framework [43] incorporates a Hypergraph Multimodal Large Language Model (HMLLM) to jointly interpret EEG and eye-tracking ...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/384662842_Exploring_Large-Scale_Language_Models_to_Evaluate_EEG-Based_Multimodal_Data_for_Mental_Health", "content": "Similarly, the Video-SME framework [43] incorporates a Hypergraph Multimodal Large Language Model (HMLLM) to jointly interpret EEG and eye-tracking ..."}
+{"idx": 4, "title": "[AINews] o1: OpenAI's new general reasoning models •", "date": "", "ddg_snippet": "... about JinaAI's Reader-LM, a Small Language Model for web data extraction and cleaning that outperformed larger models like GPT- 4 and LLaMA -3.1-70B on ...", "subpage_snippet": "", "source": "buttondown.com", "link": "https://buttondown.com/ainews/archive/ainews-o1-openais-new-general-reasoning-models/", "content": "... about JinaAI's Reader-LM, a Small Language Model for web data extraction and cleaning that outperformed larger models like GPT- 4 and LLaMA -3.1-70B on ..."}
+{"idx": 5, "title": "Latent Fusion Jailbreak: Blending Harmful and Harmless", "date": "", "ddg_snippet": "Large language models (LLMs) demonstrate impressive capabilities in various language tasks but are susceptible to jailbreak attacks that circumvent ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2508.10029v1", "content": "Large language models (LLMs) demonstrate impressive capabilities in various language tasks but are susceptible to jailbreak attacks that circumvent ..."}
+{"idx": 6, "title": "Fighting Fire with Fire (F3): A Training-free and Efficient", "date": "", "ddg_snippet": "Recent years have witnessed increasing research attention focused on adversarial attacks targeting Large Language Models (LVLMs) (Yin et al .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2506.01064v1", "content": "Recent years have witnessed increasing research attention focused on adversarial attacks targeting Large Language Models (LVLMs) (Yin et al ."}
+{"idx": 7, "title": "Bad Likert Judge: A Novel Multi-Turn Technique to Jailbreak", "date": "", "ddg_snippet": "Furthermore, the attention mechanism in language models allows them to focus on specific parts of the input when generating a response.", "subpage_snippet": "", "source": "unit42.paloaltonetworks.com", "link": "https://unit42.paloaltonetworks.com/multi-turn-technique-jailbreaks-llms/", "content": "Furthermore, the attention mechanism in language models allows them to focus on specific parts of the input when generating a response."}
+{"idx": 8, "title": "[AINews] Not much technical happened today • Buttondown", "date": "", "ddg_snippet": "The insanity of whisper versions ( Score: 30, Comments: 14 ): The post discusses the numerous versions of Whisper , including size variations ( base, ...", "subpage_snippet": "", "source": "buttondown.com", "link": "https://buttondown.com/ainews/archive/ainews-not-much-technical-happened-today/", "content": "The insanity of whisper versions ( Score: 30, Comments: 14 ): The post discusses the numerous versions of Whisper , including size variations ( base, ..."}
+{"idx": 9, "title": "Hai Zhao - ACL Anthology", "date": "", "ddg_snippet": "Large language models (LLMs) have played a pivotal role in building communicative AI, yet they encounter the challenge of efficient updates.", "subpage_snippet": "", "source": "aclanthology.org", "link": "https://aclanthology.org/people/h/hai-zhao/", "content": "Large language models (LLMs) have played a pivotal role in building communicative AI, yet they encounter the challenge of efficient updates."}
diff --git a/data/sampled_jsons/On_the_Role_of_Attention_Heads_in_Large_Language_Model_Safety_Llama-2-7b-chat_crucial_safety_head_ab_year_2023.jsonl b/data/sampled_jsons/On_the_Role_of_Attention_Heads_in_Large_Language_Model_Safety_Llama-2-7b-chat_crucial_safety_head_ab_year_2023.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..09b9a95c319ffb8121ae1320794cbe9b71eb8eb9
--- /dev/null
+++ b/data/sampled_jsons/On_the_Role_of_Attention_Heads_in_Large_Language_Model_Safety_Llama-2-7b-chat_crucial_safety_head_ab_year_2023.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "The Edge Advantage: A Comprehensive Analysis of Sub-7B Small", "date": "", "ddg_snippet": "... that the term “ small ” is relative; a model with seven billion parameters, such as Llama 2 - 7B , is still an incredibly complex piece of ...", "subpage_snippet": "", "source": "uplatz.com", "link": "https://uplatz.com/blog/the-edge-advantage-a-comprehensive-analysis-of-sub-7b-small-language-models-for-on-device-deployment/", "content": "... that the term “ small ” is relative; a model with seven billion parameters, such as Llama 2 - 7B , is still an incredibly complex piece of ..."}
+{"idx": 1, "title": "Personality Traits in Large Language Models", "date": "", "ddg_snippet": "This results in diverse distributions of paired data ( one point estimate per model ) required for evaluating the reliability, convergent validity ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2307.00184v4", "content": "This results in diverse distributions of paired data ( one point estimate per model ) required for evaluating the reliability, convergent validity ..."}
+{"idx": 2, "title": "Llama-Nemotron: Efficient Reasoning Models", "date": "", "ddg_snippet": "This release represents one of the largest contributions to the open source community in support of developing reasoning models .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2505.00949v5", "content": "This release represents one of the largest contributions to the open source community in support of developing reasoning models ."}
+{"idx": 3, "title": "Llama 2 vs ChatGPT for Enterprise", "date": "", "ddg_snippet": "With GPT-3 and ChatGPT (GPT 3.5) they offered general purpose model with a large parameter size (~175B to ~200B).", "subpage_snippet": "", "source": "www.clioapp.ai", "link": "https://www.clioapp.ai/article/gen-ai-deploying-llama-2-vs-using-chatgpt-for-enterprises", "content": "With GPT-3 and ChatGPT (GPT 3.5) they offered general purpose model with a large parameter size (~175B to ~200B)."}
+{"idx": 4, "title": "Evaluating the persuasive influence of political microtargeting", "date": "", "ddg_snippet": "Recent advancements in large language models (LLMs) have raised the prospect of scalable, automated, and fine-grained political microtargeting on a ...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/381257120_Evaluating_the_persuasive_influence_of_political_microtargeting_with_large_language_models", "content": "Recent advancements in large language models (LLMs) have raised the prospect of scalable, automated, and fine-grained political microtargeting on a ..."}
+{"idx": 5, "title": "LLM APIs for Integrating Large Language Models", "date": "", "ddg_snippet": "This article explores the most popular large language models and their integration capabilities for building chatbots, natural language search ...", "subpage_snippet": "", "source": "www.altexsoft.com", "link": "https://www.altexsoft.com/blog/llm-api-integration/", "content": "This article explores the most popular large language models and their integration capabilities for building chatbots, natural language search ..."}
+{"idx": 6, "title": "Aman's AI Journal • Primers • Overview of Large Language", "date": "", "ddg_snippet": "Given the prevalence of decoder-based models in the area of generative AI, the article focuses on decoder models (such as GPT-x) rather than encoder ...", "subpage_snippet": "", "source": "aman.ai", "link": "https://aman.ai/primers/ai/LLM/", "content": "Given the prevalence of decoder-based models in the area of generative AI, the article focuses on decoder models (such as GPT-x) rather than encoder ..."}
+{"idx": 7, "title": "Guardrails in OpenAI Agent SDK", "date": "", "ddg_snippet": "OpenAI ’ s new Agent SDK empowers developers to build these systems with ease, leveraging the latest advancements in large language models ...", "subpage_snippet": "", "source": "www.analyticsvidhya.com", "link": "https://www.analyticsvidhya.com/blog/2025/03/guardrails-in-openai-agent-sdk/", "content": "OpenAI ’ s new Agent SDK empowers developers to build these systems with ease, leveraging the latest advancements in large language models ..."}
+{"idx": 8, "title": "[AINews] o1 destroys Lmsys Arena, Qwen 2.5, Kyutai Moshi", "date": "", "ddg_snippet": "... in open source land, Alibaba's Qwen caught up to DeepSeek with its own Qwen 2 .5 suite of general, coding, and math models , showing better numbers ...", "subpage_snippet": "", "source": "buttondown.com", "link": "https://buttondown.com/ainews/archive/ainews-o1-destroys-lmsys-arena-qwen-25-kyutai/", "content": "... in open source land, Alibaba's Qwen caught up to DeepSeek with its own Qwen 2 .5 suite of general, coding, and math models , showing better numbers ..."}
+{"idx": 9, "title": "Research & Application – 3rd wave of AI | hessian.AI", "date": "", "ddg_snippet": "The quality of the model was improved by using linear RoPE scaling and Flash Attention 2 to enhance training efficiency and double the context length ...", "subpage_snippet": "", "source": "hessian.ai", "link": "https://hessian.ai/aisc/research-application-3rd-wave-of-ai/", "content": "The quality of the model was improved by using linear RoPE scaling and Flash Attention 2 to enhance training efficiency and double the context length ..."}
diff --git a/data/sampled_jsons/Online_Adaptive_Replanning_with_Reinforcement_Learning_for_Robotic_Manipulation_arxiv_2305.01234.jsonl b/data/sampled_jsons/Online_Adaptive_Replanning_with_Reinforcement_Learning_for_Robotic_Manipulation_arxiv_2305.01234.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..b6657c4c83c7afa8a075ca9473a301ef9297c6c6
--- /dev/null
+++ b/data/sampled_jsons/Online_Adaptive_Replanning_with_Reinforcement_Learning_for_Robotic_Manipulation_arxiv_2305.01234.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Adaptive Online Replanning with Diffusion Models", "date": "", "ddg_snippet": "arXiv :2310.09629v1 [cs.RO] 14 Oct 2023. Adaptive Online Replanning with Diffusion Models.Diffusion models have risen as a promising approach to data-driven planning , and have demonstrated impressive robotic control, reinforcement learning , and video planning performance.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2310.09629", "content": "arXiv :2310.09629v1 [cs.RO] 14 Oct 2023. Adaptive Online Replanning with Diffusion Models.Diffusion models have risen as a promising approach to data-driven planning , and have demonstrated impressive robotic control, reinforcement learning , and video planning performance."}
+{"idx": 1, "title": "Reinforcement Learning for Multi-Task Manipulation in Robotic Arm...", "date": "", "ddg_snippet": "Artificial Intelligence (AI), Reinforcement Learning (RL), Goal-Directed Manipulation , Variable Object Positions, Robotic Arm Control.", "subpage_snippet": "", "source": "journal.umy.ac.id", "link": "https://journal.umy.ac.id/index.php/jrc/article/view/27780", "content": "Artificial Intelligence (AI), Reinforcement Learning (RL), Goal-Directed Manipulation , Variable Object Positions, Robotic Arm Control."}
+{"idx": 2, "title": "Dexterous Manipulation for Multi-Fingered Robotic Hands With...", "date": "", "ddg_snippet": "Keywords: dexterous manipulation , multi-fingered robotic hand, reinforcement learning , learn from demonstration, sim2real.", "subpage_snippet": "", "source": "pmc.ncbi.nlm.nih.gov", "link": "https://pmc.ncbi.nlm.nih.gov/articles/PMC9083362/", "content": "Keywords: dexterous manipulation , multi-fingered robotic hand, reinforcement learning , learn from demonstration, sim2real."}
+{"idx": 3, "title": "Domain centralization and cross-modal reinforcement learning for ...", "date": "", "ddg_snippet": "Robotics Online Marketing Team.Qt-opt: Scalable deep reinforcement learning for vision-based robotic manipulation . arXiv :1806.10293, 2018.", "subpage_snippet": "", "source": "www.ijpaa.org", "link": "https://www.ijpaa.org/index.php/ijpaa/article/view/77", "content": "Robotics Online Marketing Team.Qt-opt: Scalable deep reinforcement learning for vision-based robotic manipulation . arXiv :1806.10293, 2018."}
+{"idx": 4, "title": "Robotics and Perception Group", "date": "", "ddg_snippet": "Learning on the Fly: Rapid Policy Adaptation via Differentiable Simulation. Arxiv 25_Pan. Learning control policies in simulation enables rapid, safe, and cost-effective development of advanced robotic capabilities.", "subpage_snippet": "", "source": "rpg.ifi.uzh.ch", "link": "https://rpg.ifi.uzh.ch/research_learning.html", "content": "Learning on the Fly: Rapid Policy Adaptation via Differentiable Simulation. Arxiv 25_Pan. Learning control policies in simulation enables rapid, safe, and cost-effective development of advanced robotic capabilities."}
+{"idx": 5, "title": "A Real-to-Sim-to-Real Approach to Robotic Manipulation with...", "date": "", "ddg_snippet": "Task specification for robotic manipulation in open-world environments is challenging. Importantly, this process requires flexible and adaptive objectives that align with human intentions and can evolve through iterative feedback.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=faFJI7mbQF", "content": "Task specification for robotic manipulation in open-world environments is challenging. Importantly, this process requires flexible and adaptive objectives that align with human intentions and can evolve through iterative feedback."}
+{"idx": 6, "title": "Deep RL at Scale: Sorting Waste in Office Buildings with a Fleet of...", "date": "", "ddg_snippet": "(DOI: 10.48550/ arXiv . 2305 .03270) We describe a system for deep reinforcement learning of robotic manipulation skills applied to a large-scale real-world task: sorting recyclables and trash in office buildings.", "subpage_snippet": "", "source": "scispace.com", "link": "https://scispace.com/papers/deep-rl-at-scale-sorting-waste-in-office-buildings-with-a-25llttm1", "content": "(DOI: 10.48550/ arXiv . 2305 .03270) We describe a system for deep reinforcement learning of robotic manipulation skills applied to a large-scale real-world task: sorting recyclables and trash in office buildings."}
+{"idx": 7, "title": "Benchmarking Reinforcement Learning Methods for Dexterous...", "date": "", "ddg_snippet": "This paper evaluates different reinforcement learning methods for dexterous robotic manipulation using a three-fingered gripper.", "subpage_snippet": "", "source": "www.aimodels.fyi", "link": "https://www.aimodels.fyi/papers/arxiv/benchmarking-reinforcement-learning-methods-dexterous-robotic-manipulation", "content": "This paper evaluates different reinforcement learning methods for dexterous robotic manipulation using a three-fingered gripper."}
+{"idx": 8, "title": "Publications | Robotic Manipulation and Mobility Lab", "date": "", "ddg_snippet": "\"Discovering Synergies for Robot Manipulation with Multi-Task Reinforcement Learning \", IEEE Intl. Conference on Robotics and Automation, 2022 [ arXiv , webpage, video].", "subpage_snippet": "", "source": "roam.me.columbia.edu", "link": "https://roam.me.columbia.edu/content/publications", "content": "\"Discovering Synergies for Robot Manipulation with Multi-Task Reinforcement Learning \", IEEE Intl. Conference on Robotics and Automation, 2022 [ arXiv , webpage, video]."}
+{"idx": 9, "title": "VIMA: Robot Manipulation with Multimodal Prompts", "date": "", "ddg_snippet": "We adapt it by replacing the output layer with robot action heads (hyperparameters listed in Table 4) and using tokenized rollout histories as inputs. We thus call it “VIMA-Flamingo”.", "subpage_snippet": "", "source": "proceedings.mlr.press", "link": "https://proceedings.mlr.press/v202/jiang23b/jiang23b.pdf", "content": "We adapt it by replacing the output layer with robot action heads (hyperparameters listed in Table 4) and using tokenized rollout histories as inputs. We thus call it “VIMA-Flamingo”."}
diff --git a/data/sampled_jsons/Orecchia_Ameranis_Tsourakakis_Talwar_'machine_with'_CPU_RAM_hardware_computing_environment.jsonl b/data/sampled_jsons/Orecchia_Ameranis_Tsourakakis_Talwar_'machine_with'_CPU_RAM_hardware_computing_environment.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..06c494219a7cb49031401bf581076c901fcdd681
--- /dev/null
+++ b/data/sampled_jsons/Orecchia_Ameranis_Tsourakakis_Talwar_'machine_with'_CPU_RAM_hardware_computing_environment.jsonl
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+{"idx": 0, "title": "Volume 235: International Conference on Machine Learning ...", "date": "", "ddg_snippet": "Proceedings of the 41st International Conference on Machine Learning Held in Vienna, Austria on 21-27 July 2024 Published as Volume 235 by the Proceedings ...", "subpage_snippet": "", "source": "proceedings.mlr.press", "link": "https://proceedings.mlr.press/v235/", "content": "Proceedings of the 41st International Conference on Machine Learning Held in Vienna, Austria on 21-27 July 2024 Published as Volume 235 by the Proceedings ..."}
+{"idx": 1, "title": "International Conference on Machine Learning (ICML 2024)", "date": "", "ddg_snippet": "27 Jul 2024 — Konstantinos Ameranis , Adela Depavia, Lorenzo Orecchia , Erasmo Tani. Scalable and Flexible Causal Discovery with an Efficient Test for ...", "subpage_snippet": "", "source": "www.proceedings.com", "link": "https://www.proceedings.com/content/076/076048webtoc.pdf", "content": "27 Jul 2024 — Konstantinos Ameranis , Adela Depavia, Lorenzo Orecchia , Erasmo Tani. Scalable and Flexible Causal Discovery with an Efficient Test for ..."}
+{"idx": 2, "title": "Proceedings of Machine Learning Research", "date": "", "ddg_snippet": "%A Lorenzo Orecchia %A Konstantinos Ameranis %A Charalampos Tsourakakis %A Kunal Talwar %B Proceedings of the 39th International Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2022 %E Kamalika Chaudhuri %E Stefanie Jegelka %E Le Song %E Csaba Szepesvari %E Gang Niu %E Sivan Sabato %F pmlr-v162-orecchia22a %I PMLR ...", "subpage_snippet": "", "source": "proceedings.mlr.press", "link": "https://proceedings.mlr.press/v162/orecchia22a.html", "content": "%A Lorenzo Orecchia %A Konstantinos Ameranis %A Charalampos Tsourakakis %A Kunal Talwar %B Proceedings of the 39th International Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2022 %E Kamalika Chaudhuri %E Stefanie Jegelka %E Le Song %E Csaba Szepesvari %E Gang Niu %E Sivan Sabato %F pmlr-v162-orecchia22a %I PMLR ..."}
+{"idx": 3, "title": "Publications | Orecchia Research Group", "date": "", "ddg_snippet": "Lorenzo Orecchia , Konstantinos Ameranis , Kunal Talwar , Charalampos Tsourakakis . Practical Almost-Linear-Time Approximation Algorithms for Hybrid and Overlapping Graph Clustering.", "subpage_snippet": "", "source": "orecchia.net", "link": "https://orecchia.net/publication/", "content": "Lorenzo Orecchia , Konstantinos Ameranis , Kunal Talwar , Charalampos Tsourakakis . Practical Almost-Linear-Time Approximation Algorithms for Hybrid and Overlapping Graph Clustering."}
+{"idx": 4, "title": "Konstantinos Ameranis's personal web page - Department of Computer Science", "date": "", "ddg_snippet": "arXiv preprint arXiv:2307.11042 Practical Almost-Linear-Time Approximation Algorithms for Hybrid and Overlapping Graph Clustering Konstantinos Ameranis , Lorenzo Orecchia , Charalampos Tsourakakis , Kunal Talwar", "subpage_snippet": "", "source": "people.cs.uchicago.edu", "link": "https://people.cs.uchicago.edu/~kameranis/", "content": "arXiv preprint arXiv:2307.11042 Practical Almost-Linear-Time Approximation Algorithms for Hybrid and Overlapping Graph Clustering Konstantinos Ameranis , Lorenzo Orecchia , Charalampos Tsourakakis , Kunal Talwar"}
+{"idx": 5, "title": "Κonstantinos Ameranis - Google Scholar", "date": "", "ddg_snippet": "Co-authors Lorenzo Orecchia University of Chicago, Computer ScienceVerified email at bu.edu Kunal Talwar Apple IncVerified email at apple.com Charalampos E. Tsourakakis Boston UniversityVerified email at bu.edu Nikolaos Vathis University of West AtticaVerified email at uniwa.gr", "subpage_snippet": "", "source": "scholar.google.com", "link": "https://scholar.google.com/citations?user=eajqSs4AAAAJ", "content": "Co-authors Lorenzo Orecchia University of Chicago, Computer ScienceVerified email at bu.edu Kunal Talwar Apple IncVerified email at apple.com Charalampos E. Tsourakakis Boston UniversityVerified email at bu.edu Nikolaos Vathis University of West AtticaVerified email at uniwa.gr"}
+{"idx": 6, "title": "PDF Lorenzo Orecchia", "date": "", "ddg_snippet": "L. Orecchia , K. Ameranis , C. Tsourakakis and K. Talwar . Practical Almost-Linear-Time Ap-proximation Algorithms for Hybrid and Overlapping Graph Clustering. ICML 2022: Proc. Intl. Conf. Machine Learning, PMLR 162:17071-17093, 2022. J. Diakonikolas, M. Fazel and L. Orecchia . Fair Packing and Covering on a Relative Scale.", "subpage_snippet": "", "source": "orecchia.net", "link": "https://orecchia.net/cvs/LongCV-lo.pdf", "content": "L. Orecchia , K. Ameranis , C. Tsourakakis and K. Talwar . Practical Almost-Linear-Time Ap-proximation Algorithms for Hybrid and Overlapping Graph Clustering. ICML 2022: Proc. Intl. Conf. Machine Learning, PMLR 162:17071-17093, 2022. J. Diakonikolas, M. Fazel and L. Orecchia . Fair Packing and Covering on a Relative Scale."}
+{"idx": 7, "title": "ICML 2024 Wednesday 07/24", "date": "", "ddg_snippet": "The Women in Machine Learning (WiML) workshop was founded in 2006 to forge connections within the relatively small community of women working in machine ...", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2024/day/7/24", "content": "The Women in Machine Learning (WiML) workshop was founded in 2006 to forge connections within the relatively small community of women working in machine ..."}
+{"idx": 8, "title": "Track: Poster Session 3", "date": "", "ddg_snippet": "Lorenzo Orecchia · Konstantinos Ameranis · Charalampos Tsourakakis · Kunal Talwar . Detecting communities in real-world networks and clustering similarity ...", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2022/session/20137", "content": "Lorenzo Orecchia · Konstantinos Ameranis · Charalampos Tsourakakis · Kunal Talwar . Detecting communities in real-world networks and clustering similarity ..."}
+{"idx": 9, "title": "ICML-2022-Paper-Digests.pdf", "date": "", "ddg_snippet": "Lorenzo Orecchia , Konstantinos Ameranis , Charalampos Tsourakakis , Kunal Talwar . HIGHLIGHT: In this work, we introduce a frame-work based on two novel ...", "subpage_snippet": "", "source": "www.paperdigest.org", "link": "https://www.paperdigest.org/wp-content/uploads/2022/07/ICML-2022-Paper-Digests.pdf", "content": "Lorenzo Orecchia , Konstantinos Ameranis , Charalampos Tsourakakis , Kunal Talwar . HIGHLIGHT: In this work, we introduce a frame-work based on two novel ..."}
diff --git a/data/sampled_jsons/Origin_Identification_for_Text-Guided_Image-to-Image_Diffusion_Models_Figure_9_Section_5.5_unseen_mo_year_2023.jsonl b/data/sampled_jsons/Origin_Identification_for_Text-Guided_Image-to-Image_Diffusion_Models_Figure_9_Section_5.5_unseen_mo_year_2023.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..cd0e8f4d5f41f2b340d5a6f08a2e5b6386ad9cfd
--- /dev/null
+++ b/data/sampled_jsons/Origin_Identification_for_Text-Guided_Image-to-Image_Diffusion_Models_Figure_9_Section_5.5_unseen_mo_year_2023.jsonl
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+{"idx": 1, "title": "origin 绘图软件中文版下载和安装教程 - CSDN博客", "date": "", "ddg_snippet": "Feb 26, 2025 · 文章详细介绍了如何下载并安装 Origin 的中文版，包括区分官方中文版和汉化版的区别，提供了2021版的稳定安装步骤，并给出了安装过程中需要注意的事项，如避免第三方软件劫持、正确输入序列号和应用补丁等。", "subpage_snippet": "", "source": "blog.csdn.net", "link": "https://blog.csdn.net/wlnh/article/details/129967199", "content": "Feb 26, 2025 · 文章详细介绍了如何下载并安装 Origin 的中文版，包括区分官方中文版和汉化版的区别，提供了2021版的稳定安装步骤，并给出了安装过程中需要注意的事项，如避免第三方软件劫持、正确输入序列号和应用补丁等。"}
+{"idx": 2, "title": "Origin 软件安装步骤（附安装包） Origin 2024 超详细下载安装教程 - 年...", "date": "", "ddg_snippet": "Jul 9 , 2025 · 今天给大家带来一篇超详细的 Origin 2024安装教程，不管你是科研新手还是数据处理老手，都能按照这篇教程轻松完成 Origin 的安装。", "subpage_snippet": "", "source": "www.cnblogs.com", "link": "https://www.cnblogs.com/xiaowangabc/articles/18975481", "content": "Jul 9 , 2025 · 今天给大家带来一篇超详细的 Origin 2024安装教程，不管你是科研新手还是数据处理老手，都能按照这篇教程轻松完成 Origin 的安装。"}
+{"idx": 3, "title": "【2024最新版】 Origin 下载安装教程超详细图文步骤（附安装包+永久使用...", "date": "", "ddg_snippet": "本文将为您提供全网最详细的 Origin 2024 中文版安装教程，从下载到安装再到完整授权，保证即使是小白用户也能一次性成功安装。", "subpage_snippet": "", "source": "zhuanlan.zhihu.com", "link": "https://zhuanlan.zhihu.com/p/1914654988405278112", "content": "本文将为您提供全网最详细的 Origin 2024 中文版安装教程，从下载到安装再到完整授权，保证即使是小白用户也能一次性成功安装。"}
+{"idx": 4, "title": "Origin - Origin 函数绘图软件- Origin 数据分析绘图软件- Origin 中文网站", "date": "", "ddg_snippet": "欢迎访问 Origin 软件的官方网站，这里提供最全面的 Origin 教程、最新版本下载、用户支持和论坛交流。 无论您是数据分析师、科研人员还是工程师，这里都有您需要的专业工具和资源，帮助您轻松进行复杂数据处理和图形绘制。", "subpage_snippet": "", "source": "www.origincn.com.cn", "link": "https://www.origincn.com.cn/", "content": "欢迎访问 Origin 软件的官方网站，这里提供最全面的 Origin 教程、最新版本下载、用户支持和论坛交流。 无论您是数据分析师、科研人员还是工程师，这里都有您需要的专业工具和资源，帮助您轻松进行复杂数据处理和图形绘制。"}
+{"idx": 5, "title": "从零开始的 Origin 新手入门教程（速成） - CSDN博客", "date": "", "ddg_snippet": "Nov 14, 2024 · 本文介绍了一款专业的科研绘图软件 Origin ，讲解了如何从零基础快速掌握画图技巧，包括使用模板、数据分析中的拟合功能，以及下载和安装对学生党的优惠策略。", "subpage_snippet": "", "source": "blog.csdn.net", "link": "https://blog.csdn.net/Hjh1906008151/article/details/123149481", "content": "Nov 14, 2024 · 本文介绍了一款专业的科研绘图软件 Origin ，讲解了如何从零基础快速掌握画图技巧，包括使用模板、数据分析中的拟合功能，以及下载和安装对学生党的优惠策略。"}
+{"idx": 6, "title": "Origin 中文网- origin 软件, origin 绘图软件, origin 软件下载", "date": "", "ddg_snippet": "Origin 中文网提供 Origin 绘图软件下载、Origin教程分享，致力于提升广大科研工作者、高校大学生的 Origin 绘图水平，解决 Origin 软件安装使用中的故障。", "subpage_snippet": "", "source": "www.originsoft.cn", "link": "http://www.originsoft.cn/", "content": "Origin 中文网提供 Origin 绘图软件下载、Origin教程分享，致力于提升广大科研工作者、高校大学生的 Origin 绘图水平，解决 Origin 软件安装使用中的故障。"}
+{"idx": 7, "title": "Origin 入门教程（一）：一张图学会 Origin - 知乎", "date": "", "ddg_snippet": "新版本的 origin 软件用起来会更顺手一些，并且功能也要多一些（并且不存在软件卡死闪退的现象）。 今天看到这篇推送的你算是赚到了，我们不但教你怎么使用 origin ，还可获取最新 origin 版本的安装包和安装教程！", "subpage_snippet": "", "source": "zhuanlan.zhihu.com", "link": "https://zhuanlan.zhihu.com/p/366668144", "content": "新版本的 origin 软件用起来会更顺手一些，并且功能也要多一些（并且不存在软件卡死闪退的现象）。 今天看到这篇推送的你算是赚到了，我们不但教你怎么使用 origin ，还可获取最新 origin 版本的安装包和安装教程！"}
+{"idx": 8, "title": "ORIGIN Definition & Meaning | Dictionary .com", "date": "", "ddg_snippet": "Origin definition: something from which anything arises or is derived; source; fountainhead.. See examples of ORIGIN used in a sentence.", "subpage_snippet": "", "source": "www.dictionary.com", "link": "https://www.dictionary.com/browse/origin", "content": "Origin definition: something from which anything arises or is derived; source; fountainhead.. See examples of ORIGIN used in a sentence."}
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diff --git a/data/sampled_jsons/OwMatch_Conditional_Self-Labeling_with_Consistency_for_Open-World_Semi-Supervised_Learning_methodolo.jsonl b/data/sampled_jsons/OwMatch_Conditional_Self-Labeling_with_Consistency_for_Open-World_Semi-Supervised_Learning_methodolo.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..004edf7d0c19e384df4e6582cd586dce753aa803
--- /dev/null
+++ b/data/sampled_jsons/OwMatch_Conditional_Self-Labeling_with_Consistency_for_Open-World_Semi-Supervised_Learning_methodolo.jsonl
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+{"idx": 0, "title": "Weak supervision - Wikipedia", "date": "", "ddg_snippet": "Weak supervision is a paradigm in machine learning , the relevance and notability of which increased with the advent of large language models due to large amount of data required to train them. It is characterized by using a combination of a small amo...", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/Weak_supervision", "content": "Weak supervision is a paradigm in machine learning , the relevance and notability of which increased with the advent of large language models due to large amount of data required to train them. It is characterized by using a combination of a small amo..."}
+{"idx": 1, "title": "OwMatch : Conditional Self - Labeling with Consistency for ...", "date": "", "ddg_snippet": "Open - world semi - supervised learning (OwSSL).FixMatch: simplifying semi - supervised learning with consistency and confidence. Advances in Neural Information Processing Systems (NeurIPS), 33:596–608, 2020.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2411.01833v1", "content": "Open - world semi - supervised learning (OwSSL).FixMatch: simplifying semi - supervised learning with consistency and confidence. Advances in Neural Information Processing Systems (NeurIPS), 33:596–608, 2020."}
+{"idx": 2, "title": "(PDF) OwMatch : Conditional Self - Labeling with Consistency for ...", "date": "", "ddg_snippet": "Abstract and Figures. Semi - supervised learning (SSL) offers a robust framework for harnessing the potential of unannotated data. Traditionally, SSL mandates that all classes possess labeled instances. However, the emergence of open - world SSL (OwSSL) introduces a more practical...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/385529038_OwMatch_Conditional_Self-Labeling_with_Consistency_for_Open-World_Semi-Supervised_Learning", "content": "Abstract and Figures. Semi - supervised learning (SSL) offers a robust framework for harnessing the potential of unannotated data. Traditionally, SSL mandates that all classes possess labeled instances. However, the emergence of open - world SSL (OwSSL) introduces a more practical..."}
+{"idx": 3, "title": "OwMatch : Conditional Self - Labeling with Consistency for ...", "date": "", "ddg_snippet": "2 Methodology Conditional Self - labeling Open - world Hierarchical Thresholding. 3 Experiments. Open - world Semi - Supervised Learning (OwSSL). References. Expensive and time-consuming labeling process limits real-world deep- learning applications.", "subpage_snippet": "", "source": "nips.cc", "link": "https://nips.cc/media/neurips-2024/Slides/93416.pdf", "content": "2 Methodology Conditional Self - labeling Open - world Hierarchical Thresholding. 3 Experiments. Open - world Semi - Supervised Learning (OwSSL). References. Expensive and time-consuming labeling process limits real-world deep- learning applications."}
+{"idx": 4, "title": "(PDF) OwMatch : Conditional Self - Labeling with Consistency for ...", "date": "", "ddg_snippet": "(DOI: 10.48550/arxiv.2411.01833) Semi - supervised learning (SSL) offers a robust framework for harnessing the potential of unannotated data.Specifically, we propose an effective framework called OwMatch , combining conditional self - labeling and open - world hierarchical thresholding.", "subpage_snippet": "", "source": "scispace.com", "link": "https://scispace.com/papers/owmatch-conditional-self-labeling-with-consistency-for-open-2qmo1h06mr3a", "content": "(DOI: 10.48550/arxiv.2411.01833) Semi - supervised learning (SSL) offers a robust framework for harnessing the potential of unannotated data.Specifically, we propose an effective framework called OwMatch , combining conditional self - labeling and open - world hierarchical thresholding."}
+{"idx": 5, "title": "OwMatch : Conditional Self - Labeling with Consistency ... | alphaXiv", "date": "", "ddg_snippet": "Abstract: Semi - supervised learning (SSL) offers a robust framework for harnessing the potential of unannotated data.Specifically, we propose an effective framework called OwMatch , combining conditional self - labeling and open - world hierarchical thresholding.", "subpage_snippet": "", "source": "www.alphaxiv.org", "link": "https://www.alphaxiv.org/abs/2411.01833", "content": "Abstract: Semi - supervised learning (SSL) offers a robust framework for harnessing the potential of unannotated data.Specifically, we propose an effective framework called OwMatch , combining conditional self - labeling and open - world hierarchical thresholding."}
+{"idx": 6, "title": "OwMatch : Conditional Self - Labeling with Consistency for ...", "date": "", "ddg_snippet": "Semi - supervised learning (SSL) offers a robust framework for harnessing the potential of unannotated data.Specifically, we propose an effective framework called _ OwMatch _, combining conditional self - labeling and open - world hierarchical thresholding.", "subpage_snippet": "", "source": "papers.cool", "link": "https://papers.cool/venue/rle9X7DQuH@OpenReview", "content": "Semi - supervised learning (SSL) offers a robust framework for harnessing the potential of unannotated data.Specifically, we propose an effective framework called _ OwMatch _, combining conditional self - labeling and open - world hierarchical thresholding."}
+{"idx": 7, "title": "OwMatch : Conditional Self - Labeling with Consistency for ...", "date": "", "ddg_snippet": "Semi - supervised learning (SSL) offers a robust framework for harnessing thepotential of unannotated data. Traditionally, SSL mandates that all classespossess labeled instances. However, the emergence of open - world SSL (OwSSL)introduces a more practical challenge, wherein unlabeled...", "subpage_snippet": "", "source": "deeplearn.org", "link": "https://deeplearn.org/arxiv/544706/owmatch:-conditional-self-labeling-with-consistency-for-open-world-semi-supervised-learning", "content": "Semi - supervised learning (SSL) offers a robust framework for harnessing thepotential of unannotated data. Traditionally, SSL mandates that all classespossess labeled instances. However, the emergence of open - world SSL (OwSSL)introduces a more practical challenge, wherein unlabeled..."}
+{"idx": 8, "title": "Advancing Machine Learning with OwSSL Techniques - Simple Science", "date": "", "ddg_snippet": "Title: OwMatch : Conditional Self - Labeling with Consistency for Open - World Semi - Supervised Learning . Abstract: Semi - supervised learning (SSL) offers a robust framework for harnessing the potential of unannotated data.", "subpage_snippet": "", "source": "scisimple.com", "link": "https://scisimple.com/en/articles/2025-05-21-advancing-machine-learning-with-owssl-techniques--ak5ompx", "content": "Title: OwMatch : Conditional Self - Labeling with Consistency for Open - World Semi - Supervised Learning . Abstract: Semi - supervised learning (SSL) offers a robust framework for harnessing the potential of unannotated data."}
+{"idx": 9, "title": "OwMatch : Conditional Self - Labeling with Consistency for ...", "date": "", "ddg_snippet": "OwMatch : a novel framework conquering open - world semi - supervised learning challenges by combining conditional self - labeling and consistency for substantially enhanced accuracy across known and unknown…", "subpage_snippet": "", "source": "deep-diver.github.io", "link": "https://deep-diver.github.io/neurips2024/posters/rle9x7dquh/", "content": "OwMatch : a novel framework conquering open - world semi - supervised learning challenges by combining conditional self - labeling and consistency for substantially enhanced accuracy across known and unknown…"}
diff --git a/data/sampled_jsons/PCA_whitening_control_data_dictionary_learning_biological_features_microscopy_year_2024.jsonl b/data/sampled_jsons/PCA_whitening_control_data_dictionary_learning_biological_features_microscopy_year_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..ea6f26f06ead69f6adb62b2924aff80250b3b985
--- /dev/null
+++ b/data/sampled_jsons/PCA_whitening_control_data_dictionary_learning_biological_features_microscopy_year_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Principal component analysis - Wikipedia", "date": "", "ddg_snippet": "PCA of a multivariate Gaussian distribution centered at with a standard deviation of 3 in roughly the direction and of 1 in the orthogonal direction. The vectors shown are the eigenvectors of the covariance matrix scaled by the square root of the cor...", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/Principal_component_analysis", "content": "PCA of a multivariate Gaussian distribution centered at with a standard deviation of 3 in roughly the direction and of 1 in the orthogonal direction. The vectors shown are the eigenvectors of the covariance matrix scaled by the square root of the cor..."}
+{"idx": 1, "title": "Towards scientific discovery with dictionary learning ...", "date": "", "ddg_snippet": "Dec 20, 2024 · We also propose Iterative Codebook Feature Learning ~ (ICFL) and combine it with a pre-processing step which uses PCA whitening from a control dataset. In our experiments, we demonstrate that both ICFL and PCA improve the selectivity of extracted features compared to TopK sparse autoencoders.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2412.16247", "content": "Dec 20, 2024 · We also propose Iterative Codebook Feature Learning ~ (ICFL) and combine it with a pre-processing step which uses PCA whitening from a control dataset. In our experiments, we demonstrate that both ICFL and PCA improve the selectivity of extracted features compared to TopK sparse autoencoders."}
+{"idx": 2, "title": "[PDF] Towards scientific discovery with dictionary learning ...", "date": "", "ddg_snippet": "Dec 20, 2024 · This work proposes a novel combination of a sparse DL algorithm, Iterative Codebook Feature Learning (ICFL), with a PCA whitening pre-processing step derived from control data , and demonstrates how this combined approach successfully retrieve biologically meaningful concepts, such as cell types and genetic perturbations. Sparse dictionary learning (DL) has emerged as a powerful approach to ...", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/Towards-scientific-discovery-with-dictionary-from-Donhauser-Ulicna/05236df2a670cfdd0132fe13de868dc15ed0876a", "content": "Dec 20, 2024 · This work proposes a novel combination of a sparse DL algorithm, Iterative Codebook Feature Learning (ICFL), with a PCA whitening pre-processing step derived from control data , and demonstrates how this combined approach successfully retrieve biologically meaningful concepts, such as cell types and genetic perturbations. Sparse dictionary learning (DL) has emerged as a powerful approach to ..."}
+{"idx": 3, "title": "Unsupervised Feature Learning and Deep Learning Tutorial Dictionary learning for integrative, multimodal and scalable ... Whitening with PCA with code demonstration - OpenGenus IQ whitening: Whitening and High-Dimensional Canonical ... Whitening with PCA with code demonstration - OpenGenus IQ Unsupervised Feature Learning and Deep Learning Tutorial whitening : Whitening and High-Dimensional Canonical Correlation Anal… Unsupervised Feature Learning and Deep Learning Tutorial Unsupervised Feature Learning and Deep Learning Tutorial whitening : Whitening and High-Dimensional Canonical Correlation Anal… Towards scientific discovery with dictionary learning ...", "date": "", "ddg_snippet": "Principal Components Analysis ( PCA ) is a dimensionality reduction algorithm that can be used to significantly speed up your unsupervised feature learning algorithm. More importantly, understanding PCA will enable us to later implement whitening , which is an important pre-processing step for many algorithms. Suppose you are training your algorithm o... See full list on ufldl.stanford.edu For our running example, we will use a dataset {x(1),x(2),…,x(m)} with n=2 dimensional inputs, so that x(i)∈ℜ2. Suppose we want to reduce the data from 2 dimensions to 1. (In practice, we might want to reduce data from 256 to 50 dimensions, say; but using lower dimensional data in our example allows us to visualize the algorithms better.) Here is o... See full list on ufldl.stanford.edu Thus, we can represent x in the (u1,u2)-basis by computing (The subscript “rot” comes from the observation that this corresponds to a rotation (and possibly reflection) of the original data .) Lets take the entire training set, and compute x(i)rot=UTx(i) for every i. Plotting this transformed data xrot, we get: This is the training set rotated into ... See full list on ufldl.stanford.edu We see that the principal direction of variation of the data is the first dimension xrot,1of this rotated data . Thus, if we want to reduce this data to one dimension, we can set More generally, if x∈ℜn and we want to reduce it to a k dimensional representation ˜x∈ℜk (where k < n), we would take the first k components of xrot, which correspond to th... See full list on ufldl.stanford.edu Now, ˜x∈ℜk is a lower-dimensional, “compressed” representation of the original x∈ℜn. Given ˜x, how can we recover an approximation ˆx to the original value of x? From an earlier section, we know that x=Uxrot. Further, we can think of ˜x as an approximation to xrot, where we have set the last n−k components to zeros. Thus, given ˜x∈ℜk, we can pad it... See full list on ufldl.stanford.edu How do we set k; i.e., how many PCA components should we retain? In our simple 2 dimensional example, it seemed natural to retain 1 out of the 2 components, but for higher dimensional data , this decision is less trivial. If k is too large, then we won’t be compressing the data much; in the limit of k=n, then we’re just using the original data (but ... See full list on ufldl.stanford.edu For PCA to work, usually we want each of the features x1,x2,…,xn to have a similar range of values to the others (and to have a mean close to zero). If you’ve used PCA on other applications before, you may therefore have separately pre-processed each feature to have zero mean and unit variance, by separately estimating the mean and variance of each... See full list on ufldl.stanford.edu We have used PCA to reduce the dimension of the data . There is a closely related preprocessing step called whitening (or, in some other literatures, sphering) which is needed for some algorithms. If we are training on images, the raw input is redundant, since adjacent pixel values are highly correlated. The goal of whitening is to make the input le... See full list on ufldl.stanford.edu We will first describe whitening using our previous 2D example. We will then describe how this can be combined with smoothing, and finally how to combine this with PCA . How can we make our input features uncorrelated with each other? We had already done this when computing x(i)rot=UTx(i). Repeating our previous figure, our plot for xrotwas: The cov... See full list on ufldl.stanford.edu Finally, it turns out that this way of getting the data to have covariance identity I isn’t unique. Concretely, if R is any orthogonal matrix, so that it satisfies RRT=RTR=I (less formally, if R is a rotation/reflection matrix), then RxPCAwhitewill also have identity covariance. In ZCA whitening , we choose R=U. We define Plotting xZCAwhite, we get:... See full list on ufldl.stanford.edu May 25, 2023 · Using dictionary learning for massively scalable integration The recent increase in publicly available single-cell datasets poses a challenge for integrative analysis. The goal of Whitening is to reduce redundancy in these images by using 2 measures: 1- making features less correlated to each other 2- making all features have the same variance The first measure is achieved when we apply PCA on the data . It is because the matrix U is orthogonal. In both PCA and PCA-cor whitening there is a sign-ambiguity in the eigenvector matrices. In order to resolve the sign-ambiguity we use eigenvector matrices with a positive diagonal so that PCA and PCA-cor cross-correlations and cross-covariances have a positive diagonal for the given ordering of the original variables. What is the difference between whitening and PCA in machine learning? Apply now. Principal Component Analysis ( PCA ) is a popular dimensionality reduction technique widely used in machine learning . Whitening (or Sphering) is a technique used to reduce redundancy in the input data . Before diving into the concept of whitening , we will first brush up our concepts of PCA . What is principal components analysis (PCA)? Principal Components Analysis (PCA) is a dimensionality reduction algorithm that can be used to significantly speed up your unsupervised feature learning algorithm. More importantly, understanding PCA will enable us to later implement whitening, which is an important pre-processing step for many algorithms. What is the difference between PCA whitening and Cholesky whitening? method=\"PCA-cor\": PCA-cor whitening is similar to PCA whitening but uses squared correlations . method=\"Cholesky\": computes a whitening matrix by applying Cholesky decomposition. This yields both a lower triangular positive diagonal whitening matrix and lower triangular positive di-agonal loadings (cross-covariance and cross-correlation). How to calculate PCA whitened data xpcawhite X P C A W H i t? To compute the PCA whitened data xPCAwhite x P C A w h i t e, use Since S S ’s diagonal contains the eigenvalues λi λ i, this turns out to be a compact way of computing xPCAwhite,i = xrot,i λi√ x P C A w h i t e, i = x r o λ i simultaneously for all i i. Finally, you can also compute the ZCA whitened data xZCAwhite x Z C A w h i t e as: Why is PCA important? More importantly, understanding PCA will enable us to later implement whitening , which is an important pre-processing step for many algorithms. Suppose you are training your algorithm on images. Then the input will be somewhat redundant, because the values of adjacent pixels in an image are highly correlated. What is the difference between ZCA-Cor and PCA whitening? method=\"ZCA-cor\": Likewise, ZCA-cor whitening leads to whitened variables that are maximally correlated (on average) with the original variables. method=\"PCA\": In contrast, PCA whitening lead to maximally compressed whitened variables , as measured by squared covariance. Sep 27, 2024 · We also propose a new DL algorithm, Iterative Codebook Feature Learning (ICFL), and combine it with a pre-processing step which uses PCA whitening from a control dataset. In our experiments, we demonstrate that both ICFL and PCA improve the selectivity or \"monosemanticity\" of extracted features compared to TopK sparse autoencoders.", "subpage_snippet": "", "source": "ufldl.stanford.edu", "link": "http://ufldl.stanford.edu/tutorial/unsupervised/PCAWhitening/", "content": "Principal Components Analysis ( PCA ) is a dimensionality reduction algorithm that can be used to significantly speed up your unsupervised feature learning algorithm. More importantly, understanding PCA will enable us to later implement whitening , which is an important pre-processing step for many algorithms. Suppose you are training your algorithm o... See full list on ufldl.stanford.edu For our running example, we will use a dataset {x(1),x(2),…,x(m)} with n=2 dimensional inputs, so that x(i)∈ℜ2. Suppose we want to reduce the data from 2 dimensions to 1. (In practice, we might want to reduce data from 256 to 50 dimensions, say; but using lower dimensional data in our example allows us to visualize the algorithms better.) Here is o... See full list on ufldl.stanford.edu Thus, we can represent x in the (u1,u2)-basis by computing (The subscript “rot” comes from the observation that this corresponds to a rotation (and possibly reflection) of the original data .) Lets take the entire training set, and compute x(i)rot=UTx(i) for every i. Plotting this transformed data xrot, we get: This is the training set rotated into ... See full list on ufldl.stanford.edu We see that the principal direction of variation of the data is the first dimension xrot,1of this rotated data . Thus, if we want to reduce this data to one dimension, we can set More generally, if x∈ℜn and we want to reduce it to a k dimensional representation ˜x∈ℜk (where k < n), we would take the first k components of xrot, which correspond to th... See full list on ufldl.stanford.edu Now, ˜x∈ℜk is a lower-dimensional, “compressed” representation of the original x∈ℜn. Given ˜x, how can we recover an approximation ˆx to the original value of x? From an earlier section, we know that x=Uxrot. Further, we can think of ˜x as an approximation to xrot, where we have set the last n−k components to zeros. Thus, given ˜x∈ℜk, we can pad it... See full list on ufldl.stanford.edu How do we set k; i.e., how many PCA components should we retain? In our simple 2 dimensional example, it seemed natural to retain 1 out of the 2 components, but for higher dimensional data , this decision is less trivial. If k is too large, then we won’t be compressing the data much; in the limit of k=n, then we’re just using the original data (but ... See full list on ufldl.stanford.edu For PCA to work, usually we want each of the features x1,x2,…,xn to have a similar range of values to the others (and to have a mean close to zero). If you’ve used PCA on other applications before, you may therefore have separately pre-processed each feature to have zero mean and unit variance, by separately estimating the mean and variance of each... See full list on ufldl.stanford.edu We have used PCA to reduce the dimension of the data . There is a closely related preprocessing step called whitening (or, in some other literatures, sphering) which is needed for some algorithms. If we are training on images, the raw input is redundant, since adjacent pixel values are highly correlated. The goal of whitening is to make the input le... See full list on ufldl.stanford.edu We will first describe whitening using our previous 2D example. We will then describe how this can be combined with smoothing, and finally how to combine this with PCA . How can we make our input features uncorrelated with each other? We had already done this when computing x(i)rot=UTx(i). Repeating our previous figure, our plot for xrotwas: The cov... See full list on ufldl.stanford.edu Finally, it turns out that this way of getting the data to have covariance identity I isn’t unique. Concretely, if R is any orthogonal matrix, so that it satisfies RRT=RTR=I (less formally, if R is a rotation/reflection matrix), then RxPCAwhitewill also have identity covariance. In ZCA whitening , we choose R=U. We define Plotting xZCAwhite, we get:... See full list on ufldl.stanford.edu May 25, 2023 · Using dictionary learning for massively scalable integration The recent increase in publicly available single-cell datasets poses a challenge for integrative analysis. The goal of Whitening is to reduce redundancy in these images by using 2 measures: 1- making features less correlated to each other 2- making all features have the same variance The first measure is achieved when we apply PCA on the data . It is because the matrix U is orthogonal. In both PCA and PCA-cor whitening there is a sign-ambiguity in the eigenvector matrices. In order to resolve the sign-ambiguity we use eigenvector matrices with a positive diagonal so that PCA and PCA-cor cross-correlations and cross-covariances have a positive diagonal for the given ordering of the original variables. What is the difference between whitening and PCA in machine learning? Apply now. Principal Component Analysis ( PCA ) is a popular dimensionality reduction technique widely used in machine learning . Whitening (or Sphering) is a technique used to reduce redundancy in the input data . Before diving into the concept of whitening , we will first brush up our concepts of PCA . What is principal components analysis (PCA)? Principal Components Analysis (PCA) is a dimensionality reduction algorithm that can be used to significantly speed up your unsupervised feature learning algorithm. More importantly, understanding PCA will enable us to later implement whitening, which is an important pre-processing step for many algorithms. What is the difference between PCA whitening and Cholesky whitening? method=\"PCA-cor\": PCA-cor whitening is similar to PCA whitening but uses squared correlations . method=\"Cholesky\": computes a whitening matrix by applying Cholesky decomposition. This yields both a lower triangular positive diagonal whitening matrix and lower triangular positive di-agonal loadings (cross-covariance and cross-correlation). How to calculate PCA whitened data xpcawhite X P C A W H i t? To compute the PCA whitened data xPCAwhite x P C A w h i t e, use Since S S ’s diagonal contains the eigenvalues λi λ i, this turns out to be a compact way of computing xPCAwhite,i = xrot,i λi√ x P C A w h i t e, i = x r o λ i simultaneously for all i i. Finally, you can also compute the ZCA whitened data xZCAwhite x Z C A w h i t e as: Why is PCA important? More importantly, understanding PCA will enable us to later implement whitening , which is an important pre-processing step for many algorithms. Suppose you are training your algorithm on images. Then the input will be somewhat redundant, because the values of adjacent pixels in an image are highly correlated. What is the difference between ZCA-Cor and PCA whitening? method=\"ZCA-cor\": Likewise, ZCA-cor whitening leads to whitened variables that are maximally correlated (on average) with the original variables. method=\"PCA\": In contrast, PCA whitening lead to maximally compressed whitened variables , as measured by squared covariance. Sep 27, 2024 · We also propose a new DL algorithm, Iterative Codebook Feature Learning (ICFL), and combine it with a pre-processing step which uses PCA whitening from a control dataset. In our experiments, we demonstrate that both ICFL and PCA improve the selectivity or \"monosemanticity\" of extracted features compared to TopK sparse autoencoders."}
+{"idx": 4, "title": "Dictionary learning for integrative, multimodal and scalable ... Whitening with PCA with code demonstration - OpenGenus IQ whitening: Whitening and High-Dimensional Canonical ... Whitening with PCA with code demonstration - OpenGenus IQ Unsupervised Feature Learning and Deep Learning Tutorial whitening : Whitening and High-Dimensional Canonical Correlation Anal… Unsupervised Feature Learning and Deep Learning Tutorial Unsupervised Feature Learning and Deep Learning Tutorial whitening : Whitening and High-Dimensional Canonical Correlation Anal… Towards scientific discovery with dictionary learning ...", "date": "", "ddg_snippet": "May 25, 2023 · Using dictionary learning for massively scalable integration The recent increase in publicly available single-cell datasets poses a challenge for integrative analysis. The goal of Whitening is to reduce redundancy in these images by using 2 measures: 1- making features less correlated to each other 2- making all features have the same variance The first measure is achieved when we apply PCA on the data . It is because the matrix U is orthogonal. In both PCA and PCA-cor whitening there is a sign-ambiguity in the eigenvector matrices. In order to resolve the sign-ambiguity we use eigenvector matrices with a positive diagonal so that PCA and PCA-cor cross-correlations and cross-covariances have a positive diagonal for the given ordering of the original variables. What is the difference between whitening and PCA in machine learning? Apply now. Principal Component Analysis ( PCA ) is a popular dimensionality reduction technique widely used in machine learning . Whitening (or Sphering) is a technique used to reduce redundancy in the input data . Before diving into the concept of whitening , we will first brush up our concepts of PCA . What is principal components analysis (PCA)? Principal Components Analysis (PCA) is a dimensionality reduction algorithm that can be used to significantly speed up your unsupervised feature learning algorithm. More importantly, understanding PCA will enable us to later implement whitening, which is an important pre-processing step for many algorithms. What is the difference between PCA whitening and Cholesky whitening? method=\"PCA-cor\": PCA-cor whitening is similar to PCA whitening but uses squared correlations . method=\"Cholesky\": computes a whitening matrix by applying Cholesky decomposition. This yields both a lower triangular positive diagonal whitening matrix and lower triangular positive di-agonal loadings (cross-covariance and cross-correlation). How to calculate PCA whitened data xpcawhite X P C A W H i t? To compute the PCA whitened data xPCAwhite x P C A w h i t e, use Since S S ’s diagonal contains the eigenvalues λi λ i, this turns out to be a compact way of computing xPCAwhite,i = xrot,i λi√ x P C A w h i t e, i = x r o λ i simultaneously for all i i. Finally, you can also compute the ZCA whitened data xZCAwhite x Z C A w h i t e as: Why is PCA important? More importantly, understanding PCA will enable us to later implement whitening , which is an important pre-processing step for many algorithms. Suppose you are training your algorithm on images. Then the input will be somewhat redundant, because the values of adjacent pixels in an image are highly correlated. What is the difference between ZCA-Cor and PCA whitening? method=\"ZCA-cor\": Likewise, ZCA-cor whitening leads to whitened variables that are maximally correlated (on average) with the original variables. method=\"PCA\": In contrast, PCA whitening lead to maximally compressed whitened variables , as measured by squared covariance. Sep 27, 2024 · We also propose a new DL algorithm, Iterative Codebook Feature Learning (ICFL), and combine it with a pre-processing step which uses PCA whitening from a control dataset. In our experiments, we demonstrate that both ICFL and PCA improve the selectivity or \"monosemanticity\" of extracted features compared to TopK sparse autoencoders.", "subpage_snippet": "", "source": "www.nature.com", "link": "https://www.nature.com/articles/s41587-023-01767-y", "content": "May 25, 2023 · Using dictionary learning for massively scalable integration The recent increase in publicly available single-cell datasets poses a challenge for integrative analysis. The goal of Whitening is to reduce redundancy in these images by using 2 measures: 1- making features less correlated to each other 2- making all features have the same variance The first measure is achieved when we apply PCA on the data . It is because the matrix U is orthogonal. In both PCA and PCA-cor whitening there is a sign-ambiguity in the eigenvector matrices. In order to resolve the sign-ambiguity we use eigenvector matrices with a positive diagonal so that PCA and PCA-cor cross-correlations and cross-covariances have a positive diagonal for the given ordering of the original variables. What is the difference between whitening and PCA in machine learning? Apply now. Principal Component Analysis ( PCA ) is a popular dimensionality reduction technique widely used in machine learning . Whitening (or Sphering) is a technique used to reduce redundancy in the input data . Before diving into the concept of whitening , we will first brush up our concepts of PCA . What is principal components analysis (PCA)? Principal Components Analysis (PCA) is a dimensionality reduction algorithm that can be used to significantly speed up your unsupervised feature learning algorithm. More importantly, understanding PCA will enable us to later implement whitening, which is an important pre-processing step for many algorithms. What is the difference between PCA whitening and Cholesky whitening? method=\"PCA-cor\": PCA-cor whitening is similar to PCA whitening but uses squared correlations . method=\"Cholesky\": computes a whitening matrix by applying Cholesky decomposition. This yields both a lower triangular positive diagonal whitening matrix and lower triangular positive di-agonal loadings (cross-covariance and cross-correlation). How to calculate PCA whitened data xpcawhite X P C A W H i t? To compute the PCA whitened data xPCAwhite x P C A w h i t e, use Since S S ’s diagonal contains the eigenvalues λi λ i, this turns out to be a compact way of computing xPCAwhite,i = xrot,i λi√ x P C A w h i t e, i = x r o λ i simultaneously for all i i. Finally, you can also compute the ZCA whitened data xZCAwhite x Z C A w h i t e as: Why is PCA important? More importantly, understanding PCA will enable us to later implement whitening , which is an important pre-processing step for many algorithms. Suppose you are training your algorithm on images. Then the input will be somewhat redundant, because the values of adjacent pixels in an image are highly correlated. What is the difference between ZCA-Cor and PCA whitening? method=\"ZCA-cor\": Likewise, ZCA-cor whitening leads to whitened variables that are maximally correlated (on average) with the original variables. method=\"PCA\": In contrast, PCA whitening lead to maximally compressed whitened variables , as measured by squared covariance. Sep 27, 2024 · We also propose a new DL algorithm, Iterative Codebook Feature Learning (ICFL), and combine it with a pre-processing step which uses PCA whitening from a control dataset. In our experiments, we demonstrate that both ICFL and PCA improve the selectivity or \"monosemanticity\" of extracted features compared to TopK sparse autoencoders."}
+{"idx": 5, "title": "Whitening with PCA with code demonstration - OpenGenus IQ", "date": "", "ddg_snippet": "The goal of Whitening is to reduce redundancy in these images by using 2 measures: 1- making features less correlated to each other 2- making all features have the same variance The first measure is achieved when we apply PCA on the data . It is because the matrix U is orthogonal.", "subpage_snippet": "", "source": "iq.opengenus.org", "link": "https://iq.opengenus.org/whitening-with-pca/", "content": "The goal of Whitening is to reduce redundancy in these images by using 2 measures: 1- making features less correlated to each other 2- making all features have the same variance The first measure is achieved when we apply PCA on the data . It is because the matrix U is orthogonal."}
+{"idx": 6, "title": "whitening: Whitening and High-Dimensional Canonical ...", "date": "", "ddg_snippet": "In both PCA and PCA-cor whitening there is a sign-ambiguity in the eigenvector matrices. In order to resolve the sign-ambiguity we use eigenvector matrices with a positive diagonal so that PCA and PCA-cor cross-correlations and cross-covariances have a positive diagonal for the given ordering of the original variables.", "subpage_snippet": "", "source": "cran.r-project.org", "link": "https://cran.r-project.org/web/packages/whitening/whitening.pdf", "content": "In both PCA and PCA-cor whitening there is a sign-ambiguity in the eigenvector matrices. In order to resolve the sign-ambiguity we use eigenvector matrices with a positive diagonal so that PCA and PCA-cor cross-correlations and cross-covariances have a positive diagonal for the given ordering of the original variables."}
+{"idx": 7, "title": "Towards scientific discovery with dictionary learning ...", "date": "", "ddg_snippet": "Sep 27, 2024 · We also propose a new DL algorithm, Iterative Codebook Feature Learning (ICFL), and combine it with a pre-processing step which uses PCA whitening from a control dataset. In our experiments, we demonstrate that both ICFL and PCA improve the selectivity or \"monosemanticity\" of extracted features compared to TopK sparse autoencoders.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=uDIiL89ViX", "content": "Sep 27, 2024 · We also propose a new DL algorithm, Iterative Codebook Feature Learning (ICFL), and combine it with a pre-processing step which uses PCA whitening from a control dataset. In our experiments, we demonstrate that both ICFL and PCA improve the selectivity or \"monosemanticity\" of extracted features compared to TopK sparse autoencoders."}
+{"idx": 8, "title": "ICML Poster Towards scientific discovery with dictionary learning ...", "date": "", "ddg_snippet": "Sparse dictionary learning (DL) has become a popular tool for interpreting trained large language models (LLM).We propose a novel combination of a DL algorithm, Iterative Codebook Feature Learning ~(ICFL), with a pre-processing step using PCA whitening from a control dataset.", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/poster/44540", "content": "Sparse dictionary learning (DL) has become a popular tool for interpreting trained large language models (LLM).We propose a novel combination of a DL algorithm, Iterative Codebook Feature Learning ~(ICFL), with a pre-processing step using PCA whitening from a control dataset."}
+{"idx": 9, "title": "machinelearningmastery.com/ principal - component - analysis -for...", "date": "", "ddg_snippet": "Principal Component Analysis for Visualization - Machine Learning Mastery.", "subpage_snippet": "", "source": "machinelearningmastery.com", "link": "https://machinelearningmastery.com/principal-component-analysis-for-visualization/", "content": "Principal Component Analysis for Visualization - Machine Learning Mastery."}
diff --git a/data/sampled_jsons/PMR_dataset_Pedestrian_Motion_Reconstruction_SLOPER4D_struggles_capturing_complex_extreme_scenarios_.jsonl b/data/sampled_jsons/PMR_dataset_Pedestrian_Motion_Reconstruction_SLOPER4D_struggles_capturing_complex_extreme_scenarios_.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..922dc01e79c46d243a95ef483597d9668529dc00
--- /dev/null
+++ b/data/sampled_jsons/PMR_dataset_Pedestrian_Motion_Reconstruction_SLOPER4D_struggles_capturing_complex_extreme_scenarios_.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "SLOPER 4 D : A Scene-Aware Dataset for Global 4 D Human Pose...", "date": "", "ddg_snippet": "Capturing System. Global Human Motion and Scene. Figure 1. Using the head-mounted LiDAR and camera to scan the IMUs wearer, we construct SLOPER 4 D , a large scene-aware dataset for global 4 D human pose estimation in urban environments.", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2023/papers/Dai_SLOPER4D_A_Scene-Aware_Dataset_for_Global_4D_Human_Pose_Estimation_CVPR_2023_paper.pdf", "content": "Capturing System. Global Human Motion and Scene. Figure 1. Using the head-mounted LiDAR and camera to scan the IMUs wearer, we construct SLOPER 4 D , a large scene-aware dataset for global 4 D human pose estimation in urban environments."}
+{"idx": 1, "title": "SLOPER4D: A Scene-Aware Dataset for Global 4D Human Pose ...", "date": "", "ddg_snippet": "Mar 18, 2024 · Dataset 15 sequences of 12 human subjects in 10 scenes in urban environments (1k – 30k m 2 ) 100k+ frames multi-source data (20 Hz) including 2D / 3D annotations and 3D scenes; 7 km+ human motions . Every human subject signed permission to release their motion data for research purposes.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/climbingdaily/SLOPER4D", "content": "Mar 18, 2024 · Dataset 15 sequences of 12 human subjects in 10 scenes in urban environments (1k – 30k m 2 ) 100k+ frames multi-source data (20 Hz) including 2D / 3D annotations and 3D scenes; 7 km+ human motions . Every human subject signed permission to release their motion data for research purposes."}
+{"idx": 2, "title": "Pedestrian Motion Reconstruction: A Large-scale Benchmark via ...", "date": "", "ddg_snippet": "Jan 22, 2025 · Abstract: Reconstructing pedestrian motion from dynamic sensors, with a focus on pedestrian intention, is crucial for advancing autonomous driving safety. However, this task is challenging due to data limitations arising from technical complexities , safety, and cost concerns.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=YOpa6dTrpt", "content": "Jan 22, 2025 · Abstract: Reconstructing pedestrian motion from dynamic sensors, with a focus on pedestrian intention, is crucial for advancing autonomous driving safety. However, this task is challenging due to data limitations arising from technical complexities , safety, and cost concerns."}
+{"idx": 3, "title": "P M RECONSTRUCTION: A LARGE SCALE BENCHMARK VIA MIXED REALITY ...", "date": "", "ddg_snippet": "SLOPER4D (Dai et al., 2023) is the first scene-natural 3D human motion dataset captured with wearable sensors, but it struggles with capturing complex and extreme scenarios , such as collisions .", "subpage_snippet": "", "source": "proceedings.iclr.cc", "link": "https://proceedings.iclr.cc/paper_files/paper/2025/file/f3342358d0792ea201dc86d69570946b-Paper-Conference.pdf", "content": "SLOPER4D (Dai et al., 2023) is the first scene-natural 3D human motion dataset captured with wearable sensors, but it struggles with capturing complex and extreme scenarios , such as collisions ."}
+{"idx": 4, "title": "SLOPER4D DATASET - LiDAR Human", "date": "", "ddg_snippet": "By downloading the dataset you accept the following License. Dataset breakdown 15 sequences of 12 human subjects in 10 scenes in urban environments (1k – 30k $m^2$) 100k+ frames multi-source data (20 Hz) including 2D / 3D annotations and 3D scenes; 7 km+ human motions .", "subpage_snippet": "", "source": "www.lidarhumanmotion.net", "link": "http://www.lidarhumanmotion.net/data-sloper4d/", "content": "By downloading the dataset you accept the following License. Dataset breakdown 15 sequences of 12 human subjects in 10 scenes in urban environments (1k – 30k $m^2$) 100k+ frames multi-source data (20 Hz) including 2D / 3D annotations and 3D scenes; 7 km+ human motions ."}
+{"idx": 5, "title": "GitHub - coding-rachal/PMRDataset", "date": "", "ddg_snippet": "Our PMR dataset is known for complementing extrem scenarios for real-world datasets and exploring more about the pedestrian -vehicle interaction modes. Therefore, we show some rare cases for pedestrian -vehicle interactions here. For each case, we provide videos captured from two distinct vehicles.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/coding-rachal/PMRDataset", "content": "Our PMR dataset is known for complementing extrem scenarios for real-world datasets and exploring more about the pedestrian -vehicle interaction modes. Therefore, we show some rare cases for pedestrian -vehicle interactions here. For each case, we provide videos captured from two distinct vehicles."}
+{"idx": 6, "title": "SLOPER4D: A Scene-Aware Dataset for Global 4D Human Pose ...", "date": "", "ddg_snippet": "We propose the first large-scale urban-level human pose dataset with multi-modal capture data and rich human-scene annotations. We propose an effective joint optimization method for acquiring accurate human motions in both local and global by integrating LiDAR SLAM results, IMU poses, and scene constraints. PE task on SLOP", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2303.09095", "content": "We propose the first large-scale urban-level human pose dataset with multi-modal capture data and rich human-scene annotations. We propose an effective joint optimization method for acquiring accurate human motions in both local and global by integrating LiDAR SLAM results, IMU poses, and scene constraints. PE task on SLOP"}
+{"idx": 7, "title": "Joint Optimization for 4 D Human-Scene Reconstruction in the Wild", "date": "", "ddg_snippet": "In this paper, we aim to capture human-scene interactions by tackling monocular 4 D human-scene reconstruction , which reconstructs both the 4 D global human motion and the 3D scene from web videos.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2501.02158v1", "content": "In this paper, we aim to capture human-scene interactions by tackling monocular 4 D human-scene reconstruction , which reconstructs both the 4 D global human motion and the 3D scene from web videos."}
+{"idx": 8, "title": "(PDF) Learning to Generate Diverse Pedestrian Movements from Web...", "date": "", "ddg_snippet": "pedestrian behaviors and diverse motion contexts. Table 5: Benchmarking results of our autolabeling pipeline on the SLOPER 4 D dataset .PACER+: On-Demand Pedestrian Animation Controller in Driving Scenarios .", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/384811375_Learning_to_Generate_Diverse_Pedestrian_Movements_from_Web_Videos_with_Noisy_Labels", "content": "pedestrian behaviors and diverse motion contexts. Table 5: Benchmarking results of our autolabeling pipeline on the SLOPER 4 D dataset .PACER+: On-Demand Pedestrian Animation Controller in Driving Scenarios ."}
+{"idx": 9, "title": "SLOPER 4 D : A Scene-Aware Dataset for Global... | Papers With Code", "date": "", "ddg_snippet": "Add or remove datasets introduced in this paper: SLOPER 4 D .Eventually, SLOPER 4 D consists of 15 sequences of human motions , each of which has a trajectory length of more than 200 meters (up to 1,300 meters) and covers an area of more than 2,000.", "subpage_snippet": "", "source": "paperswithcode.com", "link": "https://paperswithcode.com/paper/sloper4d-a-scene-aware-dataset-for-global-4d", "content": "Add or remove datasets introduced in this paper: SLOPER 4 D .Eventually, SLOPER 4 D consists of 15 sequences of human motions , each of which has a trajectory length of more than 200 meters (up to 1,300 meters) and covers an area of more than 2,000."}
diff --git a/data/sampled_jsons/PPO_alternatives_RLHF_robustness_TRPO_A2C_SAC_policy_gradient_improvements_year_2024.jsonl b/data/sampled_jsons/PPO_alternatives_RLHF_robustness_TRPO_A2C_SAC_policy_gradient_improvements_year_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..960a15e300f99a9ad3b5b7c653707991709e9843
--- /dev/null
+++ b/data/sampled_jsons/PPO_alternatives_RLHF_robustness_TRPO_A2C_SAC_policy_gradient_improvements_year_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "[1707.06347] Proximal Policy Optimization Algorithms", "date": "", "ddg_snippet": "Whereas standard policy gradient methods perform one gradient update per data sample, we propose a novel objective function that enables multiple epochs of minibatch updates. The new methods, which we call proximal policy optimization ( PPO )...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/1707.06347", "content": "Whereas standard policy gradient methods perform one gradient update per data sample, we propose a novel objective function that enables multiple epochs of minibatch updates. The new methods, which we call proximal policy optimization ( PPO )..."}
+{"idx": 1, "title": "From REINFORCE to PPO : The Complete On- Policy ... | Taewoon Kim", "date": "", "ddg_snippet": "LLM Fine-tuning: Modern techniques like RLHF (Reinforcement Learning from Human Feedback) rely heavily on policy gradient methods. Scalable Training: On- policy methods like PPO power the training of state-of-the-art models across multiple domains.", "subpage_snippet": "", "source": "taewoon.kim", "link": "https://taewoon.kim/2025-08-07-on-policy-rl/", "content": "LLM Fine-tuning: Modern techniques like RLHF (Reinforcement Learning from Human Feedback) rely heavily on policy gradient methods. Scalable Training: On- policy methods like PPO power the training of state-of-the-art models across multiple domains."}
+{"idx": 2, "title": "Proximal Policy Optimization ( PPO )", "date": "", "ddg_snippet": "Today we'll learn about Proximal Policy Optimization ( PPO ), an architecture that improves our agent's training stability by avoiding too large policy updates. To do that, we use a ratio that will indicates the difference between our current and old policy and clip this ratio from a specific range.", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/blog/deep-rl-ppo", "content": "Today we'll learn about Proximal Policy Optimization ( PPO ), an architecture that improves our agent's training stability by avoiding too large policy updates. To do that, we use a ratio that will indicates the difference between our current and old policy and clip this ratio from a specific range."}
+{"idx": 3, "title": "Natural Policy Gradients , TRPO , A 2 C | Holly Grimm", "date": "", "ddg_snippet": "Natural Policy Gradient improves on the standard policy gradient algorithm by approximating an optimization problem using a Fisher Information Matrix and step size.", "subpage_snippet": "", "source": "hollygrimm.com", "link": "https://hollygrimm.com/posts/rl_adv_pg/", "content": "Natural Policy Gradient improves on the standard policy gradient algorithm by approximating an optimization problem using a Fisher Information Matrix and step size."}
+{"idx": 4, "title": "Policy Gradient Method in Reinforcement Learning... - aigreeks.com", "date": "", "ddg_snippet": "PPO refines the Policy Gradient Method in Reinforcement Learning for stability.Clipping prevents large policy shifts, ensuring trust-region-like updates. PPO ’s ease of use and performance make it a go-to for tasks like game AI, where it outperforms TRPO with less complexity.", "subpage_snippet": "", "source": "aigreeks.com", "link": "https://aigreeks.com/policy-gradient-method-in-reinforcement-learning/", "content": "PPO refines the Policy Gradient Method in Reinforcement Learning for stability.Clipping prevents large policy shifts, ensuring trust-region-like updates. PPO ’s ease of use and performance make it a go-to for tasks like game AI, where it outperforms TRPO with less complexity."}
+{"idx": 5, "title": "Reinforcement Learning From Human Feedback ( RLHF )... - Freedium", "date": "", "ddg_snippet": "A deep dive into training an LLM and using Reinforcement Learning from Human Feedback ( RLHF ) with PPO to align it with human values.", "subpage_snippet": "", "source": "freedium.cfd", "link": "https://freedium.cfd/7ce6c4ddc8e6", "content": "A deep dive into training an LLM and using Reinforcement Learning from Human Feedback ( RLHF ) with PPO to align it with human values."}
+{"idx": 6, "title": "Proximal Policy Optimization Algorithms, Schulman et al, 2017 | PDF", "date": "", "ddg_snippet": "The document discusses Proximal Policy Optimization ( PPO ), a reinforcement learning algorithm introduced by Schulman et al. in 2017, which aims to improve the performance and data efficiency of existing methods like TRPO while being simpler to implement.", "subpage_snippet": "", "source": "www.slideshare.net", "link": "https://www.slideshare.net/slideshow/proximal-policy-optimization-algorithms-schulman-et-al-2017/238343413", "content": "The document discusses Proximal Policy Optimization ( PPO ), a reinforcement learning algorithm introduced by Schulman et al. in 2017, which aims to improve the performance and data efficiency of existing methods like TRPO while being simpler to implement."}
+{"idx": 7, "title": "PPO in PyTorch: Exploring the implementation details of the algorithm...", "date": "", "ddg_snippet": "PPO is a popular method that has recently contributed to advancements in LLM alignment through reinforcement learning from human feedback ( RLHF ). Understanding how PPO works is crucial for those interested in LLMs. It is an on- policy policy gradient method.", "subpage_snippet": "", "source": "www.linkedin.com", "link": "https://www.linkedin.com/pulse/ppo-pytorch-exploring-implementation-details-which-karunakaran-phd-ahd0c", "content": "PPO is a popular method that has recently contributed to advancements in LLM alignment through reinforcement learning from human feedback ( RLHF ). Understanding how PPO works is crucial for those interested in LLMs. It is an on- policy policy gradient method."}
+{"idx": 8, "title": "Proximal Policy Optimization | OpenAI", "date": "", "ddg_snippet": "Baselines: PPO , PPO 2, ACER, and TRPO .This runs approximately 3x faster than the current PPO baseline on Atari. In addition, we’re releasing an implementation of Actor Critic with Experience Replay (ACER), a sample-efficient policy gradient algorithm.", "subpage_snippet": "", "source": "openai.com", "link": "https://openai.com/index/openai-baselines-ppo/", "content": "Baselines: PPO , PPO 2, ACER, and TRPO .This runs approximately 3x faster than the current PPO baseline on Atari. In addition, we’re releasing an implementation of Actor Critic with Experience Replay (ACER), a sample-efficient policy gradient algorithm."}
+{"idx": 9, "title": "ppo _cartpole - Colab", "date": "", "ddg_snippet": "Proximal Policy Optimization. PPO is a policy gradient method and can be used for environments with either discrete or continuous action spaces. It trains a stochastic policy in an on- policy way. Also, it utilizes the actor critic method.", "subpage_snippet": "", "source": "colab.research.google.com", "link": "https://colab.research.google.com/github/keras-team/keras-io/blob/master/examples/rl/ipynb/ppo_cartpole.ipynb", "content": "Proximal Policy Optimization. PPO is a policy gradient method and can be used for environments with either discrete or continuous action spaces. It trains a stochastic policy in an on- policy way. Also, it utilizes the actor critic method."}
diff --git a/data/sampled_jsons/Parallel_Simulation_for_Log-concave_Sampling_Algorithm_1_pseudocode.jsonl b/data/sampled_jsons/Parallel_Simulation_for_Log-concave_Sampling_Algorithm_1_pseudocode.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..1ba6622f369b9a7a76b8562b5631b2a5cbd80005
--- /dev/null
+++ b/data/sampled_jsons/Parallel_Simulation_for_Log-concave_Sampling_Algorithm_1_pseudocode.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Hamiltonian Descent Algorithms for Optimization: Accelerated", "date": "", "ddg_snippet": "... inspired computational algorithms such as Hamiltonian Monte Carlo ( HMC ) (Duane et al.,, 1987 ) , a classical method widely employed for sampling ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2505.12553v2", "content": "... inspired computational algorithms such as Hamiltonian Monte Carlo ( HMC ) (Duane et al.,, 1987 ) , a classical method widely employed for sampling ..."}
+{"idx": 1, "title": "Split Gibbs Discrete Diffusion Posterior Sampling", "date": "", "ddg_snippet": "... works on diffusion posterior sampling [ 7 , 35 , 30 , 48 ] primarily focus on continuous diffusion models operating in the Euclidean space for ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2503.01161v2", "content": "... works on diffusion posterior sampling [ 7 , 35 , 30 , 48 ] primarily focus on continuous diffusion models operating in the Euclidean space for ..."}
+{"idx": 2, "title": "WO1988002156A2 - Digital simulation system for generating", "date": "", "ddg_snippet": "For instance, the key requirements of nap-of-the-earth flight for high scene content, coupled with great detail, are addressed parametrically by this ...", "subpage_snippet": "", "source": "patents.google.com", "link": "https://patents.google.com/patent/WO1988002156A2/en", "content": "For instance, the key requirements of nap-of-the-earth flight for high scene content, coupled with great detail, are addressed parametrically by this ..."}
+{"idx": 3, "title": "US5317689A - Digital visual and sensor simulation system for", "date": "", "ddg_snippet": "For instance, the key requirements of nap-of-the-earth flight for high scene content, coupled with great detail, are addressed parametrically by this ...", "subpage_snippet": "", "source": "patents.google.com", "link": "https://patents.google.com/patent/US5317689A/en", "content": "For instance, the key requirements of nap-of-the-earth flight for high scene content, coupled with great detail, are addressed parametrically by this ..."}
+{"idx": 4, "title": "(PDF) Heuristic Algorithms for the Unconstrained Binary", "date": "", "ddg_snippet": "We present two heuristic algorithms based upon tabu search and simulated annealing for this problem. ... for most problems tabu search dominates ...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/2661228_Heuristic_Algorithms_for_the_Unconstrained_Binary_Quadratic_Programming_Problem", "content": "We present two heuristic algorithms based upon tabu search and simulated annealing for this problem. ... for most problems tabu search dominates ..."}
+{"idx": 5, "title": "Fast PET Reconstruction with Variance Reduction and Prior-Aware", "date": "", "ddg_snippet": "The insights gained from these simulations directly contributed to the design of our submitted algorithms , which formed the basis of the winning ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2506.04976v1", "content": "The insights gained from these simulations directly contributed to the design of our submitted algorithms , which formed the basis of the winning ..."}
+{"idx": 6, "title": "Simultaneous Modeling of Disease Screening and Severity", "date": "", "ddg_snippet": "... simultaneously? A simple way would be to define the absence of disease as the lowest category and to set higher categories for increasing disease ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2309.04685v3", "content": "... simultaneously? A simple way would be to define the absence of disease as the lowest category and to set higher categories for increasing disease ..."}
+{"idx": 7, "title": "Downloads", "date": "", "ddg_snippet": "A First-Order Algorithmic Framework for Wasserstein Distributionally Robust Logistic Regression ... A Polynomial Time Algorithm for Log - Concave ...", "subpage_snippet": "", "source": "neurips.cc", "link": "https://neurips.cc/Downloads/2019", "content": "A First-Order Algorithmic Framework for Wasserstein Distributionally Robust Logistic Regression ... A Polynomial Time Algorithm for Log - Concave ..."}
+{"idx": 8, "title": "BOOK PROPOSAL", "date": "", "ddg_snippet": "One of the leading algorithms textbooks, \" Introduction to Algorithms \" by Thomas Cormen, Charles Leiserson, Ronald Rivest, and Clifford ...", "subpage_snippet": "", "source": "www.patrickkellogg.com", "link": "https://www.patrickkellogg.com/school/papers/manning/TheAccidentalProgrammerProposal.html", "content": "One of the leading algorithms textbooks, \" Introduction to Algorithms \" by Thomas Cormen, Charles Leiserson, Ronald Rivest, and Clifford ..."}
+{"idx": 9, "title": "JuliaCon 2017, Berkeley, CA", "date": "", "ddg_snippet": "... land in terms of libraries and frameworks in Julia – demonstrating to the audience how one can implement state-of-the-art Deep Learning models for ...", "subpage_snippet": "", "source": "juliacon.org", "link": "https://juliacon.org/2017/talks", "content": "... land in terms of libraries and frameworks in Julia – demonstrating to the audience how one can implement state-of-the-art Deep Learning models for ..."}
diff --git a/data/sampled_jsons/PowerPaint_classifier-free_guidance.jsonl b/data/sampled_jsons/PowerPaint_classifier-free_guidance.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..015c40121f03dfca334ba7654161339ed6cc5ebb
--- /dev/null
+++ b/data/sampled_jsons/PowerPaint_classifier-free_guidance.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "PowerPaint", "date": "", "ddg_snippet": "Specifically, Pobj can be used as a negative prompt with classifier - free guidance sampling for effective object removal. We further introduce Pshape for ...", "subpage_snippet": "", "source": "powerpaint.github.io", "link": "https://powerpaint.github.io/", "content": "Specifically, Pobj can be used as a negative prompt with classifier - free guidance sampling for effective object removal. We further introduce Pshape for ..."}
+{"idx": 1, "title": "Sanster/PowerPaint-V1-stable-diffusion-inpainting at main", "date": "", "ddg_snippet": "25 Dec 2023 — # For classifier free guidance, we need to do two forward passes . # Here we concatenate the unconditional and text embeddings into a single ...", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/Sanster/PowerPaint-V1-stable-diffusion-inpainting/blob/main/pipeline_PowerPaint_ControlNet.py", "content": "25 Dec 2023 — # For classifier free guidance, we need to do two forward passes . # Here we concatenate the unconditional and text embeddings into a single ..."}
+{"idx": 2, "title": "Free Lunch in the Input Conditions of Text-Guided Inpainting", "date": "", "ddg_snippet": "30 Nov 2024 — Unlike classifier-free guidance, FreeCond enhances not only prompt-adherence but also mask-fitting and image quality . However, excessive ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2412.00427v1", "content": "30 Nov 2024 — Unlike classifier-free guidance, FreeCond enhances not only prompt-adherence but also mask-fitting and image quality . However, excessive ..."}
+{"idx": 3, "title": "Diffree: Text-Guided Shape-Free Inpainting", "date": "", "ddg_snippet": "Classifier-free guidance is employed during sampling with a 5% random drop rate for conditioning, enabling balance between diversity and fidelity. 2 ...", "subpage_snippet": "", "source": "www.emergentmind.com", "link": "https://www.emergentmind.com/topics/diffree-text-guided-shape-free-object-inpainting", "content": "Classifier-free guidance is employed during sampling with a 5% random drop rate for conditioning, enabling balance between diversity and fidelity. 2 ..."}
+{"idx": 4, "title": "Learning with Task Prompts for High-Quality Versatile ...", "date": "", "ddg_snippet": "23 Jul 2024 — The classifier-free guidance strategy works by decreasing the likelihood conditioned on the negative prompt and increasing the likelihood ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2312.03594v4", "content": "23 Jul 2024 — The classifier-free guidance strategy works by decreasing the likelihood conditioned on the negative prompt and increasing the likelihood ..."}
+{"idx": 5, "title": "Daily Papers", "date": "", "ddg_snippet": "We explore diffusion models for the problem of text-conditional image synthesis and compare two different guidance strategies: CLIP guidance and classifier - free ...", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/papers?q=text-guided+inpainting", "content": "We explore diffusion models for the problem of text-conditional image synthesis and compare two different guidance strategies: CLIP guidance and classifier - free ..."}
+{"idx": 6, "title": "Erasing Entity Cleanly via Amodal Entity Segmentation and ...", "date": "", "ddg_snippet": "by Y Zhu · 2025 — For the erasure task, it introduces classifier-free guidance by using the generation prompt as the negative prompt to reduce sun- dries. CLIPAway [8] proposes a ...", "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": "by Y Zhu · 2025 — For the erasure task, it introduces classifier-free guidance by using the generation prompt as the negative prompt to reduce sun- dries. CLIPAway [8] proposes a ..."}
+{"idx": 7, "title": "Faster and More Data-Efficient Training of Diffusion Models", "date": "", "ddg_snippet": "by Z Wang · Cited by 337 — If applying classifier - free guidance to Patch Diffusion, our model could reach 2.74 FID score on ImageNet-1K, which matches the state-of-the-art performance but ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=iv2sTQtbst", "content": "by Z Wang · Cited by 337 — If applying classifier - free guidance to Patch Diffusion, our model could reach 2.74 FID score on ImageNet-1K, which matches the state-of-the-art performance but ..."}
+{"idx": 8, "title": "Shape‐Conditioned Human Motion Diffusion Model with ...", "date": "", "ddg_snippet": "17 Apr 2025 — To avoid the need for auxiliary classifier training, Ho and Salimans propose a classifier-free guidance [HS22]. The significant success of ...", "subpage_snippet": "", "source": "onlinelibrary.wiley.com", "link": "https://onlinelibrary.wiley.com/doi/10.1111/cgf.70065?af=R", "content": "17 Apr 2025 — To avoid the need for auxiliary classifier training, Ho and Salimans propose a classifier-free guidance [HS22]. The significant success of ..."}
+{"idx": 9, "title": "Creating a diffusion model from scratch in PyTorch to learn ...", "date": "", "ddg_snippet": "This repo is composed of DDPM, DDIM, and Classifier-Free guided models trained on ImageNet 64x64. More information can be found below.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/gmongaras/Diffusion_models_from_scratch", "content": "This repo is composed of DDPM, DDIM, and Classifier-Free guided models trained on ImageNet 64x64. More information can be found below."}
diff --git a/data/sampled_jsons/Provably_Efficient_Risk-Aware_Preference-Based_Reinforcement_Learning.jsonl b/data/sampled_jsons/Provably_Efficient_Risk-Aware_Preference-Based_Reinforcement_Learning.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..fb30a6c7d40bcae4af620fb167c772bd1a015f5b
--- /dev/null
+++ b/data/sampled_jsons/Provably_Efficient_Risk-Aware_Preference-Based_Reinforcement_Learning.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "RA-PbRL: Provably Eficient Risk-Aware Preference-Based ...", "date": "", "ddg_snippet": "Abstract Reinforcement Learning from Human Feedback (RLHF) has recently surged in popularity, particularly for aligning large language models and other AI sys-tems with human intentions. At its core, RLHF can be viewed as a special-ized instance of Preference-based Reinforcement Learning (PbRL), where the preferences specifically originate from human judgments rather than arbitrary evaluators ...", "subpage_snippet": "", "source": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper_files/paper/2024/file/7016d7b7b6e3c05b2128ac5b3aae492d-Paper-Conference.pdf", "content": "Abstract Reinforcement Learning from Human Feedback (RLHF) has recently surged in popularity, particularly for aligning large language models and other AI sys-tems with human intentions. At its core, RLHF can be viewed as a special-ized instance of Preference-based Reinforcement Learning (PbRL), where the preferences specifically originate from human judgments rather than arbitrary evaluators ..."}
+{"idx": 1, "title": "Risk-Aware Preference-baser Reinforcement Learning (RA-PbRL)", "date": "", "ddg_snippet": "Code for paper \"RA-PbRL: Provably Efficient Risk-Aware Preference-Based Reinforcement Learning \" Code Setup Documentation Libraries (>= Python 3.12.4) For more information on the version specifics, see the environment. yaml file. To import the environment, execute the following command prompt commands:", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/aguilarjose11/PbRLNeurips", "content": "Code for paper \"RA-PbRL: Provably Efficient Risk-Aware Preference-Based Reinforcement Learning \" Code Setup Documentation Libraries (>= Python 3.12.4) For more information on the version specifics, see the environment. yaml file. To import the environment, execute the following command prompt commands:"}
+{"idx": 2, "title": "RA-PbRL: Provably Efficient Risk-Aware Preference-Based ...", "date": "", "ddg_snippet": "Oct 31, 2024 · Reinforcement Learning from Human Feedback (RLHF) has recently surged in popularity, particularly for aligning large language models and other AI systems with human intentions. At its core, RLHF can be viewed as a specialized instance of Preference-based Reinforcement Learning (PbRL), where the preferences specifically originate from human judgments rather than arbitrary evaluators. Despite ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2410.23569", "content": "Oct 31, 2024 · Reinforcement Learning from Human Feedback (RLHF) has recently surged in popularity, particularly for aligning large language models and other AI systems with human intentions. At its core, RLHF can be viewed as a specialized instance of Preference-based Reinforcement Learning (PbRL), where the preferences specifically originate from human judgments rather than arbitrary evaluators. Despite ..."}
+{"idx": 3, "title": "Human-in-the-loop: Provably Efficient Preference-based ... RA-PbRL: Provably Efficient Risk-Aware Preference-Based... RA-PbRL: Provably Efficient Risk - Aware Preference - Based Reinforcement RA-PbRL: Provably Efficient Risk - Aware Preference - Based Reinforcement Human-in-the-loop: Provably Efficient Preference - based Reinforcement … Human-in-the-loop: Provably Efficient Preference - based Reinforcement … Human-in-the-loop: Provably Efficient Preference - based Reinforcement … RA-PbRL: Provably Efficient Risk - Aware Preference - Based Reinforcement RA-PbRL: Provably Efficient Risk-Aware Preference-Based ...", "date": "", "ddg_snippet": "We study human-in-the-loop reinforcement learn -ing (RL) with trajectory preferences , where in-stead of receiving a numeric reward at each step, the RL agent only receives preferences over tra-jectory pairs from a human overseer. The goal of the RL agent is to learn the optimal policy which is most preferred by the human overseer. Despite the empiri... See full list on proceedings.mlr.press In this section, we present the main results for preference - based RL. We first propose a novel algorithm called Preference - based Optimistic Planning (PbOP) and estab-lish the regret upper bound for it. To show the sharpness of our result, we also prove an information-theoretic lower bound in the linear case. See full list on proceedings.mlr.press In this subsection, we establish the lower bound for PbRL in the linear setting, which is derived using the reduction from the problem of RL with once-per-episode feedback. Firstly, we show the reduction from the problem of RL with once-per-episode feedback setting to the PbRL setting. Specifically, suppose we have an algorithm ALG for PbRL problem... See full list on proceedings.mlr.press In the previous section, we propose a sample-eficient al-gorithm with near-optimal regret for the problem of PbRL with trajectory feedback. However, this setting cannot cover some other RL situations with preference feedback. For example, in robotics, sampling new trajectories can be ex-pensive and time-consuming compared with labeling prefer-ences... See full list on proceedings.mlr.press Sep 25, 2024 · Abstract: Reinforcement Learning from Human Feedback (RLHF) has recently surged in popularity, particularly for aligning large language models and other AI systems with human intentions. At its core, RLHF can be viewed as a specialized instance of Preference-based Reinforcement Learning (PbRL), where the preferences specifically originate from human judgments rather than arbitrary evaluators ... What is rlhf (preference-based reinforcement learning)? At its core, RLHF can be viewed as a specialized instance of Preference - based Reinforcement Learning (PbRL), where the preferences specifically originate from human judgments rather than arbitrary evaluators. What is reinforcement learning from human feedback (rlhf)? Reinforcement Learning from Human Feedback (RLHF) has recently surged in popularity, particularly for aligning large language models and other AI systems with human intentions. What is reinforcement learning? Introduction Reinforcement learning (RL) is concerned with sequential decision-making problems in which the agent interacts with the environment to maximize its cumulative rewards. What is preference-based RL (pbrl)? The goal of the RL agent is to learn the optimal policy which is most preferred by the human overseer . Despite the empirical successes, the theoretical understanding of preference-based RL (PbRL) is only limited to the tabular case. How does human-in-the-loop reinforcement learn-ing work? We study human-in-the-loop reinforcement learn-ing (RL) with trajectory preferences, where in-stead of receiving a numeric reward at each step, the RL agent only receives preferences over tra-jectory pairs from a human overseer. The goal of the RL agent is to learn the optimal policy which is most preferred by the human overseer. Are nested and static risk-aware objectives compatible with pbrl? To address this, we explore and prove the applicability of two risk-aware objectives to PbRL : nested and static quantile risk objectives. We also introduce Risk-AwarePbRL (RA-PbRL), an algorithm designed to optimize both nested and static objectives. Authors Yujie Zhao, Jose Efraim Aguilar Escamill, Weyl Lu, Huazheng Wang Abstract Reinforcement Learning from Human Feedback (RLHF) has recently surged in popularity, particularly for aligning large language models and other AI systems with human intentions. At its core, RLHF can be viewed as a specialized instance of Preference-based Reinforcement Learning (PbRL), where the preferences ...", "subpage_snippet": "", "source": "proceedings.mlr.press", "link": "https://proceedings.mlr.press/v162/chen22ag/chen22ag.pdf", "content": "We study human-in-the-loop reinforcement learn -ing (RL) with trajectory preferences , where in-stead of receiving a numeric reward at each step, the RL agent only receives preferences over tra-jectory pairs from a human overseer. The goal of the RL agent is to learn the optimal policy which is most preferred by the human overseer. Despite the empiri... See full list on proceedings.mlr.press In this section, we present the main results for preference - based RL. We first propose a novel algorithm called Preference - based Optimistic Planning (PbOP) and estab-lish the regret upper bound for it. To show the sharpness of our result, we also prove an information-theoretic lower bound in the linear case. See full list on proceedings.mlr.press In this subsection, we establish the lower bound for PbRL in the linear setting, which is derived using the reduction from the problem of RL with once-per-episode feedback. Firstly, we show the reduction from the problem of RL with once-per-episode feedback setting to the PbRL setting. Specifically, suppose we have an algorithm ALG for PbRL problem... See full list on proceedings.mlr.press In the previous section, we propose a sample-eficient al-gorithm with near-optimal regret for the problem of PbRL with trajectory feedback. However, this setting cannot cover some other RL situations with preference feedback. For example, in robotics, sampling new trajectories can be ex-pensive and time-consuming compared with labeling prefer-ences... See full list on proceedings.mlr.press Sep 25, 2024 · Abstract: Reinforcement Learning from Human Feedback (RLHF) has recently surged in popularity, particularly for aligning large language models and other AI systems with human intentions. At its core, RLHF can be viewed as a specialized instance of Preference-based Reinforcement Learning (PbRL), where the preferences specifically originate from human judgments rather than arbitrary evaluators ... What is rlhf (preference-based reinforcement learning)? At its core, RLHF can be viewed as a specialized instance of Preference - based Reinforcement Learning (PbRL), where the preferences specifically originate from human judgments rather than arbitrary evaluators. What is reinforcement learning from human feedback (rlhf)? Reinforcement Learning from Human Feedback (RLHF) has recently surged in popularity, particularly for aligning large language models and other AI systems with human intentions. What is reinforcement learning? Introduction Reinforcement learning (RL) is concerned with sequential decision-making problems in which the agent interacts with the environment to maximize its cumulative rewards. What is preference-based RL (pbrl)? The goal of the RL agent is to learn the optimal policy which is most preferred by the human overseer . Despite the empirical successes, the theoretical understanding of preference-based RL (PbRL) is only limited to the tabular case. How does human-in-the-loop reinforcement learn-ing work? We study human-in-the-loop reinforcement learn-ing (RL) with trajectory preferences, where in-stead of receiving a numeric reward at each step, the RL agent only receives preferences over tra-jectory pairs from a human overseer. The goal of the RL agent is to learn the optimal policy which is most preferred by the human overseer. Are nested and static risk-aware objectives compatible with pbrl? To address this, we explore and prove the applicability of two risk-aware objectives to PbRL : nested and static quantile risk objectives. We also introduce Risk-AwarePbRL (RA-PbRL), an algorithm designed to optimize both nested and static objectives. Authors Yujie Zhao, Jose Efraim Aguilar Escamill, Weyl Lu, Huazheng Wang Abstract Reinforcement Learning from Human Feedback (RLHF) has recently surged in popularity, particularly for aligning large language models and other AI systems with human intentions. At its core, RLHF can be viewed as a specialized instance of Preference-based Reinforcement Learning (PbRL), where the preferences ..."}
+{"idx": 4, "title": "RA-PbRL: Provably Efficient Risk-Aware Preference-Based...", "date": "", "ddg_snippet": "Sep 25, 2024 · Abstract: Reinforcement Learning from Human Feedback (RLHF) has recently surged in popularity, particularly for aligning large language models and other AI systems with human intentions. At its core, RLHF can be viewed as a specialized instance of Preference-based Reinforcement Learning (PbRL), where the preferences specifically originate from human judgments rather than arbitrary evaluators ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=JNDcFOczOf", "content": "Sep 25, 2024 · Abstract: Reinforcement Learning from Human Feedback (RLHF) has recently surged in popularity, particularly for aligning large language models and other AI systems with human intentions. At its core, RLHF can be viewed as a specialized instance of Preference-based Reinforcement Learning (PbRL), where the preferences specifically originate from human judgments rather than arbitrary evaluators ..."}
+{"idx": 5, "title": "RA-PbRL: Provably Efficient Risk-Aware Preference-Based ...", "date": "", "ddg_snippet": "Authors Yujie Zhao, Jose Efraim Aguilar Escamill, Weyl Lu, Huazheng Wang Abstract Reinforcement Learning from Human Feedback (RLHF) has recently surged in popularity, particularly for aligning large language models and other AI systems with human intentions. At its core, RLHF can be viewed as a specialized instance of Preference-based Reinforcement Learning (PbRL), where the preferences ...", "subpage_snippet": "", "source": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper_files/paper/2024/hash/7016d7b7b6e3c05b2128ac5b3aae492d-Abstract-Conference.html", "content": "Authors Yujie Zhao, Jose Efraim Aguilar Escamill, Weyl Lu, Huazheng Wang Abstract Reinforcement Learning from Human Feedback (RLHF) has recently surged in popularity, particularly for aligning large language models and other AI systems with human intentions. At its core, RLHF can be viewed as a specialized instance of Preference-based Reinforcement Learning (PbRL), where the preferences ..."}
+{"idx": 6, "title": "RA-PbRL: Provably Efficient Risk - Aware Preference - Based ...", "date": "", "ddg_snippet": "We develop a provably efficient (both computationally and statistically) algorithm, RA-PbRL, for nested and static quantile risk - aware objectives.A survey of preference - based reinforcement learning methods.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2410.23569v1/", "content": "We develop a provably efficient (both computationally and statistically) algorithm, RA-PbRL, for nested and static quantile risk - aware objectives.A survey of preference - based reinforcement learning methods."}
+{"idx": 7, "title": "RA-PbRL: Provably Efficient Risk - Aware Preference - Based ...", "date": "", "ddg_snippet": "Reinforcement Learning from Human Feedback (RLHF) has recently surged in popularity, particularly for aligning large language models and other AI systems with human intentions. At its core, RLHF can be viewed as a specialized instance of Preference - based Reinforcement Learning ...", "subpage_snippet": "", "source": "papers.nips.cc", "link": "https://papers.nips.cc/paper_files/paper/2024/hash/7016d7b7b6e3c05b2128ac5b3aae492d-Abstract-Conference.html", "content": "Reinforcement Learning from Human Feedback (RLHF) has recently surged in popularity, particularly for aligning large language models and other AI systems with human intentions. At its core, RLHF can be viewed as a specialized instance of Preference - based Reinforcement Learning ..."}
+{"idx": 8, "title": "RA-PbRL: Provably Efficient Risk - Aware Preference - Based ...", "date": "", "ddg_snippet": "Preference - based Reinforcement Learning (PbRL) studies the problem where agents receive only preferences over pairs of trajectories in each episode. Traditional approaches in this field have predominantly focused on the mean reward or utility criterion.", "subpage_snippet": "", "source": "www.aimodels.fyi", "link": "https://www.aimodels.fyi/papers/arxiv/ra-pbrl-provably-efficient-risk-aware-preference", "content": "Preference - based Reinforcement Learning (PbRL) studies the problem where agents receive only preferences over pairs of trajectories in each episode. Traditional approaches in this field have predominantly focused on the mean reward or utility criterion."}
+{"idx": 9, "title": "RA-PbRL: Provably Efficient Risk - Aware Preference - Based ...", "date": "", "ddg_snippet": "At its core, RLHF can be viewed as a specialized instance of Preference - based Reinforcement Learning (PbRL), where the preferences specifically originate from human judgments rather than arbitrary evaluators.", "subpage_snippet": "", "source": "synthical.com", "link": "https://synthical.com/article/RA-PbRL:-Provably-Efficient-Risk-Aware-Preference-Based-Reinforcement-Learning-baf1c16a-6c49-45cc-9e7e-b12a3550842b", "content": "At its core, RLHF can be viewed as a specialized instance of Preference - based Reinforcement Learning (PbRL), where the preferences specifically originate from human judgments rather than arbitrary evaluators."}
diff --git a/data/sampled_jsons/Proximal_Policy_Optimization_Algorithms_A2C_comparison_Atari_results_table_year_2017.jsonl b/data/sampled_jsons/Proximal_Policy_Optimization_Algorithms_A2C_comparison_Atari_results_table_year_2017.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..4d8f0527b9456771fca3705ea672224189e627e1
--- /dev/null
+++ b/data/sampled_jsons/Proximal_Policy_Optimization_Algorithms_A2C_comparison_Atari_results_table_year_2017.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Proximal Policy Gradient (PPO) - CleanRL", "date": "", "ddg_snippet": "To achieve this, see how we matched the implementation details in our blog post The 37 Implementation Details of Proximal Policy Optimization .", "subpage_snippet": "", "source": "docs.cleanrl.dev", "link": "https://docs.cleanrl.dev/rl-algorithms/ppo/", "content": "To achieve this, see how we matched the implementation details in our blog post The 37 Implementation Details of Proximal Policy Optimization ."}
+{"idx": 1, "title": "Comparison Of Popular Machine Learning Algorithms |", "date": "", "ddg_snippet": "Navigating the Algorithmic Maze: Choosing the Right Path for Your Machine Learning Journey ... K-Nearest Neighbors (k-NN): Proximity-Based Wisdom in ...", "subpage_snippet": "", "source": "www.prodigitalweb.com", "link": "https://www.prodigitalweb.com/comparison-of-popular-machine-learning-algorithms/", "content": "Navigating the Algorithmic Maze: Choosing the Right Path for Your Machine Learning Journey ... K-Nearest Neighbors (k-NN): Proximity-Based Wisdom in ..."}
+{"idx": 2, "title": "1 Introduction", "date": "", "ddg_snippet": "We propose an LLM-based generative optimization approach for developing Atari game-playing agents, where policies are represented as modular Python ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2508.19506v1", "content": "We propose an LLM-based generative optimization approach for developing Atari game-playing agents, where policies are represented as modular Python ..."}
+{"idx": 3, "title": "TDRM: Smooth Reward Models with Temporal Difference for LLM RL", "date": "", "ddg_snippet": "Furthermore, during online Reinforcement Learning (RL) training — whether via Group Relative Policy Optimization (GRPO) [ 28 ] used in R1 [ 5 ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2509.15110v1", "content": "Furthermore, during online Reinforcement Learning (RL) training — whether via Group Relative Policy Optimization (GRPO) [ 28 ] used in R1 [ 5 ..."}
+{"idx": 4, "title": "CaRL: Learning Scalable Planning Policies with Simple Rewards", "date": "", "ddg_snippet": "... reward reduces the performance of Proximal Policy Optimization ... Algorithm : We train our model with Proximal Policy Optimization (PPO) [ 24 ] .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2504.17838v3", "content": "... reward reduces the performance of Proximal Policy Optimization ... Algorithm : We train our model with Proximal Policy Optimization (PPO) [ 24 ] ."}
+{"idx": 5, "title": "Simplifying Deep Temporal Difference Learning", "date": "", "ddg_snippet": "Out of this myriad of algorithmic combinations, proximal policy optimisation (PPO) (Schulman et al., 2017 ) has emerged as the de facto choice for ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2407.04811v2", "content": "Out of this myriad of algorithmic combinations, proximal policy optimisation (PPO) (Schulman et al., 2017 ) has emerged as the de facto choice for ..."}
+{"idx": 6, "title": "Automated Placement of Analog Integrated Circuits using", "date": "", "ddg_snippet": "In Section 6 , results are presented, including a comparison of automated and manual placements provided by industry partner STMicroelectronics ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2411.02406v2", "content": "In Section 6 , results are presented, including a comparison of automated and manual placements provided by industry partner STMicroelectronics ..."}
+{"idx": 7, "title": "Spinning Up as a Deep RL Researcher — Spinning Up", "date": "", "ddg_snippet": "Which algorithms ? You should probably start with vanilla policy gradient (also called REINFORCE ), DQN , A2C (the synchronous version of A3C ), PPO ...", "subpage_snippet": "", "source": "spinningup.openai.com", "link": "https://spinningup.openai.com/en/latest/spinningup/spinningup.html", "content": "Which algorithms ? You should probably start with vanilla policy gradient (also called REINFORCE ), DQN , A2C (the synchronous version of A3C ), PPO ..."}
+{"idx": 8, "title": "Reinforcement Learning[Book]", "date": "", "ddg_snippet": "Reinforcement learning (RL) will deliver one of the biggest breakthroughs in AI over the next decade, enabling algorithms to learn from their ...", "subpage_snippet": "", "source": "www.oreilly.com", "link": "https://www.oreilly.com/library/view/reinforcement-learning/9781492072386/", "content": "Reinforcement learning (RL) will deliver one of the biggest breakthroughs in AI over the next decade, enabling algorithms to learn from their ..."}
+{"idx": 9, "title": "The Evolution of Reinforcement Learning in Quantitative", "date": "", "ddg_snippet": "... optimise for various performance measures, RL must contend with the high variability and noise inherent in financial data, which can lead to ...", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.1145/3733714?cookieSet=1", "content": "... optimise for various performance measures, RL must contend with the high variability and noise inherent in financial data, which can lead to ..."}
diff --git a/data/sampled_jsons/Python_SBOM_generator_Syft_CycloneDX_pipdeptree_year_2023.jsonl b/data/sampled_jsons/Python_SBOM_generator_Syft_CycloneDX_pipdeptree_year_2023.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..21a1800b660785b1d6ea6699816bf6880e4c3907
--- /dev/null
+++ b/data/sampled_jsons/Python_SBOM_generator_Syft_CycloneDX_pipdeptree_year_2023.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Generate Python SBOMs: pipdeptree vs Syft - anchore.com", "date": "", "ddg_snippet": "SBOM (software bill of materials) generation is becoming increasingly important for software supply chain security and compliance. Several approaches exist for generating SBOMs for Python projects, each with its own strengths. In this post, we'll explore two popular methods: using pipdeptree with cyclonedx -py and Syft . We'll examine their differences and see why Syft is better for many use ...", "subpage_snippet": "", "source": "anchore.com", "link": "https://anchore.com/blog/python-sbom-generation/", "content": "SBOM (software bill of materials) generation is becoming increasingly important for software supply chain security and compliance. Several approaches exist for generating SBOMs for Python projects, each with its own strengths. In this post, we'll explore two popular methods: using pipdeptree with cyclonedx -py and Syft . We'll examine their differences and see why Syft is better for many use ..."}
+{"idx": 1, "title": "GitHub - CycloneDX/cyclonedx-python: CycloneDX Software Bill of ...", "date": "", "ddg_snippet": "This tool generates Software Bill of material ( SBOM ) documents in OWASP CycloneDX format. This is probably the most accurate, complete SBOM generator for any python -related projects. Supported data sources are: Python (virtual) environment Poetry manifest and lockfile Pipenv manifest and lockfile Pip's requirements.txt format PDM manifest and lockfile are not explicitly supported. However, PDM ...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/CycloneDX/cyclonedx-python", "content": "This tool generates Software Bill of material ( SBOM ) documents in OWASP CycloneDX format. This is probably the most accurate, complete SBOM generator for any python -related projects. Supported data sources are: Python (virtual) environment Poetry manifest and lockfile Pipenv manifest and lockfile Pip's requirements.txt format PDM manifest and lockfile are not explicitly supported. However, PDM ..."}
+{"idx": 2, "title": "How to Generate SBOMs for Python Packages with `pipdeptree` and ...", "date": "", "ddg_snippet": "Software Bill of Materials ( SBOMs ) are essential for ensuring transparency and security in software supply chains. This guide will show you how to use pipdeptree and cyclonedx -py to generate SBOMs for Python projects, including all transient dependencies. We'll also reference a comprehensive guide on generating SBOMs for Python packages using Docker and Django CMS.", "subpage_snippet": "", "source": "sbomify.com", "link": "https://sbomify.com/2024/07/30/generate-sboms-for-python-packages-with-pipdeptree-and-cyclonedx-py/", "content": "Software Bill of Materials ( SBOMs ) are essential for ensuring transparency and security in software supply chains. This guide will show you how to use pipdeptree and cyclonedx -py to generate SBOMs for Python projects, including all transient dependencies. We'll also reference a comprehensive guide on generating SBOMs for Python packages using Docker and Django CMS."}
+{"idx": 3, "title": "Python SBOM Generation Complete Guide - pip, Poetry, Conda", "date": "", "ddg_snippet": "Learn how to generate Software Bill of Materials ( SBOM ) for Python projects in 2025 using pip, Poetry, Conda, CycloneDX 1.6, SPDX 3.0, and security scanning ...", "subpage_snippet": "", "source": "sbomgenerator.com", "link": "https://sbomgenerator.com/guides/python", "content": "Learn how to generate Software Bill of Materials ( SBOM ) for Python projects in 2025 using pip, Poetry, Conda, CycloneDX 1.6, SPDX 3.0, and security scanning ..."}
+{"idx": 4, "title": "cyclonedx-bom · PyPI", "date": "", "ddg_snippet": "Project description CycloneDX Python SBOM Generation Tool This tool generates Software Bill of material ( SBOM ) documents in OWASP CycloneDX format. This is probably the most accurate, complete SBOM generator for any python -related projects. Supported data sources are: Python (virtual) environment Poetry manifest and lockfile Pipenv manifest and ...", "subpage_snippet": "", "source": "pypi.org", "link": "https://pypi.org/project/cyclonedx-bom/", "content": "Project description CycloneDX Python SBOM Generation Tool This tool generates Software Bill of material ( SBOM ) documents in OWASP CycloneDX format. This is probably the most accurate, complete SBOM generator for any python -related projects. Supported data sources are: Python (virtual) environment Poetry manifest and lockfile Pipenv manifest and ..."}
+{"idx": 5, "title": "CycloneDX SBOM Generation Tool for Python", "date": "", "ddg_snippet": "CycloneDX SBOM Generation Tool for Python This tool generates Software Bill of material ( SBOM ) documents in OWASP CycloneDX format. Supported data sources are: Python (virtual) environment Poetry manifest and lockfile Pipenv manifest and lockfile Pip's requirements file format format PDM manifest and lockfile support is not implemented, yet. However, PDM's Python virtual environments are ...", "subpage_snippet": "", "source": "cyclonedx-bom-tool.readthedocs.io", "link": "https://cyclonedx-bom-tool.readthedocs.io/en/latest/", "content": "CycloneDX SBOM Generation Tool for Python This tool generates Software Bill of material ( SBOM ) documents in OWASP CycloneDX format. Supported data sources are: Python (virtual) environment Poetry manifest and lockfile Pipenv manifest and lockfile Pip's requirements file format format PDM manifest and lockfile support is not implemented, yet. However, PDM's Python virtual environments are ..."}
+{"idx": 6, "title": "How to Generate SBOM for Python Applications - Medium", "date": "", "ddg_snippet": "In the last two blog we saw about SBOM , Now today in this blog we will see how to generate SBOM for Python Applications. The process will differ if we have containers. Generating an SBOM for your ...", "subpage_snippet": "", "source": "medium.com", "link": "https://medium.com/@achanandhi.m/how-to-generate-sbom-for-python-applications-d2fe87b95ee3", "content": "In the last two blog we saw about SBOM , Now today in this blog we will see how to generate SBOM for Python Applications. The process will differ if we have containers. Generating an SBOM for your ..."}
+{"idx": 7, "title": "Generating an SBOM from requirements.txt without including CycloneDX's ...", "date": "", "ddg_snippet": "Currently, I am trying to generate a SBOM for a Python project and following the direct instructions in the documentation (installing CycloneDX via pip, running pip freeze, and then running cyclonedx_py on my requirements.txt leads to my SBOM being filled with unwanted CycloneDX dependencies. What is the recommended way of generating an SBOM from requirements.txt without including CycloneDX's ...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/CycloneDX/cyclonedx-python/discussions/487", "content": "Currently, I am trying to generate a SBOM for a Python project and following the direct instructions in the documentation (installing CycloneDX via pip, running pip freeze, and then running cyclonedx_py on my requirements.txt leads to my SBOM being filled with unwanted CycloneDX dependencies. What is the recommended way of generating an SBOM from requirements.txt without including CycloneDX's ..."}
+{"idx": 8, "title": "Creating SBOMs from Python Projects | SBOMgen", "date": "", "ddg_snippet": "Creates CycloneDX Software Bill of Materials ( SBOM ) from Python projects and environments. positional arguments: environment Build an SBOM from Python (virtual) environment requirements Build an SBOM from Pip requirements pipenv Build an SBOM from Pipenv manifest poetry Build an SBOM from Poetry project options: -h, --help show this ...", "subpage_snippet": "", "source": "reestwick.github.io", "link": "https://reestwick.github.io/sbomgen/tutorials/creating-python-sbom/creating-python-sbom.html", "content": "Creates CycloneDX Software Bill of Materials ( SBOM ) from Python projects and environments. positional arguments: environment Build an SBOM from Python (virtual) environment requirements Build an SBOM from Pip requirements pipenv Build an SBOM from Pipenv manifest poetry Build an SBOM from Poetry project options: -h, --help show this ..."}
+{"idx": 9, "title": "sbom4python · PyPI", "date": "", "ddg_snippet": "The SBOM4Python is a free, open source tool to generate a SBOM (Software Bill of Materials) for an installed Python module in a number of formats including SPDX and CycloneDX .", "subpage_snippet": "", "source": "pypi.org", "link": "https://pypi.org/project/sbom4python/", "content": "The SBOM4Python is a free, open source tool to generate a SBOM (Software Bill of Materials) for an installed Python module in a number of formats including SPDX and CycloneDX ."}
diff --git a/data/sampled_jsons/Qi_et_al._2024_arxiv.jsonl b/data/sampled_jsons/Qi_et_al._2024_arxiv.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..02c12d0e590d32d30cf1a3cdb2b3570a9f749322
--- /dev/null
+++ b/data/sampled_jsons/Qi_et_al._2024_arxiv.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Qi (standard) - Wikipedia", "date": "", "ddg_snippet": "A Qi system consists of two types of devices – the Base Station, which is connected to a power source and provides inductive power, and Mobile Devices, which consume inductive power.", "subpage_snippet": "", "source": "en.m.wikipedia.org", "link": "https://en.m.wikipedia.org/wiki/Qi_(standard)", "content": "A Qi system consists of two types of devices – the Base Station, which is connected to a power source and provides inductive power, and Mobile Devices, which consume inductive power."}
+{"idx": 1, "title": "QI Definition & Meaning - Merriam-Webster", "date": "", "ddg_snippet": "The meaning of QI is vital energy that is held to animate the body internally and is of central importance in some Eastern systems of medical treatment (such as acupuncture) and of exercise or self-defense (such as tai chi).", "subpage_snippet": "", "source": "www.merriam-webster.com", "link": "https://www.merriam-webster.com/dictionary/qi", "content": "The meaning of QI is vital energy that is held to animate the body internally and is of central importance in some Eastern systems of medical treatment (such as acupuncture) and of exercise or self-defense (such as tai chi)."}
+{"idx": 2, "title": "What Is Qi ? (and Other Concepts) | Taking Charge of Your ...", "date": "", "ddg_snippet": "Two concepts that are unique and fundamental to Chinese medicine are Qi (usually translated as \"vital energy\") and yin and yang (the harmony of all the opposite elements and forces that make up existence).", "subpage_snippet": "", "source": "www.takingcharge.csh.umn.edu", "link": "https://www.takingcharge.csh.umn.edu/what-qi-and-other-concepts", "content": "Two concepts that are unique and fundamental to Chinese medicine are Qi (usually translated as \"vital energy\") and yin and yang (the harmony of all the opposite elements and forces that make up existence)."}
+{"idx": 3, "title": "What Is Qi Energy, What Influences It and How to Balance It ...", "date": "", "ddg_snippet": "Oct 1, 2019 · Qi refers to the vital energy that constantly circulates throughout your body. The concept of qi originates in China. You could say that life is qi .", "subpage_snippet": "", "source": "www.learning-mind.com", "link": "https://www.learning-mind.com/qi-energy-balance/", "content": "Oct 1, 2019 · Qi refers to the vital energy that constantly circulates throughout your body. The concept of qi originates in China. You could say that life is qi ."}
+{"idx": 4, "title": "What is Qi? And why you should know it - The Qi", "date": "", "ddg_snippet": "Jan 26, 2019 · In many Asian cultures, Qi , also spelled chi or ch’i, is the life force that every person and thing has. Traditional Chinese medicine and acupuncture both address the concept of Qi ; if you’re feeling a little under the weather, an unbalanced qi may be the culprit.", "subpage_snippet": "", "source": "the-qi.com", "link": "https://the-qi.com/blogs/journal/what-is-qi-and-why-you-should-know-it", "content": "Jan 26, 2019 · In many Asian cultures, Qi , also spelled chi or ch’i, is the life force that every person and thing has. Traditional Chinese medicine and acupuncture both address the concept of Qi ; if you’re feeling a little under the weather, an unbalanced qi may be the culprit."}
+{"idx": 5, "title": "Qi (Chi): The Taoist Principle of Life Force - Learn Religions", "date": "", "ddg_snippet": "Jun 25, 2019 · This article discusses the Taoist principle of \" qi ,\" the vibratory life-force energy that animates all things.", "subpage_snippet": "", "source": "www.learnreligions.com", "link": "https://www.learnreligions.com/what-is-qi-chi-3183052", "content": "Jun 25, 2019 · This article discusses the Taoist principle of \" qi ,\" the vibratory life-force energy that animates all things."}
+{"idx": 6, "title": "What is Qi : Understanding the Animating Force of the Universe", "date": "", "ddg_snippet": "Feb 2, 2025 · What is Qi ? Qi is the Animating Force and Material of the Universe. It can be particle, wave, energy or matter and is directed by the information it contains. When someone says, “he has good energy” it’s their experience of that person’s Qi , as energy, that they are talking about.", "subpage_snippet": "", "source": "renxueamericas.org", "link": "https://renxueamericas.org/what-is-qi/", "content": "Feb 2, 2025 · What is Qi ? Qi is the Animating Force and Material of the Universe. It can be particle, wave, energy or matter and is directed by the information it contains. When someone says, “he has good energy” it’s their experience of that person’s Qi , as energy, that they are talking about."}
+{"idx": 7, "title": "qi noun - Definition, pictures, pronunciation and usage notes ...", "date": "", "ddg_snippet": "Definition of qi noun in Oxford Advanced Learner's Dictionary. Meaning, pronunciation, picture, example sentences, grammar, usage notes, synonyms and more.", "subpage_snippet": "", "source": "www.oxfordlearnersdictionaries.com", "link": "https://www.oxfordlearnersdictionaries.com/definition/english/qi", "content": "Definition of qi noun in Oxford Advanced Learner's Dictionary. Meaning, pronunciation, picture, example sentences, grammar, usage notes, synonyms and more."}
+{"idx": 8, "title": "Qi - New World Encyclopedia", "date": "", "ddg_snippet": "Qi, also commonly spelled ch'i (in Wade-Giles romanization) or ki (in romanized Japanese), is a fundamental concept of traditional Chinese culture . Qi is believed to be part of everything that exists, as a “life force” or “spiritual energy” that pervades the natural world.", "subpage_snippet": "", "source": "www.newworldencyclopedia.org", "link": "https://www.newworldencyclopedia.org/entry/Qi", "content": "Qi, also commonly spelled ch'i (in Wade-Giles romanization) or ki (in romanized Japanese), is a fundamental concept of traditional Chinese culture . Qi is believed to be part of everything that exists, as a “life force” or “spiritual energy” that pervades the natural world."}
+{"idx": 9, "title": "The Basics of Qi - HowStuffWorks", "date": "", "ddg_snippet": "It is the fundamental power underlying all the activities of nature as well as the vital life force of the human body. For example, the force of a thunderstorm can be understood in terms of its qi : The power of qi can be observed in the fallen trees and buildings in the storm's aftermath.", "subpage_snippet": "", "source": "health.howstuffworks.com", "link": "https://health.howstuffworks.com/wellness/natural-medicine/chinese/qi.htm", "content": "It is the fundamental power underlying all the activities of nature as well as the vital life force of the human body. For example, the force of a thunderstorm can be understood in terms of its qi : The power of qi can be observed in the fallen trees and buildings in the storm's aftermath."}
diff --git a/data/sampled_jsons/Qixin_Zhang_Zongqi_Wan_multi-agent_submodular_coordination_target_tracking_adversarial.jsonl b/data/sampled_jsons/Qixin_Zhang_Zongqi_Wan_multi-agent_submodular_coordination_target_tracking_adversarial.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..2f81b484791a72273beb998ffd26cba8b21677bc
--- /dev/null
+++ b/data/sampled_jsons/Qixin_Zhang_Zongqi_Wan_multi-agent_submodular_coordination_target_tracking_adversarial.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Near-Optimal Online Learning for Multi-Agent Submodular Coordination ...", "date": "", "ddg_snippet": "Near-Optimal Online Learning for Multi-Agent Submodular Coordination : Tight Approximation and Communication Efficiency Qixin Zhang , Zongqi Wan , Yu Yang, Li Shen, Dacheng Tao", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2502.05028", "content": "Near-Optimal Online Learning for Multi-Agent Submodular Coordination : Tight Approximation and Communication Efficiency Qixin Zhang , Zongqi Wan , Yu Yang, Li Shen, Dacheng Tao"}
+{"idx": 1, "title": "N -o Online Learning for Multi a S Coordination: Tight Ap Proximation ...", "date": "", "ddg_snippet": "1 INTRODUCTION Recent years have witnessed an upsurge in research focused on leveraging submodular functions to coordinate the actions of multiple agents in accomplishing tasks that are spatially distributed. A compelling example is the dynamic deployment of mobile sensors, particularly unmanned aerial vehicles (UAVs), for multi - target tracking (Zhou et al., 2018; Corah & Michael, 2021) as ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=i8dYPGdB1C", "content": "1 INTRODUCTION Recent years have witnessed an upsurge in research focused on leveraging submodular functions to coordinate the actions of multiple agents in accomplishing tasks that are spatially distributed. A compelling example is the dynamic deployment of mobile sensors, particularly unmanned aerial vehicles (UAVs), for multi - target tracking (Zhou et al., 2018; Corah & Michael, 2021) as ..."}
+{"idx": 2, "title": "Zongqi Wan", "date": "", "ddg_snippet": "(Under the supervision of Prof. Jialin Zhang and Prof. Xiaoming Sun.) Before that, I received a B.Sc. from the Hua Loo-Keng class at Shandong University, majoring in applied mathematics. I am interested in several directions of theoretical computer science and machine learning, including bandit theory, submodular maximization, and mechanism design.", "subpage_snippet": "", "source": "zongqiwan.com", "link": "https://zongqiwan.com/", "content": "(Under the supervision of Prof. Jialin Zhang and Prof. Xiaoming Sun.) Before that, I received a B.Sc. from the Hua Loo-Keng class at Shandong University, majoring in applied mathematics. I am interested in several directions of theoretical computer science and machine learning, including bandit theory, submodular maximization, and mechanism design."}
+{"idx": 3, "title": "Near-Optimal Online Learning for Multi-Agent Submodular Coordination ...", "date": "", "ddg_snippet": "Authors Qixin ZHANG , Zongqi Wan , Yu Yang, Li Shen, Dacheng Tao Abstract Coordinating multiple agents to collaboratively maximize submodular functions in unpredictable environments is a critical task with numerous applications in machine learning, robot planning and control. The existing approaches, such as the OSG algorithm, are often hindered by their poor approximation guarantees and the ...", "subpage_snippet": "", "source": "proceedings.iclr.cc", "link": "https://proceedings.iclr.cc/paper_files/paper/2025/hash/3340ee1e4a8bad8d32c35721712b4d0a-Abstract-Conference.html", "content": "Authors Qixin ZHANG , Zongqi Wan , Yu Yang, Li Shen, Dacheng Tao Abstract Coordinating multiple agents to collaboratively maximize submodular functions in unpredictable environments is a critical task with numerous applications in machine learning, robot planning and control. The existing approaches, such as the OSG algorithm, are often hindered by their poor approximation guarantees and the ..."}
+{"idx": 4, "title": "NeurIPS Poster Effective Policy Learning for Multi-Agent Online ...", "date": "", "ddg_snippet": "Abstract: In this paper, we present two effective policy learning algorithms for multi-agent online coordination (MA-OC) problem. The first one, **MA-SPL**, not only can achieve the optimal (1 − c e) -approximation guarantee for the MA-OC problem with submodular objectives but also can handle the unexplored α -weakly DR- submodular and (γ, β) -weakly submodular scenarios, where c is the ...", "subpage_snippet": "", "source": "neurips.cc", "link": "https://neurips.cc/virtual/2025/poster/116516", "content": "Abstract: In this paper, we present two effective policy learning algorithms for multi-agent online coordination (MA-OC) problem. The first one, **MA-SPL**, not only can achieve the optimal (1 − c e) -approximation guarantee for the MA-OC problem with submodular objectives but also can handle the unexplored α -weakly DR- submodular and (γ, β) -weakly submodular scenarios, where c is the ..."}
+{"idx": 5, "title": "Research - zongqiwan.com", "date": "", "ddg_snippet": "Near-Optimal Online Learning for Multi-Agent Submodular Coordination : Tight Approximation and Communication Efficiency Qixin Zhang , Zongqi Wan , Yu Yang, Li Shen, Dacheng Tao", "subpage_snippet": "", "source": "zongqiwan.com", "link": "https://zongqiwan.com/research.html", "content": "Near-Optimal Online Learning for Multi-Agent Submodular Coordination : Tight Approximation and Communication Efficiency Qixin Zhang , Zongqi Wan , Yu Yang, Li Shen, Dacheng Tao"}
+{"idx": 6, "title": "Qixin ZHANG · Zongqi Wan · Yu Yang · Li Shen · Dacheng Tao", "date": "", "ddg_snippet": "Abstract: Coordinating multiple agents to collaboratively maximize submodular functions in unpredictable environments is a critical task with numerous applications in machine learning, robot planning and control. The existing approaches, such as the OSG algorithm, are often hindered by their poor approximation guarantees and the rigid requirement for a fully connected communication graph. To ...", "subpage_snippet": "", "source": "iclr.cc", "link": "https://iclr.cc/virtual/2025/poster/28714", "content": "Abstract: Coordinating multiple agents to collaboratively maximize submodular functions in unpredictable environments is a critical task with numerous applications in machine learning, robot planning and control. The existing approaches, such as the OSG algorithm, are often hindered by their poor approximation guarantees and the rigid requirement for a fully connected communication graph. To ..."}
+{"idx": 7, "title": "Zongqi Wan - OpenReview", "date": "", "ddg_snippet": "Near-Optimal Online Learning for Multi-Agent Submodular Coordination : Tight Approximation and Communication Efficiency Qixin Zhang , Zongqi Wan , Yu Yang, Li Shen, Dacheng Tao Published: 31 Dec 2024, Last Modified: 12 Aug 2025 CoRR 2025", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/profile?id=~Zongqi_Wan1", "content": "Near-Optimal Online Learning for Multi-Agent Submodular Coordination : Tight Approximation and Communication Efficiency Qixin Zhang , Zongqi Wan , Yu Yang, Li Shen, Dacheng Tao Published: 31 Dec 2024, Last Modified: 12 Aug 2025 CoRR 2025"}
+{"idx": 8, "title": "Near-optimal Online Learning for Multi-agent Submodular Coordination ...", "date": "", "ddg_snippet": "This result significantly improves the (1/1+ c)-approximation provided by the state-of-the-art OSG algorithm. Finally, we demonstrate the effectiveness of our proposed algorithms through simulation-based multi - target tracking .", "subpage_snippet": "", "source": "scholars.cityu.edu.hk", "link": "https://scholars.cityu.edu.hk/en/publications/near-optimal-online-learning-for-multi-agent-submodular-coordinat", "content": "This result significantly improves the (1/1+ c)-approximation provided by the state-of-the-art OSG algorithm. Finally, we demonstrate the effectiveness of our proposed algorithms through simulation-based multi - target tracking ."}
+{"idx": 9, "title": "[2502.05028] Near-Optimal Online Learning for Multi-Agent Submodular ...", "date": "", "ddg_snippet": "This result significantly improves the $ (\\frac {1} {1+c})$-approximation provided by the state-of-the-art OSG algorithm. Finally, we demonstrate the effectiveness of our proposed algorithms through simulation-based multi - target tracking .", "subpage_snippet": "", "source": "export.arxiv.org", "link": "http://export.arxiv.org/abs/2502.05028", "content": "This result significantly improves the $ (\\frac {1} {1+c})$-approximation provided by the state-of-the-art OSG algorithm. Finally, we demonstrate the effectiveness of our proposed algorithms through simulation-based multi - target tracking ."}
diff --git a/data/sampled_jsons/QvqnPVGWAN_Blink_of_an_eye_simple_theory_feature_localization_generative_models_github_year_2024.jsonl b/data/sampled_jsons/QvqnPVGWAN_Blink_of_an_eye_simple_theory_feature_localization_generative_models_github_year_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..019ce3f9fdf98463e06342ee16faf6999c530c40
--- /dev/null
+++ b/data/sampled_jsons/QvqnPVGWAN_Blink_of_an_eye_simple_theory_feature_localization_generative_models_github_year_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "ICML Poster Blink of an eye : a simple theory for feature localization ...", "date": "", "ddg_snippet": "This phenomenon is not unique to autoregressive models : in diffusion models , key features of the final output are decided in narrow “critical windows” of the generation process. In this work we develop a simple , unifying theory to explain this phenomenon.", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/poster/45312", "content": "This phenomenon is not unique to autoregressive models : in diffusion models , key features of the final output are decided in narrow “critical windows” of the generation process. In this work we develop a simple , unifying theory to explain this phenomenon."}
+{"idx": 1, "title": "Blink of an eye : a simple theory for feature localization in...", "date": "", "ddg_snippet": "Introduces \" blink of an eye \" theory to explain rapid feature emergence. Shows features appear in critical time windows during model training. Provides mathematical framework for understanding feature localization . Demonstrates theory works across different model architectures.", "subpage_snippet": "", "source": "www.aimodels.fyi", "link": "https://www.aimodels.fyi/papers/arxiv/blink-eye-simple-theory-feature-localization-generative", "content": "Introduces \" blink of an eye \" theory to explain rapid feature emergence. Shows features appear in critical time windows during model training. Provides mathematical framework for understanding feature localization . Demonstrates theory works across different model architectures."}
+{"idx": 2, "title": "In the Blink of an Eye - Walter Murch's Editing Theory ... - YouTube", "date": "", "ddg_snippet": "Murch’s theory of assigning the human blink as emotional punctuation is a great editing technique to keep in the back of your mind as your deciding where to ...", "subpage_snippet": "", "source": "www.youtube.com", "link": "https://www.youtube.com/watch?v=0_rHsWleVmw", "content": "Murch’s theory of assigning the human blink as emotional punctuation is a great editing technique to keep in the back of your mind as your deciding where to ..."}
+{"idx": 3, "title": "GitHub - vshal-47/Modern-Local-LLM-UI-For-Ollama: Modern Local...", "date": "", "ddg_snippet": "A simple , cross-platform, and self-contained web UI for interacting with local Ollama models . Built with Go.Automatically detects your installed Ollama models . Saves chat history in your browser. Allows deleting and switching between conversations. Stop generation mid-response.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/vshal-47/Modern-Local-LLM-UI-For-Ollama", "content": "A simple , cross-platform, and self-contained web UI for interacting with local Ollama models . Built with Go.Automatically detects your installed Ollama models . Saves chat history in your browser. Allows deleting and switching between conversations. Stop generation mid-response."}
+{"idx": 4, "title": "Flutter gen-l10n localization . Can't import 'package... - Stack Overfl...", "date": "", "ddg_snippet": "I am writing flutter application with localization support. It worked locally, I generated code using flutter gen-l10n and was able to use import 'package:flutter_gen/l10n/app_ localizations .dart'; . Then I decided to automate code compilation using github actions...", "subpage_snippet": "", "source": "stackoverflow.com", "link": "https://stackoverflow.com/questions/76905937/flutter-gen-l10n-localization-cant-import-packageflutter-gen-l10n-app-locali", "content": "I am writing flutter application with localization support. It worked locally, I generated code using flutter gen-l10n and was able to use import 'package:flutter_gen/l10n/app_ localizations .dart'; . Then I decided to automate code compilation using github actions..."}
+{"idx": 5, "title": "Website translation and localization tools | Webflow", "date": "", "ddg_snippet": "“ Localization allows us to ship localized sites 4X faster, save on dev costs, and create custom experiences that will significantly boost engagement in our target markets.”", "subpage_snippet": "", "source": "webflow.com", "link": "https://webflow.com/feature/localization", "content": "“ Localization allows us to ship localized sites 4X faster, save on dev costs, and create custom experiences that will significantly boost engagement in our target markets.”"}
+{"idx": 6, "title": "Face Swap Online Free", "date": "", "ddg_snippet": "Want to generate face swap images in batches? Batch Swap Now.Face Swap is a technology or feature that allows users to interchange faces in photos or videos, often achieved through the use of specialized software or applications.", "subpage_snippet": "", "source": "remaker.ai", "link": "https://remaker.ai/face-swap-free/", "content": "Want to generate face swap images in batches? Batch Swap Now.Face Swap is a technology or feature that allows users to interchange faces in photos or videos, often achieved through the use of specialized software or applications."}
+{"idx": 7, "title": "An open platform for evaluating AI through human preference", "date": "", "ddg_snippet": "Find the best AI for you. Compare answers across top AI models , share your feedback and power our public leaderboard. Inputs are processed by third-party AI and responses may be inaccurate.", "subpage_snippet": "", "source": "lmarena.ai", "link": "https://lmarena.ai/", "content": "Find the best AI for you. Compare answers across top AI models , share your feedback and power our public leaderboard. Inputs are processed by third-party AI and responses may be inaccurate."}
+{"idx": 8, "title": "Gemini 2.5 Flash Image Generator – Google NanoBanana Free Online", "date": "", "ddg_snippet": "Simply describe the changes you want, and the model adjusts step by step until you’re satisfied. Realistic Outputs with Gemini Banana Model .Discover the consistent character generation and powerful editing features that make Nano Banana AI a reliable image generator and editor.", "subpage_snippet": "", "source": "nanobananaart.ai", "link": "https://nanobananaart.ai/", "content": "Simply describe the changes you want, and the model adjusts step by step until you’re satisfied. Realistic Outputs with Gemini Banana Model .Discover the consistent character generation and powerful editing features that make Nano Banana AI a reliable image generator and editor."}
+{"idx": 9, "title": "AI Clothes Remover Online (Free, No Sign-up)", "date": "", "ddg_snippet": "Myimg AI Clothes Remover focuses on photo undressing. With a uniquely trained DeepNude AI model , it removes clothing from images instantly and effortlessly. One-Click, Prompt-Free. Say goodbye to text prompts and endless retries.", "subpage_snippet": "", "source": "www.myimg.ai", "link": "https://www.myimg.ai/ai-clothes-remover", "content": "Myimg AI Clothes Remover focuses on photo undressing. With a uniquely trained DeepNude AI model , it removes clothing from images instantly and effortlessly. One-Click, Prompt-Free. Say goodbye to text prompts and endless retries."}
diff --git a/data/sampled_jsons/R2GenGPT_BLEU-4_0.124_METransformer_comparison.jsonl b/data/sampled_jsons/R2GenGPT_BLEU-4_0.124_METransformer_comparison.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..e5a31c93342ecd509262a01e64323376ebc54b27
--- /dev/null
+++ b/data/sampled_jsons/R2GenGPT_BLEU-4_0.124_METransformer_comparison.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "R2GenGPT: Radiology Report Generation with frozen LLMs", "date": "", "ddg_snippet": "by Z Wang · 2023 · Cited by 132 — We propose R2GenGPT , which is a novel solution that aligns visual features with the word embedding space of LLMs using an efficient visual alignment module.", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S2950162823000334", "content": "by Z Wang · 2023 · Cited by 132 — We propose R2GenGPT , which is a novel solution that aligns visual features with the word embedding space of LLMs using an efficient visual alignment module."}
+{"idx": 1, "title": "KARGEN: Knowledge-enhanced Automated Radiology ...", "date": "", "ddg_snippet": "9 Sept 2024 — On MIMIC-CXR, our BLEU - 4 score sees a noteworthy improvement of 4.5%, rising from 0.134 to 0.140. Although our CIDEr score of 0.289 is lower ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2409.05370v1", "content": "9 Sept 2024 — On MIMIC-CXR, our BLEU - 4 score sees a noteworthy improvement of 4.5%, rising from 0.134 to 0.140. Although our CIDEr score of 0.289 is lower ..."}
+{"idx": 2, "title": "Pre-training and Benchmarking for X-ray Medical Report ...", "date": "", "ddg_snippet": "by X Wang — Specifically, our method im- proves the BLEU - 4 metric by 6% compared to R2GenGPT . ... fall short in performance compared to Transformer-based models on complex ...", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2025/supplemental/Wang_CXPMRG-Bench_Pre-training_and_CVPR_2025_supplemental.pdf", "content": "by X Wang — Specifically, our method im- proves the BLEU - 4 metric by 6% compared to R2GenGPT . ... fall short in performance compared to Transformer-based models on complex ..."}
+{"idx": 3, "title": "CmEAA: Cross-modal Enhancement and Alignment ...", "date": "", "ddg_snippet": "by X Huang · 2025 · Cited by 2 — Specifi- cally, in comparison to (a), (d) achieves an average improvement of 4.9% across the BLEU -1, BLEU -. 4 , and ROUGE-L metrics, ... 11 pages", "subpage_snippet": "", "source": "aclanthology.org", "link": "https://aclanthology.org/2025.coling-main.571.pdf", "content": "by X Huang · 2025 · Cited by 2 — Specifi- cally, in comparison to (a), (d) achieves an average improvement of 4.9% across the BLEU -1, BLEU -. 4 , and ROUGE-L metrics, ... 11 pages"}
+{"idx": 4, "title": "Multimodal Dual-Path Large-Model Decoding for Radiology ...", "date": "", "ddg_snippet": "Precision Recall F1 Score BLEU-1 BLEU - 4 METEOR ROUGE ... Table 1: Comparison with SOTA methods on the Findings section. ... ), R2GenGPT (Wang et al., 2023c), and.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf/33ca11883559f737b6fa25e6968d10dc4b36c126.pdf", "content": "Precision Recall F1 Score BLEU-1 BLEU - 4 METEOR ROUGE ... Table 1: Comparison with SOTA methods on the Findings section. ... ), R2GenGPT (Wang et al., 2023c), and."}
+{"idx": 5, "title": "BIDIRECTIONAL LEARNING FOR THE VISUAL REP", "date": "", "ddg_snippet": "by Z Chen — BLEU-1 increases from 0.419 (EKAGen) to 0.427, BLEU-2 from 0.267. ( R2GenGPT ) to 0.285, BLEU-3 from 0.186 ( R2GenGPT ) to 0.202, and BLEU - 4 from 0.142. (MedM2G) ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=gZue5gHQHp", "content": "by Z Chen — BLEU-1 increases from 0.419 (EKAGen) to 0.427, BLEU-2 from 0.267. ( R2GenGPT ) to 0.285, BLEU-3 from 0.186 ( R2GenGPT ) to 0.202, and BLEU - 4 from 0.142. (MedM2G) ..."}
+{"idx": 6, "title": "Enhanced Contrastive Learning with Multi-view Longitudinal ...", "date": "", "ddg_snippet": "by K Liu · 2025 · Cited by 11 — We identify the best model as the one with the highest sum of BLEU - 4 , F1 RadGraph, and F1 score on the validation set, and we report its performance on the test ... 12 pages", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2025/papers/Liu_Enhanced_Contrastive_Learning_with_Multi-view_Longitudinal_Data_for_Chest_X-ray_CVPR_2025_paper.pdf", "content": "by K Liu · 2025 · Cited by 11 — We identify the best model as the one with the highest sum of BLEU - 4 , F1 RadGraph, and F1 score on the validation set, and we report its performance on the test ... 12 pages"}
+{"idx": 7, "title": "Radar: Enhancing Radiology Report Generation with ...", "date": "", "ddg_snippet": "20 May 2025 — Radar improves report generation by systematically leveraging both the internal knowledge of an LLM and externally retrieved information.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2505.14318v1", "content": "20 May 2025 — Radar improves report generation by systematically leveraging both the internal knowledge of an LLM and externally retrieved information."}
+{"idx": 8, "title": "Radiology report generation using automatic keyword ...", "date": "", "ddg_snippet": "by Z He · 2025 · Cited by 1 — We developed a novel deep-learning based radiology report generation method for preparing high-quality and explainable radiology report for chest X-ray images.", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S001048252500976X", "content": "by Z He · 2025 · Cited by 1 — We developed a novel deep-learning based radiology report generation method for preparing high-quality and explainable radiology report for chest X-ray images."}
+{"idx": 9, "title": "RADAR: Enhancing Radiology Report Generation with ...", "date": "", "ddg_snippet": "by W Hou · 2025 · Cited by 1 — By comparing BACKBONE and BACKBONE-V2, we find that fine-tuning the vision encoder to incor- porate domain-specific knowledge is crucial for. 16 pages", "subpage_snippet": "", "source": "aclanthology.org", "link": "https://aclanthology.org/2025.acl-long.1279.pdf", "content": "by W Hou · 2025 · Cited by 1 — By comparing BACKBONE and BACKBONE-V2, we find that fine-tuning the vision encoder to incor- porate domain-specific knowledge is crucial for. 16 pages"}
diff --git a/data/sampled_jsons/RAG_Stability_Score_RSS_formula_4ufjBV6S4I_RAGGED_paper.jsonl b/data/sampled_jsons/RAG_Stability_Score_RSS_formula_4ufjBV6S4I_RAGGED_paper.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..f164b0c8bbd6122f49d4779d215432e780079baa
--- /dev/null
+++ b/data/sampled_jsons/RAG_Stability_Score_RSS_formula_4ufjBV6S4I_RAGGED_paper.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Retrieval-augmented generation - Wikipedia", "date": "", "ddg_snippet": "Retrieval-augmented generation is a technique that enables large language models to retrieve and incorporate new information. With RAG , LLMs do not respond to user queries until they refer to a specified set of documents.", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/Retrieval-augmented_generation", "content": "Retrieval-augmented generation is a technique that enables large language models to retrieve and incorporate new information. With RAG , LLMs do not respond to user queries until they refer to a specified set of documents."}
+{"idx": 1, "title": "RAGGED : Towards Informed Design of Scalable and Stable RAG ...", "date": "", "ddg_snippet": "Retrieval-augmented generation ( RAG ) enhances language models by integrating external knowledge, but its effectiveness is highly dependent on system configuration. Improper retrieval settings can degrade performance, making RAG less reliable than closed-book generation.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=4ufjBV6S4I", "content": "Retrieval-augmented generation ( RAG ) enhances language models by integrating external knowledge, but its effectiveness is highly dependent on system configuration. Improper retrieval settings can degrade performance, making RAG less reliable than closed-book generation."}
+{"idx": 2, "title": "Мультимодальные RAG и VLM против OCR + LLM: Как откровенно...", "date": "", "ddg_snippet": "ОглавлениеМультимодальные RAG (MM- RAG )Пример применения мультимодальной RAG для PDF-файлов", "subpage_snippet": "", "source": "blogs.epsilonmetrics.ru", "link": "https://blogs.epsilonmetrics.ru/multimodalnaya-rag-i-vlm-vmesto-ocr-i-llm/", "content": "ОглавлениеМультимодальные RAG (MM- RAG )Пример применения мультимодальной RAG для PDF-файлов"}
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+{"idx": 5, "title": "ГДЗ по английскому языку 5 класс учебник Ваулина, Дули – стр 20", "date": "", "ddg_snippet": "1. one, two, three, four 2. one, four , seven, ten 3. ten, eight, six, four 4. two, four , six, eight. Номер 3. Write the number.", "subpage_snippet": "", "source": "gdz-raketa.ru", "link": "https://gdz-raketa.ru/english/5-klass/uchebnik-vaulina/stranitsa-20/", "content": "1. one, two, three, four 2. one, four , seven, ten 3. ten, eight, six, four 4. two, four , six, eight. Номер 3. Write the number."}
+{"idx": 6, "title": "Учебник Spotlight 6 класс. Страница 17 - ГДЗ Решебник по...", "date": "", "ddg_snippet": "− Послушай и повтори. January − январь February − февраль March − март April − апрель May − май June − июнь July − июль August...", "subpage_snippet": "", "source": "xn----btbeegalms2a3a1h.xn--p1ai", "link": "https://xn----btbeegalms2a3a1h.xn--p1ai/uchebnik-spotlight-6-klass-stranica-17/", "content": "− Послушай и повтори. January − январь February − февраль March − март April − апрель May − май June − июнь July − июль August..."}
+{"idx": 7, "title": "Упражнения на времена Present с ответами", "date": "", "ddg_snippet": "5 упражнений на тренировку времен группы презент в английском языке с ответами для продолжающих. Отработка Present Simple , Present Continuous, Present Perfect.", "subpage_snippet": "", "source": "EnglishWeb.ru", "link": "https://EnglishWeb.ru/grammar/present-tenses-exercises.html", "content": "5 упражнений на тренировку времен группы презент в английском языке с ответами для продолжающих. Отработка Present Simple , Present Continuous, Present Perfect."}
+{"idx": 8, "title": "Manus: General AI agent that bridges mind and action", "date": "", "ddg_snippet": "Manus is a general AI agent that turns your thoughts into actions. It excels at various tasks in work and life, getting everything done while you rest.", "subpage_snippet": "", "source": "manus.im", "link": "https://manus.im/", "content": "Manus is a general AI agent that turns your thoughts into actions. It excels at various tasks in work and life, getting everything done while you rest."}
+{"idx": 9, "title": "Властелин Колец Все Части: 1, 2, 3, 4, 5, 6 Смотреть Онлайн...", "date": "", "ddg_snippet": "Кинотрилогии «Властелин Колец» и «Хоббит» тесно связаны одной Вселенной и сюжетными событиями. В хронологическом порядке сперва идет история о хоббите по имени Бильбо Бэггинс, хотя фильмы этой трилогии вышли намного позже первых кинолент.", "subpage_snippet": "", "source": "lord-of-ringgs-lordfilm.ru", "link": "https://lord-of-ringgs-lordfilm.ru/", "content": "Кинотрилогии «Властелин Колец» и «Хоббит» тесно связаны одной Вселенной и сюжетными событиями. В хронологическом порядке сперва идет история о хоббите по имени Бильбо Бэггинс, хотя фильмы этой трилогии вышли намного позже первых кинолент."}
diff --git a/data/sampled_jsons/RL_on_Incorrect_Synthetic_Data_Scales_the_Efficiency_of_LLM_Math_Reasoning_algorithm_DPO.jsonl b/data/sampled_jsons/RL_on_Incorrect_Synthetic_Data_Scales_the_Efficiency_of_LLM_Math_Reasoning_algorithm_DPO.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..4b937a8e416ee73bd8d3b4441649ee9501a31094
--- /dev/null
+++ b/data/sampled_jsons/RL_on_Incorrect_Synthetic_Data_Scales_the_Efficiency_of_LLM_Math_Reasoning_algorithm_DPO.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "(PDF) RL on Incorrect Synthetic Data Scales the Efficiency of LLM ...", "date": "", "ddg_snippet": "negative synthetic data and how they are fed t o supervised finetuning (SFT; positive syntheti c data from more capable m odels), rejection finetuning (RFT ; positive self-generated synthetic data from the SFT m odel) and step-level RL (via per-step DPO ) algorithms .", "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": "negative synthetic data and how they are fed t o supervised finetuning (SFT; positive syntheti c data from more capable m odels), rejection finetuning (RFT ; positive self-generated synthetic data from the SFT m odel) and step-level RL (via per-step DPO ) algorithms ."}
+{"idx": 1, "title": "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": 2, "title": "RL on Incorrect Synthetic Data Scales the Efficiency of LLM ... RL on Incorrect Synthetic Data Scales the Efficiency of LLM ... RL on Incorrect Synthetic Data · MinWoo Park Reinforcement Learning for LLM Reasoning Images Learning to Reason by Failing: Offline RL on Sub-optimal ... RL on Incorrect Synthetic Data Scales the Efficiency of LLM ... Reinforcement Learning for LLM Reasoning [2406.14532] RL on Incorrect Synthetic Data Scales the Efficiency of LL… Reinforcement Learning for LLM Reasoning RL on Incorrect Synthetic Data Scales the Efficiency of LLM Math dblp: RL on Incorrect Synthetic Data Scales the Efficiency of ...", "date": "", "ddg_snippet": "Jun 20, 2024 · 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 ... Jun 20, 2024 · 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 ... Jun 20, 2024 · 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. 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 View all Jun 13, 2024 · This paper studies finetuning LLM with synthetic data for ( math ) reasoning problems. Specifically, this paper shows that using synthetic positive data from a fine-tuned learner has better sample complexity than using data from capable LLMs (e.g. ChatGPT). 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. Does training with RL improve efficiency of learning compared to LLMs? Real-world is stochastic, this is little data compared to LLMs! Takeaway: Training with RL can help improve efficiency of learning! Summary: Still the old recipes and RL ideas are helpful! RL on Incorrect Synthetic Data Scales the Efficiency of LLM Math Reasoning by Eight-Fold. Setlur, Garg, Geng, Garg, Smith, Kumar. NeurIPS 2024 Does training on model-generated synthetic data help or hurt math reasoning? 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. Is LLM test-time compute more effective than scaling model parameters? Snell et al. Scaling LLM Test-Time Compute Optimally can be more Effective than Scaling Model Parameters. ICLR 2025 (Oral). Snell et al. Scaling LLM Test-Time Compute Optimally can be more Effective than Scaling Model Parameters. ICLR 2025 (Oral). Can synthetic data address data scarcity? Thus while in principle, synthetic data could potentially address data scarcity , it must be designed in an appropriate manner to be eff ective. However, this has been hard due to a lack o f an understanding of how synthetic data contributes to LLM beha vior. Jul 16, 2024 · Bibliographic details on RL on Incorrect Synthetic Data Scales the Efficiency of LLM Math Reasoning by Eight-Fold.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2406.14532", "content": "Jun 20, 2024 · 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 ... Jun 20, 2024 · 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 ... Jun 20, 2024 · 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. 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 View all Jun 13, 2024 · This paper studies finetuning LLM with synthetic data for ( math ) reasoning problems. Specifically, this paper shows that using synthetic positive data from a fine-tuned learner has better sample complexity than using data from capable LLMs (e.g. ChatGPT). 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. Does training with RL improve efficiency of learning compared to LLMs? Real-world is stochastic, this is little data compared to LLMs! Takeaway: Training with RL can help improve efficiency of learning! Summary: Still the old recipes and RL ideas are helpful! RL on Incorrect Synthetic Data Scales the Efficiency of LLM Math Reasoning by Eight-Fold. Setlur, Garg, Geng, Garg, Smith, Kumar. NeurIPS 2024 Does training on model-generated synthetic data help or hurt math reasoning? 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. Is LLM test-time compute more effective than scaling model parameters? Snell et al. Scaling LLM Test-Time Compute Optimally can be more Effective than Scaling Model Parameters. ICLR 2025 (Oral). Snell et al. Scaling LLM Test-Time Compute Optimally can be more Effective than Scaling Model Parameters. ICLR 2025 (Oral). Can synthetic data address data scarcity? Thus while in principle, synthetic data could potentially address data scarcity , it must be designed in an appropriate manner to be eff ective. However, this has been hard due to a lack o f an understanding of how synthetic data contributes to LLM beha vior. Jul 16, 2024 · Bibliographic details on RL on Incorrect Synthetic Data Scales the Efficiency of LLM Math Reasoning by Eight-Fold."}
+{"idx": 3, "title": "RL on Incorrect Synthetic Data · MinWoo Park", "date": "", "ddg_snippet": "Jun 20, 2024 · 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": "dsdanielpark.github.io", "link": "https://dsdanielpark.github.io/llm/2024-06-25-RLonIncorrectSyntheticData.html", "content": "Jun 20, 2024 · 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": 4, "title": "RL on Incorrect Synthetic Data Scales the Efficiency of LLM ...", "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": "dblp: RL on Incorrect Synthetic Data Scales the Efficiency of ...", "date": "", "ddg_snippet": "Jul 16, 2024 · Bibliographic details on RL on Incorrect Synthetic Data Scales the Efficiency of LLM Math Reasoning by Eight-Fold.", "subpage_snippet": "", "source": "dblp.org", "link": "https://dblp.org/rec/journals/corr/abs-2406-14532", "content": "Jul 16, 2024 · Bibliographic details on RL on Incorrect Synthetic Data Scales the Efficiency of LLM Math Reasoning by Eight-Fold."}
+{"idx": 6, "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.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2406.14532v1", "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."}
+{"idx": 7, "title": "RL on Incorrect Synthetic Data Scales", "date": "", "ddg_snippet": "Negative Data Identifies Spurious Steps with Advantage Estimates. RL on Incorrect Synthetic Data Scales the Efficiency of LLM Math Reasoning by Eight-Fold.", "subpage_snippet": "", "source": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper_files/paper/2024/file/4b77d5b896c321a29277524a98a50215-Paper-Conference.pdf", "content": "Negative Data Identifies Spurious Steps with Advantage Estimates. RL on Incorrect Synthetic Data Scales the Efficiency of LLM Math Reasoning by Eight-Fold."}
+{"idx": 8, "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.", "subpage_snippet": "", "source": "papers.nips.cc", "link": "https://papers.nips.cc/paper_files/paper/2024/hash/4b77d5b896c321a29277524a98a50215-Abstract-Conference.html", "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."}
+{"idx": 9, "title": "RL on Incorrect Synthetic Data Scales the Efficiency of LLM Math ...", "date": "", "ddg_snippet": "The study investigates the impact of synthetic data , both correct and incorrect , on the fine-tuning of LLMs for enhanced math reasoning using supervised fine-tuning (SFT) and reinforcement learning ( RL ).", "subpage_snippet": "", "source": "www.emergentmind.com", "link": "https://www.emergentmind.com/papers/2406.14532", "content": "The study investigates the impact of synthetic data , both correct and incorrect , on the fine-tuning of LLMs for enhanced math reasoning using supervised fine-tuning (SFT) and reinforcement learning ( RL )."}
diff --git a/data/sampled_jsons/RL_on_Incorrect_Synthetic_Data_Scales_the_Efficiency_of_LLM_Math_Reasoning_by_Eight-Fold.jsonl b/data/sampled_jsons/RL_on_Incorrect_Synthetic_Data_Scales_the_Efficiency_of_LLM_Math_Reasoning_by_Eight-Fold.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..9b52d3cc1fda57caf3950361501713735a7759a0
--- /dev/null
+++ b/data/sampled_jsons/RL_on_Incorrect_Synthetic_Data_Scales_the_Efficiency_of_LLM_Math_Reasoning_by_Eight-Fold.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "RL on Incorrect Synthetic Data Scales the Efficiency of LLM ... RL on Incorrect Synthetic Data Scales the Efficiency of LLM ... RL on Incorrect Synthetic Data Scales the Efficiency of LLM ... Reinforcement Learning for LLM Reasoning Images Achieving 8× Performance Gains with Reinforcement Learning on ... dblp: RL on Incorrect Synthetic Data Scales the Efficiency of ... Reinforcement Learning for LLM Reasoning [2406.14532] RL on Incorrect Synthetic Data Scales the Efficiency of … Reinforcement Learning for LLM Reasoning scaling-LLM-math-synthetic-data/README.md at master - GitHub", "date": "", "ddg_snippet": "Jun 20, 2024 · 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. Jun 20, 2024 · 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 ... Sep 26, 2024 · Current methods for training LLMs often rely on finetuning with synthetic data generated by powerful models. However, simply using positive examples can lead to overfitting and poor generalization. This paper investigates the use of synthetic data for enhancing LLM math reasoning capabilities. Takeaway: Training with RL can help improve efficiency of learning! Summary: Still the old recipes and RL ideas are helpful! RL on Incorrect Synthetic Data Scales the Efficiency of LLM Math Reasoning by Eight - Fold . Setlur, Garg, Geng, Garg, Smith, Kumar. NeurIPS 2024. Setlur, Nagpal, Fisch, Geng, Eisenstein, R. Agarwal, A. Agarwal, Berant, Kumar. View all Jul 1, 2024 · In a new paper RL on Incorrect Synthetic Data Scales the Efficiency of LLM Math Reasoning by Eight - Fold , a research team from Carnegie Mellon University, Google DeepMind and MultiOn provides insights into how synthetic data affects performance. Jul 16, 2024 · Bibliographic details on RL on Incorrect Synthetic Data Scales the Efficiency of LLM Math Reasoning by Eight - Fold . Does training with RL improve efficiency of learning compared to LLMs? Real-world is stochastic, this is little data compared to LLMs! Takeaway: Training with RL can help improve efficiency of learning! Summary: Still the old recipes and RL ideas are helpful! RL on Incorrect Synthetic Data Scales the Efficiency of LLM Math Reasoning by Eight-Fold. Setlur, Garg, Geng, Garg, Smith, Kumar. NeurIPS 2024 Does training on model-generated synthetic data help or hurt math reasoning? 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. Is LLM test-time compute more effective than scaling model parameters? Snell et al. Scaling LLM Test-Time Compute Optimally can be more Effective than Scaling Model Parameters. ICLR 2025 (Oral). Snell et al. Scaling LLM Test-Time Compute Optimally can be more Effective than Scaling Model Parameters. ICLR 2025 (Oral). Code and data used in the paper: \"Training on Incorrect Synthetic Data via RL Scales LLM Math Reasoning Eight - Fold \"", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2406.14532", "content": "Jun 20, 2024 · 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. Jun 20, 2024 · 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 ... Sep 26, 2024 · Current methods for training LLMs often rely on finetuning with synthetic data generated by powerful models. However, simply using positive examples can lead to overfitting and poor generalization. This paper investigates the use of synthetic data for enhancing LLM math reasoning capabilities. Takeaway: Training with RL can help improve efficiency of learning! Summary: Still the old recipes and RL ideas are helpful! RL on Incorrect Synthetic Data Scales the Efficiency of LLM Math Reasoning by Eight - Fold . Setlur, Garg, Geng, Garg, Smith, Kumar. NeurIPS 2024. Setlur, Nagpal, Fisch, Geng, Eisenstein, R. Agarwal, A. Agarwal, Berant, Kumar. View all Jul 1, 2024 · In a new paper RL on Incorrect Synthetic Data Scales the Efficiency of LLM Math Reasoning by Eight - Fold , a research team from Carnegie Mellon University, Google DeepMind and MultiOn provides insights into how synthetic data affects performance. Jul 16, 2024 · Bibliographic details on RL on Incorrect Synthetic Data Scales the Efficiency of LLM Math Reasoning by Eight - Fold . Does training with RL improve efficiency of learning compared to LLMs? Real-world is stochastic, this is little data compared to LLMs! Takeaway: Training with RL can help improve efficiency of learning! Summary: Still the old recipes and RL ideas are helpful! RL on Incorrect Synthetic Data Scales the Efficiency of LLM Math Reasoning by Eight-Fold. Setlur, Garg, Geng, Garg, Smith, Kumar. NeurIPS 2024 Does training on model-generated synthetic data help or hurt math reasoning? 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. Is LLM test-time compute more effective than scaling model parameters? Snell et al. Scaling LLM Test-Time Compute Optimally can be more Effective than Scaling Model Parameters. ICLR 2025 (Oral). Snell et al. Scaling LLM Test-Time Compute Optimally can be more Effective than Scaling Model Parameters. ICLR 2025 (Oral). Code and data used in the paper: \"Training on Incorrect Synthetic Data via RL Scales LLM Math Reasoning Eight - Fold \""}
+{"idx": 1, "title": "RL on Incorrect Synthetic Data Scales the Efficiency of LLM ...", "date": "", "ddg_snippet": "Jun 20, 2024 · 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 ...", "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": "Jun 20, 2024 · 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 ..."}
+{"idx": 2, "title": "RL on Incorrect Synthetic Data Scales the Efficiency of LLM ...", "date": "", "ddg_snippet": "Sep 26, 2024 · Current methods for training LLMs often rely on finetuning with synthetic data generated by powerful models. However, simply using positive examples can lead to overfitting and poor generalization. This paper investigates the use of synthetic data for enhancing LLM math reasoning capabilities.", "subpage_snippet": "", "source": "deep-diver.github.io", "link": "https://deep-diver.github.io/neurips2024/posters/9m87e9keq1/", "content": "Sep 26, 2024 · Current methods for training LLMs often rely on finetuning with synthetic data generated by powerful models. However, simply using positive examples can lead to overfitting and poor generalization. This paper investigates the use of synthetic data for enhancing LLM math reasoning capabilities."}
+{"idx": 3, "title": "Reinforcement Learning for LLM Reasoning", "date": "", "ddg_snippet": "Takeaway: Training with RL can help improve efficiency of learning! Summary: Still the old recipes and RL ideas are helpful! RL on Incorrect Synthetic Data Scales the Efficiency of LLM Math Reasoning by Eight - Fold . Setlur, Garg, Geng, Garg, Smith, Kumar. NeurIPS 2024. Setlur, Nagpal, Fisch, Geng, Eisenstein, R. Agarwal, A. Agarwal, Berant, Kumar.", "subpage_snippet": "", "source": "cs224r.stanford.edu", "link": "https://cs224r.stanford.edu/slides/10_cs224r-rl_for_reasoning_lecture.pdf", "content": "Takeaway: Training with RL can help improve efficiency of learning! Summary: Still the old recipes and RL ideas are helpful! RL on Incorrect Synthetic Data Scales the Efficiency of LLM Math Reasoning by Eight - Fold . Setlur, Garg, Geng, Garg, Smith, Kumar. NeurIPS 2024. Setlur, Nagpal, Fisch, Geng, Eisenstein, R. Agarwal, A. Agarwal, Berant, Kumar."}
+{"idx": 4, "title": "dblp: RL on Incorrect Synthetic Data Scales the Efficiency of ...", "date": "", "ddg_snippet": "Jul 16, 2024 · Bibliographic details on RL on Incorrect Synthetic Data Scales the Efficiency of LLM Math Reasoning by Eight - Fold .", "subpage_snippet": "", "source": "dblp.org", "link": "https://dblp.org/rec/journals/corr/abs-2406-14532", "content": "Jul 16, 2024 · Bibliographic details on RL on Incorrect Synthetic Data Scales the Efficiency of LLM Math Reasoning by Eight - Fold ."}
+{"idx": 5, "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": 6, "title": "ExPO: Unlocking Hard Reasoning with Self-Explanation-Guided", "date": "", "ddg_snippet": "... LLMs ) have significantly advanced the frontier of artificial general intelligence, with models demonstrating increasingly strong performance on ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2507.02834v1", "content": "... LLMs ) have significantly advanced the frontier of artificial general intelligence, with models demonstrating increasingly strong performance on ..."}
+{"idx": 7, "title": "synthesis data | Synced", "date": "", "ddg_snippet": "In a new paper RL on Incorrect Synthetic Data Scales the Efficiency of LLM Math Reasoning by Eight - Fold , a research team provides insights into how ...", "subpage_snippet": "", "source": "syncedreview.com", "link": "https://syncedreview.com/tag/synthesis-data/", "content": "In a new paper RL on Incorrect Synthetic Data Scales the Efficiency of LLM Math Reasoning by Eight - Fold , a research team provides insights into how ..."}
+{"idx": 8, "title": "Achieving 8× Performance Gains with Reinforcement Learning on ...", "date": "", "ddg_snippet": "Jul 1, 2024 · In a new paper RL on Incorrect Synthetic Data Scales the Efficiency of LLM Math Reasoning by Eight - Fold , a research team from Carnegie Mellon University, Google DeepMind and MultiOn provides insights into how synthetic data affects performance.", "subpage_snippet": "", "source": "syncedreview.com", "link": "https://syncedreview.com/2024/07/01/achieving-8x-performance-gains-with-reinforcement-learning-on-synthetic-data-in-large-language-models/", "content": "Jul 1, 2024 · In a new paper RL on Incorrect Synthetic Data Scales the Efficiency of LLM Math Reasoning by Eight - Fold , a research team from Carnegie Mellon University, Google DeepMind and MultiOn provides insights into how synthetic data affects performance."}
+{"idx": 9, "title": "Improving Value-based Process Verifier via Low-Cost Variance", "date": "", "ddg_snippet": "... ComMCS outperforms regression-based optimization method by 2. 8 points, the non- variance-reduced baseline by 2.2 points on MATH -500 on Best- of -32 ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2508.10539v1", "content": "... ComMCS outperforms regression-based optimization method by 2. 8 points, the non- variance-reduced baseline by 2.2 points on MATH -500 on Best- of -32 ..."}
diff --git a/data/sampled_jsons/RL_on_Incorrect_Synthetic_Data_Scales_the_Efficiency_of_LLM_Math_Reasoning_by_Eight-Fold_Figure_2_pe.jsonl b/data/sampled_jsons/RL_on_Incorrect_Synthetic_Data_Scales_the_Efficiency_of_LLM_Math_Reasoning_by_Eight-Fold_Figure_2_pe.jsonl
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index 0000000000000000000000000000000000000000..33702ae3f407e72a197db114e1567560b0cb3917
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@@ -0,0 +1,10 @@
+{"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": "arxiv.org", "link": "https://arxiv.org/html/2406.14532v1", "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": "RL on Incorrect Synthetic Data Scales the Efficiency of LLM Math ...", "date": "", "ddg_snippet": "First, we find that while the typical approach of finetuning a model on synthetic correct or positive problem-solution pairs generated by capable models offers modest performance gains, sampling more correct solutions from the finetuned learner itself followed by subsequent fine-tuning on this self-generated data doubles the efficiency of the ...", "subpage_snippet": "", "source": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper_files/paper/2024/hash/4b77d5b896c321a29277524a98a50215-Abstract-Conference.html", "content": "First, we find that while the typical approach of finetuning a model on synthetic correct or positive problem-solution pairs generated by capable models offers modest performance gains, sampling more correct solutions from the finetuned learner itself followed by subsequent fine-tuning on this self-generated data doubles the efficiency of the ..."}
+{"idx": 3, "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": 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": "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": "RL on Incorrect Synthetic Data Scales the Efficiency of LLM Math ...", "date": "", "ddg_snippet": "RL on Incorrect Synthetic Data Scales the Efficiency of LLM Math Reasoning by Eight-Fold : Paper and Code. 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": "www.catalyzex.com", "link": "https://www.catalyzex.com/paper/rl-on-incorrect-synthetic-data-scales-the", "content": "RL on Incorrect Synthetic Data Scales the Efficiency of LLM Math Reasoning by Eight-Fold : Paper and Code. 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": 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": "Overview The study investigates the impact of synthetic data , both correct and incorrect , on the fine-tuning of LLMs for enhanced math reasoning using supervised fine-tuning (SFT) and reinforcement learning ( RL ). The authors find that incorporating self-generated positive responses doubles efficiency , while including negative data for per-step verification results in an eight-fold increase in ...", "subpage_snippet": "", "source": "www.emergentmind.com", "link": "https://www.emergentmind.com/papers/2406.14532", "content": "Overview The study investigates the impact of synthetic data , both correct and incorrect , on the fine-tuning of LLMs for enhanced math reasoning using supervised fine-tuning (SFT) and reinforcement learning ( RL ). The authors find that incorporating self-generated positive responses doubles efficiency , while including negative data for per-step verification results in an eight-fold increase in ..."}
+{"idx": 9, "title": "Learning to Reason by Failing: Offline RL on Sub-optimal Rollouts ...", "date": "", "ddg_snippet": "This paper investigates the helps/hurts of training models on model-generated synthetic data for math reasoning . They first conduct an empirical study and then propose to construct negative samples to address spurious correlations in SFT/RFT policy. The reviewers agree that the research problem is significant. The observations are insightful and are well-motivated and grounded with theorems ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=v2PV1yCFJk", "content": "This paper investigates the helps/hurts of training models on model-generated synthetic data for math reasoning . They first conduct an empirical study and then propose to construct negative samples to address spurious correlations in SFT/RFT policy. The reviewers agree that the research problem is significant. The observations are insightful and are well-motivated and grounded with theorems ..."}
diff --git a/data/sampled_jsons/RL_on_Incorrect_Synthetic_Data_Section_6.2_credit_assignment_spurious_correlations.jsonl b/data/sampled_jsons/RL_on_Incorrect_Synthetic_Data_Section_6.2_credit_assignment_spurious_correlations.jsonl
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index 0000000000000000000000000000000000000000..2146df0a894d73c91cdffaff2ed0b055208ebd25
--- /dev/null
+++ b/data/sampled_jsons/RL_on_Incorrect_Synthetic_Data_Section_6.2_credit_assignment_spurious_correlations.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "RL on Incorrect Synthetic Data Scales the Efficiency of LLM Math ...", "date": "", "ddg_snippet": "We will next discuss scaling results for negative data , and then in Section 6.3 show how per-step credit assignment improves generalization and suppresses irrelevant and incorrect steps appearing in a response, making it possible to make more use of the same synthetic data .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2406.14532v1", "content": "We will next discuss scaling results for negative data , and then in Section 6.3 show how per-step credit assignment improves generalization and suppresses irrelevant and incorrect steps appearing in a response, making it possible to make more use of the same synthetic data ."}
+{"idx": 1, "title": "PDF Avoiding Spurious Correlations Via Logit Cor Rection", "date": "", "ddg_snippet": "ABSTRACT Empirical studies suggest that machine learning models trained with empirical risk minimization (ERM) often rely on attributes that may be spuriously correlated with the class labels. Such models typically lead to poor performance during inference for data lacking such correlations . In this work, we explicitly consider a situation where potential spurious correlations are present in ...", "subpage_snippet": "", "source": "assets.amazon.science", "link": "https://assets.amazon.science/8b/13/5d0086ef4289a0de85cf13623580/avoiding-spurious-correlations-via-logit-correction.pdf", "content": "ABSTRACT Empirical studies suggest that machine learning models trained with empirical risk minimization (ERM) often rely on attributes that may be spuriously correlated with the class labels. Such models typically lead to poor performance during inference for data lacking such correlations . In this work, we explicitly consider a situation where potential spurious correlations are present in ..."}
+{"idx": 2, "title": "DeepMind：强化学习处理不正确的合成数据，使大模型数学推理效率提高八倍 [RL on Incorrect Synthetic Data ...", "date": "", "ddg_snippet": "6. Negative Synthetic Data Enables Per-Step Credit Assignment （负合成数据实现逐步奖励分配） 在这一章节中，作者深入探讨了负合成数据的关键作用，并介绍了逐步奖励分配（Per-Step Credit Assignment ）的机制。", "subpage_snippet": "", "source": "assh83.com", "link": "https://assh83.com/2024/10/27/deepmind：强化学习处理不正确的合成数据，使大模型数/", "content": "6. Negative Synthetic Data Enables Per-Step Credit Assignment （负合成数据实现逐步奖励分配） 在这一章节中，作者深入探讨了负合成数据的关键作用，并介绍了逐步奖励分配（Per-Step Credit Assignment ）的机制。"}
+{"idx": 3, "title": "PDF Understanding and Mitigating Spurious Correlations in Text ...", "date": "", "ddg_snippet": "In this pa-per, we examine the implications of spurious correlations through a novel perspective called neighborhood analysis, which shows how spu-rious correlations lead unrelated words to er-roneously cluster together in the embedding space.", "subpage_snippet": "", "source": "www.csie.ntu.edu.tw", "link": "https://www.csie.ntu.edu.tw/~htlin/paper/doc/eacl24spurious.pdf", "content": "In this pa-per, we examine the implications of spurious correlations through a novel perspective called neighborhood analysis, which shows how spu-rious correlations lead unrelated words to er-roneously cluster together in the embedding space."}
+{"idx": 4, "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": 5, "title": "PDF Influence Tuning: Demoting Spurious Correlations via Instance ...", "date": "", "ddg_snippet": "Abstract Among the most critical limitations of deep learning NLP models are their lack of inter-pretability, and their reliance on spurious cor-relations . Prior work proposed various ap-proaches to interpreting the black-box models to unveil the spurious correlations , but the re-search was primarily used in human-computer interaction scenarios. It still remains underex-plored whether or how ...", "subpage_snippet": "", "source": "aclanthology.org", "link": "https://aclanthology.org/2021.findings-emnlp.374.pdf", "content": "Abstract Among the most critical limitations of deep learning NLP models are their lack of inter-pretability, and their reliance on spurious cor-relations . Prior work proposed various ap-proaches to interpreting the black-box models to unveil the spurious correlations , but the re-search was primarily used in human-computer interaction scenarios. It still remains underex-plored whether or how ..."}
+{"idx": 6, "title": "Severing Spurious Correlations with Data Pruning - arXiv.org", "date": "", "ddg_snippet": "Spurious Correlations are correlations that a network learns between simple, weakly predictive spurious features present in a fraction of the training data and the class label. These correlations are problematic as a network may prefer them over strongly predictive invariant correlations when making a prediction.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2503.18258v1", "content": "Spurious Correlations are correlations that a network learns between simple, weakly predictive spurious features present in a fraction of the training data and the class label. These correlations are problematic as a network may prefer them over strongly predictive invariant correlations when making a prediction."}
+{"idx": 7, "title": "Towards Mitigating Spurious Correlations in the Wild: A Benchmark & a ...", "date": "", "ddg_snippet": "Despite the growing body of recent works on remedying spurious correlations , the lack of a standardized benchmark hinders reproducible evaluation and comparison of the proposed solutions.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/371758556_Towards_Mitigating_Spurious_Correlations_in_the_Wild_A_Benchmark_a_more_Realistic_Dataset", "content": "Despite the growing body of recent works on remedying spurious correlations , the lack of a standardized benchmark hinders reproducible evaluation and comparison of the proposed solutions."}
+{"idx": 8, "title": "Vineppo: Unlocking Rl Potential for Llm Reasoning Through Refined ...", "date": "", "ddg_snippet": "Large language models (LLMs) are increasingly applied to complex reasoning tasks that require executing several complex steps before receiving any reward. Properly assigning credit to these steps is essential for enhancing model perfor-mance. Proximal Policy Optimization (PPO), a state-of-the-art reinforcement learning ( RL ) algorithm used for LLM finetuning, employs value networks to tackle ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=5mJrGtXVwz", "content": "Large language models (LLMs) are increasingly applied to complex reasoning tasks that require executing several complex steps before receiving any reward. Properly assigning credit to these steps is essential for enhancing model perfor-mance. Proximal Policy Optimization (PPO), a state-of-the-art reinforcement learning ( RL ) algorithm used for LLM finetuning, employs value networks to tackle ..."}
+{"idx": 9, "title": "PDF Bad Habits: Policy Confounding and Out-of-Trajectory Generalization in RL", "date": "", "ddg_snippet": "This repetitive exposure fosters spurious correlations between certain observations and rewards. Agents may then pick up on these correlations and develop simplistic habits tailored to the specific set of trajectories dictated by their policy.", "subpage_snippet": "", "source": "rlj.cs.umass.edu", "link": "https://rlj.cs.umass.edu/2024/papers/RLJ_RLC_2024_216.pdf", "content": "This repetitive exposure fosters spurious correlations between certain observations and rewards. Agents may then pick up on these correlations and develop simplistic habits tailored to the specific set of trajectories dictated by their policy."}
diff --git a/data/sampled_jsons/Rafailov_et_al._2023_Direct_Preference_Optimization_Your_Language_Model_is_Secretly_a_Reward_Model.jsonl b/data/sampled_jsons/Rafailov_et_al._2023_Direct_Preference_Optimization_Your_Language_Model_is_Secretly_a_Reward_Model.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..95fe1c76398e9640d67c261b5326b4382362c193
--- /dev/null
+++ b/data/sampled_jsons/Rafailov_et_al._2023_Direct_Preference_Optimization_Your_Language_Model_is_Secretly_a_Reward_Model.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Katy Perry - Wikipedia", "date": "", "ddg_snippet": "Katheryn Elizabeth Hudson (born October 25, 1984), known professionally as Katy Perry , is an American singer, songwriter, and television personality. She is one of the best-selling music artists in history, having sold over 151 million records worldwide. Perry is known for her influence on pop music and her camp style, being dubbed the \"Queen of Camp\" by Vogue and Rolling Stone. The world's ...", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/Katy_Perry", "content": "Katheryn Elizabeth Hudson (born October 25, 1984), known professionally as Katy Perry , is an American singer, songwriter, and television personality. She is one of the best-selling music artists in history, having sold over 151 million records worldwide. Perry is known for her influence on pop music and her camp style, being dubbed the \"Queen of Camp\" by Vogue and Rolling Stone. The world's ..."}
+{"idx": 1, "title": "Katy Perry | Official Site", "date": "", "ddg_snippet": "5 days ago · The official Katy Perry website.12/07/2025 Abu Dhabi Grand Prix Abu Dhabi BUY", "subpage_snippet": "", "source": "www.katyperry.com", "link": "https://www.katyperry.com/", "content": "5 days ago · The official Katy Perry website.12/07/2025 Abu Dhabi Grand Prix Abu Dhabi BUY"}
+{"idx": 2, "title": "Katy Perry | Songs, Husband, Space, Age, & Facts | Britannica", "date": "", "ddg_snippet": "Aug 26, 2025 · Katy Perry is an American pop singer who gained fame for a string of anthemic and often sexually suggestive hit songs, as well as for a playfully cartoonish sense of style. Her popular singles include ‘I Kissed a Girl,’ ‘Last Friday Night (T.G.I.F.),’ ‘Roar,’ and ‘Rise.’", "subpage_snippet": "", "source": "www.britannica.com", "link": "https://www.britannica.com/biography/Katy-Perry", "content": "Aug 26, 2025 · Katy Perry is an American pop singer who gained fame for a string of anthemic and often sexually suggestive hit songs, as well as for a playfully cartoonish sense of style. Her popular singles include ‘I Kissed a Girl,’ ‘Last Friday Night (T.G.I.F.),’ ‘Roar,’ and ‘Rise.’"}
+{"idx": 3, "title": "Katy Perry Says She's 'Continuing to Move Forward' in Letter to...", "date": "", "ddg_snippet": "1 day ago · Katy Perry is reflecting on her past year. In a letter to her fans posted to Instagram on Monday, Sept. 22, Perry , 40, got personal while marking the anniversary of her 2024 album 143.", "subpage_snippet": "", "source": "people.com", "link": "https://people.com/katy-perry-reflects-on-past-year-in-letter-to-her-fans-11815083", "content": "1 day ago · Katy Perry is reflecting on her past year. In a letter to her fans posted to Instagram on Monday, Sept. 22, Perry , 40, got personal while marking the anniversary of her 2024 album 143."}
+{"idx": 4, "title": "Katy Perry on Rollercoaster Year After Orlando Bloom Break Up", "date": "", "ddg_snippet": "1 day ago · Katy Perry marked the anniversary of her album 143 by celebrating how the milestone has inspired her to let go, months after ending her engagement to Orlando Bloom.", "subpage_snippet": "", "source": "www.eonline.com", "link": "https://www.eonline.com/news/1422900/katy-perry-on-rollercoaster-year-after-orlando-bloom-break-up", "content": "1 day ago · Katy Perry marked the anniversary of her album 143 by celebrating how the milestone has inspired her to let go, months after ending her engagement to Orlando Bloom."}
+{"idx": 5, "title": "Katy Perry Tells Fans She's ‘Continuing to Move Forward’", "date": "", "ddg_snippet": "1 day ago · Katy Perry is marking the one-year anniversary of her album 143. The singer, 40, took to Instagram on Monday, September 22, to share several behind-the-scenes photos and videos, as well as a ...", "subpage_snippet": "", "source": "www.usmagazine.com", "link": "https://www.usmagazine.com/entertainment/news/katy-perry-tells-fans-shes-continuing-to-move-forward/", "content": "1 day ago · Katy Perry is marking the one-year anniversary of her album 143. The singer, 40, took to Instagram on Monday, September 22, to share several behind-the-scenes photos and videos, as well as a ..."}
+{"idx": 6, "title": "Katy Perry - YouTube", "date": "", "ddg_snippet": "Katy Perry - I'M HIS, HE'S MINE ft. Doechii (Official Video) Katy Perry 12M views11 months ago CC 3:46", "subpage_snippet": "", "source": "www.youtube.com", "link": "https://www.youtube.com/c/@katyperry", "content": "Katy Perry - I'M HIS, HE'S MINE ft. Doechii (Official Video) Katy Perry 12M views11 months ago CC 3:46"}
+{"idx": 7, "title": "Katy Perry Shares Emotional Statement About the Future Two ... -...", "date": "", "ddg_snippet": "20 hours ago · Katy Perry shared a rare, heartfelt message on Instagram on September 22. In her message, she wrote that she was ready \"to move forward, and to dream.\" Her message comes after she reportedly ...", "subpage_snippet": "", "source": "www.yahoo.com", "link": "https://www.yahoo.com/entertainment/music/articles/katy-perry-shares-emotional-statement-142854834.html?fr=sycsrp_catchall", "content": "20 hours ago · Katy Perry shared a rare, heartfelt message on Instagram on September 22. In her message, she wrote that she was ready \"to move forward, and to dream.\" Her message comes after she reportedly ..."}
+{"idx": 8, "title": "Katy Perry Reflects on ‘Testing Years in Spotlight’ Amid Album...", "date": "", "ddg_snippet": "1 day ago · Katy Perry got nostalgic in her recent Instagram post celebrating the one-year anniversary of her album, “143.” In the post, the pop icon reflected on the year, thanking her fans.", "subpage_snippet": "", "source": "www.realitytea.com", "link": "https://www.realitytea.com/2025/09/23/katy-perry-143-album-anniversary-tribute-post-instagram/", "content": "1 day ago · Katy Perry got nostalgic in her recent Instagram post celebrating the one-year anniversary of her album, “143.” In the post, the pop icon reflected on the year, thanking her fans."}
+{"idx": 9, "title": "Katy Perry says she's done \"forcing\" her career after ... - NME", "date": "", "ddg_snippet": "11 hours ago · Katy Perry has looked back at her \"rollercoaster year\", and said that she is done “forcing” her career in a new post.", "subpage_snippet": "", "source": "www.nme.com", "link": "https://www.nme.com/news/music/katy-perry-says-shes-done-forcing-her-career-after-rollercoaster-year-of-143-3894764", "content": "11 hours ago · Katy Perry has looked back at her \"rollercoaster year\", and said that she is done “forcing” her career in a new post."}
diff --git a/data/sampled_jsons/ResearchGate_Sanity_Checking_Causal_Representation_Learning_Figure_3_MCC_scores.jsonl b/data/sampled_jsons/ResearchGate_Sanity_Checking_Causal_Representation_Learning_Figure_3_MCC_scores.jsonl
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index 0000000000000000000000000000000000000000..ca3d6e69e4920b9a18637173f130b4e70e440aeb
--- /dev/null
+++ b/data/sampled_jsons/ResearchGate_Sanity_Checking_Causal_Representation_Learning_Figure_3_MCC_scores.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "(PDF) The Third Pillar of Causal Analysis? A Measurement...", "date": "", "ddg_snippet": "Intuitively, MCC measures the component-wise correspondence. between the learned representation .J. L. Gamella, S. Bing, and J. Runge. Sanity checking causal representation learning on a simple real-world.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/392085755_The_Third_Pillar_of_Causal_Analysis_A_Measurement_Perspective_on_Causal_Representations", "content": "Intuitively, MCC measures the component-wise correspondence. between the learned representation .J. L. Gamella, S. Bing, and J. Runge. Sanity checking causal representation learning on a simple real-world."}
+{"idx": 1, "title": "[2502.20099] Sanity Checking Causal Representation Learning on...", "date": "", "ddg_snippet": "We evaluate methods for causal representation learning (CRL) on a simple, real-world system where these methods are expected to work.24 pages, 12 figures . Subjects: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Methodology (stat.ME). Cite as", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2502.20099", "content": "We evaluate methods for causal representation learning (CRL) on a simple, real-world system where these methods are expected to work.24 pages, 12 figures . Subjects: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Methodology (stat.ME). Cite as"}
+{"idx": 2, "title": "Sanity Checking Causal Representation Learning on a Simp...", "date": "", "ddg_snippet": "This paper provides a sanity check to validate advances in causal representation learning .", "subpage_snippet": "", "source": "bytez.com", "link": "https://bytez.com/docs/icml/44652/paper", "content": "This paper provides a sanity check to validate advances in causal representation learning ."}
+{"idx": 3, "title": "Sanity Checking Causal Representation Learning on a Simple...", "date": "", "ddg_snippet": "A study evaluates causal representation learning methods on a controlled optical experiment, revealing a gap between theory and practice.The paper seeks to validate advances in causal representation learning (CRL) by providing a sanity check .", "subpage_snippet": "", "source": "powerdrill.ai", "link": "https://powerdrill.ai/discover/summary-sanity-checking-causal-representation-learning-on-cm7p9dbhq6e6507m0bf9fj22h", "content": "A study evaluates causal representation learning methods on a controlled optical experiment, revealing a gap between theory and practice.The paper seeks to validate advances in causal representation learning (CRL) by providing a sanity check ."}
+{"idx": 4, "title": "Sanity Checking Causal Representation Learning on a Simple...", "date": "", "ddg_snippet": "We evaluate methods for causal representation learning (CRL) on a simple, real-world system where these methods are expected to work.We select methods representative of different approaches to CRL and find that they all fail to recover the underlying causal factors.", "subpage_snippet": "", "source": "synthical.com", "link": "https://synthical.com/article/Sanity-Checking-Causal-Representation-Learning-on-a-Simple-Real-World-System-ea56ed1c-68a5-4a60-8384-8b1132108289", "content": "We evaluate methods for causal representation learning (CRL) on a simple, real-world system where these methods are expected to work.We select methods representative of different approaches to CRL and find that they all fail to recover the underlying causal factors."}
+{"idx": 5, "title": "GitHub - simonbing/CRLSanityCheck", "date": "", "ddg_snippet": "figures . Sanity Checking Causal Representation Learning on a Simple Real-World System.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/simonbing/CRLSanityCheck", "content": "figures . Sanity Checking Causal Representation Learning on a Simple Real-World System."}
+{"idx": 6, "title": "Sanity Checking Causal Representation Learning on a Simple...", "date": "", "ddg_snippet": "We evaluate methods for causal representation learning (CRL) on a simple, real-world system where these methods are expected to work.We select methods representative of different approaches to CRL and find that they all fail to recover the underlying causal factors.", "subpage_snippet": "", "source": "paperswithcode.com", "link": "https://paperswithcode.com/paper/sanity-checking-causal-representation", "content": "We evaluate methods for causal representation learning (CRL) on a simple, real-world system where these methods are expected to work.We select methods representative of different approaches to CRL and find that they all fail to recover the underlying causal factors."}
+{"idx": 7, "title": "Causal Representations", "date": "", "ddg_snippet": "Score -based Causal Representation Learning with Interventions. arXiv preprint arXiv:2301.08230, 2023. Figure 3 : Causal graph of SCM 1 and SCM 2.", "subpage_snippet": "", "source": "proceedings.mlr.press", "link": "https://proceedings.mlr.press/v236/bing24a/bing24a.pdf", "content": "Score -based Causal Representation Learning with Interventions. arXiv preprint arXiv:2301.08230, 2023. Figure 3 : Causal graph of SCM 1 and SCM 2."}
+{"idx": 8, "title": "TU Berlin - Cited by 49 - representation learning - causality - clima...", "date": "", "ddg_snippet": "2021. Invariance & causal representation learning : Prospects and limitations. Sanity checking causal representation learning on a simple real-world system.", "subpage_snippet": "", "source": "scholar.google.com", "link": "https://scholar.google.com/citations?user=ydKQzzQAAAAJ&hl=en", "content": "2021. Invariance & causal representation learning : Prospects and limitations. Sanity checking causal representation learning on a simple real-world system."}
+{"idx": 9, "title": "Interventional Causal Representation Learning | alphaXiv", "date": "", "ddg_snippet": "Abstract: Causal representation learning seeks to extract high-level latent factors from low-level sensory data.", "subpage_snippet": "", "source": "www.alphaxiv.org", "link": "https://www.alphaxiv.org/overview/2209.11924v4", "content": "Abstract: Causal representation learning seeks to extract high-level latent factors from low-level sensory data."}
diff --git a/data/sampled_jsons/Rew_short_temporal_reward_equation_1_feint_behaviors.jsonl b/data/sampled_jsons/Rew_short_temporal_reward_equation_1_feint_behaviors.jsonl
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index 0000000000000000000000000000000000000000..0c10e4aa604f30f29083329d643c5b5a3478e01c
--- /dev/null
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@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Getting Started with REW - Audio Science Review (ASR) Forum", "date": "", "ddg_snippet": "Introduction The purpose of this document is to provide a basic step-by-step guide for novice users of Room Equalization Wizard ( REW ), freeware audio measurement software authored by John Mulcahy. This guide focuses on the enhancements to REW that support the use of USB microphones and HDMI connectivity. Since this guide was developed, there have been many significant enhancements and advanced ...", "subpage_snippet": "", "source": "www.audiosciencereview.com", "link": "https://www.audiosciencereview.com/forum/index.php?attachments/rew-101-hts-current-version-pdf.66155/", "content": "Introduction The purpose of this document is to provide a basic step-by-step guide for novice users of Room Equalization Wizard ( REW ), freeware audio measurement software authored by John Mulcahy. This guide focuses on the enhancements to REW that support the use of USB microphones and HDMI connectivity. Since this guide was developed, there have been many significant enhancements and advanced ..."}
+{"idx": 1, "title": "Room Measurement Tutorial for Dummies Part 1", "date": "", "ddg_snippet": "Feb 13, 2016 · I could have mis-understood things but I believe the way the UMIK- 1 is recognized by REW , etc: was part of REW 's newer package updates some time back, maybe even post the time Amir wrote the tutorial?", "subpage_snippet": "", "source": "www.audiosciencereview.com", "link": "https://www.audiosciencereview.com/forum/index.php?threads/room-measurement-tutorial-for-dummies-part-1.4/", "content": "Feb 13, 2016 · I could have mis-understood things but I believe the way the UMIK- 1 is recognized by REW , etc: was part of REW 's newer package updates some time back, maybe even post the time Amir wrote the tutorial?"}
+{"idx": 2, "title": "REW-like app for mobile (Android/iOS) - Audio Science Review...", "date": "", "ddg_snippet": "Sep 7, 2022 · I'm thinking about one advantage of a USB mic: portability. Could a smartphone use the UMIK- 1 and its calibration profile? If so: Is there a recommended app for iOS/Android that's similar to Room EQ Wizard? Is there a \"gold standard\" app in this forum, like REW is, or is there just a mish-mash of assorted apps?", "subpage_snippet": "", "source": "www.audiosciencereview.com", "link": "https://www.audiosciencereview.com/forum/index.php?threads/rew-like-app-for-mobile-android-ios.37151/", "content": "Sep 7, 2022 · I'm thinking about one advantage of a USB mic: portability. Could a smartphone use the UMIK- 1 and its calibration profile? If so: Is there a recommended app for iOS/Android that's similar to Room EQ Wizard? Is there a \"gold standard\" app in this forum, like REW is, or is there just a mish-mash of assorted apps?"}
+{"idx": 3, "title": "A1 EVO Neuron - Room EQ by OCA | AVS Forum", "date": "", "ddg_snippet": "Dec 23, 2024 · READ FIRST TWO POSTS BEFORE ASKING QUESTIONS give feedback and report bugs, but without sentiment bias downloads - A1 Evo Neuron - Google Drive REW beta 68 or higher is needed to run with Neuron - REW Beta Release - REW API beta releases or Index of /installers REW beta changelog - REW beta...", "subpage_snippet": "", "source": "www.avsforum.com", "link": "https://www.avsforum.com/threads/a1-evo-neuron-room-eq-by-oca.3316571/", "content": "Dec 23, 2024 · READ FIRST TWO POSTS BEFORE ASKING QUESTIONS give feedback and report bugs, but without sentiment bias downloads - A1 Evo Neuron - Google Drive REW beta 68 or higher is needed to run with Neuron - REW Beta Release - REW API beta releases or Index of /installers REW beta changelog - REW beta..."}
+{"idx": 4, "title": "Getting Started with REW: A Step-by-Step Guide - AVS Forum", "date": "", "ddg_snippet": "AVR. You are encouraged to visit the REW forum, and to thoroughly read the REW Cabling Basics and the REW Online Help documents. Suggestions to improve this document are welcome. Please direct your comments and corrections by sending a PM to AVS Forum user AustinJerry. Heartfelt thanks to AVS Forum user KBarnes701 for his assistance in creating this guide.", "subpage_snippet": "", "source": "www.avsforum.com", "link": "https://www.avsforum.com/attachments/rew-101-v1-5-pdf.66479/", "content": "AVR. You are encouraged to visit the REW forum, and to thoroughly read the REW Cabling Basics and the REW Online Help documents. Suggestions to improve this document are welcome. Please direct your comments and corrections by sending a PM to AVS Forum user AustinJerry. Heartfelt thanks to AVS Forum user KBarnes701 for his assistance in creating this guide."}
+{"idx": 5, "title": "REW, What Do I Need To Do To Add A Harman Curve?", "date": "", "ddg_snippet": "May 5, 2021 · Hi, I'm in the EQ settings for REW . My target right now is flat, is it possible in this screen to change my target to an Harman curve? And how would I do this? Any feedback is welcome, and thank you.", "subpage_snippet": "", "source": "www.audiosciencereview.com", "link": "https://www.audiosciencereview.com/forum/index.php?threads/rew-what-do-i-need-to-do-to-add-a-harman-curve.28673/", "content": "May 5, 2021 · Hi, I'm in the EQ settings for REW . My target right now is flat, is it possible in this screen to change my target to an Harman curve? And how would I do this? Any feedback is welcome, and thank you."}
+{"idx": 6, "title": "How do I create custom target curves? Excel? REW? - AVS Forum", "date": "", "ddg_snippet": "Dec 20, 2024 · REW has the option to generate a room curve with a basic linear rise/fall but is there a way to generate a curved target? Or is Excel the best way to generate curves? If so, how do I do it? I have the text files for various target curves, and I'm curious how people generated these files for use with REW .", "subpage_snippet": "", "source": "www.avsforum.com", "link": "https://www.avsforum.com/threads/how-do-i-create-custom-target-curves-excel-rew.3316434/", "content": "Dec 20, 2024 · REW has the option to generate a room curve with a basic linear rise/fall but is there a way to generate a curved target? Or is Excel the best way to generate curves? If so, how do I do it? I have the text files for various target curves, and I'm curious how people generated these files for use with REW ."}
+{"idx": 7, "title": "Simplified REW Setup and Use (USB Mic & HDMI...", "date": "", "ddg_snippet": "Jan 6, 2013 · The purpose of this thread is to explain how to both physically hook up the connections between your computer and AVR/Pre-Pro to get started with REW (Room EQ Wizard) and to share information on the proper use of REW including proper techniques for both measuring and interpreting graphs, what...", "subpage_snippet": "", "source": "www.avsforum.com", "link": "https://www.avsforum.com/threads/simplified-rew-setup-and-use-usb-mic-hdmi-connection-including-measurement-techniques-and-how-to-interpret-graphs.1449924/", "content": "Jan 6, 2013 · The purpose of this thread is to explain how to both physically hook up the connections between your computer and AVR/Pre-Pro to get started with REW (Room EQ Wizard) and to share information on the proper use of REW including proper techniques for both measuring and interpreting graphs, what..."}
+{"idx": 8, "title": "A collection of speaker target responses in .csv/.txt format", "date": "", "ddg_snippet": "Aug 29, 2019 · In order to use REW 's automatic EQ feature, I started collecting and converting every speaker target that I could find into the necessary .txt format. In this thread I want to share my work so far, as well as ask you guys for target responses that I may have missed. Comparison: So...", "subpage_snippet": "", "source": "www.audiosciencereview.com", "link": "https://www.audiosciencereview.com/forum/index.php?threads/a-collection-of-speaker-target-responses-in-csv-txt-format.16401/", "content": "Aug 29, 2019 · In order to use REW 's automatic EQ feature, I started collecting and converting every speaker target that I could find into the necessary .txt format. In this thread I want to share my work so far, as well as ask you guys for target responses that I may have missed. Comparison: So..."}
+{"idx": 9, "title": "Help with REW RTA measurement using the MMM method", "date": "", "ddg_snippet": "May 6, 2021 · Greetings, I have recently acquired a UMIK- 1 and started learning how to use REW for measurement and correction. First I did the swipe based measurement in a single position, using the excellent guide at and the measurement curve made a lot of sense, in terms of levels and slopes. Then I...", "subpage_snippet": "", "source": "www.audiosciencereview.com", "link": "https://www.audiosciencereview.com/forum/index.php?threads/help-with-rew-rta-measurement-using-the-mmm-method.24131/", "content": "May 6, 2021 · Greetings, I have recently acquired a UMIK- 1 and started learning how to use REW for measurement and correction. First I did the swipe based measurement in a single position, using the excellent guide at and the measurement curve made a lot of sense, in terms of levels and slopes. Then I..."}
diff --git a/data/sampled_jsons/Robust_fine-tuning_of_zero-shot_models_Wortsman_abstract_WiSE-FT_mechanism_year_2021.jsonl b/data/sampled_jsons/Robust_fine-tuning_of_zero-shot_models_Wortsman_abstract_WiSE-FT_mechanism_year_2021.jsonl
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index 0000000000000000000000000000000000000000..7749927a03a85a427e47a161041a554c6bda4342
--- /dev/null
+++ b/data/sampled_jsons/Robust_fine-tuning_of_zero-shot_models_Wortsman_abstract_WiSE-FT_mechanism_year_2021.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "PDF Robust fine-tuning of zero-shot models - CVF Open Access", "date": "", "ddg_snippet": "In a concrete application, a zero-shot model can be fine-tuned on extra application-specific data, which often yields large performance gains on the target distribution. However, in the experiments of Radford et al. [79] and Pham et al. [75], fine-tuning comes at the cost of robustness: across several natural distribution shifts, the accuracy of their fine-tuned models is lower than that of ...", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2022/papers/Wortsman_Robust_Fine-Tuning_of_Zero-Shot_Models_CVPR_2022_paper.pdf", "content": "In a concrete application, a zero-shot model can be fine-tuned on extra application-specific data, which often yields large performance gains on the target distribution. However, in the experiments of Radford et al. [79] and Pham et al. [75], fine-tuning comes at the cost of robustness: across several natural distribution shifts, the accuracy of their fine-tuned models is lower than that of ..."}
+{"idx": 1, "title": "[2109.01903] Robust fine-tuning of zero-shot models - arXiv.org", "date": "", "ddg_snippet": "Large pre-trained models such as CLIP or ALIGN offer consistent accuracy across a range of data distributions when performing zero-shot inference (i.e., without fine-tuning on a specific dataset). Although existing fine-tuning methods substantially improve accuracy on a given target distribution, they often reduce robustness to distribution shifts. We address this tension by introducing a ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2109.01903", "content": "Large pre-trained models such as CLIP or ALIGN offer consistent accuracy across a range of data distributions when performing zero-shot inference (i.e., without fine-tuning on a specific dataset). Although existing fine-tuning methods substantially improve accuracy on a given target distribution, they often reduce robustness to distribution shifts. We address this tension by introducing a ..."}
+{"idx": 2, "title": "GitHub - mlfoundations/wise-ft: Robust fine-tuning of zero-shot models", "date": "", "ddg_snippet": "Robust fine-tuning of zero-shot models This repository contains code for the paper Robust fine-tuning of zero-shot models by Mitchell Wortsman *, Gabriel Ilharco*, Jong Wook Kim, Mike Li, Simon Kornblith, Rebecca Roelofs, Raphael Gontijo-Lopes, Hannaneh Hajishirzi, Ali Farhadi, Hongseok Namkoong, Ludwig Schmidt.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/mlfoundations/wise-ft", "content": "Robust fine-tuning of zero-shot models This repository contains code for the paper Robust fine-tuning of zero-shot models by Mitchell Wortsman *, Gabriel Ilharco*, Jong Wook Kim, Mike Li, Simon Kornblith, Rebecca Roelofs, Raphael Gontijo-Lopes, Hannaneh Hajishirzi, Ali Farhadi, Hongseok Namkoong, Ludwig Schmidt."}
+{"idx": 3, "title": "Robust fine-tuning of zero-shot models - OpenReview", "date": "", "ddg_snippet": "WiSE-FT simply applies the strategy to models (zero-shot & fine-tuned) trained with different data. The paper directly used CLIP as a zero-shot backbone, however, to verify the effectiveness of WiSE-FT as a universal robust fine-tuning approach, the paper should conduct more experiments with other pretrained models to serve as zero-shot backbones.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=yrbF6ekqQ9w", "content": "WiSE-FT simply applies the strategy to models (zero-shot & fine-tuned) trained with different data. The paper directly used CLIP as a zero-shot backbone, however, to verify the effectiveness of WiSE-FT as a universal robust fine-tuning approach, the paper should conduct more experiments with other pretrained models to serve as zero-shot backbones."}
+{"idx": 4, "title": "Paper page - Robust fine-tuning of zero-shot models - Hugging Face", "date": "", "ddg_snippet": "Robust fine-tuning of zero-shot models ... Mitchell Wortsman , Gabriel Ilharco , Jong Wook Kim ,", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/papers/2109.01903", "content": "Robust fine-tuning of zero-shot models ... Mitchell Wortsman , Gabriel Ilharco , Jong Wook Kim ,"}
+{"idx": 5, "title": "Robust fine-tuning of zero-shot models - ResearchGate", "date": "", "ddg_snippet": "Download Citation | On Jun 1, 2022, Mitchell Wortsman and others published Robust fine-tuning of zero-shot models | Find, read and cite all the research you need on ResearchGate", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/363906816_Robust_fine-tuning_of_zero-shot_models", "content": "Download Citation | On Jun 1, 2022, Mitchell Wortsman and others published Robust fine-tuning of zero-shot models | Find, read and cite all the research you need on ResearchGate"}
+{"idx": 6, "title": "Robust fine-tuning of zero-shot models - computer.org", "date": "", "ddg_snippet": "Large pre-trained models such as CLIP or ALIGN offer consistent accuracy across a range of data distributions when performing zero-shot inference (i.e., without fine-tuning on a specific dataset). Although existing fine-tuning methods substantially improve accuracy on a given target distribution, they often reduce robustness to distribution shifts. We address this tension by introducing a ...", "subpage_snippet": "", "source": "www.computer.org", "link": "https://www.computer.org/csdl/proceedings-article/cvpr/2022/694600h949/1H0KScRrx8k", "content": "Large pre-trained models such as CLIP or ALIGN offer consistent accuracy across a range of data distributions when performing zero-shot inference (i.e., without fine-tuning on a specific dataset). Although existing fine-tuning methods substantially improve accuracy on a given target distribution, they often reduce robustness to distribution shifts. We address this tension by introducing a ..."}
+{"idx": 7, "title": "Robust fine-tuning of zero-shot models - NIPS", "date": "", "ddg_snippet": "We address this tension by introducing a simple and effective method for improving robustness: ensembling the weights of the zero-shot and fine-tuned models ( WiSE-FT ). Compared to standard fine-tuning, WiSE-FT provides large accuracy improvements out-of-distribution, while matching or improving in-distribution accuracy.", "subpage_snippet": "", "source": "nips.cc", "link": "https://nips.cc/virtual/2021/35489", "content": "We address this tension by introducing a simple and effective method for improving robustness: ensembling the weights of the zero-shot and fine-tuned models ( WiSE-FT ). Compared to standard fine-tuning, WiSE-FT provides large accuracy improvements out-of-distribution, while matching or improving in-distribution accuracy."}
+{"idx": 8, "title": "Robust Fine-tuning of Zero-shot Models via Variance ...", "date": "", "ddg_snippet": "by B Zhu · 2024 · Cited by 4 — Robust fine-tuning of zero-shot models . Vision-language models like CLIP [20] have demonstrated outstanding improvements in robustness. It is ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2411.06966?", "content": "by B Zhu · 2024 · Cited by 4 — Robust fine-tuning of zero-shot models . Vision-language models like CLIP [20] have demonstrated outstanding improvements in robustness. It is ..."}
+{"idx": 9, "title": "Robust Fine-tuning of Zero-shot Models via Variance ...", "date": "", "ddg_snippet": "11 Nov 2024 — Robust fine-tuning of zero-shot models . Vision-language models like ... When fine-tuning E2E- FT models, we adhere to Wortsman et al.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2411.06966v1", "content": "11 Nov 2024 — Robust fine-tuning of zero-shot models . Vision-language models like ... When fine-tuning E2E- FT models, we adhere to Wortsman et al."}
diff --git a/data/sampled_jsons/Rubin_1974_potential_outcomes_abstract.jsonl b/data/sampled_jsons/Rubin_1974_potential_outcomes_abstract.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..c64012be34a6c09606b0dcc6dd1faa1447b14950
--- /dev/null
+++ b/data/sampled_jsons/Rubin_1974_potential_outcomes_abstract.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Rubin causal model - Wikipedia", "date": "", "ddg_snippet": "The Rubin causal model, also known as the Neyman– Rubin causal model, is an approach to the statistical analysis of cause and effect based on the framework of potential outcomes , named after Donald Rubin . The name \" Rubin causal model\" was fi...", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/Rubin_causal_model", "content": "The Rubin causal model, also known as the Neyman– Rubin causal model, is an approach to the statistical analysis of cause and effect based on the framework of potential outcomes , named after Donald Rubin . The name \" Rubin causal model\" was fi..."}
+{"idx": 1, "title": "Causality: Rubin ( 1974 )", "date": "", "ddg_snippet": "Rubin (2005) Causal inference using potential outcomes : design, modeling, decisions. Rubin ’s ( 1974 ) abstract . A discussion of matching, randomization, random sampling, and other methods of controlling extraneous variation is presented.", "subpage_snippet": "", "source": "hedibert.org", "link": "https://hedibert.org/wp-content/uploads/2015/10/causality-meeting2.pdf", "content": "Rubin (2005) Causal inference using potential outcomes : design, modeling, decisions. Rubin ’s ( 1974 ) abstract . A discussion of matching, randomization, random sampling, and other methods of controlling extraneous variation is presented."}
+{"idx": 2, "title": "(PDF) Potential Outcomes , Counterfactuals, and Structural Modelling...", "date": "", "ddg_snippet": "Abstract . This paper examines the potential outcome model developed by Rubin and its counterfac- tual underpinnings as developed by Lewis. Though a major contribution of Rubin 's potential outcome model has been to stress the importance of the design stage, we recall the main method...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/242520646_Potential_Outcomes_Counterfactuals_and_Structural_Modelling_Causal_Approaches_in_the_Social_Sciences", "content": "Abstract . This paper examines the potential outcome model developed by Rubin and its counterfac- tual underpinnings as developed by Lewis. Though a major contribution of Rubin 's potential outcome model has been to stress the importance of the design stage, we recall the main method..."}
+{"idx": 3, "title": "4 Potential Outcomes Causal Model – <span style='font-weight: 700...", "date": "", "ddg_snippet": "In the potential outcomes tradition (Splawa-Neyman 1923; D. Rubin 1974 ), a causal effect is defined as a comparison between two states of the world.", "subpage_snippet": "", "source": "mixtape.scunning.com", "link": "https://mixtape.scunning.com/04-potential_outcomes", "content": "In the potential outcomes tradition (Splawa-Neyman 1923; D. Rubin 1974 ), a causal effect is defined as a comparison between two states of the world."}
+{"idx": 4, "title": "Causality without potential outcomes and the dynamic... | DeepAI", "date": "", "ddg_snippet": "Several approaches to causal inference from observational studies have been proposed. Since the proposal of Rubin ( 1974 ) many works have developed a counterfactual approach to causality, statistically formalized by potential outcomes .", "subpage_snippet": "", "source": "deepai.org", "link": "https://deepai.org/publication/causality-without-potential-outcomes-and-the-dynamic-approach", "content": "Several approaches to causal inference from observational studies have been proposed. Since the proposal of Rubin ( 1974 ) many works have developed a counterfactual approach to causality, statistically formalized by potential outcomes ."}
+{"idx": 5, "title": "Design-Based Inference under Random Potential Outcomes via Riesz...", "date": "", "ddg_snippet": "We introduce a general framework for design-based causal inference that accommodates random potential outcomes , thereby extending the classical Neyman- Rubin setup in which outcomes are treated as fixed.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2505.01324v4", "content": "We introduce a general framework for design-based causal inference that accommodates random potential outcomes , thereby extending the classical Neyman- Rubin setup in which outcomes are treated as fixed."}
+{"idx": 6, "title": "potential outcomes – Andi Fugard", "date": "", "ddg_snippet": "Hmmm, what group will have a better outcome …? The first example Rubin gave in his classic “Estimating causal effects of treatments in randomized and nonrandomized studies” ( Rubin , 1974 , p. 689) had the following two groups", "subpage_snippet": "", "source": "andifugard.info", "link": "https://andifugard.info/tag/potential-outcomes/", "content": "Hmmm, what group will have a better outcome …? The first example Rubin gave in his classic “Estimating causal effects of treatments in randomized and nonrandomized studies” ( Rubin , 1974 , p. 689) had the following two groups"}
+{"idx": 7, "title": "(PDF) Direct and Indirect Causal Effects via Potential Outcomes", "date": "", "ddg_snippet": "2. Potential outcomes and the assignment mechanism – the RCM The key organizing principle for addressing the topic of direct and indirect causal effects will be based on the concept of ‘principal stratification’ (Frangakis & Rubin , 2002).", "subpage_snippet": "", "source": "www.academia.edu", "link": "https://www.academia.edu/105740272/Direct_and_Indirect_Causal_Effects_via_Potential_Outcomes_", "content": "2. Potential outcomes and the assignment mechanism – the RCM The key organizing principle for addressing the topic of direct and indirect causal effects will be based on the concept of ‘principal stratification’ (Frangakis & Rubin , 2002)."}
+{"idx": 8, "title": "Health Policy Analysis from a Potential Outcomes Perspective...", "date": "", "ddg_snippet": "For binary policies, Rubin ( 1974 , 1977) developed the potential outcomes framework (POF). We propose a generally applicable extension of the POF (EPOF) which covers a broad range of policy analytic contexts.", "subpage_snippet": "", "source": "www.openread.academy", "link": "https://www.openread.academy/paper/reading?corpusId=78558320", "content": "For binary policies, Rubin ( 1974 , 1977) developed the potential outcomes framework (POF). We propose a generally applicable extension of the POF (EPOF) which covers a broad range of policy analytic contexts."}
+{"idx": 9, "title": "PSY 503: Foundations of Psychological Methods - Lecture...", "date": "", "ddg_snippet": "Lecture 3: Introduction to Causality, Potential Outcomes , and Experimental Design.Framework developed by Donald Rubin ( Rubin , 1974 , 1975) Mathematical denition of causal eects at the individual level Establishes the impossibility of measuring causal eects for an individual.", "subpage_snippet": "", "source": "www.robingomila.com", "link": "https://www.robingomila.com/files/503/fall_2020/lecture3_intro_causality_potential_outcomes.pdf", "content": "Lecture 3: Introduction to Causality, Potential Outcomes , and Experimental Design.Framework developed by Donald Rubin ( Rubin , 1974 , 1975) Mathematical denition of causal eects at the individual level Establishes the impossibility of measuring causal eects for an individual."}
diff --git a/data/sampled_jsons/SAFE_algorithm_sparse_optimization_dual_variable_multiplier_update.jsonl b/data/sampled_jsons/SAFE_algorithm_sparse_optimization_dual_variable_multiplier_update.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..47b8e9f73ca070232d9b121b125fc5171b3ea66e
--- /dev/null
+++ b/data/sampled_jsons/SAFE_algorithm_sparse_optimization_dual_variable_multiplier_update.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Safe: Finding Sparse and Flat Minima to Improve Pruning", "date": "", "ddg_snippet": "7 Jun 2025 — Motivated by recent studies in robust optimization , we aim to tackle this problem by finding subnetworks that are both sparse and flat at the ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2506.06866v1", "content": "7 Jun 2025 — Motivated by recent studies in robust optimization , we aim to tackle this problem by finding subnetworks that are both sparse and flat at the ..."}
+{"idx": 1, "title": "SAFE: Finding Sparse and Flat Minima to Improve Pruning", "date": "", "ddg_snippet": "by D Lee · 2025 — In this section, we present a detailed derivation of our flatness-inducing sparsification algorithm Sparsification via. ADMM with Flatness ...", "subpage_snippet": "", "source": "www.arxiv.org", "link": "https://www.arxiv.org/pdf/2506.06866", "content": "by D Lee · 2025 — In this section, we present a detailed derivation of our flatness-inducing sparsification algorithm Sparsification via. ADMM with Flatness ..."}
+{"idx": 2, "title": "SAFE: Finding Sparse and Flat Minima to Improve Pruning", "date": "", "ddg_snippet": "This motivates us to propose SAFE, a constrained optimization algorithm that aims to find better sparse solutions through enforcing flatness, where we ...", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/poster/46658", "content": "This motivates us to propose SAFE, a constrained optimization algorithm that aims to find better sparse solutions through enforcing flatness, where we ..."}
+{"idx": 3, "title": "Joint sample and feature selection via sparse primal ...", "date": "", "ddg_snippet": "by YH Shao · 2019 · Cited by 20 — SPDLSSVM solves an L 1 -norm based sparse primal and dual optimization problem to obtain the final classifier with sample and feature selection ...", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/abs/pii/S0950705119303685", "content": "by YH Shao · 2019 · Cited by 20 — SPDLSSVM solves an L 1 -norm based sparse primal and dual optimization problem to obtain the final classifier with sample and feature selection ..."}
+{"idx": 4, "title": "Limited memory bundle DC algorithm for sparse pairwise ...", "date": "", "ddg_snippet": "by N Karmitsa · 2025 · Cited by 1 — We propose a novel limited memory bundle DC algorithm (LMB-DCA) for large-scale nonsmooth DC optimization and apply it as an underlying solver in the SparsePKL ...", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/article/10.1007/s10898-025-01481-w", "content": "by N Karmitsa · 2025 · Cited by 1 — We propose a novel limited memory bundle DC algorithm (LMB-DCA) for large-scale nonsmooth DC optimization and apply it as an underlying solver in the SparsePKL ..."}
+{"idx": 5, "title": "FACTS Devices Allocation via Sparse Optimization", "date": "", "ddg_snippet": "by C Duan · 2015 · Cited by 72 — A new algorithm is proposed to solve these sparse optimiza- tion problems, which consists of the following four parts: 1) sparsity-inducing norms [13], 2) ...", "subpage_snippet": "", "source": "ieeexplore.ieee.org", "link": "https://ieeexplore.ieee.org/iel7/59/7410135/07112196.pdf", "content": "by C Duan · 2015 · Cited by 72 — A new algorithm is proposed to solve these sparse optimiza- tion problems, which consists of the following four parts: 1) sparsity-inducing norms [13], 2) ..."}
+{"idx": 6, "title": "Responsive Safety in Reinforcement Learning by PID ...", "date": "", "ddg_snippet": "by A Stooke · 2020 · Cited by 395 — Our contribution is to expand the scope of possible Lagrange multiplier update rules beyond (4), by interpreting the over- all learning algorithm as a dynamical ... 11 pages", "subpage_snippet": "", "source": "proceedings.mlr.press", "link": "http://proceedings.mlr.press/v119/stooke20a/stooke20a.pdf", "content": "by A Stooke · 2020 · Cited by 395 — Our contribution is to expand the scope of possible Lagrange multiplier update rules beyond (4), by interpreting the over- all learning algorithm as a dynamical ... 11 pages"}
+{"idx": 7, "title": "OFF-POLICY PRIMAL-DUAL SAFE REINFORCEMENT ...", "date": "", "ddg_snippet": "by Z Wu · Cited by 8 — Primal- dual safe RL methods commonly perform iterations between the primal update of the policy and the dual update of the Lagrange Multiplier .", "subpage_snippet": "", "source": "proceedings.iclr.cc", "link": "https://proceedings.iclr.cc/paper_files/paper/2024/file/2f8b56543953d60f262fb2c4b85c50b3-Paper-Conference.pdf", "content": "by Z Wu · Cited by 8 — Primal- dual safe RL methods commonly perform iterations between the primal update of the policy and the dual update of the Lagrange Multiplier ."}
+{"idx": 8, "title": "Sparse optimization models with robust sketching and ...", "date": "", "ddg_snippet": "by V Pham · 2018 · Cited by 1 — The so-called safe feature elimination procedure, introduced in [39], allows the algorithm to cheaply detect that some of the components of β ...", "subpage_snippet": "", "source": "www2.eecs.berkeley.edu", "link": "https://www2.eecs.berkeley.edu/Pubs/TechRpts/2018/EECS-2018-145.pdf", "content": "by V Pham · 2018 · Cited by 1 — The so-called safe feature elimination procedure, introduced in [39], allows the algorithm to cheaply detect that some of the components of β ..."}
+{"idx": 9, "title": "On PI Controllers for Updating Lagrange Multipliers in ...", "date": "", "ddg_snippet": "by M Sohrabi · Cited by 4 — In this work, we propose a PI-like update rule for the dual variables in a Lagrangian min-max game. We prove our algorithm generalizes momentum methods and we ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=8eYgCnsQmp", "content": "by M Sohrabi · Cited by 4 — In this work, we propose a PI-like update rule for the dual variables in a Lagrangian min-max game. We prove our algorithm generalizes momentum methods and we ..."}
diff --git a/data/sampled_jsons/SCAMP-7_address-event_interface_data_encoding.jsonl b/data/sampled_jsons/SCAMP-7_address-event_interface_data_encoding.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..fdb8309516945a1dd40c0edf13d8217cabc7b6c3
--- /dev/null
+++ b/data/sampled_jsons/SCAMP-7_address-event_interface_data_encoding.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Data‐Driven Technology in Event‐Based Vision - Sun - 2021", "date": "", "ddg_snippet": "28 Mar 2021 — This paper reveals the great prospects of event -based data -driven technology and presents a comprehensive overview of this field.", "subpage_snippet": "", "source": "onlinelibrary.wiley.com", "link": "https://onlinelibrary.wiley.com/doi/10.1155/2021/6689337", "content": "28 Mar 2021 — This paper reveals the great prospects of event -based data -driven technology and presents a comprehensive overview of this field."}
+{"idx": 1, "title": "An Event-Driven Ultra-Low-Power Smart Visual Sensor", "date": "", "ddg_snippet": "The data bandwidth generated by the tra- ditional imager is 80KBps, 31x higher than that of our sensor thanks to the address coding readout style. Moreover ...", "subpage_snippet": "", "source": "scispace.com", "link": "https://scispace.com/pdf/an-event-driven-ultra-low-power-smart-visual-sensor-3szh4wrkg5.pdf", "content": "The data bandwidth generated by the tra- ditional imager is 80KBps, 31x higher than that of our sensor thanks to the address coding readout style. Moreover ..."}
+{"idx": 2, "title": "SCAMP's stigmergic model of social conflict", "date": "", "ddg_snippet": "by HVD Parunak · 2023 · Cited by 13 — The fundamental dynamic of SCAMP is agent choice over alternatives, and the main alternatives are recorded in a Causal Event Graph (CEG), a ...", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/article/10.1007/s10588-021-09347-8", "content": "by HVD Parunak · 2023 · Cited by 13 — The fundamental dynamic of SCAMP is agent choice over alternatives, and the main alternatives are recorded in a Causal Event Graph (CEG), a ..."}
+{"idx": 3, "title": "Data-Driven Technology in Event-Based Vision", "date": "", "ddg_snippet": "In event -based visual systems comprising perception and processing, algorithms in the processing part are designed to maintain the intrinsic advantages of ...", "subpage_snippet": "", "source": "scispace.com", "link": "https://scispace.com/pdf/data-driven-technology-in-event-based-vision-16l34xkrng.pdf", "content": "In event -based visual systems comprising perception and processing, algorithms in the processing part are designed to maintain the intrinsic advantages of ..."}
+{"idx": 4, "title": "Point-Feature Tracking For Pixel Processor Arrays", "date": "", "ddg_snippet": "SCAMP - 7 has 256x256 pixel-processors, each which can capture light, store and process data within its local memory registers, and transfer data to neighbouring ...", "subpage_snippet": "", "source": "cvpr.thecvf.com", "link": "https://cvpr.thecvf.com/virtual/2025/poster/32867", "content": "SCAMP - 7 has 256x256 pixel-processors, each which can capture light, store and process data within its local memory registers, and transfer data to neighbouring ..."}
+{"idx": 5, "title": "Track: Poster Session 2 - CVPR", "date": "", "ddg_snippet": "13 Jun 2025 — Our approach enables simultaneous spatial and spectral renderings by encoding material properties from multi-view 3D hyperspectral datasets.", "subpage_snippet": "", "source": "cvpr.thecvf.com", "link": "https://cvpr.thecvf.com/virtual/2025/session/35266", "content": "13 Jun 2025 — Our approach enables simultaneous spatial and spectral renderings by encoding material properties from multi-view 3D hyperspectral datasets."}
+{"idx": 6, "title": "Optimized Lightweight Autoencoder System for Distributed ...", "date": "", "ddg_snippet": "4 May 2025 — On-chip Encoder Backbone: The encoder ℰ generates a low-dimensional latent representation, reducing data bandwidth and communication overhead ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2505.02256v1", "content": "4 May 2025 — On-chip Encoder Backbone: The encoder ℰ generates a low-dimensional latent representation, reducing data bandwidth and communication overhead ..."}
+{"idx": 7, "title": "Exploring machine learning methods for the identification ...", "date": "", "ddg_snippet": "by G Iuhasz · 2025 · Cited by 3 — SCAMP -ML is a research project that aims to move towards Industry 4.0 of heterogeneous production environments by using smart sensors and human–machine ...", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S2542660525000216", "content": "by G Iuhasz · 2025 · Cited by 3 — SCAMP -ML is a research project that aims to move towards Industry 4.0 of heterogeneous production environments by using smart sensors and human–machine ..."}
+{"idx": 8, "title": "In-Sensor Visual Perception and Inference", "date": "", "ddg_snippet": "by Y Liu · 2023 · Cited by 13 — The SCAMP PPA is a visual device that operates directly on the current, enabling focal-plane image processing with useful results as an output ...", "subpage_snippet": "", "source": "spj.science.org", "link": "https://spj.science.org/doi/10.34133/icomputing.0043", "content": "by Y Liu · 2023 · Cited by 13 — The SCAMP PPA is a visual device that operates directly on the current, enabling focal-plane image processing with useful results as an output ..."}
+{"idx": 9, "title": "Bit-serial architecture for optical computing", "date": "", "ddg_snippet": "by VP Heuring · 1992 · Cited by 71 — Since the SCAMP is a single- address machine, N bits of each instruction contain a memory address and 6 bits are used for control. This leads to a minor.", "subpage_snippet": "", "source": "opg.optica.org", "link": "https://opg.optica.org/viewmedia.cfm?uri=ao-31-17-3213&seq=0", "content": "by VP Heuring · 1992 · Cited by 71 — Since the SCAMP is a single- address machine, N bits of each instruction contain a memory address and 6 bits are used for control. This leads to a minor."}
diff --git a/data/sampled_jsons/SLHAMR_PA-MPJPE_score_single_moving_camera_PMR_dataset_pedestrian_motion_reconstruction.jsonl b/data/sampled_jsons/SLHAMR_PA-MPJPE_score_single_moving_camera_PMR_dataset_pedestrian_motion_reconstruction.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..3a89243628a8a28d4f370bff7e9996668140c0eb
--- /dev/null
+++ b/data/sampled_jsons/SLHAMR_PA-MPJPE_score_single_moving_camera_PMR_dataset_pedestrian_motion_reconstruction.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Physics-based Human Pose Estimation from a Single Moving RGB...", "date": "", "ddg_snippet": "4DHu-mans achieves the lowest MPJPE for motion reconstruction , while WHAM performs best at PA - MPJPE . Our method excels in trajectory estimation, outperforming others in W-MPJPE and RTE, while its WA-MPJPE is close to 4DHu-mans.", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2025W/RHOBIN/papers/Aytekin_Physics-based_Human_Pose_Estimation_from_a_Single_Moving_RGB_Camera_CVPRW_2025_paper.pdf", "content": "4DHu-mans achieves the lowest MPJPE for motion reconstruction , while WHAM performs best at PA - MPJPE . Our method excels in trajectory estimation, outperforming others in W-MPJPE and RTE, while its WA-MPJPE is close to 4DHu-mans."}
+{"idx": 1, "title": "GitHub - coding-rachal/PMRDataset", "date": "", "ddg_snippet": "Official code respository for the paper \" Pedestrian Motion Reconstruction : A Large-scale Benchmark via Mixed Reality Rendering with Multiple Perspectives and Modalities\". PMR dataset is a large-scale, comprehensive dataset derived from an innovative mixed reality platform that includes third-person perspective RGB videos from moving vehicles, LiDAR data from vehicles, and the egocentric ...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/coding-rachal/PMRDataset", "content": "Official code respository for the paper \" Pedestrian Motion Reconstruction : A Large-scale Benchmark via Mixed Reality Rendering with Multiple Perspectives and Modalities\". PMR dataset is a large-scale, comprehensive dataset derived from an innovative mixed reality platform that includes third-person perspective RGB videos from moving vehicles, LiDAR data from vehicles, and the egocentric ..."}
+{"idx": 2, "title": "Pedestrian Motion Reconstruction: A Large-scale Benchmark via ...", "date": "", "ddg_snippet": "Jan 22, 2025 · We introduce the Pedestrian Motion Reconstruction ( PMR ) dataset , which focuses on pedestrian intention to reconstruct behavior using multiple perspectives and modalities. PMR is developed from a mixed reality platform that combines real-world realism with the extensive, accurate labels of simulations, thereby reducing costs and risks.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=YOpa6dTrpt", "content": "Jan 22, 2025 · We introduce the Pedestrian Motion Reconstruction ( PMR ) dataset , which focuses on pedestrian intention to reconstruct behavior using multiple perspectives and modalities. PMR is developed from a mixed reality platform that combines real-world realism with the extensive, accurate labels of simulations, thereby reducing costs and risks."}
+{"idx": 3, "title": "P M RECONSTRUCTION: A LARGE SCALE BENCHMARK VIA MIXED REALITY ...", "date": "", "ddg_snippet": "We introduce the Pedestrian Motion Reconstruction ( PMR ) dataset , which focuses on pedestrian intention to reconstruct behavior using multiple perspectives and modalities. PMR is developed from a mixed reality platform that combines real-world realism with the extensive, accurate labels of simulations, thereby reducing costs and risks.", "subpage_snippet": "", "source": "proceedings.iclr.cc", "link": "https://proceedings.iclr.cc/paper_files/paper/2025/file/f3342358d0792ea201dc86d69570946b-Paper-Conference.pdf", "content": "We introduce the Pedestrian Motion Reconstruction ( PMR ) dataset , which focuses on pedestrian intention to reconstruct behavior using multiple perspectives and modalities. PMR is developed from a mixed reality platform that combines real-world realism with the extensive, accurate labels of simulations, thereby reducing costs and risks."}
+{"idx": 4, "title": "Decoupling Human and Camera Motion from Videos in the Wild", "date": "", "ddg_snippet": "Given a video of moving people (left), we present SLAHMR: Simultaneous Localication and Human Mesh Recovery, a method to recover the global trajectories of the people and cameras in the world coordinate frame (right top). Prior methods model human motion in the camera coordinate frame (right bottom), and cannot recover the global displacement of the athlete.", "subpage_snippet": "", "source": "vye16.github.io", "link": "https://vye16.github.io/slahmr/", "content": "Given a video of moving people (left), we present SLAHMR: Simultaneous Localication and Human Mesh Recovery, a method to recover the global trajectories of the people and cameras in the world coordinate frame (right top). Prior methods model human motion in the camera coordinate frame (right bottom), and cannot recover the global displacement of the athlete."}
+{"idx": 5, "title": "Mean Per Joint Position Error (MPJPE) - oecd.ai", "date": "", "ddg_snippet": "Mar 15, 2024 · The lower the MPJPE , the better the performance of the algorithm. In the context of 3D human pose estimation, the MPJPE is calculated as the mean Euclidean distance between the predicted 3D joint locations and the corresponding ground truth joint locations.", "subpage_snippet": "", "source": "oecd.ai", "link": "https://oecd.ai/en/catalogue/metrics/mean-per-joint-position-error-mpjpe", "content": "Mar 15, 2024 · The lower the MPJPE , the better the performance of the algorithm. In the context of 3D human pose estimation, the MPJPE is calculated as the mean Euclidean distance between the predicted 3D joint locations and the corresponding ground truth joint locations."}
+{"idx": 6, "title": "PA MPJPE comparison with state-of-the-art methods on the ...", "date": "", "ddg_snippet": "Download scientific diagram | PA MPJPE comparison with state-of-the-art methods on the Human3.6M dataset using Protocol 1. * used extra synthetic data for training. from publication: Camera ...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/figure/PA-MPJPE-comparison-with-state-of-the-art-methods-on-the-Human36M-dataset-using-Protocol_tbl3_334735304", "content": "Download scientific diagram | PA MPJPE comparison with state-of-the-art methods on the Human3.6M dataset using Protocol 1. * used extra synthetic data for training. from publication: Camera ..."}
+{"idx": 7, "title": "EventEgo3D++: 3D Human Motion Capture from a Head-Mounted...", "date": "", "ddg_snippet": "Existing methods that rely on RGB cameras often fail under these conditions. To address these limitations, we introduce EventEgo3D++, the first approach that leverages a monocular event camera with a fisheye lens for 3D human motion capture.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2502.07869v1", "content": "Existing methods that rely on RGB cameras often fail under these conditions. To address these limitations, we introduce EventEgo3D++, the first approach that leverages a monocular event camera with a fisheye lens for 3D human motion capture."}
+{"idx": 8, "title": "EventEgo3D++: 3D Human Motion Capture from A Head-Mounted...", "date": "", "ddg_snippet": "By mounting a single event camera on a head-mounted display (HMD), we eliminate the need for additional body-worn sensors, thus simplifying the setup and avoiding drift issues.We report the MPJPE and PA - MPJPE evaluated on the EE3D-R dataset .", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/article/10.1007/s11263-025-02489-1", "content": "By mounting a single event camera on a head-mounted display (HMD), we eliminate the need for additional body-worn sensors, thus simplifying the setup and avoiding drift issues.We report the MPJPE and PA - MPJPE evaluated on the EE3D-R dataset ."}
+{"idx": 9, "title": "Mean Per Joint Position Error ( MPJPE ) on H3.6M when trained on...", "date": "", "ddg_snippet": "Finally, we animate the translated gloss using a 3D keypoint-based motion system trained on Sign3D-WLASL, a dataset we created by extracting body, hand, and face key points from real ASL videos in the WLASL dataset .", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/figure/Mean-Per-Joint-Position-Error-MPJPE-on-H36M-when-trained-on-H36M-indicates_tbl2_338509165", "content": "Finally, we animate the translated gloss using a 3D keypoint-based motion system trained on Sign3D-WLASL, a dataset we created by extracting body, hand, and face key points from real ASL videos in the WLASL dataset ."}
diff --git "a/data/sampled_jsons/SPD_Sync-Point_Drop_Algorithm_1_sensitivity_threshold_\317\2041_\317\2042_tensor_parallelism.jsonl" "b/data/sampled_jsons/SPD_Sync-Point_Drop_Algorithm_1_sensitivity_threshold_\317\2041_\317\2042_tensor_parallelism.jsonl"
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index 0000000000000000000000000000000000000000..acc49dbbdae8bd56d89996f7aa897851c9514ef8
--- /dev/null
+++ "b/data/sampled_jsons/SPD_Sync-Point_Drop_Algorithm_1_sensitivity_threshold_\317\2041_\317\2042_tensor_parallelism.jsonl"
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "SPD: Sync-Point Drop for Efficient Tensor Parallelism of ...", "date": "", "ddg_snippet": "by HB Kim · 2025 — In the sequence, the processing of a block is classified into three sensitivity categories based on predefined threshold criteria ( τ1 and τ2 ).", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2502.20727", "content": "by HB Kim · 2025 — In the sequence, the processing of a block is classified into three sensitivity categories based on predefined threshold criteria ( τ1 and τ2 )."}
+{"idx": 1, "title": "SPD: Sync-Point Drop for Efficient Tensor Parallelism of ...", "date": "", "ddg_snippet": "by HB Kim — This paper introduces Sync - Point Drop ( SPD ), a novel technique to mitigate communication bottlenecks in distributed LLM inference by selectively skipping ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=23zxLtvder&referrer=[the+profile+of+Minsik+Cho](/profile?id=~Minsik_Cho1)", "content": "by HB Kim — This paper introduces Sync - Point Drop ( SPD ), a novel technique to mitigate communication bottlenecks in distributed LLM inference by selectively skipping ..."}
+{"idx": 2, "title": "SPD: Sync-Point Drop for Efficient Tensor Parallelism of ...", "date": "", "ddg_snippet": "Algorithm 1 shows the overall process of applying SPD in a multi-tiered block-wise approach with measured sync sensitivity . Based on the sync sensitivity ...", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/poster/46606", "content": "Algorithm 1 shows the overall process of applying SPD in a multi-tiered block-wise approach with measured sync sensitivity . Based on the sync sensitivity ..."}
+{"idx": 3, "title": "SPD: SYNC-POINT DROP FOR EFFICIENT TENSOR PAR", "date": "", "ddg_snippet": "Algorithm 1 Sync - point drop based on sensitivity ... sync sensitivity identification algorithm flow of a model in the parallel ... threshold criterion ( τ1 and τ2 ) ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf/41db95002c5da44fac80ce28aa5bf70de802581b.pdf", "content": "Algorithm 1 Sync - point drop based on sensitivity ... sync sensitivity identification algorithm flow of a model in the parallel ... threshold criterion ( τ1 and τ2 ) ..."}
+{"idx": 4, "title": "PassMark BurnInTest software - PC Reliability and Load Testing", "date": "", "ddg_snippet": "Supports up to 1024 cores and up to 32 sockets ... Added separate threshold option for SSD, now HDD and SSD can have different temperature thresholds", "subpage_snippet": "", "source": "www.passmark.com", "link": "https://www.passmark.com/products/burnintest/history.php", "content": "Supports up to 1024 cores and up to 32 sockets ... Added separate threshold option for SSD, now HDD and SSD can have different temperature thresholds"}
+{"idx": 5, "title": "Domain Decomposition Methods in Science and ...", "date": "", "ddg_snippet": "The implementation of Algorithm 1 was in C++ and the parallelization uses the OpenMP multithreading directives. We run two different types of experiments ...", "subpage_snippet": "", "source": "dokumen.pub", "link": "https://dokumen.pub/domain-decomposition-methods-in-science-and-engineering-xxvii-lecture-notes-in-computational-science-and-engineering-149-1st-ed-2024-3031507681-9783031507687.html", "content": "The implementation of Algorithm 1 was in C++ and the parallelization uses the OpenMP multithreading directives. We run two different types of experiments ..."}
+{"idx": 6, "title": "Parallel Processing and Applied Mathematics", "date": "", "ddg_snippet": "The framework is based on two main concepts: 1 ) identifying the issues caused by parallelization and, hence, the non-associativity of floating- point computations ...", "subpage_snippet": "", "source": "dokumen.pub", "link": "https://dokumen.pub/parallel-processing-and-applied-mathematics-14th-international-conference-ppam-2022-gdansk-poland-september-1114-2022-revised-selected-papers-lecture-notes-in-computer-science-13826-3031304411-9783031304415.html", "content": "The framework is based on two main concepts: 1 ) identifying the issues caused by parallelization and, hence, the non-associativity of floating- point computations ..."}
+{"idx": 7, "title": "Advances in Robot Control", "date": "", "ddg_snippet": "In this connection, Chapter 1 introduces a new viewpoint by linking the definition to the singularity of the inverse kinematic solutions. The problem is ...", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/content/pdf/10.1007/978-3-540-37347-6.pdf", "content": "In this connection, Chapter 1 introduces a new viewpoint by linking the definition to the singularity of the inverse kinematic solutions. The problem is ..."}
+{"idx": 8, "title": "vocab.txt", "date": "", "ddg_snippet": "... 1 2 3 4 5 6 7 8 9 : ; ? @ [ \\ ] ^ _ ` a b c d e f g h i j k l m n o p ... algorithm denot es ##orm follows obtain ##ension ##est ##ust ##eth map ...", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/InriaValda/cc_math_bert_ep10/resolve/ade7b905b65bc71993ee8a4b2e26bf37a57e40b5/vocab.txt?download=true", "content": "... 1 2 3 4 5 6 7 8 9 : ; ? @ [ \\ ] ^ _ ` a b c d e f g h i j k l m n o p ... algorithm denot es ##orm follows obtain ##ension ##est ##ust ##eth map ..."}
+{"idx": 9, "title": "BurnInTest Windows Changelog", "date": "", "ddg_snippet": "2D Test - Monitor Test, Fixed issue where it displays ( 1 of 0) & added some basic test info (cycles, ops, errors) on tile", "subpage_snippet": "", "source": "www.softpedia.com", "link": "https://www.softpedia.com/progChangelog/BurnInTest-Professional-Changelog-3299.html", "content": "2D Test - Monitor Test, Fixed issue where it displays ( 1 of 0) & added some basic test info (cycles, ops, errors) on tile"}
diff --git a/data/sampled_jsons/Sanity_Checking_Causal_Representation_Learning_Simple_Real-World_System_Malus_law_polarizer_cos_thet.jsonl b/data/sampled_jsons/Sanity_Checking_Causal_Representation_Learning_Simple_Real-World_System_Malus_law_polarizer_cos_thet.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..65006a5278e71f909e1cc809fcea47a0411b6236
--- /dev/null
+++ b/data/sampled_jsons/Sanity_Checking_Causal_Representation_Learning_Simple_Real-World_System_Malus_law_polarizer_cos_thet.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Sanity Checking Causal Representation Learning on a Simple ...", "date": "", "ddg_snippet": "We evaluate methods for causal representation learning (CRL) on a simple , real - world system where these methods are expected to work.", "subpage_snippet": "", "source": "bytez.com", "link": "https://bytez.com/docs/icml/44652/paper", "content": "We evaluate methods for causal representation learning (CRL) on a simple , real - world system where these methods are expected to work."}
+{"idx": 1, "title": "Sanity Checking Causal Representation Learning on a Simple ...", "date": "", "ddg_snippet": "We evaluate methods for causal representation learning (CRL) on a simple , real - world system where these methods are expected to work.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2502.20099", "content": "We evaluate methods for causal representation learning (CRL) on a simple , real - world system where these methods are expected to work."}
+{"idx": 2, "title": "Sanity Checking Causal Representation Learning on a Simple ...", "date": "", "ddg_snippet": "This work proposes a real - world benchmark for CRL methods, focusing on a simple physical system involving light polarization.The paper seeks to validate advances in causal representation learning (CRL) by providing a sanity check .", "subpage_snippet": "", "source": "powerdrill.ai", "link": "https://powerdrill.ai/discover/summary-sanity-checking-causal-representation-learning-on-cm7p9dbhq6e6507m0bf9fj22h", "content": "This work proposes a real - world benchmark for CRL methods, focusing on a simple physical system involving light polarization.The paper seeks to validate advances in causal representation learning (CRL) by providing a sanity check ."}
+{"idx": 3, "title": "ICML Poster Sanity Checking Causal Representation Learning on...", "date": "", "ddg_snippet": "We evaluate methods for causal representation learning (CRL) on a simple , real - world system where these methods are expected to work. The system consists of a controlled optical experiment specifically built for this purpose...", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/poster/44652", "content": "We evaluate methods for causal representation learning (CRL) on a simple , real - world system where these methods are expected to work. The system consists of a controlled optical experiment specifically built for this purpose..."}
+{"idx": 4, "title": "TU Berlin - Cited by 49 - representation learning - causality - clima...", "date": "", "ddg_snippet": "Sanity checking causal representation learning on a simple real - world system .S Bing, J Wahl, J Runge. UAI 2025 Workshop on Causal Abstractions and Representations , 0. The system can't perform the operation now.", "subpage_snippet": "", "source": "scholar.google.com", "link": "https://scholar.google.com/citations?user=ydKQzzQAAAAJ&hl=en", "content": "Sanity checking causal representation learning on a simple real - world system .S Bing, J Wahl, J Runge. UAI 2025 Workshop on Causal Abstractions and Representations , 0. The system can't perform the operation now."}
+{"idx": 5, "title": "Malus ’ Law : Theory, Formula, Diagram, and Applications", "date": "", "ddg_snippet": "Malus ’ Law Formula. Consider a beam of light that is polarized after passing through the polarizer , and its electric field oscillates in one plane. The axis of the polarizer lies on this plane.", "subpage_snippet": "", "source": "www.sciencefacts.net", "link": "https://www.sciencefacts.net/malus-law.html", "content": "Malus ’ Law Formula. Consider a beam of light that is polarized after passing through the polarizer , and its electric field oscillates in one plane. The axis of the polarizer lies on this plane."}
+{"idx": 6, "title": "Malus Law : Formula, Derivation, Graph & Applications for JEE", "date": "", "ddg_snippet": "Learn Malus Law in Physics with formula, derivation, graph, and real -life applications. Ace JEE, NEET, and school exams with clear explanations and solved examples.", "subpage_snippet": "", "source": "www.vedantu.com", "link": "https://www.vedantu.com/jee-main/physics-malus-law", "content": "Learn Malus Law in Physics with formula, derivation, graph, and real -life applications. Ace JEE, NEET, and school exams with clear explanations and solved examples."}
+{"idx": 7, "title": "GitHub - a-bissell/microfluidic- causal -chamber: A microfluidics based...", "date": "", "ddg_snippet": "Datasets for the 2025 benchmark paper \" Sanity Checking Causal Representation Learning on a Simple Real - World System \" by Juan L. Gamella*, Simon Bing*, and Jakob Runge.Measurements of light intensity displaying Malus ' law , used in the symbolic regression task in Fig.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/a-bissell/microfluidic-causal-chamber", "content": "Datasets for the 2025 benchmark paper \" Sanity Checking Causal Representation Learning on a Simple Real - World System \" by Juan L. Gamella*, Simon Bing*, and Jakob Runge.Measurements of light intensity displaying Malus ' law , used in the symbolic regression task in Fig."}
+{"idx": 8, "title": "Causal Representation Learning for Instantaneous... | OpenReview", "date": "", "ddg_snippet": "A causal representation learning method that can identify causal variables with instantaneous effects and their graph from temporal sequences with interventions.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=itZ6ggvMnzS", "content": "A causal representation learning method that can identify causal variables with instantaneous effects and their graph from temporal sequences with interventions."}
+{"idx": 9, "title": "(PDF) The Third Pillar of Causal Analysis? A Measurement...", "date": "", "ddg_snippet": "Sanity checking causal representation learning on a simple real - world .The study of learning causal structure with latent variables has advanced the understanding of the world by uncovering causal relationships and latent factors, e.g., Causal Representation Learning (CRL).", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/392085755_The_Third_Pillar_of_Causal_Analysis_A_Measurement_Perspective_on_Causal_Representations", "content": "Sanity checking causal representation learning on a simple real - world .The study of learning causal structure with latent variables has advanced the understanding of the world by uncovering causal relationships and latent factors, e.g., Causal Representation Learning (CRL)."}
diff --git a/data/sampled_jsons/Schulman_PPO_2017_arXiv1707.06347_Atari_A2C_performance_table.jsonl b/data/sampled_jsons/Schulman_PPO_2017_arXiv1707.06347_Atari_A2C_performance_table.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..9fcc611b2455f8bcc4539a3e0a00f1aa988ef9bf
--- /dev/null
+++ b/data/sampled_jsons/Schulman_PPO_2017_arXiv1707.06347_Atari_A2C_performance_table.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "[1707.06347] Proximal Policy Optimization Algorithms - arXiv.org", "date": "", "ddg_snippet": "Our experiments test PPO on a collection of benchmark tasks, including simulated robotic locomotion and Atari game playing, and we show that PPO outperforms other online policy gradient methods, and overall strikes a favorable balance between sample complexity, simplicity, and wall-time.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/1707.06347", "content": "Our experiments test PPO on a collection of benchmark tasks, including simulated robotic locomotion and Atari game playing, and we show that PPO outperforms other online policy gradient methods, and overall strikes a favorable balance between sample complexity, simplicity, and wall-time."}
+{"idx": 1, "title": "Playing Atari Space Invaders with Sparse Cosine Optimized Policy Evolution", "date": "", "ddg_snippet": "Following the success of Q-Learning based approaches on the Atari benchmark, Schulman et al. ( 2017 ) utilized Prox-imal Policy Optimization ( PPO ) as a scalable, robust and simple method for training game agents ( Schulman et al. 2017 ; Tan et al. 2024).", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2508.08526", "content": "Following the success of Q-Learning based approaches on the Atari benchmark, Schulman et al. ( 2017 ) utilized Prox-imal Policy Optimization ( PPO ) as a scalable, robust and simple method for training game agents ( Schulman et al. 2017 ; Tan et al. 2024)."}
+{"idx": 2, "title": "A Comparative Study of Deep Reinforcement Learning Models: Dqn Vs Ppo ...", "date": "", "ddg_snippet": "Abstract. This study conducts a comparative analysis of three advanced Deep Reinforcement Learning models - Deep Q-Networks (DQN), Proximal Policy Optimization ( PPO ), and Advantage Actor-Critic ( A2C ) - exclusively within the BreakOut Atari game environment. Our research aims to assess the performance and effectiveness of these models in a singular, controlled setting. Through rigorous ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2407.14151v1", "content": "Abstract. This study conducts a comparative analysis of three advanced Deep Reinforcement Learning models - Deep Q-Networks (DQN), Proximal Policy Optimization ( PPO ), and Advantage Actor-Critic ( A2C ) - exclusively within the BreakOut Atari game environment. Our research aims to assess the performance and effectiveness of these models in a singular, controlled setting. Through rigorous ..."}
+{"idx": 3, "title": "PDF John Schulman, Filip Wolski, Prafulla Dhariwal, Alec Radford, Oleg ...", "date": "", "ddg_snippet": "Here we include a comparison of PPO against A2C on a larger collection of 49 Atari games. Figure 6 shows the learning curves of each of three random seeds, while Table 6 shows the mean performance . 10", "subpage_snippet": "", "source": "ivr-ahnu.cn", "link": "http://ivr-ahnu.cn/paper/Proximal+policy+optimization+algorithms.pdf", "content": "Here we include a comparison of PPO against A2C on a larger collection of 49 Atari games. Figure 6 shows the learning curves of each of three random seeds, while Table 6 shows the mean performance . 10"}
+{"idx": 4, "title": "arXiv.org e-Print archive", "date": "", "ddg_snippet": "This paper introduces Proximal Policy Optimization algorithms, a new family of reinforcement learning methods optimizing policies through interaction and surrogate objective functions.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/1707.06347", "content": "This paper introduces Proximal Policy Optimization algorithms, a new family of reinforcement learning methods optimizing policies through interaction and surrogate objective functions."}
+{"idx": 5, "title": "PDF Proximal Policy Optimization Algorithms - TUM", "date": "", "ddg_snippet": "Proximal Policy Optimization - Experiments Atari Domain (discrete control, episodic environment) experiments on 49 Atari games from the Arcade Learning Environment [Bel+13] comparison with other approaches ( A2C [Mni+16] and ACER [Wan+16])", "subpage_snippet": "", "source": "dvl.in.tum.de", "link": "https://dvl.in.tum.de/slides/automl-ss19/01_stadler_ppo.pdf", "content": "Proximal Policy Optimization - Experiments Atari Domain (discrete control, episodic environment) experiments on 49 Atari games from the Arcade Learning Environment [Bel+13] comparison with other approaches ( A2C [Mni+16] and ACER [Wan+16])"}
+{"idx": 6, "title": "PDF A Comparison of Policy Gradient Methods for Multitask Learning", "date": "", "ddg_snippet": "Abstract This paper compares two policy gradient methods using multitask learning (MTL) on the Atari visual environments. These environments are complex and take millions of time steps to learn. This paper investigates Advantage Actor-Critic ( A2C ) and Proximal Policy Optimization's ( PPO ) performance on one, two and four tasks from the Arcade Learning Environment. The results show that agents ...", "subpage_snippet": "", "source": "honors.cs.umd.edu", "link": "http://honors.cs.umd.edu/uploads/thesis/file/211/CNalty_-_Honors_Thesis.pdf", "content": "Abstract This paper compares two policy gradient methods using multitask learning (MTL) on the Atari visual environments. These environments are complex and take millions of time steps to learn. This paper investigates Advantage Actor-Critic ( A2C ) and Proximal Policy Optimization's ( PPO ) performance on one, two and four tasks from the Arcade Learning Environment. The results show that agents ..."}
+{"idx": 7, "title": "Paper page - Proximal Policy Optimization Algorithms", "date": "", "ddg_snippet": "Proximal Policy Optimization Algorithms ... John Schulman , Filip Wolski , Prafulla Dhariwal , Alec Radford ,", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/papers/1707.06347", "content": "Proximal Policy Optimization Algorithms ... John Schulman , Filip Wolski , Prafulla Dhariwal , Alec Radford ,"}
+{"idx": 8, "title": "Proximal Policy Optimization Algorithms - ADS", "date": "", "ddg_snippet": "Our experiments test PPO on a collection of benchmark tasks, including simulated robotic locomotion and Atari game playing, and we show that PPO outperforms other online policy gradient methods, and overall strikes a favorable balance between sample complexity, simplicity, and wall-time.", "subpage_snippet": "", "source": "ui.adsabs.harvard.edu", "link": "https://ui.adsabs.harvard.edu/abs/2017arXiv170706347S/abstract", "content": "Our experiments test PPO on a collection of benchmark tasks, including simulated robotic locomotion and Atari game playing, and we show that PPO outperforms other online policy gradient methods, and overall strikes a favorable balance between sample complexity, simplicity, and wall-time."}
+{"idx": 9, "title": "Proximal Policy Optimization Algorithms | Request PDF", "date": "", "ddg_snippet": "Proximal Policy Optimization ( PPO ) introduces the constraint by using a clip function that sets a upper and lower bound for the gradient step ( Schulman et al. [ 2017 ]).", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/318584439_Proximal_Policy_Optimization_Algorithms", "content": "Proximal Policy Optimization ( PPO ) introduces the constraint by using a clip function that sets a upper and lower bound for the gradient step ( Schulman et al. [ 2017 ])."}
diff --git a/data/sampled_jsons/Sean_Michael_Kerner_TechTarget_CrowdStrike_2024.jsonl b/data/sampled_jsons/Sean_Michael_Kerner_TechTarget_CrowdStrike_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..782ae75bfc3b2bbde3370f17d5d35daf826e21c9
--- /dev/null
+++ b/data/sampled_jsons/Sean_Michael_Kerner_TechTarget_CrowdStrike_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "CrowdStrike outage explained: What caused it and what's next - TechTarget", "date": "", "ddg_snippet": "What might be considered the largest IT outage in history was triggered by a botched software update from security vendor CrowdStrike , affecting millions of Windows systems around the world. Insurers estimate the outage will cost U.S. Fortune 500 companies $5.4 billion. The outage occurred July 19, 2024 , with millions of Windows systems failing and showing the infamous blue screen of death ...", "subpage_snippet": "", "source": "www.techtarget.com", "link": "https://www.techtarget.com/WhatIs/feature/Explaining-the-largest-IT-outage-in-history-and-whats-next", "content": "What might be considered the largest IT outage in history was triggered by a botched software update from security vendor CrowdStrike , affecting millions of Windows systems around the world. Insurers estimate the outage will cost U.S. Fortune 500 companies $5.4 billion. The outage occurred July 19, 2024 , with millions of Windows systems failing and showing the infamous blue screen of death ..."}
+{"idx": 1, "title": "Understanding the CrowdStrike Attack | ProWriters", "date": "", "ddg_snippet": "On July 19, 2024 , the world got an unwanted glimpse of what might have happened had the Y2K \"millennium bug\" struck more than two decades earlier. A software update gone wrong caused millions of computing devices to crash. As a result, chaos plagued airlines, hospitals, banks, and retailers for days. The so-called \" CrowdStrike attack\" wasn't Y2K, as IT expert Sean Michael Kerner ...", "subpage_snippet": "", "source": "prowritersins.com", "link": "https://prowritersins.com/cyber-insurance-blog/crowdstrike-cyber-attack/", "content": "On July 19, 2024 , the world got an unwanted glimpse of what might have happened had the Y2K \"millennium bug\" struck more than two decades earlier. A software update gone wrong caused millions of computing devices to crash. As a result, chaos plagued airlines, hospitals, banks, and retailers for days. The so-called \" CrowdStrike attack\" wasn't Y2K, as IT expert Sean Michael Kerner ..."}
+{"idx": 2, "title": "what a great piece. | Sean M. Kerner - LinkedIn Perú", "date": "", "ddg_snippet": "CrowdStrike's recent global outage has exposed concerning flaws in their development practices, highlighting broader issues in the cybersecurity industry. Ex-employees allege that quality control was not prioritised at the company, with speed of delivery taking precedence over thorough testing.", "subpage_snippet": "", "source": "pe.linkedin.com", "link": "https://pe.linkedin.com/posts/seanmkerner_crowdstrike-ex-employees-quality-control-activity-7240372450792722432-FcwC", "content": "CrowdStrike's recent global outage has exposed concerning flaws in their development practices, highlighting broader issues in the cybersecurity industry. Ex-employees allege that quality control was not prioritised at the company, with speed of delivery taking precedence over thorough testing."}
+{"idx": 3, "title": "(PDF) A Deep Dive Into the 2024 CrowdStrike Outage - ResearchGate", "date": "", "ddg_snippet": "PDF | On Sep 3, 2024 , Damilola Ajewole published A Deep Dive Into the 2024 CrowdStrike Outage | Find, read and cite all the research you need on ResearchGate", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/383689259_A_Deep_Dive_Into_the_2024_CrowdStrike_Outage", "content": "PDF | On Sep 3, 2024 , Damilola Ajewole published A Deep Dive Into the 2024 CrowdStrike Outage | Find, read and cite all the research you need on ResearchGate"}
+{"idx": 4, "title": "Sean Michael Kerner - ITPro Today", "date": "", "ddg_snippet": "Explore the latest news and expert commentary by Sean Michael Kerner , brought to you by the Contributor of ITPro Today", "subpage_snippet": "", "source": "www.itprotoday.com", "link": "https://www.itprotoday.com/author/sean-michael-kerner", "content": "Explore the latest news and expert commentary by Sean Michael Kerner , brought to you by the Contributor of ITPro Today"}
+{"idx": 5, "title": "Analyzing the CrowdStrike Outage: What Went Wrong, Why It Happened, and ...", "date": "", "ddg_snippet": "The recent CrowdStrike outage has sent shockwaves through the global IT landscape, leading many to consider it one of the biggest failures in tech history. Affecting over 8 million machines globally, including critical systems in airports, hospitals, banks, the sheer scale and impact of this outage is staggering. But what really happened? Who's to blame? And what can we learn from this ...", "subpage_snippet": "", "source": "brendancraven.com", "link": "https://brendancraven.com/2024/09/19/analyzing-the-crowdstrike-outage-what-went-wrong-why-it-happened-and-what-we-can-learn/", "content": "The recent CrowdStrike outage has sent shockwaves through the global IT landscape, leading many to consider it one of the biggest failures in tech history. Affecting over 8 million machines globally, including critical systems in airports, hospitals, banks, the sheer scale and impact of this outage is staggering. But what really happened? Who's to blame? And what can we learn from this ..."}
+{"idx": 6, "title": "Sean Michael Kerner - TechTarget", "date": "", "ddg_snippet": "Sean Michael Kerner is an IT consultant, technology enthusiast and tinkerer, and has been known to spend his spare time immersed in the study of the Klingon language and satellite pictures of Area 51.", "subpage_snippet": "", "source": "www.techtarget.com", "link": "https://www.techtarget.com/contributor/Sean-M-Kerner", "content": "Sean Michael Kerner is an IT consultant, technology enthusiast and tinkerer, and has been known to spend his spare time immersed in the study of the Klingon language and satellite pictures of Area 51."}
+{"idx": 7, "title": "Sean Michael Kerner, Author at VentureBeat", "date": "", "ddg_snippet": "Sean Michael Kerner is a freelance journalist that has spent the last two decades covering enterprise and open source technologies.", "subpage_snippet": "", "source": "venturebeat.com", "link": "https://venturebeat.com/author/sean-michael-kerner/", "content": "Sean Michael Kerner is a freelance journalist that has spent the last two decades covering enterprise and open source technologies."}
+{"idx": 8, "title": "Explaining the largest IT outage in history and what's next", "date": "", "ddg_snippet": "IT expert Sean Michael Kerner shares an article on TechTarget explaining the outage, the businesses affected, and recovery times for businesses to get \"back to normal\". What caused the outage? The CrowdStrike Falcon platform was the main culprit.", "subpage_snippet": "", "source": "www.nogalis.com", "link": "https://www.nogalis.com/2024/07/22/explaining-the-largest-it-outage-in-history-and-whats-next/", "content": "IT expert Sean Michael Kerner shares an article on TechTarget explaining the outage, the businesses affected, and recovery times for businesses to get \"back to normal\". What caused the outage? The CrowdStrike Falcon platform was the main culprit."}
+{"idx": 9, "title": "Major Internet Outages are Getting Bigger and Occurring More Often: A ...", "date": "", "ddg_snippet": "Recovery time estimates ranged from days to months, but 99% of affected Windows systems were back online by the end of July ( Kerner 2024 ). The Global Payroll Association said that many workers would experience a delay in their monthly pay following the IT outage ( 2024 ). The outage also had a major financial and reputational impact on CrowdStrike .", "subpage_snippet": "", "source": "blog.castac.org", "link": "https://blog.castac.org/2025/02/major-internet-outages-are-getting-bigger-and-occurring-more-often-a-reflection-on-the-crowdstrike-it-outage/", "content": "Recovery time estimates ranged from days to months, but 99% of affected Windows systems were back online by the end of July ( Kerner 2024 ). The Global Payroll Association said that many workers would experience a delay in their monthly pay following the IT outage ( 2024 ). The outage also had a major financial and reputational impact on CrowdStrike ."}
diff --git a/data/sampled_jsons/Section_2_Global_Optimization_with_a_Power-Transformed_Objective_and_Gaussian_Smoothing_Dvijotham.jsonl b/data/sampled_jsons/Section_2_Global_Optimization_with_a_Power-Transformed_Objective_and_Gaussian_Smoothing_Dvijotham.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..8e90b0bc7db01f0429ba85e8b584d15ed5c539c8
--- /dev/null
+++ b/data/sampled_jsons/Section_2_Global_Optimization_with_a_Power-Transformed_Objective_and_Gaussian_Smoothing_Dvijotham.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Global Optimization with a Power-Transformed Objective ...", "date": "", "ddg_snippet": "17 Jul 2025 — We propose a novel method, namely Gaussian Smoothing with a Power-Transformed Objective (GS-PowerOpt), that solves global optimization problems in two steps.", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/poster/46360", "content": "17 Jul 2025 — We propose a novel method, namely Gaussian Smoothing with a Power-Transformed Objective (GS-PowerOpt), that solves global optimization problems in two steps."}
+{"idx": 1, "title": "Global Optimization with A Power-Transformed Objective ...", "date": "", "ddg_snippet": "6 Dec 2024 — Therefore, in this work, we propose a new method, namely the Gaussian Smoothing with a Power-transformed Objective (GSPTO), for solving the ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2412.05204v1", "content": "6 Dec 2024 — Therefore, in this work, we propose a new method, namely the Gaussian Smoothing with a Power-transformed Objective (GSPTO), for solving the ..."}
+{"idx": 2, "title": "Global Optimization with a Power-Transformed Objective and ...", "date": "", "ddg_snippet": "We propose a novel method, namely Gaussian. Smoothing with a Power-Transformed Objective . (GS-PowerOpt), that solves global optimization.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/attachment?id=6ojzpDczIY&name=pdf", "content": "We propose a novel method, namely Gaussian. Smoothing with a Power-Transformed Objective . (GS-PowerOpt), that solves global optimization."}
+{"idx": 3, "title": "Global Optimization with a Power-Transformed Objective and ...", "date": "", "ddg_snippet": "Our proposed algorithm, GS-PowerOpt, is significantly faster than the standard homotopy method for optimization , both empirically and.", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/media/icml-2025/Slides/46360_JdTUNKo.pdf", "content": "Our proposed algorithm, GS-PowerOpt, is significantly faster than the standard homotopy method for optimization , both empirically and."}
+{"idx": 4, "title": "Double Sampling Randomized Smoothing", "date": "", "ddg_snippet": "We concretize DSRS by generalized Gaussian smooth - ing mechanisms and propose a method to efficiently compute the certified radius for given classifiers. • We ...", "subpage_snippet": "", "source": "proceedings.mlr.press", "link": "https://proceedings.mlr.press/v162/li22aa/li22aa.pdf", "content": "We concretize DSRS by generalized Gaussian smooth - ing mechanisms and propose a method to efficiently compute the certified radius for given classifiers. • We ..."}
+{"idx": 5, "title": "Optimization Methods Applied to Power Systems", "date": "", "ddg_snippet": "Page 1. Volume 2 . Optimization . Methods Applied to Power Systems. Francisco G ... Objective Particle Swarm Optimization for Determination of. Congestion ...", "subpage_snippet": "", "source": "unglueit-files.s3.amazonaws.com", "link": "https://unglueit-files.s3.amazonaws.com/ebf/22fc9b7c8069454d9409f964725ba072.pdf", "content": "Page 1. Volume 2 . Optimization . Methods Applied to Power Systems. Francisco G ... Objective Particle Swarm Optimization for Determination of. Congestion ..."}
+{"idx": 6, "title": "Energy Minimization Methods in Computer Vision and ...", "date": "", "ddg_snippet": "by SUSMR Walk · 2015 — This book contains 36 original research articles that cover the whole spectrum of energy minimization in computer vision and pattern recognition, including.", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/content/pdf/10.1007/978-3-319-14612-6.pdf", "content": "by SUSMR Walk · 2015 — This book contains 36 original research articles that cover the whole spectrum of energy minimization in computer vision and pattern recognition, including."}
+{"idx": 7, "title": "Machine Learning Dec 2024", "date": "", "ddg_snippet": "Title: Global Optimization with A Power-Transformed Objective and Gaussian Smoothing . Chen Xu. Subjects: Optimization and Control (math.OC); Machine Learning ...", "subpage_snippet": "", "source": "www.arxiv.org", "link": "https://www.arxiv.org/list/cs.LG/2024-12?skip=1775&show=2000", "content": "Title: Global Optimization with A Power-Transformed Objective and Gaussian Smoothing . Chen Xu. Subjects: Optimization and Control (math.OC); Machine Learning ..."}
+{"idx": 8, "title": "Advancing Real-World Image Dehazing", "date": "", "ddg_snippet": "In this paper, we start from the definition of feature corre- lation and construct the decorrelated bilevel optimization objective using the statistical ...", "subpage_snippet": "", "source": "ieeexplore.ieee.org", "link": "https://ieeexplore.ieee.org/iel8/34/4359286/10564179.pdf", "content": "In this paper, we start from the definition of feature corre- lation and construct the decorrelated bilevel optimization objective using the statistical ..."}
+{"idx": 9, "title": "Accepted papers", "date": "", "ddg_snippet": "Gaussian Loss Smoothing Enables ... Multi- objective Bayesian Optimization with Heuristic Objectives for Biomedical and Molecular Data Analysis Workflows.", "subpage_snippet": "", "source": "jmlr.org", "link": "https://jmlr.org/tmlr/papers/", "content": "Gaussian Loss Smoothing Enables ... Multi- objective Bayesian Optimization with Heuristic Objectives for Biomedical and Molecular Data Analysis Workflows."}
diff --git a/data/sampled_jsons/Section_6_Scaling_Archetypal-SAE_norm_constraint_explicitly_OR_explicitly_state_OR_explicitly_states_year_2024.jsonl b/data/sampled_jsons/Section_6_Scaling_Archetypal-SAE_norm_constraint_explicitly_OR_explicitly_state_OR_explicitly_states_year_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..b3fce4d096aeef85df13099ba1bddae6b7ad1f33
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+++ b/data/sampled_jsons/Section_6_Scaling_Archetypal-SAE_norm_constraint_explicitly_OR_explicitly_state_OR_explicitly_states_year_2024.jsonl
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+{"idx": 0, "title": "Extracting Sparse Representations with Matching Pursuit", "date": "", "ddg_snippet": "3 Jun 2025 — Since the SAE architecture is not explicitly biased against such representations, is it possible SAEs remain performant towards expressing ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2506.03093v1", "content": "3 Jun 2025 — Since the SAE architecture is not explicitly biased against such representations, is it possible SAEs remain performant towards expressing ..."}
+{"idx": 1, "title": "Deep Causal Transcoding: A Framework for Mechanistically", "date": "", "ddg_snippet": "Scaling to deeper models—constant depth horizon suffices: To get some sense of whether the method scales to larger models, I train exponential dct s ...", "subpage_snippet": "", "source": "turntrout.com", "link": "https://turntrout.com/deep-causal-transcoding", "content": "Scaling to deeper models—constant depth horizon suffices: To get some sense of whether the method scales to larger models, I train exponential dct s ..."}
+{"idx": 2, "title": "Mechanistic Interpretability Should Prioritize Feature ...", "date": "", "ddg_snippet": "by X Song · 2025 — Unlike some earlier L1 SAEs that explicitly constrain decoder column norms to unity during optimization, this approach omits such a ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2505.20254?", "content": "by X Song · 2025 — Unlike some earlier L1 SAEs that explicitly constrain decoder column norms to unity during optimization, this approach omits such a ..."}
+{"idx": 3, "title": "Daily Papers", "date": "", "ddg_snippet": "In this study, we enhance ATD interpretability by using Sparse Autoencoders ( SAE ) to extract features from Gemma-2-2b residual stream.", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/papers?q=sparse+autoencoders", "content": "In this study, we enhance ATD interpretability by using Sparse Autoencoders ( SAE ) to extract features from Gemma-2-2b residual stream."}
+{"idx": 4, "title": "The Functional Reasoning Design Language (FRDL)", "date": "", "ddg_snippet": "This section discusses the state of standards and ongoing re- search in FHA to motivate and contextualize the development of. FRDL in Section 3. 2.1 FHA ...", "subpage_snippet": "", "source": "ntrs.nasa.gov", "link": "https://ntrs.nasa.gov/api/citations/20250006622/downloads/twocolumn_formatted.pdf?attachment=true", "content": "This section discusses the state of standards and ongoing re- search in FHA to motivate and contextualize the development of. FRDL in Section 3. 2.1 FHA ..."}
+{"idx": 5, "title": "Daily Papers", "date": "", "ddg_snippet": "Sparse Autoencoders (SAEs) have emerged as a popular tool for interpreting the hidden states of large language models (LLMs). By learning to reconstruct ...", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/papers?q=SAE+architectures", "content": "Sparse Autoencoders (SAEs) have emerged as a popular tool for interpreting the hidden states of large language models (LLMs). By learning to reconstruct ..."}
+{"idx": 6, "title": "Demystifying Decision-Making of Deep RL through Validated ...", "date": "", "ddg_snippet": "by A Dara · 2025 — archetype . Model performance is assessed using the average L1 norm on predicted vehicle states (ego speed, leader speed, headway). 4.2 ... 30 pages", "subpage_snippet": "", "source": "www2.eecs.berkeley.edu", "link": "https://www2.eecs.berkeley.edu/Pubs/TechRpts/2025/EECS-2025-51.pdf", "content": "by A Dara · 2025 — archetype . Model performance is assessed using the average L1 norm on predicted vehicle states (ego speed, leader speed, headway). 4.2 ... 30 pages"}
+{"idx": 7, "title": "Coding Framework - an overview | ScienceDirect Topics", "date": "", "ddg_snippet": "Adaboost, Naive Bayes, and SVM classifiers are used to classify 6 emotion states . Happy, love, sad, anger and surprise are emotion classes used for key ...", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/topics/computer-science/coding-framework", "content": "Adaboost, Naive Bayes, and SVM classifiers are used to classify 6 emotion states . Happy, love, sad, anger and surprise are emotion classes used for key ..."}
+{"idx": 8, "title": "insight", "date": "", "ddg_snippet": "1 Dec 2024 — 6 . This chapter explicitly states each trade study life cycle must consider cost. ( chapter 6.7.6.1) and system safety aspects. ( chapter ...", "subpage_snippet": "", "source": "www.incose.org", "link": "https://www.incose.org/docs/default-source/insight/insight_v23-4_1231.pdf?sfvrsn=26d07fc7_2", "content": "1 Dec 2024 — 6 . This chapter explicitly states each trade study life cycle must consider cost. ( chapter 6.7.6.1) and system safety aspects. ( chapter ..."}
+{"idx": 9, "title": "Measurement of Trust in Automation: A Narrative Review ...", "date": "", "ddg_snippet": "by SC Kohn · 2021 · Cited by 282 — This comprehensive narrative review addresses known methods that have been used to capture TiA. We examined measurements deployed in existing empirical works.", "subpage_snippet": "", "source": "pmc.ncbi.nlm.nih.gov", "link": "https://pmc.ncbi.nlm.nih.gov/articles/PMC8562383/", "content": "by SC Kohn · 2021 · Cited by 282 — This comprehensive narrative review addresses known methods that have been used to capture TiA. We examined measurements deployed in existing empirical works."}
diff --git a/data/sampled_jsons/Self-Paced_Learning_Physics-Informed_Neural_Networks.jsonl b/data/sampled_jsons/Self-Paced_Learning_Physics-Informed_Neural_Networks.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..531647965e9f7e03dddcb184b06314736978330b
--- /dev/null
+++ b/data/sampled_jsons/Self-Paced_Learning_Physics-Informed_Neural_Networks.jsonl
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+{"idx": 0, "title": "papasearch.net/Neural_Network/NeuralNetwork1.html", "date": "", "ddg_snippet": "... program that reached a virtually unthinkable level of play using only reinforcement learning and self -play in order to train its neural networks .", "subpage_snippet": "", "source": "papasearch.net", "link": "https://papasearch.net/Neural_Network/NeuralNetwork1.html", "content": "... program that reached a virtually unthinkable level of play using only reinforcement learning and self -play in order to train its neural networks ."}
+{"idx": 1, "title": "Artificial Intelligent _ Machine Learning _ Neural Networks", "date": "", "ddg_snippet": "Neural Network is a powerful machine learning tool used for classification & prediction. ... The concept of neural network can be thought ...", "subpage_snippet": "", "source": "www.apprentus.com", "link": "https://www.apprentus.com/en/private-lessons/online/tutoring/computer-science/machine-learning-neural-networks-matlab", "content": "Neural Network is a powerful machine learning tool used for classification & prediction. ... The concept of neural network can be thought ..."}
+{"idx": 2, "title": "Whack-a-mole Online Learning: Physics-Informed Neural Network", "date": "", "ddg_snippet": "This paper proposes a novel Physics - Informed Neural Networks (PINNs) approach called Whack-a-mole Online Learning (WamOL) to address this multi ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2411.02375v1", "content": "This paper proposes a novel Physics - Informed Neural Networks (PINNs) approach called Whack-a-mole Online Learning (WamOL) to address this multi ..."}
+{"idx": 3, "title": "Machine Learning – Ng – Self-Paced « Another", "date": "", "ddg_snippet": "Topics include: (i) Supervised learning (parametric/non-parametric algorithms, support vector machines, kernels, neural networks ).", "subpage_snippet": "", "source": "tm.durusau.net", "link": "http://tm.durusau.net/?p=61491", "content": "Topics include: (i) Supervised learning (parametric/non-parametric algorithms, support vector machines, kernels, neural networks )."}
+{"idx": 4, "title": "US7398259B2 - Training of a physical neural network - Google", "date": "", "ddg_snippet": "a neural network is an information-processing network ... Neural networks can thus be utilized as an advanced technique for processing information.", "subpage_snippet": "", "source": "patents.google.com", "link": "https://patents.google.com/patent/US7398259B2/en", "content": "a neural network is an information-processing network ... Neural networks can thus be utilized as an advanced technique for processing information."}
+{"idx": 5, "title": "US5832466A - System and method for dynamic learning control in", "date": "", "ddg_snippet": "Learning in an artificial neural network is defined to be any change in a network s memory (weight matrix) and can be categorised as supervised or ...", "subpage_snippet": "", "source": "patents.google.com", "link": "https://patents.google.com/patent/US5832466A/en", "content": "Learning in an artificial neural network is defined to be any change in a network s memory (weight matrix) and can be categorised as supervised or ..."}
+{"idx": 6, "title": "Deep Learning & Neural Networks with Python - Keras: For", "date": "", "ddg_snippet": "Learn Deep Learning and Neural Networks with Python using Keras. This beginner's guide covers essential concepts, coding examples, and hands-on ...", "subpage_snippet": "", "source": "www.learnfly.com", "link": "https://www.learnfly.com/deep-learning-neural-networks-python-keras-for-dummies", "content": "Learn Deep Learning and Neural Networks with Python using Keras. This beginner's guide covers essential concepts, coding examples, and hands-on ..."}
+{"idx": 7, "title": "Learning Deep Learning: Theory and Practice of Neural Networks,", "date": "", "ddg_snippet": "Learning Deep Learning : Theory and Practice of Neural Networks , Computer Vision, Natural Language Processing, and Transformers Using TensorFlow ...", "subpage_snippet": "", "source": "www.amazon.in", "link": "https://www.amazon.in/Learning-Deep-Processing-Transformers-TensorFlow/dp/0137470355", "content": "Learning Deep Learning : Theory and Practice of Neural Networks , Computer Vision, Natural Language Processing, and Transformers Using TensorFlow ..."}
+{"idx": 8, "title": "Self-adaptive physics-informed neural networks - ScienceDirect", "date": "", "ddg_snippet": "In this paper, we introduced Self -Adaptive Physics - Informed Neural Networks , a novel class of physics -constrained neural networks . This approach uses a similar conceptual framework as soft self -attention mechanisms in computer vision, in that the network identifies which inputs are most important to its own training.", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S0021999122007859", "content": "In this paper, we introduced Self -Adaptive Physics - Informed Neural Networks , a novel class of physics -constrained neural networks . This approach uses a similar conceptual framework as soft self -attention mechanisms in computer vision, in that the network identifies which inputs are most important to its own training."}
+{"idx": 9, "title": "[2009.04544] Self-Adaptive Physics-Informed Neural Networks using a ...", "date": "", "ddg_snippet": "Physics - Informed Neural Networks (PINNs) have emerged recently as a promising application of deep neural networks to the numerical solution of nonlinear partial differential equations (PDEs). However, it has been recognized that adaptive procedures are needed to force the neural network to fit accurately the stubborn spots in the solution of \"stiff\" PDEs. In this paper, we propose a ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2009.04544", "content": "Physics - Informed Neural Networks (PINNs) have emerged recently as a promising application of deep neural networks to the numerical solution of nonlinear partial differential equations (PDEs). However, it has been recognized that adaptive procedures are needed to force the neural network to fit accurately the stubborn spots in the solution of \"stiff\" PDEs. In this paper, we propose a ..."}
diff --git a/data/sampled_jsons/Self-Refine_Iterative_Refinement_with_Self-Feedback_full_abstract_text.jsonl b/data/sampled_jsons/Self-Refine_Iterative_Refinement_with_Self-Feedback_full_abstract_text.jsonl
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+{"idx": 0, "title": "Credit Builder: Build Credit & Add to Your Savings with Self", "date": "", "ddg_snippet": "Build your credit with Self 's Credit Builder Account & secured Self Visa® Credit Card. Ideal for credit building, no hard check, & reports to all three bureaus.", "subpage_snippet": "", "source": "www.self.inc", "link": "https://www.self.inc/", "content": "Build your credit with Self 's Credit Builder Account & secured Self Visa® Credit Card. Ideal for credit building, no hard check, & reports to all three bureaus."}
+{"idx": 1, "title": "Log In To Your Self Financial Account", "date": "", "ddg_snippet": "Use this page to access your account at Self Financial, Inc., formerly known as Self Lender.", "subpage_snippet": "", "source": "www.self.inc", "link": "https://www.self.inc/login", "content": "Use this page to access your account at Self Financial, Inc., formerly known as Self Lender."}
+{"idx": 2, "title": "Sign Up For Your Credit Builder Account - Self", "date": "", "ddg_snippet": "Use Self to build credit while you save! Your Self Credit Builder Account includes credit education to help you reach your goals.", "subpage_snippet": "", "source": "www.self.inc", "link": "https://www.self.inc/signup", "content": "Use Self to build credit while you save! Your Self Credit Builder Account includes credit education to help you reach your goals."}
+{"idx": 3, "title": "How can we help? - support.self.inc", "date": "", "ddg_snippet": "Self Financial Public Community Home Back to Self .inc | Support: 1 (877) 883-0999", "subpage_snippet": "", "source": "support.self.inc", "link": "https://support.self.inc/s/", "content": "Self Financial Public Community Home Back to Self .inc | Support: 1 (877) 883-0999"}
+{"idx": 4, "title": "Credit Builder Loans to Build Credit | Self", "date": "", "ddg_snippet": "Elevate your credit score with Self 's Credit Builder Account. Accessible options to build credit, no credit check, & reports to all bureaus.", "subpage_snippet": "", "source": "www.self.inc", "link": "https://www.self.inc/credit-builder-loan", "content": "Elevate your credit score with Self 's Credit Builder Account. Accessible options to build credit, no credit check, & reports to all bureaus."}
+{"idx": 5, "title": "The Self Card — unlocked for everyone.", "date": "", "ddg_snippet": "Explore the secured Self Visa® Credit Card. Build your credit & payment history, plus no hard pull.", "subpage_snippet": "", "source": "www.self.inc", "link": "https://www.self.inc/visa-secured-credit-card", "content": "Explore the secured Self Visa® Credit Card. Build your credit & payment history, plus no hard pull."}
+{"idx": 6, "title": "Leading Credit Building Company - About Self Financial", "date": "", "ddg_snippet": "Hi. We're Self . We're here to help you build credit and savings and reach your financial goals. It all started with some missed payments. Self began in 2015 after a mistake with Founder James Garvey’s credit card that tanked his credit score.", "subpage_snippet": "", "source": "www.self.inc", "link": "https://www.self.inc/about", "content": "Hi. We're Self . We're here to help you build credit and savings and reach your financial goals. It all started with some missed payments. Self began in 2015 after a mistake with Founder James Garvey’s credit card that tanked his credit score."}
+{"idx": 7, "title": "Self - Credit Builder Loans by Self - Credit Building App Online", "date": "", "ddg_snippet": "Self helps you build credit with credit builder loans. A credit builder loan (or account) is a tiny loan that you have to save in a CD.", "subpage_snippet": "", "source": "www.self.inc", "link": "https://www.self.inc/home/dashboard", "content": "Self helps you build credit with credit builder loans. A credit builder loan (or account) is a tiny loan that you have to save in a CD."}
+{"idx": 8, "title": "Turn your rent payments into Credit Building opportunities - Self", "date": "", "ddg_snippet": "Self is your partner in building credit - providing you with the toolbox you need to build credit. We believe that you work hard for your money, so let your money work hard for you.", "subpage_snippet": "", "source": "www.self.inc", "link": "https://www.self.inc/learn/product/rent-reporting", "content": "Self is your partner in building credit - providing you with the toolbox you need to build credit. We believe that you work hard for your money, so let your money work hard for you."}
+{"idx": 9, "title": "Contact Support", "date": "", "ddg_snippet": "Self Financial Public Community Home Back to Self .inc | Support: 1 (877) 883-0999 Expand search", "subpage_snippet": "", "source": "support.self.inc", "link": "https://support.self.inc/s/contactsupport", "content": "Self Financial Public Community Home Back to Self .inc | Support: 1 (877) 883-0999 Expand search"}
diff --git a/data/sampled_jsons/Self-Refine_Iterative_Refinement_with_Self-Feedback_medical_imaging_NLP_general_framework.jsonl b/data/sampled_jsons/Self-Refine_Iterative_Refinement_with_Self-Feedback_medical_imaging_NLP_general_framework.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..498d927a7a18bcd80fbe0b20d32959e183c17e7e
--- /dev/null
+++ b/data/sampled_jsons/Self-Refine_Iterative_Refinement_with_Self-Feedback_medical_imaging_NLP_general_framework.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "R星更新慢，进度条一直不动怎么办？ - 知乎", "date": "", "ddg_snippet": "Feb 25, 2020 · 不用挂 加速器 不要用Steam打开R星里的游戏（大概也就是gta5了），直接右键以管理员身份启动Rockstar Games Launcher 然后你就会发现，进度条在以肉眼可见的速度前进 点个赞同 ️你哦", "subpage_snippet": "", "source": "www.zhihu.com", "link": "https://www.zhihu.com/question/374575825", "content": "Feb 25, 2020 · 不用挂 加速器 不要用Steam打开R星里的游戏（大概也就是gta5了），直接右键以管理员身份启动Rockstar Games Launcher 然后你就会发现，进度条在以肉眼可见的速度前进 点个赞同 ️你哦"}
+{"idx": 1, "title": "如何看待Rockstar Games (即R星）？ - 知乎", "date": "", "ddg_snippet": "Feb 14, 2019 · R星林肯（Rockstar Lincoln） 1999年R星收购了位于英格兰林肯市的Tarantula Studios，这个团队最初曾参与到GTA的主机移植版中，包括存在感不高的任天堂GBC掌机版。 如今R星林肯已经转入幕后，专门负责R星游戏的质量保证和本地化翻译，LOGO背景是黄绿色。", "subpage_snippet": "", "source": "www.zhihu.com", "link": "https://www.zhihu.com/question/311872122", "content": "Feb 14, 2019 · R星林肯（Rockstar Lincoln） 1999年R星收购了位于英格兰林肯市的Tarantula Studios，这个团队最初曾参与到GTA的主机移植版中，包括存在感不高的任天堂GBC掌机版。 如今R星林肯已经转入幕后，专门负责R星游戏的质量保证和本地化翻译，LOGO背景是黄绿色。"}
+{"idx": 2, "title": "R星、育碧、EA和动视暴雪之间那个更厉害？ - 知乎", "date": "", "ddg_snippet": "Nov 19, 2018 · R星很稳，做游戏更追求品质细节，旗下游戏都是销量口碑双丰收的.游戏销量就和他做游戏一样稳得一批，形成了只要是R星出品玩家都乐意掏钱买账，当之无愧的业内游戏质量最好！缺点就是不敢创新，最近几年开发的游戏都是属于老IP续作，游戏玩法方面也并不出彩，中规中矩类型！育碧游戏公式 ...", "subpage_snippet": "", "source": "www.zhihu.com", "link": "https://www.zhihu.com/question/301930405", "content": "Nov 19, 2018 · R星很稳，做游戏更追求品质细节，旗下游戏都是销量口碑双丰收的.游戏销量就和他做游戏一样稳得一批，形成了只要是R星出品玩家都乐意掏钱买账，当之无愧的业内游戏质量最好！缺点就是不敢创新，最近几年开发的游戏都是属于老IP续作，游戏玩法方面也并不出彩，中规中矩类型！育碧游戏公式 ..."}
+{"idx": 3, "title": "荒野大镖客2游戏里照片模式拍的图片怎么找？在电脑的哪个 ...", "date": "", "ddg_snippet": "Sep 27, 2021 · 在 r星客户端 里，不能保存到本地，建议你用英伟达的那个 截录屏，alt+f1就可以截图，你把照片模式的那些参数隐藏后没区别的。不想用alt+f1就先按alt+z里面有快捷键设置。", "subpage_snippet": "", "source": "www.zhihu.com", "link": "https://www.zhihu.com/question/488136528", "content": "Sep 27, 2021 · 在 r星客户端 里，不能保存到本地，建议你用英伟达的那个 截录屏，alt+f1就可以截图，你把照片模式的那些参数隐藏后没区别的。不想用alt+f1就先按alt+z里面有快捷键设置。"}
+{"idx": 4, "title": "为什么TAKE TWO这个超大游戏公司在国内似乎知名度很低？", "date": "", "ddg_snippet": "Aug 4, 2021 · R星和2K在硬核玩家群体里的知名度应该不低吧，前者因为GTA系列和荒野大镖客系列，后者因为NBA 2K系列、文明系列、生化奇兵系列。", "subpage_snippet": "", "source": "www.zhihu.com", "link": "https://www.zhihu.com/question/477137574", "content": "Aug 4, 2021 · R星和2K在硬核玩家群体里的知名度应该不低吧，前者因为GTA系列和荒野大镖客系列，后者因为NBA 2K系列、文明系列、生化奇兵系列。"}
+{"idx": 5, "title": "r星官网在哪 - 百度知道", "date": "", "ddg_snippet": "Apr 11, 2024 · R星，全称为Rockstar Games，是游戏发行商Take-Two Interactive旗下的游戏开发分公司，成立于1998年，总部设在美国纽约。 该公司的作品中，最为知名的是《侠盗猎车手》系列，该系列自1997年发行第一部作品便受到广泛的欢迎，成为R星的一部大作。", "subpage_snippet": "", "source": "zhidao.baidu.com", "link": "https://zhidao.baidu.com/question/1506179597375211459.html", "content": "Apr 11, 2024 · R星，全称为Rockstar Games，是游戏发行商Take-Two Interactive旗下的游戏开发分公司，成立于1998年，总部设在美国纽约。 该公司的作品中，最为知名的是《侠盗猎车手》系列，该系列自1997年发行第一部作品便受到广泛的欢迎，成为R星的一部大作。"}
+{"idx": 6, "title": "R星怎么和EPIC解绑？ - 知乎", "date": "", "ddg_snippet": "然后R星还会发来电子邮件，需要你提供social club昵称和邮箱地址，epic ID以及epic账户界面的截图，根据它的要求来就行了。 大家可以看看我怎么回复的 然后就是等待R星的回复，然后就成功解绑啦！", "subpage_snippet": "", "source": "www.zhihu.com", "link": "https://www.zhihu.com/question/396805794", "content": "然后R星还会发来电子邮件，需要你提供social club昵称和邮箱地址，epic ID以及epic账户界面的截图，根据它的要求来就行了。 大家可以看看我怎么回复的 然后就是等待R星的回复，然后就成功解绑啦！"}
+{"idx": 7, "title": "为什么R星做游戏这么牛啊？ - 知乎", "date": "", "ddg_snippet": "R星美术总监 Aaron Garbut（带过各种GTA项目） 参见： Grand Theft Auto IV's Aaron Garbut: Part 1 Interview • Page 1 • Eurogamer.net 角色动作 GTA4角色动作软件用的是 NaturalMotion Endorphin （估计GTA5用的也是这个） 引自网络： “Endorphin是英国 Natural Motion 公司所最新研发的角色动态生成软件，里面拥有英国牛津大学实验室 ...", "subpage_snippet": "", "source": "www.zhihu.com", "link": "https://www.zhihu.com/question/21093833?sort=created", "content": "R星美术总监 Aaron Garbut（带过各种GTA项目） 参见： Grand Theft Auto IV's Aaron Garbut: Part 1 Interview • Page 1 • Eurogamer.net 角色动作 GTA4角色动作软件用的是 NaturalMotion Endorphin （估计GTA5用的也是这个） 引自网络： “Endorphin是英国 Natural Motion 公司所最新研发的角色动态生成软件，里面拥有英国牛津大学实验室 ..."}
+{"idx": 8, "title": "R星，k2公司的游戏突然开始扫盘，并时时调用网络摄像头 ...", "date": "", "ddg_snippet": "Jul 6, 2025 · R星，k2公司的游戏突然开始扫盘，并时时调用网络摄像头合法吗？游戏协议中对中外玩家区别对待了没？", "subpage_snippet": "", "source": "www.zhihu.com", "link": "https://www.zhihu.com/question/1916027349968871882", "content": "Jul 6, 2025 · R星，k2公司的游戏突然开始扫盘，并时时调用网络摄像头合法吗？游戏协议中对中外玩家区别对待了没？"}
+{"idx": 9, "title": "怎么查到与steam账号绑定的r星账号_百度知道", "date": "", "ddg_snippet": "Jun 11, 2020 · 怎么查到与steam账号绑定的r星账号1、首先在电脑中启动steam客户端，然后点击左上角的三横线标志。2、然后在弹出的设置菜单中，选择设置按钮，如下图所示。3、接着在设置菜单的工具栏中，选择账号连接按钮。4、在账", "subpage_snippet": "", "source": "zhidao.baidu.com", "link": "https://zhidao.baidu.com/question/566151461477095204.html", "content": "Jun 11, 2020 · 怎么查到与steam账号绑定的r星账号1、首先在电脑中启动steam客户端，然后点击左上角的三横线标志。2、然后在弹出的设置菜单中，选择设置按钮，如下图所示。3、接着在设置菜单的工具栏中，选择账号连接按钮。4、在账"}
diff --git a/data/sampled_jsons/Self-refine_Iterative_Refinement_Self-Feedback_Madaan_2024_year_2024.jsonl b/data/sampled_jsons/Self-refine_Iterative_Refinement_Self-Feedback_Madaan_2024_year_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..404159fc4bb0290bb634b1a1bf0173f74ac1caa8
--- /dev/null
+++ b/data/sampled_jsons/Self-refine_Iterative_Refinement_Self-Feedback_Madaan_2024_year_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "[2303.17651] Self - Refine : Iterative Refinement with Self - Feedback", "date": "", "ddg_snippet": "Self - Refine does not require any supervised training data, additional training, or reinforcement learning, and instead uses a single LLM as the generator, refiner, and feedback provider.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2303.17651", "content": "Self - Refine does not require any supervised training data, additional training, or reinforcement learning, and instead uses a single LLM as the generator, refiner, and feedback provider."}
+{"idx": 1, "title": "GitHub - madaan / self - refine : LLMs can generate feedback on their...", "date": "", "ddg_snippet": "Self - Refine : Iterative Refinement with Self - Feedback .Nov 2023: Added visual self - refine examples and colabs. Use GPT4-V to write tikz code for diagrams, and improve them iteratively .", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/madaan/self-refine", "content": "Self - Refine : Iterative Refinement with Self - Feedback .Nov 2023: Added visual self - refine examples and colabs. Use GPT4-V to write tikz code for diagrams, and improve them iteratively ."}
+{"idx": 2, "title": "Self - Refine : Iterative Refinement with Self - Feedback | DeepAI", "date": "", "ddg_snippet": "Just as people then refine their text, we introduce SELF - REFINE , a framework for similarly improving initial outputs from LLMs through iterative feedback and refinement . The main idea is to generate an output using an LLM, then allow the same model to provide multi-aspect feedback for its...", "subpage_snippet": "", "source": "deepai.org", "link": "https://deepai.org/publication/self-refine-iterative-refinement-with-self-feedback", "content": "Just as people then refine their text, we introduce SELF - REFINE , a framework for similarly improving initial outputs from LLMs through iterative feedback and refinement . The main idea is to generate an output using an LLM, then allow the same model to provide multi-aspect feedback for its..."}
+{"idx": 3, "title": "Self - Refine : Iterative Refinement with Self - Feedback for LLMs", "date": "", "ddg_snippet": "Refining through feedback : Self - Refine enhances Large Language Model outputs by iteratively improving initial results based on model feedback . Practical application : It is a simple three-step process: generate output, get feedback , and refine the answer...", "subpage_snippet": "", "source": "learnprompting.org", "link": "https://learnprompting.org/docs/advanced/self_criticism/self_refine", "content": "Refining through feedback : Self - Refine enhances Large Language Model outputs by iteratively improving initial results based on model feedback . Practical application : It is a simple three-step process: generate output, get feedback , and refine the answer..."}
+{"idx": 4, "title": "Self - Refine : Iterative Refinement with Self - Feedback", "date": "", "ddg_snippet": "Self - Refine is a framework for iteratively improving outputs from LLMs through a process of iterative creation with feedback .", "subpage_snippet": "", "source": "selfrefine.info", "link": "https://selfrefine.info/", "content": "Self - Refine is a framework for iteratively improving outputs from LLMs through a process of iterative creation with feedback ."}
+{"idx": 5, "title": "madaan / self - refine - Githubissues", "date": "", "ddg_snippet": "Self - Refine : Iterative Refinement with Self - Feedback . With Self - Refine , LLMs can generate feedback on their work, use it to improve the output, and repeat this process.", "subpage_snippet": "", "source": "githubissues.com", "link": "https://githubissues.com/madaan/self-refine/readme", "content": "Self - Refine : Iterative Refinement with Self - Feedback . With Self - Refine , LLMs can generate feedback on their work, use it to improve the output, and repeat this process."}
+{"idx": 6, "title": "[PDF] Self - Refine : Iterative Refinement with Self - Feedback", "date": "", "ddg_snippet": "Self - Refine is introduced, an approach for improving initial outputs from LLMs through iterative feedback and refinement that demonstrates that even state-of-the-art LLMs like GPT-4 can be further improved at test time using this simple, standalone approach.", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/Self-Refine:-Iterative-Refinement-with-Madaan-Tandon/3aaf6a2cbad5850ad81ab5c163599cb3d523436f", "content": "Self - Refine is introduced, an approach for improving initial outputs from LLMs through iterative feedback and refinement that demonstrates that even state-of-the-art LLMs like GPT-4 can be further improved at test time using this simple, standalone approach."}
+{"idx": 7, "title": "Self - Refine : Iterative Refinement with Self - Feedback - Notes by Lex", "date": "", "ddg_snippet": "Describes the Self - Refine approach to Reflection: LLM generates an output, then provides feedback on its work and refines the output based on that feedback , effectively simulating a human writer's process of editing their work. The approach is motivated by how humans refine their...", "subpage_snippet": "", "source": "notesbylex.com", "link": "https://notesbylex.com/self-refine-iterative-refinement-with-self-feedback", "content": "Describes the Self - Refine approach to Reflection: LLM generates an output, then provides feedback on its work and refines the output based on that feedback , effectively simulating a human writer's process of editing their work. The approach is motivated by how humans refine their..."}
+{"idx": 8, "title": "Roam Research Notes on “ SELF - REFINE : Iterative Refinement with...”", "date": "", "ddg_snippet": "SELF - REFINE mimics human iterative refinement , where an initial draft is revised based on self - feedback . Iterative Process: The process uses two steps: FEEDBACK and REFINE , iterating until no further improvements are needed.", "subpage_snippet": "", "source": "www.marknagelberg.com", "link": "https://www.marknagelberg.com/roam-research-notes-on-self-refine-iterative-refinement-with-self-feedback-by-madaan-et-al/", "content": "SELF - REFINE mimics human iterative refinement , where an initial draft is revised based on self - feedback . Iterative Process: The process uses two steps: FEEDBACK and REFINE , iterating until no further improvements are needed."}
+{"idx": 9, "title": "Self - Refinement Module in AI", "date": "", "ddg_snippet": "Self - refinement modules iteratively optimize model outputs using feedback , boosting performance, generalization, and error mitigation in AI systems.", "subpage_snippet": "", "source": "www.emergentmind.com", "link": "https://www.emergentmind.com/topics/self-refinement-module", "content": "Self - refinement modules iteratively optimize model outputs using feedback , boosting performance, generalization, and error mitigation in AI systems."}
diff --git a/data/sampled_jsons/Shen_Lee_2019_randomized_midpoint_method_Picard_iterations_log-concave_sampling.jsonl b/data/sampled_jsons/Shen_Lee_2019_randomized_midpoint_method_Picard_iterations_log-concave_sampling.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..653fd61990df8ec81e4592c3e7beee155398b833
--- /dev/null
+++ b/data/sampled_jsons/Shen_Lee_2019_randomized_midpoint_method_Picard_iterations_log-concave_sampling.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "The Randomized Midpoint Method for Log-Concave Sampling", "date": "", "ddg_snippet": "Abstract page for arXiv paper 1909.05503: The Randomized Midpoint Method for Log-Concave Sampling", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/1909.05503", "content": "Abstract page for arXiv paper 1909.05503: The Randomized Midpoint Method for Log-Concave Sampling"}
+{"idx": 1, "title": "PDF The Randomized Midpoint Method for Log-Concave Sampling", "date": "", "ddg_snippet": "Section 2 provides background information on solving the log-concave sampling problem, while Section 3 introduces our notations and assumptions about the function f.", "subpage_snippet": "", "source": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper/2019/file/eb86d510361fc23b59f18c1bc9802cc6-Paper.pdf", "content": "Section 2 provides background information on solving the log-concave sampling problem, while Section 3 introduces our notations and assumptions about the function f."}
+{"idx": 2, "title": "Faster Diffusion Sampling with Randomized Midpoints: Sequential and ...", "date": "", "ddg_snippet": "In this work, we propose a new scheme inspired by Shen and Lee's randomized midpoint method for log-concave sampling ( Shen & Lee , 2019 ).", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=MT3aOfXIbY", "content": "In this work, we propose a new scheme inspired by Shen and Lee's randomized midpoint method for log-concave sampling ( Shen & Lee , 2019 )."}
+{"idx": 3, "title": "The randomized midpoint method for log-concave sampling", "date": "", "ddg_snippet": "To solve the sampling problem, we propose a new framework to discretize stochastic differential equations. We apply this framework to discretize and simulate ULD, which converges to the target distribution p*. The framework can be used to solve not only the log-concave sampling problem, but any problem that involves simulating (stochastic) differential equations.", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/abs/10.5555/3454287.3454475", "content": "To solve the sampling problem, we propose a new framework to discretize stochastic differential equations. We apply this framework to discretize and simulate ULD, which converges to the target distribution p*. The framework can be used to solve not only the log-concave sampling problem, but any problem that involves simulating (stochastic) differential equations."}
+{"idx": 4, "title": "Reviews: The Randomized Midpoint Method for Log-Concave Sampling", "date": "", "ddg_snippet": "4. The statement of the Theorem 3 is awkward: what does it mean \"Algorithm 1 can find a random point X\"? Maybe a statement on the distribution at step N is better. Significance: This paper improves the previous rate for log-concave sampling and provides new ideas for designing new sampling algorithms.", "subpage_snippet": "", "source": "papers.nips.cc", "link": "https://papers.nips.cc/paper_files/paper/2019/file/eb86d510361fc23b59f18c1bc9802cc6-Reviews.html", "content": "4. The statement of the Theorem 3 is awkward: what does it mean \"Algorithm 1 can find a random point X\"? Maybe a statement on the distribution at step N is better. Significance: This paper improves the previous rate for log-concave sampling and provides new ideas for designing new sampling algorithms."}
+{"idx": 5, "title": "PDF The Randomized Midpoint Method for Log-Concave Sampling", "date": "", "ddg_snippet": "The Randomized Midpoint Method for Log-Concave Sampling Ruoqi Shen University of Washington shenr3@cs.washington.edu Yin Tat Lee University of Washington and Microsoft Research yintat@uw.edu", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/1909.05503.pdf", "content": "The Randomized Midpoint Method for Log-Concave Sampling Ruoqi Shen University of Washington shenr3@cs.washington.edu Yin Tat Lee University of Washington and Microsoft Research yintat@uw.edu"}
+{"idx": 6, "title": "ICLR Poster Faster Diffusion Sampling with Randomized Midpoints ...", "date": "", "ddg_snippet": "In this work, we propose a new scheme inspired by Shen and Lee's randomized midpoint method for log-concave sampling ( Shen & Lee , 2019 ). We prove that this approach achieves the best known dimension dependence for sampling from arbitrary smooth distributions in total variation distance (O ~ (d 5 / 12) compared to O ~ (d) from prior work).", "subpage_snippet": "", "source": "iclr.cc", "link": "https://iclr.cc/virtual/2025/poster/29931", "content": "In this work, we propose a new scheme inspired by Shen and Lee's randomized midpoint method for log-concave sampling ( Shen & Lee , 2019 ). We prove that this approach achieves the best known dimension dependence for sampling from arbitrary smooth distributions in total variation distance (O ~ (d 5 / 12) compared to O ~ (d) from prior work)."}
+{"idx": 7, "title": "The Randomized Midpoint Method for Log-Concave Sampling", "date": "", "ddg_snippet": "Ruoqi Shen · Yin Tat Lee East Exhibition Hall B, C #163 Keywords: [ Optimization ] [ Probabilistic Methods ] [ MCMC ] [ Abstract ]", "subpage_snippet": "", "source": "neurips.cc", "link": "https://neurips.cc/virtual/2019/poster/14500", "content": "Ruoqi Shen · Yin Tat Lee East Exhibition Hall B, C #163 Keywords: [ Optimization ] [ Probabilistic Methods ] [ MCMC ] [ Abstract ]"}
+{"idx": 8, "title": "The Randomized Midpoint Method for Log-Concave Sampling", "date": "", "ddg_snippet": "Ruoqi Shen , Yin Tat Lee . The Randomized Midpoint Method for Log-Concave Sampling . In Hanna M. Wallach, Hugo Larochelle, Alina Beygelzimer, Florence d'Alché-Buc, Edward A. Fox, Roman Garnett, editors, Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019 , NeurIPS 2019 , 8-14 December 2019 , Vancouver, BC, Canada. pages 2098-2109 ...", "subpage_snippet": "", "source": "researchr.org", "link": "https://researchr.org/publication/ShenL19-9", "content": "Ruoqi Shen , Yin Tat Lee . The Randomized Midpoint Method for Log-Concave Sampling . In Hanna M. Wallach, Hugo Larochelle, Alina Beygelzimer, Florence d'Alché-Buc, Edward A. Fox, Roman Garnett, editors, Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019 , NeurIPS 2019 , 8-14 December 2019 , Vancouver, BC, Canada. pages 2098-2109 ..."}
+{"idx": 9, "title": "Faster Diffusion-based Sampling with Randomized Midpoints:...", "date": "", "ddg_snippet": "In this work, we propose a new discretization scheme for diffusion models inspired by Shen and Lee's randomized midpoint method for log-concave sampling~\\cite {ShenL19}.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=KhIros9JTC", "content": "In this work, we propose a new discretization scheme for diffusion models inspired by Shen and Lee's randomized midpoint method for log-concave sampling~\\cite {ShenL19}."}
diff --git a/data/sampled_jsons/Solver-in-the-Loop_Learning_from_Differentiable_Physics_to_Interact_with_Iterative_PDE-Solvers.jsonl b/data/sampled_jsons/Solver-in-the-Loop_Learning_from_Differentiable_Physics_to_Interact_with_Iterative_PDE-Solvers.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..3d21a49c9abf5d9d9dfb241a07c8abefc5e9e652
--- /dev/null
+++ b/data/sampled_jsons/Solver-in-the-Loop_Learning_from_Differentiable_Physics_to_Interact_with_Iterative_PDE-Solvers.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "GitHub - tum-pbs/Solver-in-the-Loop: Solver-in-the-Loop: Learning from ...", "date": "", "ddg_snippet": "Solver-in-the-Loop This is the source code repository for the NeurIPS'20 paper \" Solver-in-the-Loop: Learning from Differentiable Physics to Interact with Iterative PDE-Solvers \" by Kiwon Um, Robert Brand, Yun (Raymond) Fei, Philipp Holl, Nils Thuerey. Additional information: project page, NeurIPS 2020 page.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/tum-pbs/Solver-in-the-Loop", "content": "Solver-in-the-Loop This is the source code repository for the NeurIPS'20 paper \" Solver-in-the-Loop: Learning from Differentiable Physics to Interact with Iterative PDE-Solvers \" by Kiwon Um, Robert Brand, Yun (Raymond) Fei, Philipp Holl, Nils Thuerey. Additional information: project page, NeurIPS 2020 page."}
+{"idx": 1, "title": "Solver-in-the-loop | Proceedings of the 34th International Conference ...", "date": "", "ddg_snippet": "Index Terms Solver-in-the-loop: learning from differentiable physics to interact with iterative PDE-solvers Computing methodologies Machine learning Learning paradigms", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.5555/3495724.3496237", "content": "Index Terms Solver-in-the-loop: learning from differentiable physics to interact with iterative PDE-solvers Computing methodologies Machine learning Learning paradigms"}
+{"idx": 2, "title": "Solver-in-the-Loop: Learning from Differentiable Physics to Interact ...", "date": "", "ddg_snippet": "Appendix for Solver-in-the-Loop: Learning from Differentiable Physics to Interact with Iterative PDE-Solvers Below, we give additional details regarding the steps and numerical methods employed in each of the interaction variants discussed in the main text.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2007.00016", "content": "Appendix for Solver-in-the-Loop: Learning from Differentiable Physics to Interact with Iterative PDE-Solvers Below, we give additional details regarding the steps and numerical methods employed in each of the interaction variants discussed in the main text."}
+{"idx": 3, "title": "Solver-in-the-Loop/README.md at master - GitHub", "date": "", "ddg_snippet": "Solver-in-the-Loop This is the source code repository for the NeurIPS'20 paper \" Solver-in-the-Loop: Learning from Differentiable Physics to Interact with Iterative PDE-Solvers \" by Kiwon Um, Robert Brand, Yun (Raymond) Fei, Philipp Holl, Nils Thuerey. Additional information: project page, NeurIPS 2020 page.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/tum-pbs/Solver-in-the-Loop/blob/master/README.md", "content": "Solver-in-the-Loop This is the source code repository for the NeurIPS'20 paper \" Solver-in-the-Loop: Learning from Differentiable Physics to Interact with Iterative PDE-Solvers \" by Kiwon Um, Robert Brand, Yun (Raymond) Fei, Philipp Holl, Nils Thuerey. Additional information: project page, NeurIPS 2020 page."}
+{"idx": 4, "title": "Solver-in-the-loop | Proceedings of the 34th International Conference ...", "date": "", "ddg_snippet": "Solver-in-the-loop: learning from differentiable physics to interact with iterative PDE-solvers", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/full/10.5555/3495724.3496237", "content": "Solver-in-the-loop: learning from differentiable physics to interact with iterative PDE-solvers"}
+{"idx": 5, "title": "Review for NeurIPS paper: Solver-in-the-Loop: Learning from ...", "date": "", "ddg_snippet": "Solver-in-the-Loop: Learning from Differentiable Physics to Interact with Iterative PDE-Solvers", "subpage_snippet": "", "source": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper_files/paper/2020/file/43e4e6a6f341e00671e123714de019a8-Review.html", "content": "Solver-in-the-Loop: Learning from Differentiable Physics to Interact with Iterative PDE-Solvers"}
+{"idx": 6, "title": "Solver-in-the-Loop: Learning from Differentiable Physics to ...", "date": "", "ddg_snippet": "by K Um · 2020 · Cited by 349 — Solver-in-the-Loop: Learning from Differentiable Physics to Interact with Iterative PDE-Solvers . Authors:Kiwon Um, Robert Brand, Yun (Raymond) ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2007.00016", "content": "by K Um · 2020 · Cited by 349 — Solver-in-the-Loop: Learning from Differentiable Physics to Interact with Iterative PDE-Solvers . Authors:Kiwon Um, Robert Brand, Yun (Raymond) ..."}
+{"idx": 7, "title": "Solver-in-the-Loop: Learning from Differentiable Physics to ...", "date": "", "ddg_snippet": "by K Um · 2020 · Cited by 349 — Finding accurate solutions to partial differential equations (PDEs) is a crucial task in all scientific and engineering disciplines. 12 pages", "subpage_snippet": "", "source": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper/2020/file/43e4e6a6f341e00671e123714de019a8-Paper.pdf", "content": "by K Um · 2020 · Cited by 349 — Finding accurate solutions to partial differential equations (PDEs) is a crucial task in all scientific and engineering disciplines. 12 pages"}
+{"idx": 8, "title": "Solver-in-the-Loop: Learning from Differentiable Physics to...", "date": "", "ddg_snippet": "by K Um · Cited by 348 — Solver-in-the-Loop: Learning from Differentiable Physics to Interact with Iterative PDE-Solvers . Accepted Papers by Program Chairs ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=9Go-qmHB2PG", "content": "by K Um · Cited by 348 — Solver-in-the-Loop: Learning from Differentiable Physics to Interact with Iterative PDE-Solvers . Accepted Papers by Program Chairs ..."}
+{"idx": 9, "title": "Solver-in-the-Loop: Learning from Differentiable Physics to ...", "date": "", "ddg_snippet": "Solver-in-the-Loop: Learning from Differentiable Physics to Interact with Iterative PDE-Solvers · Kiwon Um, Yun Fei, +2 authors. Nils Thürey · Published in Neural ...", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/Solver-in-the-Loop:-Learning-from-Differentiable-to-Um-Fei/8995944de6f298c6a439675e31068ab8258722ba", "content": "Solver-in-the-Loop: Learning from Differentiable Physics to Interact with Iterative PDE-Solvers · Kiwon Um, Yun Fei, +2 authors. Nils Thürey · Published in Neural ..."}
diff --git a/data/sampled_jsons/Song_et_al_2021_score-based_generative_modeling_stochastic_differential_equations_abstract_year_2021.jsonl b/data/sampled_jsons/Song_et_al_2021_score-based_generative_modeling_stochastic_differential_equations_abstract_year_2021.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..6f00ecf39afc462083723339ff7379e86ee987ae
--- /dev/null
+++ b/data/sampled_jsons/Song_et_al_2021_score-based_generative_modeling_stochastic_differential_equations_abstract_year_2021.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Score-Based Generative Modeling through Stochastic Differential Equations", "date": "", "ddg_snippet": "Creating noise from data is easy; creating data from noise is generative modeling . We present a stochastic differential equation (SDE) that smoothly transforms a complex data distribution to a known prior distribution by slowly injecting noise, and a corresponding reverse-time SDE that transforms the prior distribution back into the data distribution by slowly removing the noise. Crucially ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2011.13456", "content": "Creating noise from data is easy; creating data from noise is generative modeling . We present a stochastic differential equation (SDE) that smoothly transforms a complex data distribution to a known prior distribution by slowly injecting noise, and a corresponding reverse-time SDE that transforms the prior distribution back into the data distribution by slowly removing the noise. Crucially ..."}
+{"idx": 1, "title": "Generative Modeling by Estimating Gradients of the Data ... - Yang Song", "date": "", "ddg_snippet": "By generalizing the number of noise scales to infinity, we further proved that score-based generative models and diffusion probabilistic models can both be viewed as discretizations to stochastic differential equations determined by score functions.", "subpage_snippet": "", "source": "yang-song.net", "link": "https://yang-song.net/blog/2021/score/", "content": "By generalizing the number of noise scales to infinity, we further proved that score-based generative models and diffusion probabilistic models can both be viewed as discretizations to stochastic differential equations determined by score functions."}
+{"idx": 2, "title": "PDF S -B GENERATIVE MODELING THROUGH S DIFFERENTIAL EQUATIONS - OpenReview", "date": "", "ddg_snippet": "tpxq. et al.,2020). To enable new sampling methods and further extend the capabilities of score-based generative models, we propose a unified framework that generalizes previous approaches through the lens of stochastic differential equations (SDEs). Specifically, instead of perturbing data with a finite number of noise distributions, we consider a continuum of distributions that evolve ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf/ef0eadbe07115b0853e964f17aa09d811cd490f1.pdf", "content": "tpxq. et al.,2020). To enable new sampling methods and further extend the capabilities of score-based generative models, we propose a unified framework that generalizes previous approaches through the lens of stochastic differential equations (SDEs). Specifically, instead of perturbing data with a finite number of noise distributions, we consider a continuum of distributions that evolve ..."}
+{"idx": 3, "title": "Score-Based Generative Modeling through Stochastic Differential Equations", "date": "", "ddg_snippet": "We show that this framework encapsulates previous approaches in score-based generative modeling and diffusion probabilistic modeling , allowing for new sampling procedures and new modeling capabilities. In particular, we introduce a predictor-corrector framework to correct errors in the evolution of the discretized reverse-time SDE.", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/Score-Based-Generative-Modeling-through-Stochastic-Song-Sohl-Dickstein/633e2fbfc0b21e959a244100937c5853afca4853", "content": "We show that this framework encapsulates previous approaches in score-based generative modeling and diffusion probabilistic modeling , allowing for new sampling procedures and new modeling capabilities. In particular, we introduce a predictor-corrector framework to correct errors in the evolution of the discretized reverse-time SDE."}
+{"idx": 4, "title": "ICLR 2021 Score-Based Generative Modeling through Stochastic ...", "date": "", "ddg_snippet": "We show that this framework encapsulates previous approaches in score-based generative modeling and diffusion probabilistic modeling , allowing for new sampling procedures and new modeling capabilities. In particular, we introduce a predictor-corrector framework to correct errors in the evolution of the discretized reverse-time SDE.", "subpage_snippet": "", "source": "iclr.cc", "link": "https://iclr.cc/virtual/2021/oral/3402", "content": "We show that this framework encapsulates previous approaches in score-based generative modeling and diffusion probabilistic modeling , allowing for new sampling procedures and new modeling capabilities. In particular, we introduce a predictor-corrector framework to correct errors in the evolution of the discretized reverse-time SDE."}
+{"idx": 5, "title": "PDF Literature Review: Score-Based Generative Modeling through Stochastic ...", "date": "", "ddg_snippet": "Key references: Anderson, Brian D O (1982). Reverse-time diffusion equation models, Stochastic Process. Appl., 12(3): pp.313-326 Sarkka, Simo and Arno Solin (2019). Applied stochastic differential equations , CUP Song , Yang et al . ( 2021 ). Score-Based Generative Modeling through Stochastic Differential Equations , arXiv:2011.13456v2", "subpage_snippet": "", "source": "parleyyang.github.io", "link": "https://parleyyang.github.io/Cam/LT2021/Songpaper.pdf", "content": "Key references: Anderson, Brian D O (1982). Reverse-time diffusion equation models, Stochastic Process. Appl., 12(3): pp.313-326 Sarkka, Simo and Arno Solin (2019). Applied stochastic differential equations , CUP Song , Yang et al . ( 2021 ). Score-Based Generative Modeling through Stochastic Differential Equations , arXiv:2011.13456v2"}
+{"idx": 6, "title": "score_sde/README.md at main · yang-song/score_sde · GitHub", "date": "", "ddg_snippet": "We propose a unified framework that generalizes and improves previous work on score-based generative models through the lens of stochastic differential equations (SDEs). In particular, we can transform data to a simple noise distribution with a continuous-time stochastic process described by an SDE.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/yang-song/score_sde/blob/main/README.md", "content": "We propose a unified framework that generalizes and improves previous work on score-based generative models through the lens of stochastic differential equations (SDEs). In particular, we can transform data to a simple noise distribution with a continuous-time stochastic process described by an SDE."}
+{"idx": 7, "title": "dblp: Score-Based Generative Modeling through Stochastic Differential ...", "date": "", "ddg_snippet": "Bibliographic details on Score-Based Generative Modeling through Stochastic Differential Equations .", "subpage_snippet": "", "source": "dblp.org", "link": "https://dblp.org/rec/conf/iclr/0011SKKEP21", "content": "Bibliographic details on Score-Based Generative Modeling through Stochastic Differential Equations ."}
+{"idx": 8, "title": "Paper review: Score-Based Generative Modeling through Stochastic ...", "date": "", "ddg_snippet": "In this study, inspired by the work and code provided by Song et al . [17], we conducted experiments using a score based generative model trained on the FashionMNIST dataset, that uses Stochastic Differential Equations (SDE) to revert the noise corruption step. Our code is made available at", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=Lq316r4Lx9", "content": "In this study, inspired by the work and code provided by Song et al . [17], we conducted experiments using a score based generative model trained on the FashionMNIST dataset, that uses Stochastic Differential Equations (SDE) to revert the noise corruption step. Our code is made available at"}
+{"idx": 9, "title": "Score-Based Generative Modeling through Stochastic Differential Equations", "date": "", "ddg_snippet": "We propose a unified framework that generalizes and improves previous work on score-based generative models through the lens of stochastic differential equations (SDEs). In particular, we can transform data to a simple noise distribution with a continuous-time stochastic process described by an SDE.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/yang-song/score_sde", "content": "We propose a unified framework that generalizes and improves previous work on score-based generative models through the lens of stochastic differential equations (SDEs). In particular, we can transform data to a simple noise distribution with a continuous-time stochastic process described by an SDE."}
diff --git a/data/sampled_jsons/SpeechSSM_Long-Form_Speech_Generation_with_Spoken_Language_Models_paper.jsonl b/data/sampled_jsons/SpeechSSM_Long-Form_Speech_Generation_with_Spoken_Language_Models_paper.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..62585f801dadb20f6f4fc9602f1dbd300082058e
--- /dev/null
+++ b/data/sampled_jsons/SpeechSSM_Long-Form_Speech_Generation_with_Spoken_Language_Models_paper.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Long-Form Speech Generation with Spoken Language ...", "date": "", "ddg_snippet": "by SJ Park · 2024 · Cited by 9 — We consider the generative modeling of speech over multiple minutes, a requirement for long - form multimedia generation and audio-native voice assistants.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2412.18603", "content": "by SJ Park · 2024 · Cited by 9 — We consider the generative modeling of speech over multiple minutes, a requirement for long - form multimedia generation and audio-native voice assistants."}
+{"idx": 1, "title": "Long-Form Speech Generation with Spoken Language ...", "date": "", "ddg_snippet": "by SJ Park · Cited by 9 — The paper proposes SpeechSSM , a spoken language model based on state-space models that can generate long - form audio in a single pass. The paper also ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=4AmFA0qNQ2", "content": "by SJ Park · Cited by 9 — The paper proposes SpeechSSM , a spoken language model based on state-space models that can generate long - form audio in a single pass. The paper also ..."}
+{"idx": 2, "title": "Long-Form Speech Generation with Spoken Language Models", "date": "", "ddg_snippet": "Our work makes initial progress on naturalistic, audio-native, long - form speech generation : We introduce SpeechSSM , the first spoken language model for long- ...", "subpage_snippet": "", "source": "google.github.io", "link": "https://google.github.io/tacotron/publications/speechssm/", "content": "Our work makes initial progress on naturalistic, audio-native, long - form speech generation : We introduce SpeechSSM , the first spoken language model for long- ..."}
+{"idx": 3, "title": "Long-Form Speech Generation with Spoken Language ...", "date": "", "ddg_snippet": "24 Dec 2024 — We propose SpeechSSM , the first speech language model to learn from and sample long - form spoken audio (eg, 16 minutes of read or extemporaneous speech) in a ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2412.18603v1", "content": "24 Dec 2024 — We propose SpeechSSM , the first speech language model to learn from and sample long - form spoken audio (eg, 16 minutes of read or extemporaneous speech) in a ..."}
+{"idx": 4, "title": "Long-Form Speech Generation with Spoken Language ...", "date": "", "ddg_snippet": "24 Dec 2024 — SpeechSSM is a speech language model that generates long - form spoken audio without text intermediates using linear-time sequence modeling ...", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/papers/2412.18603", "content": "24 Dec 2024 — SpeechSSM is a speech language model that generates long - form spoken audio without text intermediates using linear-time sequence modeling ..."}
+{"idx": 5, "title": "Long-Form Speech Generation with Spoken Language ...", "date": "", "ddg_snippet": "by SJ Park · Cited by 9 — The result is SpeechSSM , a new (textless) spoken language model family (2B, 9B) designed for long - form generation , being the first to model and generate ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=4AmFA0qNQ2", "content": "by SJ Park · Cited by 9 — The result is SpeechSSM , a new (textless) spoken language model family (2B, 9B) designed for long - form generation , being the first to model and generate ..."}
+{"idx": 6, "title": "Long-Form Speech Generation with Spoken Language Models", "date": "", "ddg_snippet": "Key takeaway: ' SpeechSSM is the first speech language model to generate long - form speech (up to 16 minutes) in a single decoding session without text ...", "subpage_snippet": "", "source": "www.consensus.app", "link": "https://www.consensus.app/papers/details/a0d61097acb75bdea9e625e732087c65/", "content": "Key takeaway: ' SpeechSSM is the first speech language model to generate long - form speech (up to 16 minutes) in a single decoding session without text ..."}
+{"idx": 7, "title": "[Literature Review] Long-Form Speech Generation with ...", "date": "", "ddg_snippet": "The paper titled \" Long - Form Speech Generation with Spoken Language Models \" discusses advancements in the modeling of long - form spoken audio.", "subpage_snippet": "", "source": "www.themoonlight.io", "link": "https://www.themoonlight.io/en/review/long-form-speech-generation-with-spoken-language-models", "content": "The paper titled \" Long - Form Speech Generation with Spoken Language Models \" discusses advancements in the modeling of long - form spoken audio."}
+{"idx": 8, "title": "google-deepmind/librispeech-long", "date": "", "ddg_snippet": "LibriSpeech-Long is a benchmark dataset for long - form speech generation and processing. Released as part of \" Long - Form Speech Generation with Spoken Language ...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/google-deepmind/librispeech-long", "content": "LibriSpeech-Long is a benchmark dataset for long - form speech generation and processing. Released as part of \" Long - Form Speech Generation with Spoken Language ..."}
+{"idx": 9, "title": "KAIST researcher Se Jin Park develops 'SpeechSSM,' opening ...", "date": "", "ddg_snippet": "KAIST researcher Se Jin Park develops ' SpeechSSM ,' opening up possibilities for a 24-hour AI voice assistant · Paper Title: Long - Form Speech Generation with ...", "subpage_snippet": "", "source": "www.eurekalert.org", "link": "https://www.eurekalert.org/news-releases/1090061", "content": "KAIST researcher Se Jin Park develops ' SpeechSSM ,' opening up possibilities for a 24-hour AI voice assistant · Paper Title: Long - Form Speech Generation with ..."}
diff --git a/data/sampled_jsons/Statistical_Collusion_by_Collectives_on_Learning_Platforms_Figure_1_y_=_Poor_chart_year_2023.jsonl b/data/sampled_jsons/Statistical_Collusion_by_Collectives_on_Learning_Platforms_Figure_1_y_=_Poor_chart_year_2023.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..8f12e9651b278d6eb99071bb9649e98993985802
--- /dev/null
+++ b/data/sampled_jsons/Statistical_Collusion_by_Collectives_on_Learning_Platforms_Figure_1_y_=_Poor_chart_year_2023.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Statistical Collusion by Collectives on Learning Platforms GitHub - GauthierE/statistical-collusion Statistical Collusion by Collectives on Learning Platforms ... Statistical Collusion by Collectives on Learning Platforms Statistical Collusion by Collectives on Learning Platforms arXiv:2502.04879v1 [stat.ML] 7 Feb 2025 Statistical Collusion by Collectives on Learning Platforms", "date": "", "ddg_snippet": "Feb 7, 2025 · 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 ... statistical - collusion This repository contains the code for reproducing the experiments and figures presented in the paper Statistical Collusion by Collectives on Learning Platforms . 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... May 1 , 2025 · The paper explores the statistical inference for collective action in learning platforms , in particular, the paper examines not only the classic signal planting procedure introduced by Hardt et al, but also signal unplanting as well as signal erasing, both of which require statistical inferences for defining the optimal strategy h. Abstract 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. Feb 10, 2025 · Collective Action in Machine Learning . We draw on the work of Hardt et al. (2023), who investigate the following problem: a population of individuals (x, y ) ∈ X × Y , drawn i.i.d. from a distribution D, interacts with a platform . A collective of relative size α ∈ (0, 1 ) forms within this population with the goal of influencing the platform . The collective ’s influence is quantified by a ... 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/abs/2502.04879", "content": "Feb 7, 2025 · 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 ... statistical - collusion This repository contains the code for reproducing the experiments and figures presented in the paper Statistical Collusion by Collectives on Learning Platforms . 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... May 1 , 2025 · The paper explores the statistical inference for collective action in learning platforms , in particular, the paper examines not only the classic signal planting procedure introduced by Hardt et al, but also signal unplanting as well as signal erasing, both of which require statistical inferences for defining the optimal strategy h. Abstract 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. Feb 10, 2025 · Collective Action in Machine Learning . We draw on the work of Hardt et al. (2023), who investigate the following problem: a population of individuals (x, y ) ∈ X × Y , drawn i.i.d. from a distribution D, interacts with a platform . A collective of relative size α ∈ (0, 1 ) forms within this population with the goal of influencing the platform . The collective ’s influence is quantified by a ... 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": 1, "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": 2, "title": "Statistical Collusion by Collectives on Learning Platforms ...", "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": 3, "title": "Statistical Collusion by Collectives on Learning Platforms", "date": "", "ddg_snippet": "May 1 , 2025 · The paper explores the statistical inference for collective action in learning platforms , in particular, the paper examines not only the classic signal planting procedure introduced by Hardt et al, but also signal unplanting as well as signal erasing, both of which require statistical inferences for defining the optimal strategy h.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=46yLEXtav4", "content": "May 1 , 2025 · The paper explores the statistical inference for collective action in learning platforms , in particular, the paper examines not only the classic signal planting procedure introduced by Hardt et al, but also signal unplanting as well as signal erasing, both of which require statistical inferences for defining the optimal strategy h."}
+{"idx": 4, "title": "Statistical Collusion by Collectives on Learning Platforms", "date": "", "ddg_snippet": "Abstract 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": "Abstract 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": 5, "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": 6, "title": "Statistical Collusion by Collectives on Learning Platforms", "date": "", "ddg_snippet": "by E Gauthier · 2025 · Cited by 2 — Lower bound from Theorem 3.3 with y * = Poor for different values of n with N = 500, 000, N = 1 , 000, 000, and N = 2, 000, 000. 4.2. Signal unplanting. We now ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2502.04879?", "content": "by E Gauthier · 2025 · Cited by 2 — Lower bound from Theorem 3.3 with y * = Poor for different values of n with N = 500, 000, N = 1 , 000, 000, and N = 2, 000, 000. 4.2. Signal unplanting. We now ..."}
+{"idx": 7, "title": "Statistical Collusion by Collectives on Learning Platforms", "date": "", "ddg_snippet": "Line 403: y ∗ ∈ {Good, Average, Poor } -> y ∗ ∈ {Good (G), Average (A), Poor (P)}. if possible: Propose an alternative notation for n e s t as it can lead ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=46yLEXtav4&noteId=vzYAAeoKXq", "content": "Line 403: y ∗ ∈ {Good, Average, Poor } -> y ∗ ∈ {Good (G), Average (A), Poor (P)}. if possible: Propose an alternative notation for n e s t as it can lead ..."}
+{"idx": 8, "title": "Statistical Collusion by Collectives on Learning Platforms", "date": "", "ddg_snippet": "7 Feb 2025 — The labels assigned to each vehicle reflect the car's evaluation, categorized into four classes: Excellent, Good, Average, or Poor . Further ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2502.04879v1", "content": "7 Feb 2025 — The labels assigned to each vehicle reflect the car's evaluation, categorized into four classes: Excellent, Good, Average, or Poor . Further ..."}
+{"idx": 9, "title": "Collusion Detection in Reputation Systems for Peer-to- ...", "date": "", "ddg_snippet": "by Z Li · Cited by 7 — In collu- sion, two or more malicious peers conspire to give each other high local reputation values and (or) give all other peers low local reputation values ...", "subpage_snippet": "", "source": "www.cs.virginia.edu", "link": "https://www.cs.virginia.edu/~hs6ms/publishedPaper/Conference/2012/ICPP2012.pdf", "content": "by Z Li · Cited by 7 — In collu- sion, two or more malicious peers conspire to give each other high local reputation values and (or) give all other peers low local reputation values ..."}
diff --git a/data/sampled_jsons/Stress-Testing_Capability_Elicitation_With_Password-Locked_Models_PDF.jsonl b/data/sampled_jsons/Stress-Testing_Capability_Elicitation_With_Password-Locked_Models_PDF.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..48aa37b70c51bdd62943ce9a282a916bc3004f7a
--- /dev/null
+++ b/data/sampled_jsons/Stress-Testing_Capability_Elicitation_With_Password-Locked_Models_PDF.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "[2405.19550] Stress-Testing Capability Elicitation With", "date": "", "ddg_snippet": "View a PDF of the paper titled Stress - Testing Capability Elicitation With Password - Locked Models , by Ryan Greenblatt and 3 other authors", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2405.19550", "content": "View a PDF of the paper titled Stress - Testing Capability Elicitation With Password - Locked Models , by Ryan Greenblatt and 3 other authors"}
+{"idx": 1, "title": "The Elicitation Game: Evaluating Capability Elicitation", "date": "", "ddg_snippet": "We evaluate a variety of capability elicitation techniques on password - locked and circuit-broken model organisms, in both a multiple-choice question ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2502.02180v2", "content": "We evaluate a variety of capability elicitation techniques on password - locked and circuit-broken model organisms, in both a multiple-choice question ..."}
+{"idx": 2, "title": "System Card: Claude Opus 4 & Claude Sonnet 4 | PDF", "date": "", "ddg_snippet": "... cdn.anthropic.com/4263b940cabb546aa0e3283f35b686f4f3b2ff47. pdf ... Per capita expenditure prediction using model stacking based on satellite ima...", "subpage_snippet": "", "source": "www.slideshare.net", "link": "https://www.slideshare.net/slideshow/system-card-claude-opus-4-claude-sonnet-4/279651386", "content": "... cdn.anthropic.com/4263b940cabb546aa0e3283f35b686f4f3b2ff47. pdf ... Per capita expenditure prediction using model stacking based on satellite ima..."}
+{"idx": 3, "title": "Stress Factors in mAb Drug Substance Production Processes:", "date": "", "ddg_snippet": "If you don't remember your password , you can reset it by entering your email address and clicking the Reset Password button.", "subpage_snippet": "", "source": "www.jpharmsci.org", "link": "https://www.jpharmsci.org/article/S0022-3549(19)30633-1/fulltext", "content": "If you don't remember your password , you can reset it by entering your email address and clicking the Reset Password button."}
+{"idx": 4, "title": "Invited Review: Genes Involved in the Bovine Heat Stress", "date": "", "ddg_snippet": "... exposed to 3 environments during 3 trials: 1) thermoneutral, 2) heat stress , and 3) heat stressing and solar radiation (identical to trial 2 but with ...", "subpage_snippet": "", "source": "www.journalofdairyscience.org", "link": "https://www.journalofdairyscience.org/article/S0022-0302(08)71386-9/fulltext", "content": "... exposed to 3 environments during 3 trials: 1) thermoneutral, 2) heat stress , and 3) heat stressing and solar radiation (identical to trial 2 but with ..."}
+{"idx": 5, "title": "Model Tampering Attacks Enable More Rigorous Evaluations of LLM", "date": "", "ddg_snippet": "As a complementary method for eliciting harmful behaviors, we propose evaluating LLMs with model tampering attacks which allow for modifications to ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2502.05209v3", "content": "As a complementary method for eliciting harmful behaviors, we propose evaluating LLMs with model tampering attacks which allow for modifications to ..."}
+{"idx": 6, "title": "Noise Injection Reveals Hidden Capabilities of Sandbagging", "date": "", "ddg_snippet": "In a similar manner, anti-refusal fine-tuning has also been utilized to elicit capabilities from password - locked models .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2412.01784v1", "content": "In a similar manner, anti-refusal fine-tuning has also been utilized to elicit capabilities from password - locked models ."}
+{"idx": 7, "title": "Phasic Dopamine Signals in the Nucleus Accumbens that Cause", "date": "", "ddg_snippet": "To survive, organisms must learn how to interact appropriately with their environment—i.e., to approach positive stimuli, to avoid triggers that ...", "subpage_snippet": "", "source": "www.cell.com", "link": "https://www.cell.com/current-biology/fulltext/S0960-9822(18)30366-X", "content": "To survive, organisms must learn how to interact appropriately with their environment—i.e., to approach positive stimuli, to avoid triggers that ..."}
+{"idx": 8, "title": "Interval-Based Secretomics Unravels Acute-Phase Response in", "date": "", "ddg_snippet": "However, not all cell systems are compatible with these approaches and the required labeling reagents lead to metabolic stress and may impair protein ...", "subpage_snippet": "", "source": "www.mcponline.org", "link": "https://www.mcponline.org/article/S1535-9476(22)00049-4/fulltext", "content": "However, not all cell systems are compatible with these approaches and the required labeling reagents lead to metabolic stress and may impair protein ..."}
+{"idx": 9, "title": "Specification of Physiologic and Disease States by Distinct", "date": "", "ddg_snippet": "Advances in chemical genomics, gene editing, and model systems now permit deconstruction of the complex interplay between these protein states and ...", "subpage_snippet": "", "source": "www.cell.com", "link": "https://www.cell.com/cell/fulltext/S0092-8674(17)31307-7", "content": "Advances in chemical genomics, gene editing, and model systems now permit deconstruction of the complex interplay between these protein states and ..."}
diff --git a/data/sampled_jsons/SwinIR_Image_Restoration_Using_Swin_Transformer_arxiv.jsonl b/data/sampled_jsons/SwinIR_Image_Restoration_Using_Swin_Transformer_arxiv.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..f97ea36cc3f17841673187a5d5ae933a8a88c6be
--- /dev/null
+++ b/data/sampled_jsons/SwinIR_Image_Restoration_Using_Swin_Transformer_arxiv.jsonl
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+{"idx": 0, "title": "[2108.10257] SwinIR: Image Restoration Using Swin Transformer - arXiv.org", "date": "", "ddg_snippet": "Image restoration is a long-standing low-level vision problem that aims to restore high-quality images from low-quality images (e.g., downscaled, noisy and compressed images). While state-of-the-art image restoration methods are based on convolutional neural networks, few attempts have been made with Transformers which show impressive performance on high-level vision tasks. In this paper, we ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2108.10257", "content": "Image restoration is a long-standing low-level vision problem that aims to restore high-quality images from low-quality images (e.g., downscaled, noisy and compressed images). While state-of-the-art image restoration methods are based on convolutional neural networks, few attempts have been made with Transformers which show impressive performance on high-level vision tasks. In this paper, we ..."}
+{"idx": 1, "title": "SwinIR: Image Restoration Using Swin Transformer - GitHub", "date": "", "ddg_snippet": "SwinIR: Image Restoration Using Swin Transformer Jingyun Liang, Jiezhang Cao, Guolei Sun, Kai Zhang, Luc Van Gool, Radu Timofte Computer Vision Lab, ETH Zurich Gradio Web Demo This repository is the official PyTorch implementation of SwinIR: Image Restoration Using Shifted Window Transformer ( arxiv , supp, pretrained models, visual results).", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/JingyunLiang/SwinIR", "content": "SwinIR: Image Restoration Using Swin Transformer Jingyun Liang, Jiezhang Cao, Guolei Sun, Kai Zhang, Luc Van Gool, Radu Timofte Computer Vision Lab, ETH Zurich Gradio Web Demo This repository is the official PyTorch implementation of SwinIR: Image Restoration Using Shifted Window Transformer ( arxiv , supp, pretrained models, visual results)."}
+{"idx": 2, "title": "PDF SwinIR: Image Restoration Using Swin Transformer", "date": "", "ddg_snippet": "Abstract Image restoration is a long-standing low-level vision problem that aims to restore high-quality images from low-quality images (e.g., downscaled, noisy and compressed im-ages). While state-of-the-art image restoration methods are based on convolutional neural networks, few attempts have been made with Transformers which show impressive per-formance on high-level vision tasks. In this ...", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/ICCV2021W/AIM/papers/Liang_SwinIR_Image_Restoration_Using_Swin_Transformer_ICCVW_2021_paper.pdf", "content": "Abstract Image restoration is a long-standing low-level vision problem that aims to restore high-quality images from low-quality images (e.g., downscaled, noisy and compressed im-ages). While state-of-the-art image restoration methods are based on convolutional neural networks, few attempts have been made with Transformers which show impressive per-formance on high-level vision tasks. In this ..."}
+{"idx": 3, "title": "Paper page - SwinIR: Image Restoration Using Swin Transformer", "date": "", "ddg_snippet": "Abstract SwinIR, a model based on Swin Transformers, outperforms existing image restoration methods on various tasks with fewer parameters.", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/papers/2108.10257", "content": "Abstract SwinIR, a model based on Swin Transformers, outperforms existing image restoration methods on various tasks with fewer parameters."}
+{"idx": 4, "title": "SwinIR: Image Restoration Using Swin Transformer - NASA/ADS", "date": "", "ddg_snippet": "Image restoration is a long-standing low-level vision problem that aims to restore high-quality images from low-quality images (e.g., downscaled, noisy and compressed images). While state-of-the-art image restoration methods are based on convolutional neural networks, few attempts have been made with Transformers which show impressive performance on high-level vision tasks. In this paper, we ...", "subpage_snippet": "", "source": "ui.adsabs.harvard.edu", "link": "https://ui.adsabs.harvard.edu/abs/2021arXiv210810257L/abstract", "content": "Image restoration is a long-standing low-level vision problem that aims to restore high-quality images from low-quality images (e.g., downscaled, noisy and compressed images). While state-of-the-art image restoration methods are based on convolutional neural networks, few attempts have been made with Transformers which show impressive performance on high-level vision tasks. In this paper, we ..."}
+{"idx": 5, "title": "[2108.10257] SwinIR: Image Restoration Using Swin Transformer - ar5iv", "date": "", "ddg_snippet": "Image restoration is a long-standing low-level vision problem that aims to restore high-quality images from low-quality images (e.g., downscaled, noisy and compressed images). While state-of-the-art image restoration m…", "subpage_snippet": "", "source": "ar5iv.labs.arxiv.org", "link": "https://ar5iv.labs.arxiv.org/html/2108.10257", "content": "Image restoration is a long-standing low-level vision problem that aims to restore high-quality images from low-quality images (e.g., downscaled, noisy and compressed images). While state-of-the-art image restoration m…"}
+{"idx": 6, "title": "JingyunLiang/SwinIR: SwinIR: Image Restoration Using Swin Transformer", "date": "", "ddg_snippet": "This repository is the official PyTorch implementation of SwinIR: Image Restoration Using Shifted Window Transformer ( arxiv , supp, pretrained models, visual results). SwinIR achieves state-of-the-art performance in", "subpage_snippet": "", "source": "gitmemories.com", "link": "https://gitmemories.com/index.php/JingyunLiang/SwinIR", "content": "This repository is the official PyTorch implementation of SwinIR: Image Restoration Using Shifted Window Transformer ( arxiv , supp, pretrained models, visual results). SwinIR achieves state-of-the-art performance in"}
+{"idx": 7, "title": "SwinIR: Image Restoration Using Swin Transformer - IEEE Xplore", "date": "", "ddg_snippet": "Image restoration is a long-standing low-level vision problem that aims to restore high-quality images from low-quality images (e.g., downscaled, noisy and compressed images). While state-of-the-art image restoration methods are based on convolutional neural networks, few attempts have been made with Transformers which show impressive performance on high-level vision tasks. In this paper, we ...", "subpage_snippet": "", "source": "ieeexplore.ieee.org", "link": "https://ieeexplore.ieee.org/abstract/document/9607618", "content": "Image restoration is a long-standing low-level vision problem that aims to restore high-quality images from low-quality images (e.g., downscaled, noisy and compressed images). While state-of-the-art image restoration methods are based on convolutional neural networks, few attempts have been made with Transformers which show impressive performance on high-level vision tasks. In this paper, we ..."}
+{"idx": 8, "title": "SwinIR/README.md at main · JingyunLiang/SwinIR · GitHub", "date": "", "ddg_snippet": "SwinIR: Image Restoration Using Swin Transformer Jingyun Liang, Jiezhang Cao, Guolei Sun, Kai Zhang, Luc Van Gool, Radu Timofte Computer Vision Lab, ETH Zurich Gradio Web Demo This repository is the official PyTorch implementation of SwinIR: Image Restoration Using Shifted Window Transformer ( arxiv , supp, pretrained models, visual results).", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/JingyunLiang/SwinIR/blob/main/README.md", "content": "SwinIR: Image Restoration Using Swin Transformer Jingyun Liang, Jiezhang Cao, Guolei Sun, Kai Zhang, Luc Van Gool, Radu Timofte Computer Vision Lab, ETH Zurich Gradio Web Demo This repository is the official PyTorch implementation of SwinIR: Image Restoration Using Shifted Window Transformer ( arxiv , supp, pretrained models, visual results)."}
+{"idx": 9, "title": "Rethinking LayerNorm in Image Restoration Transformers", "date": "", "ddg_snippet": "Figure 1 : Evolution of feature magnitudes and channel-axis feature entropy during training an Image Restoration (IR) Transformer using conventional ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2504.06629v1", "content": "Figure 1 : Evolution of feature magnitudes and channel-axis feature entropy during training an Image Restoration (IR) Transformer using conventional ..."}
diff --git a/data/sampled_jsons/Symmetric_Reinforcement_Learning_Loss_Equation_7_RA2C.jsonl b/data/sampled_jsons/Symmetric_Reinforcement_Learning_Loss_Equation_7_RA2C.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..d9a24d2776d9464a0b474ac0b4709fd60d159ff3
--- /dev/null
+++ b/data/sampled_jsons/Symmetric_Reinforcement_Learning_Loss_Equation_7_RA2C.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Reinforcement learning - Wikipedia", "date": "", "ddg_snippet": "Reinforcement learning (RL) is an interdisciplinary area of machine learning and optimal control concerned with how an intelligent agent should take actions in a dynamic environment in order to maximize a reward signal. Reinforcement learning is one of the three basic machine learning paradigms, alongside supervised learning and unsupervised learning . Reinforcement learning differs from ...", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/Reinforcement_learning", "content": "Reinforcement learning (RL) is an interdisciplinary area of machine learning and optimal control concerned with how an intelligent agent should take actions in a dynamic environment in order to maximize a reward signal. Reinforcement learning is one of the three basic machine learning paradigms, alongside supervised learning and unsupervised learning . Reinforcement learning differs from ..."}
+{"idx": 1, "title": "Symmetric Reinforcement Learning Loss for Robust Learning on ...", "date": "", "ddg_snippet": "In this work, we focus on RL algorithms that share learning difficulties with cross-entropy loss , especially for low-probability predictions. To enhance stability, we adapt reverse cross-entropy (RCE) from supervised learning for noisy data, defining a symmetric RL loss . We demonstrate performance improvements across various tasks and scales.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2405.17618v3", "content": "In this work, we focus on RL algorithms that share learning difficulties with cross-entropy loss , especially for low-probability predictions. To enhance stability, we adapt reverse cross-entropy (RCE) from supervised learning for noisy data, defining a symmetric RL loss . We demonstrate performance improvements across various tasks and scales."}
+{"idx": 2, "title": "GitHub - shashacks/Symmetric_RL", "date": "", "ddg_snippet": "Symmetric Reinforcement Learning Loss for Robust Learning on Diverse Tasks and Model Scales Reinforcement learning (RL) training is inherently unstable due to factors such as moving targets and high gradient variance.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/shashacks/Symmetric_RL", "content": "Symmetric Reinforcement Learning Loss for Robust Learning on Diverse Tasks and Model Scales Reinforcement learning (RL) training is inherently unstable due to factors such as moving targets and high gradient variance."}
+{"idx": 3, "title": "Reinforcement Learning Algorithms and Equations", "date": "", "ddg_snippet": "This write-up is intended as a collection of common algorithms and equations in reinforcement learning , deep reinforcement learning , decision making under uncertainty, approximate dynamic programming, and stochastic optimization methods.", "subpage_snippet": "", "source": "web.stanford.edu", "link": "https://web.stanford.edu/~mossr/pdf/rl_algs.pdf", "content": "This write-up is intended as a collection of common algorithms and equations in reinforcement learning , deep reinforcement learning , decision making under uncertainty, approximate dynamic programming, and stochastic optimization methods."}
+{"idx": 4, "title": "SYMMETRIC REINFORCEMENT LEARNING LOSS FOR ROBUST LEARNING ON ...", "date": "", "ddg_snippet": "We propose the symmetric RL loss for A2C and PPO, along with the gradient analysis that aligns with the gradient behavior of robust loss functions used in noisy classification tasks in Section 4.3.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=9oq0iY2Jxx", "content": "We propose the symmetric RL loss for A2C and PPO, along with the gradient analysis that aligns with the gradient behavior of robust loss functions used in noisy classification tasks in Section 4.3."}
+{"idx": 5, "title": "On Learning Symmetric Locomotion - xbpeng.github.io", "date": "", "ddg_snippet": "By encouraging symmetric motion, the learning may be faster, converge to more eficient solutions, and be more aesthetically pleasing. We describe, compare, and evaluate four practical methods for encouraging mo-tion symmetry . These are implemented via particular choices of structure for the policy network, data duplication, or via the loss ...", "subpage_snippet": "", "source": "xbpeng.github.io", "link": "https://xbpeng.github.io/projects/SymLoco/SymLoco_2018.pdf", "content": "By encouraging symmetric motion, the learning may be faster, converge to more eficient solutions, and be more aesthetically pleasing. We describe, compare, and evaluate four practical methods for encouraging mo-tion symmetry . These are implemented via particular choices of structure for the policy network, data duplication, or via the loss ..."}
+{"idx": 6, "title": "\"Unlocking Stability in Reinforcement Learning: A Symmetric ...", "date": "", "ddg_snippet": "Hey folks, just came across a fascinating research paper on enhancing robustness in Reinforcement Learning tasks using a symmetric RL loss derived from supervised learning . The study delves into the benefits of Symmetric A2C and Symmetric PPO across different tasks and model scales.", "subpage_snippet": "", "source": "www.reddit.com", "link": "https://www.reddit.com/r/languagemodeldigest/comments/1dloqiw/unlocking_stability_in_reinforcement_learning_a/", "content": "Hey folks, just came across a fascinating research paper on enhancing robustness in Reinforcement Learning tasks using a symmetric RL loss derived from supervised learning . The study delves into the benefits of Symmetric A2C and Symmetric PPO across different tasks and model scales."}
+{"idx": 7, "title": "Symmetric Reinforcement Learning Loss for Robust", "date": "", "ddg_snippet": "Symmetric reinforcement learning loss . Gradient analysis. Experiments.The Symmetric Reinforcement Learning (SRL) loss Lsrl consists of two parts like SCE ( Equation 6): the original RL loss Lrl (A 2 C or PPO) and the corresponding reverse RL loss Lrev ( RA 2 C or RPPO).", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2405.17618", "content": "Symmetric reinforcement learning loss . Gradient analysis. Experiments.The Symmetric Reinforcement Learning (SRL) loss Lsrl consists of two parts like SCE ( Equation 6): the original RL loss Lrl (A 2 C or PPO) and the corresponding reverse RL loss Lrev ( RA 2 C or RPPO)."}
+{"idx": 8, "title": "ICML Poster Symmetric Reinforcement Learning Loss for Robust...", "date": "", "ddg_snippet": "Reinforcement learning (RL) training is inherently unstable due to factors such as moving targets and high gradient variance.To enhance stability, we adapt reverse cross-entropy (RCE) from supervised learning for noisy data, defining a symmetric RL loss .", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/poster/44897", "content": "Reinforcement learning (RL) training is inherently unstable due to factors such as moving targets and high gradient variance.To enhance stability, we adapt reverse cross-entropy (RCE) from supervised learning for noisy data, defining a symmetric RL loss ."}
+{"idx": 9, "title": "Symmetric Reinforcement Learning Loss for Robust Learning on...", "date": "", "ddg_snippet": "The Symmetric Reinforcement Learning (SRL) Loss presented in this paper offers a promising new approach to improving the robustness and performance of reinforcement learning models.", "subpage_snippet": "", "source": "www.aimodels.fyi", "link": "https://www.aimodels.fyi/papers/arxiv/symmetric-reinforcement-learning-loss-robust-learning-diverse", "content": "The Symmetric Reinforcement Learning (SRL) Loss presented in this paper offers a promising new approach to improving the robustness and performance of reinforcement learning models."}
diff --git a/data/sampled_jsons/TCE_Li_et_al.,_2024_reinforcement_learning.jsonl b/data/sampled_jsons/TCE_Li_et_al.,_2024_reinforcement_learning.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..cb1ec0cbddd9207fefd001fd0ebc9ee211af73f9
--- /dev/null
+++ b/data/sampled_jsons/TCE_Li_et_al.,_2024_reinforcement_learning.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "DeepSeek-R1 incentivizes reasoning in LLMs through reinforcement learning", "date": "", "ddg_snippet": "A new artificial intelligence model, DeepSeek-R1, is introduced, demonstrating that the reasoning abilities of large language models can be incentivized through pure reinforcement learning ...", "subpage_snippet": "", "source": "www.nature.com", "link": "https://www.nature.com/articles/s41586-025-09422-z", "content": "A new artificial intelligence model, DeepSeek-R1, is introduced, demonstrating that the reasoning abilities of large language models can be incentivized through pure reinforcement learning ..."}
+{"idx": 1, "title": "Reinforcement Learning with Token-level Feedback for Controllable Text ...", "date": "", "ddg_snippet": "To tackle that, we propose a novel reinforcement learning algorithm named TOLE which formulates TOken-LEvel rewards for controllable text generation, and employs a \"first-quantize-then-noise\" paradigm to enhance the robustness of the RL algorithm.", "subpage_snippet": "", "source": "aclanthology.org", "link": "https://aclanthology.org/2024.findings-naacl.111/", "content": "To tackle that, we propose a novel reinforcement learning algorithm named TOLE which formulates TOken-LEvel rewards for controllable text generation, and employs a \"first-quantize-then-noise\" paradigm to enhance the robustness of the RL algorithm."}
+{"idx": 2, "title": "TOP-ERL: Transformer-based Off-Policy Episodic Reinforcement Learning", "date": "", "ddg_snippet": "This work introduces Transformer-based Off-Policy Episodic Reinforcement Learning (TOP-ERL), a novel algorithm that enables off-policy updates in the ERL framework. In ERL, policies predict entire ...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/384929465_TOP-ERL_Transformer-based_Off-Policy_Episodic_Reinforcement_Learning", "content": "This work introduces Transformer-based Off-Policy Episodic Reinforcement Learning (TOP-ERL), a novel algorithm that enables off-policy updates in the ERL framework. In ERL, policies predict entire ..."}
+{"idx": 3, "title": "PDF Top-erl: Transformer Based Off-policy E Reinforcement Learning", "date": "", "ddg_snippet": "a et al., 2018a) where an action is sampled in each time step. The action selection concept in ERL is promising as sh wn in recent works in RL (Otto et al., 2022; Li et al., 2024 ). Similar insights have been made in the field of Imitation Learning , where predicting action sequences instead of single ac-tions ha", "subpage_snippet": "", "source": "proceedings.iclr.cc", "link": "https://proceedings.iclr.cc/paper_files/paper/2025/file/6debbef99fd649051ee96f8cb80e232d-Paper-Conference.pdf", "content": "a et al., 2018a) where an action is sampled in each time step. The action selection concept in ERL is promising as sh wn in recent works in RL (Otto et al., 2022; Li et al., 2024 ). Similar insights have been made in the field of Imitation Learning , where predicting action sequences instead of single ac-tions ha"}
+{"idx": 4, "title": "Privacy Preserved Reinforcement Learning Model Using Generative AI for ...", "date": "", "ddg_snippet": "Modernized learning environments are designed based on personalized E-Learning system, due to the availability of enriched content and flexibility in the learning system. This paper proposed a personalized enriched course recommendation method for an e-learning environment using reinforcement techniques.", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/abs/10.1109/TCE.2024.3398824", "content": "Modernized learning environments are designed based on personalized E-Learning system, due to the availability of enriched content and flexibility in the learning system. This paper proposed a personalized enriched course recommendation method for an e-learning environment using reinforcement techniques."}
+{"idx": 5, "title": "On Transforming Reinforcement Learning With Transformers: The ...", "date": "", "ddg_snippet": "Transformers, originally devised for natural language processing (NLP), have also produced significant successes in computer vision (CV). Due to their strong expression power, researchers are investigating ways to deploy transformers for reinforcement learning (RL), and transformer-based models have manifested their potential in representative RL benchmarks. In this paper, we collect and ...", "subpage_snippet": "", "source": "ieeexplore.ieee.org", "link": "https://ieeexplore.ieee.org/abstract/document/10546317", "content": "Transformers, originally devised for natural language processing (NLP), have also produced significant successes in computer vision (CV). Due to their strong expression power, researchers are investigating ways to deploy transformers for reinforcement learning (RL), and transformer-based models have manifested their potential in representative RL benchmarks. In this paper, we collect and ..."}
+{"idx": 6, "title": "PDF Settling the sample complexity of model-based offline reinforcement ...", "date": "", "ddg_snippet": "1. Introduction. Reinforcement learning (RL) has recently achieved superhuman per-formance in the gaming frontier, such as the game of Go (Silver et al . (2017)), under the premise that vast amounts of training data can be obtained.", "subpage_snippet": "", "source": "projecteuclid.org", "link": "https://projecteuclid.org/journals/annals-of-statistics/volume-52/issue-1/Settling-the-sample-complexity-of-model-based-offline-reinforcement-learning/10.1214/23-AOS2342.pdf", "content": "1. Introduction. Reinforcement learning (RL) has recently achieved superhuman per-formance in the gaming frontier, such as the game of Go (Silver et al . (2017)), under the premise that vast amounts of training data can be obtained."}
+{"idx": 7, "title": "Cooperative Multi-Agent Reinforcement Learning Framework for Edge ...", "date": "", "ddg_snippet": "F. Song et al., \"Evolutionary multi-objective reinforcement learning based trajectory control and task offloading in UAV-assisted mobile edge computing,\" IEEE Trans. Mobile Comput., vol. 22, no. 12, pp. 7387-7405, Dec. 2023.", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.1109/TCE.2024.3416822", "content": "F. Song et al., \"Evolutionary multi-objective reinforcement learning based trajectory control and task offloading in UAV-assisted mobile edge computing,\" IEEE Trans. Mobile Comput., vol. 22, no. 12, pp. 7387-7405, Dec. 2023."}
+{"idx": 8, "title": "Efficient and scalable reinforcement learning for large-scale ... - Nature", "date": "", "ddg_snippet": "Article Open access Published: 03 September 2024 Efficient and scalable reinforcement learning for large-scale network control Chengdong Ma, Aming Li , Yali Du, Hao Dong & Yaodong Yang", "subpage_snippet": "", "source": "www.nature.com", "link": "https://www.nature.com/articles/s42256-024-00879-7", "content": "Article Open access Published: 03 September 2024 Efficient and scalable reinforcement learning for large-scale network control Chengdong Ma, Aming Li , Yali Du, Hao Dong & Yaodong Yang"}
+{"idx": 9, "title": "Optimizing Language Models with Fair and Stable Reward Composition in ...", "date": "", "ddg_snippet": "Jiahui Li , Hanlin Zhang, Fengda Zhang, Tai-Wei Chang, Kun Kuang, Long Chen, and Jun Zhou. 2024 . Optimizing Language Models with Fair and Stable Reward Composition in Reinforcement Learning .", "subpage_snippet": "", "source": "aclanthology.org", "link": "https://aclanthology.org/2024.emnlp-main.565/", "content": "Jiahui Li , Hanlin Zhang, Fengda Zhang, Tai-Wei Chang, Kun Kuang, Long Chen, and Jun Zhou. 2024 . Optimizing Language Models with Fair and Stable Reward Composition in Reinforcement Learning ."}
diff --git a/data/sampled_jsons/TOP-ERL_paper_random_segment_length_better_performance_Section_5.3_authors_reasoning.jsonl b/data/sampled_jsons/TOP-ERL_paper_random_segment_length_better_performance_Section_5.3_authors_reasoning.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..2471051ffc28adfccc196850c5ecafb1f3323bfc
--- /dev/null
+++ b/data/sampled_jsons/TOP-ERL_paper_random_segment_length_better_performance_Section_5.3_authors_reasoning.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "484 questions with answers in HEAT EXCHANGERS | Science topic", "date": "", "ddg_snippet": "Coaxial designs allow for better thermal contact with the surrounding soil due to a larger surface area per unit length .", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/topic/Heat-Exchangers", "content": "Coaxial designs allow for better thermal contact with the surrounding soil due to a larger surface area per unit length ."}
+{"idx": 1, "title": "Adversarial Graph Fusion for Incomplete Multi-view", "date": "", "ddg_snippet": "... sources or modalities, significantly enhancing the performance of downstream tasks, such as autonomous driving [ 1 , 2 ] and precision health [ 3 ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2509.15955v1", "content": "... sources or modalities, significantly enhancing the performance of downstream tasks, such as autonomous driving [ 1 , 2 ] and precision health [ 3 ..."}
+{"idx": 2, "title": "BASIL: Best-Action Symbolic Interpretable Learning for Evolving", "date": "", "ddg_snippet": "... policies with state-of-the-art performance in comparison with top deep reinforcement learning techniques, while vastly outperforming traditional ERL ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2506.00328v1", "content": "... policies with state-of-the-art performance in comparison with top deep reinforcement learning techniques, while vastly outperforming traditional ERL ..."}
+{"idx": 3, "title": "Susannne Wenzel – StachnissLab", "date": "", "ddg_snippet": "The focus of this paper lies in a well-reasoned representation of these segments by means of geometric primitives, such as straight line segments as ...", "subpage_snippet": "", "source": "www.ipb.uni-bonn.de", "link": "http://www.ipb.uni-bonn.de/people/susanne-wenzel/", "content": "The focus of this paper lies in a well-reasoned representation of these segments by means of geometric primitives, such as straight line segments as ..."}
+{"idx": 4, "title": "Sequential linked data: The state of affairs - IOS Press", "date": "", "ddg_snippet": "In this section , we specialise the methodology previously introduced in [ 14 ] to pragmatically evaluate the performance of competing models for the ...", "subpage_snippet": "", "source": "content.iospress.com", "link": "https://content.iospress.com/articles/semantic-web/sw210436", "content": "In this section , we specialise the methodology previously introduced in [ 14 ] to pragmatically evaluate the performance of competing models for the ..."}
+{"idx": 5, "title": "Uncategorized | Bert Simonovich's Design Notes", "date": "", "ddg_snippet": "Adjusting Dk values within a 3 -D field solver to fit measured results based on as-fabricated PCB cross- section (x- section ) dimensions provides an ...", "subpage_snippet": "", "source": "blog.lamsimenterprises.com", "link": "https://blog.lamsimenterprises.com/category/uncategorized/", "content": "Adjusting Dk values within a 3 -D field solver to fit measured results based on as-fabricated PCB cross- section (x- section ) dimensions provides an ..."}
+{"idx": 6, "title": "(PDF) Holistic evaluation in multi-model databases benchmarking", "date": "", "ddg_snippet": "As more and more platforms are developed to deal with multi-model data, it has become crucial to establish a benchmark for evaluating the performance ...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/337804572_Holistic_evaluation_in_multi-model_databases_benchmarking", "content": "As more and more platforms are developed to deal with multi-model data, it has become crucial to establish a benchmark for evaluating the performance ..."}
+{"idx": 7, "title": "US5701256A - Method and apparatus for biological sequence", "date": "", "ddg_snippet": "In that case, when the local alignment score is greater than or equal to 20 bits (each bit represents a 2:1 odds that a match is not random ) then the ...", "subpage_snippet": "", "source": "patents.google.com", "link": "https://patents.google.com/patent/US5701256A/en", "content": "In that case, when the local alignment score is greater than or equal to 20 bits (each bit represents a 2:1 odds that a match is not random ) then the ..."}
+{"idx": 8, "title": "Virtuoso Open-Source Edition", "date": "", "ddg_snippet": "... s place therein be? In this paper , we explore the inevitable coming together of RDF, analytic oriented databasing, web scale computing and reasoning ...", "subpage_snippet": "", "source": "vos.openlinksw.com", "link": "https://vos.openlinksw.com/owiki/wiki/VOS/VirtDirectionsChallengesSemdata", "content": "... s place therein be? In this paper , we explore the inevitable coming together of RDF, analytic oriented databasing, web scale computing and reasoning ..."}
+{"idx": 9, "title": "There's life above 20 kilohertz! A survey of musical instrument", "date": "", "ddg_snippet": "Author 's Notes, May 4, 2000 At the request of people involved in standards-setting for audio, who wanted this information made available as soon as ...", "subpage_snippet": "", "source": "www.cco.caltech.edu", "link": "https://www.cco.caltech.edu/~boyk/spectra/spectra.htm", "content": "Author 's Notes, May 4, 2000 At the request of people involved in standards-setting for audio, who wanted this information made available as soon as ..."}
diff --git a/data/sampled_jsons/TRSSL_Trust-region_semi-supervised_learning_methodology_algorithm.jsonl b/data/sampled_jsons/TRSSL_Trust-region_semi-supervised_learning_methodology_algorithm.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..a16b700a5c06ae4258dc170e018f9216c637ff69
--- /dev/null
+++ b/data/sampled_jsons/TRSSL_Trust-region_semi-supervised_learning_methodology_algorithm.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "nayeemrizve/ TRSSL : \"Towards Realistic Semi - Supervised Learning ...\"", "date": "", "ddg_snippet": "Semi - supervised learning ( SSL ) complements the annotated training data with a large corpus of unlabeled data to reduce annotation cost. The standard SSL approach assumes unlabeled data are from the same distribution as annotated data.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/nayeemrizve/TRSSL", "content": "Semi - supervised learning ( SSL ) complements the annotated training data with a large corpus of unlabeled data to reduce annotation cost. The standard SSL approach assumes unlabeled data are from the same distribution as annotated data."}
+{"idx": 1, "title": "Semi - Supervised Learning : Techniques & Examples [2024]", "date": "", "ddg_snippet": "Learn what Semi - Supervised Learning is, how it works, and what its limitations are. Explore the most exciting research papers and datasets, and start labeling your data on V7.", "subpage_snippet": "", "source": "www.v7labs.com", "link": "https://www.v7labs.com/blog/semi-supervised-learning-guide", "content": "Learn what Semi - Supervised Learning is, how it works, and what its limitations are. Explore the most exciting research papers and datasets, and start labeling your data on V7."}
+{"idx": 2, "title": "Semi - Supervised Learning in ML - GeeksforGeeks", "date": "", "ddg_snippet": "Semi - supervised learning is a hybrid machine learning approach which uses both supervised and unsupervised learning . It uses a small amount of labelled data combined with a large amount of unlabelled data to train models.", "subpage_snippet": "", "source": "www.geeksforgeeks.org", "link": "https://www.geeksforgeeks.org/machine-learning/ml-semi-supervised-learning/", "content": "Semi - supervised learning is a hybrid machine learning approach which uses both supervised and unsupervised learning . It uses a small amount of labelled data combined with a large amount of unlabelled data to train models."}
+{"idx": 3, "title": "What Is Semi - Supervised Learning ? | IBM", "date": "", "ddg_snippet": "Unlike semi - supervised (and fully supervised ) learning , unsupervised learning algorithms use neither labeled data nor loss functions.The low-density assumption states that the decision boundary between classes should not pass through high-density regions .", "subpage_snippet": "", "source": "www.ibm.com", "link": "https://www.ibm.com/think/topics/semi-supervised-learning", "content": "Unlike semi - supervised (and fully supervised ) learning , unsupervised learning algorithms use neither labeled data nor loss functions.The low-density assumption states that the decision boundary between classes should not pass through high-density regions ."}
+{"idx": 4, "title": "Trust - region algorithms : probabilistic complexity and intrinsic noise...", "date": "", "ddg_snippet": "A trust - region algorithm is presented for finding approximate minimizers of smooth unconstrained functions whose values and derivatives are subject to random noise. It is shown that, under suitable probabilistic assumptions, the new method finds (in expectation) an epsilon-approximate...", "subpage_snippet": "", "source": "optimization-online.org", "link": "https://optimization-online.org/2021/12/8723/", "content": "A trust - region algorithm is presented for finding approximate minimizers of smooth unconstrained functions whose values and derivatives are subject to random noise. It is shown that, under suitable probabilistic assumptions, the new method finds (in expectation) an epsilon-approximate..."}
+{"idx": 5, "title": "Trust region methods for deep reinforcement learning", "date": "", "ddg_snippet": "Trust region methods . The basic building block of Machine learning is to optimize some kind of objective function like the mean squared loss or max log-likelihood.", "subpage_snippet": "", "source": "readmedium.com", "link": "https://readmedium.com/trust-region-methods-for-deep-reinforcement-learning-e7e2a8460284", "content": "Trust region methods . The basic building block of Machine learning is to optimize some kind of objective function like the mean squared loss or max log-likelihood."}
+{"idx": 6, "title": "Semi -Super v ised", "date": "", "ddg_snippet": "As a eld, semi - supervised learning uses a diverse set of tools and illustrates, on a small scale, the sophisticated machinery developed in various branches of machine learning such as kernel methods or Bayesian techniques.", "subpage_snippet": "", "source": "www.molgen.mpg.de", "link": "https://www.molgen.mpg.de/3659531/MITPress--SemiSupervised-Learning.pdf", "content": "As a eld, semi - supervised learning uses a diverse set of tools and illustrates, on a small scale, the sophisticated machinery developed in various branches of machine learning such as kernel methods or Bayesian techniques."}
+{"idx": 7, "title": "8 Clustering Algorithms in Machine Learning that All Data Scientists...", "date": "", "ddg_snippet": "You can go with supervised learning , semi - supervised learning , or unsupervised learning . In supervised learning you have labeled data, so you have outputs that you know for sure are the correct values for your inputs. That's like knowing car prices based on features like make...", "subpage_snippet": "", "source": "www.freecodecamp.org", "link": "https://www.freecodecamp.org/news/8-clustering-algorithms-in-machine-learning-that-all-data-scientists-should-know/", "content": "You can go with supervised learning , semi - supervised learning , or unsupervised learning . In supervised learning you have labeled data, so you have outputs that you know for sure are the correct values for your inputs. That's like knowing car prices based on features like make..."}
+{"idx": 8, "title": "(PDF) Semi - supervised learning : a brief review", "date": "", "ddg_snippet": "niques of Semi - supervised learning ( SSL ) such as self-training, co-training, multi-view learning , TSVMs methods .", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/324050146_Semi-supervised_learning_a_brief_review", "content": "niques of Semi - supervised learning ( SSL ) such as self-training, co-training, multi-view learning , TSVMs methods ."}
+{"idx": 9, "title": "Trust region Bayesian optimization — trieste 4.2.2 documentation", "date": "", "ddg_snippet": "In trust region Bayesian optimization, a probabilistic model, often a Gaussian Process, replaces simpler models like quadratics to inform the optimization.", "subpage_snippet": "", "source": "secondmind-labs.github.io", "link": "https://secondmind-labs.github.io/trieste/4.2.2/notebooks/trust_region.html", "content": "In trust region Bayesian optimization, a probabilistic model, often a Gaussian Process, replaces simpler models like quadratics to inform the optimization."}
diff --git a/data/sampled_jsons/Table_1_GPT-4_MedQA_real-world_accuracy_Medical_Large_Language_Model_Benchmarks_Should_Prioritize_Co.jsonl b/data/sampled_jsons/Table_1_GPT-4_MedQA_real-world_accuracy_Medical_Large_Language_Model_Benchmarks_Should_Prioritize_Co.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..931a049c4ffc125cbf1b8a146bc9bc1aea907f26
--- /dev/null
+++ b/data/sampled_jsons/Table_1_GPT-4_MedQA_real-world_accuracy_Medical_Large_Language_Model_Benchmarks_Should_Prioritize_Co.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Medical Large Language Model Benchmarks Should Prioritize ...", "date": "", "ddg_snippet": "The results in Table 1 show a modest correlation between LLM performance on the MedQA benchmark and real - world cases. In a scenario of perfect criterion validity , we would expect. α𝛼\\alphaitalic_α.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2503.10694v1", "content": "The results in Table 1 show a modest correlation between LLM performance on the MedQA benchmark and real - world cases. In a scenario of perfect criterion validity , we would expect. α𝛼\\alphaitalic_α."}
+{"idx": 1, "title": "Latest AI Research LLM Agents, Medical LLMs, And More", "date": "", "ddg_snippet": "Medical Large Language Models: Revolutionizing Healthcare.DeVisE: Behavioral Testing of Medical Large Language Models focuses on testing the behavior of medical LLMs. This is essential for ensuring that these models perform reliably in real - world clinical settings.", "subpage_snippet": "", "source": "codemeld.org", "link": "https://codemeld.org/blog/latest-ai-research-llm-agents", "content": "Medical Large Language Models: Revolutionizing Healthcare.DeVisE: Behavioral Testing of Medical Large Language Models focuses on testing the behavior of medical LLMs. This is essential for ensuring that these models perform reliably in real - world clinical settings."}
+{"idx": 2, "title": "ICML 2025 Sneak Peek: The New Laws of AI | Medium", "date": "", "ddg_snippet": "Another paper, “Position: Medical Large Language Model Benchmarks Should Prioritize Construct Validity ,” argues that current benchmarks for high-stakes domains like medicine fail to meet the rigorous standards of scientific validity required for trustworthy evaluation.", "subpage_snippet": "", "source": "medium.com", "link": "https://medium.com/foundation-models-deep-dive/icml-2025-sneak-peek-the-new-laws-of-ai-1210789c66a2", "content": "Another paper, “Position: Medical Large Language Model Benchmarks Should Prioritize Construct Validity ,” argues that current benchmarks for high-stakes domains like medicine fail to meet the rigorous standards of scientific validity required for trustworthy evaluation."}
+{"idx": 3, "title": "Articles by Frances Dean | Synthical", "date": "", "ddg_snippet": "Medical Large Language Model Benchmarks Should Prioritize Construct Validity . 12 March 2025 by Ahmed Alaa and others. Computation and Language.", "subpage_snippet": "", "source": "synthical.com", "link": "https://synthical.com/profile/d8b88aa7-cbcf-4d81-a315-a1ff4be5f516/articles", "content": "Medical Large Language Model Benchmarks Should Prioritize Construct Validity . 12 March 2025 by Ahmed Alaa and others. Computation and Language."}
+{"idx": 4, "title": "GitHub - Xuchen-Li/llm-arxiv-daily: Automatically update arXiv papers...", "date": "", "ddg_snippet": "A Hierarchical Test Platform for Vision Language Model (VLM)-Integrated Real - World Autonomous Driving. Yupeng Zhou et.al.SituatedThinker: Grounding LLM Reasoning with Real - World through Situated Thinking.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/Xuchen-Li/llm-arxiv-daily", "content": "A Hierarchical Test Platform for Vision Language Model (VLM)-Integrated Real - World Autonomous Driving. Yupeng Zhou et.al.SituatedThinker: Grounding LLM Reasoning with Real - World through Situated Thinking."}
+{"idx": 5, "title": "Franny Dean - Google Scholar", "date": "", "ddg_snippet": "2021. Position: Medical Large Language Model Benchmarks Should Prioritize Construct Validity .2020. Med- Real 2Sim: Non-Invasive Medical Digital Twins using Physics-Informed Self-Supervised Learning.", "subpage_snippet": "", "source": "scholar.google.com", "link": "https://scholar.google.com/citations?user=Y04UhM4AAAAJ&hl=en", "content": "2021. Position: Medical Large Language Model Benchmarks Should Prioritize Construct Validity .2020. Med- Real 2Sim: Non-Invasive Medical Digital Twins using Physics-Informed Self-Supervised Learning."}
+{"idx": 6, "title": "Latest 15 Papers - June 08, 2025 - Githubissues", "date": "", "ddg_snippet": "Medical Large Language Models.CancerLLM: A Large Language Model in Cancer Domain. 2025-04-01. new v...", "subpage_snippet": "", "source": "githubissues.com", "link": "https://githubissues.com/somewordstoolate/DailyArXiv/17", "content": "Medical Large Language Models.CancerLLM: A Large Language Model in Cancer Domain. 2025-04-01. new v..."}
+{"idx": 7, "title": "3: Consulting the Record: AI Consistently Fails the... - AI Now Institute", "date": "", "ddg_snippet": "...validity—meaning that the test used to evaluate them is an accurate measure of the concept it’s intended to measure.65Ahmed Alla et al., “ Medical Large Language Model Benchmarks Should Prioritize Construct Validity ,” ()arXiv()", "subpage_snippet": "", "source": "ainowinstitute.org", "link": "https://ainowinstitute.org/publications/research/3-consulting-the-record-ai-consistently-fails-the-public", "content": "...validity—meaning that the test used to evaluate them is an accurate measure of the concept it’s intended to measure.65Ahmed Alla et al., “ Medical Large Language Model Benchmarks Should Prioritize Construct Validity ,” ()arXiv()"}
+{"idx": 8, "title": "ICML 2025 Accepted Paper List: Position Track - Paper Copilot", "date": "", "ddg_snippet": "How to use the paper list below: - Overview: This table presents papers from the ICML conference, year 2025.Position: Medical Large Language Model Benchmarks Should Prioritize Construct Validity . OR.", "subpage_snippet": "", "source": "papercopilot.com", "link": "https://papercopilot.com/paper-list/icml-paper-list/icml-2025-paper-list-position-track/", "content": "How to use the paper list below: - Overview: This table presents papers from the ICML conference, year 2025.Position: Medical Large Language Model Benchmarks Should Prioritize Construct Validity . OR."}
+{"idx": 9, "title": "Tom Hartvigsen - Publications", "date": "", "ddg_snippet": "Medical Large Language Model Benchmarks Should Prioritize Construct Validity . Ahmed Alaa, Thomas Hartvigsen, Niloufar Golchini, Shiladitya Dutta, Frances Dean, Inioluwa Deborah Raji, Travis Zack.Oral Spotlight. Real - world Relevance of Generative Counterfactual Explanations.", "subpage_snippet": "", "source": "www.tomhartvigsen.com", "link": "https://www.tomhartvigsen.com/publications", "content": "Medical Large Language Model Benchmarks Should Prioritize Construct Validity . Ahmed Alaa, Thomas Hartvigsen, Niloufar Golchini, Shiladitya Dutta, Frances Dean, Inioluwa Deborah Raji, Travis Zack.Oral Spotlight. Real - world Relevance of Generative Counterfactual Explanations."}
diff --git a/data/sampled_jsons/Table_2_Results_of_crystal_system_classification_and_space_group_classification_Bidirectional-GRU_F1.jsonl b/data/sampled_jsons/Table_2_Results_of_crystal_system_classification_and_space_group_classification_Bidirectional-GRU_F1.jsonl
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index 0000000000000000000000000000000000000000..3d2420c39585bfe3f2578562647159ad4df05ae4
--- /dev/null
+++ b/data/sampled_jsons/Table_2_Results_of_crystal_system_classification_and_space_group_classification_Bidirectional-GRU_F1.jsonl
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+{"idx": 0, "title": "Comparative Analysis of LSTM, GRU, and BERT Models for ... - DiVA", "date": "", "ddg_snippet": "Objectives: The objective of this study is to perform a comparative analysis of the Deep Learning (DL) models namely Long Short-Term Memory (LSTM), Gated Recurrent Unit ( GRU ), and Bidirectional Encoder Representations from Transform-ers (BERT) for fake news detection. By evaluating the performance of these models using metrics such as accuracy, precision, recall, and F1 -score, the study aims ...", "subpage_snippet": "", "source": "www.diva-portal.org", "link": "https://www.diva-portal.org/smash/get/diva2:1980396/FULLTEXT01.pdf", "content": "Objectives: The objective of this study is to perform a comparative analysis of the Deep Learning (DL) models namely Long Short-Term Memory (LSTM), Gated Recurrent Unit ( GRU ), and Bidirectional Encoder Representations from Transform-ers (BERT) for fake news detection. By evaluating the performance of these models using metrics such as accuracy, precision, recall, and F1 -score, the study aims ..."}
+{"idx": 1, "title": "datnguyen-tien204/Vietnamese-News-Classification - GitHub", "date": "", "ddg_snippet": "An NLP project for Vietnamese news classification using custom Bi- GRU and Bi-LSTM networks. Features an end-to-end pipeline with PhoBERT embeddings for high-accuracy text categorization. - datnguye...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/datnguyen-tien204/Vietnamese-News-Classification", "content": "An NLP project for Vietnamese news classification using custom Bi- GRU and Bi-LSTM networks. Features an end-to-end pipeline with PhoBERT embeddings for high-accuracy text categorization. - datnguye..."}
+{"idx": 2, "title": "Hybrid transformer Bi-GRU model with modified ada-belief ...", "date": "", "ddg_snippet": "Sep 11, 2025 · The proposed hybrid transformer model allows the representation of the consumer reviews text into word vectors, which are then passed into the downstream Bidirectional-GRU architecture for further refinement and semantic extraction from given words, subsequently helping to identify spam or ham in the given consumer reviews text.", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/article/10.1007/s41870-025-02699-3", "content": "Sep 11, 2025 · The proposed hybrid transformer model allows the representation of the consumer reviews text into word vectors, which are then passed into the downstream Bidirectional-GRU architecture for further refinement and semantic extraction from given words, subsequently helping to identify spam or ham in the given consumer reviews text."}
+{"idx": 3, "title": "DCATCN: A temporal convolutional network and cross-attention ...", "date": "", "ddg_snippet": "2 days ago · By using a Cross-Attention mechanism to aggregate bidirectional information within a TCN, the framework realizes dynamic interactive modeling of forward and backward information, enhancing the model’s responsiveness to key temporal segments and effectively improving detection accuracy for complex anomalies.", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S2542660525002732", "content": "2 days ago · By using a Cross-Attention mechanism to aggregate bidirectional information within a TCN, the framework realizes dynamic interactive modeling of forward and backward information, enhancing the model’s responsiveness to key temporal segments and effectively improving detection accuracy for complex anomalies."}
+{"idx": 4, "title": "Prototype Learning for Medical Time Series Classification via", "date": "", "ddg_snippet": "No special permission is required to reuse all or part of the article published by MDPI, including figures and tables .", "subpage_snippet": "", "source": "www.mdpi.com", "link": "https://www.mdpi.com/1424-8220/24/8/2655", "content": "No special permission is required to reuse all or part of the article published by MDPI, including figures and tables ."}
+{"idx": 5, "title": "Video Classification of Cloth Simulations: Deep Learning and", "date": "", "ddg_snippet": "No special permission is required to reuse all or part of the article published by MDPI, including figures and tables .", "subpage_snippet": "", "source": "www.mdpi.com", "link": "https://www.mdpi.com/1424-8220/24/2/549", "content": "No special permission is required to reuse all or part of the article published by MDPI, including figures and tables ."}
+{"idx": 6, "title": "Employing Machine Learning and Deep Learning Models for Mental", "date": "", "ddg_snippet": "No special permission is required to reuse all or part of the article published by MDPI, including figures and tables .", "subpage_snippet": "", "source": "www.mdpi.com", "link": "https://www.mdpi.com/2079-3197/13/8/186", "content": "No special permission is required to reuse all or part of the article published by MDPI, including figures and tables ."}
+{"idx": 7, "title": "ImageNet Classification with Deep Convolutional Neural Networks", "date": "", "ddg_snippet": "After AlexNet [12] showed the effectiveness of deep feature vectors in classifying images, various CNNs such as VGGNet [13], and ResNet [14] have ...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/267960550_ImageNet_Classification_with_Deep_Convolutional_Neural_Networks", "content": "After AlexNet [12] showed the effectiveness of deep feature vectors in classifying images, various CNNs such as VGGNet [13], and ResNet [14] have ..."}
+{"idx": 8, "title": "SimXRD-4M: Big Simulated X-ray Diffraction Data Accelerate ...", "date": "", "ddg_snippet": "Table 2 : Results of crystal system classification and space group classification . Inference time is measured for a batch size of 100 samples and experiments are run on a GeForce RTX 3090 GPU.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2406.15469v1", "content": "Table 2 : Results of crystal system classification and space group classification . Inference time is measured for a batch size of 100 samples and experiments are run on a GeForce RTX 3090 GPU."}
+{"idx": 9, "title": "arXiv:2406.15469v1 [cond-mat.mtrl-sci] 15 Jun 2024", "date": "", "ddg_snippet": "nd evaluate 21 sequence learning models on SimXRD. The results indicate that existing neural networks st. uggle with low-frequency crys-tal classifications . The present work highlights the academic significance and the engineering novelty of simu.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2406.15469v1", "content": "nd evaluate 21 sequence learning models on SimXRD. The results indicate that existing neural networks st. uggle with low-frequency crys-tal classifications . The present work highlights the academic significance and the engineering novelty of simu."}
diff --git a/data/sampled_jsons/Taming_Knowledge_Conflicts_in_Language_Models_JUICE_algorithm_dual-run_mechanism_Section_3.2_year_2023-2024.jsonl b/data/sampled_jsons/Taming_Knowledge_Conflicts_in_Language_Models_JUICE_algorithm_dual-run_mechanism_Section_3.2_year_2023-2024.jsonl
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index 0000000000000000000000000000000000000000..576b63adad7e8546898e3a4a6601fa41313f77fd
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@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Taming Knowledge Conflicts in Language Models", "date": "", "ddg_snippet": "by G Li · 2025 — Algorithm. We propose JUICE , a simple yet effective method to steer an LM toward parametric or contextual knowledge without finetuning, leveraging a dual-run.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2503.10996", "content": "by G Li · 2025 — Algorithm. We propose JUICE , a simple yet effective method to steer an LM toward parametric or contextual knowledge without finetuning, leveraging a dual-run."}
+{"idx": 1, "title": "Daily Papers", "date": "", "ddg_snippet": "7 days ago — Taming Knowledge Conflicts in Language Models · Language Models (LMs) often encounter knowledge conflicts when parametric memory contradicts ...", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/papers?q=double+attention", "content": "7 days ago — Taming Knowledge Conflicts in Language Models · Language Models (LMs) often encounter knowledge conflicts when parametric memory contradicts ..."}
+{"idx": 2, "title": "Daily Papers", "date": "", "ddg_snippet": "Taming Knowledge Conflicts in Language Models · Language Models (LMs) often encounter knowledge conflicts when parametric memory contradicts contextual ...", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/papers?q=attribute+knowledge", "content": "Taming Knowledge Conflicts in Language Models · Language Models (LMs) often encounter knowledge conflicts when parametric memory contradicts contextual ..."}
+{"idx": 3, "title": "ICML 2025 2025 Spotlight Posters", "date": "", "ddg_snippet": "Taming Knowledge Conflicts in Language Models . Spotlight Poster. Gaotang Li · Yuzhong Chen · Hanghang Tong. [ East Exhibition Hall A-B ]. thumbnail Abstract.", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/events/2025SpotlightPosters", "content": "Taming Knowledge Conflicts in Language Models . Spotlight Poster. Gaotang Li · Yuzhong Chen · Hanghang Tong. [ East Exhibition Hall A-B ]. thumbnail Abstract."}
+{"idx": 4, "title": "codefuse-ai/Awesome-Code-LLM", "date": "", "ddg_snippet": "[TMLR] A curated list of language modeling researches for code (and other software engineering activities), plus related datasets.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/codefuse-ai/Awesome-Code-LLM", "content": "[TMLR] A curated list of language modeling researches for code (and other software engineering activities), plus related datasets."}
+{"idx": 5, "title": "Towards Controllable Generative AI with Intrinsic ...", "date": "", "ddg_snippet": "by A Liu · 2025 — In this section , we list the detailed algorithms for pruning ( Section 3.2 .1), growing (Sec- tion 3.2 .3), circuit flows computation (Definition 7), and mini ... 308 pages", "subpage_snippet": "", "source": "escholarship.org", "link": "https://escholarship.org/content/qt4xc1x5bb/qt4xc1x5bb_noSplash_6acd65b313cdbcc06103f7b9b1103f4c.pdf", "content": "by A Liu · 2025 — In this section , we list the detailed algorithms for pruning ( Section 3.2 .1), growing (Sec- tion 3.2 .3), circuit flows computation (Definition 7), and mini ... 308 pages"}
+{"idx": 6, "title": "Uday Kamath, Kevin Keenan, Garrett Somers, Sarah ...", "date": "", "ddg_snippet": "The book 'Large Language Models : A Deep Dive' provides a comprehensive guide to understanding and utilizing large language models (LLMs), covering their ...", "subpage_snippet": "", "source": "www.scribd.com", "link": "https://www.scribd.com/document/838169388/Uday-Kamath-Kevin-Keenan-Garrett-Somers-Sarah-Sorenson-Large-Language-Models-a-Deep-Dive-Springer-2024", "content": "The book 'Large Language Models : A Deep Dive' provides a comprehensive guide to understanding and utilizing large language models (LLMs), covering their ..."}
+{"idx": 7, "title": "NeurIPS 2024 Schedule", "date": "", "ddg_snippet": "How Do Large Language Models Acquire Factual Knowledge During Pretraining? Fine-Tuning Large Vision- Language Models as Decision-Making Agents via ...", "subpage_snippet": "", "source": "neurips.cc", "link": "https://neurips.cc/virtual/2024/calendar", "content": "How Do Large Language Models Acquire Factual Knowledge During Pretraining? Fine-Tuning Large Vision- Language Models as Decision-Making Agents via ..."}
+{"idx": 8, "title": "15th USENIX Symposium on Operating Systems Design ...", "date": "", "ddg_snippet": "by S IX · 2021 — ... conflicts with the papers under consideration. PC members nominated ... Mechanism to Tame Hybrid Transaction/Analytical Processing ... 579 pages", "subpage_snippet": "", "source": "www.usenix.org", "link": "https://www.usenix.org/system/files/osdi21_full_proceedings.pdf", "content": "by S IX · 2021 — ... conflicts with the papers under consideration. PC members nominated ... Mechanism to Tame Hybrid Transaction/Analytical Processing ... 579 pages"}
+{"idx": 9, "title": "Artificial Intelligence - A Modern Approach Third Edition - GitHub", "date": "", "ddg_snippet": "Our coverage of planning goes into more depth on contingent planning in partially observable environments and includes a new approach to hierarchical planning. 1,151 pages", "subpage_snippet": "", "source": "raw.githubusercontent.com", "link": "https://raw.githubusercontent.com/yanshengjia/ml-road/47cadb02faa756f85fd2f058e31221cc8223b97a/resources/Artificial+Intelligence+-+A+Modern+Approach+(3rd+Edition).pdf", "content": "Our coverage of planning goes into more depth on contingent planning in partially observable environments and includes a new approach to hierarchical planning. 1,151 pages"}
diff --git a/data/sampled_jsons/Taming_Knowledge_Conflicts_in_Language_Models_full_paper.jsonl b/data/sampled_jsons/Taming_Knowledge_Conflicts_in_Language_Models_full_paper.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..c413206287673ef018d91b971e42ad035f714d3b
--- /dev/null
+++ b/data/sampled_jsons/Taming_Knowledge_Conflicts_in_Language_Models_full_paper.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "[2503.10996] Taming Knowledge Conflicts in Language Models", "date": "", "ddg_snippet": "Abstract page for arXiv paper 2503.10996: Taming Knowledge Conflicts in Language Models .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2503.10996", "content": "Abstract page for arXiv paper 2503.10996: Taming Knowledge Conflicts in Language Models ."}
+{"idx": 1, "title": "(PDF) Taming Knowledge Conflicts in Language Models", "date": "", "ddg_snippet": "Taming Knowledge Conflicts in Language Models . Gaotang Li 1Yuzhong Chen 2Hanghang Tong 1. Abstract.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": "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. Abstract.In this paper , we study how language models respond to. varying degrees of knowledge conflict and propose methods. to regulate these behaviors."}
+{"idx": 2, "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": 3, "title": "Mitigating Knowledge Conflicts in Language ... | Papers With Code", "date": "", "ddg_snippet": "- Language Modeling Document Summarization Question Answering Language Modelling Hallucination. Not in the list?New method full name (e.g. Rectified Linear Unit): Paper where method was first introduced", "subpage_snippet": "", "source": "paperswithcode.com", "link": "https://paperswithcode.com/paper/mitigating-knowledge-conflicts-in-language", "content": "- Language Modeling Document Summarization Question Answering Language Modelling Hallucination. Not in the list?New method full name (e.g. Rectified Linear Unit): Paper where method was first introduced"}
+{"idx": 4, "title": "Mitigating Knowledge Conflicts in Language Model -Driven Question...", "date": "", "ddg_snippet": "Research focuses on mitigating knowledge conflicts in large language models during question answering. Introduces a novel method called Knowledge Conflict Resolution (KCR). Improves answer accuracy by 12-15% compared to baseline methods.", "subpage_snippet": "", "source": "www.aimodels.fyi", "link": "https://www.aimodels.fyi/papers/arxiv/mitigating-knowledge-conflicts-language-model-driven-question", "content": "Research focuses on mitigating knowledge conflicts in large language models during question answering. Introduces a novel method called Knowledge Conflict Resolution (KCR). Improves answer accuracy by 12-15% compared to baseline methods."}
+{"idx": 5, "title": "Mitigating Knowledge Conflicts in Language Model -Driven Question...", "date": "", "ddg_snippet": "The paper addresses the issue of knowledge conflicts in language models , particularly focusing on the phenomenon of factual hallucination during question answering tasks.", "subpage_snippet": "", "source": "powerdrill.ai", "link": "https://powerdrill.ai/discover/discover-Mitigating-Knowledge-Conflicts-cm3oxw8lig9sd01cdpvnv0z7z", "content": "The paper addresses the issue of knowledge conflicts in language models , particularly focusing on the phenomenon of factual hallucination during question answering tasks."}
+{"idx": 6, "title": "Addressing Knowledge Conflicts in Language Models", "date": "", "ddg_snippet": "#Understanding Knowledge Conflicts . Language models use two types of knowledge : Internal Memory and External Context. Internal memory is the knowledge that the model has learned during training. This is often factual but can become outdated.", "subpage_snippet": "", "source": "scisimple.com", "link": "https://scisimple.com/en/articles/2025-09-03-addressing-knowledge-conflicts-in-language-models--a9pwq1z", "content": "#Understanding Knowledge Conflicts . Language models use two types of knowledge : Internal Memory and External Context. Internal memory is the knowledge that the model has learned during training. This is often factual but can become outdated."}
+{"idx": 7, "title": "Taming Knowledge Conflicts in", "date": "", "ddg_snippet": "Taming Knowledge Conflicts in Language Models . Gaotang Li†, Yuzhong Chen‡, Hanghang Tong†. †University of Illinois Urbana-Champaign ‡ VISA Research Contact: gaotang3@illinois.edu.", "subpage_snippet": "", "source": "actionable-interpretability.github.io", "link": "https://actionable-interpretability.github.io/posters/ICML_25_JuICE_Poster+-+Gaotang+Li.pdf", "content": "Taming Knowledge Conflicts in Language Models . Gaotang Li†, Yuzhong Chen‡, Hanghang Tong†. †University of Illinois Urbana-Champaign ‡ VISA Research Contact: gaotang3@illinois.edu."}
+{"idx": 8, "title": "GitHub - pillowsofwind/ Knowledge - Conflicts -Survey: [EMNLP 2024]...", "date": "", "ddg_snippet": "Studying Large Language Model Behaviors Under Realistic Knowledge Conflicts , arXiv 2024, [ Paper ]. Characterizing mechanisms for factual recall in language models , EMNLP 2023, [ Paper ].", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/pillowsofwind/Knowledge-Conflicts-Survey", "content": "Studying Large Language Model Behaviors Under Realistic Knowledge Conflicts , arXiv 2024, [ Paper ]. Characterizing mechanisms for factual recall in language models , EMNLP 2023, [ Paper ]."}
+{"idx": 9, "title": "Analysing the Residual Stream of Language Models Under...", "date": "", "ddg_snippet": "Large language models (LLMs) can store a significant amount of factual knowledge in their parameters. However, their parametric knowledge may conflict with the information provided in the context.", "subpage_snippet": "", "source": "www.chatpaper.ai", "link": "https://www.chatpaper.ai/paper/c44f2560-bad6-4c19-9286-d77dc4ac2237", "content": "Large language models (LLMs) can store a significant amount of factual knowledge in their parameters. However, their parametric knowledge may conflict with the information provided in the context."}
diff --git a/data/sampled_jsons/Temporally-Correlated_ERL_Li_et_al._2024_abstract_year_2024.jsonl b/data/sampled_jsons/Temporally-Correlated_ERL_Li_et_al._2024_abstract_year_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..8a9a193d1883ac3322bc8ad51ffcb9dc8e594e6a
--- /dev/null
+++ b/data/sampled_jsons/Temporally-Correlated_ERL_Li_et_al._2024_abstract_year_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Is there a generic word in English that means \"through time\"?", "date": "", "ddg_snippet": "Upon reflection, I guess \" temporally \" is actually the closest word to use. I was just hoping for a word that meant literally \"spans time\". I guess the implication of using the word \" temporally \", because it deals with time, also often implies over the course of time.", "subpage_snippet": "", "source": "english.stackexchange.com", "link": "https://english.stackexchange.com/questions/33608/is-there-a-generic-word-in-english-that-means-through-time", "content": "Upon reflection, I guess \" temporally \" is actually the closest word to use. I was just hoping for a word that meant literally \"spans time\". I guess the implication of using the word \" temporally \", because it deals with time, also often implies over the course of time."}
+{"idx": 1, "title": "How to choose between \"spatial\" or \"spatially\"?", "date": "", "ddg_snippet": "Dec 8, 2014 · Improved seam merging for temporally and spatially video resizing with structure and motion preservation I am not sure about the adverbs: it is correct to say spatially video resizing or spatial video resizing (or both of the alternatives are wrong)?", "subpage_snippet": "", "source": "english.stackexchange.com", "link": "https://english.stackexchange.com/questions/212491/how-to-choose-between-spatial-or-spatially", "content": "Dec 8, 2014 · Improved seam merging for temporally and spatially video resizing with structure and motion preservation I am not sure about the adverbs: it is correct to say spatially video resizing or spatial video resizing (or both of the alternatives are wrong)?"}
+{"idx": 2, "title": "Single-word request: an adjective meaning \"spatial OR temporal\"", "date": "", "ddg_snippet": "Sep 22, 2024 · Consider deictic, defined at dictionary.com as follows in its second entry: deictic adjective 2. Grammar. specifying identity or spatial or temporal location from the perspective of one or more of the participants in an act of speech or writing, in the context of either an external situation or the surrounding discourse, as we, you, here, there, now, then, this, that, the former, or the latter ...", "subpage_snippet": "", "source": "english.stackexchange.com", "link": "https://english.stackexchange.com/questions/626173/single-word-request-an-adjective-meaning-spatial-or-temporal", "content": "Sep 22, 2024 · Consider deictic, defined at dictionary.com as follows in its second entry: deictic adjective 2. Grammar. specifying identity or spatial or temporal location from the perspective of one or more of the participants in an act of speech or writing, in the context of either an external situation or the surrounding discourse, as we, you, here, there, now, then, this, that, the former, or the latter ..."}
+{"idx": 3, "title": "What, in the context of time, is the equivalent of \"equidistant\"", "date": "", "ddg_snippet": "To change that default frame, you could say that something is ' temporally equidistant.' For example, \"Noon is temporally equidistant from 11 AM and 1 PM\" or \"The journeys from New York City to Washington and Boston are temporally equidistant.\" A quick Google search indicates that the phrase is fairly common in physics and philosophy papers.", "subpage_snippet": "", "source": "english.stackexchange.com", "link": "https://english.stackexchange.com/questions/80066/what-in-the-context-of-time-is-the-equivalent-of-equidistant", "content": "To change that default frame, you could say that something is ' temporally equidistant.' For example, \"Noon is temporally equidistant from 11 AM and 1 PM\" or \"The journeys from New York City to Washington and Boston are temporally equidistant.\" A quick Google search indicates that the phrase is fairly common in physics and philosophy papers."}
+{"idx": 4, "title": "synonyms - English Language & Usage Stack Exchange", "date": "", "ddg_snippet": "Perhaps the word temporally (adverb) is what you could be looking for? E.g., the adjective \"spatiotemporal\" describes something in terms of space (\"spatio-\") and time (\"temporal\"). See here for some definitions of the word temporal (adjective), in which the second definition can be located and identified apparently with what you're looking for: (formal) connected with or limited by time - a ...", "subpage_snippet": "", "source": "english.stackexchange.com", "link": "https://english.stackexchange.com/questions/181282/a-word-for-in-terms-of-time", "content": "Perhaps the word temporally (adverb) is what you could be looking for? E.g., the adjective \"spatiotemporal\" describes something in terms of space (\"spatio-\") and time (\"temporal\"). See here for some definitions of the word temporal (adjective), in which the second definition can be located and identified apparently with what you're looking for: (formal) connected with or limited by time - a ..."}
+{"idx": 5, "title": "single word requests - Chronologically relevant - English...", "date": "", "ddg_snippet": "Feb 16, 2016 · Consider temporally . Definition: In a certain order, with regards to time. The term describes how things are related to each other in terms of time. Examples: I thought the two events that I wanted to attend were at the same time, but I later found out they were temporally arranged, which meant that I could attend one and then the other without missing anything. The organization of the ...", "subpage_snippet": "", "source": "english.stackexchange.com", "link": "https://english.stackexchange.com/questions/307819/chronologically-relevant", "content": "Feb 16, 2016 · Consider temporally . Definition: In a certain order, with regards to time. The term describes how things are related to each other in terms of time. Examples: I thought the two events that I wanted to attend were at the same time, but I later found out they were temporally arranged, which meant that I could attend one and then the other without missing anything. The organization of the ..."}
+{"idx": 6, "title": "Difference in meaning between did you speak to... and have you...", "date": "", "ddg_snippet": "Possible Duplicate: How do the tenses in English correspond temporally to one another? What's the difference in meaning between \"Did you speak to the landlord this morning?\" and \"Have you spoke...", "subpage_snippet": "", "source": "english.stackexchange.com", "link": "https://english.stackexchange.com/questions/70722/difference-in-meaning-between-did-you-speak-to-and-have-you-spoken-to", "content": "Possible Duplicate: How do the tenses in English correspond temporally to one another? What's the difference in meaning between \"Did you speak to the landlord this morning?\" and \"Have you spoke..."}
+{"idx": 7, "title": "\"temporal\" vs. \"temporary\" [closed] - English Language & Usage...", "date": "", "ddg_snippet": "Is there any difference between the two? In a technical document I have used a phrase \"this is a temporal solution\" and my coworker told that he'd use \"temporary\" in the context. Is one of the two...", "subpage_snippet": "", "source": "english.stackexchange.com", "link": "https://english.stackexchange.com/questions/7539/temporal-vs-temporary", "content": "Is there any difference between the two? In a technical document I have used a phrase \"this is a temporal solution\" and my coworker told that he'd use \"temporary\" in the context. Is one of the two..."}
+{"idx": 8, "title": "terminology - The relation between \"temporal\" and \"time\" -...", "date": "", "ddg_snippet": "Feb 3, 2013 · Where to look: temporal in ODO The word temporal is an adjectival form of the word time. adjective 2 relating to time This is supported by the use of temporal in the entry for time: Old English tīma, of Germanic origin; related to tide, which it superseded in temporal senses. In English, any noun can theoretically be made attributive [made into what is effectively an adjective], and that ...", "subpage_snippet": "", "source": "english.stackexchange.com", "link": "https://english.stackexchange.com/questions/102816/the-relation-between-temporal-and-time", "content": "Feb 3, 2013 · Where to look: temporal in ODO The word temporal is an adjectival form of the word time. adjective 2 relating to time This is supported by the use of temporal in the entry for time: Old English tīma, of Germanic origin; related to tide, which it superseded in temporal senses. In English, any noun can theoretically be made attributive [made into what is effectively an adjective], and that ..."}
+{"idx": 9, "title": "verbs - \"log in to\" or \"log into\" or \"login to\" - English...", "date": "", "ddg_snippet": "When writing an instruction about connecting to a computer using ssh, telnet, etc., I'm not sure what spacing to use in this familiar spoken phrase: \"Log in to host.com\" \"Log into host.com\" \"Login to", "subpage_snippet": "", "source": "english.stackexchange.com", "link": "https://english.stackexchange.com/questions/5302/log-in-to-or-log-into-or-login-to", "content": "When writing an instruction about connecting to a computer using ssh, telnet, etc., I'm not sure what spacing to use in this familiar spoken phrase: \"Log in to host.com\" \"Log into host.com\" \"Login to"}
diff --git a/data/sampled_jsons/Temporally-Correlated_ERL_sitearxiv.org_Li_et_al._2024_year_2024.jsonl b/data/sampled_jsons/Temporally-Correlated_ERL_sitearxiv.org_Li_et_al._2024_year_2024.jsonl
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@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Open the Black Box: Step-based Policy Updates for Temporally-Correlated ...", "date": "", "ddg_snippet": "In this work, we introduce a novel ERL algorithm, Temporally-Correlated Episodic RL (TCE), which effectively utilizes step information in episodic policy updates, opening the 'black box' in existing ERL methods while retaining the smooth and consistent exploration in parameter space.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2401.11437", "content": "In this work, we introduce a novel ERL algorithm, Temporally-Correlated Episodic RL (TCE), which effectively utilizes step information in episodic policy updates, opening the 'black box' in existing ERL methods while retaining the smooth and consistent exploration in parameter space."}
+{"idx": 1, "title": "PDF arXiv:2401.11437v1 [cs.LG] 21 Jan 2024", "date": "", "ddg_snippet": "x ERL methods like Otto et al. (2022). Our contributions are summarized as: (a) We propose TCE, a novel RL framework that integrates step-based information into the policy updates of ERL , while preserving the broad exploration scope and traje", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2401.11437.pdf", "content": "x ERL methods like Otto et al. (2022). Our contributions are summarized as: (a) We propose TCE, a novel RL framework that integrates step-based information into the policy updates of ERL , while preserving the broad exploration scope and traje"}
+{"idx": 2, "title": "[2412.04775] A Temporally Correlated Latent Exploration for ...", "date": "", "ddg_snippet": "Efficient exploration remains one of the longstanding problems of deep reinforcement learning. Instead of depending solely on extrinsic rewards from the environments, existing methods use intrinsic rewards to enhance exploration. However, we demonstrate that these methods are vulnerable to Noisy TV and stochasticity. To tackle this problem, we propose Temporally Correlated Latent Exploration ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2412.04775", "content": "Efficient exploration remains one of the longstanding problems of deep reinforcement learning. Instead of depending solely on extrinsic rewards from the environments, existing methods use intrinsic rewards to enhance exploration. However, we demonstrate that these methods are vulnerable to Noisy TV and stochasticity. To tackle this problem, we propose Temporally Correlated Latent Exploration ..."}
+{"idx": 3, "title": "A arXiv:2410.09536v4 [cs.LG] 15 Mar 2025", "date": "", "ddg_snippet": "ecially in settings with dense re-wards. To address this issue, a most recently proposed method, Temporally-Correlated ERL (TCE) ( Li et al ., 2024 ) introduced a more eficient update scheme that \"opens the black-box\" and utilizes sub-segment information for policy update while retai", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2410.09536", "content": "ecially in settings with dense re-wards. To address this issue, a most recently proposed method, Temporally-Correlated ERL (TCE) ( Li et al ., 2024 ) introduced a more eficient update scheme that \"opens the black-box\" and utilizes sub-segment information for policy update while retai"}
+{"idx": 4, "title": "A Temporally Correlated Latent Exploration for Reinforcement Learning", "date": "", "ddg_snippet": "It has been demonstrated that temporally correlated noise for action selection enhances exploration in both on-policy and off-policy RL (Eberhard et al ., 2023; Hollenstein et al ., 2024 ).", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2412.04775v1", "content": "It has been demonstrated that temporally correlated noise for action selection enhances exploration in both on-policy and off-policy RL (Eberhard et al ., 2023; Hollenstein et al ., 2024 )."}
+{"idx": 5, "title": "Temporally Correlated Task Scheduling for Sequence Learning", "date": "", "ddg_snippet": "Abstract Sequence learning has attracted much research attention from the machine learning community in recent years. In many applications, a sequence learning task is usually associated with multiple temporally correlated auxiliary tasks, which are different in terms of how much input information to use or which future step to predict. For exam-ple, (i) in simultaneous machine translation ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2007.05290", "content": "Abstract Sequence learning has attracted much research attention from the machine learning community in recent years. In many applications, a sequence learning task is usually associated with multiple temporally correlated auxiliary tasks, which are different in terms of how much input information to use or which future step to predict. For exam-ple, (i) in simultaneous machine translation ..."}
+{"idx": 6, "title": "PDF Multivariate Probabilistic Time Series Forecasting with Correlated Errors", "date": "", "ddg_snippet": "Abstract Modeling the correlations among errors is closely associated with how accurately the model can quantify predictive uncertainty in probabilistic time series forecasting. Recent multivariate mod-els have made significant progress in accounting for contemporaneous correlations among errors, while a common assumption on these errors is that they are temporally independent for the sake of ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2402.01000.pdf", "content": "Abstract Modeling the correlations among errors is closely associated with how accurately the model can quantify predictive uncertainty in probabilistic time series forecasting. Recent multivariate mod-els have made significant progress in accounting for contemporaneous correlations among errors, while a common assumption on these errors is that they are temporally independent for the sake of ..."}
+{"idx": 7, "title": "Diversity-aware Buffer for Coping with Temporally Correlated Data ...", "date": "", "ddg_snippet": "This ensures that the model cannot drift too far apart from the source model. In cases of temporally correlated data, another challenge arises in estimating reliable batch normalization (BN) statistics. For robust statistics, NOTE [6] introduced an instance-aware batch normalization and RoTTA [7] employs a robust batch normalization variant.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2401.00989v1", "content": "This ensures that the model cannot drift too far apart from the source model. In cases of temporally correlated data, another challenge arises in estimating reliable batch normalization (BN) statistics. For robust statistics, NOTE [6] introduced an instance-aware batch normalization and RoTTA [7] employs a robust batch normalization variant."}
+{"idx": 8, "title": "Chunking the Critic: A Transformer-based Soft Actor-Critic with N-Step ...", "date": "", "ddg_snippet": "2.2 Transformer-based Critics for Episodic RL Transformer-based Off-Policy Episodic RL (TOP- ERL ) ( Li et al ., 2024a) introduces architectural innovation through a transformer-based critic that processes trajectory segments rather than full episodes.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2503.03660v1", "content": "2.2 Transformer-based Critics for Episodic RL Transformer-based Off-Policy Episodic RL (TOP- ERL ) ( Li et al ., 2024a) introduces architectural innovation through a transformer-based critic that processes trajectory segments rather than full episodes."}
+{"idx": 9, "title": "[2407.04811] Simplifying Deep Temporal Difference Learning", "date": "", "ddg_snippet": "Q-learning played a foundational role in the field reinforcement learning (RL). However, TD algorithms with off-policy data, such as Q-learning, or nonlinear function approximation like deep neural networks require several additional tricks to stabilise training, primarily a large replay buffer and target networks. Unfortunately, the delayed updating of frozen network parameters in the target ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2407.04811", "content": "Q-learning played a foundational role in the field reinforcement learning (RL). However, TD algorithms with off-policy data, such as Q-learning, or nonlinear function approximation like deep neural networks require several additional tricks to stabilise training, primarily a large replay buffer and target networks. Unfortunately, the delayed updating of frozen network parameters in the target ..."}
diff --git a/data/sampled_jsons/The_Value_of_Prediction_in_Identifying_the_Worst-Off_equation_2_policy_value_Gaussian_bivariate_norm.jsonl b/data/sampled_jsons/The_Value_of_Prediction_in_Identifying_the_Worst-Off_equation_2_policy_value_Gaussian_bivariate_norm.jsonl
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--- /dev/null
+++ b/data/sampled_jsons/The_Value_of_Prediction_in_Identifying_the_Worst-Off_equation_2_policy_value_Gaussian_bivariate_norm.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Normal distribution - Wikipedia", "date": "", "ddg_snippet": "6.1 Generating values from normal distribution. 6. 2 Numerical approximations for the normal cumulative distribution function and normal quantile function.A random variable with a Gaussian distribution is said to be normally distributed, and is called a normal deviate.", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/Normal_distribution", "content": "6.1 Generating values from normal distribution. 6. 2 Numerical approximations for the normal cumulative distribution function and normal quantile function.A random variable with a Gaussian distribution is said to be normally distributed, and is called a normal deviate."}
+{"idx": 1, "title": "The Value of Prediction in Identifying the Worst - Off", "date": "", "ddg_snippet": "( Policy Value in Gaussian Setting) Let.The decision-maker has (at least) two pathways to raise the policy value , which we refer to as policy levers", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=26JsumCG0z", "content": "( Policy Value in Gaussian Setting) Let.The decision-maker has (at least) two pathways to raise the policy value , which we refer to as policy levers"}
+{"idx": 2, "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": 3, "title": "probability - Bivariate Gaussian Independence - Mathematics Stack...", "date": "", "ddg_snippet": "is the common value of the off -diagonal elements of this matrix, which I get to be σ 2 (ac+bd+(bc+ad)ρ). , and this is zero when ρ=−db+acad+bc , just as you've found by your method. Apart from the errors in the coefficients of f.", "subpage_snippet": "", "source": "math.stackexchange.com", "link": "https://math.stackexchange.com/questions/4190292/bivariate-gaussian-independence", "content": "is the common value of the off -diagonal elements of this matrix, which I get to be σ 2 (ac+bd+(bc+ad)ρ). , and this is zero when ρ=−db+acad+bc , just as you've found by your method. Apart from the errors in the coefficients of f."}
+{"idx": 4, "title": "tensorflow - Negative values in Log likelihood of a bivariate gaussian", "date": "", "ddg_snippet": "I am trying to implement a loss function which tries to minimize the negative log likelihood of obtaining ground truth values (x,y) from predicted bivariate gaussian distribution parameters.", "subpage_snippet": "", "source": "stackoverflow.com", "link": "https://stackoverflow.com/questions/43031731/negative-values-in-log-likelihood-of-a-bivariate-gaussian", "content": "I am trying to implement a loss function which tries to minimize the negative log likelihood of obtaining ground truth values (x,y) from predicted bivariate gaussian distribution parameters."}
+{"idx": 5, "title": "Gaussian Function -- from Wolfram MathWorld", "date": "", "ddg_snippet": "In one dimension, the Gaussian function is the probability density function of the normal distributionsometimes also called the frequency curve. The full width at half maximum (FWHM) for a Gaussian is found by finding the half-maximum points x_0 .", "subpage_snippet": "", "source": "mathworld.wolfram.com", "link": "https://mathworld.wolfram.com/GaussianFunction.html", "content": "In one dimension, the Gaussian function is the probability density function of the normal distributionsometimes also called the frequency curve. The full width at half maximum (FWHM) for a Gaussian is found by finding the half-maximum points x_0 ."}
+{"idx": 6, "title": "[ICML 2025] The Value of Prediction in Identifying the Worst - Off", "date": "", "ddg_snippet": "Improving Predictions : Enhancing a model's predictive power (measured by R ² ). Expanding Access: Increasing the screening capacity (α), or the proportion of the population that can be evaluated and supported.", "subpage_snippet": "", "source": "arxiviq.substack.com", "link": "https://arxiviq.substack.com/p/icml-2025-the-value-of-prediction", "content": "Improving Predictions : Enhancing a model's predictive power (measured by R ² ). Expanding Access: Increasing the screening capacity (α), or the proportion of the population that can be evaluated and supported."}
+{"idx": 7, "title": "Visualizing the Bivariate Gaussian ... - Never Open Always Closed", "date": "", "ddg_snippet": "Visualizing the Bivariate Gaussian Distribution in Python. The values present in the right diagonal represent the joint covariance between two components of the corresponding random variables . If the value is +ve, that means there is positive covariance between the two random variables ...", "subpage_snippet": "", "source": "neveropen.tech", "link": "https://neveropen.tech/visualizing-the-bivariate-gaussian-distribution-in-python/", "content": "Visualizing the Bivariate Gaussian Distribution in Python. The values present in the right diagonal represent the joint covariance between two components of the corresponding random variables . If the value is +ve, that means there is positive covariance between the two random variables ..."}
+{"idx": 8, "title": "7. Conditional Bivariate Gaussians — Data Science Topics...", "date": "", "ddg_snippet": "7.3. Modeling conditional bivariate gaussian . After we simulate the data, we can estimate the means, variances, standard deviations and correlations from the data.Here, we evaluate the log probability of the data given the two models. Remeber Bayes’ Theorem.", "subpage_snippet": "", "source": "datascience.oneoffcoder.com", "link": "https://datascience.oneoffcoder.com/bivariate-cond-gaussian.html", "content": "7.3. Modeling conditional bivariate gaussian . After we simulate the data, we can estimate the means, variances, standard deviations and correlations from the data.Here, we evaluate the log probability of the data given the two models. Remeber Bayes’ Theorem."}
+{"idx": 9, "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."}
diff --git a/data/sampled_jsons/The_illusion_of_state_in_state-space_models_abstract_year_2024.jsonl b/data/sampled_jsons/The_illusion_of_state_in_state-space_models_abstract_year_2024.jsonl
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index 0000000000000000000000000000000000000000..028f6123de8d4a40f88530dd9fb2645be6786232
--- /dev/null
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@@ -0,0 +1,10 @@
+{"idx": 0, "title": "[2404.08819] The Illusion of State in State-Space Models", "date": "", "ddg_snippet": "by W Merrill · 2024 · Cited by 89 — Abstract : State - space models (SSMs) have emerged as a potential alternative architecture for building large language models (LLMs) compared ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2404.08819", "content": "by W Merrill · 2024 · Cited by 89 — Abstract : State - space models (SSMs) have emerged as a potential alternative architecture for building large language models (LLMs) compared ..."}
+{"idx": 1, "title": "The illusion of state in state-space models", "date": "", "ddg_snippet": "by W Merrill · 2024 · Cited by 89 — The \" state \" in common SSMs is an illusion : S4, Mamba, and related models have similar expressiveness limitations to nonrecurrent models like transformers.", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.5555/3692070.3693514", "content": "by W Merrill · 2024 · Cited by 89 — The \" state \" in common SSMs is an illusion : S4, Mamba, and related models have similar expressiveness limitations to nonrecurrent models like transformers."}
+{"idx": 2, "title": "The Illusion of State in State-Space Models", "date": "", "ddg_snippet": "by W Merrill · 2024 · Cited by 89 — Abstract. State-space models (SSMs) have emerged as a potential alternative to transformers . One theoret- ical weakness of transformers is ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2404.08819", "content": "by W Merrill · 2024 · Cited by 89 — Abstract. State-space models (SSMs) have emerged as a potential alternative to transformers . One theoret- ical weakness of transformers is ..."}
+{"idx": 3, "title": "The illusion of state in state-space models", "date": "", "ddg_snippet": "7 Jun 2024 — The lexical features are abstracted away and the network begins to understand meaning directly. Of course, you'd still have differentiation by ...", "subpage_snippet": "", "source": "news.ycombinator.com", "link": "https://news.ycombinator.com/item?id=40607733", "content": "7 Jun 2024 — The lexical features are abstracted away and the network begins to understand meaning directly. Of course, you'd still have differentiation by ..."}
+{"idx": 4, "title": "Transformers are Better than State Space Models at ...", "date": "", "ddg_snippet": "We find that transformers outperform GSSMs in terms of efficiency and generalization on synthetic tasks that require copying the context.", "subpage_snippet": "", "source": "www.reddit.com", "link": "https://www.reddit.com/r/MachineLearning/comments/1aj9swv/repeat_after_me_transformers_are_better_than/", "content": "We find that transformers outperform GSSMs in terms of efficiency and generalization on synthetic tasks that require copying the context."}
+{"idx": 5, "title": "Attention, State Space Models, and Recurrent Neural ...", "date": "", "ddg_snippet": "by J Sieber · 2024 · Cited by 25 — The illusion of state in state-space models . arXiv preprint arXiv:2404.08819 ... The abstract and/or introduction should clearly state the claims made, including ...", "subpage_snippet": "", "source": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper_files/paper/2024/file/f271a36160097fbdb06a9adeb1605343-Paper-Conference.pdf", "content": "by J Sieber · 2024 · Cited by 25 — The illusion of state in state-space models . arXiv preprint arXiv:2404.08819 ... The abstract and/or introduction should clearly state the claims made, including ..."}
+{"idx": 6, "title": "radarFudan/Awesome-state-space-models", "date": "", "ddg_snippet": "From generalization analysis to optimization designs for state space models (https://arxiv.org/abs/2405.02670). The Illusion of State in State-Space Models ( ...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/radarFudan/Awesome-state-space-models", "content": "From generalization analysis to optimization designs for state space models (https://arxiv.org/abs/2405.02670). The Illusion of State in State-Space Models ( ..."}
+{"idx": 7, "title": "Jackson Petty", "date": "", "ddg_snippet": "The illusion of state in state-space models . W Merrill, J Petty, A Sabharwal ... How abstract is linguistic generalization in large language models?", "subpage_snippet": "", "source": "scholar.google.com", "link": "https://scholar.google.com/citations?user=hCYSiTgAAAAJ&hl=en", "content": "The illusion of state in state-space models . W Merrill, J Petty, A Sabharwal ... How abstract is linguistic generalization in large language models?"}
+{"idx": 8, "title": "William Merrill", "date": "", "ddg_snippet": "The Illusion of State in State-Space Models . William Merrill, Jackson Petty, and Ashish Sabharwal. In ICML, Jul 2024. HTML. Can You Learn Semantics Through Next ...", "subpage_snippet": "", "source": "lambdaviking.com", "link": "https://lambdaviking.com/", "content": "The Illusion of State in State-Space Models . William Merrill, Jackson Petty, and Ashish Sabharwal. In ICML, Jul 2024. HTML. Can You Learn Semantics Through Next ..."}
+{"idx": 9, "title": "Chimera: Effectively Modeling Multivariate Time Series with...", "date": "", "ddg_snippet": "5 Nov 2024 — This paper introduces Chimera, a novel two-dimensional State Space Model (SSM) for multivariate time series modeling. The method aims to capture ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=ncYGjx2vnE&noteId=SB0dLuJeL7", "content": "5 Nov 2024 — This paper introduces Chimera, a novel two-dimensional State Space Model (SSM) for multivariate time series modeling. The method aims to capture ..."}
diff --git a/data/sampled_jsons/Theorem_3.2_function-valued_Gaussian_processes_multi-output_Gaussian_processes_linearization.jsonl b/data/sampled_jsons/Theorem_3.2_function-valued_Gaussian_processes_multi-output_Gaussian_processes_linearization.jsonl
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@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Learning Gaussian processes which map functions to functions", "date": "", "ddg_snippet": "Vector- Valued Gaussian Processes on Riemannian Manifolds via Gauge Independent Projected Kernels .” In Advances in Neural Information Processing ...", "subpage_snippet": "", "source": "danmackinlay.name", "link": "https://danmackinlay.name/notebook/gp_regression_functional.html", "content": "Vector- Valued Gaussian Processes on Riemannian Manifolds via Gauge Independent Projected Kernels .” In Advances in Neural Information Processing ..."}
+{"idx": 1, "title": "Gaussian Processes and Reproducing Kernels: Connections and", "date": "", "ddg_snippet": "This monograph studies the relations between two approaches using positive definite kernels: probabilistic methods using Gaussian processes , and non ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2506.17366v1", "content": "This monograph studies the relations between two approaches using positive definite kernels: probabilistic methods using Gaussian processes , and non ..."}
+{"idx": 2, "title": "Toward Efficient Influence Function: Dropout as a Compression", "date": "", "ddg_snippet": "... Gaussian projections (Park et al.,, 2023 ) or principal component analysis (PCA) (Choe et al.,, 2024 ) , alleviate the challenge posed by the large ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2509.15651v1", "content": "... Gaussian projections (Park et al.,, 2023 ) or principal component analysis (PCA) (Choe et al.,, 2024 ) , alleviate the challenge posed by the large ..."}
+{"idx": 3, "title": "Linear Operators and Stochastic Partial Differential Equations", "date": "", "ddg_snippet": "... Gaussian process prior to regularize the solution and integrate a normalization constraint into the Gaussian process via two approaches: by ...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/221079156_Linear_Operators_and_Stochastic_Partial_Differential_Equations_in_Gaussian_Process_Regression", "content": "... Gaussian process prior to regularize the solution and integrate a normalization constraint into the Gaussian process via two approaches: by ..."}
+{"idx": 4, "title": "Newest 'gpy' Questions - Stack Overflow", "date": "", "ddg_snippet": "I have trained successfully a multi - output Gaussian Process model using an GPy.models.GPCoregionalizedRegression model of the GPy package.", "subpage_snippet": "", "source": "stackoverflow.com", "link": "https://stackoverflow.com/questions/tagged/gpy", "content": "I have trained successfully a multi - output Gaussian Process model using an GPy.models.GPCoregionalizedRegression model of the GPy package."}
+{"idx": 5, "title": "ASCII - 2023 - Annual activity report", "date": "", "ddg_snippet": "We are developing the continuous time formulation of this problem and allow for diffusion control and for possible competing multiple agents and ...", "subpage_snippet": "", "source": "radar.inria.fr", "link": "https://radar.inria.fr/report/2023/ascii/index.html", "content": "We are developing the continuous time formulation of this problem and allow for diffusion control and for possible competing multiple agents and ..."}
+{"idx": 6, "title": "Optimal Control Policy of Unreliable Production Systems", "date": "", "ddg_snippet": "... Optimized Multi - Output Gaussian Process ... Please note that many of the page functionalities won't work as expected without javascript enabled.", "subpage_snippet": "", "source": "www.mdpi.com", "link": "https://www.mdpi.com/2071-1050/16/13/5760", "content": "... Optimized Multi - Output Gaussian Process ... Please note that many of the page functionalities won't work as expected without javascript enabled."}
+{"idx": 7, "title": "(PDF) Generative AI for Bayesian Computation", "date": "", "ddg_snippet": "To illustrate our methodology, we provide three examples: a stylized synthetic example, a traffic flow prediction problem and we analyze the well ...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/371009080_Generative_AI_for_Bayesian_Computation", "content": "To illustrate our methodology, we provide three examples: a stylized synthetic example, a traffic flow prediction problem and we analyze the well ..."}
+{"idx": 8, "title": "Retrocausality in Quantum Mechanics (Stanford Encyclopedia of", "date": "", "ddg_snippet": "... process of electromagnetic radiation emission and absorption should be thought of as an interaction between a source and an absorber rather than as an ...", "subpage_snippet": "", "source": "plato.stanford.edu", "link": "https://plato.stanford.edu/entries/qm-retrocausality/", "content": "... process of electromagnetic radiation emission and absorption should be thought of as an interaction between a source and an absorber rather than as an ..."}
+{"idx": 9, "title": "Newest 'geospatial' Questions - Data Science Stack", "date": "", "ddg_snippet": "Problem Statement Imagine there are two almost identical images with annotations (bounding boxes for certain objects), where one is so-called golden ...", "subpage_snippet": "", "source": "datascience.stackexchange.com", "link": "https://datascience.stackexchange.com/questions/tagged/geospatial", "content": "Problem Statement Imagine there are two almost identical images with annotations (bounding boxes for certain objects), where one is so-called golden ..."}
diff --git a/data/sampled_jsons/Theorem_3.4_upper_bound_Catoni_Contextual_Bandits_are_Robust_to_Heavy-tailed_Rewards.jsonl b/data/sampled_jsons/Theorem_3.4_upper_bound_Catoni_Contextual_Bandits_are_Robust_to_Heavy-tailed_Rewards.jsonl
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index 0000000000000000000000000000000000000000..737ce042acb63436b0c09f104ab923bd8f23859b
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+++ b/data/sampled_jsons/Theorem_3.4_upper_bound_Catoni_Contextual_Bandits_are_Robust_to_Heavy-tailed_Rewards.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Catoni Contextual Bandits are Robust to Heavy-tailed Rewards", "date": "", "ddg_snippet": "This theorem provides a variance-dependent upper bound when variances are unknown, which matches the upper bound when variances are observed ( Theorem 3 . 4 ) up to a slightly worse dependence on the eluder dimension.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=5IpVe9PH14", "content": "This theorem provides a variance-dependent upper bound when variances are unknown, which matches the upper bound when variances are observed ( Theorem 3 . 4 ) up to a slightly worse dependence on the eluder dimension."}
+{"idx": 1, "title": "A Sharp Upper Bound for the Expected Interval Occupation Time ...", "date": "", "ddg_snippet": "The optimal control attains the upper bound ] in (1.5). Secondly, to prove Theorem 1.1, we consider the same control problem as above but with the aim of maximizing the expected occupation time of X in the interval [l u , ] up to some finite time horizon t.", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/content/pdf/10.1007/s10959-025-01446-5.pdf", "content": "The optimal control attains the upper bound ] in (1.5). Secondly, to prove Theorem 1.1, we consider the same control problem as above but with the aim of maximizing the expected occupation time of X in the interval [l u , ] up to some finite time horizon t."}
+{"idx": 2, "title": "Low-rank matrix recovery under heavy-tailed errors", "date": "", "ddg_snippet": "This paper proposes convex relaxation based robust methods to recover approximately low-rank matrices in the presence of heavy - tailed and asymmetric errors, allowing for heteroscedasticity. We focus on three archetypal applications in matrix recovery: matrix compressed sensing, matrix completion and multitask regression. Statis-tically, we provide sub-Gaussian-type deviation bounds when the ...", "subpage_snippet": "", "source": "par.nsf.gov", "link": "https://par.nsf.gov/servlets/purl/10520389", "content": "This paper proposes convex relaxation based robust methods to recover approximately low-rank matrices in the presence of heavy - tailed and asymmetric errors, allowing for heteroscedasticity. We focus on three archetypal applications in matrix recovery: matrix compressed sensing, matrix completion and multitask regression. Statis-tically, we provide sub-Gaussian-type deviation bounds when the ..."}
+{"idx": 3, "title": "Heavy-Tailed Linear Bandits: Huber Regression with One-Pass", "date": "", "ddg_snippet": "Table 1: Comparisons of our regret bounds and computational complexity to previous best-known results for heavy-tailed linear bandits .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2503.00419v1", "content": "Table 1: Comparisons of our regret bounds and computational complexity to previous best-known results for heavy-tailed linear bandits ."}
+{"idx": 4, "title": "Kronecker Powers, Orthogonal Vectors, and the Asymptotic Spectrum", "date": "", "ddg_snippet": "Strassen’s duality theorem asserts that this family of obstructions is complete; that is, the asymptotic upper bound exactly matches the best α lower bound provided by the -volumes. This equivalence is the essence of our duality theorem. To make all these arguments rigorous, see further details in Section 4.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2509.14489", "content": "Strassen’s duality theorem asserts that this family of obstructions is complete; that is, the asymptotic upper bound exactly matches the best α lower bound provided by the -volumes. This equivalence is the essence of our duality theorem. To make all these arguments rigorous, see further details in Section 4."}
+{"idx": 5, "title": "Lecture notes for 21-301: Combinatorics - CMU", "date": "", "ddg_snippet": "These are lecture notes for Math 21-301: Combinatorics, taught during the Fall semester 2017 at Carnegie Mellon University. My goal for this course is to give you", "subpage_snippet": "", "source": "www.math.cmu.edu", "link": "https://www.math.cmu.edu/~mtait/21-301Fall2017/Notes.pdf", "content": "These are lecture notes for Math 21-301: Combinatorics, taught during the Fall semester 2017 at Carnegie Mellon University. My goal for this course is to give you"}
+{"idx": 6, "title": "How Balanced Should Causal Covariates Be?", "date": "", "ddg_snippet": "... the balancing be, in order to minimize the error of the final estimate? Current guidance is derived from heuristic or asymptotic analyses, which are ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2503.03860v1", "content": "... the balancing be, in order to minimize the error of the final estimate? Current guidance is derived from heuristic or asymptotic analyses, which are ..."}
+{"idx": 7, "title": "Extended UCB Policies for Multi-Armed Bandit Problems", "date": "", "ddg_snippet": "... robust UCB generalizes Lattimore’s seminary work (for moments of orders p = 4 p= 4 and q = 2 q=2 ) to arbitrarily chosen p > q > 1 p > q ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/1112.1768v5", "content": "... robust UCB generalizes Lattimore’s seminary work (for moments of orders p = 4 p= 4 and q = 2 q=2 ) to arbitrarily chosen p > q > 1 p > q ..."}
+{"idx": 8, "title": "The Stats Map · Active Statistical Inference", "date": "", "ddg_snippet": "But suppose we are in a supervised learning setting and you get to choose which data points to label. How should you label in them in order to ...", "subpage_snippet": "", "source": "thestatsmap.com", "link": "https://thestatsmap.com/active-statistical-inference", "content": "But suppose we are in a supervised learning setting and you get to choose which data points to label. How should you label in them in order to ..."}
+{"idx": 9, "title": "ICML 2022 Papers", "date": "", "ddg_snippet": "Adversarially Robust Models may not Transfer Better ... Representation Topology Divergence: A Method for Comparing Neural Network Representations.", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2022/papers.html", "content": "Adversarially Robust Models may not Transfer Better ... Representation Topology Divergence: A Method for Comparing Neural Network Representations."}
diff --git a/data/sampled_jsons/Trockman_Kolter_mimetic_initialization_self-attention_identity_matrix_year_2023.jsonl b/data/sampled_jsons/Trockman_Kolter_mimetic_initialization_self-attention_identity_matrix_year_2023.jsonl
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index 0000000000000000000000000000000000000000..9b1b0185b1ab5b2e381da40b627109fd020806d8
--- /dev/null
+++ b/data/sampled_jsons/Trockman_Kolter_mimetic_initialization_self-attention_identity_matrix_year_2023.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "[2305.09828] Mimetic Initialization of Self - Attention Layers", "date": "", "ddg_snippet": "Authors:Asher Trockman , J. Zico Kolter .Our initialization scheme is closed form, learning-free, and very simple: we set the product of the query and key weights to be approximately the identity , and the product of the value and projection weights to approximately the negative identity .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2305.09828", "content": "Authors:Asher Trockman , J. Zico Kolter .Our initialization scheme is closed form, learning-free, and very simple: we set the product of the query and key weights to be approximately the identity , and the product of the value and projection weights to approximately the negative identity ."}
+{"idx": 1, "title": "Mimetic Initialization of Self-Attention Layers - LOVO AI", "date": "", "ddg_snippet": "Aug 23, 2023 · To address this problem, a paper written by Asher Trockman , J. Zico Kolter proposes mimetic initialization . As its name suggests, the self-attention weights of the transformers are initialized in a way that mimics those of their trained counterparts, allowing for a faster convergence", "subpage_snippet": "", "source": "lovo.ai", "link": "https://lovo.ai/post/mimetic-initialization-of-self-attention-layers", "content": "Aug 23, 2023 · To address this problem, a paper written by Asher Trockman , J. Zico Kolter proposes mimetic initialization . As its name suggests, the self-attention weights of the transformers are initialized in a way that mimics those of their trained counterparts, allowing for a faster convergence"}
+{"idx": 2, "title": "Mimetic Initialization of Self-Attention Layers", "date": "", "ddg_snippet": "Our initialization scheme is closed form, learning-free, and very simple: we set the product of the query and key weights to be approximately the identity , and the product of the value and projection weights to ap-proximately the negative identity .", "subpage_snippet": "", "source": "proceedings.mlr.press", "link": "https://proceedings.mlr.press/v202/trockman23a/trockman23a.pdf", "content": "Our initialization scheme is closed form, learning-free, and very simple: we set the product of the query and key weights to be approximately the identity , and the product of the value and projection weights to ap-proximately the negative identity ."}
+{"idx": 3, "title": "Mimetic Initialization of Self-Attention Layers - ResearchGate", "date": "", "ddg_snippet": "May 16, 2023 · Download Citation | Mimetic Initialization of Self-Attention Layers | It is notoriously difficult to train Transformers on small datasets; typically, large pre-trained models are instead used as ...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/370841580_Mimetic_Initialization_of_Self-Attention_Layers", "content": "May 16, 2023 · Download Citation | Mimetic Initialization of Self-Attention Layers | It is notoriously difficult to train Transformers on small datasets; typically, large pre-trained models are instead used as ..."}
+{"idx": 4, "title": "Mimetic Initialization of Self-Attention Layers - DeepAI", "date": "", "ddg_snippet": "May 16, 2023 · Mimetic Initialization of Self-Attention Layers 05/16/2023 ∙ by Asher Trockman , et al.", "subpage_snippet": "", "source": "deepai.org", "link": "https://deepai.org/publication/mimetic-initialization-of-self-attention-layers", "content": "May 16, 2023 · Mimetic Initialization of Self-Attention Layers 05/16/2023 ∙ by Asher Trockman , et al."}
+{"idx": 5, "title": "Mimetic initialization of self-attention layers | Proceedings ...", "date": "", "ddg_snippet": "Jul 23, 2023 · Our initialization scheme is closed form, learning-free, and very simple: we set the product of the query and key weights to be approximately the identity , and the product of the value and projection weights to approximately the negative identity .", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.5555/3618408.3619843", "content": "Jul 23, 2023 · Our initialization scheme is closed form, learning-free, and very simple: we set the product of the query and key weights to be approximately the identity , and the product of the value and projection weights to approximately the negative identity ."}
+{"idx": 6, "title": "Mimetic Initialization of Self-Attention Layers - arXiv.org", "date": "", "ddg_snippet": "Our initialization scheme is closed form, learning-free, and very simple: we set the product of the query and key weights to be approximately the identity , and the product of the value and projection weights to ap-proximately the negative identity .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2305.09828", "content": "Our initialization scheme is closed form, learning-free, and very simple: we set the product of the query and key weights to be approximately the identity , and the product of the value and projection weights to ap-proximately the negative identity ."}
+{"idx": 7, "title": "Mimetic Initialization of Self - Attention Layers | Papers With Code", "date": "", "ddg_snippet": "Surprisingly, we find that simply initializing the weights of self - attention layers so that they \"look\" more like their pre-trained counterparts allows us to train vanilla Transformers faster and to higher final accuracies, particularly on vision tasks such as CIFAR-10 and ImageNet classification...", "subpage_snippet": "", "source": "paperswithcode.com", "link": "https://paperswithcode.com/paper/mimetic-initialization-of-self-attention", "content": "Surprisingly, we find that simply initializing the weights of self - attention layers so that they \"look\" more like their pre-trained counterparts allows us to train vanilla Transformers faster and to higher final accuracies, particularly on vision tasks such as CIFAR-10 and ImageNet classification..."}
+{"idx": 8, "title": "Asher Trockman - Google Akademik", "date": "", "ddg_snippet": "Mimetic initialization of self - attention layers. A Trockman , JZ Kolter . International Conference on Machine Learning, 34456-34468, 2023.", "subpage_snippet": "", "source": "scholar.google.de", "link": "https://scholar.google.de/citations?user=xd3LCGcAAAAJ&hl=tr", "content": "Mimetic initialization of self - attention layers. A Trockman , JZ Kolter . International Conference on Machine Learning, 34456-34468, 2023."}
+{"idx": 9, "title": "Articles by Asher Trockman | Synthical", "date": "", "ddg_snippet": "Mimetic Initialization Helps State Space Models Learn to Recall. Mimetic Initialization of Self - Attention Layers. 16 May 2023 by Asher Trockman and J. Zico Kolter .", "subpage_snippet": "", "source": "synthical.com", "link": "https://synthical.com/profile/29d4130e-0f30-4582-a5ad-a920c7f5ea06/articles", "content": "Mimetic Initialization Helps State Space Models Learn to Recall. Mimetic Initialization of Self - Attention Layers. 16 May 2023 by Asher Trockman and J. Zico Kolter ."}
diff --git a/data/sampled_jsons/Two_Moon_dataset_empirical_convergence_Wasserstein_distance_sample_size.jsonl b/data/sampled_jsons/Two_Moon_dataset_empirical_convergence_Wasserstein_distance_sample_size.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..083e59e8eba9901ac1fac088797d3a278f6bde83
--- /dev/null
+++ b/data/sampled_jsons/Two_Moon_dataset_empirical_convergence_Wasserstein_distance_sample_size.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Preconditioned Regularized Wasserstein Proximal Sampling Methods", "date": "", "ddg_snippet": "Recently, [ 45 ] proposed a noise-free sampling based on the regularized Wasserstein proximal operator (RWPO), called the backwards regularized ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2509.01685v1", "content": "Recently, [ 45 ] proposed a noise-free sampling based on the regularized Wasserstein proximal operator (RWPO), called the backwards regularized ..."}
+{"idx": 1, "title": "Boosting decision trees for Main Belt Asteroid selection in", "date": "", "ddg_snippet": "... distance have an accuracy of about 1 m, whereas the influence of the MBAs on the Mars orbit can reach a few kilometers (Standish & Fienga, 2002 ) ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2505.10487v1", "content": "... distance have an accuracy of about 1 m, whereas the influence of the MBAs on the Mars orbit can reach a few kilometers (Standish & Fienga, 2002 ) ..."}
+{"idx": 2, "title": "Diffusion map particle systems for generative modeling", "date": "", "ddg_snippet": "... two ideas by using diffusion maps to directly approximate (the gradient of the inverse of) the generator of the Langevin diffusion process from ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2304.00200v5", "content": "... two ideas by using diffusion maps to directly approximate (the gradient of the inverse of) the generator of the Langevin diffusion process from ..."}
+{"idx": 3, "title": "Model-free Estimation of Latent Structure via Multiscale", "date": "", "ddg_snippet": "Consider the two -dimensional example shown in Figure 1 , which displays the density, its contour plot, and a scatter plot of samples generated from ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2410.22248v2", "content": "Consider the two -dimensional example shown in Figure 1 , which displays the density, its contour plot, and a scatter plot of samples generated from ..."}
+{"idx": 4, "title": "Parameter Effects in ReCom Ensembles", "date": "", "ddg_snippet": "In particular, we will borrow from [ CCD + 21 ] , which uses the Kolmogorov-Smirnov distance to assess convergence for Colorado congressional ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2505.21326v1", "content": "In particular, we will borrow from [ CCD + 21 ] , which uses the Kolmogorov-Smirnov distance to assess convergence for Colorado congressional ..."}
+{"idx": 5, "title": "ASCL.net - Browsing Codes", "date": "", "ddg_snippet": "Bulk parameters include the mass and radius of the planet (and those of the other planets involved in the integration), the size and mass of the host ...", "subpage_snippet": "", "source": "ascl.net", "link": "http://ascl.net/code/all/page/4/limit/100/order/title/listmode/full/dir/desc", "content": "Bulk parameters include the mass and radius of the planet (and those of the other planets involved in the integration), the size and mass of the host ..."}
+{"idx": 6, "title": "2020.02.24 Learning papers — Eye On AI", "date": "", "ddg_snippet": "Deep Neural Networks for the Correction of Mie Scattering in Fourier-Transformed Infrared Spectra of Biological Samples by Arne P.", "subpage_snippet": "", "source": "www.eye-on.ai", "link": "https://www.eye-on.ai/ai-research-watch-papers/2020/2/24/20200224-learning-papers", "content": "Deep Neural Networks for the Correction of Mie Scattering in Fourier-Transformed Infrared Spectra of Biological Samples by Arne P."}
+{"idx": 7, "title": "Newest 'time-complexity' Questions - Cross Validated", "date": "", "ddg_snippet": "Given 2 samples of size $n$ and each datapoint has feature dimension $d$ -- the goal is to compute the Wasserstein -1 distance between the 2 samples .", "subpage_snippet": "", "source": "stats.stackexchange.com", "link": "https://stats.stackexchange.com/questions/tagged/time-complexity", "content": "Given 2 samples of size $n$ and each datapoint has feature dimension $d$ -- the goal is to compute the Wasserstein -1 distance between the 2 samples ."}
+{"idx": 8, "title": "UAI 2020", "date": "", "ddg_snippet": "Our finite- sample analysis provides theoretical justification for choosing stepsizes for this two timescale algorithm for faster convergence in ...", "subpage_snippet": "", "source": "www.auai.org", "link": "https://www.auai.org/~w-auai/uai2020/accepted.php", "content": "Our finite- sample analysis provides theoretical justification for choosing stepsizes for this two timescale algorithm for faster convergence in ..."}
+{"idx": 9, "title": "NeurIPS 2022 Oral-Equivalent Papers", "date": "", "ddg_snippet": "... performance than previous state-of-the-art methods, like TinyBERT; ( 2 ) extreme quantization plus layer reduction is able to reduce the model size by ...", "subpage_snippet": "", "source": "neurips.cc", "link": "https://neurips.cc/virtual/2022/events/highlighted", "content": "... performance than previous state-of-the-art methods, like TinyBERT; ( 2 ) extreme quantization plus layer reduction is able to reduce the model size by ..."}
diff --git a/data/sampled_jsons/Unbiased_Pairwise_Learning_from_Biased_Implicit_Feedback_Saito_2020_abstract.jsonl b/data/sampled_jsons/Unbiased_Pairwise_Learning_from_Biased_Implicit_Feedback_Saito_2020_abstract.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..071fefc3b20bd9907f30a7a00f235ebbaedb11a2
--- /dev/null
+++ b/data/sampled_jsons/Unbiased_Pairwise_Learning_from_Biased_Implicit_Feedback_Saito_2020_abstract.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Unbiased Pairwise Learning from Biased Implicit Feedback", "date": "", "ddg_snippet": "2017. Unbiased learning-to-rank with biased feedback.Yuta Saito , Suguru Yaginuma, Yuta Nishino, Hayato Sakata, and Kazuhide Nakata. 2020 . Unbiased Recommender Learning from Missing-Not-At-Random Implicit Feedback .", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.1145/3409256.3409812", "content": "2017. Unbiased learning-to-rank with biased feedback.Yuta Saito , Suguru Yaginuma, Yuta Nishino, Hayato Sakata, and Kazuhide Nakata. 2020 . Unbiased Recommender Learning from Missing-Not-At-Random Implicit Feedback ."}
+{"idx": 1, "title": "GitHub - usaito/ unbiased - pairwise -rec: (ICTIR 2020 ) \"Unbiased...\"", "date": "", "ddg_snippet": "(ICTIR 2020 ) \" Unbiased Pairwise Learning from Biased Implicit Feedback \".", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/usaito/unbiased-pairwise-rec", "content": "(ICTIR 2020 ) \" Unbiased Pairwise Learning from Biased Implicit Feedback \"."}
+{"idx": 2, "title": "Unbiased Pairwise Learning from Biased Implicit Feedback", "date": "", "ddg_snippet": "Unbiased Pairwise Learning from Implicit Feedback for Recommender Systems without Biased Variance Control.Unbiased Learning to Rank with Biased Continuous Feedback. Yi RenHongyan TangSiwen Zhu.", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/Unbiased-Pairwise-Learning-from-Biased-Implicit-Saito/2aff8673be4db3e7c9c81e7fe37c7dcd08b52405", "content": "Unbiased Pairwise Learning from Implicit Feedback for Recommender Systems without Biased Variance Control.Unbiased Learning to Rank with Biased Continuous Feedback. Yi RenHongyan TangSiwen Zhu."}
+{"idx": 3, "title": "Unbiased Pairwise Learning from Biased Implicit Feedback", "date": "", "ddg_snippet": "Implicit feedback is prevalent in real-world scenarios and is widely used in the construction of recommender systems. However, the application of implicit feedback data is much more complicated than its explicit counterpart because it provides only positive feedback...", "subpage_snippet": "", "source": "usait0.com", "link": "https://usait0.com/en/publication/2020/ictir2020/", "content": "Implicit feedback is prevalent in real-world scenarios and is widely used in the construction of recommender systems. However, the application of implicit feedback data is much more complicated than its explicit counterpart because it provides only positive feedback..."}
+{"idx": 4, "title": "Unbiased Pairwise Learning from Biased Implicit Feedback", "date": "", "ddg_snippet": "View. Show abstract . ... Existing studies have developed unbiased recommender learning methods [33,38,39,63] to estimate true user preferences from implicit feedback under the missing-not-at-random (MNAR) assumption [29,43,62]. They formulated a new loss function to eliminate...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/347579058_Unbiased_Pairwise_Learning_from_Biased_Implicit_Feedback", "content": "View. Show abstract . ... Existing studies have developed unbiased recommender learning methods [33,38,39,63] to estimate true user preferences from implicit feedback under the missing-not-at-random (MNAR) assumption [29,43,62]. They formulated a new loss function to eliminate..."}
+{"idx": 5, "title": "Unbiased Pairwise Learning from Biased Implicit Feedback .", "date": "", "ddg_snippet": "↓ скачать. Saito , Y. ( 2020 ). Unbiased Pairwise Learning from Biased Implicit Feedback . Proceedings of the 2020 ACM SIGIR on International Conference on Theory of Information Retrieval. doi:10.1145/3409256.3409812.", "subpage_snippet": "", "source": "sci-hub.se", "link": "https://sci-hub.se/10.1145/3409256.3409812", "content": "↓ скачать. Saito , Y. ( 2020 ). Unbiased Pairwise Learning from Biased Implicit Feedback . Proceedings of the 2020 ACM SIGIR on International Conference on Theory of Information Retrieval. doi:10.1145/3409256.3409812."}
+{"idx": 6, "title": "Yuta Saito - Google Scholar", "date": "", "ddg_snippet": "2020 . Unbiased pairwise learning from biased implicit feedback .Y Saito , G Morishita, S Yasui. Proceedings of the 43rd International ACM SIGIR Conference on Research and …, 2020 .", "subpage_snippet": "", "source": "scholar.google.com", "link": "https://scholar.google.com/citations?user=pw4hwS8AAAAJ&hl=en", "content": "2020 . Unbiased pairwise learning from biased implicit feedback .Y Saito , G Morishita, S Yasui. Proceedings of the 43rd International ACM SIGIR Conference on Research and …, 2020 ."}
+{"idx": 7, "title": "Proceedings of the 2020 ACM SIGIR on International... | ScienceGate", "date": "", "ddg_snippet": "Unbiased Pairwise Learning from Biased Implicit Feedback .Learning to Rank Entities for Set Expansion from Unstructured Data.", "subpage_snippet": "", "source": "www.sciencegate.app", "link": "https://www.sciencegate.app/source/2061186952", "content": "Unbiased Pairwise Learning from Biased Implicit Feedback .Learning to Rank Entities for Set Expansion from Unstructured Data."}
+{"idx": 8, "title": "Publications - Yuta Saito", "date": "", "ddg_snippet": "Yuta Saito . Unbiased Pairwise Learning from Biased Implicit Feedback International Conference on the Theory of Information Retrieval (ICTIR 2020 ).", "subpage_snippet": "", "source": "usaito.github.io", "link": "https://usaito.github.io/publications/", "content": "Yuta Saito . Unbiased Pairwise Learning from Biased Implicit Feedback International Conference on the Theory of Information Retrieval (ICTIR 2020 )."}
+{"idx": 9, "title": "Cross Pairwise Ranking for Unbiased Item Recommendation", "date": "", "ddg_snippet": "2020 . Unbiased Pairwise Learning from Biased Implicit Feedback .[30] Yuta Saito , Suguru Yaginuma, Yuta Nishino, Hayato Sakata, and Kazuhide Nakata. 2020 . Unbiased recommender learning from missing-not-at-random implicit feedback . In WSDM.", "subpage_snippet": "", "source": "staff.ustc.edu.cn", "link": "http://staff.ustc.edu.cn/~hexn/papers/www22-cpr.pdf", "content": "2020 . Unbiased Pairwise Learning from Biased Implicit Feedback .[30] Yuta Saito , Suguru Yaginuma, Yuta Nishino, Hayato Sakata, and Kazuhide Nakata. 2020 . Unbiased recommender learning from missing-not-at-random implicit feedback . In WSDM."}
diff --git a/data/sampled_jsons/Universal_Transformers_Dehghani_et_al._2019_official_abstract_year_2019.jsonl b/data/sampled_jsons/Universal_Transformers_Dehghani_et_al._2019_official_abstract_year_2019.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..2cc0414ef981ec999942d219bf74a14f06148564
--- /dev/null
+++ b/data/sampled_jsons/Universal_Transformers_Dehghani_et_al._2019_official_abstract_year_2019.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Abstract page for arXiv paper 1807.03819: Universal Transformers", "date": "", "ddg_snippet": "Title: Universal Transformers . Authors:Mostafa Dehghani , Stephan Gouws, Oriol Vinyals, Jakob Uszkoreit, Łukasz Kaiser. View a PDF of the paper titled Universal Transformers , by Mostafa Dehghani and 4 other authors.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/1807.03819", "content": "Title: Universal Transformers . Authors:Mostafa Dehghani , Stephan Gouws, Oriol Vinyals, Jakob Uszkoreit, Łukasz Kaiser. View a PDF of the paper titled Universal Transformers , by Mostafa Dehghani and 4 other authors."}
+{"idx": 1, "title": "Universal Transformers - Iterative Refinement for Better... | Medium", "date": "", "ddg_snippet": "Universal Transformers : Recursion Over Representations. The Universal Transformer changes one crucial detail: instead of stacking a fixed number of different layers, it reuses the same layer recurrently. At each iteration: Tokens exchange information via self-attention.", "subpage_snippet": "", "source": "medium.com", "link": "https://medium.com/@jain.sm/universal-transformers-iterative-refinement-for-better-reasoning-a1fca43fea56", "content": "Universal Transformers : Recursion Over Representations. The Universal Transformer changes one crucial detail: instead of stacking a fixed number of different layers, it reuses the same layer recurrently. At each iteration: Tokens exchange information via self-attention."}
+{"idx": 2, "title": "Lessons on Parameter Sharing across Layers in Transformers", "date": "", "ddg_snippet": "3The original Universal Transformers ( Dehghani et al ., 2019 ) use the sinusoidal positional encoding for each layer and adaptive computation time technique (Graves, 2017) but we omitted them in this study to focus on the difference among parameter sharing strategies.", "subpage_snippet": "", "source": "aclanthology.org", "link": "https://aclanthology.org/2023.sustainlp-1.5.pdf", "content": "3The original Universal Transformers ( Dehghani et al ., 2019 ) use the sinusoidal positional encoding for each layer and adaptive computation time technique (Graves, 2017) but we omitted them in this study to focus on the difference among parameter sharing strategies."}
+{"idx": 3, "title": "Lessons on Parameter Sharing across Layers in Transformers | DeepAI", "date": "", "ddg_snippet": "The proposed approach relaxes a widely used technique, which shares parameters for one layer with all layers such as Universal Transformers ( Dehghani et al ., 2019 ), to increase the efficiency in the computational time.", "subpage_snippet": "", "source": "deepai.org", "link": "https://deepai.org/publication/lessons-on-parameter-sharing-across-layers-in-transformers", "content": "The proposed approach relaxes a widely used technique, which shares parameters for one layer with all layers such as Universal Transformers ( Dehghani et al ., 2019 ), to increase the efficiency in the computational time."}
+{"idx": 4, "title": "Faster Depth-Adaptive Transformers", "date": "", "ddg_snippet": "2019 ) Transformer (Vaswani et al .Unlike Universal Transformer ( Dehghani et al . 2019 ) iteratively applies ACT on the same layer, we dynamically adjust the amount of both computa-tion and model capacity.", "subpage_snippet": "", "source": "cdn.aaai.org", "link": "https://cdn.aaai.org/ojs/17584/17584-13-21078-1-2-20210518.pdf", "content": "2019 ) Transformer (Vaswani et al .Unlike Universal Transformer ( Dehghani et al . 2019 ) iteratively applies ACT on the same layer, we dynamically adjust the amount of both computa-tion and model capacity."}
+{"idx": 5, "title": "Are Transformers Turing-complete? A Good Disguise Is All You Need.", "date": "", "ddg_snippet": "Figure 2 from Dehghani et al . 2019 . Apart from that, their model is—to their own admission—different from “vanilla” transformers which are used in large language models. While transformers normally have a fixed depth, their model can run an arbitrary number of layers for an input.", "subpage_snippet": "", "source": "lifeiscomputation.com", "link": "https://lifeiscomputation.com/transformers-are-not-turing-complete/", "content": "Figure 2 from Dehghani et al . 2019 . Apart from that, their model is—to their own admission—different from “vanilla” transformers which are used in large language models. While transformers normally have a fixed depth, their model can run an arbitrary number of layers for an input."}
+{"idx": 6, "title": "With f eedback M emory", "date": "", "ddg_snippet": "Universal Transformer ( Dehghani et al ., 2018) share the parameters between the layers of a Transformer , leading a recurrent network in depth.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=OCm0rwa1lx1", "content": "Universal Transformer ( Dehghani et al ., 2018) share the parameters between the layers of a Transformer , leading a recurrent network in depth."}
+{"idx": 7, "title": "SDMTR: A Brain-inspired Transformer for Relation Inference", "date": "", "ddg_snippet": "For example, the Universal Transformer ( Dehghani et al ., 2018) combines the parallelizability and global receptive field of feed-forward sequence models like Transformers with the recurrent inductive bias of RNNs.", "subpage_snippet": "", "source": "proceedings.mlr.press", "link": "https://proceedings.mlr.press/v238/zeng24a/zeng24a.pdf", "content": "For example, the Universal Transformer ( Dehghani et al ., 2018) combines the parallelizability and global receptive field of feed-forward sequence models like Transformers with the recurrent inductive bias of RNNs."}
+{"idx": 8, "title": "Dictformer: tiny transformer with shared dictionary", "date": "", "ddg_snippet": "Universal Transformer ( Dehghani . et al ., 2019 ) achie ves ∼1 BLEU score improvement with 1.4× less parameters, but the Mult-Adds. numbers are increased by ∼4.3× , thereby significantly prolonging the NLP inference latency and.", "subpage_snippet": "", "source": "www.readkong.com", "link": "https://www.readkong.com/page/dictformer-tiny-transformer-with-shared-dictionary-1951167", "content": "Universal Transformer ( Dehghani . et al ., 2019 ) achie ves ∼1 BLEU score improvement with 1.4× less parameters, but the Mult-Adds. numbers are increased by ∼4.3× , thereby significantly prolonging the NLP inference latency and."}
+{"idx": 9, "title": "Google & DeepMind Study the Interactions Between Scaling Laws and...", "date": "", "ddg_snippet": "Funnel Transformers (Dai et al ., 2020). The study also looks at non- transformer architectures that include Lightweight Convolutions (Wu et al ., 2019 ), Dynamic Convolutions (Wu et al ., 2019 ), and MLP-Mixers (Tolstikhin et al ., 2021).", "subpage_snippet": "", "source": "syncedreview.com", "link": "https://syncedreview.com/2022/07/27/google-deepmind-study-the-interactions-between-scaling-laws-and-neural-network-architectures/", "content": "Funnel Transformers (Dai et al ., 2020). The study also looks at non- transformer architectures that include Lightweight Convolutions (Wu et al ., 2019 ), Dynamic Convolutions (Wu et al ., 2019 ), and MLP-Mixers (Tolstikhin et al ., 2021)."}
diff --git a/data/sampled_jsons/VACB_algorithm_Unknown-Variance_OLS_regret_bound_R_logarithmic_polynomial_Catoni.jsonl b/data/sampled_jsons/VACB_algorithm_Unknown-Variance_OLS_regret_bound_R_logarithmic_polynomial_Catoni.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..0eb3ea090a7a589e19b7f5afe51f8354c4d78fb9
--- /dev/null
+++ b/data/sampled_jsons/VACB_algorithm_Unknown-Variance_OLS_regret_bound_R_logarithmic_polynomial_Catoni.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Catoni Contextual Bandits are Robust to Heavy-tailed ...", "date": "", "ddg_snippet": "by C Ye · 2025 · Cited by 1 — The algorithm enjoys a variance -based regret bound with only polynomial dependence on R . When the per-round variance is unknown , our proposed ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2502.02486?", "content": "by C Ye · 2025 · Cited by 1 — The algorithm enjoys a variance -based regret bound with only polynomial dependence on R . When the per-round variance is unknown , our proposed ..."}
+{"idx": 1, "title": "Catoni Contextual Bandits are Robust to Heavy-tailed ...", "date": "", "ddg_snippet": "In this paper, we study the design of contextual bandit algorithms that can leverage such structures to have regret guarantees dependent polynomially on the ...", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/poster/46438", "content": "In this paper, we study the design of contextual bandit algorithms that can leverage such structures to have regret guarantees dependent polynomially on the ..."}
+{"idx": 2, "title": "ICML 2025 2025 Spotlight Posters", "date": "", "ddg_snippet": "With additional dependence on the fourth moment, our algorithm also enjoys a variance -based bound with logarithmic reward-range dependence. Moreover, we ...", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/events/2025SpotlightPosters", "content": "With additional dependence on the fourth moment, our algorithm also enjoys a variance -based bound with logarithmic reward-range dependence. Moreover, we ..."}
+{"idx": 3, "title": "Contributions to Representation Learning with Graph ...", "date": "", "ddg_snippet": "... ( log )- variance vectors for each node by µ and by log σ14, respectively, they set: µ = Encoderµ(A, X) and log σ = Encoderσ(A, X). (2.35). As is the case for ... 270 pages", "subpage_snippet": "", "source": "guillaumesalhagalvan.com", "link": "https://guillaumesalhagalvan.com/doc/phdthesis_gsalhagalvan.pdf", "content": "... ( log )- variance vectors for each node by µ and by log σ14, respectively, they set: µ = Encoderµ(A, X) and log σ = Encoderσ(A, X). (2.35). As is the case for ... 270 pages"}
+{"idx": 4, "title": "the annals", "date": "", "ddg_snippet": "by R HAN · 2022 — The effectiveness of the proposed algorithm is demonstrated in a suite of problems in high-dimensional statistics, including singular value. 80 pages", "subpage_snippet": "", "source": "www.imstat.org", "link": "https://www.imstat.org/publications/aos/aos_50_1/aos_50_1.pdf", "content": "by R HAN · 2022 — The effectiveness of the proposed algorithm is demonstrated in a suite of problems in high-dimensional statistics, including singular value. 80 pages"}
+{"idx": 5, "title": "Statistical Foundations Of Data Science [1", "date": "", "ddg_snippet": "The essence of the above algorithm is to compute the common vectors {wj }pj=1 first and then utilize (2.40). RIDGE REGRESSION 2.6.5 41. Kernel ridge regression", "subpage_snippet": "", "source": "dokumen.pub", "link": "https://dokumen.pub/statistical-foundations-of-data-science-1nbsped-1466510846-9781466510845-1466510854-9781466510852-0429527616-9780429527616-0367512629-9780367512620-0429542313-9780429542312-0429096283-9780429096280.html", "content": "The essence of the above algorithm is to compute the common vectors {wj }pj=1 first and then utilize (2.40). RIDGE REGRESSION 2.6.5 41. Kernel ridge regression"}
+{"idx": 6, "title": "", "date": "", "ddg_snippet": "", "subpage_snippet": "", "source": "", "link": "", "content": ""}
+{"idx": 7, "title": "Learning Theory and Kernel Machines", "date": "", "ddg_snippet": "This volume contains papers presented at the joint 16th Annual Conference on. Learning Theory (COLT) and the 7th Annual Workshop on Kernel Machines,.", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/content/pdf/10.1007/b12006.pdf", "content": "This volume contains papers presented at the joint 16th Annual Conference on. Learning Theory (COLT) and the 7th Annual Workshop on Kernel Machines,."}
+{"idx": 8, "title": "Lecture Notes in Artificial Intelligence 6322", "date": "", "ddg_snippet": "The European Conference on Machine Learning and Principles and Practice of. Knowledge Discovery in Databases, ECML PKDD 2010, was held in Barcelona,.", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/content/pdf/10.1007/978-3-642-15883-4.pdf", "content": "The European Conference on Machine Learning and Principles and Practice of. Knowledge Discovery in Databases, ECML PKDD 2010, was held in Barcelona,."}
+{"idx": 9, "title": "(PDF) Computer Science in Sport-Special emphasis: Football", "date": "", "ddg_snippet": "This paper explores the intersection of computer science and sports, with a primary focus on football. It discusses the implications of incorporating ...", "subpage_snippet": "", "source": "www.academia.edu", "link": "https://www.academia.edu/1457305/Computer_Science_in_Sport_Special_emphasis_Football", "content": "This paper explores the intersection of computer science and sports, with a primary focus on football. It discusses the implications of incorporating ..."}
diff --git a/data/sampled_jsons/VBench_[25]_generative_models_evaluation_year_2023.jsonl b/data/sampled_jsons/VBench_[25]_generative_models_evaluation_year_2023.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..67f07f7fc3782068c8ad18cb8ecf7c8c57d564bd
--- /dev/null
+++ b/data/sampled_jsons/VBench_[25]_generative_models_evaluation_year_2023.jsonl
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+{"idx": 0, "title": "Comprehensive Benchmark Suite for Video Generative Models", "date": "", "ddg_snippet": "by Z Huang · 2024 · Cited by 605 — VBench Evaluation Results of Video Generative . Models . We visualize the evaluation results of four video gen- eration models in 16 VBench dimensions. We ... 12 pages", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2024/papers/Huang_VBench_Comprehensive_Benchmark_Suite_for_Video_Generative_Models_CVPR_2024_paper.pdf", "content": "by Z Huang · 2024 · Cited by 605 — VBench Evaluation Results of Video Generative . Models . We visualize the evaluation results of four video gen- eration models in 16 VBench dimensions. We ... 12 pages"}
+{"idx": 1, "title": "VBench++: Comprehensive and Versatile Benchmark Suite ...", "date": "", "ddg_snippet": "20 Nov 2024 — Beyond assessing technical quality, VBench++ evaluates the trustworthiness of video generative models , providing a more holistic view of model ...", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/papers/2411.13503", "content": "20 Nov 2024 — Beyond assessing technical quality, VBench++ evaluates the trustworthiness of video generative models , providing a more holistic view of model ..."}
+{"idx": 2, "title": "VBench-2.0: Advancing Video Generation Benchmark ...", "date": "", "ddg_snippet": "Initially, video generative models primarily relied on conventional evaluation metrics such as Fréchet inception distance (FID) [67] , Inception Score (IS) [68] ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2503.21755v2", "content": "Initially, video generative models primarily relied on conventional evaluation metrics such as Fréchet inception distance (FID) [67] , Inception Score (IS) [68] ..."}
+{"idx": 3, "title": "VBench: Comprehensive Benchmark Suite for Video ...", "date": "", "ddg_snippet": "by Z Huang · 2024 · Cited by 604 — To this end, we present VBench , a comprehensive benchmark suite that dissects “video generation quality” into specific, hierarchical, and disentangled ...", "subpage_snippet": "", "source": "ieeexplore.ieee.org", "link": "https://ieeexplore.ieee.org/document/10657096/", "content": "by Z Huang · 2024 · Cited by 604 — To this end, we present VBench , a comprehensive benchmark suite that dissects “video generation quality” into specific, hierarchical, and disentangled ..."}
+{"idx": 4, "title": "VBench++: Comprehensive and Versatile Benchmark Suite ...", "date": "", "ddg_snippet": "20 Nov 2024 — We present VBench , a comprehensive benchmark suite that dissects “video generation quality” into specific, hierarchical, and disentangled dimensions.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2411.13503v1", "content": "20 Nov 2024 — We present VBench , a comprehensive benchmark suite that dissects “video generation quality” into specific, hierarchical, and disentangled dimensions."}
+{"idx": 5, "title": "GenAI Arena: An Open Evaluation Platform for Generative ...", "date": "", "ddg_snippet": "24 Oct 2024 — This paper proposes an open platform GENAI-ARENA to evaluate different image and video generative models , where users can actively participate ...", "subpage_snippet": "", "source": "nips.cc", "link": "https://nips.cc/virtual/2024/poster/97878", "content": "24 Oct 2024 — This paper proposes an open platform GENAI-ARENA to evaluate different image and video generative models , where users can actively participate ..."}
+{"idx": 6, "title": "Efficient and Promptable Evaluation Framework for Visual ...", "date": "", "ddg_snippet": "The Evaluation Agent framework is fully open-sourced to advance research in visual generative models and their efficient evaluation . Pipeline of Evaluation ...", "subpage_snippet": "", "source": "vchitect.github.io", "link": "https://vchitect.github.io/Evaluation-Agent-project/", "content": "The Evaluation Agent framework is fully open-sourced to advance research in visual generative models and their efficient evaluation . Pipeline of Evaluation ..."}
+{"idx": 7, "title": "VBench: Comprehensive Benchmark Suite for Video ...", "date": "", "ddg_snippet": "VBench Evaluation Results of. Video Generative Models - For each dimension, we map the maximum score achieved by one of the T2V models to 0.8, and the ...", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2024/supplemental/Huang_VBench_Comprehensive_Benchmark_CVPR_2024_supplemental.pdf", "content": "VBench Evaluation Results of. Video Generative Models - For each dimension, we map the maximum score achieved by one of the T2V models to 0.8, and the ..."}
+{"idx": 8, "title": "Vbench: Comprehensive Benchmark Suite For Video ...", "date": "", "ddg_snippet": "VBench is a comprehensive benchmark suite designed to evaluate video generative models by breaking down video generation quality into 16 distinct, ...", "subpage_snippet": "", "source": "www.scribd.com", "link": "https://www.scribd.com/document/890468466/2311-17982v1-3", "content": "VBench is a comprehensive benchmark suite designed to evaluate video generative models by breaking down video generation quality into 16 distinct, ..."}
+{"idx": 9, "title": "VBench: Comprehensive Benchmark Suite for Video ...", "date": "", "ddg_snippet": "29 Nov 2023 — We present VBench , a comprehensive benchmark suite that dissects video generation quality into specific, hierarchical, and disentangled dimensions.", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/papers/2311.17982", "content": "29 Nov 2023 — We present VBench , a comprehensive benchmark suite that dissects video generation quality into specific, hierarchical, and disentangled dimensions."}
diff --git a/data/sampled_jsons/VRSBench_Table_1_Comparison_between_existing_remote_sensing_vision-language_datasets.jsonl b/data/sampled_jsons/VRSBench_Table_1_Comparison_between_existing_remote_sensing_vision-language_datasets.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..302f9d9d5e672ad4a537f9fd71a3f7d827ffd2b5
--- /dev/null
+++ b/data/sampled_jsons/VRSBench_Table_1_Comparison_between_existing_remote_sensing_vision-language_datasets.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "VRSBench", "date": "", "ddg_snippet": "VRSBench features a diverse collection of open-set question-answer pairs in natural language . Table 1 . Comparison between existing remote sensing vision - ...", "subpage_snippet": "", "source": "vrsbench.github.io", "link": "https://vrsbench.github.io/", "content": "VRSBench features a diverse collection of open-set question-answer pairs in natural language . Table 1 . Comparison between existing remote sensing vision - ..."}
+{"idx": 1, "title": "VRSBench: A Versatile Vision-Language Benchmark ...", "date": "", "ddg_snippet": "9 Dec 2024 — Table 1: Comparison between existing remote sensing vision-language datasets and our VRSBench dataset. Values in parentheses in the Caption ...", "subpage_snippet": "", "source": "neurips.cc", "link": "https://neurips.cc/virtual/2024/poster/97530", "content": "9 Dec 2024 — Table 1: Comparison between existing remote sensing vision-language datasets and our VRSBench dataset. Values in parentheses in the Caption ..."}
+{"idx": 2, "title": "VRSBench: A Versatile Vision-Language Benchmark ...", "date": "", "ddg_snippet": "Comparison between the proposed VRSBench dataset and the existing remote sensing vision - language datasets shows the advantages of VRSBench . Also, the ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=kwDOxOmGE0&referrer=[the+profile+of+Jian+Ding](/profile?id=~Jian_Ding3)", "content": "Comparison between the proposed VRSBench dataset and the existing remote sensing vision - language datasets shows the advantages of VRSBench . Also, the ..."}
+{"idx": 3, "title": "VRSBench: A Versatile Vision-Language Benchmark ...", "date": "", "ddg_snippet": "18 Jun 2024 — Table 1: Comparison between existing remote sensing vision-language datasets and our VRSBench dataset. Values in parentheses in the Caption ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2406.12384v1", "content": "18 Jun 2024 — Table 1: Comparison between existing remote sensing vision-language datasets and our VRSBench dataset. Values in parentheses in the Caption ..."}
+{"idx": 4, "title": "VRSBench", "date": "", "ddg_snippet": "by X Li · 2024 · Cited by 49 — Table 1: Comparison between existing remote sensing vision-language datasets and our VRSBench dataset. Values in parentheses in the Caption ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2406.12384?", "content": "by X Li · 2024 · Cited by 49 — Table 1: Comparison between existing remote sensing vision-language datasets and our VRSBench dataset. Values in parentheses in the Caption ..."}
+{"idx": 5, "title": "VRSBench: A Versatile Vision-Language Benchmark ...", "date": "", "ddg_snippet": "by X Li · Cited by 50 — Current remote sensing VQA dataset contains close-set answers. Table 1. Comparison between existing remote sensing vision-language datasets and VRSBench ...", "subpage_snippet": "", "source": "neurips.cc", "link": "https://neurips.cc/media/neurips-2024/Slides/97530.pdf", "content": "by X Li · Cited by 50 — Current remote sensing VQA dataset contains close-set answers. Table 1. Comparison between existing remote sensing vision-language datasets and VRSBench ..."}
+{"idx": 6, "title": "VRSBench: A Versatile Vision-Language Benchmark Dataset ...", "date": "", "ddg_snippet": "18 Jun 2024 — This paper introduces VRSBench , a versatile vision-language benchmark dataset for comprehensive remote sensing image understanding model ...", "subpage_snippet": "", "source": "liner.com", "link": "https://liner.com/review/vrsbench-a-versatile-visionlanguage-benchmark-dataset-for-remote-sensing-image", "content": "18 Jun 2024 — This paper introduces VRSBench , a versatile vision-language benchmark dataset for comprehensive remote sensing image understanding model ..."}
+{"idx": 7, "title": "Supplementary of VRSBench: A Versatile Benchmark for ...", "date": "", "ddg_snippet": "by XLJDM Elhoseiny — VRSBench is designed to facilitate the development and evaluation of vision - language models in remote sensing , providing a comprehensive set of annotations.", "subpage_snippet": "", "source": "papers.nips.cc", "link": "https://papers.nips.cc/paper_files/paper/2024/file/05b7f821234f66b78f99e7803fffa78a-Supplemental-Datasets_and_Benchmarks_Track.pdf", "content": "by XLJDM Elhoseiny — VRSBench is designed to facilitate the development and evaluation of vision - language models in remote sensing , providing a comprehensive set of annotations."}
+{"idx": 8, "title": "xiang709/VRSBench · Datasets at Hugging Face", "date": "", "ddg_snippet": "It facilitates the training and evaluation of vision - language models across a broad spectrum of remote sensing image understanding tasks. Using datasets .", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/datasets/xiang709/VRSBench", "content": "It facilitates the training and evaluation of vision - language models across a broad spectrum of remote sensing image understanding tasks. Using datasets ."}
+{"idx": 9, "title": "VRSBench: a versatile vision-language benchmark dataset for ...", "date": "", "ddg_snippet": "We introduce a new benchmark designed to advance the development of generalpurpose, large-scale vision - language models for remote sensing images.", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.5555/3737916.3738022", "content": "We introduce a new benchmark designed to advance the development of generalpurpose, large-scale vision - language models for remote sensing images."}
diff --git a/data/sampled_jsons/VRSBench_arxiv_html.jsonl b/data/sampled_jsons/VRSBench_arxiv_html.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..ceaa37306b8a8087fa7b852100f43647606f6d23
--- /dev/null
+++ b/data/sampled_jsons/VRSBench_arxiv_html.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "VRSBench: A Versatile Vision-Language Benchmark Dataset for Remote ...", "date": "", "ddg_snippet": "VRSBench comprises 29,614 images, each enriched with human-verified detailed captions, complex object referring, and question-answer pairs, check Table 1 for a detailed comparison with existing datasets. This dataset facilitates the training and evaluation of vision-language models across a spectrum of remote sensing image understanding tasks.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2406.12384v2", "content": "VRSBench comprises 29,614 images, each enriched with human-verified detailed captions, complex object referring, and question-answer pairs, check Table 1 for a detailed comparison with existing datasets. This dataset facilitates the training and evaluation of vision-language models across a spectrum of remote sensing image understanding tasks."}
+{"idx": 1, "title": "GitHub - lx709/VRSBench", "date": "", "ddg_snippet": "VRSBench is a Versatile Vision-Language Benchmark for Remote Sensing Image Understanding. It consists of 29,614 remote sensing images with detailed captions, 52,472 object refers, and 3123,221 visual question-answer pairs.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/lx709/VRSBench", "content": "VRSBench is a Versatile Vision-Language Benchmark for Remote Sensing Image Understanding. It consists of 29,614 remote sensing images with detailed captions, 52,472 object refers, and 3123,221 visual question-answer pairs."}
+{"idx": 2, "title": "VRSBench:", "date": "", "ddg_snippet": "VRSBench provides a large-scale collection of human-verified, high-quality captions rich in object details. VRSBench offers more realistic object refers in which each referring sentence unambiguously identifies an object among multiple similar ones within the same category.", "subpage_snippet": "", "source": "vrsbench.github.io", "link": "https://vrsbench.github.io/", "content": "VRSBench provides a large-scale collection of human-verified, high-quality captions rich in object details. VRSBench offers more realistic object refers in which each referring sentence unambiguously identifies an object among multiple similar ones within the same category."}
+{"idx": 3, "title": "PDF Supplementary of VRSBench: A Versatile Benchmark for Vision Language ...", "date": "", "ddg_snippet": "1.1 Overview VRSBench consists of 29,614 remote sensing images with detailed captions, 52,472 object refers, 123,221 visual question-answer pairs. VRSBench is designed to facilitate the development and evaluation of vision-language models in remote sensing, providing a comprehensive set of annotations including detailed captions, visual grounding, and visual question answering. This section ...", "subpage_snippet": "", "source": "papers.nips.cc", "link": "https://papers.nips.cc/paper_files/paper/2024/file/05b7f821234f66b78f99e7803fffa78a-Supplemental-Datasets_and_Benchmarks_Track.pdf", "content": "1.1 Overview VRSBench consists of 29,614 remote sensing images with detailed captions, 52,472 object refers, 123,221 visual question-answer pairs. VRSBench is designed to facilitate the development and evaluation of vision-language models in remote sensing, providing a comprehensive set of annotations including detailed captions, visual grounding, and visual question answering. This section ..."}
+{"idx": 4, "title": "VRSBench", "date": "", "ddg_snippet": "The VRSBench dataset is constructed to support the development and evaluation of vision-language models in the context of remote sensing image understanding. It addresses limitations in existing datasets, such as task-specific design, limited object-level detail, and inconsistent annotation quality. VRSBench contains 29,614 remote sensing images, each paired with a human-verified caption. It ...", "subpage_snippet": "", "source": "www.eod-grss-ieee.com", "link": "https://www.eod-grss-ieee.com/dataset-detail/UjNWY0FMSStDYU5MTHlJNVE5bXllQT09", "content": "The VRSBench dataset is constructed to support the development and evaluation of vision-language models in the context of remote sensing image understanding. It addresses limitations in existing datasets, such as task-specific design, limited object-level detail, and inconsistent annotation quality. VRSBench contains 29,614 remote sensing images, each paired with a human-verified caption. It ..."}
+{"idx": 5, "title": "[PDF] VRSBench: A Versatile Vision-Language Benchmark Dataset for ...", "date": "", "ddg_snippet": "A new benchmark designed to advance the development of general-purpose, large-scale vision-language models for remote sensing images, and evaluated state-of-the-art models on this benchmark for three vision-language tasks: image captioning, visual grounding, and visual question answering. Expand View PDF on arXiv Save to Library Create Alert Cite", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/VRSBench:-A-Versatile-Vision-Language-Benchmark-for-Li-Ding/c28110e1d66ee586f611bea5c331920bdacc5d4b", "content": "A new benchmark designed to advance the development of general-purpose, large-scale vision-language models for remote sensing images, and evaluated state-of-the-art models on this benchmark for three vision-language tasks: image captioning, visual grounding, and visual question answering. Expand View PDF on arXiv Save to Library Create Alert Cite"}
+{"idx": 6, "title": "PDF VRSBench: A Versatile Vision-Language - neurips.cc", "date": "", "ddg_snippet": "Benchmark Tasks VRSBench -Cap: This challenge requires the prediction of a comprehensive description for a given remote sensing image, encapsulating intricate details and contextual relevance. VRSBench -Ref: The task involves identifying and localizing specific objects or features within a given remote sensing image based on a textual description.", "subpage_snippet": "", "source": "neurips.cc", "link": "https://neurips.cc/media/neurips-2024/Slides/97530.pdf", "content": "Benchmark Tasks VRSBench -Cap: This challenge requires the prediction of a comprehensive description for a given remote sensing image, encapsulating intricate details and contextual relevance. VRSBench -Ref: The task involves identifying and localizing specific objects or features within a given remote sensing image based on a textual description."}
+{"idx": 7, "title": "README.md · xiang709/VRSBench at main - Hugging Face", "date": "", "ddg_snippet": "VRSBench VRSBench is a Versatile Vision-Language Benchmark for Remote Sensing Image Understanding. It consists of 29,614 remote sensing images with detailed captions, 52,472 object refers, and 3123,221 visual question-answer pairs.", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/datasets/xiang709/VRSBench/blob/main/README.md", "content": "VRSBench VRSBench is a Versatile Vision-Language Benchmark for Remote Sensing Image Understanding. It consists of 29,614 remote sensing images with detailed captions, 52,472 object refers, and 3123,221 visual question-answer pairs."}
+{"idx": 8, "title": "VRSBench: A Versatile Vision-Language Benchmark Dataset for Remote ...", "date": "", "ddg_snippet": "Exploring these improvement opportunities, we present a Versatile vision-language Benchmark for Remote Sensing image understanding, termed VRSBench . This benchmark comprises 29,614 images, with 29,614 human-verified detailed captions, 52,472 object references, and 123,221 question-answer pairs.", "subpage_snippet": "", "source": "papers.nips.cc", "link": "https://papers.nips.cc/paper_files/paper/2024/hash/05b7f821234f66b78f99e7803fffa78a-Abstract-Datasets_and_Benchmarks_Track.html", "content": "Exploring these improvement opportunities, we present a Versatile vision-language Benchmark for Remote Sensing image understanding, termed VRSBench . This benchmark comprises 29,614 images, with 29,614 human-verified detailed captions, 52,472 object references, and 123,221 question-answer pairs."}
+{"idx": 9, "title": "VRSBench: A Versatile Vision-Language Benchmark Dataset for Remote ...", "date": "", "ddg_snippet": "We introduce a new benchmark designed to advance the development of general-purpose, large-scale vision-language models for remote sensing images. Although several vision-language datasets in remote sensing have been proposed to pursue this goal, existing datasets are typically tailored to single tasks, lack detailed object information, or suffer from inadequate quality control. Exploring ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2406.12384", "content": "We introduce a new benchmark designed to advance the development of general-purpose, large-scale vision-language models for remote sensing images. Although several vision-language datasets in remote sensing have been proposed to pursue this goal, existing datasets are typically tailored to single tasks, lack detailed object information, or suffer from inadequate quality control. Exploring ..."}
diff --git a/data/sampled_jsons/Video-ColBERT_Contextualized_Late_Interaction_for_Text-to-Video_Retrieval_CVPR_paper_reference_[63].jsonl b/data/sampled_jsons/Video-ColBERT_Contextualized_Late_Interaction_for_Text-to-Video_Retrieval_CVPR_paper_reference_[63].jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..b692331a6535dc843a6eaf56ae206144be1ff5d2
--- /dev/null
+++ b/data/sampled_jsons/Video-ColBERT_Contextualized_Late_Interaction_for_Text-to-Video_Retrieval_CVPR_paper_reference_[63].jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Video - ColBERT : Contextualized Late Interaction for Text - to - Video ...", "date": "", "ddg_snippet": "Figure 1. VIDEO - COLBERT architecture, combining token-wise interaction on both static frame features (green) and temporally contextualized video features (red). (e.g. through mean pooling), then use a single dot product for similarity calculation at retrieval time.", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2025/papers/Reddy_Video-ColBERT_Contextualized_Late_Interaction_for_Text-to-Video_Retrieval_CVPR_2025_paper.pdf", "content": "Figure 1. VIDEO - COLBERT architecture, combining token-wise interaction on both static frame features (green) and temporally contextualized video features (red). (e.g. through mean pooling), then use a single dot product for similarity calculation at retrieval time."}
+{"idx": 1, "title": "Video - ColBERT : Contextualized Late Interaction for Text - to - Video ...", "date": "", "ddg_snippet": "Video - ColBERT is built upon 3 main components: a fine-grained spatial and temporal token-wise interaction , query and visual expansions, and a dual sigmoid loss during training. We find that this interaction and training paradigm leads to strong individual...", "subpage_snippet": "", "source": "papers.cool", "link": "https://papers.cool/venue/Reddy_Video-ColBERT_Contextualized_Late_Interaction_for_Text-to-Video_Retrieval@CVPR2025@CVF", "content": "Video - ColBERT is built upon 3 main components: a fine-grained spatial and temporal token-wise interaction , query and visual expansions, and a dual sigmoid loss during training. We find that this interaction and training paradigm leads to strong individual..."}
+{"idx": 2, "title": "Johns Hopkins researchers to highlight work at CVPR 2025", "date": "", "ddg_snippet": "“ Video - ColBERT : Contextualized Late Interaction for Text - to - Video Retrieval ” by Arun Reddy, Alexander Martin; Eugene Yang; Andrew Yates, Kate Sanders; Kenton Murray; Reno Kriz; Celso M. de Melo, Benjamin Van Durme, and Rama Chellappa.", "subpage_snippet": "", "source": "www.cs.jhu.edu", "link": "https://www.cs.jhu.edu/news/johns-hopkins-researchers-to-highlight-work-at-cvpr-2025/", "content": "“ Video - ColBERT : Contextualized Late Interaction for Text - to - Video Retrieval ” by Arun Reddy, Alexander Martin; Eugene Yang; Andrew Yates, Kate Sanders; Kenton Murray; Reno Kriz; Celso M. de Melo, Benjamin Van Durme, and Rama Chellappa."}
+{"idx": 3, "title": "GitHub - 666zz666/ CVPR 2025-The-vast-majority-of- papers ...", "date": "", "ddg_snippet": "Video -CoIBERT: Contextualized Late Interaction for Text - to - Video Retrieval , Arun Reddy, Alexander Martin, Eugene Yang, Andrew Yates, Kate Sanders, Kenton Murray, Reno Kriz, Celso M. de Melo, Benjamin Van Durme, Rama Chellappa.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/666zz666/CVPR2025-The-vast-majority-of-papers", "content": "Video -CoIBERT: Contextualized Late Interaction for Text - to - Video Retrieval , Arun Reddy, Alexander Martin, Eugene Yang, Andrew Yates, Kate Sanders, Kenton Murray, Reno Kriz, Celso M. de Melo, Benjamin Van Durme, Rama Chellappa."}
+{"idx": 4, "title": "ColBERT and Late Interaction | qdrant/fastembed | DeepWiki", "date": "", "ddg_snippet": "Late interaction is a retrieval approach that defers token-to-token interaction between queries and documents until the final ranking stage, offering improved precision compared to traditional dense embeddings.", "subpage_snippet": "", "source": "deepwiki.com", "link": "https://deepwiki.com/qdrant/fastembed/7.3-colbert-and-late-interaction", "content": "Late interaction is a retrieval approach that defers token-to-token interaction between queries and documents until the final ranking stage, offering improved precision compared to traditional dense embeddings."}
+{"idx": 5, "title": "ContextIQ: A Multimodal Expert-Based Video Retrieval System for...", "date": "", "ddg_snippet": "[7] Video - ColBERT : Contextualized Late Interaction for Text - to - Video Retrieval . In this work, we tackle the problem of text - to - video retrieval (T2VR). AArun ReddyAAlexander Martin.", "subpage_snippet": "", "source": "www.bohrium.com", "link": "https://www.bohrium.com/paper-details/contextiq-a-multimodal-expert-based-video-retrieval-system-for-contextual-advertising/1058628209777573922-108597", "content": "[7] Video - ColBERT : Contextualized Late Interaction for Text - to - Video Retrieval . In this work, we tackle the problem of text - to - video retrieval (T2VR). AArun ReddyAAlexander Martin."}
+{"idx": 6, "title": "CVPR 2025 Accepted Papers -CSDN博客", "date": "", "ddg_snippet": "Text - to -Image Model for Image Restoration Junyuan Deng · Xinyi Wu · Yongxing Yang · Congchao Zhu · Song Wang · Zhenyao Wu Synergizing Motion and Appearance: Multi-Scale Compensatory Codebooks for Talking Head Video Generation Shuling Zhao...", "subpage_snippet": "", "source": "blog.csdn.net", "link": "https://blog.csdn.net/u013963578/article/details/146183100", "content": "Text - to -Image Model for Image Restoration Junyuan Deng · Xinyi Wu · Yongxing Yang · Congchao Zhu · Song Wang · Zhenyao Wu Synergizing Motion and Appearance: Multi-Scale Compensatory Codebooks for Talking Head Video Generation Shuling Zhao..."}
+{"idx": 7, "title": "[ CVPR 2025] Video - ColBERT : Contextualized Late Interaction for ...", "date": "", "ddg_snippet": "О сервисе Прессе Авторские права Связаться с нами Авторам Рекламодателям...", "subpage_snippet": "", "source": "www.youtube.com", "link": "https://www.youtube.com/watch?v=TtcPgsih2TQ", "content": "О сервисе Прессе Авторские права Связаться с нами Авторам Рекламодателям..."}
+{"idx": 8, "title": "Alexander Martin - Google Scholar", "date": "", "ddg_snippet": "Video - ColBERT : Contextualized Late Interaction for Text - to - Video Retrieval .Proceedings of the Computer Vision and Pattern Recognition Conference, 19691 …, 2025.", "subpage_snippet": "", "source": "scholar.google.com", "link": "https://scholar.google.com/citations?user=6_4ethMAAAAJ&hl=en", "content": "Video - ColBERT : Contextualized Late Interaction for Text - to - Video Retrieval .Proceedings of the Computer Vision and Pattern Recognition Conference, 19691 …, 2025."}
+{"idx": 9, "title": "Reno KRIZ | Doctor of Philosophy | University of Pennsylvania...", "date": "", "ddg_snippet": "Video - ColBERT : Contextualized Late Interaction for Text - to - Video Retrieval . Preprint.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/profile/Reno-Kriz", "content": "Video - ColBERT : Contextualized Late Interaction for Text - to - Video Retrieval . Preprint."}
diff --git a/data/sampled_jsons/Video-ColBERT_Contextualized_Late_Interaction_for_Text-to-Video_Retrieval_full_text.jsonl b/data/sampled_jsons/Video-ColBERT_Contextualized_Late_Interaction_for_Text-to-Video_Retrieval_full_text.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..676b4c4dea1428bd41b7ce875d10f84114b36018
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+++ b/data/sampled_jsons/Video-ColBERT_Contextualized_Late_Interaction_for_Text-to-Video_Retrieval_full_text.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Contextualized Late Interaction for Text-to-Video Retrieval", "date": "", "ddg_snippet": "by A Reddy · 2025 · Cited by 5 — Abstract page for arXiv paper 2503.19009: Video-ColBERT: Contextualized Late Interaction for Text-to-Video Retrieval . ... Full text . Search.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2503.19009", "content": "by A Reddy · 2025 · Cited by 5 — Abstract page for arXiv paper 2503.19009: Video-ColBERT: Contextualized Late Interaction for Text-to-Video Retrieval . ... Full text . Search."}
+{"idx": 1, "title": "Contextualized Late Interaction for Text-to-Video Retrieval", "date": "", "ddg_snippet": "by A Reddy · 2025 · Cited by 5 — Video-ColBERT: Contextualized Late Interaction for Text-to-Video Retrieval . Arun Reddy1,2* Alexander Martin2* Eugene Yang2,3. Andrew Yates2,3. Kate Sanders2. 11 pages", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2025/papers/Reddy_Video-ColBERT_Contextualized_Late_Interaction_for_Text-to-Video_Retrieval_CVPR_2025_paper.pdf", "content": "by A Reddy · 2025 · Cited by 5 — Video-ColBERT: Contextualized Late Interaction for Text-to-Video Retrieval . Arun Reddy1,2* Alexander Martin2* Eugene Yang2,3. Andrew Yates2,3. Kate Sanders2. 11 pages"}
+{"idx": 2, "title": "Contextualized Late Interaction for Text-to-Video Retrieval", "date": "", "ddg_snippet": "24 Mar 2025 — Video-ColBERT: Contextualized Late Interaction for Text-to-Video Retrieval ... SigLIP, on the other hand, uses full (i.e. bidirectional) ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2503.19009v1", "content": "24 Mar 2025 — Video-ColBERT: Contextualized Late Interaction for Text-to-Video Retrieval ... SigLIP, on the other hand, uses full (i.e. bidirectional) ..."}
+{"idx": 3, "title": "Contextualized Late Interaction for Text-to-Video Retrieval", "date": "", "ddg_snippet": "Video-ColBERT: Contextualized Late Interaction for Text-to-Video Retrieval . Arun Reddy · Alexander Martin · Eugene Yang · Andrew Yates · Kate Sanders ...", "subpage_snippet": "", "source": "cvpr.thecvf.com", "link": "https://cvpr.thecvf.com/virtual/2025/poster/33381", "content": "Video-ColBERT: Contextualized Late Interaction for Text-to-Video Retrieval . Arun Reddy · Alexander Martin · Eugene Yang · Andrew Yates · Kate Sanders ..."}
+{"idx": 4, "title": "Contextualized Late Interaction for Text-to-Video Retrieval", "date": "", "ddg_snippet": "Video-ColBERT: Contextualized Late Interaction for Text-to-Video Retrieval · Arun Reddy, Alexander Martin, +7 authors. Ramalingam Chellappa · Published in ...", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/bff2f91c763830a2d14dbbbeca150e92ede02323", "content": "Video-ColBERT: Contextualized Late Interaction for Text-to-Video Retrieval · Arun Reddy, Alexander Martin, +7 authors. Ramalingam Chellappa · Published in ..."}
+{"idx": 5, "title": "Contextualized Late Interaction for Text-to-Video Retrieval", "date": "", "ddg_snippet": "Paper. Video-ColBERT: Contextualized Late Interaction for Text-to-Video Retrieval . Published Mar 24, 2025 · Arun Reddy, Alexander Martin, Eugene Yang +7 more.", "subpage_snippet": "", "source": "www.consensus.app", "link": "https://www.consensus.app/papers/details/ad9f18cebf0c56b49ec8885b375473b9/", "content": "Paper. Video-ColBERT: Contextualized Late Interaction for Text-to-Video Retrieval . Published Mar 24, 2025 · Arun Reddy, Alexander Martin, Eugene Yang +7 more."}
+{"idx": 6, "title": "Contextualized Late Interaction for Text-to-Video Retrieval", "date": "", "ddg_snippet": "The paper \" Video-ColBERT: Contextualized Late Interaction for Text-to-Video Retrieval ... However, these methods still face limitations as they typically compress ...", "subpage_snippet": "", "source": "www.alphaxiv.org", "link": "https://www.alphaxiv.org/overview/2503.19009", "content": "The paper \" Video-ColBERT: Contextualized Late Interaction for Text-to-Video Retrieval ... However, these methods still face limitations as they typically compress ..."}
+{"idx": 7, "title": "Revision History for Video-ColBERT: Contextualized Late...", "date": "", "ddg_snippet": "Title: Video-ColBERT: Contextualized Late Interaction for Text-to-Video Retrieval . Authors: Arun V. Reddy, Alexander Martin 0006, Eugene Yang, Andrew Yates, ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/revisions?id=z802hMi65G", "content": "Title: Video-ColBERT: Contextualized Late Interaction for Text-to-Video Retrieval . Authors: Arun V. Reddy, Alexander Martin 0006, Eugene Yang, Andrew Yates, ..."}
+{"idx": 8, "title": "Video-ColBERT: Contextualized Late Interaction for Text-to- ...", "date": "", "ddg_snippet": "Can AI now understand videos well enough to find the exact scene you're thinking of? Video-ColBERT: Contextualized Late Interaction for Text-to-Video Retrieval .", "subpage_snippet": "", "source": "www.aimodels.fyi", "link": "https://www.aimodels.fyi/papers/arxiv/video-colbert-contextualized-late-interaction-text-to", "content": "Can AI now understand videos well enough to find the exact scene you're thinking of? Video-ColBERT: Contextualized Late Interaction for Text-to-Video Retrieval ."}
+{"idx": 9, "title": "Sumit", "date": "", "ddg_snippet": "Video-ColBERT: Contextualized Late Interaction for Text-to-Video Retrieval Introduces a mechanism for fine-grained similarity assessment ...", "subpage_snippet": "", "source": "x.com", "link": "https://x.com/_reachsumit/status/1904777629875548314", "content": "Video-ColBERT: Contextualized Late Interaction for Text-to-Video Retrieval Introduces a mechanism for fine-grained similarity assessment ..."}
diff --git a/data/sampled_jsons/Video-ColBERT_X-CLIP_spatial_spatio-temporal_visual_features.jsonl b/data/sampled_jsons/Video-ColBERT_X-CLIP_spatial_spatio-temporal_visual_features.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..5f91624f3cf9c42ebaa1203b0b40c26185c1038c
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@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Video-ColBERT: Contextualized Late Interaction for Text-to ...", "date": "", "ddg_snippet": "In this work, we introduced Video-ColBERT , a novel approach for text-to- video retrieval that uses efficient fine-grained interactions with both spatial and spatio-temporal visual features .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2503.19009v1", "content": "In this work, we introduced Video-ColBERT , a novel approach for text-to- video retrieval that uses efficient fine-grained interactions with both spatial and spatio-temporal visual features ."}
+{"idx": 1, "title": "Exciting Research Alert: Video-ColBERT - A Breakthrough in ...", "date": "", "ddg_snippet": "Fine-grained spatial and temporal token-wise interaction - Unlike traditional approaches that compress videos into single vectors, Video-ColBERT performs MeanMaxSim (MMS) operations on both ...", "subpage_snippet": "", "source": "www.linkedin.com", "link": "https://www.linkedin.com/posts/singhsidhukuldeep_exciting-research-alert-video-colbert-activity-7336927383549071360-eAP7", "content": "Fine-grained spatial and temporal token-wise interaction - Unlike traditional approaches that compress videos into single vectors, Video-ColBERT performs MeanMaxSim (MMS) operations on both ..."}
+{"idx": 2, "title": "Spatial–temporal video grounding with cross-modal ...", "date": "", "ddg_snippet": "May 1, 2025 · Afterwards, the cross-modal spatial – temporal features are input into a transformer decoder to produce the spatial – temporal video grounding outcomes. Concurrently, our methodology integrates a dual-level contrastive learning strategy to augment the representations of both visual and textual modalities across temporal and spatial dimensions.", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S0957417425002726", "content": "May 1, 2025 · Afterwards, the cross-modal spatial – temporal features are input into a transformer decoder to produce the spatial – temporal video grounding outcomes. Concurrently, our methodology integrates a dual-level contrastive learning strategy to augment the representations of both visual and textual modalities across temporal and spatial dimensions."}
+{"idx": 3, "title": "Video-ColBERT: Contextualized Late Interaction for Text-to ...", "date": "", "ddg_snippet": "In this work, we introduced VIDEO-COLBERT , a novel approach for text-to- video retrieval that uses eficient fine-grained interactions with both spatial and spatio-temporal visual features .", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2025/papers/Reddy_Video-ColBERT_Contextualized_Late_Interaction_for_Text-to-Video_Retrieval_CVPR_2025_paper.pdf", "content": "In this work, we introduced VIDEO-COLBERT , a novel approach for text-to- video retrieval that uses eficient fine-grained interactions with both spatial and spatio-temporal visual features ."}
+{"idx": 4, "title": "ST-CLIP: Spatio-Temporal Enhanced CLIP Towards Dense Video ... Video-ColBERT: Contextualized Late Interaction for Text-to ... Video -ColBERT: Contextualized Late Interaction for Text-to-Video Retrieval Spatial – temporal video grounding with cross-modal understanding an… Spatial – temporal video grounding with cross-modal understanding an… Spatial – temporal video grounding with cross-modal understanding an… Spatial – temporal video grounding with cross-modal understanding an… [2503.19009] Video-ColBERT: Contextualized Late Interaction ...", "date": "", "ddg_snippet": "Jul 31, 2024 · A video , seen as a collection of ordered images, contains rich spatial and temporal information that plays a vital role in video understanding tasks. To model spatial and temporal interactions, some works [7, 8] employ the transformer [9] architecture for visual encoding. They directly sample T frames from the raw video and then use the transformer to process each independent 2D frame ... In this work, we introduced VIDEO-COLBERT , a novel ap-proach for text-to- video retrieval that uses fine-grained in-teractions with both spatial and spatio-temporal visual fea-tures . How does video-Colbert work? Video - ColBERT is built upon 3 main components: a fine-grained spatial and temporal token-wise interaction, query and visual expansions, and a dual sigmoid loss during training. We find that this interaction and training paradigm leads to strong individual, yet compatible, representations for encoding video content. Is fine-grained spatial–temporal encoding effective? This demonstrates the efficacy of the fine-grained spatial–temporal encoding in our CUTE model, which enables it to accurately model the spatial–temporal relationship between videos and texts. Is cute a good tool for capturing spatial information? Additionally, although CUTE effectively captures detailed temporal and spatial information , it may struggle with videos that contain multiple segments matching the query text or when the query text requires locating the spatial positions of multiple target subjects within the video, limitations that some other methods are better equipped to handle. How does roialign work in spatial contrastive learning? In the context of spatial contrastive learning, we employ RoIAlign (He, Gkioxari, Dollár, & Girshick, 2017) to extract regional features , which are then represented as feature maps with dimensions P × P, where P is set to 3. What is stvgbert's new cross-modal feature learning module? STVGBert (Su et al., 2021) introduces a new cross-modal feature learning module that models spatial – temporal information and simultaneously aligns cross-modal representation. Mar 24, 2025 · Video-ColBERT is built upon 3 main components: a fine-grained spatial and temporal token-wise interaction, query and visual expansions, and a dual sigmoid loss during training. We find that this interaction and training paradigm leads to strong individual, yet compatible, representations for encoding video content.", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/chapter/10.1007/978-981-97-5612-4_34", "content": "Jul 31, 2024 · A video , seen as a collection of ordered images, contains rich spatial and temporal information that plays a vital role in video understanding tasks. To model spatial and temporal interactions, some works [7, 8] employ the transformer [9] architecture for visual encoding. They directly sample T frames from the raw video and then use the transformer to process each independent 2D frame ... In this work, we introduced VIDEO-COLBERT , a novel ap-proach for text-to- video retrieval that uses fine-grained in-teractions with both spatial and spatio-temporal visual fea-tures . How does video-Colbert work? Video - ColBERT is built upon 3 main components: a fine-grained spatial and temporal token-wise interaction, query and visual expansions, and a dual sigmoid loss during training. We find that this interaction and training paradigm leads to strong individual, yet compatible, representations for encoding video content. Is fine-grained spatial–temporal encoding effective? This demonstrates the efficacy of the fine-grained spatial–temporal encoding in our CUTE model, which enables it to accurately model the spatial–temporal relationship between videos and texts. Is cute a good tool for capturing spatial information? Additionally, although CUTE effectively captures detailed temporal and spatial information , it may struggle with videos that contain multiple segments matching the query text or when the query text requires locating the spatial positions of multiple target subjects within the video, limitations that some other methods are better equipped to handle. How does roialign work in spatial contrastive learning? In the context of spatial contrastive learning, we employ RoIAlign (He, Gkioxari, Dollár, & Girshick, 2017) to extract regional features , which are then represented as feature maps with dimensions P × P, where P is set to 3. What is stvgbert's new cross-modal feature learning module? STVGBert (Su et al., 2021) introduces a new cross-modal feature learning module that models spatial – temporal information and simultaneously aligns cross-modal representation. Mar 24, 2025 · Video-ColBERT is built upon 3 main components: a fine-grained spatial and temporal token-wise interaction, query and visual expansions, and a dual sigmoid loss during training. We find that this interaction and training paradigm leads to strong individual, yet compatible, representations for encoding video content."}
+{"idx": 5, "title": "Video-ColBERT: Contextualized Late Interaction for Text-to ...", "date": "", "ddg_snippet": "In this work, we introduced VIDEO-COLBERT , a novel ap-proach for text-to- video retrieval that uses fine-grained in-teractions with both spatial and spatio-temporal visual fea-tures .", "subpage_snippet": "", "source": "www.celsodemelo.net", "link": "https://www.celsodemelo.net/static/publications/Video_ColBERT_CVPR_2025_DistA.pdf", "content": "In this work, we introduced VIDEO-COLBERT , a novel ap-proach for text-to- video retrieval that uses fine-grained in-teractions with both spatial and spatio-temporal visual fea-tures ."}
+{"idx": 6, "title": "[2503.19009] Video-ColBERT: Contextualized Late Interaction ...", "date": "", "ddg_snippet": "Mar 24, 2025 · Video-ColBERT is built upon 3 main components: a fine-grained spatial and temporal token-wise interaction, query and visual expansions, and a dual sigmoid loss during training. We find that this interaction and training paradigm leads to strong individual, yet compatible, representations for encoding video content.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2503.19009", "content": "Mar 24, 2025 · Video-ColBERT is built upon 3 main components: a fine-grained spatial and temporal token-wise interaction, query and visual expansions, and a dual sigmoid loss during training. We find that this interaction and training paradigm leads to strong individual, yet compatible, representations for encoding video content."}
+{"idx": 7, "title": "TDS- CLIP : Temporal Difference Side Network for Efficient Video ...", "date": "", "ddg_snippet": ". III-B Visual Temporal Encoder. The spatial semantic features from the frozen CLIP ViT can guide the model in learning the appearance and background information of video frames, but for video understanding tasks, the model needs to have temporal modeling capabilities.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2408.10688v2", "content": ". III-B Visual Temporal Encoder. The spatial semantic features from the frozen CLIP ViT can guide the model in learning the appearance and background information of video frames, but for video understanding tasks, the model needs to have temporal modeling capabilities."}
+{"idx": 8, "title": "Robustness Analysis of Video -Language Models", "date": "", "ddg_snippet": "Next, we add temporal perturbations to address the time dimension and video compression to address video -specific distribution shifts as well as spatio - temporal . The total set of visual perturbations fall into 5 categories: Noise, Blur, Temporal, Camera and Digital.", "subpage_snippet": "", "source": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper_files/paper/2022/file/de6ff07cbd222c10d694c2b2f732aceb-Paper-Datasets_and_Benchmarks.pdf", "content": "Next, we add temporal perturbations to address the time dimension and video compression to address video -specific distribution shifts as well as spatio - temporal . The total set of visual perturbations fall into 5 categories: Noise, Blur, Temporal, Camera and Digital."}
+{"idx": 9, "title": "Human emotion recognition from videos using spatio - temporal and...", "date": "", "ddg_snippet": "For facial expressions spatio - temporal features are extracted from visual streams. Principal component analysis (PCA) is applied for dimensionality reduction of the visual features and capturing 97 % of variances.", "subpage_snippet": "", "source": "www.bohrium.com", "link": "https://www.bohrium.com/paper-details/human-emotion-recognition-from-videos-using-spatio-temporal-and-audio-features/813263401592553473-2658", "content": "For facial expressions spatio - temporal features are extracted from visual streams. Principal component analysis (PCA) is applied for dimensionality reduction of the visual features and capturing 97 % of variances."}
diff --git a/data/sampled_jsons/WWW_2024_accepted_papers_proceedings_blockchain_year_2024.jsonl b/data/sampled_jsons/WWW_2024_accepted_papers_proceedings_blockchain_year_2024.jsonl
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index 0000000000000000000000000000000000000000..6c218ac9ae28a100a6eb0cd5712f287033bb0139
--- /dev/null
+++ b/data/sampled_jsons/WWW_2024_accepted_papers_proceedings_blockchain_year_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Blockchain - Wikipedia", "date": "", "ddg_snippet": "To prolong the blockchain , bitcoin uses Hashcash puzzles. While Hashcash was designed in 1997 by Adam Back, the original idea was first proposed by Cynthia Dwork and Moni Naor and Eli Ponyatovski in their 1992 paper \"Pricing via Processing or Combatting Junk Mail\".", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/Blockchain", "content": "To prolong the blockchain , bitcoin uses Hashcash puzzles. While Hashcash was designed in 1997 by Adam Back, the original idea was first proposed by Cynthia Dwork and Moni Naor and Eli Ponyatovski in their 1992 paper \"Pricing via Processing or Combatting Junk Mail\"."}
+{"idx": 1, "title": "Блокчейн — Википедия", "date": "", "ddg_snippet": "Блокчейн (англ. blockchain [1], изначально block chain[2] — цепь из блоков) — выстроенная по определённым правилам непрерывная последовательная цепочка блоков (связный список), содержащих какую-либо информацию.", "subpage_snippet": "", "source": "ru.wikipedia.org", "link": "https://ru.wikipedia.org/wiki/Блокчейн", "content": "Блокчейн (англ. blockchain [1], изначально block chain[2] — цепь из блоков) — выстроенная по определённым правилам непрерывная последовательная цепочка блоков (связный список), содержащих какую-либо информацию."}
+{"idx": 2, "title": "Bitget Report: Female-Led Blockchain Startups Receive... - Chainwire", "date": "", "ddg_snippet": "Victoria, Seychelles, January 11th, 2024 , Chainwire Bitget, the world’s leading cryptocurrency exchange and Web3 company, has released a study analyzing the latest trends in the blockchain startup funding landscape and their influence on gender-based disparity in investment...", "subpage_snippet": "", "source": "chainwire.org", "link": "https://chainwire.org/2024/01/11/bitget-report-female-led-blockchain-startups-receive-only-6-of-overall-funding/", "content": "Victoria, Seychelles, January 11th, 2024 , Chainwire Bitget, the world’s leading cryptocurrency exchange and Web3 company, has released a study analyzing the latest trends in the blockchain startup funding landscape and their influence on gender-based disparity in investment..."}
+{"idx": 3, "title": "An Appraisal of the National Blockchain Policy... - Omaplex Law Firm", "date": "", "ddg_snippet": "The National Blockchain Policy for Nigeria, 2023, pg.5 ↑. The NDEPS is a policy framework and action plan developed by the government in 2019 to realign the Nigerian economy to take advantage of the numerous opportunities digital technology has to offer.", "subpage_snippet": "", "source": "omaplex.com.ng", "link": "https://omaplex.com.ng/an-appraisal-of-the-national-blockchain-policy-and-its-likely-impact-on-the-nigerian-blockchain-ecosystem/", "content": "The National Blockchain Policy for Nigeria, 2023, pg.5 ↑. The NDEPS is a policy framework and action plan developed by the government in 2019 to realign the Nigerian economy to take advantage of the numerous opportunities digital technology has to offer."}
+{"idx": 4, "title": "The Intrepid Report Issue #51 - Blockchain ... - Blockchain Review", "date": "", "ddg_snippet": "We’ve got thoughts from the Blockchain Review on Smart Cities and the new digital economy, as well as insights on the next steps for blockchain technology, and much more.Articles, white papers , ebooks, and more. The Second Blockchain Bubble is Now Complete — What’s Next?", "subpage_snippet": "", "source": "blockchainreview.io", "link": "https://blockchainreview.io/the-intrepid-report-issue-51-blockchain-review/", "content": "We’ve got thoughts from the Blockchain Review on Smart Cities and the new digital economy, as well as insights on the next steps for blockchain technology, and much more.Articles, white papers , ebooks, and more. The Second Blockchain Bubble is Now Complete — What’s Next?"}
+{"idx": 5, "title": "Embracing Blockchain Interoperability: A Gateway to Innovation and...", "date": "", "ddg_snippet": "Blockchain interoperability represents a paradigm shift in the way we conceive decentralized systems. No longer confined to isolated networks, blockchain platforms can now exchange data, assets, or value with ease, transcending the limitations of siloed environments.", "subpage_snippet": "", "source": "www.linkedin.com", "link": "https://www.linkedin.com/pulse/embracing-blockchain-interoperability-gateway-a7puf", "content": "Blockchain interoperability represents a paradigm shift in the way we conceive decentralized systems. No longer confined to isolated networks, blockchain platforms can now exchange data, assets, or value with ease, transcending the limitations of siloed environments."}
+{"idx": 6, "title": "Make no mistake, Bitcoin and Blockchain will disrupt... - Memeburn", "date": "", "ddg_snippet": "Bitcoin and Blockchain technology has surged forward as the potential for the next biggest disrupter in the finance industry. Last year Wall Street showed just how serious it was about using their tech when larger financial institutions like Citi, Nasdaq, Visa and others invested in Chain.com...", "subpage_snippet": "", "source": "memeburn.com", "link": "https://memeburn.com/2016/04/make-no-mistake-bitcoin-and-blockchain-will-disr/", "content": "Bitcoin and Blockchain technology has surged forward as the potential for the next biggest disrupter in the finance industry. Last year Wall Street showed just how serious it was about using their tech when larger financial institutions like Citi, Nasdaq, Visa and others invested in Chain.com..."}
+{"idx": 7, "title": "Blockchain is a booming technology and a buzzword among... | Medium", "date": "", "ddg_snippet": "For simplicity, let’s assume Blockchain as a secured database, in which if any information is changed or tampered with, the whole system gets to know about it. You might be confused as to how it is possible. Blockchain is a chain of various fragmented entities which are called Blocks.", "subpage_snippet": "", "source": "medium.com", "link": "https://medium.com/@sasteyashwant/blockchain-is-a-booming-technology-and-a-buzzword-among-all-techies-nowadays-so-lets-dive-in-and-2efbff8f6e38", "content": "For simplicity, let’s assume Blockchain as a secured database, in which if any information is changed or tampered with, the whole system gets to know about it. You might be confused as to how it is possible. Blockchain is a chain of various fragmented entities which are called Blocks."}
+{"idx": 8, "title": "Блокчейн-технологии и криптовалюты: проблемы...", "date": "", "ddg_snippet": "2024 / Мирончук Вадим Анатольевич, Золкин Александр Леонидович, Журавлева Ирина Витальевна, Попова Елена Александровна. Понятие и сущность блокчейн-технологии в системе цифровой экономики.", "subpage_snippet": "", "source": "cyberleninka.ru", "link": "https://cyberleninka.ru/article/n/blokcheyn-tehnologii-i-kriptovalyuty-problemy-bezopasnosti-i-ih-primenenie-v-razlichnyh-otraslyah-ekonomiki", "content": "2024 / Мирончук Вадим Анатольевич, Золкин Александр Леонидович, Журавлева Ирина Витальевна, Попова Елена Александровна. Понятие и сущность блокчейн-технологии в системе цифровой экономики."}
+{"idx": 9, "title": "Lombard: Bitcoin Capital Markets Onchain", "date": "", "ddg_snippet": "Founded in 2024 , Lombard pioneered Bitcoin's integration into DeFi with LBTC, the leading liquid staked token, secured by a consortium of top institutions.", "subpage_snippet": "", "source": "www.lombard.finance", "link": "https://www.lombard.finance/", "content": "Founded in 2024 , Lombard pioneered Bitcoin's integration into DeFi with LBTC, the leading liquid staked token, secured by a consortium of top institutions."}
diff --git a/data/sampled_jsons/WWW_2024_proceedings_program_papers.jsonl b/data/sampled_jsons/WWW_2024_proceedings_program_papers.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..e400be5b013227318bf6ff900c93ed39b7141d8f
--- /dev/null
+++ b/data/sampled_jsons/WWW_2024_proceedings_program_papers.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "2024 Optical Fiber Communications Conference and Exhibition (OFC ...", "date": "", "ddg_snippet": "Read all the papers in 2024 Optical Fiber Communications Conference and Exhibition (OFC) | IEEE Conference | IEEE Xplore", "subpage_snippet": "", "source": "ieeexplore.ieee.org", "link": "https://ieeexplore.ieee.org/xpl/conhome/10526487/proceeding", "content": "Read all the papers in 2024 Optical Fiber Communications Conference and Exhibition (OFC) | IEEE Conference | IEEE Xplore"}
+{"idx": 1, "title": "Proceedings of the International Conference for High Performance ...", "date": "", "ddg_snippet": "SC24 continued the tradition of balancing a robust Technical Program with a variety of program elements like Technical Papers , Panels, Workshops, and Birds of a Feather sessions; an expansive and vibrant exhibit floor with a record number of industry and academic exhibit booths; and programming for Students@SC and Inclusivity participants.", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/proceedings/10.5555/3703596", "content": "SC24 continued the tradition of balancing a robust Technical Program with a variety of program elements like Technical Papers , Panels, Workshops, and Birds of a Feather sessions; an expansive and vibrant exhibit floor with a record number of industry and academic exhibit booths; and programming for Students@SC and Inclusivity participants."}
+{"idx": 2, "title": "Proceedings & Archives - SC24", "date": "", "ddg_snippet": "Technical Program Publications acm opentoc Enables the download of SC24 Technical Papers content from the ACM Digital Library at no charge in perpetuity. PapersArchived in the ACM Digital Library and IEEE Xplore, members of SIGHPC or subscribers to", "subpage_snippet": "", "source": "sc24.supercomputing.org", "link": "https://sc24.supercomputing.org/program/proceedings-archives/", "content": "Technical Program Publications acm opentoc Enables the download of SC24 Technical Papers content from the ACM Digital Library at no charge in perpetuity. PapersArchived in the ACM Digital Library and IEEE Xplore, members of SIGHPC or subscribers to"}
+{"idx": 3, "title": "Proceedings - IEEE PES General Meeting", "date": "", "ddg_snippet": "The system will email you your information. Download Instructions: 1. From the On-line Proceedings Home Page, click on the \"Download Proceedings \" button. 2. Uncompress or extract the \"PESGM2024_Proceedings.zip\" file to a folder on your computer or to a USB drive. 3. The PDFs of all of the conference papers will be in the \"PDF FILES ...", "subpage_snippet": "", "source": "pes-gm.org", "link": "https://pes-gm.org/seattle-2024/program/proceedings/", "content": "The system will email you your information. Download Instructions: 1. From the On-line Proceedings Home Page, click on the \"Download Proceedings \" button. 2. Uncompress or extract the \"PESGM2024_Proceedings.zip\" file to a folder on your computer or to a USB drive. 3. The PDFs of all of the conference papers will be in the \"PDF FILES ..."}
+{"idx": 4, "title": "2024 AOM Annual Meeting Proceedings including Best Papers", "date": "", "ddg_snippet": "2024 AOM Annual Meeting Proceedings including Best Papers", "subpage_snippet": "", "source": "my.aom.org", "link": "https://my.aom.org/program2024/proceedings.aspx", "content": "2024 AOM Annual Meeting Proceedings including Best Papers"}
+{"idx": 5, "title": "Publications - INCOM 2024", "date": "", "ddg_snippet": "Web version: ifac.floq.live Proceedings The INCOM 2024 proceedings is edited by Prof. Fazel Ansari and Prof. Sebastian Schlund and will be published as volume 58, number 19, of IFAC-PapersOnline. Further information will be provided soon. Proceedings are the final collection of papers . Proceedings are produced after the conference.", "subpage_snippet": "", "source": "www.incom2024.org", "link": "https://www.incom2024.org/publications/", "content": "Web version: ifac.floq.live Proceedings The INCOM 2024 proceedings is edited by Prof. Fazel Ansari and Prof. Sebastian Schlund and will be published as volume 58, number 19, of IFAC-PapersOnline. Further information will be provided soon. Proceedings are the final collection of papers . Proceedings are produced after the conference."}
+{"idx": 6, "title": "Conference Proceedings | ISIT2024 - IEEE ISIT", "date": "", "ddg_snippet": "The ISIT 2024 Proceedings are available to download until August 9, 2024 . Be sure to save a copy before this time.There are three ways to access the conference proceedings or individual papers during the conference. To access the proceedings / papers , you will need to authenticate using the following credentials:Email: Email address with which you registered.Confirmation Code: Confirmation ...", "subpage_snippet": "", "source": "2024.ieee-isit.org", "link": "https://2024.ieee-isit.org/conference-proceedings", "content": "The ISIT 2024 Proceedings are available to download until August 9, 2024 . Be sure to save a copy before this time.There are three ways to access the conference proceedings or individual papers during the conference. To access the proceedings / papers , you will need to authenticate using the following credentials:Email: Email address with which you registered.Confirmation Code: Confirmation ..."}
+{"idx": 7, "title": "IPDPS 2024 Proceedings Online & Mobile App", "date": "", "ddg_snippet": "The IPDPS 2024 conference proceedings are published in two volumes with two separate ISBN's: one for the regular conference technical program (IPDPS) and one for the workshops (IPDPSW). This volume also includes the poster-elect papers that will be presented as Conference Posters and a compendium of the PhD Forum poster abstracts.", "subpage_snippet": "", "source": "www.ipdps.org", "link": "https://www.ipdps.org/ipdps2024/2024-proceedings.html", "content": "The IPDPS 2024 conference proceedings are published in two volumes with two separate ISBN's: one for the regular conference technical program (IPDPS) and one for the workshops (IPDPSW). This volume also includes the poster-elect papers that will be presented as Conference Posters and a compendium of the PhD Forum poster abstracts."}
+{"idx": 8, "title": "ICTIR 2024 - Proceedings", "date": "", "ddg_snippet": "Venue Organizers SIGIR 2024 Proceedings ICTIR 2024 | Washington D.C. USA | July 13 2024 Google Sites Report abuse", "subpage_snippet": "", "source": "www.ictir2024.org", "link": "https://www.ictir2024.org/proceedings", "content": "Venue Organizers SIGIR 2024 Proceedings ICTIR 2024 | Washington D.C. USA | July 13 2024 Google Sites Report abuse"}
+{"idx": 9, "title": "PDF Proceedings & Program Guide ISCAP Conference 2024", "date": "", "ddg_snippet": "Baltimore, Maryland - November 6 - 9, 2024 Share your conference achievements with other ISCAP Members on our social media! Stay in touch after the conference, learn about next year's conference, and more. #ISCAP ISCAP Facebook Group ISCAP LinkedIn Group", "subpage_snippet": "", "source": "iscap.us", "link": "https://iscap.us/docs/program2024.pdf", "content": "Baltimore, Maryland - November 6 - 9, 2024 Share your conference achievements with other ISCAP Members on our social media! Stay in touch after the conference, learn about next year's conference, and more. #ISCAP ISCAP Facebook Group ISCAP LinkedIn Group"}
diff --git a/data/sampled_jsons/WWW_2024_reinforcement_learning_sitewww2024.thewebconf.org.jsonl b/data/sampled_jsons/WWW_2024_reinforcement_learning_sitewww2024.thewebconf.org.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..9d1f2fbc7cff01231703bcaa0054739aa83926a2
--- /dev/null
+++ b/data/sampled_jsons/WWW_2024_reinforcement_learning_sitewww2024.thewebconf.org.jsonl
@@ -0,0 +1,7 @@
+{"idx": 0, "title": "International World Wide Web Conference 2024 ( WWW 2024 )", "date": "", "ddg_snippet": "The Web Conference 2024 (formerly known as WWW ) will take place in Singapore, from May 13 to 17, 2024 . We invite short paper contributions to the Health Day.", "subpage_snippet": "", "source": "www2024.thewebconf.org", "link": "https://www2024.thewebconf.org/calls/health-day/", "content": "The Web Conference 2024 (formerly known as WWW ) will take place in Singapore, from May 13 to 17, 2024 . We invite short paper contributions to the Health Day."}
+{"idx": 1, "title": "International World Wide Web Conference 2024 ( WWW 2024 )", "date": "", "ddg_snippet": "**The Web Conference 2024 will be held in Singapore, from May 13 to May 17, 2024 . The committee led by our General Chairs: Tat-Seng Chua (National University of Singapore) and Chong-Wah Ngo (Singapore Management University) welcome you to Singapore in May 2024 !", "subpage_snippet": "", "source": "www2024.thewebconf.org", "link": "https://www2024.thewebconf.org/about/newsletters/", "content": "**The Web Conference 2024 will be held in Singapore, from May 13 to May 17, 2024 . The committee led by our General Chairs: Tat-Seng Chua (National University of Singapore) and Chong-Wah Ngo (Singapore Management University) welcome you to Singapore in May 2024 !"}
+{"idx": 2, "title": "International World Wide Web Conference 2024 ( WWW 2024 )", "date": "", "ddg_snippet": "Seoul ToT 2024 Press Release.Photos 16 to 17 May 2024 . Accepted Papers - Research Tracks.", "subpage_snippet": "", "source": "www2024.thewebconf.org", "link": "https://www2024.thewebconf.org/accepted/research-tracks/", "content": "Seoul ToT 2024 Press Release.Photos 16 to 17 May 2024 . Accepted Papers - Research Tracks."}
+{"idx": 3, "title": "International World Wide Web Conference 2024 ( WWW 2024 )", "date": "", "ddg_snippet": "Companion ACM Digital Library. Seoul ToT 2024 Press Release. Photos 13 to 15 May 2024 .", "subpage_snippet": "", "source": "www2024.thewebconf.org", "link": "https://www2024.thewebconf.org/accepted/industry/", "content": "Companion ACM Digital Library. Seoul ToT 2024 Press Release. Photos 13 to 15 May 2024 ."}
+{"idx": 4, "title": "International World Wide Web Conference 2024 ( WWW 2024 )", "date": "", "ddg_snippet": "Health Day. Date: May 14, 2024 . The emergence of novel Artificial Intelligence (AI) and Machine Learning (ML) technologies is poised to significantly transform clinical care practices.", "subpage_snippet": "", "source": "www2024.thewebconf.org", "link": "https://www2024.thewebconf.org/program/special-days/", "content": "Health Day. Date: May 14, 2024 . The emergence of novel Artificial Intelligence (AI) and Machine Learning (ML) technologies is poised to significantly transform clinical care practices."}
+{"idx": 5, "title": "International World Wide Web Conference 2024 ( WWW 2024 )", "date": "", "ddg_snippet": "Companion ACM Digital Library. Seoul ToT 2024 Press Release. Photos 13 to 15 May 2024 .Accepted Papers - Short Papers. Can we Soft Prompt LLMs for Graph Learning Tasks?", "subpage_snippet": "", "source": "www2024.thewebconf.org", "link": "https://www2024.thewebconf.org/accepted/short-papers/", "content": "Companion ACM Digital Library. Seoul ToT 2024 Press Release. Photos 13 to 15 May 2024 .Accepted Papers - Short Papers. Can we Soft Prompt LLMs for Graph Learning Tasks?"}
+{"idx": 6, "title": "", "date": "", "ddg_snippet": "", "subpage_snippet": "", "source": "", "link": "", "content": ""}
diff --git a/data/sampled_jsons/Wampler_et_al._2010_character_animation_two-player_adversarial_games_year_2010.jsonl b/data/sampled_jsons/Wampler_et_al._2010_character_animation_two-player_adversarial_games_year_2010.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..d66a5d327bdd17f64d619d18e1cff6d7686334c6
--- /dev/null
+++ b/data/sampled_jsons/Wampler_et_al._2010_character_animation_two-player_adversarial_games_year_2010.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "PDF Character Animation in Two-Player Adversarial Games", "date": "", "ddg_snippet": "The root assumption upon which our method is based is that the characters act simultaneously, in contrast to previous adversarial character animation techniques which model the players as taking turns. This closely matches the structure of many real-world games and sports, and captures the reason it often pays to be unpredictable in these games . In turn-based approaches the best way to act is ...", "subpage_snippet": "", "source": "grail.cs.washington.edu", "link": "https://grail.cs.washington.edu/wp-content/uploads/2015/08/wampler2010cai.pdf", "content": "The root assumption upon which our method is based is that the characters act simultaneously, in contrast to previous adversarial character animation techniques which model the players as taking turns. This closely matches the structure of many real-world games and sports, and captures the reason it often pays to be unpredictable in these games . In turn-based approaches the best way to act is ..."}
+{"idx": 1, "title": "Character animation in two-player adversarial games", "date": "", "ddg_snippet": "The incorporation of randomness is critical for the believability and effectiveness of controllers for characters in competitive games . We present a fully automatic method for generating intelligent real-time controllers for characters in such a game . ...", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/abs/10.1145/1805964.1805970", "content": "The incorporation of randomness is critical for the believability and effectiveness of controllers for characters in competitive games . We present a fully automatic method for generating intelligent real-time controllers for characters in such a game . ..."}
+{"idx": 2, "title": "Sci-Hub | Character animation in two-player adversarial games. ACM ...", "date": "", "ddg_snippet": "Sci-Hub | Character animation in two-player adversarial games . ACM Transactions on Graphics, 29 (3), 1-13 | 10.1145/1805964.1805970 hubto open science ↓ save", "subpage_snippet": "", "source": "sci-hub.st", "link": "https://sci-hub.st/10.1145/1805964.1805970", "content": "Sci-Hub | Character animation in two-player adversarial games . ACM Transactions on Graphics, 29 (3), 1-13 | 10.1145/1805964.1805970 hubto open science ↓ save"}
+{"idx": 3, "title": "Adversarial Character Animation | PDF | Basis (Linear Algebra ... - Scribd", "date": "", "ddg_snippet": "Character Animation in Two-Player Adversarial Games KEVIN WAMPLER , ERIK ANDERSEN, EVAN HERBST, YONGJOON LEE, and ZORAN POPOVIC University of Washington The incorporation of randomness is critical for the believability and effectiveness of controllers for characters in competitive games . We present a fully automatic method for generating intelligent real-time controllers for characters in such ...", "subpage_snippet": "", "source": "www.scribd.com", "link": "https://www.scribd.com/document/332112195/Adversarial-Character-Animation", "content": "Character Animation in Two-Player Adversarial Games KEVIN WAMPLER , ERIK ANDERSEN, EVAN HERBST, YONGJOON LEE, and ZORAN POPOVIC University of Washington The incorporation of randomness is critical for the believability and effectiveness of controllers for characters in competitive games . We present a fully automatic method for generating intelligent real-time controllers for characters in such ..."}
+{"idx": 4, "title": "Character animation in two-player adversarial games - 百度学术", "date": "", "ddg_snippet": "The incorporation of randomness is critical for the believability and effectiveness of controllers for characters in competitive games . We present a fully automatic method for generating intelligent real-time controllers for characters in such a game . Our approach uses game theory to deal with the ramifications of the characters acting simultaneously, and generates controllers which employ ...", "subpage_snippet": "", "source": "xueshu.baidu.com", "link": "https://xueshu.baidu.com/usercenter/paper/show?paperid=19acb930c84762c35ee65e6cef54bb96&site=xueshu_se", "content": "The incorporation of randomness is critical for the believability and effectiveness of controllers for characters in competitive games . We present a fully automatic method for generating intelligent real-time controllers for characters in such a game . Our approach uses game theory to deal with the ramifications of the characters acting simultaneously, and generates controllers which employ ..."}
+{"idx": 5, "title": "(PDF) Character Animation in Two-Player Adversarial …grail.cs ...", "date": "", "ddg_snippet": "26 Character Animation in Two-Player Adversarial Games KEVIN WAMPLER , ERIKANDERSEN, EVAN HERBST, YONGJOON LEE, and ZORAN POPOVI ´ C University of Washington The incorporation of randomness is critical for the believability and effectiveness of controllers for characters in competitive games . We present a fully automatic method for generating intelligent real-time controllers for characters in ...", "subpage_snippet": "", "source": "dokumen.tips", "link": "https://dokumen.tips/documents/character-animation-in-two-player-adversarial-grailcs-26-character-animation.html", "content": "26 Character Animation in Two-Player Adversarial Games KEVIN WAMPLER , ERIKANDERSEN, EVAN HERBST, YONGJOON LEE, and ZORAN POPOVI ´ C University of Washington The incorporation of randomness is critical for the believability and effectiveness of controllers for characters in competitive games . We present a fully automatic method for generating intelligent real-time controllers for characters in ..."}
+{"idx": 6, "title": "Character animation in two-player adversarial games", "date": "", "ddg_snippet": "The approach uses game theory to deal with the ramifications of the characters acting simultaneously, and generates controllers which employ both long-term planning and an intelligent use of randomness, exhibiting nuanced strategies based on unpredictability. The incorporation of randomness is critical for the believability and effectiveness of controllers for characters in competitive games ...", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/Character-animation-in-two-player-adversarial-games-Wampler-Andersen/aab9ee829c871af99ca5c91dbf594ad9e0674a79", "content": "The approach uses game theory to deal with the ramifications of the characters acting simultaneously, and generates controllers which employ both long-term planning and an intelligent use of randomness, exhibiting nuanced strategies based on unpredictability. The incorporation of randomness is critical for the believability and effectiveness of controllers for characters in competitive games ..."}
+{"idx": 7, "title": "Introduction to 3D Character Animation - GAMES105", "date": "", "ddg_snippet": "Character Animation in Two-Player Adversarial Games KEVIN WAMPLER,ERIK ANDERSEN, EVAN HERBST, YONGJOON LEE, and ZORAN POPOVIC Univoersity of Washington Near-optimal Character Animation with Continuous Control Adrien Treuille \\ (\\quad\\) Yongjoon Lee \\ (\\quad\\) Zoran Popovic University of Washington Motion Graph＋AI，实现高级控制", "subpage_snippet": "", "source": "caterpillarstudygroup.github.io", "link": "https://caterpillarstudygroup.github.io/GAMES105_mdbook/Introduction.html", "content": "Character Animation in Two-Player Adversarial Games KEVIN WAMPLER,ERIK ANDERSEN, EVAN HERBST, YONGJOON LEE, and ZORAN POPOVIC Univoersity of Washington Near-optimal Character Animation with Continuous Control Adrien Treuille \\ (\\quad\\) Yongjoon Lee \\ (\\quad\\) Zoran Popovic University of Washington Motion Graph＋AI，实现高级控制"}
+{"idx": 8, "title": "Kevin Wampler's research works | Adobe Inc., San Jose and other places", "date": "", "ddg_snippet": "Using an action-level graph, a game tree structure can also be established to organize collaborative and adversarial behaviors among multiple characters [Shum et al. 2007 [Shum et al. , 2008a ...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/scientific-contributions/Kevin-Wampler-69898549", "content": "Using an action-level graph, a game tree structure can also be established to organize collaborative and adversarial behaviors among multiple characters [Shum et al. 2007 [Shum et al. , 2008a ..."}
+{"idx": 9, "title": "PDF Generating and Ranking Diverse Multi-Character Interactions", "date": "", "ddg_snippet": "Game theory and motion graphs have also been used to control the motion of two characters in an adversarial game [ Wampler et al. 2010 ]. The scope of motion synthesis algorithms based on min-max searching and adversarial games are often lim-ited to interactions between two characters .", "subpage_snippet": "", "source": "studios.disneyresearch.com", "link": "https://studios.disneyresearch.com/wp-content/uploads/2019/03/Generating-and-Ranking-Diverse-Multi-Character-Interactions-Paper.pdf", "content": "Game theory and motion graphs have also been used to control the motion of two characters in an adversarial game [ Wampler et al. 2010 ]. The scope of motion synthesis algorithms based on min-max searching and adversarial games are often lim-ited to interactions between two characters ."}
diff --git a/data/sampled_jsons/Wang_METransformer_radiology_report_generation_MIMIC-CXR_BLEU-4_year_2023.jsonl b/data/sampled_jsons/Wang_METransformer_radiology_report_generation_MIMIC-CXR_BLEU-4_year_2023.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..7230f4e3993070c53c461199a193d2667977c5b5
--- /dev/null
+++ b/data/sampled_jsons/Wang_METransformer_radiology_report_generation_MIMIC-CXR_BLEU-4_year_2023.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "METransformer: Radiology Report Generation by Transformer ...", "date": "", "ddg_snippet": "by Z Wang · 2023 · Cited by 167 — Bleu 4 and CIDEr scores by using different numbers of expert tokens on IU-Xray and MIMIC - CXR dataset. are used, the final prediction result is obtained by ...", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2023/papers/Wang_METransformer_Radiology_Report_Generation_by_Transformer_With_Multiple_Learnable_Expert_CVPR_2023_paper.pdf", "content": "by Z Wang · 2023 · Cited by 167 — Bleu 4 and CIDEr scores by using different numbers of expert tokens on IU-Xray and MIMIC - CXR dataset. are used, the final prediction result is obtained by ..."}
+{"idx": 1, "title": "Radiology report generation using automatic keyword ...", "date": "", "ddg_snippet": "by Z He · 2025 · Cited by 1 — We developed a novel deep-learning based radiology report generation method for preparing high-quality and explainable radiology report for chest X-ray images.", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S001048252500976X", "content": "by Z He · 2025 · Cited by 1 — We developed a novel deep-learning based radiology report generation method for preparing high-quality and explainable radiology report for chest X-ray images."}
+{"idx": 2, "title": "Automatic radiology report generation with deep learning", "date": "", "ddg_snippet": "by Y Li · 2025 — Experiments on the MIMIC - CXR dataset show that AP-ISG outperforms previous methods such as R2Gen and R2GenCMN in BLEU - 4 , METEOR, CIDEr, and ...", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/article/10.1007/s10462-025-11337-0", "content": "by Y Li · 2025 — Experiments on the MIMIC - CXR dataset show that AP-ISG outperforms previous methods such as R2Gen and R2GenCMN in BLEU - 4 , METEOR, CIDEr, and ..."}
+{"idx": 3, "title": "RRG-Mamba: Efficient Radiology Report Generation with ...", "date": "", "ddg_snippet": "Extensive experiments on publicly available datasets, including IU X-Ray and MIMIC - CXR , demonstrate that RRG-Mamba achieves a 3.7% im- provement in BLEU - 4 score ...", "subpage_snippet": "", "source": "www.ijcai.org", "link": "https://www.ijcai.org/proceedings/2025/0824.pdf", "content": "Extensive experiments on publicly available datasets, including IU X-Ray and MIMIC - CXR , demonstrate that RRG-Mamba achieves a 3.7% im- provement in BLEU - 4 score ..."}
+{"idx": 4, "title": "Prompt-Guided Generation of Structured Chest X-Ray ...", "date": "", "ddg_snippet": "Our method introduces a prompt-guided approach to generate structured chest X-ray reports using a pre-trained large language model (LLM).", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2404.11209v1", "content": "Our method introduces a prompt-guided approach to generate structured chest X-ray reports using a pre-trained large language model (LLM)."}
+{"idx": 5, "title": "Elevating Radiology Report Generation with Longitudinal ...", "date": "", "ddg_snippet": "27 Nov 2024 — Results of NLG metrics (BLEU (BL), METEOR (M), ROUGE-L (R_L )) and CE metrics (Precision, Recall and F1) on the Longitudinal MIMIC-CXR dataset.", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/chapter/10.1007/978-3-031-73001-6_11", "content": "27 Nov 2024 — Results of NLG metrics (BLEU (BL), METEOR (M), ROUGE-L (R_L )) and CE metrics (Precision, Recall and F1) on the Longitudinal MIMIC-CXR dataset."}
+{"idx": 6, "title": "Advancements in Radiology Report Generation", "date": "", "ddg_snippet": "by D Mamdouh · 2025 · Cited by 2 — By combining the capabilities of CV and NLP, AI systems can analyze complex radiological images and extract key information from reports .", "subpage_snippet": "", "source": "pmc.ncbi.nlm.nih.gov", "link": "https://pmc.ncbi.nlm.nih.gov/articles/PMC12292164/", "content": "by D Mamdouh · 2025 · Cited by 2 — By combining the capabilities of CV and NLP, AI systems can analyze complex radiological images and extract key information from reports ."}
+{"idx": 7, "title": "R2GenGPT: Radiology Report Generation with frozen LLMs", "date": "", "ddg_snippet": "by Z Wang · 2023 · Cited by 132 — We evaluated performance using two datasets: a widely-used benchmark IU-Xray and the currently largest dataset MIMIC - CXR for medical report generation . ... BLEU - 4 ...", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S2950162823000334", "content": "by Z Wang · 2023 · Cited by 132 — We evaluated performance using two datasets: a widely-used benchmark IU-Xray and the currently largest dataset MIMIC - CXR for medical report generation . ... BLEU - 4 ..."}
+{"idx": 8, "title": "CVPR Poster METransformer: Radiology Report Generation ...", "date": "", "ddg_snippet": "The key design of our method is the introduction of multiple learnable “expert” tokens into both the transformer encoder and decoder. In the encoder, each ...", "subpage_snippet": "", "source": "cvpr.thecvf.com", "link": "https://cvpr.thecvf.com/virtual/2023/poster/21217", "content": "The key design of our method is the introduction of multiple learnable “expert” tokens into both the transformer encoder and decoder. In the encoder, each ..."}
+{"idx": 9, "title": "Author Guidelines for ECCV Submission", "date": "", "ddg_snippet": "21 Jul 2024 — Wang , Z., Liu, L., Wang , L., Zhou, L.: Metransformer : Radiology report generation by transformer with multiple learnable expert tokens. In ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2407.15158v1", "content": "21 Jul 2024 — Wang , Z., Liu, L., Wang , L., Zhou, L.: Metransformer : Radiology report generation by transformer with multiple learnable expert tokens. In ..."}
diff --git a/data/sampled_jsons/Wang_et_al._2019_reverse_cross-entropy_abstract_year_2019.jsonl b/data/sampled_jsons/Wang_et_al._2019_reverse_cross-entropy_abstract_year_2019.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..ab759b8503a5ac71cef43891e01f88003d3e8008
--- /dev/null
+++ b/data/sampled_jsons/Wang_et_al._2019_reverse_cross-entropy_abstract_year_2019.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Provable Maximum Entropy Manifold Exploration via Diffusion", "date": "", "ddg_snippet": "Recent progress in generative modeling, particularly the emergence of diffusion models (Sohl-Dickstein et al ., 2015 ; Song & Ermon, 2019 ; Ho ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2506.15385v1", "content": "Recent progress in generative modeling, particularly the emergence of diffusion models (Sohl-Dickstein et al ., 2015 ; Song & Ermon, 2019 ; Ho ..."}
+{"idx": 1, "title": "EP0888689B1 - Data compression using hybrid lossless entropy", "date": "", "ddg_snippet": "EP0888689B1 - Data compression using hybrid lossless entropy encoding of run-length codes - Google Patents ... using hybrid lossless entropy encoding ...", "subpage_snippet": "", "source": "patents.google.com", "link": "https://patents.google.com/patent/EP0888689B1/en", "content": "EP0888689B1 - Data compression using hybrid lossless entropy encoding of run-length codes - Google Patents ... using hybrid lossless entropy encoding ..."}
+{"idx": 2, "title": "Entropy | April 2021 - Browse Articles", "date": "", "ddg_snippet": "All articles published by MDPI are made immediately available worldwide under an open access license. ... special permission is required to reuse all ...", "subpage_snippet": "", "source": "www.mdpi.com", "link": "https://www.mdpi.com/1099-4300/23/4", "content": "All articles published by MDPI are made immediately available worldwide under an open access license. ... special permission is required to reuse all ..."}
+{"idx": 3, "title": "Entropy | July 2018 - Browse Articles", "date": "", "ddg_snippet": "All articles published by MDPI are made immediately available worldwide under an open access license. ... special permission is required to reuse all ...", "subpage_snippet": "", "source": "www.mdpi.com", "link": "https://www.mdpi.com/1099-4300/20/7", "content": "All articles published by MDPI are made immediately available worldwide under an open access license. ... special permission is required to reuse all ..."}
+{"idx": 4, "title": "Entropy | May 2016 - Browse Articles", "date": "", "ddg_snippet": "All articles published by MDPI are made immediately available worldwide under an open access license. ... special permission is required to reuse all ...", "subpage_snippet": "", "source": "www.mdpi.com", "link": "https://www.mdpi.com/1099-4300/18/5", "content": "All articles published by MDPI are made immediately available worldwide under an open access license. ... special permission is required to reuse all ..."}
+{"idx": 5, "title": "US20070040710A1 - Fast, Practically Optimal Entropy Encoding -", "date": "", "ddg_snippet": "If all the symbol probabilities happen to be of the form 1/2 k , where k is a positive integer, the resultant encoding minimizes the average message ...", "subpage_snippet": "", "source": "patents.google.com", "link": "https://patents.google.com/patent/US20070040710A1/en", "content": "If all the symbol probabilities happen to be of the form 1/2 k , where k is a positive integer, the resultant encoding minimizes the average message ..."}
+{"idx": 6, "title": "Multi-Modal Multi-Behavior Sequential Recommendation with", "date": "", "ddg_snippet": "... capture user interests has become a highly promising research direction in the field of sequential recommendation (Chen et al ., 2023 ; Wang et al ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2508.05352v1", "content": "... capture user interests has become a highly promising research direction in the field of sequential recommendation (Chen et al ., 2023 ; Wang et al ..."}
+{"idx": 7, "title": "Cntnap2 loss drives striatal neuron hyperexcitability and", "date": "", "ddg_snippet": "... at the juxtaparanodes of myelinated axons and is involved in the clustering of potassium channels ( Poliak et al ., 2003 ; Scott et al ., 2019 ).", "subpage_snippet": "", "source": "elifesciences.org", "link": "https://elifesciences.org/articles/100162", "content": "... at the juxtaparanodes of myelinated axons and is involved in the clustering of potassium channels ( Poliak et al ., 2003 ; Scott et al ., 2019 )."}
+{"idx": 8, "title": "Exploiting functional regions in the viral RNA genome as", "date": "", "ddg_snippet": "... protein targets and significantly expand the landscape of targetable macromolecules ( Disney, 2019 ; Kovachka et al ., 2024 ; Li and Wang , 2019 ; Ursu ...", "subpage_snippet": "", "source": "elifesciences.org", "link": "https://elifesciences.org/reviewed-preprints/103923", "content": "... protein targets and significantly expand the landscape of targetable macromolecules ( Disney, 2019 ; Kovachka et al ., 2024 ; Li and Wang , 2019 ; Ursu ..."}
+{"idx": 9, "title": "Energy loss analysis of volute centrifugal pump handling", "date": "", "ddg_snippet": "... influence of pump size on fluid ’ s non-Newtonian behavior and energy loss in a centrifugal pump is also investigated by comparing the entropy ...", "subpage_snippet": "", "source": "www.mechanics-industry.org", "link": "https://www.mechanics-industry.org/articles/meca/full_html/2024/01/mi230008/mi230008.html", "content": "... influence of pump size on fluid ’ s non-Newtonian behavior and energy loss in a centrifugal pump is also investigated by comparing the entropy ..."}
diff --git a/data/sampled_jsons/Waymo_Open_Dataset_798_scenes_202_150_10Hz_20s_frames_year_2020.jsonl b/data/sampled_jsons/Waymo_Open_Dataset_798_scenes_202_150_10Hz_20s_frames_year_2020.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..4cf306237c3b68b96af16e2d560a84875791106b
--- /dev/null
+++ b/data/sampled_jsons/Waymo_Open_Dataset_798_scenes_202_150_10Hz_20s_frames_year_2020.jsonl
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+{"idx": 0, "title": "About – Waymo Open Dataset", "date": "", "ddg_snippet": "About Waymo Open DatasetWe have released the Waymo Open Dataset publicly to aid the research community in investigating a wide range of interesting aspects of machine perception and autonomous driving technology. The Waymo Open Dataset is composed of three datasets - the Perception Dataset with high resolution sensor data and labels for 2,030 segments, the Motion Dataset with object ...", "subpage_snippet": "", "source": "waymo.com", "link": "https://waymo.com/open/about/", "content": "About Waymo Open DatasetWe have released the Waymo Open Dataset publicly to aid the research community in investigating a wide range of interesting aspects of machine perception and autonomous driving technology. The Waymo Open Dataset is composed of three datasets - the Perception Dataset with high resolution sensor data and labels for 2,030 segments, the Motion Dataset with object ..."}
+{"idx": 1, "title": "Waymo Open Dataset", "date": "", "ddg_snippet": "The Waymo Open Dataset contains 1, 150 scenes , each consisting of 20 seconds of data captured at 10 Hz (i.e., 10 frames per second, and thus 200 frames per scene ).", "subpage_snippet": "", "source": "www.ecva.net", "link": "https://www.ecva.net/papers/eccv_2022/papers_ECCV/papers/136890052.pdf", "content": "The Waymo Open Dataset contains 1, 150 scenes , each consisting of 20 seconds of data captured at 10 Hz (i.e., 10 frames per second, and thus 200 frames per scene )."}
+{"idx": 2, "title": "Download – Waymo Open Dataset", "date": "", "ddg_snippet": "Download Waymo Open DatasetHere are the datasets in the Waymo Open Dataset for downloading. The Google Cloud Storage buckets below contain all of the files. This should facilitate programmatic downloading as well as allow easy access from Google Cloud APIs.", "subpage_snippet": "", "source": "waymo.com", "link": "http://waymo.com/open/download", "content": "Download Waymo Open DatasetHere are the datasets in the Waymo Open Dataset for downloading. The Google Cloud Storage buckets below contain all of the files. This should facilitate programmatic downloading as well as allow easy access from Google Cloud APIs."}
+{"idx": 3, "title": "Waymo Open dataset - Dataset - LDM - service.tib.eu", "date": "", "ddg_snippet": "The Waymo Open dataset provides 798 training sequences and 202 validation sequences. Each sequence contains 200 frames within 20 seconds.", "subpage_snippet": "", "source": "service.tib.eu", "link": "https://service.tib.eu/ldmservice/dataset/waymo-open-dataset", "content": "The Waymo Open dataset provides 798 training sequences and 202 validation sequences. Each sequence contains 200 frames within 20 seconds."}
+{"idx": 4, "title": "waymo-research/waymo-open-dataset - GitHub Waymo Open Dataset: Panoramic Video Panoptic Segmentation A ARGOVERSE 2 AND WAYMO OPEN DATASET CONFIGURATION DETAILS About – Waymo Open Dataset Waymo Open Dataset: Panoramic Video Panoptic Segmentation", "date": "", "ddg_snippet": "We have released the Waymo Open Dataset publicly to aid the research community in making advancements in machine perception and autonomous driving technology. The Waymo Open Dataset is composed of two datasets - the Perception dataset with high resolution sensor data and labels for 2,030 scenes , and the Motion dataset with object trajectories and corresponding 3D maps for 103,354 scenes . See full list on github.com This code repository (excluding src/waymo_open_dataset/wdl_limited folder) is licensed under the Apache License, Version 2.0. The code appearing in src/waymo_open_dataset/wdl_limited is licensed under terms appearing therein. The Waymo Open Dataset itself is licensed under separate terms. Please visit https:// waymo .com/ open /terms/ for details. Code... See full list on github.com We released v1.6.1 version of the pip package with fixes for the WOSAC metrics: •Fixing a bug in validity checking for collision and offroad. See full list on github.com We released a large-scale object-centric asset dataset containing over 1.2M images and lidar observations of two major categories (vehicles and pedestrians) from the Perception Dataset (v2.0.0). •Extracted perception objects from multi-sensor data: all five cameras and the top lidar. •Lidar features include 3D point cloud sequences that support 3D object shape reconstruction. We additionally provide refined box pose through point cloud shape registration for all vehicle objects. •Camera features include sequences of camera patches from the most_visible_camera, projected lidar returns on the corresponding camera, per-pixel camera rays information, and auto-labeled 2D panoptic segmentation that supports object NeRF reconstruction. See full list on github.com This major update includes supporting code to four challenges at waymo .com/ open , and dataset updates to both the Perception and Motion Datasets. v2.0.0 of the Perception Dataset •Introduced the dataset in modular format, enabling users to selectively download only the components they need. •Includes all features in v1.4.2 of the Perception Dataset except maps. •Added a tutorial and supporting code. v1.4.2 of the Perception Dataset See full list on github.com We released v1.4.1 of the Perception dataset . •Improved the quality of the 2D video panoptic segmentation labels. See full list on github.com We released v1.4.0 of the Perception dataset . •Added 2D video panoptic segmentation labels and supporting code. See full list on github.com •Released a tutorial for the 3D Camera-Only Detection Challenge. •Added support for computing 3D-LET-APL in Python metrics ops. See Compute Metrics in the tutorial. See full list on github.com We released v1.3.2 of the Perception dataset to improve the quality and accuracy of the labels. •Updated 3D semantic segmentation labels, for better temporal consistency and to fix mislabeled points. •Updated 2D key point labels to fix image cropping issues. •Added num_top_lidar_points_in_box in dataset .proto for the 3D Camera-Only Detection Challenge. See full list on github.com We released v1.3.1 of the Perception dataset to support the 2022 Challenges and have updated this repository accordingly. •Added metrics (LET-3D-APL and LET-3D-AP) for the 3D Camera-Only Detection Challenge. •Added 80 segments of 20-second camera imagery, as a test set for the 3D Camera-Only Detection Challenge. •Added z-axis speed and acceleration in lidar label metadata. •Fixed some inconsistencies in projected_lidar_labels in dataset .proto. •Updated the default configuration for the Occupancy and Flow Challenge, switching from aggregate waypoints to subsampled waypoints. See full list on github.com We obtain consistent instance IDs across temporal frames and cameras by associating the panoptic labels from each individual image with the existing box-level annotations. 3.1 Dataset Overview The Waymo Open Dataset contains 1,150 scenes , each consisting of 20s of data captured 10Hz (i.e., 10 frames per second, and thus 200 frames per scene ). Waymo Open . The dataset contains 798 training and 202 validation sequences. Each sequence contains 20 seconds of 10Hz point clouds collected using a custom LiDAR mounted on the roof of a car. We use the same preprocessing and training configurations used on Argoverse 2; after ego motion compensation and ground point removal, the average point cloud has 79,327 points (Figure 6b). About Waymo Open DatasetWe have released the Waymo Open Dataset publicly to aid the research community in investigating a wide range of interesting aspects of machine perception and autonomous driving technology. The Waymo Open Dataset is composed of three datasets - the Perception Dataset with high resolution sensor data and labels for 2,030 segments, the Motion Dataset with object ... The Waymo Open Dataset contains 1,150 scenes , each consisting of 20 seconds of data captured at 10Hz (i.e., 10 frames per second, and thus 200 frames per scene ).", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/waymo-research/waymo-open-dataset", "content": "We have released the Waymo Open Dataset publicly to aid the research community in making advancements in machine perception and autonomous driving technology. The Waymo Open Dataset is composed of two datasets - the Perception dataset with high resolution sensor data and labels for 2,030 scenes , and the Motion dataset with object trajectories and corresponding 3D maps for 103,354 scenes . See full list on github.com This code repository (excluding src/waymo_open_dataset/wdl_limited folder) is licensed under the Apache License, Version 2.0. The code appearing in src/waymo_open_dataset/wdl_limited is licensed under terms appearing therein. The Waymo Open Dataset itself is licensed under separate terms. Please visit https:// waymo .com/ open /terms/ for details. Code... See full list on github.com We released v1.6.1 version of the pip package with fixes for the WOSAC metrics: •Fixing a bug in validity checking for collision and offroad. See full list on github.com We released a large-scale object-centric asset dataset containing over 1.2M images and lidar observations of two major categories (vehicles and pedestrians) from the Perception Dataset (v2.0.0). •Extracted perception objects from multi-sensor data: all five cameras and the top lidar. •Lidar features include 3D point cloud sequences that support 3D object shape reconstruction. We additionally provide refined box pose through point cloud shape registration for all vehicle objects. •Camera features include sequences of camera patches from the most_visible_camera, projected lidar returns on the corresponding camera, per-pixel camera rays information, and auto-labeled 2D panoptic segmentation that supports object NeRF reconstruction. See full list on github.com This major update includes supporting code to four challenges at waymo .com/ open , and dataset updates to both the Perception and Motion Datasets. v2.0.0 of the Perception Dataset •Introduced the dataset in modular format, enabling users to selectively download only the components they need. •Includes all features in v1.4.2 of the Perception Dataset except maps. •Added a tutorial and supporting code. v1.4.2 of the Perception Dataset See full list on github.com We released v1.4.1 of the Perception dataset . •Improved the quality of the 2D video panoptic segmentation labels. See full list on github.com We released v1.4.0 of the Perception dataset . •Added 2D video panoptic segmentation labels and supporting code. See full list on github.com •Released a tutorial for the 3D Camera-Only Detection Challenge. •Added support for computing 3D-LET-APL in Python metrics ops. See Compute Metrics in the tutorial. See full list on github.com We released v1.3.2 of the Perception dataset to improve the quality and accuracy of the labels. •Updated 3D semantic segmentation labels, for better temporal consistency and to fix mislabeled points. •Updated 2D key point labels to fix image cropping issues. •Added num_top_lidar_points_in_box in dataset .proto for the 3D Camera-Only Detection Challenge. See full list on github.com We released v1.3.1 of the Perception dataset to support the 2022 Challenges and have updated this repository accordingly. •Added metrics (LET-3D-APL and LET-3D-AP) for the 3D Camera-Only Detection Challenge. •Added 80 segments of 20-second camera imagery, as a test set for the 3D Camera-Only Detection Challenge. •Added z-axis speed and acceleration in lidar label metadata. •Fixed some inconsistencies in projected_lidar_labels in dataset .proto. •Updated the default configuration for the Occupancy and Flow Challenge, switching from aggregate waypoints to subsampled waypoints. See full list on github.com We obtain consistent instance IDs across temporal frames and cameras by associating the panoptic labels from each individual image with the existing box-level annotations. 3.1 Dataset Overview The Waymo Open Dataset contains 1,150 scenes , each consisting of 20s of data captured 10Hz (i.e., 10 frames per second, and thus 200 frames per scene ). Waymo Open . The dataset contains 798 training and 202 validation sequences. Each sequence contains 20 seconds of 10Hz point clouds collected using a custom LiDAR mounted on the roof of a car. We use the same preprocessing and training configurations used on Argoverse 2; after ego motion compensation and ground point removal, the average point cloud has 79,327 points (Figure 6b). About Waymo Open DatasetWe have released the Waymo Open Dataset publicly to aid the research community in investigating a wide range of interesting aspects of machine perception and autonomous driving technology. The Waymo Open Dataset is composed of three datasets - the Perception Dataset with high resolution sensor data and labels for 2,030 segments, the Motion Dataset with object ... The Waymo Open Dataset contains 1,150 scenes , each consisting of 20 seconds of data captured at 10Hz (i.e., 10 frames per second, and thus 200 frames per scene )."}
+{"idx": 5, "title": "Waymo Open Dataset: Panoramic Video Panoptic Segmentation", "date": "", "ddg_snippet": "We obtain consistent instance IDs across temporal frames and cameras by associating the panoptic labels from each individual image with the existing box-level annotations. 3.1 Dataset Overview The Waymo Open Dataset contains 1,150 scenes , each consisting of 20s of data captured 10Hz (i.e., 10 frames per second, and thus 200 frames per scene ).", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/content/pdf/10.1007/978-3-031-19818-2_4.pdf", "content": "We obtain consistent instance IDs across temporal frames and cameras by associating the panoptic labels from each individual image with the existing box-level annotations. 3.1 Dataset Overview The Waymo Open Dataset contains 1,150 scenes , each consisting of 20s of data captured 10Hz (i.e., 10 frames per second, and thus 200 frames per scene )."}
+{"idx": 6, "title": "A ARGOVERSE 2 AND WAYMO OPEN DATASET CONFIGURATION DETAILS", "date": "", "ddg_snippet": "Waymo Open . The dataset contains 798 training and 202 validation sequences. Each sequence contains 20 seconds of 10Hz point clouds collected using a custom LiDAR mounted on the roof of a car. We use the same preprocessing and training configurations used on Argoverse 2; after ego motion compensation and ground point removal, the average point cloud has 79,327 points (Figure 6b).", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/attachment?id=FRCHDhbxZF&name=supplementary_material", "content": "Waymo Open . The dataset contains 798 training and 202 validation sequences. Each sequence contains 20 seconds of 10Hz point clouds collected using a custom LiDAR mounted on the roof of a car. We use the same preprocessing and training configurations used on Argoverse 2; after ego motion compensation and ground point removal, the average point cloud has 79,327 points (Figure 6b)."}
+{"idx": 7, "title": "waymo - open - dataset /tutorial/ frames at master...", "date": "", "ddg_snippet": "Fund open source developers. The ReadME Project.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/waymo-research/waymo-open-dataset/blob/master/tutorial/frames", "content": "Fund open source developers. The ReadME Project."}
+{"idx": 8, "title": "waymo _ open _ dataset | TensorFlow Datasets", "date": "", "ddg_snippet": "The Waymo Open Dataset is comprised of high resolution sensor data collected by Waymo self-driving cars in a wide variety of conditions.", "subpage_snippet": "", "source": "www.tensorflow.org", "link": "https://www.tensorflow.org/datasets/catalog/waymo_open_dataset", "content": "The Waymo Open Dataset is comprised of high resolution sensor data collected by Waymo self-driving cars in a wide variety of conditions."}
+{"idx": 9, "title": "Waymo Open Dataset Tutorial.ipynb - Colab", "date": "", "ddg_snippet": "Visit the Waymo Open Dataset Website to download the full dataset .Refer to dataset .proto for the data format. The context contains shared information among all frames in the scene .", "subpage_snippet": "", "source": "colab.research.google.com", "link": "https://colab.research.google.com/github/waymo-research/waymo-open-dataset/blob/r1.0/tutorial/tutorial.ipynb", "content": "Visit the Waymo Open Dataset Website to download the full dataset .Refer to dataset .proto for the data format. The context contains shared information among all frames in the scene ."}
diff --git a/data/sampled_jsons/Web_Conference_2024_proceedings_WWW2024_papers.jsonl b/data/sampled_jsons/Web_Conference_2024_proceedings_WWW2024_papers.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..cbd03e626ef538ef98946399a711dce506c8b361
--- /dev/null
+++ b/data/sampled_jsons/Web_Conference_2024_proceedings_WWW2024_papers.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "International World Wide Web Conference 2024 ( WWW2024 | The", "date": "", "ddg_snippet": "... paper contributions to The Web Conference 2024 ... Each accepted paper will be included in the Companion Proceedings of the Web Conference 2024 .", "subpage_snippet": "", "source": "www2024.thewebconf.org", "link": "https://www2024.thewebconf.org/calls/short-papers/", "content": "... paper contributions to The Web Conference 2024 ... Each accepted paper will be included in the Companion Proceedings of the Web Conference 2024 ."}
+{"idx": 1, "title": "Home | International World Wide Web Conference 2024 ( WWW2024 |", "date": "", "ddg_snippet": "The 2024 ACM Web Conference will offer a high quality program made of research sessions, posters and demonstrations, a PhD symposium for the junior ...", "subpage_snippet": "", "source": "www2024.thewebconf.org", "link": "https://www2024.thewebconf.org/", "content": "The 2024 ACM Web Conference will offer a high quality program made of research sessions, posters and demonstrations, a PhD symposium for the junior ..."}
+{"idx": 2, "title": "International World Wide Web Conference 2024 ( WWW2024 | The", "date": "", "ddg_snippet": "... paper contributions to the Emerging World Symposium ... Each accepted paper will be included in the Companion Proceedings of the Web Conference 2024 .", "subpage_snippet": "", "source": "www2024.thewebconf.org", "link": "https://www2024.thewebconf.org/calls/emerging-world-symposium/", "content": "... paper contributions to the Emerging World Symposium ... Each accepted paper will be included in the Companion Proceedings of the Web Conference 2024 ."}
+{"idx": 3, "title": "International World Wide Web Conference 2024 ( WWW2024 | The", "date": "", "ddg_snippet": "Each accepted paper will be included in the Companion Proceedings of the Web Conference 2024 . ... proceedings , every accepted paper must be covered by ...", "subpage_snippet": "", "source": "www2024.thewebconf.org", "link": "https://www2024.thewebconf.org/calls/resource/", "content": "Each accepted paper will be included in the Companion Proceedings of the Web Conference 2024 . ... proceedings , every accepted paper must be covered by ..."}
+{"idx": 4, "title": "International World Wide Web Conference 2024 ( WWW2024 | The", "date": "", "ddg_snippet": "The Web Conference 2024 features two special events that are included in the Main Conference and Passport registrations.", "subpage_snippet": "", "source": "www2024.thewebconf.org", "link": "https://www2024.thewebconf.org/program/social-events/", "content": "The Web Conference 2024 features two special events that are included in the Main Conference and Passport registrations."}
+{"idx": 5, "title": "Call for papers – WebAndTheCity", "date": "", "ddg_snippet": "... Web Conference ’ s organizers will require at least one registration per paper published in either the main Proceedings or in the Companion ...", "subpage_snippet": "", "source": "webandthecity.home.blog", "link": "https://webandthecity.home.blog/2024/11/21/cfp/", "content": "... Web Conference ’ s organizers will require at least one registration per paper published in either the main Proceedings or in the Companion ..."}
+{"idx": 6, "title": "WebAndTheCity – 11th International Smart City Workshop", "date": "", "ddg_snippet": "... of the workshop series with the label “The Web and Smart Cities ” , which will take place in Sydney, in conjunction with The Web Conference ...", "subpage_snippet": "", "source": "webandthecity.home.blog", "link": "https://webandthecity.home.blog/", "content": "... of the workshop series with the label “The Web and Smart Cities ” , which will take place in Sydney, in conjunction with The Web Conference ..."}
+{"idx": 7, "title": "Call for Papers – Web4All 2024", "date": "", "ddg_snippet": "W4A is co-located with the 33rd International World Wide Web Conference , TheWebConf ’ 24 ( https:// www2024 .thewebconf.org/ ).", "subpage_snippet": "", "source": "www.w4a.info", "link": "http://www.w4a.info/2024/call-for-papers/", "content": "W4A is co-located with the 33rd International World Wide Web Conference , TheWebConf ’ 24 ( https:// www2024 .thewebconf.org/ )."}
+{"idx": 8, "title": "Dirk Ahlers - Personal page and publication list", "date": "", "ddg_snippet": "Web and Smart Cities – 10th International Smart City ... 14th Temporal Web Analytics Workshop (TempWeb 2024 ) @ The Web Conference 2024 ( WWW2024 )", "subpage_snippet": "", "source": "www.informatik.uni-oldenburg.de", "link": "http://www.informatik.uni-oldenburg.de/~there/", "content": "Web and Smart Cities – 10th International Smart City ... 14th Temporal Web Analytics Workshop (TempWeb 2024 ) @ The Web Conference 2024 ( WWW2024 )"}
+{"idx": 9, "title": "高旻-重庆大学大数据与软件学院", "date": "", "ddg_snippet": "Companion Proceedings of the ACM Web Conference 2025 ( WWW 2025, CCF A ) (Accepted) ... Companion Proceedings of the ACM Web Conference 2024 .", "subpage_snippet": "", "source": "www.cse.cqu.edu.cn", "link": "http://www.cse.cqu.edu.cn/info/2095/7111.htm", "content": "Companion Proceedings of the ACM Web Conference 2025 ( WWW 2025, CCF A ) (Accepted) ... Companion Proceedings of the ACM Web Conference 2024 ."}
diff --git a/data/sampled_jsons/What_Makes_and_Breaks_Safety_Fine-tuning_A_Mechanistic_Jain_Lubana_learning_rates.jsonl b/data/sampled_jsons/What_Makes_and_Breaks_Safety_Fine-tuning_A_Mechanistic_Jain_Lubana_learning_rates.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..2c52a10c189b844041b5c6e22d62a163060ada1a
--- /dev/null
+++ b/data/sampled_jsons/What_Makes_and_Breaks_Safety_Fine-tuning_A_Mechanistic_Jain_Lubana_learning_rates.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Why LLM Safety Guardrails Collapse After Fine-tuning: A", "date": "", "ddg_snippet": "Then, in the supervised fine - tuning phase, it is aligned with human preferences and safety principles using curated instruction-following datasets ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2506.05346v1", "content": "Then, in the supervised fine - tuning phase, it is aligned with human preferences and safety principles using curated instruction-following datasets ..."}
+{"idx": 1, "title": "LoRA is All You Need for Safety Alignment of Reasoning LLMs", "date": "", "ddg_snippet": "For instruction fine - tuning , safety degradation is shown across various model architectures and optimization strategies, including full-model and ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2507.17075v1", "content": "For instruction fine - tuning , safety degradation is shown across various model architectures and optimization strategies, including full-model and ..."}
+{"idx": 2, "title": "Immunization against harmful fine-tuning attacks", "date": "", "ddg_snippet": "... a formal characterisation of the harmful fine - tuning problem, we provide a thorough description of what a successful defense must comprise of and ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2402.16382v2", "content": "... a formal characterisation of the harmful fine - tuning problem, we provide a thorough description of what a successful defense must comprise of and ..."}
+{"idx": 3, "title": "LLMSymGuard: A Symbolic Safety Guardrail Framework Leveraging", "date": "", "ddg_snippet": "Current methods for developing safety guardrails primarily rely on supervised fine - tuning Jain et al. ... fine - tuning based guardrails, resulting ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2508.16325v1", "content": "Current methods for developing safety guardrails primarily rely on supervised fine - tuning Jain et al. ... fine - tuning based guardrails, resulting ..."}
+{"idx": 4, "title": "A Mechanistic Understanding of Alignment Algorithms: A Case", "date": "", "ddg_snippet": "Based on this understanding, we demonstrate the ease of re-activating these vectors to generate toxic outputs, and thus undoing the alignment learned ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2401.01967v1", "content": "Based on this understanding, we demonstrate the ease of re-activating these vectors to generate toxic outputs, and thus undoing the alignment learned ..."}
+{"idx": 5, "title": "Noise Injection Systemically Degrades Large Language Model", "date": "", "ddg_snippet": "... safety guardrails—namely alignment fine - tuning and alignment preference optimization—demonstrate significant vulnerability to activation noise ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2505.13500v1", "content": "... safety guardrails—namely alignment fine - tuning and alignment preference optimization—demonstrate significant vulnerability to activation noise ..."}
+{"idx": 6, "title": "The Elicitation Game: Evaluating Capability Elicitation", "date": "", "ddg_snippet": "Previous work by (Greenblatt et al., 2024 ) has found that supervised fine - tuning (SFT) and reinforcement learning (RL) are effective at eliciting ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2502.02180v2", "content": "Previous work by (Greenblatt et al., 2024 ) has found that supervised fine - tuning (SFT) and reinforcement learning (RL) are effective at eliciting ..."}
+{"idx": 7, "title": "No Two Devils Alike: Unveiling Distinct Mechanisms of", "date": "", "ddg_snippet": "... safety alignment of Large Language Models (LLMs) is found fragile and could be easily attacked through different strategies, such as through ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2405.16229v1", "content": "... safety alignment of Large Language Models (LLMs) is found fragile and could be easily attacked through different strategies, such as through ..."}
+{"idx": 8, "title": "Model Tampering Attacks Enable More Rigorous Evaluations of LLM", "date": "", "ddg_snippet": "To answer these questions, we pit state-of-the-art methods for unlearning and safety fine - tuning in LLMs against a suite of input-space and model ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2502.05209v3", "content": "To answer these questions, we pit state-of-the-art methods for unlearning and safety fine - tuning in LLMs against a suite of input-space and model ..."}
+{"idx": 9, "title": "From Flat to Hierarchical : Extracting Sparse Representations", "date": "", "ddg_snippet": "A ) Linearly accessible concepts : abstract directions that are approximately orthogonal and independently interpretable, as assumed by the Linear ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2506.03093v1", "content": "A ) Linearly accessible concepts : abstract directions that are approximately orthogonal and independently interpretable, as assumed by the Linear ..."}
diff --git a/data/sampled_jsons/What_Makes_and_Breaks_Safety_Fine-tuning_NeurIPS_2024_section_4_learning_rates_year_2024.jsonl b/data/sampled_jsons/What_Makes_and_Breaks_Safety_Fine-tuning_NeurIPS_2024_section_4_learning_rates_year_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..ddf39d26363d2c5a44a4b22d6cd1c836f34393ff
--- /dev/null
+++ b/data/sampled_jsons/What_Makes_and_Breaks_Safety_Fine-tuning_NeurIPS_2024_section_4_learning_rates_year_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Layer-Aware Representation Filtering: Purifying Finetuning Data", "date": "", "ddg_snippet": "... and increasing accessibility of LLMs, fine - tuning aligned models has become a critical step for adapting them to real-world applications, which makes ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2507.18631v1", "content": "... and increasing accessibility of LLMs, fine - tuning aligned models has become a critical step for adapting them to real-world applications, which makes ..."}
+{"idx": 1, "title": "SC-LoRA: Balancing Efficient Fine-tuning and Knowledge", "date": "", "ddg_snippet": "... 2024 ) feeds fine - tuning data into the model, applies sigular value decomposition to activation covariance matrices, and takes top singular vectors as ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2505.23724v1", "content": "... 2024 ) feeds fine - tuning data into the model, applies sigular value decomposition to activation covariance matrices, and takes top singular vectors as ..."}
+{"idx": 2, "title": "NeurIPS 2024 – Research Impact & Leadership", "date": "", "ddg_snippet": "... NeurIPS ’24 research, “Navigating the Safety Landscape: Measuring Risks in Finetuning Large Language Models ” is in collaboration with IBM ...", "subpage_snippet": "", "source": "sites.gatech.edu", "link": "https://sites.gatech.edu/research/neurips-2024/", "content": "... NeurIPS ’24 research, “Navigating the Safety Landscape: Measuring Risks in Finetuning Large Language Models ” is in collaboration with IBM ..."}
+{"idx": 3, "title": "Highlights of NeurIPS 2023 from Reading All 3584 Abstracts |", "date": "", "ddg_snippet": "... and finals week disaster, I decided to spend my winter break going through and reading every single abstract of the accepted papers in the NeurIPS ...", "subpage_snippet": "", "source": "alexzhang13.github.io", "link": "https://alexzhang13.github.io/blog/2024/neurips2023/", "content": "... and finals week disaster, I decided to spend my winter break going through and reading every single abstract of the accepted papers in the NeurIPS ..."}
+{"idx": 4, "title": "Pretraining vs. Fine-tuning: What Are the Differences?", "date": "", "ddg_snippet": "... is to develop generalized representations and pattern recognition abilities by exposing the model to vast amounts of diverse data before fine - tuning ...", "subpage_snippet": "", "source": "www.lightly.ai", "link": "https://www.lightly.ai/blog/pretraining-vs-finetuning", "content": "... is to develop generalized representations and pattern recognition abilities by exposing the model to vast amounts of diverse data before fine - tuning ..."}
+{"idx": 5, "title": "Software:ChatGPT - HandWiki", "date": "", "ddg_snippet": "... 4 , both of which are members of OpenAI's proprietary series of generative pre-trained transformer (GPT) models, based on the transformer architecture ...", "subpage_snippet": "", "source": "handwiki.org", "link": "https://handwiki.org/wiki/Software:ChatGPT", "content": "... 4 , both of which are members of OpenAI's proprietary series of generative pre-trained transformer (GPT) models, based on the transformer architecture ..."}
+{"idx": 6, "title": "Untitled Document", "date": "", "ddg_snippet": "... LLMs) enter high-stakes domains like education, healthcare, governance, and law, their behavior must reliably reflect human-aligned values and safety ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2506.13901v1", "content": "... LLMs) enter high-stakes domains like education, healthcare, governance, and law, their behavior must reliably reflect human-aligned values and safety ..."}
+{"idx": 7, "title": "Noise-Robustness Through Noise: Asymmetric LoRA Adaption with", "date": "", "ddg_snippet": "... of fine - tuning , we focus on the router’s ability to guide both clean and noisy data and the normal experts’ capacity for learning from clean data.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2505.23868v4", "content": "... of fine - tuning , we focus on the router’s ability to guide both clean and noisy data and the normal experts’ capacity for learning from clean data."}
+{"idx": 8, "title": "Tamper-Resistant Safeguards for Open-Weight LLMs", "date": "", "ddg_snippet": "... undergo fine - tuning to implement safeguards [ 3 , 42 , 55 ] , using methods such as reinforcement learning from human feedback (RLHF) [ 7 , 43 ] and ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2408.00761v4", "content": "... undergo fine - tuning to implement safeguards [ 3 , 42 , 55 ] , using methods such as reinforcement learning from human feedback (RLHF) [ 7 , 43 ] and ..."}
+{"idx": 9, "title": "Downloads", "date": "", "ddg_snippet": "A Linearly Convergent Method for Non-Smooth Non-Convex Optimization on the Grassmannian with Applications to Robust Subspace and Dictionary Learning", "subpage_snippet": "", "source": "neurips.cc", "link": "https://neurips.cc/Downloads/2019", "content": "A Linearly Convergent Method for Non-Smooth Non-Convex Optimization on the Grassmannian with Applications to Robust Subspace and Dictionary Learning"}
diff --git "a/data/sampled_jsons/What_Makes_and_Breaks_Safety_Fine-tuning_\316\267M_\316\267S_synthetic_experiments_Section_4_learning_rates.jsonl" "b/data/sampled_jsons/What_Makes_and_Breaks_Safety_Fine-tuning_\316\267M_\316\267S_synthetic_experiments_Section_4_learning_rates.jsonl"
new file mode 100644
index 0000000000000000000000000000000000000000..195aa9d089fe6cfb53fdab6f695938050c1714a8
--- /dev/null
+++ "b/data/sampled_jsons/What_Makes_and_Breaks_Safety_Fine-tuning_\316\267M_\316\267S_synthetic_experiments_Section_4_learning_rates.jsonl"
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Synthetic biology - Wikipedia", "date": "", "ddg_snippet": "Synthetic biology ( SynBio ) is a multidisciplinary field of science that focuses on living systems and organisms, and it applies engineering ...", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/Synthetic_biology", "content": "Synthetic biology ( SynBio ) is a multidisciplinary field of science that focuses on living systems and organisms, and it applies engineering ..."}
+{"idx": 1, "title": "What Makes and Breaks Safety Fine-tuning? A Mechanistic ...", "date": "", "ddg_snippet": "by S Jain · Cited by 26 — The empirical results indicate that safety fine-tuning encourages the formation of different clusters for safe and unsafe samples, reducing the ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=JEflV4nRlH", "content": "by S Jain · Cited by 26 — The empirical results indicate that safety fine-tuning encourages the formation of different clusters for safe and unsafe samples, reducing the ..."}
+{"idx": 2, "title": "What Makes and Breaks Safety Fine-tuning? A Mechanistic ...", "date": "", "ddg_snippet": "Using this, we investigate three well-known safety fine - tuning methods—supervised safety fine - tuning , direct preference optimization, and unlearning—and provide ...", "subpage_snippet": "", "source": "neurips.cc", "link": "https://neurips.cc/virtual/2024/poster/95726", "content": "Using this, we investigate three well-known safety fine - tuning methods—supervised safety fine - tuning , direct preference optimization, and unlearning—and provide ..."}
+{"idx": 3, "title": "How does fine-tuning affect your model? Mechanistic analysis on ...", "date": "", "ddg_snippet": "We note that the results for ηM learning rate look worse than the ηS learning rate around 103 iterations because ηM is too big of a learning rate , forcing the.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf/df4e2e54162007448f58766149ea7fca1c363e72.pdf", "content": "We note that the results for ηM learning rate look worse than the ηS learning rate around 103 iterations because ηM is too big of a learning rate , forcing the."}
+{"idx": 4, "title": "Machine Learning and Knowledge Discovery in Databases", "date": "", "ddg_snippet": "18 Sept 2017 — Topics covered include deep learning, time series mining, text mining, for a variety of applications such as e-commerce, fraud detection, ...", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/content/pdf/10.1007/978-3-319-71273-4.pdf", "content": "18 Sept 2017 — Topics covered include deep learning, time series mining, text mining, for a variety of applications such as e-commerce, fraud detection, ..."}
+{"idx": 5, "title": "Collaborative Computing: Networking, Applications and ...", "date": "", "ddg_snippet": "As can be seen that before the analysis work, lots of corpora must be manually collected and tagged for training the supervised machine learning models.", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/content/pdf/10.1007/978-3-030-00916-8.pdf", "content": "As can be seen that before the analysis work, lots of corpora must be manually collected and tagged for training the supervised machine learning models."}
+{"idx": 6, "title": "Unlock the Correlation between Supervised Fine-Tuning and", "date": "", "ddg_snippet": "Within the fine - tuning , supervised fine - tuning (SFT), and reinforcement learning (RL) are often used to improve the model’ s zero-shot ability.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2406.10305v2", "content": "Within the fine - tuning , supervised fine - tuning (SFT), and reinforcement learning (RL) are often used to improve the model’ s zero-shot ability."}
+{"idx": 7, "title": "AI Safety IV: Sparks of Misalignment - Synthesis AI", "date": "", "ddg_snippet": "... and 2025, AI safety researchers have discovered that our most advanced language models—including GPT- 4 and Claude—can engage in sophisticated ...", "subpage_snippet": "", "source": "synthesis.ai", "link": "https://synthesis.ai/2025/06/19/ai-safety-iv-sparks-of-misalignment/", "content": "... and 2025, AI safety researchers have discovered that our most advanced language models—including GPT- 4 and Claude—can engage in sophisticated ..."}
+{"idx": 8, "title": "‘AI safety’ directory · Gwern.net", "date": "", "ddg_snippet": "Expanding on What We Missed With Sycophancy: A Deeper Dive on Our Findings, What Went Wrong, and Future Changes We’re Making ”, OpenAI 2025", "subpage_snippet": "", "source": "gwern.net", "link": "https://gwern.net/doc/reinforcement-learning/safe/index", "content": "Expanding on What We Missed With Sycophancy: A Deeper Dive on Our Findings, What Went Wrong, and Future Changes We’re Making ”, OpenAI 2025"}
+{"idx": 9, "title": "AI #26: Fine Tuning Time - by Zvi Mowshowitz", "date": "", "ddg_snippet": "What Rowan does not mention is that GPT- 4 has the edge in raw intelligence and general capability, and also the ability to set system instructions is ...", "subpage_snippet": "", "source": "thezvi.substack.com", "link": "https://thezvi.substack.com/p/ai-26-fine-tuning-time", "content": "What Rowan does not mention is that GPT- 4 has the edge in raw intelligence and general capability, and also the ability to set system instructions is ..."}
diff --git a/data/sampled_jsons/Wolpert_No_Free_Lunch_theorem_abstract.jsonl b/data/sampled_jsons/Wolpert_No_Free_Lunch_theorem_abstract.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..f2e027bb943bec762a8fd3ecf93793227b887d26
--- /dev/null
+++ b/data/sampled_jsons/Wolpert_No_Free_Lunch_theorem_abstract.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "No free lunch theorem - Wikipedia", "date": "", "ddg_snippet": "In mathematical folklore, the \" no free lunch \" (NFL) theorem (sometimes pluralized) of David Wolpert and William Macready, alludes to the saying \"no such thing as a free lunch \", that is, there are no easy shortcuts to success.", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/No_free_lunch_theorem", "content": "In mathematical folklore, the \" no free lunch \" (NFL) theorem (sometimes pluralized) of David Wolpert and William Macready, alludes to the saying \"no such thing as a free lunch \", that is, there are no easy shortcuts to success."}
+{"idx": 1, "title": "No Free Lunch Theorems For Optimization - Evolutionary ...", "date": "", "ddg_snippet": "David H. Wolpert and William G. Macready. Abstract — A framework is developed to explore the connection between effective optimization algorithms and the problems they are solving.", "subpage_snippet": "", "source": "www.cs.ubc.ca", "link": "https://www.cs.ubc.ca/~hutter/earg/papers07/00585893.pdf", "content": "David H. Wolpert and William G. Macready. Abstract — A framework is developed to explore the connection between effective optimization algorithms and the problems they are solving."}
+{"idx": 2, "title": "No Free Lunch Theorems for Search - jmvidal.cse.sc.edu A No Free Lunch theorem for multi-objective optimization No Free Lunch Theorems No free lunch theorem - Wikipedia No Free Lunch Theorems No Free Lunch Theorems A No Free Lunch theorem for multi-objective optimization No Free Lunch Theorems A No Free Lunch theorem for multi-objective optimization (PDF) No Free Lunch Theorems for Optimization - ResearchGate (PDF) No Free Lunch Theorems for Optimization - ResearchGate", "date": "", "ddg_snippet": "@TechReport{ wolpert95a , author = {David H. Wolpert and William G. Macready}, title = { No Free Lunch Theorems for Search}, institution = {Santa Fe Institute}, year = 1995, abstract = {We show that all algorithms that search for an extremum of a cost function perform exactly the same, when averaged over all possible cost functions Oct 15, 2010 · The No Free Lunch theorem (Schumacher et al., 2001; Wolpert and Macready, 1997 [8], [10]) is a foundational impossibility result in black-box optimization stating that no optimization technique has performance superior to any other over any set of functions closed under permutation. The no free lunch theorem for search and optimization ( Wolpert and Macready 1997) applies to finite spaces and algorithms that do not resample points. All algorithms that search for an extremum of a cost function perform exactly the same when averaged over all possible cost functions. In mathematical folklore, the \" no free lunch \" (NFL) theorem (sometimes pluralized) of David Wolpert and William Macready, alludes to the saying \"no such thing as a free lunch \", that is, there are no easy shortcuts to success. What is the no free lunch theorem for search and optimization? The no free lunch theorem for search and optimization (Wolpert and Macready 1997) applies to finite spaces and algorithms that do not resample points . All algorithms that search for an extremum of a cost function perform exactly the same when averaged over all possible cost functions. What does Wolpert's 'no free lunch theorem' mean? Wolpert's result, in essence, formalizes Hume, extends him and calls the whole of science into question. The no free lunch theorem for search and optimization (Wolpert and Macready 1997) applies to finite spaces and algorithms that do not resample points. What is structural no free lunch theorem? Structural No Free Lunch theorem If we restrict our attention to performance metrics that measure algorithm performance with respect to some structure over the objective values and search algorithms that attempt to optimize with respect to this structure, we can obtain meaningful NFL results for classes of functions not closed under permutation. What is an almost no free lunch (anfl) theorem? \"an Almost No Free Lunch (ANFL) theorem shows that for each function which can be optimized efficiently by a search heuristic there can be constructed many related functions where the same heuristic is bad. Are there free lunch and free leftovers theorems for multiobjective optimization problems? No Free Lunch and Free Leftovers theorems for multiobjective optimization problems David Corne, Joshua Knowles, Some multiobjective optimizers are better than others, in: Proceedings of 2003 IEEE Congress on Evolutionary Computation, 2003, pp. 2506–2512. What is the no-free-lunch theorem? Additionally, the No-Free-Lunch Theorem states that no single algorithm can universally outperform others across all optimization problems . This underscores the necessity of developing specialized algorithms, such as those discussed here, tailored to specific problem domains 36 . Jan 1, 1997 · PDF | On Jan 1, 1997, D. H. Wolpert and others published No Free Lunch Theorems for Optimization | Find, read and cite all the research you need on ResearchGate", "subpage_snippet": "", "source": "jmvidal.cse.sc.edu", "link": "https://jmvidal.cse.sc.edu/lib/wolpert95a.html", "content": "@TechReport{ wolpert95a , author = {David H. Wolpert and William G. Macready}, title = { No Free Lunch Theorems for Search}, institution = {Santa Fe Institute}, year = 1995, abstract = {We show that all algorithms that search for an extremum of a cost function perform exactly the same, when averaged over all possible cost functions Oct 15, 2010 · The No Free Lunch theorem (Schumacher et al., 2001; Wolpert and Macready, 1997 [8], [10]) is a foundational impossibility result in black-box optimization stating that no optimization technique has performance superior to any other over any set of functions closed under permutation. The no free lunch theorem for search and optimization ( Wolpert and Macready 1997) applies to finite spaces and algorithms that do not resample points. All algorithms that search for an extremum of a cost function perform exactly the same when averaged over all possible cost functions. In mathematical folklore, the \" no free lunch \" (NFL) theorem (sometimes pluralized) of David Wolpert and William Macready, alludes to the saying \"no such thing as a free lunch \", that is, there are no easy shortcuts to success. What is the no free lunch theorem for search and optimization? The no free lunch theorem for search and optimization (Wolpert and Macready 1997) applies to finite spaces and algorithms that do not resample points . All algorithms that search for an extremum of a cost function perform exactly the same when averaged over all possible cost functions. What does Wolpert's 'no free lunch theorem' mean? Wolpert's result, in essence, formalizes Hume, extends him and calls the whole of science into question. The no free lunch theorem for search and optimization (Wolpert and Macready 1997) applies to finite spaces and algorithms that do not resample points. What is structural no free lunch theorem? Structural No Free Lunch theorem If we restrict our attention to performance metrics that measure algorithm performance with respect to some structure over the objective values and search algorithms that attempt to optimize with respect to this structure, we can obtain meaningful NFL results for classes of functions not closed under permutation. What is an almost no free lunch (anfl) theorem? \"an Almost No Free Lunch (ANFL) theorem shows that for each function which can be optimized efficiently by a search heuristic there can be constructed many related functions where the same heuristic is bad. Are there free lunch and free leftovers theorems for multiobjective optimization problems? No Free Lunch and Free Leftovers theorems for multiobjective optimization problems David Corne, Joshua Knowles, Some multiobjective optimizers are better than others, in: Proceedings of 2003 IEEE Congress on Evolutionary Computation, 2003, pp. 2506–2512. What is the no-free-lunch theorem? Additionally, the No-Free-Lunch Theorem states that no single algorithm can universally outperform others across all optimization problems . This underscores the necessity of developing specialized algorithms, such as those discussed here, tailored to specific problem domains 36 . Jan 1, 1997 · PDF | On Jan 1, 1997, D. H. Wolpert and others published No Free Lunch Theorems for Optimization | Find, read and cite all the research you need on ResearchGate"}
+{"idx": 3, "title": "A No Free Lunch theorem for multi-objective optimization", "date": "", "ddg_snippet": "Oct 15, 2010 · The No Free Lunch theorem (Schumacher et al., 2001; Wolpert and Macready, 1997 [8], [10]) is a foundational impossibility result in black-box optimization stating that no optimization technique has performance superior to any other over any set of functions closed under permutation.", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S0020019010002449", "content": "Oct 15, 2010 · The No Free Lunch theorem (Schumacher et al., 2001; Wolpert and Macready, 1997 [8], [10]) is a foundational impossibility result in black-box optimization stating that no optimization technique has performance superior to any other over any set of functions closed under permutation."}
+{"idx": 4, "title": "No Free Lunch Theorems", "date": "", "ddg_snippet": "The no free lunch theorem for search and optimization ( Wolpert and Macready 1997) applies to finite spaces and algorithms that do not resample points. All algorithms that search for an extremum of a cost function perform exactly the same when averaged over all possible cost functions.", "subpage_snippet": "", "source": "no-free-lunch.org", "link": "http://no-free-lunch.org/", "content": "The no free lunch theorem for search and optimization ( Wolpert and Macready 1997) applies to finite spaces and algorithms that do not resample points. All algorithms that search for an extremum of a cost function perform exactly the same when averaged over all possible cost functions."}
+{"idx": 5, "title": "(PDF) No Free Lunch Theorems for Optimization - ResearchGate", "date": "", "ddg_snippet": "Jan 1, 1997 · PDF | On Jan 1, 1997, D. H. Wolpert and others published No Free Lunch Theorems for Optimization | Find, read and cite all the research you need on ResearchGate", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/281266055_No_Free_Lunch_Theorems_for_Optimization", "content": "Jan 1, 1997 · PDF | On Jan 1, 1997, D. H. Wolpert and others published No Free Lunch Theorems for Optimization | Find, read and cite all the research you need on ResearchGate"}
+{"idx": 6, "title": "The No Free Lunch theorem for dummies - LessWrong 2.0 viewer", "date": "", "ddg_snippet": "See discussion by Eliezer Yudkowsky at Arbital: No - Free - Lunch theorems are often irrelevant and A reply to Francois Chollet on intelligence explosion ...", "subpage_snippet": "", "source": "www.greaterwrong.com", "link": "https://www.greaterwrong.com/posts/Dyt2TDdMGHDkXPcpp/the-no-free-lunch-theorem-for-dummies", "content": "See discussion by Eliezer Yudkowsky at Arbital: No - Free - Lunch theorems are often irrelevant and A reply to Francois Chollet on intelligence explosion ..."}
+{"idx": 7, "title": "The Supervised Learning No-Free-Lunch Theorems | TransferLab", "date": "", "ddg_snippet": "... Learning No - Free - Lunch Theorems , David H. ... This paper reviews the supervised learning versions of the no - free - lunch theorems in a simplified form.", "subpage_snippet": "", "source": "transferlab.ai", "link": "https://transferlab.ai/refs/wolpert_supervised_2002/", "content": "... Learning No - Free - Lunch Theorems , David H. ... This paper reviews the supervised learning versions of the no - free - lunch theorems in a simplified form."}
+{"idx": 8, "title": "The No Free Lunch theorem for dummies — LessWrong", "date": "", "ddg_snippet": "See discussion by Eliezer Yudkowsky at Arbital: No - Free - Lunch theorems are often irrelevant and A reply to Francois Chollet on intelligence explosion ...", "subpage_snippet": "", "source": "www.lesswrong.com", "link": "https://www.lesswrong.com/posts/Dyt2TDdMGHDkXPcpp/the-no-free-lunch-theorem-for-dummies", "content": "See discussion by Eliezer Yudkowsky at Arbital: No - Free - Lunch theorems are often irrelevant and A reply to Francois Chollet on intelligence explosion ..."}
+{"idx": 9, "title": "The No Free Lunch theorems and their Razor — LessWrong", "date": "", "ddg_snippet": "The No Free Lunch (NFL) family of theorems contains some of the most misunderstood theorems of machine learning. ... can call this the No Free Lunch ...", "subpage_snippet": "", "source": "www.lesswrong.com", "link": "https://www.lesswrong.com/posts/sdrCBWpqyvNBJSZH5/the-no-free-lunch-theorems-and-their-razor", "content": "The No Free Lunch (NFL) family of theorems contains some of the most misunderstood theorems of machine learning. ... can call this the No Free Lunch ..."}
diff --git a/data/sampled_jsons/XLRS-Bench_captioning_cost_per_image_$1.5_$2.5_Figure_4.jsonl b/data/sampled_jsons/XLRS-Bench_captioning_cost_per_image_$1.5_$2.5_Figure_4.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..517c29e99ee6bfa00b00f806cbb28c2351c63933
--- /dev/null
+++ b/data/sampled_jsons/XLRS-Bench_captioning_cost_per_image_$1.5_$2.5_Figure_4.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "(PDF) XLRS - Bench : Could Your Multimodal LLMs Understand...", "date": "", "ddg_snippet": "Figure 1. A typical example from our XLRS - Bench . XLRS - Bench focuses on extremely large ultra-high-resolution RS imagery , integrating.Outputs: correct annotations. Input: processed images , prompts. Cost : at least $2000 for tokens of GPT- 4 o, $ 1 . 5 - $ 2 . 5 per image .", "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": "Figure 1. A typical example from our XLRS - Bench . XLRS - Bench focuses on extremely large ultra-high-resolution RS imagery , integrating.Outputs: correct annotations. Input: processed images , prompts. Cost : at least $2000 for tokens of GPT- 4 o, $ 1 . 5 - $ 2 . 5 per image ."}
+{"idx": 1, "title": "XLRS - Bench : Could Your Multimodal LLMs Understand Extremely...", "date": "", "ddg_snippet": "Figure 2 . Advantages of XLRS - Bench : XLRS - Bench boasts an average image size that is 24 times larger than existing datasets. Figure 3. XLRS - Bench evaluates the perception and reasoning capabilities of MLLMs across three levels and 16 sub-tasks.", "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": "Figure 2 . Advantages of XLRS - Bench : XLRS - Bench boasts an average image size that is 24 times larger than existing datasets. Figure 3. XLRS - Bench evaluates the perception and reasoning capabilities of MLLMs across three levels and 16 sub-tasks."}
+{"idx": 2, "title": "[2503.23771] XLRS - Bench : Could Your Multimodal LLMs Understand...", "date": "", "ddg_snippet": "View a PDF of the paper titled XLRS - Bench : Could Your Multimodal LLMs Understand Extremely Large Ultra-High-Resolution Remote Sensing Imagery ?, by Fengxiang Wang and 11 other authors.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2503.23771", "content": "View a PDF of the paper titled XLRS - Bench : Could Your Multimodal LLMs Understand Extremely Large Ultra-High-Resolution Remote Sensing Imagery ?, by Fengxiang Wang and 11 other authors."}
+{"idx": 3, "title": "Runway Act-Two: AI Performance Capture & Character Animation", "date": "", "ddg_snippet": "Transform Any Performance IntoProfessional Animation. Runway Act-Two uses AI to capture your full-body performance, facial expressions, and hand gestures from just a webcam video—then transfers it to any character in seconds.", "subpage_snippet": "", "source": "acttwo.cv", "link": "https://acttwo.cv/", "content": "Transform Any Performance IntoProfessional Animation. Runway Act-Two uses AI to capture your full-body performance, facial expressions, and hand gestures from just a webcam video—then transfers it to any character in seconds."}
+{"idx": 4, "title": "XLRS - Bench", "date": "", "ddg_snippet": "XLRS - Bench features the largest image sizes available, 10∼20× than that of existing datasets, with 840 images out of all images at a resolution of 10,000×10,000 pixels 2 . High-quality Annotation.", "subpage_snippet": "", "source": "xlrs-bench.github.io", "link": "https://xlrs-bench.github.io/home_page.html", "content": "XLRS - Bench features the largest image sizes available, 10∼20× than that of existing datasets, with 840 images out of all images at a resolution of 10,000×10,000 pixels 2 . High-quality Annotation."}
+{"idx": 5, "title": "Диаграмма без названия - draw.io", "date": "", "ddg_snippet": "UML 2 . 5 .AWS18 / Cost Management.", "subpage_snippet": "", "source": "app.diagrams.net", "link": "https://app.diagrams.net/", "content": "UML 2 . 5 .AWS18 / Cost Management."}
+{"idx": 6, "title": "Бесплатная нейросеть онлайн | Чат GPT на русском для... — ruGPT", "date": "", "ddg_snippet": "4 ,82 на основании отзывовРейтинг 4 ,82. avatar image . Более 6 млн пользователейБолее 6 млн.", "subpage_snippet": "", "source": "rugpt.io", "link": "https://rugpt.io/", "content": "4 ,82 на основании отзывовРейтинг 4 ,82. avatar image . Более 6 млн пользователейБолее 6 млн."}
+{"idx": 7, "title": "Artguru - Free Online AI Art Generator: Create AI Art from Text & Photo", "date": "", "ddg_snippet": "AI Image Generator from Text.Text-to-Art lets you create images from your text descriptions. Photo-to-Art transforms your existing photos to various styles like painting, anime, or cyberpunk.", "subpage_snippet": "", "source": "www.artguru.ai", "link": "https://www.artguru.ai/", "content": "AI Image Generator from Text.Text-to-Art lets you create images from your text descriptions. Photo-to-Art transforms your existing photos to various styles like painting, anime, or cyberpunk."}
+{"idx": 8, "title": "PDF в JPG — Конвертируйте PDF в изображения онлайн бесплатно", "date": "", "ddg_snippet": "Удобный онлайн-инструмент конвертации страниц PDF в изображения или извлечения отдельных изображений из PDF. Бесплатно, без загрузок и водяных знаков.", "subpage_snippet": "", "source": "smallpdf.com", "link": "https://smallpdf.com/ru/pdf-to-jpg", "content": "Удобный онлайн-инструмент конвертации страниц PDF в изображения или извлечения отдельных изображений из PDF. Бесплатно, без загрузок и водяных знаков."}
+{"idx": 9, "title": "DeepSeek нейросеть на русском - DeepSeek", "date": "", "ddg_snippet": "Модели DeepSeek V 2 , DeepSeek R 1 , DeepSeek V 3 и V 3. 1 — искусственный интеллект для любых задач.", "subpage_snippet": "", "source": "deepseek-chat.ru", "link": "https://deepseek-chat.ru/", "content": "Модели DeepSeek V 2 , DeepSeek R 1 , DeepSeek V 3 и V 3. 1 — искусственный интеллект для любых задач."}
diff --git a/data/sampled_jsons/XLRS-Bench_semi-automated_captioning_pipeline_$2,000_1,000_images_Table_1_Figure_4_year_2024.jsonl b/data/sampled_jsons/XLRS-Bench_semi-automated_captioning_pipeline_$2,000_1,000_images_Table_1_Figure_4_year_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..10324e3cc4db6e708bdb2fad15a2532392f305c4
--- /dev/null
+++ b/data/sampled_jsons/XLRS-Bench_semi-automated_captioning_pipeline_$2,000_1,000_images_Table_1_Figure_4_year_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "CVPR Poster XLRS-Bench: Could Your Multimodal LLMs ...", "date": "", "ddg_snippet": "14 Jun 2025 — We present XLRS - Bench : a comprehensive benchmark for evaluating the perception and reasoning capabilities of MLLMs in ultra-high-resolution RS scenarios.", "subpage_snippet": "", "source": "cvpr.thecvf.com", "link": "https://cvpr.thecvf.com/virtual/2025/poster/35068", "content": "14 Jun 2025 — We present XLRS - Bench : a comprehensive benchmark for evaluating the perception and reasoning capabilities of MLLMs in ultra-high-resolution RS scenarios."}
+{"idx": 1, "title": "XLRS-Bench: Could Your Multimodal LLMs Understand ...", "date": "", "ddg_snippet": "31 Mar 2025 — Detailed captions and multi- image inputs resulted in extensive token usage with GPT-4o, costing over $2,000 for captioning 1,000 images . However ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2503.23771v1", "content": "31 Mar 2025 — Detailed captions and multi- image inputs resulted in extensive token usage with GPT-4o, costing over $2,000 for captioning 1,000 images . However ..."}
+{"idx": 2, "title": "XLRS-Bench: Could Your Multimodal LLMs Understand ...", "date": "", "ddg_snippet": "by F Wang · 2025 · Cited by 7 — For captioning , we use a semi - automated pipeline to interpret and generate text annotations of the image , as shown in Figure 4 . We begin by dividing each image ... 12 pages", "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": "by F Wang · 2025 · Cited by 7 — For captioning , we use a semi - automated pipeline to interpret and generate text annotations of the image , as shown in Figure 4 . We begin by dividing each image ... 12 pages"}
+{"idx": 3, "title": "ICLR 2025 Spotlights", "date": "", "ddg_snippet": "We design a semi - automated annotation pipeline to obtain 49,979 Question-Answer (QA) pairs of 1,353 streaming videos, which includes generating QA chains ...", "subpage_snippet": "", "source": "iclr.cc", "link": "https://iclr.cc/virtual/2025/events/spotlight-posters", "content": "We design a semi - automated annotation pipeline to obtain 49,979 Question-Answer (QA) pairs of 1,353 streaming videos, which includes generating QA chains ..."}
+{"idx": 4, "title": "Daily Papers", "date": "", "ddg_snippet": "7 days ago — The benchmark is constructed through a semi - automated pipeline that combines GPT- 4 -generated prompts with expert validation to ensure domain ...", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/papers?q=on-the-fly+baselines", "content": "7 days ago — The benchmark is constructed through a semi - automated pipeline that combines GPT- 4 -generated prompts with expert validation to ensure domain ..."}
+{"idx": 5, "title": "ICLR 2025 - Bird's-eye views of conference proceedings", "date": "", "ddg_snippet": "We design a semi - automated annotation pipeline to obtain 49,979 Question-Answer (QA) pairs of 1,353 streaming videos, which includes generating QA chains ...", "subpage_snippet": "", "source": "www.confviews.com", "link": "https://www.confviews.com/iclr2025", "content": "We design a semi - automated annotation pipeline to obtain 49,979 Question-Answer (QA) pairs of 1,353 streaming videos, which includes generating QA chains ..."}
+{"idx": 6, "title": "", "date": "", "ddg_snippet": "", "subpage_snippet": "", "source": "", "link": "", "content": ""}
+{"idx": 7, "title": "A Survey of Scientific Large Language Models", "date": "", "ddg_snippet": "Scientific Large Language Models (Sci-LLMs) are transforming how knowledge is represented, integrated, and applied in scientific research, ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2508.21148v1", "content": "Scientific Large Language Models (Sci-LLMs) are transforming how knowledge is represented, integrated, and applied in scientific research, ..."}
+{"idx": 8, "title": "open-sciencelab/Awesome-Scientific-Datasets-and-LLMs", "date": "", "ddg_snippet": "A curated collection of papers, datasets, and resources on Scientific Datasets and Large Language Models (LLMs) ...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/open-sciencelab/Awesome-Scientific-Datasets-and-LLMs", "content": "A curated collection of papers, datasets, and resources on Scientific Datasets and Large Language Models (LLMs) ..."}
+{"idx": 9, "title": "Advanced Turbine Aerothermal Research Rig (ATARR) Data ...", "date": "", "ddg_snippet": "by C Haldeman · 1995 — The ATARR Data Acquisition System (DAS) comprised of the instrumentation front-ends, the amplifier-digitizer components, the software suite, and ...", "subpage_snippet": "", "source": "apps.dtic.mil", "link": "https://apps.dtic.mil/sti/tr/pdf/ADA315427.pdf", "content": "by C Haldeman · 1995 — The ATARR Data Acquisition System (DAS) comprised of the instrumentation front-ends, the amplifier-digitizer components, the software suite, and ..."}
diff --git a/data/sampled_jsons/YjBrt82S3v_Symmetric_Reinforcement_Learning_Loss_Section_5.4_PPO_instability_analysis.jsonl b/data/sampled_jsons/YjBrt82S3v_Symmetric_Reinforcement_Learning_Loss_Section_5.4_PPO_instability_analysis.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..f1140a559f04362b219619cd44af95c9e5f1e8ea
--- /dev/null
+++ b/data/sampled_jsons/YjBrt82S3v_Symmetric_Reinforcement_Learning_Loss_Section_5.4_PPO_instability_analysis.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Proximal Policy Optimization ( PPO ) - How to train Large... - YouTube", "date": "", "ddg_snippet": "Reinforcement Learning with Human Feedback (RLHF) is a method used for training Large Language Models (LLMs).", "subpage_snippet": "", "source": "www.youtube.com", "link": "https://www.youtube.com/watch?v=TjHH_--7l8g", "content": "Reinforcement Learning with Human Feedback (RLHF) is a method used for training Large Language Models (LLMs)."}
+{"idx": 1, "title": "A Survey Examination of Psychological Support in the Workplace", "date": "", "ddg_snippet": "We wanted to investigate the landscape of psychological support in the contemporary American workplace. As the significance of employee well-being grows, organizations recognize the need to provide effective psychological support to foster a healthy work environment.", "subpage_snippet": "", "source": "jcss.ut.ac.ir", "link": "https://jcss.ut.ac.ir/article_102845.html", "content": "We wanted to investigate the landscape of psychological support in the contemporary American workplace. As the significance of employee well-being grows, organizations recognize the need to provide effective psychological support to foster a healthy work environment."}
+{"idx": 2, "title": "Magistral Small 1.2 and Magistral Medium 1.2 are here", "date": "", "ddg_snippet": "This version builds directly on Mistral Small 1.1 . Engineers applied supervised fine-tuning using traces from Magistral Medium, followed by reinforcement learning stages.", "subpage_snippet": "", "source": "apidog.com", "link": "https://apidog.com/blog/magistral-small-1-2-magistral-medium-1-2/", "content": "This version builds directly on Mistral Small 1.1 . Engineers applied supervised fine-tuning using traces from Magistral Medium, followed by reinforcement learning stages."}
+{"idx": 3, "title": "Попробуйте ChatGPT-4o бесплатно онлайн - на основе API...", "date": "", "ddg_snippet": "GPT-4 V Has Extensive Application Scenarios. The image recognition capabilities of GPT-4 V can be widely applied to ecommerce, document digitization, accessibility services, language learning , and many other fields.", "subpage_snippet": "", "source": "gpt4v.net", "link": "https://gpt4v.net/ru", "content": "GPT-4 V Has Extensive Application Scenarios. The image recognition capabilities of GPT-4 V can be widely applied to ecommerce, document digitization, accessibility services, language learning , and many other fields."}
+{"idx": 4, "title": "Journal of Medical Internet Research - Evaluating the Utility of...", "date": "", "ddg_snippet": "Eventually, of the 1044 studies, 12 (1.12%) met the inclusion criteria, validating the feasibility of wearable sensors in the early diagnosis of PD. Most (10/12, 83%) were cross- sectional studies, with 1 longitudinal and 1 mixed-design study.", "subpage_snippet": "", "source": "www.jmir.org", "link": "https://www.jmir.org/2025/1/e69422/", "content": "Eventually, of the 1044 studies, 12 (1.12%) met the inclusion criteria, validating the feasibility of wearable sensors in the early diagnosis of PD. Most (10/12, 83%) were cross- sectional studies, with 1 longitudinal and 1 mixed-design study."}
+{"idx": 5, "title": "sciencedirect.com/science/article/pii/S1063520312001042", "date": "", "ddg_snippet": "...for computing the spectra of real symmetric tridiagonal matrices, Appl.", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S1063520312001042", "content": "...for computing the spectra of real symmetric tridiagonal matrices, Appl."}
+{"idx": 6, "title": "Как скачать видео с Рутуба: 5 простых способов [2025]", "date": "", "ddg_snippet": "Как скачать видео с Рутуба: пошаговые инструкции. 4.5 Оценок: 158 (Ваша: ) User icon Автор: Ксения Лаптева.", "subpage_snippet": "", "source": "video-editor.su", "link": "https://video-editor.su/kak-skachat-video-s-rutube.php", "content": "Как скачать видео с Рутуба: пошаговые инструкции. 4.5 Оценок: 158 (Ваша: ) User icon Автор: Ксения Лаптева."}
+{"idx": 7, "title": "Анатолий Шарий – Telegram", "date": "", "ddg_snippet": "9.24K videos. 12 files. 6.82K links. Реклама: @sharij_team Отправь интересный контент и информацию: @shariy_time_bot Почта: anatolijsharij@sharij.pro.", "subpage_snippet": "", "source": "t.me", "link": "https://t.me/s/ASupersharij", "content": "9.24K videos. 12 files. 6.82K links. Реклама: @sharij_team Отправь интересный контент и информацию: @shariy_time_bot Почта: anatolijsharij@sharij.pro."}
+{"idx": 8, "title": "GBS-82XX experiments part 2 – Something interesting is under way", "date": "", "ddg_snippet": "Fixing the random speckles on the display I believe I have found the cause of the occasional white speckles seen on the display. The default setting for the GBS-82XX board is to clock the…", "subpage_snippet": "", "source": "ianstedman.wordpress.com", "link": "https://ianstedman.wordpress.com/2015/01/25/gbs-82xx-experiments-part-2/", "content": "Fixing the random speckles on the display I believe I have found the cause of the occasional white speckles seen on the display. The default setting for the GBS-82XX board is to clock the…"}
+{"idx": 9, "title": "Overview Leaderboard | LMArena", "date": "", "ddg_snippet": "This page gives you a snapshot of each Arena, you can explore deeper insights in their dedicated tabs. Learn more about it here.", "subpage_snippet": "", "source": "lmarena.ai", "link": "https://lmarena.ai/leaderboard", "content": "This page gives you a snapshot of each Arena, you can explore deeper insights in their dedicated tabs. Learn more about it here."}
diff --git a/data/sampled_jsons/YjBrt82S3v_Symmetric_Reinforcement_Learning_Loss_equation_7_L_ra2c_reverse_A2C.jsonl b/data/sampled_jsons/YjBrt82S3v_Symmetric_Reinforcement_Learning_Loss_equation_7_L_ra2c_reverse_A2C.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..6a912450d5f98b749d49d524e4de3bab15f4c9c6
--- /dev/null
+++ b/data/sampled_jsons/YjBrt82S3v_Symmetric_Reinforcement_Learning_Loss_equation_7_L_ra2c_reverse_A2C.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Symmetric Reinforcement Learning Loss for Robust Learning on ...", "date": "", "ddg_snippet": "We present Symmetric RL loss , inspired by Symmetric Cross Entropy (SCE) (Wang et al., 2019) from supervised learning, to enhance RL robustness. By incorporating re - verse information through SCE, we develop SA2C and SPPO, extending standard A2C and PPO algorithms.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=YjBrt82S3v", "content": "We present Symmetric RL loss , inspired by Symmetric Cross Entropy (SCE) (Wang et al., 2019) from supervised learning, to enhance RL robustness. By incorporating re - verse information through SCE, we develop SA2C and SPPO, extending standard A2C and PPO algorithms."}
+{"idx": 1, "title": "Symmetric Reinforcement Learning Loss for Robust Learning on ... Symmetry-Informed Reinforcement Learning and its Application ... GitHub - shashacks/Symmetric_RL Data-Efficient Deep Reinforcement Learning with Symmetric ... Symmetric Reinforcement Learning Loss for Robust Learning on ... Symmetric Reinforcement Learning Loss for Robust Learning on ...", "date": "", "ddg_snippet": "May 27, 2024 · In this work, we improve the stability of RL training by adapting the reverse cross entropy (RCE) from supervised learning for noisy data to define a symmetric RL loss . We demonstrate performance improvements across various tasks and scales. Symmetry is ubiquitous in nature, physics, and mathematics. However, a classical symmetry -agnostic reinforcement learning (RL) approach cannot guarantee to resp. In this work, we improve the stability of RL training by adapting the reverse cross entropy (RCE) from supervised learning for noisy data to define a symmetric RL loss . We demonstrate performance improvements across various tasks and scales. Data augmentation methods have proven highly effective in supervised learning domains where semantic-invariant perturbations can be easily applied to the labele May 27, 2024 · The symmetric RL loss function is a novel approach that makes reinforcement learning more resistant to noisy feedback. It works by modifying how the AI calculates its learning errors, similar to methods used in supervised learning. Sep 25, 2024 · In this work, we enhance the stability of the RL training procedure by adapting reverse cross-entropy (RCE) from supervised learning for noisy data to define a symmetric RL loss . We demonstrate performance improvements across various tasks and scales.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2405.17618", "content": "May 27, 2024 · In this work, we improve the stability of RL training by adapting the reverse cross entropy (RCE) from supervised learning for noisy data to define a symmetric RL loss . We demonstrate performance improvements across various tasks and scales. Symmetry is ubiquitous in nature, physics, and mathematics. However, a classical symmetry -agnostic reinforcement learning (RL) approach cannot guarantee to resp. In this work, we improve the stability of RL training by adapting the reverse cross entropy (RCE) from supervised learning for noisy data to define a symmetric RL loss . We demonstrate performance improvements across various tasks and scales. Data augmentation methods have proven highly effective in supervised learning domains where semantic-invariant perturbations can be easily applied to the labele May 27, 2024 · The symmetric RL loss function is a novel approach that makes reinforcement learning more resistant to noisy feedback. It works by modifying how the AI calculates its learning errors, similar to methods used in supervised learning. Sep 25, 2024 · In this work, we enhance the stability of the RL training procedure by adapting reverse cross-entropy (RCE) from supervised learning for noisy data to define a symmetric RL loss . We demonstrate performance improvements across various tasks and scales."}
+{"idx": 2, "title": "Symmetry-Informed Reinforcement Learning and its Application ...", "date": "", "ddg_snippet": "Symmetry is ubiquitous in nature, physics, and mathematics. However, a classical symmetry -agnostic reinforcement learning (RL) approach cannot guarantee to resp.", "subpage_snippet": "", "source": "ieeexplore.ieee.org", "link": "https://ieeexplore.ieee.org/document/10054413", "content": "Symmetry is ubiquitous in nature, physics, and mathematics. However, a classical symmetry -agnostic reinforcement learning (RL) approach cannot guarantee to resp."}
+{"idx": 3, "title": "GitHub - shashacks/Symmetric_RL", "date": "", "ddg_snippet": "In this work, we improve the stability of RL training by adapting the reverse cross entropy (RCE) from supervised learning for noisy data to define a symmetric RL loss . We demonstrate performance improvements across various tasks and scales.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/shashacks/Symmetric_RL", "content": "In this work, we improve the stability of RL training by adapting the reverse cross entropy (RCE) from supervised learning for noisy data to define a symmetric RL loss . We demonstrate performance improvements across various tasks and scales."}
+{"idx": 4, "title": "Data-Efficient Deep Reinforcement Learning with Symmetric ...", "date": "", "ddg_snippet": "Data augmentation methods have proven highly effective in supervised learning domains where semantic-invariant perturbations can be easily applied to the labele", "subpage_snippet": "", "source": "ieeexplore.ieee.org", "link": "https://ieeexplore.ieee.org/document/9956417", "content": "Data augmentation methods have proven highly effective in supervised learning domains where semantic-invariant perturbations can be easily applied to the labele"}
+{"idx": 5, "title": "Symmetric Reinforcement Learning Loss for Robust Learning on ...", "date": "", "ddg_snippet": "May 27, 2024 · The symmetric RL loss function is a novel approach that makes reinforcement learning more resistant to noisy feedback. It works by modifying how the AI calculates its learning errors, similar to methods used in supervised learning.", "subpage_snippet": "", "source": "www.promptlayer.com", "link": "https://www.promptlayer.com/research-papers/symmetric-reinforcement-learning-loss-for-robust-learning-on-diverse-tasks-and-model-scales", "content": "May 27, 2024 · The symmetric RL loss function is a novel approach that makes reinforcement learning more resistant to noisy feedback. It works by modifying how the AI calculates its learning errors, similar to methods used in supervised learning."}
+{"idx": 6, "title": "Symmetric Reinforcement Learning Loss for Robust Learning on ...", "date": "", "ddg_snippet": "Sep 25, 2024 · In this work, we enhance the stability of the RL training procedure by adapting reverse cross-entropy (RCE) from supervised learning for noisy data to define a symmetric RL loss . We demonstrate performance improvements across various tasks and scales.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=9oq0iY2Jxx", "content": "Sep 25, 2024 · In this work, we enhance the stability of the RL training procedure by adapting reverse cross-entropy (RCE) from supervised learning for noisy data to define a symmetric RL loss . We demonstrate performance improvements across various tasks and scales."}
+{"idx": 7, "title": "geometry - Solving symmetric equations in three unknowns by...", "date": "", "ddg_snippet": "Learn more about Teams. Solving symmetric equations in three unknowns by reducing the number of unknowns from $3$ to $2$.Assuming $ a ^ 2 - c ^2 \\gt 0 $, then the solution of this equation is.", "subpage_snippet": "", "source": "math.stackexchange.com", "link": "https://math.stackexchange.com/questions/4859590/solving-symmetric-equations-in-three-unknowns-by-reducing-the-number-of-unknowns", "content": "Learn more about Teams. Solving symmetric equations in three unknowns by reducing the number of unknowns from $3$ to $2$.Assuming $ a ^ 2 - c ^2 \\gt 0 $, then the solution of this equation is."}
+{"idx": 8, "title": "Gemini - Google DeepMind", "date": "", "ddg_snippet": "Gemini 2.5 Deep Think. An enhanced reasoning mode that uses cutting edge research techniques in parallel thinking and reinforcement learning to significantly improve Gemini’s ability to solve complex problems. Try with Google AI Ultra. View model card.64.6%. 66. 7 %. 78.3%. 82.8%.", "subpage_snippet": "", "source": "deepmind.google", "link": "https://deepmind.google/models/gemini/", "content": "Gemini 2.5 Deep Think. An enhanced reasoning mode that uses cutting edge research techniques in parallel thinking and reinforcement learning to significantly improve Gemini’s ability to solve complex problems. Try with Google AI Ultra. View model card.64.6%. 66. 7 %. 78.3%. 82.8%."}
+{"idx": 9, "title": "An interactive demo for developers to try the new text-to-speech model...", "date": "", "ddg_snippet": "An interactive demo for developers to try the new text-to-speech model in the OpenAI API...", "subpage_snippet": "", "source": "www.openai.fm", "link": "https://www.openai.fm/", "content": "An interactive demo for developers to try the new text-to-speech model in the OpenAI API..."}
diff --git a/data/sampled_jsons/adaptive_replanning_reinforcement_learning_robotics_2023.jsonl b/data/sampled_jsons/adaptive_replanning_reinforcement_learning_robotics_2023.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..1bf372ef3e3c6e9391a3749825f2aaa8e042e060
--- /dev/null
+++ b/data/sampled_jsons/adaptive_replanning_reinforcement_learning_robotics_2023.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "[2304.12046] When to Replan? An Adaptive Replanning Strategy for...", "date": "", "ddg_snippet": "Based on this insight, we then derive a new adaptive replanning strategy based on deep reinforcement learning , which can learn from experience to decide appropriate replanning timings in the given environment and planning setups.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2304.12046", "content": "Based on this insight, we then derive a new adaptive replanning strategy based on deep reinforcement learning , which can learn from experience to decide appropriate replanning timings in the given environment and planning setups."}
+{"idx": 1, "title": "Implementing Reinforcement Learning for Robots using Tensorflow.js", "date": "", "ddg_snippet": "Cylon.js is a JavaScript framework for robotics , physical computing, and the Internet of Things using Node.js. It provides a simple, yet powerful way to create JavaScript robots that incorporate multiple, different hardware devices at the same time.", "subpage_snippet": "", "source": "discuss.ai.google.dev", "link": "https://discuss.ai.google.dev/t/implementing-reinforcement-learning-for-robots-using-tensorflow-js/31458", "content": "Cylon.js is a JavaScript framework for robotics , physical computing, and the Internet of Things using Node.js. It provides a simple, yet powerful way to create JavaScript robots that incorporate multiple, different hardware devices at the same time."}
+{"idx": 2, "title": "ARiADNE: A Reinforcement learning approach using Attention-based...", "date": "", "ddg_snippet": "(DOI: 10.1109/ICRA48891. 2023 .10160565) In autonomous robot exploration tasks, a mobile robot needs to actively explore and map an unknown environment as fast as possible.", "subpage_snippet": "", "source": "scispace.com", "link": "https://scispace.com/papers/ariadne-a-reinforcement-learning-approach-using-attention-2d7czt3d", "content": "(DOI: 10.1109/ICRA48891. 2023 .10160565) In autonomous robot exploration tasks, a mobile robot needs to actively explore and map an unknown environment as fast as possible."}
+{"idx": 3, "title": "Enhancing Autonomous Industrial Navigation: Deep Reinforcement ...", "date": "", "ddg_snippet": "This paper shows how deep reinforcement learning helps industrial robots avoid obstacles. The goal is to create a solid framework that lets a robotic agent autonomously travel from a starting point to an endpoint, avoiding obstacles.", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/chapter/10.1007/978-3-031-81455-6_9", "content": "This paper shows how deep reinforcement learning helps industrial robots avoid obstacles. The goal is to create a solid framework that lets a robotic agent autonomously travel from a starting point to an endpoint, avoiding obstacles."}
+{"idx": 4, "title": "SMART: Self-Morphing Adaptive Replanning Tree | IEEE Xplore", "date": "", "ddg_snippet": "The letter presents an algorithm, called Self-Morphing Adaptive Replanning Tree (SMART), that facilitates fast replanning in dynamic environments.Published in: IEEE Robotics and Automation Letters ( Volume: 8 , Issue: 11 , November 2023 ).", "subpage_snippet": "", "source": "ieeexplore.ieee.org", "link": "https://ieeexplore.ieee.org/document/10250928", "content": "The letter presents an algorithm, called Self-Morphing Adaptive Replanning Tree (SMART), that facilitates fast replanning in dynamic environments.Published in: IEEE Robotics and Automation Letters ( Volume: 8 , Issue: 11 , November 2023 )."}
+{"idx": 5, "title": "DREAM: Adaptive Reinforcement Learning Based On Attention...", "date": "", "ddg_snippet": "As reinforcement learning (RL) for multi-hop reasoning on traditional knowledge graphs starts showing superior explainability and performance in recent advances, it has opened up opportunities for exploring RL techniques on TKG reasoning.", "subpage_snippet": "", "source": "blog.gdeltproject.org", "link": "https://blog.gdeltproject.org/dream-adaptive-reinforcement-learning-based-on-attention-mechanism-for-temporal-knowledge-graph-reasoning/", "content": "As reinforcement learning (RL) for multi-hop reasoning on traditional knowledge graphs starts showing superior explainability and performance in recent advances, it has opened up opportunities for exploring RL techniques on TKG reasoning."}
+{"idx": 6, "title": "Context-Aware Deep Reinforcement Learning for Autonomous...", "date": "", "ddg_snippet": "Proceedings of The 7th Conference on Robot Learning , PMLR 229:1425-1436, 2023 . Abstract. Mapless navigation refers to a challenging task where a mobile robot must rapidly navigate to a predefined destination using its partial knowledge of the environment...", "subpage_snippet": "", "source": "proceedings.mlr.press", "link": "https://proceedings.mlr.press/v229/liang23a", "content": "Proceedings of The 7th Conference on Robot Learning , PMLR 229:1425-1436, 2023 . Abstract. Mapless navigation refers to a challenging task where a mobile robot must rapidly navigate to a predefined destination using its partial knowledge of the environment..."}
+{"idx": 7, "title": "Online Replanning Through MoveIt! withouth stopping motion", "date": "", "ddg_snippet": "January 4, 2023 . Replanning because of an interruption (#111).Using Reinforcement Learning to Perform Motion Planning for a YuMi Robot .", "subpage_snippet": "", "source": "discourse.openrobotics.org", "link": "https://discourse.openrobotics.org/t/online-replanning-through-moveit-withouth-stopping-motion/5024", "content": "January 4, 2023 . Replanning because of an interruption (#111).Using Reinforcement Learning to Perform Motion Planning for a YuMi Robot ."}
+{"idx": 8, "title": "Reinforcement Learning Allows Underwater Robots To... - Astrobiology", "date": "", "ddg_snippet": "July 29, 2023 .Specifically, this new work reveals that reinforcement learning , widely used in the field of control and robotics , as well as in the development of tools related to natural language processing such as ChatGPT, allows underwater robots to learn what actions to perform...", "subpage_snippet": "", "source": "astrobiology.com", "link": "https://astrobiology.com/2023/07/reinforcement-learning-allows-underwater-robots-to-locate-and-track-objects-underwater.html", "content": "July 29, 2023 .Specifically, this new work reveals that reinforcement learning , widely used in the field of control and robotics , as well as in the development of tools related to natural language processing such as ChatGPT, allows underwater robots to learn what actions to perform..."}
+{"idx": 9, "title": "Seminar: Robot Learning — Robot Learning Lab", "date": "", "ddg_snippet": "Requirements: Basic knowledge of Deep Learning or Reinforcement Learning . Remarks: Topics will be assigned for the seminar via a preference voting.Deep Reactive Policy: Learning Reactive Manipulator Motion Planning for Dynamic Environments.", "subpage_snippet": "", "source": "rl.uni-freiburg.de", "link": "https://rl.uni-freiburg.de/teaching/ws25/robotlearning", "content": "Requirements: Basic knowledge of Deep Learning or Reinforcement Learning . Remarks: Topics will be assigned for the seminar via a preference voting.Deep Reactive Policy: Learning Reactive Manipulator Motion Planning for Dynamic Environments."}
diff --git a/data/sampled_jsons/advantage_CQT_over_STFT_music_signals_year_2020-2024.jsonl b/data/sampled_jsons/advantage_CQT_over_STFT_music_signals_year_2020-2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..a22bbede45d1b4d39668045766578093f0b2cbb4
--- /dev/null
+++ b/data/sampled_jsons/advantage_CQT_over_STFT_music_signals_year_2020-2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Spectral Representations: Mel Spectrograms, CQT, and HCQT", "date": "", "ddg_snippet": "Unlike STFT ’s fixed window size, CQT ’s window size varies— longer for low frequencies, shorter for high ones.", "subpage_snippet": "", "source": "www.jhonatanlopez.com", "link": "https://www.jhonatanlopez.com/advanced-spectral-representations-audio-analysis/", "content": "Unlike STFT ’s fixed window size, CQT ’s window size varies— longer for low frequencies, shorter for high ones."}
+{"idx": 1, "title": "An Intro to Wavelets for Computer Musicians | Nathan Ho", "date": "", "ddg_snippet": "Computer musicians with a penchant for unusual and bizarre DSP (for instance, people who read niche blogs devoted to the topic) tend to get ...", "subpage_snippet": "", "source": "nathan.ho.name", "link": "https://nathan.ho.name/posts/wavelets/", "content": "Computer musicians with a penchant for unusual and bizarre DSP (for instance, people who read niche blogs devoted to the topic) tend to get ..."}
+{"idx": 2, "title": "US9779706B2 - Context-dependent piano music transcription with", "date": "", "ddg_snippet": "G10H2250/00 — Aspects of algorithms or signal processing methods without intrinsic musical character, yet specifically adapted for or used in ...", "subpage_snippet": "", "source": "patents.google.com", "link": "https://patents.google.com/patent/US9779706B2/en", "content": "G10H2250/00 — Aspects of algorithms or signal processing methods without intrinsic musical character, yet specifically adapted for or used in ..."}
+{"idx": 3, "title": "fft - Most efficient way to extract frequency components of", "date": "", "ddg_snippet": "I'm trying to work out how (if possible) to extract the frequency components of an arbitrary audio sample (typically music ) in an FFT-like manner ...", "subpage_snippet": "", "source": "dsp.stackexchange.com", "link": "https://dsp.stackexchange.com/questions/3580/most-efficient-way-to-extract-frequency-components-of-audio-signal-with-near-hum", "content": "I'm trying to work out how (if possible) to extract the frequency components of an arbitrary audio sample (typically music ) in an FFT-like manner ..."}
+{"idx": 4, "title": "Diffusion Models for Audio Restoration Invited paper for the", "date": "", "ddg_snippet": "Traditional audio restoration methods exploit statistical properties of audio signals , such as auto-regressive modeling for click removal [ 1 ] or ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2402.09821v3", "content": "Traditional audio restoration methods exploit statistical properties of audio signals , such as auto-regressive modeling for click removal [ 1 ] or ..."}
+{"idx": 5, "title": "Advanced Signal Analysis in Detecting Replay Attacks for", "date": "", "ddg_snippet": "Specifically, the CQA method offers significant computational advantages over the traditional CQT method, while the MA method shows a superior ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2403.01130v3", "content": "Specifically, the CQA method offers significant computational advantages over the traditional CQT method, while the MA method shows a superior ..."}
+{"idx": 6, "title": "(PDF) A Real-Time Variable-Q Non-Stationary Gabor Transform for", "date": "", "ddg_snippet": "The simulation results showed that the proposed approach is suitable for pitch shifting of both speech and music signals .", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/292361602_A_Real-Time_Variable-Q_Non-Stationary_Gabor_Transform_for_Pitch_Shifting", "content": "The simulation results showed that the proposed approach is suitable for pitch shifting of both speech and music signals ."}
+{"idx": 7, "title": "Literature | SigSep", "date": "", "ddg_snippet": "... gain between (0) and (1) to apply on each element of the TF representation of the mixture (e.g., an STFT ) in order to estimate the desired signal .", "subpage_snippet": "", "source": "sigsep.github.io", "link": "https://sigsep.github.io/literature/", "content": "... gain between (0) and (1) to apply on each element of the TF representation of the mixture (e.g., an STFT ) in order to estimate the desired signal ."}
+{"idx": 8, "title": "Constant-Q transform - HandWiki", "date": "", "ddg_snippet": "In general, the transform is well suited to musical data, and this can be seen in some of its advantages compared to the fast Fourier transform.", "subpage_snippet": "", "source": "handwiki.org", "link": "https://handwiki.org/wiki/Constant-Q_transform", "content": "In general, the transform is well suited to musical data, and this can be seen in some of its advantages compared to the fast Fourier transform."}
+{"idx": 9, "title": "An Educational Guide through the FMP Notebooks for Teaching and", "date": "", "ddg_snippet": "Efficient Retrieval of Music Recordings Using Graph-Based Index Structures ... Overview Contact Careers News Press Blog", "subpage_snippet": "", "source": "www.mdpi.com", "link": "https://www.mdpi.com/2624-6120/2/2/18", "content": "Efficient Retrieval of Music Recordings Using Graph-Based Index Structures ... Overview Contact Careers News Press Blog"}
diff --git a/data/sampled_jsons/advantage_of_CQT_over_STFT_for_music_signals.jsonl b/data/sampled_jsons/advantage_of_CQT_over_STFT_for_music_signals.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..c3b1c082abdf6ff737cd5e965ac2ffe83d9f4e32
--- /dev/null
+++ b/data/sampled_jsons/advantage_of_CQT_over_STFT_for_music_signals.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Time–frequency analysis for music signals - Wikipedia", "date": "", "ddg_snippet": "Time–frequency analysis for music signals is one of the applications of time–frequency analysis. Musical sound can be more complicated than human vocal sound, occupying a wider band of frequency.", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/Time–frequency_analysis_for_music_signals", "content": "Time–frequency analysis for music signals is one of the applications of time–frequency analysis. Musical sound can be more complicated than human vocal sound, occupying a wider band of frequency."}
+{"idx": 1, "title": "A Comparison of CQT Spectrogram with STFT -based Acoustic", "date": "", "ddg_snippet": "and is the window function which, except in a small region, possesses a value of zero. The STFT is normally visualized by means of its spectrogram, which is the intensity plot of STFT magnitude over time. An example of an STFT spectrogram is shown in Figure 2.", "subpage_snippet": "", "source": "jad.shahroodut.ac.ir", "link": "https://jad.shahroodut.ac.ir/article_2707_3c17cc7bd491d721623526b32fa63822.pdf", "content": "and is the window function which, except in a small region, possesses a value of zero. The STFT is normally visualized by means of its spectrogram, which is the intensity plot of STFT magnitude over time. An example of an STFT spectrogram is shown in Figure 2."}
+{"idx": 2, "title": "P ipeline for M usical t imbre t ransfer", "date": "", "ddg_snippet": "CQT for Music Representation.Time-frequency analysis refers to techniques that aim to measure how the signal ’s frequency domain representation changes over time. Short Time Fourier Transform ( STFT ) The STFT is one of the most commonly applied techniques for this purpose.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/1811.09620", "content": "CQT for Music Representation.Time-frequency analysis refers to techniques that aim to measure how the signal ’s frequency domain representation changes over time. Short Time Fourier Transform ( STFT ) The STFT is one of the most commonly applied techniques for this purpose."}
+{"idx": 3, "title": "comparing_audio_representations_for synthesis_with_GANs", "date": "", "ddg_snippet": "The main disadvantage of CQT over STFT is the loss of perceptual reconstruction quality due to the frequency scaling in lower frequencies. In the following examples, we train a model using a pseudo invertible CQT as audio representations.", "subpage_snippet": "", "source": "sites.google.com", "link": "https://sites.google.com/view/audio-synthesis-with-gans", "content": "The main disadvantage of CQT over STFT is the loss of perceptual reconstruction quality due to the frequency scaling in lower frequencies. In the following examples, we train a model using a pseudo invertible CQT as audio representations."}
+{"idx": 4, "title": "Audio signal feature extraction for analysis | by Athina B | Medium", "date": "", "ddg_snippet": "Musical signals are volatile, their statistics change over time so it would be rather meaningless to compute a single Fourier transform over an entire 11-minute song.", "subpage_snippet": "", "source": "athina-b.medium.com", "link": "https://athina-b.medium.com/audio-signal-feature-extraction-for-analysis-507861717dc1", "content": "Musical signals are volatile, their statistics change over time so it would be rather meaningless to compute a single Fourier transform over an entire 11-minute song."}
+{"idx": 5, "title": "Comparison of STFT and IIR- CQT spectrograms of (a) speech signal ...", "date": "", "ddg_snippet": "Due to computational efficiency, shorttime Fourier transform ( STFT ) is employed for evaluating the CQT [37]. The IIR- CQT spectrogram is widely used in the audio signal analysis as it provides good spectral and temporal resolution at lower and higher frequency values, respectively. ...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/figure/Comparison-of-STFT-and-IIR-CQT-spectrograms-of-a-speech-signal-using-the-STFT-b-music_fig5_329249822", "content": "Due to computational efficiency, shorttime Fourier transform ( STFT ) is employed for evaluating the CQT [37]. The IIR- CQT spectrogram is widely used in the audio signal analysis as it provides good spectral and temporal resolution at lower and higher frequency values, respectively. ..."}
+{"idx": 6, "title": "Music Transcription Using Deep", "date": "", "ddg_snippet": "Methods widely used for preprocessing audio signals include STFT (Short-Time Fourier Transform), Mel Filterbank Cepstrum (MFCC) and constant Q transform ( CQT ). Signal processing methods for music transcription.", "subpage_snippet": "", "source": "cs229.stanford.edu", "link": "https://cs229.stanford.edu/proj2017/final-reports/5242716.pdf", "content": "Methods widely used for preprocessing audio signals include STFT (Short-Time Fourier Transform), Mel Filterbank Cepstrum (MFCC) and constant Q transform ( CQT ). Signal processing methods for music transcription."}
+{"idx": 7, "title": "Automatic Piano Sheet Music Transcription with Machine", "date": "", "ddg_snippet": "...reasons for choosing CQT over STFT , the first reason is that CQT has much lower dimensional representation than the STFT in which it is useful when using neural network acoustic models as it reduces the number of parameters in the model and the second reason is that CQT is...", "subpage_snippet": "", "source": "thescipub.com", "link": "https://thescipub.com/pdf/jcssp.2021.178.187.pdf", "content": "...reasons for choosing CQT over STFT , the first reason is that CQT has much lower dimensional representation than the STFT in which it is useful when using neural network acoustic models as it reduces the number of parameters in the model and the second reason is that CQT is..."}
+{"idx": 8, "title": "A Comparison of Audio Preprocessing Methods for Music Autotagging...", "date": "", "ddg_snippet": "Based on the STFT as preprocessing method for audio signals , CNNs are used in Music Information Retrieval (MIR) as well. Especially for autotagging of music , they have become state-of-the-art.", "subpage_snippet": "", "source": "repositum.tuwien.at", "link": "https://repositum.tuwien.at/bitstream/20.500.12708/20348/1/Damboeck+Maximilian+-+2022+-+A+comparison+of+audio+preprocessing+methods+for...pdf", "content": "Based on the STFT as preprocessing method for audio signals , CNNs are used in Music Information Retrieval (MIR) as well. Especially for autotagging of music , they have become state-of-the-art."}
+{"idx": 9, "title": "TimbreTron: A WaveNet(CycleGAN( CQT (Audio)))", "date": "", "ddg_snippet": "Background: STFT and CQT . Equivariance of Convolution over Log-Frequency Axis with respect to Translation. Spectrogram Processing Details. Waveform Reconstruction from Spectrogram Representation Background.", "subpage_snippet": "", "source": "nips2018creativity.github.io", "link": "https://nips2018creativity.github.io/doc/timbretron.pdf", "content": "Background: STFT and CQT . Equivariance of Convolution over Log-Frequency Axis with respect to Translation. Spectrogram Processing Details. Waveform Reconstruction from Spectrogram Representation Background."}
diff --git a/data/sampled_jsons/adversarial_review_algorithm_contentiousness_variable_delta_modulation_parameter_debate_rounds_year_2024.jsonl b/data/sampled_jsons/adversarial_review_algorithm_contentiousness_variable_delta_modulation_parameter_debate_rounds_year_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..a49561e819eaafaf8f3ea1c5c4b57e73c02d9794
--- /dev/null
+++ b/data/sampled_jsons/adversarial_review_algorithm_contentiousness_variable_delta_modulation_parameter_debate_rounds_year_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Adversarial Multi-Agent Evaluation of Large Language Models through...", "date": "", "ddg_snippet": "The authors further present two theorems respectively claiming that (1) aggregated multi-advocate arguments lead to greater score differentiation than those obtained using iterative debates , and (2) that multi-advocate argumentation requires fewer rounds of interaction to receive the same level of score differentiation as iterative debate .", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=06ZvHHBR0i", "content": "The authors further present two theorems respectively claiming that (1) aggregated multi-advocate arguments lead to greater score differentiation than those obtained using iterative debates , and (2) that multi-advocate argumentation requires fewer rounds of interaction to receive the same level of score differentiation as iterative debate ."}
+{"idx": 1, "title": "PDF An Adversarial Behavior Model for Contextual Ethical Alignment in Large ...", "date": "", "ddg_snippet": "After each round , the contentiousness level is decreased by dividing it by a modulation parameter δ. This gradual reduction steers the discussion towards a more cooperative tone.", "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": "After each round , the contentiousness level is decreased by dividing it by a modulation parameter δ. This gradual reduction steers the discussion towards a more cooperative tone."}
+{"idx": 2, "title": "PDF MultiAgent Collaboration Attack: Investigating Adversarial Attacks in ...", "date": "", "ddg_snippet": "Each response is then shared with the other models for reconsideration and revision over several rounds . An example of this debate is illustrated in Figure2. To simulate an adversarial attack, the adversary selectsanincorrectanswerandtriestopersuadethe other agents to accept it as correct.", "subpage_snippet": "", "source": "aclanthology.org", "link": "https://aclanthology.org/2024.findings-emnlp.407.pdf", "content": "Each response is then shared with the other models for reconsideration and revision over several rounds . An example of this debate is illustrated in Figure2. To simulate an adversarial attack, the adversary selectsanincorrectanswerandtriestopersuadethe other agents to accept it as correct."}
+{"idx": 3, "title": "Multi-LLM Debate: Framework, Principals, and Interventions", "date": "", "ddg_snippet": "The paper presents a theoretical framework for studying multi-LLM debate , and further proposes two types interventions to debate processes. The framework establishes principles of debates , including the diversity of model responses and shared misconceptions among the models. The first type of intervention is pruning, which filters responses to be included in the response, and the second type ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=sy7eSEXdPC", "content": "The paper presents a theoretical framework for studying multi-LLM debate , and further proposes two types interventions to debate processes. The framework establishes principles of debates , including the diversity of model responses and shared misconceptions among the models. The first type of intervention is pruning, which filters responses to be included in the response, and the second type ..."}
+{"idx": 4, "title": "Robustness Assessment of DL-based Automatic Modulation Classification ...", "date": "", "ddg_snippet": "Deep learning (DL) based automatic modulation classification (AMC) is widely used in communication systems. However, the vulnerability of deep neural networks (DNNs) to adversarial perturbations reveals inherent robustness limitations, emphasizing the necessity of robustness testing. Conventional adversarial attacks often fail to maintain effectiveness in practical communication scenarios due ...", "subpage_snippet": "", "source": "ieeexplore.ieee.org", "link": "https://ieeexplore.ieee.org/document/11098472", "content": "Deep learning (DL) based automatic modulation classification (AMC) is widely used in communication systems. However, the vulnerability of deep neural networks (DNNs) to adversarial perturbations reveals inherent robustness limitations, emphasizing the necessity of robustness testing. Conventional adversarial attacks often fail to maintain effectiveness in practical communication scenarios due ..."}
+{"idx": 5, "title": "(PDF) Adversarial Multi-Agent Evaluation of Large Language Models ...", "date": "", "ddg_snippet": "4 Adversarial Multi-Agent Evaluation of Large Language Models through Iterati ve Debates Algorithm 1 Multi-Advocate One- Round Evaluation (MORE) 1: Initialize A 1={A 11, A12 , A13}", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/384698689_Adversarial_Multi-Agent_Evaluation_of_Large_Language_Models_through_Iterative_Debates", "content": "4 Adversarial Multi-Agent Evaluation of Large Language Models through Iterati ve Debates Algorithm 1 Multi-Advocate One- Round Evaluation (MORE) 1: Initialize A 1={A 11, A12 , A13}"}
+{"idx": 6, "title": "Minimizing Hallucinations and Communication Costs: Adversarial Debate ...", "date": "", "ddg_snippet": "The voting mechanism is embedded in this adversarial debate to determine the necessity of conducting some external knowledge retrieval. Moreover, we employ entropy compression to minimize the number of conversational tokens and turns, in order to handle tasks that are sensitive to communication efficiency.", "subpage_snippet": "", "source": "www.mdpi.com", "link": "https://www.mdpi.com/2076-3417/15/7/3676", "content": "The voting mechanism is embedded in this adversarial debate to determine the necessity of conducting some external knowledge retrieval. Moreover, we employ entropy compression to minimize the number of conversational tokens and turns, in order to handle tasks that are sensitive to communication efficiency."}
+{"idx": 7, "title": "Adversarial Multi-Agent Evaluation of Large Language Models through ...", "date": "", "ddg_snippet": "The rapid advancement of large language models (LLMs) has revolutionized the field of natural language processing, enabling the development of increasingly sophisticated AI systems capable of generating human-like text, engaging in dialogue, and performing complex language tasks [5]. As these models grow in size and capability, the challenge of accurately evaluating their performance and ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2410.04663v2", "content": "The rapid advancement of large language models (LLMs) has revolutionized the field of natural language processing, enabling the development of increasingly sophisticated AI systems capable of generating human-like text, engaging in dialogue, and performing complex language tasks [5]. As these models grow in size and capability, the challenge of accurately evaluating their performance and ..."}
+{"idx": 8, "title": "A Checks-and-Balances Framework for Context-Aware Ethical AI Alignment", "date": "", "ddg_snippet": "After each round , the level of contentiousness is reduced by dividing it by a modulation parameter δ. This gradual reduction steers the discussion towards a more cooperative tone.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2502.00136v2", "content": "After each round , the level of contentiousness is reduced by dividing it by a modulation parameter δ. This gradual reduction steers the discussion towards a more cooperative tone."}
+{"idx": 9, "title": "Integrating Emotional and Linguistic Models for Ethical Compliance in ...", "date": "", "ddg_snippet": "The debate starts with the contentiousness level at percent 90 90\\% 90 %, adjusting through a modulation parameter 𝛿 \\ delta italic_δ. Following each round of debate , contentiousness is decreased by dividing it by 𝛿 \\ delta italic_δ, steering the discussion towards a more cooperative tone.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2405.07076", "content": "The debate starts with the contentiousness level at percent 90 90\\% 90 %, adjusting through a modulation parameter 𝛿 \\ delta italic_δ. Following each round of debate , contentiousness is decreased by dividing it by 𝛿 \\ delta italic_δ, steering the discussion towards a more cooperative tone."}
diff --git a/data/sampled_jsons/alternating_optimization_algorithm_termination_condition_convergence_X^{t+1}_-_X^t_F.jsonl b/data/sampled_jsons/alternating_optimization_algorithm_termination_condition_convergence_X^{t+1}_-_X^t_F.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..0e9a82772b649a83b0b65fbe2ef0a877174f2b30
--- /dev/null
+++ b/data/sampled_jsons/alternating_optimization_algorithm_termination_condition_convergence_X^{t+1}_-_X^t_F.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Expectation–maximization algorithm - Wikipedia", "date": "", "ddg_snippet": "The convergence analysis of the Dempster–Laird–Rubin algorithm was flawed and a correct convergence analysis was published by C.", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/Expectation-maximization_algorithm", "content": "The convergence analysis of the Dempster–Laird–Rubin algorithm was flawed and a correct convergence analysis was published by C."}
+{"idx": 1, "title": "Chapter 6 Alternating minimization - University of Michigan", "date": "", "ddg_snippet": "This chapter develops alternating minimization algorithms that are suitable for such problems. ation and signal/image processing. In image processing, an early approach was it These coordinate descent methods can be very useful, but also can have limitations for certain nonsmooth cost functions.", "subpage_snippet": "", "source": "web.eecs.umich.edu", "link": "https://web.eecs.umich.edu/~fessler/course/598/l/n-06-alt.pdf", "content": "This chapter develops alternating minimization algorithms that are suitable for such problems. ation and signal/image processing. In image processing, an early approach was it These coordinate descent methods can be very useful, but also can have limitations for certain nonsmooth cost functions."}
+{"idx": 2, "title": "(PDF) Convergence of alternating optimization - ResearchGate", "date": "", "ddg_snippet": "Dec 1 , 2003 · Alternating optimization (AO) is an iterative procedure for minimizing f ( x ) = f (X1, X2,..., Xt) jointly over all variables by alternating restricted minimizations over the individual subsets of ...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/262318598_Convergence_of_alternating_optimization", "content": "Dec 1 , 2003 · Alternating optimization (AO) is an iterative procedure for minimizing f ( x ) = f (X1, X2,..., Xt) jointly over all variables by alternating restricted minimizations over the individual subsets of ..."}
+{"idx": 3, "title": "reference request - Optimality and convergence of Alternating ...", "date": "", "ddg_snippet": "Apr 3, 2018 · I have an optimization problem in four variable and fortunately, the problem is convex in each variable while keeping others fixed. I want to use alternating minimization (AM) based algorithm to find the optimal solution.", "subpage_snippet": "", "source": "math.stackexchange.com", "link": "https://math.stackexchange.com/questions/2696513/optimality-and-convergence-of-alternating-minimization-approaches", "content": "Apr 3, 2018 · I have an optimization problem in four variable and fortunately, the problem is convex in each variable while keeping others fixed. I want to use alternating minimization (AM) based algorithm to find the optimal solution."}
+{"idx": 4, "title": "Chapter 2: Alternating Minimization", "date": "", "ddg_snippet": "Feb 10, 2025 · Alternating minimization is often used for problems non-diferentiable optimization problems. Therefore, it is useful to analyze its convergence properties and its failure modes in the absence of diferentiability.", "subpage_snippet": "", "source": "ernestryu.com", "link": "https://ernestryu.com/courses/164/AM.pdf", "content": "Feb 10, 2025 · Alternating minimization is often used for problems non-diferentiable optimization problems. Therefore, it is useful to analyze its convergence properties and its failure modes in the absence of diferentiability."}
+{"idx": 5, "title": "Alternating Minimizations Converge to Second-Order Optimal ...", "date": "", "ddg_snippet": "This work studies the second-order convergence for both standard alternating minimization and proximal alternating minimization. We show that under mild assumptions on the (nonconvex) ob-jective function, both algorithms avoid strict sad-dles almost surely from random initialization. To-gether with known first-order convergence results, this implies that both algorithms converge to a second ...", "subpage_snippet": "", "source": "proceedings.mlr.press", "link": "https://proceedings.mlr.press/v97/li19n/li19n.pdf", "content": "This work studies the second-order convergence for both standard alternating minimization and proximal alternating minimization. We show that under mild assumptions on the (nonconvex) ob-jective function, both algorithms avoid strict sad-dles almost surely from random initialization. To-gether with known first-order convergence results, this implies that both algorithms converge to a second ..."}
+{"idx": 6, "title": "Alternating Minimization and Alternating Projection ...", "date": "", "ddg_snippet": "Alternating minimization of a function of two vectors variables provides a useful framework for the derivation of iterative optimization algorithms . The main reference for alternating minimization is the paper [32] of Csisz ́ar and Tusn ́ady. We use their three-point property and four-point property to provide a somewhat simpler proof of convergence for their alternating minimization al ...", "subpage_snippet": "", "source": "faculty.uml.edu", "link": "https://faculty.uml.edu/cbyrne/CandT.pdf", "content": "Alternating minimization of a function of two vectors variables provides a useful framework for the derivation of iterative optimization algorithms . The main reference for alternating minimization is the paper [32] of Csisz ́ar and Tusn ́ady. We use their three-point property and four-point property to provide a somewhat simpler proof of convergence for their alternating minimization al ..."}
+{"idx": 7, "title": "Lecture 19: EM algorithm and alternating minimization", "date": "", "ddg_snippet": "We saw how to mitigate this problem using the Frank-Wolfe algorithm , in which the nuclear norm projection is replaced by a linear optimization step that is solved by the power method. Here we'll see a natural approach to solve the non-convex formulation directly without any relaxation via alternating minimization. Alternating minimization ¶", "subpage_snippet": "", "source": "ee227c.github.io", "link": "https://ee227c.github.io/code/lecture19.html", "content": "We saw how to mitigate this problem using the Frank-Wolfe algorithm , in which the nuclear norm projection is replaced by a linear optimization step that is solved by the power method. Here we'll see a natural approach to solve the non-convex formulation directly without any relaxation via alternating minimization. Alternating minimization ¶"}
+{"idx": 8, "title": "Convergence of First-Order Algorithms with Momentum from the", "date": "", "ddg_snippet": "The most widely used first-order optimization methods/ algorithms are the gradient descent (GD) method [ 7 , 37 ] , the inexact proximal point method ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2505.03050v1", "content": "The most widely used first-order optimization methods/ algorithms are the gradient descent (GD) method [ 7 , 37 ] , the inexact proximal point method ..."}
+{"idx": 9, "title": "An efficient gradient-based algorithm with descent direction", "date": "", "ddg_snippet": "Leveraging the strong Wolfe conditions and assuming Lipschitz continuity, we establish the global convergence of the algorithm .", "subpage_snippet": "", "source": "peerj.com", "link": "https://peerj.com/articles/cs-2783/", "content": "Leveraging the strong Wolfe conditions and assuming Lipschitz continuity, we establish the global convergence of the algorithm ."}
diff --git a/data/sampled_jsons/arXiv2505.00998_pdf.jsonl b/data/sampled_jsons/arXiv2505.00998_pdf.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..1ede145558e7772dfeab355e8447c7e3da80e2c4
--- /dev/null
+++ b/data/sampled_jsons/arXiv2505.00998_pdf.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "[2505.00998] Deterministic-to-Stochastic Diverse Latent Feature Mapping ...", "date": "", "ddg_snippet": "Abstract page for arXiv paper 2505.00998 : Deterministic-to-Stochastic Diverse Latent Feature Mapping for Human Motion Synthesis", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2505.00998", "content": "Abstract page for arXiv paper 2505.00998 : Deterministic-to-Stochastic Diverse Latent Feature Mapping for Human Motion Synthesis"}
+{"idx": 1, "title": "arXiv.org", "date": "", "ddg_snippet": "arXiv .org", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2505.00898", "content": "arXiv .org"}
+{"idx": 2, "title": "[2505.05495] Learning 3D Persistent Embodied World Models - arXiv.org", "date": "", "ddg_snippet": "View a PDF of the paper titled Learning 3D Persistent Embodied World Models, by Siyuan Zhou and 6 other authors", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2505.05495", "content": "View a PDF of the paper titled Learning 3D Persistent Embodied World Models, by Siyuan Zhou and 6 other authors"}
+{"idx": 3, "title": "arxiv-downloader: The arXiv PDF Command-Line Interface Downloader - GitHub", "date": "", "ddg_snippet": "A command line interface to download PDF files from https:// arxiv .org. - braun-steven/ arxiv -downloader", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/braun-steven/arxiv-downloader", "content": "A command line interface to download PDF files from https:// arxiv .org. - braun-steven/ arxiv -downloader"}
+{"idx": 4, "title": "[2505.09435] Endo-CLIP: Progressive Self-Supervised Pre-training on Raw ...", "date": "", "ddg_snippet": "View a PDF of the paper titled Endo-CLIP: Progressive Self-Supervised Pre-training on Raw Colonoscopy Records, by Yili He and 9 other authors", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2505.09435", "content": "View a PDF of the paper titled Endo-CLIP: Progressive Self-Supervised Pre-training on Raw Colonoscopy Records, by Yili He and 9 other authors"}
+{"idx": 5, "title": "[2505.00788] SpatialLLM: A Compound 3D-Informed Design towards ...", "date": "", "ddg_snippet": "Abstract page for arXiv paper 2505.00788: SpatialLLM: A Compound 3D-Informed Design towards Spatially-Intelligent Large Multimodal Models", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2505.00788", "content": "Abstract page for arXiv paper 2505.00788: SpatialLLM: A Compound 3D-Informed Design towards Spatially-Intelligent Large Multimodal Models"}
+{"idx": 6, "title": "arXiv:2505.00998v1 [cs.CV] 2 May 2025", "date": "", "ddg_snippet": "Abstract 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 ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2505.00998", "content": "Abstract 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 ..."}
+{"idx": 7, "title": "dblp: Deterministic-to-Stochastic Diverse Latent Feature Mapping for ...", "date": "", "ddg_snippet": "Details and statistics show external API response JSON @ openalex.org see also: API doc @ openalex.org DOI: 10.48550/ ARXIV .2505.00998 access: open type: Informal or Other Publication metadata version: 2025-06-18 view electronic edition via DOI (open access) authority control:", "subpage_snippet": "", "source": "dblp.org", "link": "https://dblp.org/rec/journals/corr/abs-2505-00998", "content": "Details and statistics show external API response JSON @ openalex.org see also: API doc @ openalex.org DOI: 10.48550/ ARXIV .2505.00998 access: open type: Informal or Other Publication metadata version: 2025-06-18 view electronic edition via DOI (open access) authority control:"}
+{"idx": 8, "title": "Artificial Intelligence authors/titles recent submissions (455 skipped)", "date": "", "ddg_snippet": "Comments: 52 pages. This work is an improved version of the previous study at arXiv:2402.02526 Subjects:Artificial Intelligence (cs.AI); Computation and Language (cs.CL) [463] arXiv:2505.13372 [ pdf , ps, other] Title: Exploiting Symbolic Heuristics for the Synthesis of Domain-Specific Temporal Planning Guidance using Reinforcement Learning Authors: Irene Brugnara, Alessandro Valentini, Andrea ...", "subpage_snippet": "", "source": "export-test.arxiv.org", "link": "https://export-test.arxiv.org/list/cs.AI/pastweek?skip=455&show=25", "content": "Comments: 52 pages. This work is an improved version of the previous study at arXiv:2402.02526 Subjects:Artificial Intelligence (cs.AI); Computation and Language (cs.CL) [463] arXiv:2505.13372 [ pdf , ps, other] Title: Exploiting Symbolic Heuristics for the Synthesis of Domain-Specific Temporal Planning Guidance using Reinforcement Learning Authors: Irene Brugnara, Alessandro Valentini, Andrea ..."}
+{"idx": 9, "title": "Deterministic-to-Stochastic Diverse Latent Feature Mapping for Human ...", "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. However, their training process involves complex curvature trajectories, leading to unstable training process. In this paper, we propose a Deterministic-to-Stochastic ...", "subpage_snippet": "", "source": "papers.cool", "link": "https://papers.cool/arxiv/2505.00998", "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. However, their training process involves complex curvature trajectories, leading to unstable training process. In this paper, we propose a Deterministic-to-Stochastic ..."}
diff --git a/data/sampled_jsons/arXiv_2412.07435_Algorithm_1_parallel_Picard_method_lines_9-26.jsonl b/data/sampled_jsons/arXiv_2412.07435_Algorithm_1_parallel_Picard_method_lines_9-26.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..6cee99d2dbfc68dd2853cd81147d7ab860daea28
--- /dev/null
+++ b/data/sampled_jsons/arXiv_2412.07435_Algorithm_1_parallel_Picard_method_lines_9-26.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Algorithm - Wikipedia", "date": "", "ddg_snippet": "More advanced algorithms can use conditionals to divert the code execution through various routes (referred to as automated decision-making) and deduce valid inferences (referred to as automated reasoning).", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/Algorithm", "content": "More advanced algorithms can use conditionals to divert the code execution through various routes (referred to as automated decision-making) and deduce valid inferences (referred to as automated reasoning)."}
+{"idx": 1, "title": "[2412.07435] Parallel simulation for sampling under ...", "date": "", "ddg_snippet": "Dec 10, 2024 · In recent years, there has been a surge of interest in proving discretization bounds for sampling under isoperimetry and for diffusion models. As data size grows, reducing the iteration cost becomes an important goal. Inspired by the great success of the parallel simulation of the initial value problem in scientific computation, we propose parallel Picard methods for sampling tasks. Rigorous ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2412.07435", "content": "Dec 10, 2024 · In recent years, there has been a surge of interest in proving discretization bounds for sampling under isoperimetry and for diffusion models. As data size grows, reducing the iteration cost becomes an important goal. Inspired by the great success of the parallel simulation of the initial value problem in scientific computation, we propose parallel Picard methods for sampling tasks. Rigorous ..."}
+{"idx": 2, "title": "Parallel computations for Metropolis Markov chains with ...", "date": "", "ddg_snippet": "1 .4 Related literature on parallel sampling and Picard maps 2 Picard algorithms with piecewise constant map ΦΦ\\Phiroman_Φ 2. 1 Online Picard algorithm 2.2 Markov structure of Online Picard algorithms 3 Complexity of Picard algorithms for zeroth-order Monte Carlo 3. 1 Assumption on the target 3.2 Metropolis-Hastings kernels", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2506.09762v1", "content": "1 .4 Related literature on parallel sampling and Picard maps 2 Picard algorithms with piecewise constant map ΦΦ\\Phiroman_Φ 2. 1 Online Picard algorithm 2.2 Markov structure of Online Picard algorithms 3 Complexity of Picard algorithms for zeroth-order Monte Carlo 3. 1 Assumption on the target 3.2 Metropolis-Hastings kernels"}
+{"idx": 3, "title": "[2503.19731] PCM : Picard Consistency Model for Fast Parallel ... PCM : Picard Consistency Model for Fast Parallel Sampling of ... arXiv:2409.08526v2 [math.NA] 5 Jul 2025 Parallel Numerical Picard Iteration Methods | Journal of ... Parallel simulation for sampling under isoperimetry and score-based PCM : Picard Consistency Model for Fast Parallel Sampling of Diffusion Parallel Numerical Picard Iteration Methods | Journal of Scientific Parallel Numerical Picard Iteration Methods | Journal of Scientific Parallel Numerical Picard Iteration Methods | Journal of Scientific Parallel Numerical Picard Iteration Methods | Journal of Scientific Parallel simulation for sampling under isoperimetry and ...", "date": "", "ddg_snippet": "Mar 25, 2025 · Recently, diffusion models have achieved significant advances in vision, text, and robotics. However, they still face slow generation speeds due to sequential denoising processes. To address this, a parallel sampling method based on Picard iteration was introduced, effectively reducing sequential steps while ensuring exact convergence to the original output. Nonetheless, Picard iteration does ... Recently, ParaDiGMS [38] tackled this challenge with a parallel sampling method based on Picard iteration, reducing the sequential steps in diffusion sampling by performing more computations in parallel . The overall procedure is summarized in Algorithm 1 , and several computational details involved in Algorithm 1 are discussed below. Algorithm 1 Deep Picard Iteration (DPI) Algorithm Input: Number of Picard iterations K, number of data points N per iteration, number of Monte Carlo sampling M, number of epochs Eper iteration for training neural ... Mar 1 , 2023 · In this work, we propose a new class of parallel time integrators for initial-value problems based on the well-known Picard iteration. To this end, we first investigate a class of sequential integrators, known as numerical Picard iteration methods, which falls into the general framework of deferred correction methods. We show that the numerical Picard iteration methods admit a $$\\\\min (J,M+ 1 ... Can parallel Picard methods reduce the iteration cost? As data size grows, reducing the iteration cost becomes an important goal. Inspired by the great success of the parallel simulation of the initial value problem in scientific computation, we propose parallel Picard methods for sampling tasks. Why is a parallel sampling method based on Picard iteration important? However, they still face slow generation speeds due to sequential denoising processes. To address this, a parallel sampling method based on Picard iteration was introduced, effectively reducing sequential steps while ensuring exact convergence to the original output . Can a numerical Picard iteration be parallelized on different sub-intervals? In addition to the parallelization in one Picard iteration due to the property of computation segment by segment, we further investigate the parallelization between the numerical Picard iterations on different sub-intervals. We propose a new parallel method called the parallel numerical Picard iteration (PNPI) method . What is parallel numerical Picard iteration (pnpi) method? We propose a new parallel method called the parallel numerical Picard iteration (PNPI) method. Note that in the traditional numerical Picard iteration method one must complete the Picard iterations on the current interval before moving on to the next one. Are there parallel time integrators for initial-value problems based on Picard iteration? In this work, we propose a new class of parallel time integrators for initial-value problems based on the well-known Picard iteration. To this end, we first investigate a class of sequential integrators, known as numerical Picard iteration methods, which falls into the general framework of deferred correction methods. Does the parallelization of the numerical Picard iterations lead to bad convergence? However, the parallelization of the numerical Picard iterations shown in Fig. 1 leads to bad convergence of the iteration. To explain this, we focus on the second Picard iteration on \\ (I_1\\) with the initial-state condition \\ (\\eta _1^ { }\\) and a rough approximation \\ (Y_1^ { }\\). In recent years, there has been a surge of interest in proving discretization bounds for sampling under isoperimetry and for diffusion models. As data size grows, reducing the iteration cost becomes an important goal. Inspired by the great success of the parallel simulation of the initial value problem in scientific computation, we propose parallel Picard methods for sampling tasks. Rigorous ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2503.19731", "content": "Mar 25, 2025 · Recently, diffusion models have achieved significant advances in vision, text, and robotics. However, they still face slow generation speeds due to sequential denoising processes. To address this, a parallel sampling method based on Picard iteration was introduced, effectively reducing sequential steps while ensuring exact convergence to the original output. Nonetheless, Picard iteration does ... Recently, ParaDiGMS [38] tackled this challenge with a parallel sampling method based on Picard iteration, reducing the sequential steps in diffusion sampling by performing more computations in parallel . The overall procedure is summarized in Algorithm 1 , and several computational details involved in Algorithm 1 are discussed below. Algorithm 1 Deep Picard Iteration (DPI) Algorithm Input: Number of Picard iterations K, number of data points N per iteration, number of Monte Carlo sampling M, number of epochs Eper iteration for training neural ... Mar 1 , 2023 · In this work, we propose a new class of parallel time integrators for initial-value problems based on the well-known Picard iteration. To this end, we first investigate a class of sequential integrators, known as numerical Picard iteration methods, which falls into the general framework of deferred correction methods. We show that the numerical Picard iteration methods admit a $$\\\\min (J,M+ 1 ... Can parallel Picard methods reduce the iteration cost? As data size grows, reducing the iteration cost becomes an important goal. Inspired by the great success of the parallel simulation of the initial value problem in scientific computation, we propose parallel Picard methods for sampling tasks. Why is a parallel sampling method based on Picard iteration important? However, they still face slow generation speeds due to sequential denoising processes. To address this, a parallel sampling method based on Picard iteration was introduced, effectively reducing sequential steps while ensuring exact convergence to the original output . Can a numerical Picard iteration be parallelized on different sub-intervals? In addition to the parallelization in one Picard iteration due to the property of computation segment by segment, we further investigate the parallelization between the numerical Picard iterations on different sub-intervals. We propose a new parallel method called the parallel numerical Picard iteration (PNPI) method . What is parallel numerical Picard iteration (pnpi) method? We propose a new parallel method called the parallel numerical Picard iteration (PNPI) method. Note that in the traditional numerical Picard iteration method one must complete the Picard iterations on the current interval before moving on to the next one. Are there parallel time integrators for initial-value problems based on Picard iteration? In this work, we propose a new class of parallel time integrators for initial-value problems based on the well-known Picard iteration. To this end, we first investigate a class of sequential integrators, known as numerical Picard iteration methods, which falls into the general framework of deferred correction methods. Does the parallelization of the numerical Picard iterations lead to bad convergence? However, the parallelization of the numerical Picard iterations shown in Fig. 1 leads to bad convergence of the iteration. To explain this, we focus on the second Picard iteration on \\ (I_1\\) with the initial-state condition \\ (\\eta _1^ { }\\) and a rough approximation \\ (Y_1^ { }\\). In recent years, there has been a surge of interest in proving discretization bounds for sampling under isoperimetry and for diffusion models. As data size grows, reducing the iteration cost becomes an important goal. Inspired by the great success of the parallel simulation of the initial value problem in scientific computation, we propose parallel Picard methods for sampling tasks. Rigorous ..."}
+{"idx": 4, "title": "PCM : Picard Consistency Model for Fast Parallel Sampling of ...", "date": "", "ddg_snippet": "Recently, ParaDiGMS [38] tackled this challenge with a parallel sampling method based on Picard iteration, reducing the sequential steps in diffusion sampling by performing more computations in parallel .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2503.19731", "content": "Recently, ParaDiGMS [38] tackled this challenge with a parallel sampling method based on Picard iteration, reducing the sequential steps in diffusion sampling by performing more computations in parallel ."}
+{"idx": 5, "title": "arXiv:2409.08526v2 [math.NA] 5 Jul 2025", "date": "", "ddg_snippet": "The overall procedure is summarized in Algorithm 1 , and several computational details involved in Algorithm 1 are discussed below. Algorithm 1 Deep Picard Iteration (DPI) Algorithm Input: Number of Picard iterations K, number of data points N per iteration, number of Monte Carlo sampling M, number of epochs Eper iteration for training neural ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2409.08526", "content": "The overall procedure is summarized in Algorithm 1 , and several computational details involved in Algorithm 1 are discussed below. Algorithm 1 Deep Picard Iteration (DPI) Algorithm Input: Number of Picard iterations K, number of data points N per iteration, number of Monte Carlo sampling M, number of epochs Eper iteration for training neural ..."}
+{"idx": 6, "title": "Parallel Numerical Picard Iteration Methods | Journal of ...", "date": "", "ddg_snippet": "Mar 1 , 2023 · In this work, we propose a new class of parallel time integrators for initial-value problems based on the well-known Picard iteration. To this end, we first investigate a class of sequential integrators, known as numerical Picard iteration methods, which falls into the general framework of deferred correction methods. We show that the numerical Picard iteration methods admit a $$\\\\min (J,M+ 1 ...", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/article/10.1007/s10915-023-02156-y", "content": "Mar 1 , 2023 · In this work, we propose a new class of parallel time integrators for initial-value problems based on the well-known Picard iteration. To this end, we first investigate a class of sequential integrators, known as numerical Picard iteration methods, which falls into the general framework of deferred correction methods. We show that the numerical Picard iteration methods admit a $$\\\\min (J,M+ 1 ..."}
+{"idx": 7, "title": "Parallel simulation for sampling under isoperimetry and ...", "date": "", "ddg_snippet": "In recent years, there has been a surge of interest in proving discretization bounds for sampling under isoperimetry and for diffusion models. As data size grows, reducing the iteration cost becomes an important goal. Inspired by the great success of the parallel simulation of the initial value problem in scientific computation, we propose parallel Picard methods for sampling tasks. Rigorous ...", "subpage_snippet": "", "source": "xs.typicalgame.com", "link": "https://xs.typicalgame.com/citations?view_op=view_citation&hl=en&user=GkYIrlIAAAAJ&sortby=pubdate&citation_for_view=GkYIrlIAAAAJ:9aOYe38lPcwC", "content": "In recent years, there has been a surge of interest in proving discretization bounds for sampling under isoperimetry and for diffusion models. As data size grows, reducing the iteration cost becomes an important goal. Inspired by the great success of the parallel simulation of the initial value problem in scientific computation, we propose parallel Picard methods for sampling tasks. Rigorous ..."}
+{"idx": 8, "title": "Parallel", "date": "", "ddg_snippet": "Parallel Picard method for sampling under isoperimetry. arXiv : 2412 . 07435 v2 [cs.DS] 12 Dec 2024. Parallel simulation for sampling under isoperimetry and. score-based diffusion models.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2412.07435", "content": "Parallel Picard method for sampling under isoperimetry. arXiv : 2412 . 07435 v2 [cs.DS] 12 Dec 2024. Parallel simulation for sampling under isoperimetry and. score-based diffusion models."}
+{"idx": 9, "title": "Parallel simulation for sampling under isoperimetry and score-based...", "date": "", "ddg_snippet": "4 Parallel Picard method for sampling under isoperimetry.In this section, we present parallel Picard methods for sampling under isoperimetry ( Algorithm 1 ) and show it holds improved convergence rate w.r.t. the. 𝖪𝖫𝖪𝖫\\mathsf{KL}sansserif_KL.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2412.07435v1", "content": "4 Parallel Picard method for sampling under isoperimetry.In this section, we present parallel Picard methods for sampling under isoperimetry ( Algorithm 1 ) and show it holds improved convergence rate w.r.t. the. 𝖪𝖫𝖪𝖫\\mathsf{KL}sansserif_KL."}
diff --git a/data/sampled_jsons/arXiv_2502.20727_Figure_7c_LLaMA2-70B_70%_SPD_speedup.jsonl b/data/sampled_jsons/arXiv_2502.20727_Figure_7c_LLaMA2-70B_70%_SPD_speedup.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..ae7fe4d6b5cfca360a8612e662bea1edd5c9bf61
--- /dev/null
+++ b/data/sampled_jsons/arXiv_2502.20727_Figure_7c_LLaMA2-70B_70%_SPD_speedup.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "[ 2502 . 20727 ] SPD : Sync-Point Drop for efficient tensor parallelism of...", "date": "", "ddg_snippet": "...degradation during LLM inference, offering a scalable solution for diverse distributed environments: SPD offered about 20% overall inference latency reduction with < 1% accuracy regression for LLaMA 2 - 70 B inference over 8 GPUs.(or arXiv : 2502 . 20727 v1 [cs.DC] for this version).", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2502.20727", "content": "...degradation during LLM inference, offering a scalable solution for diverse distributed environments: SPD offered about 20% overall inference latency reduction with < 1% accuracy regression for LLaMA 2 - 70 B inference over 8 GPUs.(or arXiv : 2502 . 20727 v1 [cs.DC] for this version)."}
+{"idx": 1, "title": "meta-llama/ Llama - 2 -7b · Hugging Face", "date": "", "ddg_snippet": "arxiv : 2307.09288. Llama 2 is a collection of pretrained and fine-tuned generative text models ranging in scale from 7 billion to 70 billion parameters. This is the repository for the 7B pretrained model. Links to other models can be found in the index at the bottom.", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/meta-llama/Llama-2-7b", "content": "arxiv : 2307.09288. Llama 2 is a collection of pretrained and fine-tuned generative text models ranging in scale from 7 billion to 70 billion parameters. This is the repository for the 7B pretrained model. Links to other models can be found in the index at the bottom."}
+{"idx": 2, "title": "How To Install UNCENSORED Llama 2 Locally... - YouTube", "date": "", "ddg_snippet": "Welcome to the ultimate guide on how to unlock the full potential of the language model in Llama 2 by installing the uncensored version!", "subpage_snippet": "", "source": "www.youtube.com", "link": "https://www.youtube.com/watch?v=RpGXhpeH668", "content": "Welcome to the ultimate guide on how to unlock the full potential of the language model in Llama 2 by installing the uncensored version!"}
+{"idx": 3, "title": "Как дообучить LLaMA бесплатно и без программирования... / Хабр", "date": "", "ddg_snippet": "Что потребуется для запуска нейронки. Есть две версии модели: LLaMA и LLaMA 2 . LLaMA есть в размерах 7B, 13, 30B, 65B, LLaMA 2 - в размерах 7B, 13B и 70 B . 7B весит примерно 13 гб, 65B - 120 гб.", "subpage_snippet": "", "source": "habr.com", "link": "https://habr.com/ru/articles/755114/", "content": "Что потребуется для запуска нейронки. Есть две версии модели: LLaMA и LLaMA 2 . LLaMA есть в размерах 7B, 13, 30B, 65B, LLaMA 2 - в размерах 7B, 13B и 70 B . 7B весит примерно 13 гб, 65B - 120 гб."}
+{"idx": 4, "title": "togetherai/ llama - 2 - 70 b", "date": "", "ddg_snippet": "togetherai. llama - 2 - 70 b .Preview. YAML. together LLaMA - 2 ( 70 B ) model icon.", "subpage_snippet": "", "source": "hub.continue.dev", "link": "https://hub.continue.dev/togetherai/llama-2-70b", "content": "togetherai. llama - 2 - 70 b .Preview. YAML. together LLaMA - 2 ( 70 B ) model icon."}
+{"idx": 5, "title": "Ускорение инференса LLM через тензорный... — AI на vc.ru", "date": "", "ddg_snippet": "vllm serve meta-llama/Meta-Llama-3.1-405B-Instruct --tensor-parallel-size 16. Пропускная способность инференса на 16 GPU H100 (по 8 GPU на узел) с использованием синтетического набора данных (средняя длина входных данных 1024, средняя длина выходных...", "subpage_snippet": "", "source": "vc.ru", "link": "https://vc.ru/ai/2052555-uskorenie-inferensa-llm-s-pomoshchyu-tenzornogo-parallelizma", "content": "vllm serve meta-llama/Meta-Llama-3.1-405B-Instruct --tensor-parallel-size 16. Пропускная способность инференса на 16 GPU H100 (по 8 GPU на узел) с использованием синтетического набора данных (средняя длина входных данных 1024, средняя длина выходных..."}
+{"idx": 6, "title": "Построение RAG c большой языковой моделью LLM (Llama...)", "date": "", "ddg_snippet": "Узнайте, как построить RAG (Retrieval-Augmented Generation) с использованием большой языковой модели LLM ( Llama 2 ) и FAISS. Пошаговое руководство для оптимизации генерации ответов на запросы.", "subpage_snippet": "", "source": "blogs.epsilonmetrics.ru", "link": "https://blogs.epsilonmetrics.ru/guide-building-rag-llama-2-faiss/", "content": "Узнайте, как построить RAG (Retrieval-Augmented Generation) с использованием большой языковой модели LLM ( Llama 2 ) и FAISS. Пошаговое руководство для оптимизации генерации ответов на запросы."}
+{"idx": 7, "title": "Llama - 2 - 70 b inference initial untuned results with vLLM...", "date": "", "ddg_snippet": "Llama - 2 - 70 b _Server_inital_untuned. This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.", "subpage_snippet": "", "source": "gist.github.com", "link": "https://gist.github.com/dfeddema/0978af2741b988fe5b25dae2c8e7fd23", "content": "Llama - 2 - 70 b _Server_inital_untuned. This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters."}
+{"idx": 8, "title": "Llama 2 Online - Next-Gen Large Language Model by Meta", "date": "", "ddg_snippet": "Experience the power of Llama 2 , the second-generation Large Language Model by Meta. Choose from three model sizes, pre-trained on 2 trillion tokens, and fine-tuned with over a million human-annotated examples. Try it now online!", "subpage_snippet": "", "source": "www.llama2.space", "link": "https://www.llama2.space/", "content": "Experience the power of Llama 2 , the second-generation Large Language Model by Meta. Choose from three model sizes, pre-trained on 2 trillion tokens, and fine-tuned with over a million human-annotated examples. Try it now online!"}
+{"idx": 9, "title": "Alibaba-NLP/Tongyi-DeepResearch-30B-A3B | Chutes", "date": "", "ddg_snippet": "deepseek-r1- 70 b deepseek-r1- 70 b . cosy-voice-tts-16g cosy-voice-tts-16g.", "subpage_snippet": "", "source": "chutes.ai", "link": "https://chutes.ai/app/chute/0e392a49-6f77-56c4-99d6-57c06851321e", "content": "deepseek-r1- 70 b deepseek-r1- 70 b . cosy-voice-tts-16g cosy-voice-tts-16g."}
diff --git a/data/sampled_jsons/arxiv.org_2410.00844_LEnergy_equation_10.jsonl b/data/sampled_jsons/arxiv.org_2410.00844_LEnergy_equation_10.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..8ab078c419144ba5d66862d05b755d3d8dc7c58c
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+{"idx": 0, "title": "[ 2410 . 00844 ] Learning stochastic dynamics from snapshots through...", "date": "", "ddg_snippet": "arXiv : 2410 . 00844 (cs). [Submitted on 1 Oct 2024 (v1), last revised 8 May 2025 (this version, v5)].Machine Learning (cs.LG); Optimization and Control (math.OC); Computational Physics (physics.comp-ph); Quantitative Methods (q-bio.QM). Cite as: arXiv : 2410 . 00844 [cs.LG].", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2410.00844", "content": "arXiv : 2410 . 00844 (cs). [Submitted on 1 Oct 2024 (v1), last revised 8 May 2025 (this version, v5)].Machine Learning (cs.LG); Optimization and Control (math.OC); Computational Physics (physics.comp-ph); Quantitative Methods (q-bio.QM). Cite as: arXiv : 2410 . 00844 [cs.LG]."}
+{"idx": 1, "title": "Обзоры препринтов научных статей «astro-ph/ arxiv . org » за... / Хабр", "date": "", "ddg_snippet": "Ежемесячный обзор научных статей в области астрофизики от профессора МГУ Сергея Попова. Выборка интересных публикаций в области астрономии, астрофизики и физики с сайта препринтов arxiv . org .", "subpage_snippet": "", "source": "habr.com", "link": "https://habr.com/ru/articles/948996/", "content": "Ежемесячный обзор научных статей в области астрофизики от профессора МГУ Сергея Попова. Выборка интересных публикаций в области астрономии, астрофизики и физики с сайта препринтов arxiv . org ."}
+{"idx": 2, "title": "arXiv.org e-Print archive", "date": "", "ddg_snippet": "arXiv is a free distribution service and an open-access archive for nearly 2.4 million scholarly articles in the fields of physics, mathematics, computer science, quantitative biology, quantitative finance, statistics, electrical engineering and systems science, and economics. Materials on this site are not peer-reviewed by arXiv .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/", "content": "arXiv is a free distribution service and an open-access archive for nearly 2.4 million scholarly articles in the fields of physics, mathematics, computer science, quantitative biology, quantitative finance, statistics, electrical engineering and systems science, and economics. Materials on this site are not peer-reviewed by arXiv ."}
+{"idx": 3, "title": "[2410.05034] The energy-critical stochastic Zakharov system [2410.16040] Energy-Momentum tensor correlators in $ϕ^4 ... Publications - Zhenyi Zhang Laplace neural operator for solving differential equations (PDF) Average energy dissipation rates of additive implicit ...", "date": "", "ddg_snippet": "Oct 7, 2024 · The proof is based on the refined rescaling approach and a new functional framework, where both Fourier restriction and local smoothing norms are used as well as a (uniform) double endpoint Strichartz and local smoothing inequality for the Schrödinger equation with certain rough and time dependent lower order perturbations. Oct 21, 2024 · Finally, we compute the 2-point function $\\braket {\\Theta\\Theta}$ and we show that it obeys an eigenvalue equation that gives additional information about the internal structure of the Energy -Momentum tensor operator to what is already contained in its Callan-Symanzik equation . These technologies, when combined with computational frameworks such as Markov chains, stochastic differential equations (SDEs), and generative models like optimal transport and Schrödinger bridges, enable the reconstruction of dynamic cellular trajectories and cell fate decisions. Real-world problems in computational science and engineering that lack closed-form solutions often require the use of expensive numeri-cal solvers. However, recent advances in scientific machine ... Oct 10 , 2024 · A unified theoretical framework is suggested to examine the energy dissipation properties at all stages of additive implicit-explicit Runge-Kutta (IERK) methods up to fourth-order accuracy for ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2410.05034", "content": "Oct 7, 2024 · The proof is based on the refined rescaling approach and a new functional framework, where both Fourier restriction and local smoothing norms are used as well as a (uniform) double endpoint Strichartz and local smoothing inequality for the Schrödinger equation with certain rough and time dependent lower order perturbations. Oct 21, 2024 · Finally, we compute the 2-point function $\\braket {\\Theta\\Theta}$ and we show that it obeys an eigenvalue equation that gives additional information about the internal structure of the Energy -Momentum tensor operator to what is already contained in its Callan-Symanzik equation . These technologies, when combined with computational frameworks such as Markov chains, stochastic differential equations (SDEs), and generative models like optimal transport and Schrödinger bridges, enable the reconstruction of dynamic cellular trajectories and cell fate decisions. Real-world problems in computational science and engineering that lack closed-form solutions often require the use of expensive numeri-cal solvers. However, recent advances in scientific machine ... Oct 10 , 2024 · A unified theoretical framework is suggested to examine the energy dissipation properties at all stages of additive implicit-explicit Runge-Kutta (IERK) methods up to fourth-order accuracy for ..."}
+{"idx": 4, "title": "[2410.16040] Energy-Momentum tensor correlators in $ϕ^4 ... Publications - Zhenyi Zhang Laplace neural operator for solving differential equations (PDF) Average energy dissipation rates of additive implicit ...", "date": "", "ddg_snippet": "Oct 21, 2024 · Finally, we compute the 2-point function $\\braket {\\Theta\\Theta}$ and we show that it obeys an eigenvalue equation that gives additional information about the internal structure of the Energy -Momentum tensor operator to what is already contained in its Callan-Symanzik equation . These technologies, when combined with computational frameworks such as Markov chains, stochastic differential equations (SDEs), and generative models like optimal transport and Schrödinger bridges, enable the reconstruction of dynamic cellular trajectories and cell fate decisions. Real-world problems in computational science and engineering that lack closed-form solutions often require the use of expensive numeri-cal solvers. However, recent advances in scientific machine ... Oct 10 , 2024 · A unified theoretical framework is suggested to examine the energy dissipation properties at all stages of additive implicit-explicit Runge-Kutta (IERK) methods up to fourth-order accuracy for ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2410.16040", "content": "Oct 21, 2024 · Finally, we compute the 2-point function $\\braket {\\Theta\\Theta}$ and we show that it obeys an eigenvalue equation that gives additional information about the internal structure of the Energy -Momentum tensor operator to what is already contained in its Callan-Symanzik equation . These technologies, when combined with computational frameworks such as Markov chains, stochastic differential equations (SDEs), and generative models like optimal transport and Schrödinger bridges, enable the reconstruction of dynamic cellular trajectories and cell fate decisions. Real-world problems in computational science and engineering that lack closed-form solutions often require the use of expensive numeri-cal solvers. However, recent advances in scientific machine ... Oct 10 , 2024 · A unified theoretical framework is suggested to examine the energy dissipation properties at all stages of additive implicit-explicit Runge-Kutta (IERK) methods up to fourth-order accuracy for ..."}
+{"idx": 5, "title": "Живые обои Китовое облако скачать на VSThemes. org", "date": "", "ddg_snippet": "1. 10 K. Живые обои «Уставший кот с чашкой кофе».", "subpage_snippet": "", "source": "VSThemes.org", "link": "https://VSThemes.org/wallpapers/nature/72967-whale-cloud.html", "content": "1. 10 K. Живые обои «Уставший кот с чашкой кофе»."}
+{"idx": 6, "title": "Laplace neural operator for solving differential equations", "date": "", "ddg_snippet": "Real-world problems in computational science and engineering that lack closed-form solutions often require the use of expensive numeri-cal solvers. However, recent advances in scientific machine ...", "subpage_snippet": "", "source": "www.nature.com", "link": "https://www.nature.com/articles/s42256-024-00844-4.pdf", "content": "Real-world problems in computational science and engineering that lack closed-form solutions often require the use of expensive numeri-cal solvers. However, recent advances in scientific machine ..."}
+{"idx": 7, "title": "How to Calculate Kinetic Energy : 9 Steps (with Pictures) - wikiHow", "date": "", "ddg_snippet": "There are two basic forms of energy : potential and kinetic energy . Potential energy is the energy an object has relative to the position of another object.Write the equation . The formula for calculating kinetic energy (KE) is KE = 0.5 x mv2.", "subpage_snippet": "", "source": "www.wikihow.com", "link": "https://www.wikihow.com/Calculate-Kinetic-Energy", "content": "There are two basic forms of energy : potential and kinetic energy . Potential energy is the energy an object has relative to the position of another object.Write the equation . The formula for calculating kinetic energy (KE) is KE = 0.5 x mv2."}
+{"idx": 8, "title": "Калькулятор уравнений", "date": "", "ddg_snippet": "equation icon Уравнения.Значения. x equals .", "subpage_snippet": "", "source": "mathdf.com", "link": "https://mathdf.com/equ/ru/", "content": "equation icon Уравнения.Значения. x equals ."}
+{"idx": 9, "title": "Learning stochastic dynamics from snapshots through ...", "date": "", "ddg_snippet": "By leveraging Fisher regularization, our method transforms a stochastic differential equation (SDE) problem into an ordinary differential equation (ODE) constraint. Through the use of neural network modeling for growth and death, our framework models dynamics without requiring prior knowledge of these processes.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2410.00844v1", "content": "By leveraging Fisher regularization, our method transforms a stochastic differential equation (SDE) problem into an ordinary differential equation (ODE) constraint. Through the use of neural network modeling for growth and death, our framework models dynamics without requiring prior knowledge of these processes."}
diff --git a/data/sampled_jsons/arxiv.orgabs2010.01412_Foret_Sharpness-Aware_Minimization_abstract_generalization.jsonl b/data/sampled_jsons/arxiv.orgabs2010.01412_Foret_Sharpness-Aware_Minimization_abstract_generalization.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..b3c7ea431d0896f2c777ca6a11e2ec14b6f38bf5
--- /dev/null
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@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Sharpness-Aware Minimization for Efficiently Improving ...", "date": "", "ddg_snippet": "by P Foret · 2020 · Cited by 1939 — We present empirical results showing that SAM improves model generalization across a variety of benchmark datasets (eg, CIFAR-10, CIFAR-100, ImageNet, ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2010.01412", "content": "by P Foret · 2020 · Cited by 1939 — We present empirical results showing that SAM improves model generalization across a variety of benchmark datasets (eg, CIFAR-10, CIFAR-100, ImageNet, ..."}
+{"idx": 1, "title": "ReMem: Mutual Information-Aware Fine-tuning of Pretrained", "date": "", "ddg_snippet": "We find that Sharpness - Aware Minimization (SAM) ( Foret et al., 2020 ) , while traditionally used to improve model generalization , can be ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2506.23041v1", "content": "We find that Sharpness - Aware Minimization (SAM) ( Foret et al., 2020 ) , while traditionally used to improve model generalization , can be ..."}
+{"idx": 2, "title": "sharpness-aware minimization for efficiently improving ...", "date": "", "ddg_snippet": "by P Foret · 2020 · Cited by 1939 — We show via a rigorous empirical study that using SAM improves model generalization ability across a range of widely studied computer vision ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2010.01412", "content": "by P Foret · 2020 · Cited by 1939 — We show via a rigorous empirical study that using SAM improves model generalization ability across a range of widely studied computer vision ..."}
+{"idx": 3, "title": "Sharpness-aware Minimization for Efficiently Improving ...", "date": "", "ddg_snippet": "by P Foret · Cited by 1939 — We introduce a novel, effective procedure for instead simultaneously minimizing loss value and loss sharpness .", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=6Tm1mposlrM", "content": "by P Foret · Cited by 1939 — We introduce a novel, effective procedure for instead simultaneously minimizing loss value and loss sharpness ."}
+{"idx": 4, "title": "Sharpness-Aware Minimization in Genetic Programming", "date": "", "ddg_snippet": "17 May 2024 — Sharpness-Aware Minimization (SAM ) was recently introduced as a regularization procedure for training deep neural networks.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2405.10267v2", "content": "17 May 2024 — Sharpness-Aware Minimization (SAM ) was recently introduced as a regularization procedure for training deep neural networks."}
+{"idx": 5, "title": "Sharpness-aware Parameter Selection for Machine ...", "date": "", "ddg_snippet": "8 Apr 2025 — Sharpness-aware minimization for efficiently improving generalization , 2021. URL https://arxiv.org/abs/2010.01412. Foster et al. (2024)", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2504.06398v1", "content": "8 Apr 2025 — Sharpness-aware minimization for efficiently improving generalization , 2021. URL https://arxiv.org/abs/2010.01412. Foster et al. (2024)"}
+{"idx": 6, "title": "How Does Sharpness-Aware Minimization ...", "date": "", "ddg_snippet": "by K Wen · 2022 · Cited by 99 — Abstract. Sharpness-Aware Minimization (SAM ) is a highly effective regularization technique for improving the generalization of deep neural ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2211.05729", "content": "by K Wen · 2022 · Cited by 99 — Abstract. Sharpness-Aware Minimization (SAM ) is a highly effective regularization technique for improving the generalization of deep neural ..."}
+{"idx": 7, "title": "Practical Sharpness-Aware Minimization Cannot Converge ...", "date": "", "ddg_snippet": "Foret, A. Kleiner, H. Mobahi, and B. Neyshabur. Sharpness-aware minimization for efficiently improving generalization . arXiv preprint arXiv:2010.01412, 2020.", "subpage_snippet": "", "source": "neurips.cc", "link": "https://neurips.cc/virtual/2023/poster/70489", "content": "Foret, A. Kleiner, H. Mobahi, and B. Neyshabur. Sharpness-aware minimization for efficiently improving generalization . arXiv preprint arXiv:2010.01412, 2020."}
+{"idx": 8, "title": "Rethinking Sharpness-Aware Minimization as Variational Inference", "date": "", "ddg_snippet": "Abstract. Sharpness-aware minimisation (SAM ) aims to improve the generalisation of gradient-based learn- ing by seeking out flat minima.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/references/pdf?id=f1OgsmV7K", "content": "Abstract. Sharpness-aware minimisation (SAM ) aims to improve the generalisation of gradient-based learn- ing by seeking out flat minima."}
+{"idx": 9, "title": "Avoiding spurious sharpness minimization broadens ...", "date": "", "ddg_snippet": "4 Feb 2025 — Curvature regularization techniques like Sharpness Aware Minimization (\\SAM) have shown great promise in improving generalization on vision ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2502.02407v1", "content": "4 Feb 2025 — Curvature regularization techniques like Sharpness Aware Minimization (\\SAM) have shown great promise in improving generalization on vision ..."}
diff --git a/data/sampled_jsons/arxiv.orgabs2207.02269.jsonl b/data/sampled_jsons/arxiv.orgabs2207.02269.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..6b175664444939c188207de83a6d9f55a41b1610
--- /dev/null
+++ b/data/sampled_jsons/arxiv.orgabs2207.02269.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "arXiv.org e-Print archive", "date": "", "ddg_snippet": "arXiv is a free distribution service and an open-access archive for nearly 2.4 million scholarly articles in the fields of physics, mathematics, computer science, quantitative biology, quantitative finance, statistics, electrical engineering and systems science, and economics.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/", "content": "arXiv is a free distribution service and an open-access archive for nearly 2.4 million scholarly articles in the fields of physics, mathematics, computer science, quantitative biology, quantitative finance, statistics, electrical engineering and systems science, and economics."}
+{"idx": 1, "title": "[2507.02269] Numerical simulations of electron acceleration driven by ...", "date": "", "ddg_snippet": "Abstract page for arXiv paper 2507.02269: Numerical simulations of electron acceleration driven by heavy ion beams in plasma with alternating density gradients", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2507.02269", "content": "Abstract page for arXiv paper 2507.02269: Numerical simulations of electron acceleration driven by heavy ion beams in plasma with alternating density gradients"}
+{"idx": 2, "title": "[2007.02269] Rethinking Bottleneck Structure for Efficient Mobile ...", "date": "", "ddg_snippet": "Abstract page for arXiv paper 2007.02269: Rethinking Bottleneck Structure for Efficient Mobile Network Design", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2007.02269", "content": "Abstract page for arXiv paper 2007.02269: Rethinking Bottleneck Structure for Efficient Mobile Network Design"}
+{"idx": 3, "title": "[2203.02269] Do Explanations Explain? Model Knows Best - arXiv.org", "date": "", "ddg_snippet": "Abstract page for arXiv paper 2203.02269: Do Explanations Explain? Model Knows Best", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2203.02269", "content": "Abstract page for arXiv paper 2203.02269: Do Explanations Explain? Model Knows Best"}
+{"idx": 4, "title": "[2207.02269] Towards Realistic Semi-Supervised Learning - arXiv.org", "date": "", "ddg_snippet": "Deep learning is pushing the state-of-the-art in many computer vision applications. However, it relies on large annotated data repositories, and capturing the unconstrained nature of the real-world data is yet to be solved. Semi-supervised learning (SSL) complements the annotated training data with a large corpus of unlabeled data to reduce annotation cost. The standard SSL approach assumes ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2207.02269", "content": "Deep learning is pushing the state-of-the-art in many computer vision applications. However, it relies on large annotated data repositories, and capturing the unconstrained nature of the real-world data is yet to be solved. Semi-supervised learning (SSL) complements the annotated training data with a large corpus of unlabeled data to reduce annotation cost. The standard SSL approach assumes ..."}
+{"idx": 5, "title": "[2207.02269] Towards Realistic Semi-Supervised Learning", "date": "", "ddg_snippet": "Deep learning is pushing the state-of-the-art in many computer vision applications. However, it relies on large annotated data repositories, and capturing the unconstrained nature of the real-world data is yet to be so…", "subpage_snippet": "", "source": "ar5iv.labs.arxiv.org", "link": "https://ar5iv.labs.arxiv.org/abs/2207.02269", "content": "Deep learning is pushing the state-of-the-art in many computer vision applications. However, it relies on large annotated data repositories, and capturing the unconstrained nature of the real-world data is yet to be so…"}
+{"idx": 6, "title": "[2207.02069] Anatomy of a slow merger: dissecting secularly-driven ...", "date": "", "ddg_snippet": "The dozens of compact object mergers detected by LIGO/Virgo raise a key theoretical question: how do initially wide binaries shrink sufficiently quickly that they are able to merge via gravitational wave (GW) radiation within a Hubble time? One promising class of answers involves secular driving of binary eccentricity by some external tidal perturbation. This perturbation can arise due to the ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2207.02069", "content": "The dozens of compact object mergers detected by LIGO/Virgo raise a key theoretical question: how do initially wide binaries shrink sufficiently quickly that they are able to merge via gravitational wave (GW) radiation within a Hubble time? One promising class of answers involves secular driving of binary eccentricity by some external tidal perturbation. This perturbation can arise due to the ..."}
+{"idx": 7, "title": "Next-generation HPC models for future rotorcraft applications", "date": "", "ddg_snippet": "Abstract page for arXiv paper 2207.12269: Next-generation HPC models for future rotorcraft applications", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2207.12269", "content": "Abstract page for arXiv paper 2207.12269: Next-generation HPC models for future rotorcraft applications"}
+{"idx": 8, "title": "Towards Realistic Semi-Supervised Learning | Cool Papers - Immersive ...", "date": "", "ddg_snippet": "Deep learning is pushing the state-of-the-art in many computer vision applications. However, it relies on large annotated data repositories, and capturing the unconstrained nature of the real-world data is yet to be solved. Semi-supervised learning (SSL) complements the annotated training data with a large corpus of unlabeled data to reduce annotation cost. The standard SSL approach assumes ...", "subpage_snippet": "", "source": "papers.cool", "link": "https://papers.cool/arxiv/2207.02269", "content": "Deep learning is pushing the state-of-the-art in many computer vision applications. However, it relies on large annotated data repositories, and capturing the unconstrained nature of the real-world data is yet to be solved. Semi-supervised learning (SSL) complements the annotated training data with a large corpus of unlabeled data to reduce annotation cost. The standard SSL approach assumes ..."}
+{"idx": 9, "title": "\"Towards Realistic Semi-Supervised Learning.\" - dblp", "date": "", "ddg_snippet": "Bibliographic details on Towards Realistic Semi-Supervised Learning.", "subpage_snippet": "", "source": "dblp.org", "link": "https://dblp.org/rec/journals/corr/abs-2207-02269", "content": "Bibliographic details on Towards Realistic Semi-Supervised Learning."}
diff --git a/data/sampled_jsons/arxiv.orgabs2406.13909_Table_1_Appendix_Two-Room_2x11_Rnd._Experts.jsonl b/data/sampled_jsons/arxiv.orgabs2406.13909_Table_1_Appendix_Two-Room_2x11_Rnd._Experts.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..2022370d1946158aeb69e330277840bb35a8bfa1
--- /dev/null
+++ b/data/sampled_jsons/arxiv.orgabs2406.13909_Table_1_Appendix_Two-Room_2x11_Rnd._Experts.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Beyond Optimism: Exploration With Partially Observable Rewards", "date": "", "ddg_snippet": "Table 1 : Number of training steps.5), Two - Room ( 2 . ×\\times×. 11), and Corridor (20) are aligned with the remainder, showing that Our exploration strategy outperforms all the baselines.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2406.13909v2", "content": "Table 1 : Number of training steps.5), Two - Room ( 2 . ×\\times×. 11), and Corridor (20) are aligned with the remainder, showing that Our exploration strategy outperforms all the baselines."}
+{"idx": 1, "title": "(Решено) Упр.9 Модуль 1 c ГДЗ Spotlight 11 класс с переводом...", "date": "", "ddg_snippet": "6 - into (come into - унаследовать). Приведем выдержку из задания из учебника Юлия Ваулина, Джунни Дули 11 класс, Просвещение: 9 Fill in: down with, across, up with, into, over. Check in Appendix 2.", "subpage_snippet": "", "source": "reshak.ru", "link": "https://reshak.ru/otvet/otvet_txt.php?otvet1=/spotlight11/images/pic/1c_9", "content": "6 - into (come into - унаследовать). Приведем выдержку из задания из учебника Юлия Ваулина, Джунни Дули 11 класс, Просвещение: 9 Fill in: down with, across, up with, into, over. Check in Appendix 2."}
+{"idx": 2, "title": "Login | Figma", "date": "", "ddg_snippet": "Figma Data Table Library.", "subpage_snippet": "", "source": "www.figma.com", "link": "https://www.figma.com/login", "content": "Figma Data Table Library."}
+{"idx": 3, "title": "(PDF) Beyond Optimism: Exploration With Partially Observable Rewards", "date": "", "ddg_snippet": "Two - Room ( 2 × 11) Corridor(20). … River Swim (6). Figure 7: Fullsetofthetabularenvironmentsusedforourexperiments.allows Q-Learning to conv erge given enough exploration [. 50. ]. The reward model is a table .", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/381579170_Beyond_Optimism_Exploration_With_Partially_Observable_Rewards", "content": "Two - Room ( 2 × 11) Corridor(20). … River Swim (6). Figure 7: Fullsetofthetabularenvironmentsusedforourexperiments.allows Q-Learning to conv erge given enough exploration [. 50. ]. The reward model is a table ."}
+{"idx": 4, "title": "BetBoom Streamers Battle x Динамо – Telegram", "date": "", "ddg_snippet": "ТраВоМаН выступит в качестве капитана команды на BetBoom Streamers Battle х Динамо 11. Тимур также примет участие в 12 сезоне турнира, предусматривающим LAN-стадию!", "subpage_snippet": "", "source": "t.me", "link": "https://t.me/s/betboomstreamersbattle", "content": "ТраВоМаН выступит в качестве капитана команды на BetBoom Streamers Battle х Динамо 11. Тимур также примет участие в 12 сезоне турнира, предусматривающим LAN-стадию!"}
+{"idx": 5, "title": "An open platform for evaluating AI through human preference", "date": "", "ddg_snippet": "An open platform for evaluating AI through human preference...", "subpage_snippet": "", "source": "lmarena.ai", "link": "https://lmarena.ai/", "content": "An open platform for evaluating AI through human preference..."}
+{"idx": 6, "title": "Онлайн калькулятор уравнений и неравенств", "date": "", "ddg_snippet": "(x 2 + x 21 )+(x+x 1 )=4 (решить уравнение).Каталог электронных учебников для учеников 1 -11 классов. Онлайн формулы по математике и физике.", "subpage_snippet": "", "source": "findh.org", "link": "https://findh.org/4388-matematicheskij-kalkulyator.html?op=equation", "content": "(x 2 + x 21 )+(x+x 1 )=4 (решить уравнение).Каталог электронных учебников для учеников 1 -11 классов. Онлайн формулы по математике и физике."}
+{"idx": 7, "title": "How To Install KDE Plasma on Debian 13 - idroot", "date": "", "ddg_snippet": "Access the session selector from the login screen by clicking the desktop environment icon, usually located in the top-right or bottom-right corner of the login interface. Select “Plasma ( X 11 )” or “Plasma (Wayland)” based on your preference and hardware compatibility. Setting Default Session.", "subpage_snippet": "", "source": "idroot.us", "link": "https://idroot.us/install-kde-plasma-debian-13/", "content": "Access the session selector from the login screen by clicking the desktop environment icon, usually located in the top-right or bottom-right corner of the login interface. Select “Plasma ( X 11 )” or “Plasma (Wayland)” based on your preference and hardware compatibility. Setting Default Session."}
+{"idx": 8, "title": "qBittorrent - скачать бесплатно qBittorrent 4.4.5 / 5. 1 .2", "date": "", "ddg_snippet": "скачать BitTorrent BitTorrent Free 7.11.0 build 47197. BitTorrent - популярный клиент для работы с p2p-сетями, с помощью которого пользователь можно...", "subpage_snippet": "", "source": "www.SoftPortal.com", "link": "https://www.SoftPortal.com/software-18505-qbittorrent.html", "content": "скачать BitTorrent BitTorrent Free 7.11.0 build 47197. BitTorrent - популярный клиент для работы с p2p-сетями, с помощью которого пользователь можно..."}
+{"idx": 9, "title": "Авито | Объявления в Саратове: купить вещь, выбрать...", "date": "", "ddg_snippet": "Volkswagen Touareg 3.0 AT, 2010, 232 000 км. Ещё 11 фото.", "subpage_snippet": "", "source": "www.Avito.ru", "link": "https://www.Avito.ru/saratov", "content": "Volkswagen Touareg 3.0 AT, 2010, 232 000 км. Ещё 11 фото."}
diff --git a/data/sampled_jsons/arxiv.orgabs2410.23569.jsonl b/data/sampled_jsons/arxiv.orgabs2410.23569.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..bec04a464a966c060640681a1774890f58ae86c4
--- /dev/null
+++ b/data/sampled_jsons/arxiv.orgabs2410.23569.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "arXiv.org e-Print archive", "date": "", "ddg_snippet": "arXiv is a free distribution service and an open-access archive for nearly 2.4 million scholarly articles in the fields of physics, mathematics, computer science, quantitative biology, quantitative finance, statistics, electrical engineering and systems science, and economics.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/", "content": "arXiv is a free distribution service and an open-access archive for nearly 2.4 million scholarly articles in the fields of physics, mathematics, computer science, quantitative biology, quantitative finance, statistics, electrical engineering and systems science, and economics."}
+{"idx": 1, "title": "[2410.23561] A comparative study of dynamic models for gravity-driven ...", "date": "", "ddg_snippet": "Abstract page for arXiv paper 2410.23561: A comparative study of dynamic models for gravity-driven particle-laden flows", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2410.23561", "content": "Abstract page for arXiv paper 2410.23561: A comparative study of dynamic models for gravity-driven particle-laden flows"}
+{"idx": 2, "title": "[2410.23775] In-Context LoRA for Diffusion Transformers - arXiv.org", "date": "", "ddg_snippet": "Recent research arXiv:2410.15027 has explored the use of diffusion transformers (DiTs) for task-agnostic image generation by simply concatenating attention tokens across images. However, despite substantial computational resources, the fidelity of the generated images remains suboptimal. In this study, we reevaluate and streamline this framework by hypothesizing that text-to-image DiTs ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2410.23775", "content": "Recent research arXiv:2410.15027 has explored the use of diffusion transformers (DiTs) for task-agnostic image generation by simply concatenating attention tokens across images. However, despite substantial computational resources, the fidelity of the generated images remains suboptimal. In this study, we reevaluate and streamline this framework by hypothesizing that text-to-image DiTs ..."}
+{"idx": 3, "title": "[2410.00371] AHA: A Vision-Language-Model for Detecting and Reasoning ...", "date": "", "ddg_snippet": "Abstract page for arXiv paper 2410.00371: AHA: A Vision-Language-Model for Detecting and Reasoning Over Failures in Robotic Manipulation", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2410.00371", "content": "Abstract page for arXiv paper 2410.00371: AHA: A Vision-Language-Model for Detecting and Reasoning Over Failures in Robotic Manipulation"}
+{"idx": 4, "title": "Finding Articles - arXiv info", "date": "", "ddg_snippet": "All arXiv submissions are assigned a unique identifier of the form yymm.nnnnn (or arch-ive/yymmnnn for older submissions). To retrieve the abstract page a paper simply enter the identifier in the \" Search or Article-id \" box in the top right of most pages.", "subpage_snippet": "", "source": "info.arxiv.org", "link": "https://info.arxiv.org/help/find/index.html", "content": "All arXiv submissions are assigned a unique identifier of the form yymm.nnnnn (or arch-ive/yymmnnn for older submissions). To retrieve the abstract page a paper simply enter the identifier in the \" Search or Article-id \" box in the top right of most pages."}
+{"idx": 5, "title": "ar5iv - Articles from arXiv.org as responsive HTML5 web documents", "date": "", "ddg_snippet": "ar5iv offers a modern web view for arXiv's preprints. An open community resource, on a quest to a full collection of high-quality documents.", "subpage_snippet": "", "source": "ar5iv.labs.arxiv.org", "link": "https://ar5iv.labs.arxiv.org/", "content": "ar5iv offers a modern web view for arXiv's preprints. An open community resource, on a quest to a full collection of high-quality documents."}
+{"idx": 6, "title": "RA-PbRL: Provably Efficient Risk-Aware Preference-Based ...", "date": "", "ddg_snippet": "by Y Zhao · 2024 · Cited by 4 — Abstract page for arXiv paper 2410.23569 : RA-PbRL: Provably Efficient Risk-Aware Preference-Based Reinforcement Learning.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2410.23569", "content": "by Y Zhao · 2024 · Cited by 4 — Abstract page for arXiv paper 2410.23569 : RA-PbRL: Provably Efficient Risk-Aware Preference-Based Reinforcement Learning."}
+{"idx": 7, "title": "Machine Learning on X: \"RA-PbRL: Provably Efficient Risk ...", "date": "", "ddg_snippet": "RA-PbRL: Provably Efficient Risk-Aware Preference-Based Reinforcement Learning . https://arxiv.org/abs/2410.23569 · 12:38 PM · Nov 1, 2024.", "subpage_snippet": "", "source": "x.com", "link": "https://x.com/Memoirs/status/1852329333261504721", "content": "RA-PbRL: Provably Efficient Risk-Aware Preference-Based Reinforcement Learning . https://arxiv.org/abs/2410.23569 · 12:38 PM · Nov 1, 2024."}
+{"idx": 8, "title": "CAIN: Hijacking LLM-Humans Conversations via Malicious ...", "date": "", "ddg_snippet": "6 Aug 2025 — Firstly, CAIN maxim izes the adversarial effectiveness by employing an automatic sentence-level prompt generation module ... ArXiv, abs/2410.23569.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2505.16888v2", "content": "6 Aug 2025 — Firstly, CAIN maxim izes the adversarial effectiveness by employing an automatic sentence-level prompt generation module ... ArXiv, abs/2410.23569."}
+{"idx": 9, "title": "Huazheng Wang", "date": "", "ddg_snippet": ": https:// arxiv . org / abs / 2410.23569 . I will be at NeurIPS until Sunday and look forward to discussing RL with friends and colleagues! Image. 5 ...", "subpage_snippet": "", "source": "twitter.com", "link": "https://twitter.com/huazheng_wang/status/1866893623624892837", "content": ": https:// arxiv . org / abs / 2410.23569 . I will be at NeurIPS until Sunday and look forward to discussing RL with friends and colleagues! Image. 5 ..."}
diff --git a/data/sampled_jsons/arxiv2502.00921_Table_1_MMLU.jsonl b/data/sampled_jsons/arxiv2502.00921_Table_1_MMLU.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..c8749bd65a60634fa70189d4e8b8a36eb257396d
--- /dev/null
+++ b/data/sampled_jsons/arxiv2502.00921_Table_1_MMLU.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "MMLU-ProX: A Multilingual Benchmark for Advanced Large ... - arXiv.org", "date": "", "ddg_snippet": "gap that MMLU -ProX aims to address. A de-tailed comparison of the aforementioned dataset is shown in Table 1 . Among the selected benchmarks, MMLU -ProX fills an important gap by maintaining balanced distribution of languages, subjects, and questions, with a focus on data parallelization and reasoning-focused features.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2503.10497", "content": "gap that MMLU -ProX aims to address. A de-tailed comparison of the aforementioned dataset is shown in Table 1 . Among the selected benchmarks, MMLU -ProX fills an important gap by maintaining balanced distribution of languages, subjects, and questions, with a focus on data parallelization and reasoning-focused features."}
+{"idx": 1, "title": "cais/mmlu · Datasets at Hugging Face", "date": "", "ddg_snippet": "Dataset Card for MMLU Dataset Summary Measuring Massive Multitask Language Understanding by Dan Hendrycks, Collin Burns, Steven Basart, Andy Zou, Mantas Mazeika, Dawn Song, and Jacob Steinhardt (ICLR 2021). This is a massive multitask test consisting of multiple-choice questions from various branches of knowledge.", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/datasets/cais/mmlu", "content": "Dataset Card for MMLU Dataset Summary Measuring Massive Multitask Language Understanding by Dan Hendrycks, Collin Burns, Steven Basart, Andy Zou, Mantas Mazeika, Dawn Song, and Jacob Steinhardt (ICLR 2021). This is a massive multitask test consisting of multiple-choice questions from various branches of knowledge."}
+{"idx": 2, "title": "TIGER-AI-Lab/MMLU-Pro - GitHub", "date": "", "ddg_snippet": "We introduce MMLU -Pro, an enhanced benchmark designed to evaluate language understanding models across broader and more challenging tasks. Building on the Massive Multitask Language Understanding ( MMLU ) dataset, MMLU -Pro integrates more challenging, reasoning-focused questions and increases the answer choices per question from four to ten, significantly raising the difficulty and reducing the ...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/TIGER-AI-Lab/MMLU-Pro", "content": "We introduce MMLU -Pro, an enhanced benchmark designed to evaluate language understanding models across broader and more challenging tasks. Building on the Massive Multitask Language Understanding ( MMLU ) dataset, MMLU -Pro integrates more challenging, reasoning-focused questions and increases the answer choices per question from four to ten, significantly raising the difficulty and reducing the ..."}
+{"idx": 3, "title": "PDF MMLU-Pro: A More Robust and Challenging Multi-Task Language ...", "date": "", "ddg_snippet": "These issues have highlighted the need for more challenging, discriminative, and reliable datasets to track the progress of LLMs. In this paper, we introduce MMLU -Pro: a comprehensive benchmark designed for proficient-level multi-discipline language understanding and reasoning. MMLU -Pro spans 14 diverse domains including mathematics, physics, chemistry, law, engineering, psychology, and health ...", "subpage_snippet": "", "source": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper_files/paper/2024/file/ad236edc564f3e3156e1b2feafb99a24-Paper-Datasets_and_Benchmarks_Track.pdf", "content": "These issues have highlighted the need for more challenging, discriminative, and reliable datasets to track the progress of LLMs. In this paper, we introduce MMLU -Pro: a comprehensive benchmark designed for proficient-level multi-discipline language understanding and reasoning. MMLU -Pro spans 14 diverse domains including mathematics, physics, chemistry, law, engineering, psychology, and health ..."}
+{"idx": 4, "title": "GitHub - weihao1115/MMLU-ProX: [EMNLP 2025 Main] The official repo of ...", "date": "", "ddg_snippet": "MMLU -ProX is a multilingual benchmark that builds upon MMLU -Pro, extending to 29 typologically diverse languages, designed to evaluate large language models' reasoning capabilities across linguistic and cultural boundaries.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/weihao1115/MMLU-ProX", "content": "MMLU -ProX is a multilingual benchmark that builds upon MMLU -Pro, extending to 29 typologically diverse languages, designed to evaluate large language models' reasoning capabilities across linguistic and cultural boundaries."}
+{"idx": 5, "title": "Resurrecting saturated LLM benchmarks - arXiv.org", "date": "", "ddg_snippet": "However, MMLU -Pro provides ten answer options per question instead of four. Models showed larger performance drops on paired MMLU -Pro than other benchmarks, with smaller models struggling more. GPT-4o mini correctly answered only one-third of the questions it solved in the original MMLU -Pro.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2502.06738v1", "content": "However, MMLU -Pro provides ten answer options per question instead of four. Models showed larger performance drops on paired MMLU -Pro than other benchmarks, with smaller models struggling more. GPT-4o mini correctly answered only one-third of the questions it solved in the original MMLU -Pro."}
+{"idx": 6, "title": "Triangulating LLM Progress - arXiv.org", "date": "", "ddg_snippet": "Abstract We examine three evaluation paradigms: standard benchmarks (e.g., MMLU and BBH), interactive games (e.g., Signalling Games or Taboo), and cognitive tests (e.g., for working memory or theory of mind). First, we investigate which of the former two—benchmarks or games—is most effective at discriminating LLMs of varying quality. Then, inspired by human cognitive assessments, we ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2502.14359v3", "content": "Abstract We examine three evaluation paradigms: standard benchmarks (e.g., MMLU and BBH), interactive games (e.g., Signalling Games or Taboo), and cognitive tests (e.g., for working memory or theory of mind). First, we investigate which of the former two—benchmarks or games—is most effective at discriminating LLMs of varying quality. Then, inspired by human cognitive assessments, we ..."}
+{"idx": 7, "title": "MMLU-Pro/README.md at main · TIGER-AI-Lab/MMLU-Pro", "date": "", "ddg_snippet": "The code and data for \" MMLU -Pro: A More Robust and Challenging Multi-Task Language Understanding Benchmark\" [NeurIPS 2024] - TIGER-AI-Lab/ MMLU -Pro", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/TIGER-AI-Lab/MMLU-Pro/blob/main/README.md", "content": "The code and data for \" MMLU -Pro: A More Robust and Challenging Multi-Task Language Understanding Benchmark\" [NeurIPS 2024] - TIGER-AI-Lab/ MMLU -Pro"}
+{"idx": 8, "title": "GitHub - Helw150/mmlu: Measuring Massive Multitask Language ...", "date": "", "ddg_snippet": "This is the repository for Measuring Massive Multitask Language Understanding by Dan Hendrycks, Collin Burns, Steven Basart, Andy Zou, Mantas Mazeika, Dawn Song, and Jacob Steinhardt (ICLR 2021). This repository contains OpenAI API evaluation code, and the test is available for download here.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/Helw150/mmlu", "content": "This is the repository for Measuring Massive Multitask Language Understanding by Dan Hendrycks, Collin Burns, Steven Basart, Andy Zou, Mantas Mazeika, Dawn Song, and Jacob Steinhardt (ICLR 2021). This repository contains OpenAI API evaluation code, and the test is available for download here."}
+{"idx": 9, "title": "Stress Testing Generalization: How Minor Modifications Undermine Large ...", "date": "", "ddg_snippet": "For example, Qwen 2.5 1.5B's MMLU score drops from 89 to 36 when option lengths are changed without altering the question. Even GPT4o experiences a 25-point accuracy loss when question types are changed, with a 6-point drop across all three categories.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2502.12459v1", "content": "For example, Qwen 2.5 1.5B's MMLU score drops from 89 to 36 when option lengths are changed without altering the question. Even GPT4o experiences a 25-point accuracy loss when question types are changed, with a 6-point drop across all three categories."}
diff --git a/data/sampled_jsons/arxiv_2406.14532_html_Per-step_DPO_algorithm_year_2024.jsonl b/data/sampled_jsons/arxiv_2406.14532_html_Per-step_DPO_algorithm_year_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..642dd3ac27dd69c6e1b333db1cce2646df679dfd
--- /dev/null
+++ b/data/sampled_jsons/arxiv_2406.14532_html_Per-step_DPO_algorithm_year_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Step-DPO: Step-wise Preference Optimization for Long-chain Reasoning of ...", "date": "", "ddg_snippet": "This limitation stems from a lack of fine-grained process supervision. We propose a simple, effective, and data-efficient method called Step-DPO , which treats individual reasoning steps as units for preference optimization rather than evaluating answers holistically.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2406.18629", "content": "This limitation stems from a lack of fine-grained process supervision. We propose a simple, effective, and data-efficient method called Step-DPO , which treats individual reasoning steps as units for preference optimization rather than evaluating answers holistically."}
+{"idx": 1, "title": "Full-Step-DPO: Self-Supervised Preference Optimization with Step-wise ...", "date": "", "ddg_snippet": "Direct Preference Optimization ( DPO ) often struggles with long-chain mathematical reasoning. Existing approaches, such as Step-DPO , typically improve this by focusing on the first erroneous step in the reasoning chain. However, they overlook all other steps and rely heavily on humans or GPT-4 to identify erroneous steps . To address these issues, we propose Full- Step - DPO , a novel DPO framework ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2502.14356", "content": "Direct Preference Optimization ( DPO ) often struggles with long-chain mathematical reasoning. Existing approaches, such as Step-DPO , typically improve this by focusing on the first erroneous step in the reasoning chain. However, they overlook all other steps and rely heavily on humans or GPT-4 to identify erroneous steps . To address these issues, we propose Full- Step - DPO , a novel DPO framework ..."}
+{"idx": 2, "title": "Direct Value Optimization: Improving Chain-of-Thought ... - arXiv.org", "date": "", "ddg_snippet": "Step-DPO : Conducts step -level paired sam-pling of positive and negative instances based on the step value estimation, with a maximum of four pairs per question.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2502.13723", "content": "Step-DPO : Conducts step -level paired sam-pling of positive and negative instances based on the step value estimation, with a maximum of four pairs per question."}
+{"idx": 3, "title": "Abstract - arXiv.org", "date": "", "ddg_snippet": "pon DPO . Step -APO integrates advantage estimates for step -level preference pairs obtained via MCTS, enabling the model to learn fine-grained preferences between steps , identify critical plan steps , and de-emphasize errone", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2409.08642", "content": "pon DPO . Step -APO integrates advantage estimates for step -level preference pairs obtained via MCTS, enabling the model to learn fine-grained preferences between steps , identify critical plan steps , and de-emphasize errone"}
+{"idx": 4, "title": "Direct Value Optimization: - arXiv.org", "date": "", "ddg_snippet": "Step-DPO : Conducts step -level paired sampling of positive and negative instances based on the step value estimation, with a maximum of four pairs per question. Report issue for preceding element", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2502.13723", "content": "Step-DPO : Conducts step -level paired sampling of positive and negative instances based on the step value estimation, with a maximum of four pairs per question. Report issue for preceding element"}
+{"idx": 5, "title": "Step-controlled Dpo: Leveraging Stepwise Errors for Enhancing ...", "date": "", "ddg_snippet": "enerating diverse dispreferred solutions that start making errors at a specific step . We propose Step -Controlled DPO (SCDPO), an simple yet effective algorithm", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=ZRDa2IT1sQ", "content": "enerating diverse dispreferred solutions that start making errors at a specific step . We propose Step -Controlled DPO (SCDPO), an simple yet effective algorithm"}
+{"idx": 6, "title": "超越DPO!大模型精细化对齐之Step-DPO - CSDN博客", "date": "", "ddg_snippet": "7. step-dpo 对每个 step 一视同仁，一般都会存在一定的噪声，这时， [1] 里面提供的一些方法可以降低 step -level 的噪声。 同时，也能使用 prefix-sampling，计算 prefix- step 的 bon-acc 作为 step -level- dpo 的 soft-label。", "subpage_snippet": "", "source": "blog.csdn.net", "link": "https://blog.csdn.net/c9Yv2cf9I06K2A9E/article/details/141308054", "content": "7. step-dpo 对每个 step 一视同仁，一般都会存在一定的噪声，这时， [1] 里面提供的一些方法可以降低 step -level 的噪声。 同时，也能使用 prefix-sampling，计算 prefix- step 的 bon-acc 作为 step -level- dpo 的 soft-label。"}
+{"idx": 7, "title": "Generative Verifiers: Reward Modeling as Next-Token Prediction", "date": "", "ddg_snippet": "For algorithmic tasks, we report average performance using Gemma-2B on Last Letter Concat (Wei et al., 2022 ) and BBH Word Sorting (Suzgun et ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2408.15240v3", "content": "For algorithmic tasks, we report average performance using Gemma-2B on Last Letter Concat (Wei et al., 2022 ) and BBH Word Sorting (Suzgun et ..."}
+{"idx": 8, "title": "RL on Incorrect Synthetic Data Scales the Efficiency of LLM Math ...", "date": "", "ddg_snippet": "In particular, we add new preference pairs to the dataset of per-step DPO algorithm starting from positive samples. Recall from Section 4 and Algorithm 1, that for a problem 𝐱, with correct answer 𝐲 given by SFT or RFT data, Part 1 of per-step DPO estimates the expected accuracy (Q-value) of each step in a negative rollout 𝐲 ^ ∼ π ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2406.14532v1", "content": "In particular, we add new preference pairs to the dataset of per-step DPO algorithm starting from positive samples. Recall from Section 4 and Algorithm 1, that for a problem 𝐱, with correct answer 𝐲 given by SFT or RFT data, Part 1 of per-step DPO estimates the expected accuracy (Q-value) of each step in a negative rollout 𝐲 ^ ∼ π ..."}
+{"idx": 9, "title": "RLonIncorrectSyntheticDataScalesthe EfficiencyofLLMMathReasoningbyEight ...", "date": "", "ddg_snippet": "Now,wediscussanexperimentthatimprovestheperformance ofper-stepDPO[23]byrunningthefullversionoftheper- stepDPOalgorithm(Algorithm1).Inparticular,weaddnew preference pairs to the dataset of per-step DPO algorithm starting from positive samples.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2406.14532", "content": "Now,wediscussanexperimentthatimprovestheperformance ofper-stepDPO[23]byrunningthefullversionoftheper- stepDPOalgorithm(Algorithm1).Inparticular,weaddnew preference pairs to the dataset of per-step DPO algorithm starting from positive samples."}
diff --git a/data/sampled_jsons/arxiv_2501.15987_supplementary_Table_S8_computational_time_Re=4000.jsonl b/data/sampled_jsons/arxiv_2501.15987_supplementary_Table_S8_computational_time_Re=4000.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..0244f975fc53e664ac187dc756909dbde5e2f3b7
--- /dev/null
+++ b/data/sampled_jsons/arxiv_2501.15987_supplementary_Table_S8_computational_time_Re=4000.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "MultiPDENet: PDE-embedded Learning with Multi-time-stepping ... Topsicle: a method for estimating telomere length from whole ... arXiv.org e-Print archive Adaptive Computation Time for Recurrent Neural Networks [2501.09732] Inference-Time Scaling for Diffusion Models ... Does More Inference-Time Compute Really Help Robustness? Large-scale interactive retrieval in art collections using ...", "date": "", "ddg_snippet": "Jan 27, 2025 · Solving partial differential equations (PDEs) by numerical methods meet computational cost challenge for getting the accurate solution since fine grids and small time steps are required. Machine learning can accelerate this process, but struggle with weak generalizability, interpretability, and data dependency, as well as suffer in long-term prediction. To this end, we propose a PDE-embedded ... 1 day ago · Here, we introduce Topsicle, a computational method that estimates telomere length from whole genome long-read sequencing data using k-mer and change-point detection analysis. Simulations show Topsicle is robust to sequencing errors and coverage. arXiv is a free distribution service and an open-access archive for nearly 2.4 million scholarly articles in the fields of physics, mathematics, computer science, quantitative biology, quantitative finance, statistics, electrical engineering and systems science, and economics. Mar 29, 2016 · This paper introduces Adaptive Computation Time (ACT), an algorithm that allows recurrent neural networks to learn how many computational steps to take between receiving an input and emitting an output. Jan 16, 2025 · Generative models have made significant impacts across various domains, largely due to their ability to scale during training by increasing data, computational resources, and model size, a phenomenon characterized by the scaling laws. Recent research has begun to explore inference- time scaling behavior in Large Language Models (LLMs), revealing how performance can further improve with ... Abstract Recently, Zaremba et al. (2025) demonstrated that increasing inference- time computation improves robustness in large proprietary reasoning LLMs. In this paper, we first show that smaller-scale, open-source models (e.g., DeepSeek R1, Qwen3, Phi-reasoning) can also benefit from inference- time scaling using a simple budget forcing strategy. More importantly, we reveal and critically ... Finding objects and motifs across artworks is of great importance for art history as it helps to understand individual works and analyze relations between them. The advent of digitization has produced extensive digital art collections with many ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2501.15987", "content": "Jan 27, 2025 · Solving partial differential equations (PDEs) by numerical methods meet computational cost challenge for getting the accurate solution since fine grids and small time steps are required. Machine learning can accelerate this process, but struggle with weak generalizability, interpretability, and data dependency, as well as suffer in long-term prediction. To this end, we propose a PDE-embedded ... 1 day ago · Here, we introduce Topsicle, a computational method that estimates telomere length from whole genome long-read sequencing data using k-mer and change-point detection analysis. Simulations show Topsicle is robust to sequencing errors and coverage. arXiv is a free distribution service and an open-access archive for nearly 2.4 million scholarly articles in the fields of physics, mathematics, computer science, quantitative biology, quantitative finance, statistics, electrical engineering and systems science, and economics. Mar 29, 2016 · This paper introduces Adaptive Computation Time (ACT), an algorithm that allows recurrent neural networks to learn how many computational steps to take between receiving an input and emitting an output. Jan 16, 2025 · Generative models have made significant impacts across various domains, largely due to their ability to scale during training by increasing data, computational resources, and model size, a phenomenon characterized by the scaling laws. Recent research has begun to explore inference- time scaling behavior in Large Language Models (LLMs), revealing how performance can further improve with ... Abstract Recently, Zaremba et al. (2025) demonstrated that increasing inference- time computation improves robustness in large proprietary reasoning LLMs. In this paper, we first show that smaller-scale, open-source models (e.g., DeepSeek R1, Qwen3, Phi-reasoning) can also benefit from inference- time scaling using a simple budget forcing strategy. More importantly, we reveal and critically ... Finding objects and motifs across artworks is of great importance for art history as it helps to understand individual works and analyze relations between them. The advent of digitization has produced extensive digital art collections with many ..."}
+{"idx": 1, "title": "arXiv.org e-Print archive", "date": "", "ddg_snippet": "arXiv is a free distribution service and an open-access archive for nearly 2.4 million scholarly articles in the fields of physics, mathematics, computer science, quantitative biology, quantitative finance, statistics, electrical engineering and systems science, and economics.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/", "content": "arXiv is a free distribution service and an open-access archive for nearly 2.4 million scholarly articles in the fields of physics, mathematics, computer science, quantitative biology, quantitative finance, statistics, electrical engineering and systems science, and economics."}
+{"idx": 2, "title": "Adaptive Computation Time for Recurrent Neural Networks", "date": "", "ddg_snippet": "Mar 29, 2016 · This paper introduces Adaptive Computation Time (ACT), an algorithm that allows recurrent neural networks to learn how many computational steps to take between receiving an input and emitting an output.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/1603.08983", "content": "Mar 29, 2016 · This paper introduces Adaptive Computation Time (ACT), an algorithm that allows recurrent neural networks to learn how many computational steps to take between receiving an input and emitting an output."}
+{"idx": 3, "title": "[2501.09732] Inference-Time Scaling for Diffusion Models ... Does More Inference-Time Compute Really Help Robustness? Large-scale interactive retrieval in art collections using ...", "date": "", "ddg_snippet": "Jan 16, 2025 · Generative models have made significant impacts across various domains, largely due to their ability to scale during training by increasing data, computational resources, and model size, a phenomenon characterized by the scaling laws. Recent research has begun to explore inference- time scaling behavior in Large Language Models (LLMs), revealing how performance can further improve with ... Abstract Recently, Zaremba et al. (2025) demonstrated that increasing inference- time computation improves robustness in large proprietary reasoning LLMs. In this paper, we first show that smaller-scale, open-source models (e.g., DeepSeek R1, Qwen3, Phi-reasoning) can also benefit from inference- time scaling using a simple budget forcing strategy. More importantly, we reveal and critically ... Finding objects and motifs across artworks is of great importance for art history as it helps to understand individual works and analyze relations between them. The advent of digitization has produced extensive digital art collections with many ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2501.09732", "content": "Jan 16, 2025 · Generative models have made significant impacts across various domains, largely due to their ability to scale during training by increasing data, computational resources, and model size, a phenomenon characterized by the scaling laws. Recent research has begun to explore inference- time scaling behavior in Large Language Models (LLMs), revealing how performance can further improve with ... Abstract Recently, Zaremba et al. (2025) demonstrated that increasing inference- time computation improves robustness in large proprietary reasoning LLMs. In this paper, we first show that smaller-scale, open-source models (e.g., DeepSeek R1, Qwen3, Phi-reasoning) can also benefit from inference- time scaling using a simple budget forcing strategy. More importantly, we reveal and critically ... Finding objects and motifs across artworks is of great importance for art history as it helps to understand individual works and analyze relations between them. The advent of digitization has produced extensive digital art collections with many ..."}
+{"idx": 4, "title": "Does More Inference-Time Compute Really Help Robustness?", "date": "", "ddg_snippet": "Abstract Recently, Zaremba et al. (2025) demonstrated that increasing inference- time computation improves robustness in large proprietary reasoning LLMs. In this paper, we first show that smaller-scale, open-source models (e.g., DeepSeek R1, Qwen3, Phi-reasoning) can also benefit from inference- time scaling using a simple budget forcing strategy. More importantly, we reveal and critically ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2507.15974", "content": "Abstract Recently, Zaremba et al. (2025) demonstrated that increasing inference- time computation improves robustness in large proprietary reasoning LLMs. In this paper, we first show that smaller-scale, open-source models (e.g., DeepSeek R1, Qwen3, Phi-reasoning) can also benefit from inference- time scaling using a simple budget forcing strategy. More importantly, we reveal and critically ..."}
+{"idx": 5, "title": "MultiPDENet: PDE-embedded Learning with Multi-time ...", "date": "", "ddg_snippet": "27 Jan 2025 — Table S8: Computational time for a given accuracy (e.g., correlation ≥ 0.8 absent 0.8 \\geq 0.8 ≥ 0.8 ) on the NSE dataset. Iterm, R e R ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2501.15987v1", "content": "27 Jan 2025 — Table S8: Computational time for a given accuracy (e.g., correlation ≥ 0.8 absent 0.8 \\geq 0.8 ≥ 0.8 ) on the NSE dataset. Iterm, R e R ..."}
+{"idx": 6, "title": "Reaction Time Test: The simple, accurate online reaction time tester.", "date": "", "ddg_snippet": "This is a simple tool to measure your reaction time . The average (median) reaction time is 273 milliseconds, according to the data collected so far. In addition to measuring your reaction time , this test is affected by the latency of your computer and monitor.", "subpage_snippet": "", "source": "humanbenchmark.com", "link": "https://humanbenchmark.com/tests/reactiontime", "content": "This is a simple tool to measure your reaction time . The average (median) reaction time is 273 milliseconds, according to the data collected so far. In addition to measuring your reaction time , this test is affected by the latency of your computer and monitor."}
+{"idx": 7, "title": "Topsicle: a method for estimating telomere length from whole ...", "date": "", "ddg_snippet": "1 day ago · Here, we introduce Topsicle, a computational method that estimates telomere length from whole genome long-read sequencing data using k-mer and change-point detection analysis. Simulations show Topsicle is robust to sequencing errors and coverage.", "subpage_snippet": "", "source": "genomebiology.biomedcentral.com", "link": "https://genomebiology.biomedcentral.com/articles/10.1186/s13059-025-03783-4", "content": "1 day ago · Here, we introduce Topsicle, a computational method that estimates telomere length from whole genome long-read sequencing data using k-mer and change-point detection analysis. Simulations show Topsicle is robust to sequencing errors and coverage."}
+{"idx": 8, "title": "Compress PDF | Reduce PDF File Size Online for Free", "date": "", "ddg_snippet": "We’ re cloud-based, so you can reduce your file size online via any browser and on any device, from desktop to tablet and mobile. Other formats you can compress using Smallpdf.", "subpage_snippet": "", "source": "smallpdf.com", "link": "https://smallpdf.com/compress-pdf", "content": "We’ re cloud-based, so you can reduce your file size online via any browser and on any device, from desktop to tablet and mobile. Other formats you can compress using Smallpdf."}
+{"idx": 9, "title": "Large-scale interactive retrieval in art collections using ...", "date": "", "ddg_snippet": "Finding objects and motifs across artworks is of great importance for art history as it helps to understand individual works and analyze relations between them. The advent of digitization has produced extensive digital art collections with many ...", "subpage_snippet": "", "source": "pmc.ncbi.nlm.nih.gov", "link": "https://pmc.ncbi.nlm.nih.gov/articles/PMC8612525", "content": "Finding objects and motifs across artworks is of great importance for art history as it helps to understand individual works and analyze relations between them. The advent of digitization has produced extensive digital art collections with many ..."}
diff --git a/data/sampled_jsons/causal_graph_structural_equation_model_Pearl_SCM_components.jsonl b/data/sampled_jsons/causal_graph_structural_equation_model_Pearl_SCM_components.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..f9097be2070b76012576b95c5fa5bc883c245556
--- /dev/null
+++ b/data/sampled_jsons/causal_graph_structural_equation_model_Pearl_SCM_components.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Newest 'causal-diagram' Questions - Cross Validated", "date": "", "ddg_snippet": "Given the causal graph $(Z\\to X$, $Z\\to Y$, $X\\to Y)$, according to Pearl ’s intervention, the effect of intervening $X$ on $Y$ can be estimated as ...", "subpage_snippet": "", "source": "stats.stackexchange.com", "link": "https://stats.stackexchange.com/questions/tagged/causal-diagram", "content": "Given the causal graph $(Z\\to X$, $Z\\to Y$, $X\\to Y)$, according to Pearl ’s intervention, the effect of intervening $X$ on $Y$ can be estimated as ..."}
+{"idx": 1, "title": "regression - How is strength of an arrow in graphical causal", "date": "", "ddg_snippet": "From Causal Inference In Statistics, A Primer by Judea Pearl , I learnt that for linear SCMs ( Structural Causal Model ), the arrow strengths can be ...", "subpage_snippet": "", "source": "stats.stackexchange.com", "link": "https://stats.stackexchange.com/questions/605855/how-is-strength-of-an-arrow-in-graphical-causal-models-related-to-variance-of-ta", "content": "From Causal Inference In Statistics, A Primer by Judea Pearl , I learnt that for linear SCMs ( Structural Causal Model ), the arrow strengths can be ..."}
+{"idx": 2, "title": "Differentiable Cyclic Causal Discovery Under Unmeasured", "date": "", "ddg_snippet": "Most causal structure learning methods assume (i) a directed acyclic graph (DAG) with no directed cycles and (ii) complete observability, meaning no ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2508.08450v1", "content": "Most causal structure learning methods assume (i) a directed acyclic graph (DAG) with no directed cycles and (ii) complete observability, meaning no ..."}
+{"idx": 3, "title": "Causal Discovery via Quantile Partial Effect", "date": "", "ddg_snippet": "Functional Causal Models (FCMs) are a class of Structural Causal Models ( SCMs ) that impose constraints on causal mechanisms.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2509.12981v1", "content": "Functional Causal Models (FCMs) are a class of Structural Causal Models ( SCMs ) that impose constraints on causal mechanisms."}
+{"idx": 4, "title": "Causes and Explanations: A Structural-Model Approach, Part I:", "date": "", "ddg_snippet": "SCMs , also known as structural equation models (SEMs), are an object of study in their own right [13] as they form an important generalization of ...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/220487167_Causes_and_Explanations_A_Structural-Model_Approach_Part_I_Causes", "content": "SCMs , also known as structural equation models (SEMs), are an object of study in their own right [13] as they form an important generalization of ..."}
+{"idx": 5, "title": "Ergalics: Predictive Causal Models", "date": "", "ddg_snippet": "The mathematical language of structural causal models ( SCMs ) [ Pearl 2009 ] can be used as a model framework. ... SCMs is the product of several ...", "subpage_snippet": "", "source": "www2.cs.arizona.edu", "link": "http://www2.cs.arizona.edu/people/rts/ergalics/predictivecausal.html", "content": "The mathematical language of structural causal models ( SCMs ) [ Pearl 2009 ] can be used as a model framework. ... SCMs is the product of several ..."}
+{"idx": 6, "title": "Chapter 21 Causal Inference | A Guide on Data Analysis", "date": "", "ddg_snippet": "... Pearl ( Pearl and Mackenzie 2018 ) , which ... Pearl ’s Structural Causal Model ( SCM ) framework provides tools for causal inference ( Pearl 2019 ) :", "subpage_snippet": "", "source": "bookdown.org", "link": "https://bookdown.org/mike/data_analysis/sec-causal-inference.html", "content": "... Pearl ( Pearl and Mackenzie 2018 ) , which ... Pearl ’s Structural Causal Model ( SCM ) framework provides tools for causal inference ( Pearl 2019 ) :"}
+{"idx": 7, "title": "Structural learning and estimation of joint causal effects", "date": "", "ddg_snippet": "We propose a Bayesian methodology which combines structural learning of Gaussian DAG models and inference of causal effects as arising from ...", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/article/10.1007/s10260-021-00579-1", "content": "We propose a Bayesian methodology which combines structural learning of Gaussian DAG models and inference of causal effects as arising from ..."}
+{"idx": 8, "title": "An improved constraint-inference approach for causality", "date": "", "ddg_snippet": "... causal framework [ 22 ], mainly studies the average causal effect of two variables, while SCM proposed by Pearl [ 23 ] uses a causal graph to model ...", "subpage_snippet": "", "source": "pcmp.springeropen.com", "link": "https://pcmp.springeropen.com/articles/10.1186/s41601-023-00330-w", "content": "... causal framework [ 22 ], mainly studies the average causal effect of two variables, while SCM proposed by Pearl [ 23 ] uses a causal graph to model ..."}
+{"idx": 9, "title": "Newest 'identifiability' Questions - Cross Validated", "date": "", "ddg_snippet": "I'm currently using Structural vector autoregressive models by Kevin Kotzé to learn Vector Autoregression. ... in Simultaneous Equations Model ? I ...", "subpage_snippet": "", "source": "stats.stackexchange.com", "link": "https://stats.stackexchange.com/questions/tagged/identifiability", "content": "I'm currently using Structural vector autoregressive models by Kevin Kotzé to learn Vector Autoregression. ... in Simultaneous Equations Model ? I ..."}
diff --git a/data/sampled_jsons/causal_representation_learning_stochastic_mixing_function_robustness_2024_year_2024.jsonl b/data/sampled_jsons/causal_representation_learning_stochastic_mixing_function_robustness_2024_year_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..11216ee7e59cbbbe141d6121bec160a6e549b22e
--- /dev/null
+++ b/data/sampled_jsons/causal_representation_learning_stochastic_mixing_function_robustness_2024_year_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Unifying Causal Representation Learning and Foundation ...", "date": "", "ddg_snippet": "9 Dec 2024 — In this work, we relate these two approaches and study how to learn human-interpretable concepts from data.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2402.09236v2", "content": "9 Dec 2024 — In this work, we relate these two approaches and study how to learn human-interpretable concepts from data."}
+{"idx": 1, "title": "From Causal to Concept-Based Representation Learning", "date": "", "ddg_snippet": "To build intelligent machine learning systems, modern representation learning attempts to recover latent generative factors from data, such as in causal ...", "subpage_snippet": "", "source": "neurips.cc", "link": "https://neurips.cc/virtual/2024/poster/93459", "content": "To build intelligent machine learning systems, modern representation learning attempts to recover latent generative factors from data, such as in causal ..."}
+{"idx": 2, "title": "Score-based Causal Representation Learning: Linear and ...", "date": "", "ddg_snippet": "by B Varici · 2025 · Cited by 4 — Abstract. This paper addresses intervention-based causal representation learning (CRL) under a general nonparametric latent causal model and an unknown ... 90 pages", "subpage_snippet": "", "source": "www.jmlr.org", "link": "https://www.jmlr.org/papers/volume26/24-0194/24-0194.pdf", "content": "by B Varici · 2025 · Cited by 4 — Abstract. This paper addresses intervention-based causal representation learning (CRL) under a general nonparametric latent causal model and an unknown ... 90 pages"}
+{"idx": 3, "title": "unifying causal representation learning with", "date": "", "ddg_snippet": "by D Yao · 2024 · Cited by 12 — Causal representation learning (CRL) aims at recovering latent causal variables from high-dimensional observations to solve causal ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2409.02772", "content": "by D Yao · 2024 · Cited by 12 — Causal representation learning (CRL) aims at recovering latent causal variables from high-dimensional observations to solve causal ..."}
+{"idx": 4, "title": "Causal representation learning through higher-level ...", "date": "", "ddg_snippet": "by F Silva · 2024 · Cited by 2 — We explore a perspective on this problem that is directed to learning the generative process with causality -related foundations.", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.1145/3696412", "content": "by F Silva · 2024 · Cited by 2 — We explore a perspective on this problem that is directed to learning the generative process with causality -related foundations."}
+{"idx": 5, "title": "Causal Representation Learning from General Environments ...", "date": "", "ddg_snippet": "by I Ng — Causal representation learning aims to re- cover the latent causal variables and their causal relations, typically represented by di-. 23 pages", "subpage_snippet": "", "source": "raw.githubusercontent.com", "link": "https://raw.githubusercontent.com/mlresearch/v258/main/assets/ng25a/ng25a.pdf", "content": "by I Ng — Causal representation learning aims to re- cover the latent causal variables and their causal relations, typically represented by di-. 23 pages"}
+{"idx": 6, "title": "From Causal to Concept-Based Representation Learning", "date": "", "ddg_snippet": "by G Rajendran · 2024 · Cited by 7 — We sample the base distribution from a Gaussian Mixture model and experiment with both linear and nonlinear mixing functions (details deferred to Appendix G). 47 pages", "subpage_snippet": "", "source": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper_files/paper/2024/file/b76a9959151d377ddd2c77a275a97475-Paper-Conference.pdf", "content": "by G Rajendran · 2024 · Cited by 7 — We sample the base distribution from a Gaussian Mixture model and experiment with both linear and nonlinear mixing functions (details deferred to Appendix G). 47 pages"}
+{"idx": 7, "title": "Invariance & Causal Representation Learning", "date": "", "ddg_snippet": "by S Bing · Cited by 4 — Learning causal representations without assumptions is known to be fundamentally impossible, thus establishing the need for suitable inductive biases.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=lpOC6s4BcM", "content": "by S Bing · Cited by 4 — Learning causal representations without assumptions is known to be fundamentally impossible, thus establishing the need for suitable inductive biases."}
+{"idx": 8, "title": "ROBUST AGENTS LEARN CAUSAL WORLD MODELS", "date": "", "ddg_snippet": "by J Richens · Cited by 78 — Causal representation learning . Causal representation learning (CRL) aims to learn representations of data that capture unknown causal structure (Schölkopf et ... 32 pages", "subpage_snippet": "", "source": "proceedings.iclr.cc", "link": "https://proceedings.iclr.cc/paper_files/paper/2024/file/44a2b9f7bf9aec3f1fa333ad875b0ee0-Paper-Conference.pdf", "content": "by J Richens · Cited by 78 — Causal representation learning . Causal representation learning (CRL) aims to learn representations of data that capture unknown causal structure (Schölkopf et ... 32 pages"}
+{"idx": 9, "title": "Multi-Domain Causal Representation Learning via Weak ...", "date": "", "ddg_snippet": "by K Ahuja · 2024 · Cited by 19 — Causal representation learning has emerged as the center of action in causal machine learning research. In particular, multi-domain.", "subpage_snippet": "", "source": "proceedings.mlr.press", "link": "https://proceedings.mlr.press/v238/ahuja24a/ahuja24a.pdf", "content": "by K Ahuja · 2024 · Cited by 19 — Causal representation learning has emerged as the center of action in causal machine learning research. In particular, multi-domain."}
diff --git a/data/sampled_jsons/circuit_fingerprinting_attacks_tor.jsonl b/data/sampled_jsons/circuit_fingerprinting_attacks_tor.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..0d5bf2e55b12fe94bb7c072695543df67f1d6d49
--- /dev/null
+++ b/data/sampled_jsons/circuit_fingerprinting_attacks_tor.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Circuit Fingerprinting Attacks: Passive Deanonymization of Tor", "date": "", "ddg_snippet": "... and Mashael AlSabah and David Lazar and Marc Dacier and Srinivas Devadas}, title = { Circuit Fingerprinting Attacks : Passive Deanonymization of Tor ...", "subpage_snippet": "", "source": "www.usenix.org", "link": "https://www.usenix.org/conference/usenixsecurity15/technical-sessions/presentation/kwon", "content": "... and Mashael AlSabah and David Lazar and Marc Dacier and Srinivas Devadas}, title = { Circuit Fingerprinting Attacks : Passive Deanonymization of Tor ..."}
+{"idx": 1, "title": "Researchers Demonstrate New Fingerprinting Attack on Tor", "date": "", "ddg_snippet": "Website fingerprinting attacks on Tor aim to break these anonymity protections and enable an adversary observing the encrypted traffic patterns ...", "subpage_snippet": "", "source": "thehackernews.com", "link": "https://thehackernews.com/2021/11/researchers-demonstrate-new.html", "content": "Website fingerprinting attacks on Tor aim to break these anonymity protections and enable an adversary observing the encrypted traffic patterns ..."}
+{"idx": 2, "title": "Researchers Demonstrate New Fingerprinting Attack on Tor", "date": "", "ddg_snippet": "Website fingerprinting attacks on Tor aim to break these anonymity protections and enable an adversary observing the encrypted traffic patterns ...", "subpage_snippet": "", "source": "nsaneforums.com", "link": "https://nsaneforums.com/news/security-privacy-news/researchers-demonstrate-new-fingerprinting-attack-on-tor-encrypted-traffic-r3389/", "content": "Website fingerprinting attacks on Tor aim to break these anonymity protections and enable an adversary observing the encrypted traffic patterns ..."}
+{"idx": 3, "title": "A technical summary of the Usenix fingerprinting paper | The", "date": "", "ddg_snippet": "Albert Kwon, Mashael AlSabah, and others have a paper entitled Circuit Fingerprinting Attacks : Passive Deanonymization of Tor Hidden Services at the ...", "subpage_snippet": "", "source": "blog.torproject.org", "link": "https://blog.torproject.org/technical-summary-usenix-fingerprinting-paper/", "content": "Albert Kwon, Mashael AlSabah, and others have a paper entitled Circuit Fingerprinting Attacks : Passive Deanonymization of Tor Hidden Services at the ..."}
+{"idx": 4, "title": "Experimental Defense for Website Traffic Fingerprinting | The", "date": "", "ddg_snippet": "Is my privacy depending on \"a group of researchers (who) succeeded in performing this attack \" or an \"experimental defense\" nightly build by Tor ? Are ...", "subpage_snippet": "", "source": "blog.torproject.org", "link": "https://blog.torproject.org/experimental-defense-website-traffic-fingerprinting/", "content": "Is my privacy depending on \"a group of researchers (who) succeeded in performing this attack \" or an \"experimental defense\" nightly build by Tor ? Are ..."}
+{"idx": 5, "title": "Researchers mount successful attacks against Tor network—and", "date": "", "ddg_snippet": "For a while, we've been aware that circuit fingerprinting is a big issue for hidden services,\" says David Goulet, a developer with the Tor project.", "subpage_snippet": "", "source": "phys.org", "link": "https://phys.org/news/2015-07-mount-successful-tor-networkand.html", "content": "For a while, we've been aware that circuit fingerprinting is a big issue for hidden services,\" says David Goulet, a developer with the Tor project."}
+{"idx": 6, "title": "Shoring up Tor | MIT News | Massachusetts Institute of", "date": "", "ddg_snippet": "The researchers’ attack requires that the adversary’s computer serve as the guard on a Tor circuit . ... circuit fingerprinting is a big issue for ...", "subpage_snippet": "", "source": "news.mit.edu", "link": "https://news.mit.edu/2015/tor-vulnerability-0729", "content": "The researchers’ attack requires that the adversary’s computer serve as the guard on a Tor circuit . ... circuit fingerprinting is a big issue for ..."}
+{"idx": 7, "title": "Glossary | Tor Project | Support", "date": "", "ddg_snippet": "Tor uses three layers of encryption in the Tor circuit ; each relay decrypts one layer before passing the request on to the next relay.", "subpage_snippet": "", "source": "support.torproject.org", "link": "https://support.torproject.org/glossary/", "content": "Tor uses three layers of encryption in the Tor circuit ; each relay decrypts one layer before passing the request on to the next relay."}
+{"idx": 8, "title": "Tor Entry Guards - Whonix", "date": "", "ddg_snippet": "For example, suppose the attacker controls or watches the Tor relay a user chooses to enter the network, and also controls or watches the website ...", "subpage_snippet": "", "source": "www.whonix.org", "link": "https://www.whonix.org/wiki/Tor_Entry_Guards", "content": "For example, suppose the attacker controls or watches the Tor relay a user chooses to enter the network, and also controls or watches the website ..."}
+{"idx": 9, "title": "Staying anonymous | plus.maths.org", "date": "", "ddg_snippet": "The attack devised by the researchers requires the attacker 's computer to serve as the guard on a Tor circuit . ... of a circuit , computers on the Tor ...", "subpage_snippet": "", "source": "plus.maths.org", "link": "https://plus.maths.org/content/staying-anonymous", "content": "The attack devised by the researchers requires the attacker 's computer to serve as the guard on a Tor circuit . ... of a circuit , computers on the Tor ..."}
diff --git a/data/sampled_jsons/climate_activism_reddit_subreddits_climatestrike_extinctionrebellion_fridaysforfuture.jsonl b/data/sampled_jsons/climate_activism_reddit_subreddits_climatestrike_extinctionrebellion_fridaysforfuture.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..9ccfe163c7cc1346ecd9b2f541fbbfc3235fe859
--- /dev/null
+++ b/data/sampled_jsons/climate_activism_reddit_subreddits_climatestrike_extinctionrebellion_fridaysforfuture.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Yes, climate activism can win : r/ExtinctionRebellion - Reddit", "date": "", "ddg_snippet": "Timothy Snyder makes the case that solving climate change is essential for defending democracy youtube r/ ExtinctionRebellion", "subpage_snippet": "", "source": "www.reddit.com", "link": "https://www.reddit.com/r/ExtinctionRebellion/comments/19437sc/yes_climate_activism_can_win/", "content": "Timothy Snyder makes the case that solving climate change is essential for defending democracy youtube r/ ExtinctionRebellion"}
+{"idx": 1, "title": "List of environmentalist activist subreddits and websites", "date": "", "ddg_snippet": "Go to ExtinctionRebellion r/ ExtinctionRebellion • by StarBam View community ranking In the Top 5% of largest communities on Reddit", "subpage_snippet": "", "source": "www.reddit.com", "link": "https://www.reddit.com/r/ExtinctionRebellion/comments/biv0r4/list_of_environmentalist_activist_subreddits_and/", "content": "Go to ExtinctionRebellion r/ ExtinctionRebellion • by StarBam View community ranking In the Top 5% of largest communities on Reddit"}
+{"idx": 2, "title": "Causal Modeling of Climate Activism on Reddit - arXiv.org", "date": "", "ddg_snippet": "We identify as our target variable the participation in one of the subreddits afiliated with the organizations that coordinated the September 2019 global climate strikes, mainly Earth Strike, Extinc-tion Rebellion , Fridays For Future , and the Sunrise Movement (see", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2410.10562", "content": "We identify as our target variable the participation in one of the subreddits afiliated with the organizations that coordinated the September 2019 global climate strikes, mainly Earth Strike, Extinc-tion Rebellion , Fridays For Future , and the Sunrise Movement (see"}
+{"idx": 3, "title": "Extinction Rebellion | Join The Fight Against Climate and ...", "date": "", "ddg_snippet": "From our coverage of Extinction Rebellion activity around the world to commentary on climate change news and events, the Extinction Rebellion blog is the best way to stay up to date with the global fight for change.", "subpage_snippet": "", "source": "rebellion.global", "link": "https://rebellion.global/", "content": "From our coverage of Extinction Rebellion activity around the world to commentary on climate change news and events, the Extinction Rebellion blog is the best way to stay up to date with the global fight for change."}
+{"idx": 4, "title": "r/ExtinctionRebellion on Reddit: The September climate ...", "date": "", "ddg_snippet": "307 votes, 12 comments. 26K subscribers in the ExtinctionRebellion community. This is our darkest hour. Humanity finds itself embroiled in an event…", "subpage_snippet": "", "source": "www.reddit.com", "link": "https://www.reddit.com/r/ExtinctionRebellion/comments/f024gu/the_september_climate_strikes_were_the_biggest/", "content": "307 votes, 12 comments. 26K subscribers in the ExtinctionRebellion community. This is our darkest hour. Humanity finds itself embroiled in an event…"}
+{"idx": 5, "title": "Should I really join Extinction Rebellion? : r ... - Reddit", "date": "", "ddg_snippet": "So I think that the climate change is an big problem and i want to do more than going to Fridays for future (only in my Hometown) and doing little things like picking up trash that I see.", "subpage_snippet": "", "source": "www.reddit.com", "link": "https://www.reddit.com/r/ExtinctionRebellion/comments/xr7gox/should_i_really_join_extinction_rebellion/", "content": "So I think that the climate change is an big problem and i want to do more than going to Fridays for future (only in my Hometown) and doing little things like picking up trash that I see."}
+{"idx": 6, "title": "Climate Activism: Driving Change Amid Controversy", "date": "", "ddg_snippet": "Sep 5, 2024 · The paper then explores the different range of climate activism , with particular attention on three well-known groups: Last Generation, Extinction Rebellion , and Fridays for Future .", "subpage_snippet": "", "source": "mondointernazionale.org", "link": "https://mondointernazionale.org/en/focus-allegati/climate-activism-driving-change-amid-controversy", "content": "Sep 5, 2024 · The paper then explores the different range of climate activism , with particular attention on three well-known groups: Last Generation, Extinction Rebellion , and Fridays for Future ."}
+{"idx": 7, "title": "Causal Modeling of Climate Activism on Reddit", "date": "", "ddg_snippet": "14 Oct 2024 — In this work, we develop a comprehensive causal model of how and why Reddit users engage with activist communities driving mass climate protests .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2410.10562v1", "content": "14 Oct 2024 — In this work, we develop a comprehensive causal model of how and why Reddit users engage with activist communities driving mass climate protests ."}
+{"idx": 8, "title": "Causal Modeling of Climate Activism on Reddit", "date": "", "ddg_snippet": "by J Lenti · Cited by 9 — In this work, we develop a comprehensive causal model of how and why. Reddit users engage with activist communities driving mass climate protests (mainly the ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=6yBhoJn6qy", "content": "by J Lenti · Cited by 9 — In this work, we develop a comprehensive causal model of how and why. Reddit users engage with activist communities driving mass climate protests (mainly the ..."}
+{"idx": 9, "title": "r/ClimateVideo", "date": "", "ddg_snippet": "r/ClimateVideo : A new place for videos about the ongoing climate crisis. Post videos to understand, discuss and find new ways to fight this global…", "subpage_snippet": "", "source": "www.reddit.com", "link": "https://www.reddit.com/r/ClimateVideo/", "content": "r/ClimateVideo : A new place for videos about the ongoing climate crisis. Post videos to understand, discuss and find new ways to fight this global…"}
diff --git a/data/sampled_jsons/confidence_bound_formula_Orlicz_norm_distributed_learning.jsonl b/data/sampled_jsons/confidence_bound_formula_Orlicz_norm_distributed_learning.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..03db398e6a80bd026743971c0ed3fcd4027b2a65
--- /dev/null
+++ b/data/sampled_jsons/confidence_bound_formula_Orlicz_norm_distributed_learning.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Orlicz space - Wikipedia", "date": "", "ddg_snippet": "In mathematical analysis, and especially in real, harmonic analysis and functional analysis, an Orlicz space is a type of function space which generalizes the Lp spaces. Like the Lp spaces, they are Banach spaces. The spaces are named for Władysław O...", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/Orlicz_space", "content": "In mathematical analysis, and especially in real, harmonic analysis and functional analysis, an Orlicz space is a type of function space which generalizes the Lp spaces. Like the Lp spaces, they are Banach spaces. The spaces are named for Władysław O..."}
+{"idx": 1, "title": "(PDF) Orlicz norms of sequences of random variables", "date": "", "ddg_snippet": "Nis an Orlicz norm . We find out that these expressions are maximal. (up to an absolute constant) if the random variables are, in addition, required to be.is equivalent to an Orlicz norm whose Orlicz function is 2-concave. Here we give a formula for the sequence.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/38351650_Orlicz_norms_of_sequences_of_random_variables", "content": "Nis an Orlicz norm . We find out that these expressions are maximal. (up to an absolute constant) if the random variables are, in addition, required to be.is equivalent to an Orlicz norm whose Orlicz function is 2-concave. Here we give a formula for the sequence."}
+{"idx": 2, "title": "Orlicz norms and concentration inequalities for -heavy tailed random...", "date": "", "ddg_snippet": "Motivating examples of heavy-tailed distributions and adapted Orlicz norm . Orlicz norm [KR61] provides a nice tool to study the statistical uctuations of an estimator for a given family of distributions . Consider an Orlicz function Ψ : R+ → R+, that is a continuous non-decreasing.", "subpage_snippet": "", "source": "hal.science", "link": "https://hal.science/hal-03175697/document", "content": "Motivating examples of heavy-tailed distributions and adapted Orlicz norm . Orlicz norm [KR61] provides a nice tool to study the statistical uctuations of an estimator for a given family of distributions . Consider an Orlicz function Ψ : R+ → R+, that is a continuous non-decreasing."}
+{"idx": 3, "title": "$\\psi_1$- Orlicz norm of a product of two Gaussian random variables", "date": "", "ddg_snippet": "probability-theory statistics normal - distribution orlicz -spaces.Is there a direct formula or a bound for the following Orlicz norm : ‖ξη‖ψ1.", "subpage_snippet": "", "source": "math.stackexchange.com", "link": "https://math.stackexchange.com/questions/4712366/psi-1-orlicz-norm-of-a-product-of-two-gaussian-random-variables", "content": "probability-theory statistics normal - distribution orlicz -spaces.Is there a direct formula or a bound for the following Orlicz norm : ‖ξη‖ψ1."}
+{"idx": 4, "title": "Latest Research on Orlicz Norm for Machine Learning ... | Medium", "date": "", "ddg_snippet": "Abstract : Given a normalized Orlicz function M we provide an easy formula for a distribution such that, if X is a random variable distributed accordingly and X1,…,Xn are independent copies of X…On the Distribution of Random variables corresponding to Musielak- Orlicz norms (arXiv).", "subpage_snippet": "", "source": "medium.com", "link": "https://medium.com/@monocosmo77/latest-research-on-orlicz-norm-for-machine-learning-part7-55b7e62e6cc4", "content": "Abstract : Given a normalized Orlicz function M we provide an easy formula for a distribution such that, if X is a random variable distributed accordingly and X1,…,Xn are independent copies of X…On the Distribution of Random variables corresponding to Musielak- Orlicz norms (arXiv)."}
+{"idx": 5, "title": "The Bennett- Orlicz norm", "date": "", "ddg_snippet": "Here we introduce another Orlicz norm , the Bennett- Orlicz norm , which is connected to Bennett type inequalities. The new Bennett- Orlicz norm yields inequalities for expectations of maxima which are potentially somewhat tighter than those resulting from the Bernstein- Orlicz norm when...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/1703.01721", "content": "Here we introduce another Orlicz norm , the Bennett- Orlicz norm , which is connected to Bennett type inequalities. The new Bennett- Orlicz norm yields inequalities for expectations of maxima which are potentially somewhat tighter than those resulting from the Bernstein- Orlicz norm when..."}
+{"idx": 6, "title": "Orlicz Random Fourier Features", "date": "", "ddg_snippet": "Problem Formulation . Main Result. Properties of the Orlicz Norm . Proofs. Proof of Remark 2(v).To tackle this diculty, we establish a nite-sample deviation bound for a general class of polynomial-growth functions under α-exponential Orlicz condition on the distribution of the sample.", "subpage_snippet": "", "source": "jmlr.org", "link": "https://jmlr.org/papers/volume21/19-1031/19-1031.pdf", "content": "Problem Formulation . Main Result. Properties of the Orlicz Norm . Proofs. Proof of Remark 2(v).To tackle this diculty, we establish a nite-sample deviation bound for a general class of polynomial-growth functions under α-exponential Orlicz condition on the distribution of the sample."}
+{"idx": 7, "title": "pr.probability - Composing an Orlicz norm related to... - MathOverflow", "date": "", "ddg_snippet": "Stack Exchange network consists of 183 Q&A communities including Stack Overflow, the largest, most trusted online community for developers to learn , share their knowledge, and build their careers.Go to help center to learn more. Composing an Orlicz norm related to Bernstein's inequality?", "subpage_snippet": "", "source": "mathoverflow.net", "link": "https://mathoverflow.net/questions/453191/composing-an-orlicz-norm-related-to-bernsteins-inequality", "content": "Stack Exchange network consists of 183 Q&A communities including Stack Overflow, the largest, most trusted online community for developers to learn , share their knowledge, and build their careers.Go to help center to learn more. Composing an Orlicz norm related to Bernstein's inequality?"}
+{"idx": 8, "title": "Subspace Embedding and Linear Regression with Orlicz Norm", "date": "", "ddg_snippet": "Orlicz norm into a lower dimensional space with.Our experi-ments reveal that that the solution of regression under the Orlicz norm induced by Huber loss is much better than the solution given by regression under 1 or 2 norms , under natural noise distributions in practice.", "subpage_snippet": "", "source": "proceedings.mlr.press", "link": "https://proceedings.mlr.press/v80/andoni18a/andoni18a.pdf", "content": "Orlicz norm into a lower dimensional space with.Our experi-ments reveal that that the solution of regression under the Orlicz norm induced by Huber loss is much better than the solution given by regression under 1 or 2 norms , under natural noise distributions in practice."}
+{"idx": 9, "title": "Orlicz Norms of Sequences of Random Variables", "date": "", "ddg_snippet": "To approximate Orlicz norms on Rn we will use the following norm . Given a vector z ∈ Rm with z1 ≥ z2 ≥ · · · ≥ zm > 0 denote.In this sections we provide a few examples. We need the following two lemmas about the normal distribution . Lemma 12 For all x with x > 0.", "subpage_snippet": "", "source": "case.edu", "link": "https://case.edu/artsci/math/werner/publications/GLSW1.pdf", "content": "To approximate Orlicz norms on Rn we will use the following norm . Given a vector z ∈ Rm with z1 ≥ z2 ≥ · · · ≥ zm > 0 denote.In this sections we provide a few examples. We need the following two lemmas about the normal distribution . Lemma 12 For all x with x > 0."}
diff --git a/data/sampled_jsons/context_drift_computational_overhead_checks_balances_framework_ethical_AI.jsonl b/data/sampled_jsons/context_drift_computational_overhead_checks_balances_framework_ethical_AI.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..bd307fcf7af4af2d63562851c5e5704a8fd0f9eb
--- /dev/null
+++ b/data/sampled_jsons/context_drift_computational_overhead_checks_balances_framework_ethical_AI.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Integrating Reasoning and Agentic Frameworks for LLMs: A", "date": "", "ddg_snippet": "The main trade-off is increased computational overhead : maintaining and evaluating many branches is resource-intensive and requires careful ...", "subpage_snippet": "", "source": "artsen.h3x.xyz", "link": "https://artsen.h3x.xyz/blog/integrating-reasoning-and-agentic-frameworks-for-llms-a-comprehensive-review-and-unified-proposal/", "content": "The main trade-off is increased computational overhead : maintaining and evaluating many branches is resource-intensive and requires careful ..."}
+{"idx": 1, "title": "A Unified Framework for Evaluating the Effectiveness and", "date": "", "ddg_snippet": "... predictions, enabling stakeholders to recognize biases, inconsistencies, and limitations, thus ensuring ethical , equitable, and reliable use of AI ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2412.03884v2", "content": "... predictions, enabling stakeholders to recognize biases, inconsistencies, and limitations, thus ensuring ethical , equitable, and reliable use of AI ..."}
+{"idx": 2, "title": "MCP and RAG Integration: A Complete Guide", "date": "", "ddg_snippet": "Computational Overhead : Complex retrieval processes can slow down response times. Context Management: Difficulty in maintaining coherent context across multiple retrieval steps. Why integrate MCP with RAG?", "subpage_snippet": "", "source": "www.byteplus.com", "link": "https://www.byteplus.com/en/topic/541897", "content": "Computational Overhead : Complex retrieval processes can slow down response times. Context Management: Difficulty in maintaining coherent context across multiple retrieval steps. Why integrate MCP with RAG?"}
+{"idx": 3, "title": "Platform-induced Hallucination.md · GitHub", "date": "", "ddg_snippet": "Performance Overhead: Continual state checking , context introspection, or runtime schema verification may introduce latency or computational overhead , slightly impacting performance and responsiveness.", "subpage_snippet": "", "source": "gist.github.com", "link": "https://gist.github.com/fractalserpent/9687f24d18708f22e8fda324d8e75bdf", "content": "Performance Overhead: Continual state checking , context introspection, or runtime schema verification may introduce latency or computational overhead , slightly impacting performance and responsiveness."}
+{"idx": 4, "title": "Chain of Thought Explained: Step-by-Step Reasoning in... - Kingy AI", "date": "", "ddg_snippet": "6.3 Computational Overhead . Generating extended textual explanations can be more resource-intensive, especially for large-scale deployments. Each query not only seeks a final answer but also requests detailed intermediate expansions.", "subpage_snippet": "", "source": "kingy.ai", "link": "https://kingy.ai/blog/chain-of-thought-explained-step-by-step-reasoning-in-todays-top-llms/", "content": "6.3 Computational Overhead . Generating extended textual explanations can be more resource-intensive, especially for large-scale deployments. Each query not only seeks a final answer but also requests detailed intermediate expansions."}
+{"idx": 5, "title": "Diagnostics of cognitive failures in multi-agent expert systems using...", "date": "", "ddg_snippet": "This work introduces a diagnostic framework for expert systems that not only evaluates but also facilitates the transfer of expert behaviour into LLM-powered agents.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2509.15366", "content": "This work introduces a diagnostic framework for expert systems that not only evaluates but also facilitates the transfer of expert behaviour into LLM-powered agents."}
+{"idx": 6, "title": "Top 10 Best Uncensored LLMs You Can Run Locally", "date": "", "ddg_snippet": "Occasional context drift in very prolonged conversations.Fine-tuned architecture for high-quality text generation. Optimized for local execution with reduced computational overhead .", "subpage_snippet": "", "source": "fluxnsfw.ai", "link": "https://fluxnsfw.ai/posts/uncensored-llm", "content": "Occasional context drift in very prolonged conversations.Fine-tuned architecture for high-quality text generation. Optimized for local execution with reduced computational overhead ."}
+{"idx": 7, "title": "Unlocking the Potential of the Prompt Engineering Paradigm in...", "date": "", "ddg_snippet": "From the perspective of computational overhead , manual prompt crafting remains the most efficient, requiring no training cycles or additional GPU load [38,43]. It allows for rapid iteration during the development phase without dependency on high-performance infrastructure.", "subpage_snippet": "", "source": "www.mdpi.com", "link": "https://www.mdpi.com/2673-2688/6/9/206", "content": "From the perspective of computational overhead , manual prompt crafting remains the most efficient, requiring no training cycles or additional GPU load [38,43]. It allows for rapid iteration during the development phase without dependency on high-performance infrastructure."}
+{"idx": 8, "title": "Preventing Another Tessa: Modular Safety Middleware For", "date": "", "ddg_snippet": "AI assistants are increasingly deployed in health and counseling contexts , where even minor errors can result in significant harm.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2509.07022v1", "content": "AI assistants are increasingly deployed in health and counseling contexts , where even minor errors can result in significant harm."}
+{"idx": 9, "title": "RAG vs Finetuning vs Prompt Engineering: Key AI Techniques", "date": "", "ddg_snippet": "RAG excels in dynamic environments by leveraging external knowledge bases, but its computational overhead can strain systems with limited ...", "subpage_snippet": "", "source": "www.chitika.com", "link": "https://www.chitika.com/rag-vs-finetuning-vs-prompt-engineering/", "content": "RAG excels in dynamic environments by leveraging external knowledge bases, but its computational overhead can strain systems with limited ..."}
diff --git a/data/sampled_jsons/convergence_rate_n^{-12}_distribution_regression_conditional_deep_generative_models.jsonl b/data/sampled_jsons/convergence_rate_n^{-12}_distribution_regression_conditional_deep_generative_models.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..7f10f658f64dc3fadc6aeaf9bf0b4d9b768a81e3
--- /dev/null
+++ b/data/sampled_jsons/convergence_rate_n^{-12}_distribution_regression_conditional_deep_generative_models.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "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": 1, "title": "(PDF) A Likelihood Based Approach to Distribution Regression Using...", "date": "", "ddg_snippet": "Keywords: Distribution Regression ; Conditional Deep Generative Models ; Intrinsic Manifold Structure; Sieve MLE; Wasserstein Convergence .Our deep generative model focuses on the conditional distribution V|X.", "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": "Keywords: Distribution Regression ; Conditional Deep Generative Models ; Intrinsic Manifold Structure; Sieve MLE; Wasserstein Convergence .Our deep generative model focuses on the conditional distribution V|X."}
+{"idx": 2, "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": 3, "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": 4, "title": "Generative Modelling via Quantile Regression -Bohrium", "date": "", "ddg_snippet": "We link conditional generative modelling to quantile regression . We propose a suitable loss function and derive minimax convergence rates for the associated risk under smoothness assumptions imposed on the conditional distribution .", "subpage_snippet": "", "source": "www.bohrium.com", "link": "https://www.bohrium.com/paper-details/generative-modelling-via-quantile-regression/1040149472706822145-108557", "content": "We link conditional generative modelling to quantile regression . We propose a suitable loss function and derive minimax convergence rates for the associated risk under smoothness assumptions imposed on the conditional distribution ."}
+{"idx": 5, "title": "[PDF] Discussion of: “Nonparametric regression using deep neural...”", "date": "", "ddg_snippet": "A Likelihood Based Approach to Distribution Regression Using Conditional Deep Generative Models .On the rate of convergence of fully connected very deep neural network regression estimates.", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/Discussion-of:-“Nonparametric-regression-using-deep-Ghorbani-Mei/688b87f8e83080a024eb0fd7e55288de313aff8b", "content": "A Likelihood Based Approach to Distribution Regression Using Conditional Deep Generative Models .On the rate of convergence of fully connected very deep neural network regression estimates."}
+{"idx": 6, "title": "Convergence Rates - FasterCapital", "date": "", "ddg_snippet": "2 . 1 . Mathematical Framework of Convergence Rates . 2 . 2 . Convergence Speed and Its Implications.The convergence rate affects how quickly these models can adapt to changing market conditions , impacting a firm's risk management strategies.", "subpage_snippet": "", "source": "fastercapital.com", "link": "https://fastercapital.com/term/convergence-rates.html", "content": "2 . 1 . Mathematical Framework of Convergence Rates . 2 . 2 . Convergence Speed and Its Implications.The convergence rate affects how quickly these models can adapt to changing market conditions , impacting a firm's risk management strategies."}
+{"idx": 7, "title": "Deep Neural Networks for Nonparametric Interaction Models with...", "date": "", "ddg_snippet": "Deep Conditional Generative Learning: Model and Error Analysis.Show Less. TL;DR: An Ordinary Differential Equation based deep generative method for learning a conditional distribution , named the Conditional Follmer Flow, which derives a non-asymptotic convergence rate in the...", "subpage_snippet": "", "source": "scispace.com", "link": "https://scispace.com/papers/deep-neural-networks-for-nonparametric-interaction-models-2k2o1jjk", "content": "Deep Conditional Generative Learning: Model and Error Analysis.Show Less. TL;DR: An Ordinary Differential Equation based deep generative method for learning a conditional distribution , named the Conditional Follmer Flow, which derives a non-asymptotic convergence rate in the..."}
+{"idx": 8, "title": "A Deep Learning Framework for the", "date": "", "ddg_snippet": "• A new continuous conditional generative deep learning model capable to receive more than one condition (a set of cosmological parameters), with a reliable distribution .", "subpage_snippet": "", "source": "ccc.inaoep.mx", "link": "https://ccc.inaoep.mx/archivos/2021/CCC-23-005.pdf", "content": "• A new continuous conditional generative deep learning model capable to receive more than one condition (a set of cosmological parameters), with a reliable distribution ."}
+{"idx": 9, "title": "Learning Structured Output Representation using Deep Conditional ...", "date": "", "ddg_snippet": "To address this problem, we propose novel deep conditional generative models (CGMs) for output representation learning and structured prediction. In other words, we model the distribution of high-dimensional output space as a generative model conditioned on the input observation.", "subpage_snippet": "", "source": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper/2015/file/8d55a249e6baa5c06772297520da2051-Paper.pdf", "content": "To address this problem, we propose novel deep conditional generative models (CGMs) for output representation learning and structured prediction. In other words, we model the distribution of high-dimensional output space as a generative model conditioned on the input observation."}
diff --git a/data/sampled_jsons/diagonal_update_all_grids_Parallel_Simulation_for_Log-concave_Sampling_and_Score-based_Diffusion_Mod.jsonl b/data/sampled_jsons/diagonal_update_all_grids_Parallel_Simulation_for_Log-concave_Sampling_and_Score-based_Diffusion_Mod.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..74ec89d9b509c4d2bd34a7fdd2918a86db0ce4fc
--- /dev/null
+++ b/data/sampled_jsons/diagonal_update_all_grids_Parallel_Simulation_for_Log-concave_Sampling_and_Score-based_Diffusion_Mod.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Is there a way to insert a Diagonal Line in a single cell?", "date": "", "ddg_snippet": "Trying to insert a Diagonal Line across a single cell, rather then using the Strikeout line feature.", "subpage_snippet": "", "source": "support.google.com", "link": "https://support.google.com/docs/thread/262751092/is-there-a-way-to-insert-a-diagonal-line-in-a-single-cell?hl=en", "content": "Trying to insert a Diagonal Line across a single cell, rather then using the Strikeout line feature."}
+{"idx": 1, "title": "How do I make a diagonal border in Google docs?", "date": "", "ddg_snippet": "Jan 19, 2021 · I'm trying to submit an assignment to my teacher, but he is asking for a diagonal border on a table. How do I create a diagonal border?", "subpage_snippet": "", "source": "support.google.com", "link": "https://support.google.com/docs/thread/94190467/how-do-i-make-a-diagonal-border-in-google-docs?hl=en", "content": "Jan 19, 2021 · I'm trying to submit an assignment to my teacher, but he is asking for a diagonal border on a table. How do I create a diagonal border?"}
+{"idx": 2, "title": "Cloud with diagonal line through it", "date": "", "ddg_snippet": "Feb 28, 2019 · Cloud with diagonal line through it Not much just wanted to know what the cloud with diagonal through it means?", "subpage_snippet": "", "source": "support.google.com", "link": "https://support.google.com/photos/thread/1941307/cloud-with-diagonal-line-through-it?hl=en", "content": "Feb 28, 2019 · Cloud with diagonal line through it Not much just wanted to know what the cloud with diagonal through it means?"}
+{"idx": 3, "title": "Use magnification on your screen - Android Accessibility Help", "date": "", "ddg_snippet": "Allow diagonal scrolling (partial screen magnification only): Change whether diagonal scrolling is on or off. To easily move around and locate the magnified section on the screen, turn off Allow diagonal scrolling.", "subpage_snippet": "", "source": "support.google.com", "link": "https://support.google.com/accessibility/android/answer/6006949?hl=en", "content": "Allow diagonal scrolling (partial screen magnification only): Change whether diagonal scrolling is on or off. To easily move around and locate the magnified section on the screen, turn off Allow diagonal scrolling."}
+{"idx": 4, "title": "Cómo usar la ampliación en la pantalla", "date": "", "ddg_snippet": "Permitir el desplazamiento diagonal (solo ampliación parcial de la pantalla): Permite activar o desactivar el desplazamiento diagonal . Para desplazarte y encontrar fácilmente la sección ampliada en la pantalla, desactiva Desplazamiento en diagonal .", "subpage_snippet": "", "source": "support.google.com", "link": "https://support.google.com/accessibility/android/answer/6006949?hl=es-419", "content": "Permitir el desplazamiento diagonal (solo ampliación parcial de la pantalla): Permite activar o desactivar el desplazamiento diagonal . Para desplazarte y encontrar fácilmente la sección ampliada en la pantalla, desactiva Desplazamiento en diagonal ."}
+{"idx": 5, "title": "What does a square with a diagonal line thru it mean? It seems to...", "date": "", "ddg_snippet": "What does a square with a diagonal line thru it mean? It seems to be st the end of a dark line The dark line with the symbol at the end is not a road or boundary. Just runs thru a residential neighborhood.", "subpage_snippet": "", "source": "support.google.com", "link": "https://support.google.com/maps/thread/190165385/what-does-a-square-with-a-diagonal-line-thru-it-mean-it-seems-to-be-st-the-end-of-a-dark-line?hl=en", "content": "What does a square with a diagonal line thru it mean? It seems to be st the end of a dark line The dark line with the symbol at the end is not a road or boundary. Just runs thru a residential neighborhood."}
+{"idx": 6, "title": "Menggunakan pembesaran di layar - Bantuan Aksesibilitas Android", "date": "", "ddg_snippet": "Izinkan scrolling diagonal (khusus pembesaran layar sebagian): Mengubah apakah scrolling diagonal aktif atau nonaktif. Untuk berpindahdan menemukan bagian yang diperbesar di layar dengan mudah, nonaktifkan Izinkan scrolling diagonal . Tips untuk menggunakan pembesaran Rentang pembesaran dapat mencapai 8x.", "subpage_snippet": "", "source": "support.google.com", "link": "https://support.google.com/accessibility/android/answer/6006949?hl=id", "content": "Izinkan scrolling diagonal (khusus pembesaran layar sebagian): Mengubah apakah scrolling diagonal aktif atau nonaktif. Untuk berpindahdan menemukan bagian yang diperbesar di layar dengan mudah, nonaktifkan Izinkan scrolling diagonal . Tips untuk menggunakan pembesaran Rentang pembesaran dapat mencapai 8x."}
+{"idx": 7, "title": "How do I slant/tilt the column headers?", "date": "", "ddg_snippet": "I've done it once before in an old google sheet, and I can't recreate it. I know how to rotate the text, but not the column itself.", "subpage_snippet": "", "source": "support.google.com", "link": "https://support.google.com/docs/thread/163221313/how-do-i-slant-tilt-the-column-headers?hl=en", "content": "I've done it once before in an old google sheet, and I can't recreate it. I know how to rotate the text, but not the column itself."}
+{"idx": 8, "title": "Usa los editores de Documentos de Google con un lector de...", "date": "", "ddg_snippet": "Para buscar en los menús, sigue estos pasos: Presiona Option + barra diagonal . Busca un término, como accesibilidad. Activa la compatibilidad con braille Documentos, Hojas de cálculo y Presentaciones tienen un modo braille que es necesario cuando se usa una pantalla braille, pero que también se puede utilizar junto con los comentarios por voz.", "subpage_snippet": "", "source": "support.google.com", "link": "https://support.google.com/docs/answer/6282736?hl=es-419", "content": "Para buscar en los menús, sigue estos pasos: Presiona Option + barra diagonal . Busca un término, como accesibilidad. Activa la compatibilidad con braille Documentos, Hojas de cálculo y Presentaciones tienen un modo braille que es necesario cuando se usa una pantalla braille, pero que también se puede utilizar junto con los comentarios por voz."}
+{"idx": 9, "title": "Pixel phone hardware tech specs", "date": "", "ddg_snippet": "Check out the tech specs for Pixel phones. If you're interested in buying a Pixel phone, visit the Google Store. Pixel", "subpage_snippet": "", "source": "support.google.com", "link": "https://support.google.com/pixelphone/answer/7158570?hl=en", "content": "Check out the tech specs for Pixel phones. If you're interested in buying a Pixel phone, visit the Google Store. Pixel"}
diff --git a/data/sampled_jsons/diffusion_model_alignment_methods_backpropagation-free_vs_backpropagation_comparison.jsonl b/data/sampled_jsons/diffusion_model_alignment_methods_backpropagation-free_vs_backpropagation_comparison.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..c35f787075ecf1b71a69ed45c33e90f29770c046
--- /dev/null
+++ b/data/sampled_jsons/diffusion_model_alignment_methods_backpropagation-free_vs_backpropagation_comparison.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Training-Free Diffusion Model Alignment with Sampling ...", "date": "", "ddg_snippet": "by PH Yeh · Cited by 5 — Summary: This paper studies the problem of aligning diffusion models without additional training or backpropagation. The main idea is to optimize the sampling ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=tfemquulED", "content": "by PH Yeh · Cited by 5 — Summary: This paper studies the problem of aligning diffusion models without additional training or backpropagation. The main idea is to optimize the sampling ..."}
+{"idx": 1, "title": "training-free diffusion model alignment with sampling ...", "date": "", "ddg_snippet": "by PH Yeh · 2024 · Cited by 5 — To the best of our knowledge, the proposed approach is the first inference-time, backpropagation-free preference alignment method for diffusion ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2410.05760?", "content": "by PH Yeh · 2024 · Cited by 5 — To the best of our knowledge, the proposed approach is the first inference-time, backpropagation-free preference alignment method for diffusion ..."}
+{"idx": 2, "title": "DyMO: Training-Free Diffusion Model Alignment with ...", "date": "", "ddg_snippet": "25 Mar 2025 — We propose a plug- and -play training- free alignment method , DyMO, for aligning the generated images and human preferences during inference.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2412.00759v3", "content": "25 Mar 2025 — We propose a plug- and -play training- free alignment method , DyMO, for aligning the generated images and human preferences during inference."}
+{"idx": 3, "title": "Efficient Personalization of Quantized Diffusion Model without ...", "date": "", "ddg_snippet": "by H Seo · 2025 — However, these methods have limitations: 1) they mostly rely on backpropagation , unsuitable for most mobile processors, which are designed to accelerate ... 11 pages", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2025/papers/Seo_Efficient_Personalization_of_Quantized_Diffusion_Model_without_Backpropagation_CVPR_2025_paper.pdf", "content": "by H Seo · 2025 — However, these methods have limitations: 1) they mostly rely on backpropagation , unsuitable for most mobile processors, which are designed to accelerate ... 11 pages"}
+{"idx": 4, "title": "Efficient Personalization of Quantized Diffusion Model without ...", "date": "", "ddg_snippet": "Our method achieves comparable performance to prior methods in image and text alignment scores for personalizing Stable Diffusion with only forward passes while ...", "subpage_snippet": "", "source": "ignoww.github.io", "link": "https://ignoww.github.io/ZOODiP_project/", "content": "Our method achieves comparable performance to prior methods in image and text alignment scores for personalizing Stable Diffusion with only forward passes while ..."}
+{"idx": 5, "title": "A SURVEY OF BACKPROPAGATION-FREE TRAINING ...", "date": "", "ddg_snippet": "During the backward pass, gradients are computed by backpropagating this loss through the network, allowing for the adjustment of weights in the model. 12 pages", "subpage_snippet": "", "source": "www.techrxiv.org", "link": "https://www.techrxiv.org/users/756917/articles/728971/master/file/data/main-survey-fwd/main-survey-fwd.pdf", "content": "During the backward pass, gradients are computed by backpropagating this loss through the network, allowing for the adjustment of weights in the model. 12 pages"}
+{"idx": 6, "title": "xie-lab-ml/awesome-alignment-of-diffusion-models", "date": "", "ddg_snippet": "The collection of awesome papers on the alignment of diffusion models . If you are interested in the alignment of diffusion models , please refer to our survey ...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/xie-lab-ml/awesome-alignment-of-diffusion-models", "content": "The collection of awesome papers on the alignment of diffusion models . If you are interested in the alignment of diffusion models , please refer to our survey ..."}
+{"idx": 7, "title": "DyMO: Training-Free Diffusion Model Alignment with Dynamic ...", "date": "", "ddg_snippet": "by X Xie · 2025 · Cited by 3 — Comparison with Existing Methods . To verify the effectiveness of our approach, we compare our method with training-based and training- free models . The former ...", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2025/papers/Xie_DyMO_Training-Free_Diffusion_Model_Alignment_with_Dynamic_Multi-Objective_Scheduling_CVPR_2025_paper.pdf", "content": "by X Xie · 2025 · Cited by 3 — Comparison with Existing Methods . To verify the effectiveness of our approach, we compare our method with training-based and training- free models . The former ..."}
+{"idx": 8, "title": "[PDF] Diffusion Model Alignment Using Direct Preference ...", "date": "", "ddg_snippet": "This work proposes Diffusion-DPO, a method to align diffusion models to human preferences by directly optimizing on human comparison data, and fine-tuned ...", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/Diffusion-Model-Alignment-Using-Direct-Preference-Wallace-Dang/f5275c61736781d236abe6700b822f1ea62f982e", "content": "This work proposes Diffusion-DPO, a method to align diffusion models to human preferences by directly optimizing on human comparison data, and fine-tuned ..."}
+{"idx": 9, "title": "Deep learning without back-propagation : r/MachineLearning", "date": "", "ddg_snippet": "Backprop has linear complexity in terms of the number of examples. Their method has quadratic complexity in the number of examples!", "subpage_snippet": "", "source": "www.reddit.com", "link": "https://www.reddit.com/r/MachineLearning/comments/cql2yr/deep_learning_without_backpropagation/", "content": "Backprop has linear complexity in terms of the number of examples. Their method has quadratic complexity in the number of examples!"}
diff --git a/data/sampled_jsons/du_Plessis_class_prior_estimation_positive_unlabeled_learning.jsonl b/data/sampled_jsons/du_Plessis_class_prior_estimation_positive_unlabeled_learning.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..6d14b677ce0b538c01f986f95b3af10478eb6a63
--- /dev/null
+++ b/data/sampled_jsons/du_Plessis_class_prior_estimation_positive_unlabeled_learning.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Class-prior Estimation for Learning from Positive and ...", "date": "", "ddg_snippet": "by MC Plessis · 2016 · Cited by 201 — In this paper, we show that, with additional samples coming only from the positive class , the class prior of the unlabeled dataset can be estimated correctly.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/1611.01586", "content": "by MC Plessis · 2016 · Cited by 201 — In this paper, we show that, with additional samples coming only from the positive class , the class prior of the unlabeled dataset can be estimated correctly."}
+{"idx": 1, "title": "Class-prior estimation for learning from positive and ...", "date": "", "ddg_snippet": "by MC du Plessis · 2017 · Cited by 201 — Estimation of the class prior from positive and unlabeled data is of great practical importance, since it allows a classifier to be trained only ...", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/article/10.1007/s10994-016-5604-6", "content": "by MC du Plessis · 2017 · Cited by 201 — Estimation of the class prior from positive and unlabeled data is of great practical importance, since it allows a classifier to be trained only ..."}
+{"idx": 2, "title": "Class-prior Estimation for Learning from Positive and ...", "date": "", "ddg_snippet": "by M Christoffel · 2016 · Cited by 201 — Class prior estimation from positive and unlabeled data . IEICE Transactions on Information and Systems, E97-D, 2014. M. C. du Plessis, G. Niu, and M. Sugiyama.", "subpage_snippet": "", "source": "proceedings.mlr.press", "link": "http://proceedings.mlr.press/v45/Christoffel15.pdf", "content": "by M Christoffel · 2016 · Cited by 201 — Class prior estimation from positive and unlabeled data . IEICE Transactions on Information and Systems, E97-D, 2014. M. C. du Plessis, G. Niu, and M. Sugiyama."}
+{"idx": 3, "title": "Class Prior Estimation in Active Positive and Unlabeled ...", "date": "", "ddg_snippet": "by L Perini · Cited by 18 — This paper explores a different setting that combines PU learning with active learning . The learner initially has ac- cess to only unlabeled data, and positive ... 7 pages", "subpage_snippet": "", "source": "www.ijcai.org", "link": "https://www.ijcai.org/proceedings/2020/0403.pdf", "content": "by L Perini · Cited by 18 — This paper explores a different setting that combines PU learning with active learning . The learner initially has ac- cess to only unlabeled data, and positive ... 7 pages"}
+{"idx": 4, "title": "Class prior estimation for positive-unlabeled learning when ...", "date": "", "ddg_snippet": "28 Feb 2025 — We study estimation of class prior for unlabeled target samples which is possibly different from that of source population.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2502.21194v1", "content": "28 Feb 2025 — We study estimation of class prior for unlabeled target samples which is possibly different from that of source population."}
+{"idx": 5, "title": "Analysis of learning from positive and unlabeled data - Volume 1", "date": "", "ddg_snippet": "by MC Plessis · 2014 · Cited by 508 — du Plessis and M. Sugiyama. Class prior estimation from positive and unlabeled data . IEICE Transactions on Information and Systems, E97-D:1358-1362, 2014 ...", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.5555/2968826.2968905", "content": "by MC Plessis · 2014 · Cited by 508 — du Plessis and M. Sugiyama. Class prior estimation from positive and unlabeled data . IEICE Transactions on Information and Systems, E97-D:1358-1362, 2014 ..."}
+{"idx": 6, "title": "Estimating the Class Prior in Positive and Unlabeled Data ...", "date": "", "ddg_snippet": "by J Bekker · 2018 · Cited by 153 — du Plessis, M.; Niu, G.; and Sugiyama, M. 2015. Class-prior estimation for learning from positive and unlabeled data . ML. 1–30. Elkan, C., and Noto, K. 2008 ...", "subpage_snippet": "", "source": "aaai.org", "link": "https://aaai.org/ocs/index.php/AAAI/AAAI18/paper/viewPDFInterstitial/16776/16539", "content": "by J Bekker · 2018 · Cited by 153 — du Plessis, M.; Niu, G.; and Sugiyama, M. 2015. Class-prior estimation for learning from positive and unlabeled data . ML. 1–30. Elkan, C., and Noto, K. 2008 ..."}
+{"idx": 7, "title": "Analysis of Learning from Positive and Unlabeled Data", "date": "", "ddg_snippet": "by MC du Plessis · 2014 · Cited by 508 — In this paper, we first show that this problem can be solved by cost-sensitive learning between positive and unlabeled data.", "subpage_snippet": "", "source": "papers.nips.cc", "link": "https://papers.nips.cc/paper/5509-analysis-of-learning-from-positive-and-unlabeled-data", "content": "by MC du Plessis · 2014 · Cited by 508 — In this paper, we first show that this problem can be solved by cost-sensitive learning between positive and unlabeled data."}
+{"idx": 8, "title": "LEARNING FROM POSITIVE AND UNLABELED DATA", "date": "", "ddg_snippet": "by M Kato · Cited by 147 — Marthinus Christoffel du Plessis, Gang Niu, and Masashi Sugiyama. Class-prior estimation for learning from positive and unlabeled data . In ACML, pp. 221–236 ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=rJzLciCqKm", "content": "by M Kato · Cited by 147 — Marthinus Christoffel du Plessis, Gang Niu, and Masashi Sugiyama. Class-prior estimation for learning from positive and unlabeled data . In ACML, pp. 221–236 ..."}
+{"idx": 9, "title": "[PDF] Class-prior estimation for learning from positive and ...", "date": "", "ddg_snippet": "It is shown that, with additional samples coming only from the positive class , the class prior of the unlabeled dataset can be estimated correctly and the ...", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/Class-prior-estimation-for-learning-from-positive-Plessis-Niu/c5deab518eabc3698e0c932ac7188e8b8a2f6cd3", "content": "It is shown that, with additional samples coming only from the positive class , the class prior of the unlabeled dataset can be estimated correctly and the ..."}
diff --git a/data/sampled_jsons/du_Plessis_et_al._2017_class_prior_estimation_optimal_transport_year_2017.jsonl b/data/sampled_jsons/du_Plessis_et_al._2017_class_prior_estimation_optimal_transport_year_2017.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..962b5202867f4e24be74a8f06261cdec8f6af34a
--- /dev/null
+++ b/data/sampled_jsons/du_Plessis_et_al._2017_class_prior_estimation_optimal_transport_year_2017.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Dynamic laboratory X-ray phase-contrast microtomography with", "date": "", "ddg_snippet": "Algorithms that utilise spatio-temporal [ 34 ] or structure-based [ 35 ] priors are particularly capable of generating useful reconstructions for ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2504.09193v1", "content": "Algorithms that utilise spatio-temporal [ 34 ] or structure-based [ 35 ] priors are particularly capable of generating useful reconstructions for ..."}
+{"idx": 1, "title": "MoSEs : Uncertainty-Aware AI-Generated Text Detection via", "date": "", "ddg_snippet": "The second category uses neural representation as semantic features to train or fine-tune a classification model, including RoBERTa (Solaiman et al ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2509.02499v1", "content": "The second category uses neural representation as semantic features to train or fine-tune a classification model, including RoBERTa (Solaiman et al ..."}
+{"idx": 2, "title": "Two-sample comparison through additive tree models for density", "date": "", "ddg_snippet": "... estimation of the mutual information (Suzuki et al ., 2009a , b ) , conditional density estimation (Sugiyama et al ., 2010 ) , covariate shift ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2508.03059v1", "content": "... estimation of the mutual information (Suzuki et al ., 2009a , b ) , conditional density estimation (Sugiyama et al ., 2010 ) , covariate shift ..."}
+{"idx": 3, "title": "US20180012233A1 - Method and a system for allocating emissions", "date": "", "ddg_snippet": "Disclosed herein is a method and system for allocating emissions resulting from transportation of cargo comprising disaggregating the emissions for ...", "subpage_snippet": "", "source": "patents.google.com", "link": "https://patents.google.com/patent/US20180012233A1/en", "content": "Disclosed herein is a method and system for allocating emissions resulting from transportation of cargo comprising disaggregating the emissions for ..."}
+{"idx": 4, "title": "CA2501778A1 - Dynamic electrostatic aerosol collection", "date": "", "ddg_snippet": "Priority date (The priority date is an assumption and is not a legal conclusion. ... 150000008163 sugars Chemical class 0.000 description 1", "subpage_snippet": "", "source": "patents.google.com", "link": "https://patents.google.com/patent/CA2501778A1/en", "content": "Priority date (The priority date is an assumption and is not a legal conclusion. ... 150000008163 sugars Chemical class 0.000 description 1"}
+{"idx": 5, "title": "Placental and Fetal In Utero Growth Among Fetuses With", "date": "", "ddg_snippet": "Intrauterine growth retardation: definition, classification, and etiology. Clin Obstet Gynecol . ... Effects of long-term, high-altitude ...", "subpage_snippet": "", "source": "jamanetwork.com", "link": "https://jamanetwork.com/journals/jamanetworkopen/fullarticle/2833187", "content": "Intrauterine growth retardation: definition, classification, and etiology. Clin Obstet Gynecol . ... Effects of long-term, high-altitude ..."}
+{"idx": 6, "title": "ecerm.org/journal/view.php", "date": "", "ddg_snippet": "The classes of Ig present and the precise characterization of the sperm region involved in the autoimmune response were evaluated using indirect ...", "subpage_snippet": "", "source": "ecerm.org", "link": "https://ecerm.org/journal/view.php?number=1163&viewtype=pubreader", "content": "The classes of Ig present and the precise characterization of the sperm region involved in the autoimmune response were evaluated using indirect ..."}
+{"idx": 7, "title": "Hyperbolic Genome Embeddings", "date": "", "ddg_snippet": "... et al ., 2018 ; Nickel & Kiela, 2018 ) , alongside optimization techniques generalized to Riemannian manifolds (Bécigneul & Ganea, 2019 ) .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2507.21648v1", "content": "... et al ., 2018 ; Nickel & Kiela, 2018 ) , alongside optimization techniques generalized to Riemannian manifolds (Bécigneul & Ganea, 2019 ) ."}
+{"idx": 8, "title": "How do monetary and time spend explain cultural tourist", "date": "", "ddg_snippet": "... longer, but the synergies with other activities will also increase their satisfaction with their visit (Rachão et al ., 2021; Vena-Oya et al ., 2021) .", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/348826794_How_do_monetary_and_time_spend_explain_cultural_tourist_satisfaction", "content": "... longer, but the synergies with other activities will also increase their satisfaction with their visit (Rachão et al ., 2021; Vena-Oya et al ., 2021) ."}
+{"idx": 9, "title": "Genomics Reveals Complex Population History and Unexpected", "date": "", "ddg_snippet": "... to include whole genome sequencing (WGS), the congruence between genetic and genomic methods has been called into question ( McCartney-Melstad et al ...", "subpage_snippet": "", "source": "academic.oup.com", "link": "https://academic.oup.com/mbe/article/40/11/msad207/7275014", "content": "... to include whole genome sequencing (WGS), the congruence between genetic and genomic methods has been called into question ( McCartney-Melstad et al ..."}
diff --git a/data/sampled_jsons/extreme_weather_events_policy_announcements_scientific_reports_climate_activism_social_media.jsonl b/data/sampled_jsons/extreme_weather_events_policy_announcements_scientific_reports_climate_activism_social_media.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..adefe5344d31b0f71cb5801246e921e55f5a2bef
--- /dev/null
+++ b/data/sampled_jsons/extreme_weather_events_policy_announcements_scientific_reports_climate_activism_social_media.jsonl
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+{"idx": 0, "title": "A study of impact of climate change on the U.S. stock ...", "date": "", "ddg_snippet": "by C Li · 2024 · Cited by 10 — ... climate change on the NASDAQ index from the unique perspective of extreme weather events ... policy announcements on listed fishing ...", "subpage_snippet": "", "source": "www.nature.com", "link": "https://www.nature.com/articles/s41598-024-66109-7", "content": "by C Li · 2024 · Cited by 10 — ... climate change on the NASDAQ index from the unique perspective of extreme weather events ... policy announcements on listed fishing ..."}
+{"idx": 1, "title": "The coverage, framing and audience interaction with climate ...", "date": "", "ddg_snippet": "by LW Njeru · 2025 — ... extreme weather events , scientific reports and political movements. The Intergovernmental Panel on Climate Change (IPCC) fifth assessment ...", "subpage_snippet": "", "source": "ecommons.aku.edu", "link": "https://ecommons.aku.edu/cgi/viewcontent.cgi?article=3306&context=theses_dissertations", "content": "by LW Njeru · 2025 — ... extreme weather events , scientific reports and political movements. The Intergovernmental Panel on Climate Change (IPCC) fifth assessment ..."}
+{"idx": 2, "title": "Working together to achieve the Paris climate goals and ...", "date": "", "ddg_snippet": "In addition, climate change through extreme weather events , floods or sea ... scenarios: flood mitigation in Indian coastal city', Scientific Reports , 9(7783). 182 pages", "subpage_snippet": "", "source": "newclimate.org", "link": "https://newclimate.org/sites/default/files/2021/09/BMZStudy_NC_DIE_ENGLISH_FullReport.pdf", "content": "In addition, climate change through extreme weather events , floods or sea ... scenarios: flood mitigation in Indian coastal city', Scientific Reports , 9(7783). 182 pages"}
+{"idx": 3, "title": "The Ocean as a Solution to Climate Change", "date": "", "ddg_snippet": "by O Hoegh-Guldberg · Cited by 266 — occurrences of extreme weather events . The pandemic created supply chain ... IMO ambitions were recently revealed in policy announcements (July 5, 2023): ... 160 pages", "subpage_snippet": "", "source": "oceanpanel.org", "link": "https://oceanpanel.org/wp-content/uploads/2023/09/Full-Report_Ocean-Climate-Solutions-Update-1.pdf", "content": "by O Hoegh-Guldberg · Cited by 266 — occurrences of extreme weather events . The pandemic created supply chain ... IMO ambitions were recently revealed in policy announcements (July 5, 2023): ... 160 pages"}
+{"idx": 4, "title": "Signal of Sustainable Agriculture! Nonlinear Impact ...", "date": "", "ddg_snippet": "20 Aug 2025 — ... policy announcements , the ... “The Empirical Effect of Agricultural Social Services on Pesticide Inputs.” Scientific Reports 14: 15907.", "subpage_snippet": "", "source": "onlinelibrary.wiley.com", "link": "https://onlinelibrary.wiley.com/doi/10.1002/sd.70167", "content": "20 Aug 2025 — ... policy announcements , the ... “The Empirical Effect of Agricultural Social Services on Pesticide Inputs.” Scientific Reports 14: 15907."}
+{"idx": 5, "title": "Case Studies of Environmental Risk Analysis Methodologies", "date": "", "ddg_snippet": "25 Mar 2018 — ... extreme weather events ), rises in sea levels, losses of ecosystem ... Scientific reports , 2, 541. Black, F., & Scholes, M. (1973). The ... 616 pages", "subpage_snippet": "", "source": "www.ngfs.net", "link": "https://www.ngfs.net/sites/default/files/medias/documents/case_studies_of_environmental_risk_analysis_methodologies.pdf", "content": "25 Mar 2018 — ... extreme weather events ), rises in sea levels, losses of ecosystem ... Scientific reports , 2, 541. Black, F., & Scholes, M. (1973). The ... 616 pages"}
+{"idx": 6, "title": "Leveraging Deep Learning for Carbon Market Price ...", "date": "", "ddg_snippet": "by Z Zhang · 2025 · Cited by 10 — The increasing frequency of extreme weather events and rising global temperatures have highlighted the urgent need for comprehensive policy ...", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/org/science/article/pii/S1546223425000371", "content": "by Z Zhang · 2025 · Cited by 10 — The increasing frequency of extreme weather events and rising global temperatures have highlighted the urgent need for comprehensive policy ..."}
+{"idx": 7, "title": "JANUARY 2025 - Global Economic Prospects", "date": "", "ddg_snippet": "5 Jan 2025 — ... extreme weather events related to climate change , and weaker growth ... policy announcements in several large economies—most notably in ...", "subpage_snippet": "", "source": "documents1.worldbank.org", "link": "https://documents1.worldbank.org/curated/en/099310203242527356/pdf/IDU-774d653a-a3ac-4e84-bf63-bbc014107071.pdf", "content": "5 Jan 2025 — ... extreme weather events related to climate change , and weaker growth ... policy announcements in several large economies—most notably in ..."}
+{"idx": 8, "title": "Enhancing agricultural commodity price forecasting with ...", "date": "", "ddg_snippet": "by RL Manogna · 2025 · Cited by 3 — ... extreme weather events , geopolitical tensions, or pandemics. ... policy announcements ). Such enhancements could improve prediction ...", "subpage_snippet": "", "source": "www.nature.com", "link": "https://www.nature.com/articles/s41598-025-05103-z", "content": "by RL Manogna · 2025 · Cited by 3 — ... extreme weather events , geopolitical tensions, or pandemics. ... policy announcements ). Such enhancements could improve prediction ..."}
+{"idx": 9, "title": "Forecasting regional carbon prices in china with a hybrid ...", "date": "", "ddg_snippet": "by Y Yi · 2025 — Additionally, the model framework does not account for the impact of policy changes or extreme weather events . Future research could incorporate ...", "subpage_snippet": "", "source": "pmc.ncbi.nlm.nih.gov", "link": "https://pmc.ncbi.nlm.nih.gov/articles/PMC12208498/", "content": "by Y Yi · 2025 — Additionally, the model framework does not account for the impact of policy changes or extreme weather events . Future research could incorporate ..."}
diff --git a/data/sampled_jsons/fiveaiunderstanding_safety_finetuning_model.py_config_GPT_transformer_blocks.jsonl b/data/sampled_jsons/fiveaiunderstanding_safety_finetuning_model.py_config_GPT_transformer_blocks.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..65f90e96ce214124eb0033e54e69583b37924eb9
--- /dev/null
+++ b/data/sampled_jsons/fiveaiunderstanding_safety_finetuning_model.py_config_GPT_transformer_blocks.jsonl
@@ -0,0 +1,8 @@
+{"idx": 0, "title": "GitHub - fiveai/understanding_safety_finetuning: Official ...", "date": "", "ddg_snippet": "To better understand the underlying factors that make models safe via safety fine-tuning, we design a synthetic data generation framework that captures salient aspects of an unsafe input by modeling the interaction between the task the model is asked to perform (e.g., “design”) versus the specific concepts the task is asked to be performed upon (e.g., a “cycle” vs. a “bomb”). Using ...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/fiveai/understanding_safety_finetuning", "content": "To better understand the underlying factors that make models safe via safety fine-tuning, we design a synthetic data generation framework that captures salient aspects of an unsafe input by modeling the interaction between the task the model is asked to perform (e.g., “design”) versus the specific concepts the task is asked to be performed upon (e.g., a “cycle” vs. a “bomb”). Using ..."}
+{"idx": 1, "title": "fiveai / understanding_safety_finetuning Public - GitHub", "date": "", "ddg_snippet": "Official Code for What Makes and Breaks Safety Fine-tuning? A Mechanistic Study (NeurIPS 2024) - understanding_safety_finetuning/ssft at main · fiveai / understanding ...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/fiveai/understanding_safety_finetuning/tree/main/ssft", "content": "Official Code for What Makes and Breaks Safety Fine-tuning? A Mechanistic Study (NeurIPS 2024) - understanding_safety_finetuning/ssft at main · fiveai / understanding ..."}
+{"idx": 2, "title": "understanding_safety_finetuning/pretrain.py at main - GitHub", "date": "", "ddg_snippet": "fiveai / understanding_safety_finetuning Public Notifications Fork 0 Star 0 Code Issues Actions Projects Security Insights", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/fiveai/understanding_safety_finetuning/blob/main/pretrain.py", "content": "fiveai / understanding_safety_finetuning Public Notifications Fork 0 Star 0 Code Issues Actions Projects Security Insights"}
+{"idx": 3, "title": "What Makes and Breaks Safety Fine-tuning? A Mechanistic Study", "date": "", "ddg_snippet": "Sep 25, 2024 · Safety fine-tuning helps align Large Language Models (LLMs) with human preferences for their safe deployment. To better understand the underlying factors that make models safe via safety fine-tuning, we design a synthetic data generation framework that captures salient aspects of an unsafe input by modeling the interaction between the task the model is asked to perform (e.g., “design ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=JEflV4nRlH", "content": "Sep 25, 2024 · Safety fine-tuning helps align Large Language Models (LLMs) with human preferences for their safe deployment. To better understand the underlying factors that make models safe via safety fine-tuning, we design a synthetic data generation framework that captures salient aspects of an unsafe input by modeling the interaction between the task the model is asked to perform (e.g., “design ..."}
+{"idx": 4, "title": "What Makes and Breaks Safety Fine - tuning ?", "date": "", "ddg_snippet": "What drives the clustering of safe and unsafe samples: Analyzing parameter changes. Impact of safety fine - tuning on the sensitivity of the learned model . Evading the Safety Mechanism: Jailbreak and Adversarial Inputs.", "subpage_snippet": "", "source": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper_files/paper/2024/file/a9bef53eb7b0e5950d4f2d9c74a16006-Paper-Conference.pdf", "content": "What drives the clustering of safe and unsafe samples: Analyzing parameter changes. Impact of safety fine - tuning on the sensitivity of the learned model . Evading the Safety Mechanism: Jailbreak and Adversarial Inputs."}
+{"idx": 5, "title": "What Makes and Breaks Safety Fine-tuning? A Mechanistic Study", "date": "", "ddg_snippet": "Safety fine-tuning helps align Large Language Models (LLMs) with human pref-erences for their safe deployment. To better understand the underlying factors that make models safe via safety fine-tuning, we design a synthetic data gen-eration framework that captures salient aspects of an unsafe input by modeling the interaction between the task the model is asked to perform (e.g., “design ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=JEflV4nRlH", "content": "Safety fine-tuning helps align Large Language Models (LLMs) with human pref-erences for their safe deployment. To better understand the underlying factors that make models safe via safety fine-tuning, we design a synthetic data gen-eration framework that captures salient aspects of an unsafe input by modeling the interaction between the task the model is asked to perform (e.g., “design ..."}
+{"idx": 6, "title": "What Makes and Breaks Safety Fine-tuning? A Mechanistic Study", "date": "", "ddg_snippet": "To better understand the underlying factors that make models safe via safety fine-tuning, we design a synthetic data generation framework that captures salient aspects of an unsafe input by modeling the interaction between the task the model is asked to perform (e.g., “design”) versus the specific concepts the task is asked to be performed ...", "subpage_snippet": "", "source": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper_files/paper/2024/hash/a9bef53eb7b0e5950d4f2d9c74a16006-Abstract-Conference.html", "content": "To better understand the underlying factors that make models safe via safety fine-tuning, we design a synthetic data generation framework that captures salient aspects of an unsafe input by modeling the interaction between the task the model is asked to perform (e.g., “design”) versus the specific concepts the task is asked to be performed ..."}
+{"idx": 7, "title": "What makes and breaks safety fine-tuning? a mechanistic study", "date": "", "ddg_snippet": "Safety fine-tuning helps align Large Language Models (LLMs) with human preferences for their safe deployment. To better understand the underlying factors that make models safe via safety fine-tuning, we design a synthetic data generation framework that captures salient aspects of an unsafe input by modeling the interaction between the task the model is asked to perform (e.g., \"design\") versus ...", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.5555/3737916.3740879", "content": "Safety fine-tuning helps align Large Language Models (LLMs) with human preferences for their safe deployment. To better understand the underlying factors that make models safe via safety fine-tuning, we design a synthetic data generation framework that captures salient aspects of an unsafe input by modeling the interaction between the task the model is asked to perform (e.g., \"design\") versus ..."}
diff --git "a/data/sampled_jsons/generalized_static_Schr\303\266dinger_bridge_extending_to_Luo_Tseng_1992_coordinate_descent.jsonl" "b/data/sampled_jsons/generalized_static_Schr\303\266dinger_bridge_extending_to_Luo_Tseng_1992_coordinate_descent.jsonl"
new file mode 100644
index 0000000000000000000000000000000000000000..65d72e72d1efc06d0b833a860d298fc34d74a7fc
--- /dev/null
+++ "b/data/sampled_jsons/generalized_static_Schr\303\266dinger_bridge_extending_to_Luo_Tseng_1992_coordinate_descent.jsonl"
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+{"idx": 0, "title": "Schrödinger equation - Wikipedia", "date": "", "ddg_snippet": "The Schrödinger equation is not the only way to study quantum mechanical systems and make predictions. Other formulations of quantum mechanics include matrix mechanics, introduced by Werner Heisenberg, and the path integral formulation, developed chiefly by Richard Feynman.", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/Schrödinger_equation", "content": "The Schrödinger equation is not the only way to study quantum mechanical systems and make predictions. Other formulations of quantum mechanics include matrix mechanics, introduced by Werner Heisenberg, and the path integral formulation, developed chiefly by Richard Feynman."}
+{"idx": 1, "title": "[2303.16852] Diffusion Schrödinger Bridge Matching", "date": "", "ddg_snippet": "In contrast, Schrödinger bridges (SBs) compute stochastic dynamic mappings which recover entropy-regularized versions of OT. Unfortunately, existing numerical methods approximating SBs either scale poorly with dimension or accumulate errors across iterations.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2303.16852", "content": "In contrast, Schrödinger bridges (SBs) compute stochastic dynamic mappings which recover entropy-regularized versions of OT. Unfortunately, existing numerical methods approximating SBs either scale poorly with dimension or accumulate errors across iterations."}
+{"idx": 2, "title": "Tree-Based Diffusion Schrödinger Bridge with Applications to...", "date": "", "ddg_snippet": "General experimental setup. Details on the experiments. Tree-Based Diffusion Schrödinger Bridge with Applications to Wasserstein Barycenters. Luo , Z.-Q. and Tseng , P. On the convergence of the coordinate descent method for convex differen-tiable minimization.", "subpage_snippet": "", "source": "hal.science", "link": "https://hal.science/hal-04402356/document", "content": "General experimental setup. Details on the experiments. Tree-Based Diffusion Schrödinger Bridge with Applications to Wasserstein Barycenters. Luo , Z.-Q. and Tseng , P. On the convergence of the coordinate descent method for convex differen-tiable minimization."}
+{"idx": 3, "title": "A Dual Coordinate Descent Method for Large-scale Linear SVM", "date": "", "ddg_snippet": "Luo , Z.-Q., & Tseng , P. ( 1992 ). On the convergence of coordinate descent method for convex dierentiable minimization. J. Optim.We consider the analysis in ( Luo & Tseng , 1992 ), which studies coordinate descent methods for prob-lems in the following form: min.", "subpage_snippet": "", "source": "www.csie.ntu.edu.tw", "link": "https://www.csie.ntu.edu.tw/~cjlin/papers/cddual.pdf", "content": "Luo , Z.-Q., & Tseng , P. ( 1992 ). On the convergence of coordinate descent method for convex dierentiable minimization. J. Optim.We consider the analysis in ( Luo & Tseng , 1992 ), which studies coordinate descent methods for prob-lems in the following form: min."}
+{"idx": 4, "title": "Coordinate Descent · 모두를 위한 컨벡스 최적화", "date": "", "ddg_snippet": "Z. Luo and P. Tseng ( 1992 ), “On the convergence of the coordinate descent method for convex differentiable minimization”. J. Ortega and W. Rheinboldt (1970), “Iterative solution of nonlinear equations in several variables”.", "subpage_snippet": "", "source": "convex-optimization-for-all.github.io", "link": "https://convex-optimization-for-all.github.io/contents/chapter23/", "content": "Z. Luo and P. Tseng ( 1992 ), “On the convergence of the coordinate descent method for convex differentiable minimization”. J. Ortega and W. Rheinboldt (1970), “Iterative solution of nonlinear equations in several variables”."}
+{"idx": 5, "title": "(PDF) A dual coordinate descent method for large-scale linear SVM", "date": "", "ddg_snippet": "in ( Luo & Tseng , 1992 ): Theorem 1 For L1-SVM and L2-SVM, {αk,i}gen-. erated by Algorithm 1 globally converges to an optimal.We consider the analysis in ( Luo & Tseng , 1992 ), which studies coordinate descent methods for prob", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/312450680_A_dual_coordinate_descent_method_for_large-scale_linear_SVM", "content": "in ( Luo & Tseng , 1992 ): Theorem 1 For L1-SVM and L2-SVM, {αk,i}gen-. erated by Algorithm 1 globally converges to an optimal.We consider the analysis in ( Luo & Tseng , 1992 ), which studies coordinate descent methods for prob"}
+{"idx": 6, "title": "On the Linear Convergence of Descent Methods for Convex...", "date": "", "ddg_snippet": "Z.-Q. Luo , P. Tseng , On the convergence of the coordinate descent method for convex differentiable minimization, J. Optim.", "subpage_snippet": "", "source": "epubs.siam.org", "link": "https://epubs.siam.org/doi/10.1137/0330025", "content": "Z.-Q. Luo , P. Tseng , On the convergence of the coordinate descent method for convex differentiable minimization, J. Optim."}
+{"idx": 7, "title": "Diffusion Schrödinger bridges for score-based generative modeling", "date": "", "ddg_snippet": "This document discusses diffusion Schrödinger bridges as a method for score-based generative modeling, highlighting their effectiveness over traditional GANs and variational autoencoders in generating images.", "subpage_snippet": "", "source": "www.slideshare.net", "link": "https://www.slideshare.net/slideshow/diffusion-schrdinger-bridges-for-scorebased-generative-modeling/251937201", "content": "This document discusses diffusion Schrödinger bridges as a method for score-based generative modeling, highlighting their effectiveness over traditional GANs and variational autoencoders in generating images."}
+{"idx": 8, "title": "Efcient Greedy Coordinate Descent via Variable Partitioning", "date": "", "ddg_snippet": "Zhiquan Luo and Paul Tseng . On the convergence of the coordinate descent method for convex differentiable min-imization.", "subpage_snippet": "", "source": "auai.org", "link": "https://auai.org/uai2021/pdf/uai2021.219.pdf", "content": "Zhiquan Luo and Paul Tseng . On the convergence of the coordinate descent method for convex differentiable min-imization."}
+{"idx": 9, "title": "Accelerating Greedy Coordinate Descent Methods", "date": "", "ddg_snippet": "Luo , Zhi-Quan and Tseng , Paul. Error bounds and conver-gence analysis of feasible descent methods: a general approach.", "subpage_snippet": "", "source": "mitsloan.mit.edu", "link": "https://mitsloan.mit.edu/shared/ods/documents?DocumentID=4768", "content": "Luo , Zhi-Quan and Tseng , Paul. Error bounds and conver-gence analysis of feasible descent methods: a general approach."}
diff --git a/data/sampled_jsons/github.comMethodsOfMachineLearningluno_README.md.jsonl b/data/sampled_jsons/github.comMethodsOfMachineLearningluno_README.md.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..af35af51ffffdf3b0ae74c0e2f316a915195a3e9
--- /dev/null
+++ b/data/sampled_jsons/github.comMethodsOfMachineLearningluno_README.md.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "GitHub - MethodsOfMachineLearning/luno: LUNO: Linearized ...", "date": "", "ddg_snippet": "luno - Linearized Uncertainty for Neural Operators 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": "luno - Linearized Uncertainty for Neural Operators This repository contains the main algorithm of the paper \"Linearization Turns Neural Operators into Function-Valued Gaussian Processes\" by Magnani et al. (2025)."}
+{"idx": 1, "title": "luno/README.md at main · MethodsOfMachineLearning/luno · GitHub", "date": "", "ddg_snippet": "LUNO : Linearized Predictive Uncertainty in Neural Operators - luno / README.md at main · MethodsOfMachineLearning / luno", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/MethodsOfMachineLearning/luno/blob/main/README.md", "content": "LUNO : Linearized Predictive Uncertainty in Neural Operators - luno / README.md at main · MethodsOfMachineLearning / luno"}
+{"idx": 2, "title": "Getting Started | luno/workflow | DeepWiki", "date": "", "ddg_snippet": "Getting Started Relevant source files This guide provides instructions for setting up and implementing a basic workflow using the Luno Workflow engine. It covers installation, basic configuration, and the creation of a simple workflow. For detailed information about specific components, refer to Core Components and Adapters. Installation To start using the workflow engine, add the module to ...", "subpage_snippet": "", "source": "deepwiki.com", "link": "https://deepwiki.com/luno/workflow/1.3-getting-started", "content": "Getting Started Relevant source files This guide provides instructions for setting up and implementing a basic workflow using the Luno Workflow engine. It covers installation, basic configuration, and the creation of a simple workflow. For detailed information about specific components, refer to Core Components and Adapters. Installation To start using the workflow engine, add the module to ..."}
+{"idx": 3, "title": "Issues: MethodsOfMachineLearning/luno - GitHub", "date": "", "ddg_snippet": "LUNO : Linearized Predictive Uncertainty in Neural Operators - Issues · MethodsOfMachineLearning / luno", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/MethodsOfMachineLearning/luno/issues", "content": "LUNO : Linearized Predictive Uncertainty in Neural Operators - Issues · MethodsOfMachineLearning / luno"}
+{"idx": 4, "title": "Luno/README.md at master · tbhall/Luno · GitHub", "date": "", "ddg_snippet": "Machine Learning algorithm that takes a page image and extracts product information from it. - Luno / README.md at master · tbhall/ Luno", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/tbhall/Luno/blob/master/README.md?plain=1", "content": "Machine Learning algorithm that takes a page image and extracts product information from it. - Luno / README.md at master · tbhall/ Luno"}
+{"idx": 5, "title": "luno package - github.com/luno/luno-go - Go Packages", "date": "", "ddg_snippet": "Jul 8, 2025 · Provides Go library for interacting with Luno 's cryptocurrency trading platform.", "subpage_snippet": "", "source": "pkg.go.dev", "link": "https://pkg.go.dev/github.com/luno/luno-go", "content": "Jul 8, 2025 · Provides Go library for interacting with Luno 's cryptocurrency trading platform."}
+{"idx": 6, "title": "luno/README.md at main · luno-lang/luno · GitHub", "date": "", "ddg_snippet": "A small toy language with syntax heavily inspired by Lua that transpiles to JS. - luno / README.md at main · luno -lang/ luno", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/luno-lang/luno/blob/main/README.md", "content": "A small toy language with syntax heavily inspired by Lua that transpiles to JS. - luno / README.md at main · luno -lang/ luno"}
+{"idx": 7, "title": "3 Ways to Add an Image to GitHub README | Sean C Davis", "date": "", "ddg_snippet": "In other words, if you place an image file in the root your project as my-image.jpg, you could then render the image in your projects main README . md file like this...", "subpage_snippet": "", "source": "www.seancdavis.com", "link": "https://www.seancdavis.com/posts/three-ways-to-add-image-to-github-readme/", "content": "In other words, if you place an image file in the root your project as my-image.jpg, you could then render the image in your projects main README . md file like this..."}
+{"idx": 8, "title": "GitHub - papayiv/Avit0_challenge: Решается задача словесной...", "date": "", "ddg_snippet": "Repository files navigation. README . Avit0_challenge. Решается задача словесной сегментации.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/papayiv/Avit0_challenge", "content": "Repository files navigation. README . Avit0_challenge. Решается задача словесной сегментации."}
+{"idx": 9, "title": "Всё, что нужно знать о файле README . md : Зачем он нужен... | Дзен", "date": "", "ddg_snippet": "Зачем нужен README . md ? Представьте, что вы нашли на улице интересный гаджет, но без инструкции. Вы не знаете, что это, как его включить и чем он может быть вам...", "subpage_snippet": "", "source": "dzen.ru", "link": "https://dzen.ru/a/aM8WpanXJl4zUOcF", "content": "Зачем нужен README . md ? Представьте, что вы нашли на улице интересный гаджет, но без инструкции. Вы не знаете, что это, как его включить и чем он может быть вам..."}
diff --git a/data/sampled_jsons/github.commarvinli-harvardcritical-windows_year_2024.jsonl b/data/sampled_jsons/github.commarvinli-harvardcritical-windows_year_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..b1414a60ea64d885069f46c2d028ebd004eb9b89
--- /dev/null
+++ b/data/sampled_jsons/github.commarvinli-harvardcritical-windows_year_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "How Does Critical Batch Size Scale in Pre-training?", "date": "", "ddg_snippet": "In particular, the efficiency notion of critical batch size (CBS), concerning the compromise between time and compute, marks the threshold beyond ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2410.21676v4", "content": "In particular, the efficiency notion of critical batch size (CBS), concerning the compromise between time and compute, marks the threshold beyond ..."}
+{"idx": 1, "title": "network analysis – Page 8 – Computational Legal", "date": "", "ddg_snippet": "... Commission and aggregated by the Center for Responsive Politics our visualizations track large money donations to members of the 110 th Congress over ...", "subpage_snippet": "", "source": "computationallegalstudies.com", "link": "https://computationallegalstudies.com/tag/network-analysis/page/8/", "content": "... Commission and aggregated by the Center for Responsive Politics our visualizations track large money donations to members of the 110 th Congress over ..."}
+{"idx": 2, "title": "GitHub - deeenes/bioinfo-tools: Collection of Python and other", "date": "", "ddg_snippet": "You will find this collection helpful either if you are a scientist and you just realized that you need computational tools for processing your data ...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/deeenes/bioinfo-tools", "content": "You will find this collection helpful either if you are a scientist and you just realized that you need computational tools for processing your data ..."}
+{"idx": 3, "title": "The Coming Technological Singularity: How to Survive in the", "date": "", "ddg_snippet": "Save pavellishin/1049084 to your computer and use it in GitHub Desktop. ... spread across the human world view? For a while yet, the general critics ...", "subpage_snippet": "", "source": "gist.github.com", "link": "https://gist.github.com/pavellishin/1049084", "content": "Save pavellishin/1049084 to your computer and use it in GitHub Desktop. ... spread across the human world view? For a while yet, the general critics ..."}
+{"idx": 4, "title": "Hi, my name is Marvin!", "date": "", "ddg_snippet": "Sitan Chen at Harvard SEAS, I helped discover and ... Critical windows : non-asymptotic theory for feature emergence in diffusion models ” .", "subpage_snippet": "", "source": "marvinfli.com", "link": "https://marvinfli.com/", "content": "Sitan Chen at Harvard SEAS, I helped discover and ... Critical windows : non-asymptotic theory for feature emergence in diffusion models ” ."}
+{"idx": 5, "title": "Artificial intelligence leader Marvin Minsky dies at 88 - SD", "date": "", "ddg_snippet": "With more development teams today using open-source and third-party components to build out their applications, the biggest area of concern for ...", "subpage_snippet": "", "source": "sdtimes.com", "link": "https://sdtimes.com/ai/artificial-intelligence-leader-marvin-minsky-dies-at-88/", "content": "With more development teams today using open-source and third-party components to build out their applications, the biggest area of concern for ..."}
+{"idx": 6, "title": "Speakers | SCALE 14x", "date": "", "ddg_snippet": "He also previously served as director of community at GitHub , Canonical, XPRIZE, OpenAdvantage, and consulted and advised a range of organizations ...", "subpage_snippet": "", "source": "www.socallinuxexpo.org", "link": "https://www.socallinuxexpo.org/scale/14x/speakers/jeff-mccormick", "content": "He also previously served as director of community at GitHub , Canonical, XPRIZE, OpenAdvantage, and consulted and advised a range of organizations ..."}
+{"idx": 7, "title": "Speakers | SCALE 14x", "date": "", "ddg_snippet": "He also previously served as director of community at GitHub , Canonical, XPRIZE, OpenAdvantage, and consulted and advised a range of organizations ...", "subpage_snippet": "", "source": "www.socallinuxexpo.org", "link": "https://www.socallinuxexpo.org/scale/14x/speakers/dave-cramer", "content": "He also previously served as director of community at GitHub , Canonical, XPRIZE, OpenAdvantage, and consulted and advised a range of organizations ..."}
+{"idx": 8, "title": "Clue | The Sleepless Critic", "date": "", "ddg_snippet": "Welcome comedic local references varied from quips about North Shore Massachusetts accents to local landmarks to host puppets ‘GBH’s Jim Braude ...", "subpage_snippet": "", "source": "sleeplesscritic.com", "link": "https://sleeplesscritic.com/tag/clue/", "content": "Welcome comedic local references varied from quips about North Shore Massachusetts accents to local landmarks to host puppets ‘GBH’s Jim Braude ..."}
+{"idx": 9, "title": "How Nelson Mandela Influenced Me – Questions for Colleges", "date": "", "ddg_snippet": "Marvin Krislov is the current president of Oberlin College and the former vice-president and general counsel for the University of Michigan .", "subpage_snippet": "", "source": "q4colleges.com", "link": "https://q4colleges.com/2012/01/02/marvin-krislov-oberlin-college/", "content": "Marvin Krislov is the current president of Oberlin College and the former vice-president and general counsel for the University of Michigan ."}
diff --git a/data/sampled_jsons/github_ars22_scaling-LLM-math-synthetic-data_Llama_DeepSeek_base_models_Section_5.jsonl b/data/sampled_jsons/github_ars22_scaling-LLM-math-synthetic-data_Llama_DeepSeek_base_models_Section_5.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..20c8fc4de6f97530a2e9bab79e53d554916cef55
--- /dev/null
+++ b/data/sampled_jsons/github_ars22_scaling-LLM-math-synthetic-data_Llama_DeepSeek_base_models_Section_5.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Large language model - Wikipedia", "date": "", "ddg_snippet": "Machine learningand data mining. v . t. e. A large language model is a language model trained with self-supervised machine learning on a vast amount of text, designed for natural language processing tasks, especially language generation.", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/Large_language_model", "content": "Machine learningand data mining. v . t. e. A large language model is a language model trained with self-supervised machine learning on a vast amount of text, designed for natural language processing tasks, especially language generation."}
+{"idx": 1, "title": "DeepSeek - Wikipedia", "date": "", "ddg_snippet": ". Hangzhou DeepSeek Artificial Intelligence Basic Technology Research Co., Ltd., doing business as DeepSeek , is a Chinese artificial intelligence company that develops large language models . Based in Hangzhou, Zhejiang, Deepseek is owned and funded b...", "subpage_snippet": "", "source": "en.m.wikipedia.org", "link": "https://en.m.wikipedia.org/wiki/AI_DeepSeek", "content": ". Hangzhou DeepSeek Artificial Intelligence Basic Technology Research Co., Ltd., doing business as DeepSeek , is a Chinese artificial intelligence company that develops large language models . Based in Hangzhou, Zhejiang, Deepseek is owned and funded b..."}
+{"idx": 2, "title": "RLonIncorrectSyntheticDataScalesthe ...", "date": "", "ddg_snippet": "Figure1:Left: Positive/negative synthetic data :Pictorialrepresentationofoursyntheticdatapipeline,definitionsforpositiveand negativesyntheticdataandhowtheyarefedtosupervisedfinetuning(SFT;positivesyntheticdatafrommorecapablemodels), rejectionfinetuning(RFT;positiveself-generatedsyntheticdatafromtheSFTmodel)andstep-levelRL(viaper-stepDPO ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2406.14532", "content": "Figure1:Left: Positive/negative synthetic data :Pictorialrepresentationofoursyntheticdatapipeline,definitionsforpositiveand negativesyntheticdataandhowtheyarefedtosupervisedfinetuning(SFT;positivesyntheticdatafrommorecapablemodels), rejectionfinetuning(RFT;positiveself-generatedsyntheticdatafromtheSFTmodel)andstep-levelRL(viaper-stepDPO ..."}
+{"idx": 3, "title": "GitHub - ars 22 / scaling - LLM - math - synthetic - data : Code and data...", "date": "", "ddg_snippet": "ars 22 / scaling - LLM - math - synthetic - data .Code and data used in the paper: \"Training on Incorrect Synthetic Data via RL Scales LLM Math Reasoning Eight-Fold\". About.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/ars22/scaling-LLM-math-synthetic-data", "content": "ars 22 / scaling - LLM - math - synthetic - data .Code and data used in the paper: \"Training on Incorrect Synthetic Data via RL Scales LLM Math Reasoning Eight-Fold\". About."}
+{"idx": 4, "title": "RL on Incorrect Synthetic Data Scales the Efficiency of LLM ...", "date": "", "ddg_snippet": "We present the results for scaling up negative data for training the DeepSeek - Math -7B and LLama2-7B models on GSM8K and MATH in Figure 7 (a,b). Observe in Figure 7 (a,b), that for both DeepSeek - Math -7B and LLama2-7B models , per-step DPO improves performance beyond the SFT policy and the performance continues to scale favorably as data size ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2406.14532v1", "content": "We present the results for scaling up negative data for training the DeepSeek - Math -7B and LLama2-7B models on GSM8K and MATH in Figure 7 (a,b). Observe in Figure 7 (a,b), that for both DeepSeek - Math -7B and LLama2-7B models , per-step DPO improves performance beyond the SFT policy and the performance continues to scale favorably as data size ..."}
+{"idx": 5, "title": "Timeline for Code Release and Reproducibility of ... - GitHub", "date": "", "ddg_snippet": "ars22 / scaling-LLM-math-synthetic-data Public Notifications You must be signed in to change notification settings Fork 0 Star 23", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/ars22/scaling-LLM-math-synthetic-data/issues/1", "content": "ars22 / scaling-LLM-math-synthetic-data Public Notifications You must be signed in to change notification settings Fork 0 Star 23"}
+{"idx": 6, "title": "[2308.01825] Scaling Relationship on Learning Mathematical ... Zhihong Shao 邵智宏 - Zhihong Shao 邵智宏 gsm8k-ScRel by OFA-Sys - SourcePulse", "date": "", "ddg_snippet": "Aug 3, 2023 · Mathematical reasoning is a challenging task for large language models (LLMs), while the scaling relationship of it with respect to LLM capacity is under-explored. In this paper, we investigate how the pre-training loss, supervised data amount, and augmented data amount influence the reasoning performances of a supervised LLM . We find that pre-training loss is a better indicator of the model's ... DeepSeek -Prover: Advancing Theorem Proving in LLMs through Large- Scale Synthetic Data The Annual Conference on Neural Information Processing Systems (NeurIPS), MATH -AI workshop, 2024. This repository provides code and data for scaling mathematical reasoning in large language models , focusing on techniques like Supervised Fine-Tuning (SFT) and Rejection-based Fine-Tuning (RFT). It targets researchers and practitioners working on improving LLM performance in mathematical problem-solving, offering reproducible results and pre-trained checkpoints. How It Works", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2308.01825", "content": "Aug 3, 2023 · Mathematical reasoning is a challenging task for large language models (LLMs), while the scaling relationship of it with respect to LLM capacity is under-explored. In this paper, we investigate how the pre-training loss, supervised data amount, and augmented data amount influence the reasoning performances of a supervised LLM . We find that pre-training loss is a better indicator of the model's ... DeepSeek -Prover: Advancing Theorem Proving in LLMs through Large- Scale Synthetic Data The Annual Conference on Neural Information Processing Systems (NeurIPS), MATH -AI workshop, 2024. This repository provides code and data for scaling mathematical reasoning in large language models , focusing on techniques like Supervised Fine-Tuning (SFT) and Rejection-based Fine-Tuning (RFT). It targets researchers and practitioners working on improving LLM performance in mathematical problem-solving, offering reproducible results and pre-trained checkpoints. How It Works"}
+{"idx": 7, "title": "Поднимаем DeepSeek llm локально / Хабр", "date": "", "ddg_snippet": "Все уже слышали про новую модель DeepSeek r1, которая обогнала по бенчмаркам openai. Компания DeepSeek выложила веса и дистилляты в открытый доступ, поэтому мы можем их запустить.", "subpage_snippet": "", "source": "habr.com", "link": "https://habr.com/ru/articles/878836/", "content": "Все уже слышали про новую модель DeepSeek r1, которая обогнала по бенчмаркам openai. Компания DeepSeek выложила веса и дистилляты в открытый доступ, поэтому мы можем их запустить."}
+{"idx": 8, "title": "DeepSeek V 3 0324 (free) - API, Providers, Stats | OpenRouter", "date": "", "ddg_snippet": "It succeeds the [ DeepSeek V 3](/ deepseek / deepseek -chat- v 3) model and performs really well on a variety of tasks. Run DeepSeek V 3 0324 (free) with API.", "subpage_snippet": "", "source": "openrouter.ai", "link": "https://openrouter.ai/deepseek/deepseek-chat-v3-0324:free", "content": "It succeeds the [ DeepSeek V 3](/ deepseek / deepseek -chat- v 3) model and performs really well on a variety of tasks. Run DeepSeek V 3 0324 (free) with API."}
+{"idx": 9, "title": "(PDF) RL on Incorrect Synthetic Data Scales the Efficiency of LLM ...", "date": "", "ddg_snippet": "DeepSeek LLM Scaling Open-Source Language Models with Longtermism.Our evaluation results demonstrate that DeepSeek LLM 67B surpasses LLaMA -2 70B across a range of benchmarks, especially in the domains of code, mathematics , and reasoning.", "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": "DeepSeek LLM Scaling Open-Source Language Models with Longtermism.Our evaluation results demonstrate that DeepSeek LLM 67B surpasses LLaMA -2 70B across a range of benchmarks, especially in the domains of code, mathematics , and reasoning."}
diff --git a/data/sampled_jsons/gradient-based_meta-learning_MAML_model-agnostic_meta-learning_alternatives_to_evolution_strategies_year_2023.jsonl b/data/sampled_jsons/gradient-based_meta-learning_MAML_model-agnostic_meta-learning_alternatives_to_evolution_strategies_year_2023.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..eae21141fc293520350fa36657b0a37143e8f1aa
--- /dev/null
+++ b/data/sampled_jsons/gradient-based_meta-learning_MAML_model-agnostic_meta-learning_alternatives_to_evolution_strategies_year_2023.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Meta - Learning with Gradient - Based Meta - Learners | Medium", "date": "", "ddg_snippet": "Meta - learning , or “learning to learn,” is a powerful concept where a model doesn’t just learn from one dataset but learns how to adapt to multiple tasks by developing a more general understanding of how to learn.Let’s talk about MAML — the Model - Agnostic Meta - Learning algorithm.", "subpage_snippet": "", "source": "medium.com", "link": "https://medium.com/biased-algorithms/meta-learning-with-gradient-based-meta-learners-bdb3a7611f22", "content": "Meta - learning , or “learning to learn,” is a powerful concept where a model doesn’t just learn from one dataset but learns how to adapt to multiple tasks by developing a more general understanding of how to learn.Let’s talk about MAML — the Model - Agnostic Meta - Learning algorithm."}
+{"idx": 1, "title": "Meta - Learning Evolution : The Art Of Learning To... - Divi-AI.com", "date": "", "ddg_snippet": "Model - Agnostic Meta - Learning ( MAML ). Gradient - Based Meta - Learning is a general framework that utilizes gradients to learn optimal model parameters for fast adaptation.", "subpage_snippet": "", "source": "divi-ai.com", "link": "https://divi-ai.com/meta-learning-evolution-the-art-of-learning-to-learn-in-ai/", "content": "Model - Agnostic Meta - Learning ( MAML ). Gradient - Based Meta - Learning is a general framework that utilizes gradients to learn optimal model parameters for fast adaptation."}
+{"idx": 2, "title": "Meta Learning : Revolutionizing How Machines Learn and Adapt", "date": "", "ddg_snippet": "1. Model - Agnostic Meta - Learning ( MAML ).In gradient - based meta - learning , the model learns to update its parameters efficiently using gradients.", "subpage_snippet": "", "source": "www.quantzig.com", "link": "https://www.quantzig.com/blog/meta-learning-revolutionizing-machine-learning-with-adaptive-algorithms/", "content": "1. Model - Agnostic Meta - Learning ( MAML ).In gradient - based meta - learning , the model learns to update its parameters efficiently using gradients."}
+{"idx": 3, "title": "Adaptive Gradient - Based Meta - Learning Methods | Restackio", "date": "", "ddg_snippet": "Model - Agnostic Meta - Learning ( MAML ): MAML is a widely recognized algorithm that aims to find a set of model parameters that can be quickly adapted to new tasks with minimal training. The key steps include", "subpage_snippet": "", "source": "d2wozrt205r2fu.cloudfront.net", "link": "https://d2wozrt205r2fu.cloudfront.net/p/meta-learning-adaptive-gradient-answer-cat-ai", "content": "Model - Agnostic Meta - Learning ( MAML ): MAML is a widely recognized algorithm that aims to find a set of model parameters that can be quickly adapted to new tasks with minimal training. The key steps include"}
+{"idx": 4, "title": "Transforming Machine Learning with Meta - Learning Techniques", "date": "", "ddg_snippet": "Meta - Learner : This model learns to make the base learner perform better by learning any hyperparameters, learning rates, training strategies , etc.", "subpage_snippet": "", "source": "www.artiba.org", "link": "https://www.artiba.org/blog/transforming-machine-learning-with-meta-learning-techniques", "content": "Meta - Learner : This model learns to make the base learner perform better by learning any hyperparameters, learning rates, training strategies , etc."}
+{"idx": 5, "title": "FOMAML and Reptile Algorithm Analysis", "date": "", "ddg_snippet": "Model - Agnostic Meta - Learning ( MAML ).While Model - Agnostic Meta - Learning ( MAML ) provides a powerful framework for learning adaptable initializations, its reliance on second-order derivatives (computing gradients through gradients ) presents significant computational hurdles...", "subpage_snippet": "", "source": "apxml.com", "link": "https://apxml.com/courses/meta-learning-foundation-models/chapter-2-advanced-gradient-based-meta-learning/fomaml-reptile-analysis", "content": "Model - Agnostic Meta - Learning ( MAML ).While Model - Agnostic Meta - Learning ( MAML ) provides a powerful framework for learning adaptable initializations, its reliance on second-order derivatives (computing gradients through gradients ) presents significant computational hurdles..."}
+{"idx": 6, "title": "Meta - Learning for Cognitive Robotics: A Deep Dive", "date": "", "ddg_snippet": "Model - Agnostic Meta - Learning ( MAML ).Reptile: a gradient - based meta - learning algorithm that uses the gradient information to update the model parameters. The following diagram illustrates the different meta - learning techniques", "subpage_snippet": "", "source": "www.numberanalytics.com", "link": "https://www.numberanalytics.com/blog/meta-learning-cognitive-robotics-deep-dive", "content": "Model - Agnostic Meta - Learning ( MAML ).Reptile: a gradient - based meta - learning algorithm that uses the gradient information to update the model parameters. The following diagram illustrates the different meta - learning techniques"}
+{"idx": 7, "title": "Model - Agnostic Meta - Learning for Fast Adaptation of Deep... - statwiki", "date": "", "ddg_snippet": "Learning quickly is a hallmark of human intelligence, whether it involves recognizing objects from a few examples or quickly learning new skills after just minutes of experience. Meta - learning is a subfield of machine learning where automatic learnin...", "subpage_snippet": "", "source": "wiki.math.uwaterloo.ca", "link": "https://wiki.math.uwaterloo.ca/statwiki/index.php?title=Model-Agnostic_Meta-Learning_for_Fast_Adaptation_of_Deep_Networks", "content": "Learning quickly is a hallmark of human intelligence, whether it involves recognizing objects from a few examples or quickly learning new skills after just minutes of experience. Meta - learning is a subfield of machine learning where automatic learnin..."}
+{"idx": 8, "title": "ES- MAML : Simple Hessian-Free Meta Learning", "date": "", "ddg_snippet": "To avoid these issues, we propose an alternative approach to MAML based on Evolution Strategies (ES), as opposed to the policy gradient underlying previous MAML algorithms. Model - agnostic meta - learning for fast adaptation of deep networks.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/1910.01215", "content": "To avoid these issues, we propose an alternative approach to MAML based on Evolution Strategies (ES), as opposed to the policy gradient underlying previous MAML algorithms. Model - agnostic meta - learning for fast adaptation of deep networks."}
+{"idx": 9, "title": "Meta - Learning In AI: Key Concepts | Restackio", "date": "", "ddg_snippet": "Meta - Learner and Base -Learner. Model - Agnostic Meta - Learning ( MAML ): This approach focuses on finding a suitable initialization of model parameters that can be quickly adapted to new tasks with minimal gradient updates. MAML employs a two-level optimization process", "subpage_snippet": "", "source": "store-restack.vercel.app", "link": "https://store-restack.vercel.app/p/meta-learning-in-ai-answer-key-concepts-cat-ai", "content": "Meta - Learner and Base -Learner. Model - Agnostic Meta - Learning ( MAML ): This approach focuses on finding a suitable initialization of model parameters that can be quickly adapted to new tasks with minimal gradient updates. MAML employs a two-level optimization process"}
diff --git a/data/sampled_jsons/hierarchical_overlapping_clustering_graphs_cost_function_objective_function_year_2022.jsonl b/data/sampled_jsons/hierarchical_overlapping_clustering_graphs_cost_function_objective_function_year_2022.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..fc4df859463f23d588f59838b0d928a8134d9b28
--- /dev/null
+++ b/data/sampled_jsons/hierarchical_overlapping_clustering_graphs_cost_function_objective_function_year_2022.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Cluster analysis - Wikipedia", "date": "", "ddg_snippet": "Hierarchical clustering : objects that belong to a child cluster also belong to the parent cluster . Subspace clustering : while an overlapping clustering , within a uniquely defined subspace, clusters are not expected to overlap .", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/Cluster_analysis", "content": "Hierarchical clustering : objects that belong to a child cluster also belong to the parent cluster . Subspace clustering : while an overlapping clustering , within a uniquely defined subspace, clusters are not expected to overlap ."}
+{"idx": 1, "title": "ICML Poster Hierarchical Overlapping Clustering on Graphs : Cost ...", "date": "", "ddg_snippet": "Overlap and hierarchy are two prevalent phenomena in clustering , and usually coexist in a single system.To address this issue, we initiate the study of hierarchical overlapping clustering on graphs by introducing a new cost function for it.", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/poster/46447", "content": "Overlap and hierarchy are two prevalent phenomena in clustering , and usually coexist in a single system.To address this issue, we initiate the study of hierarchical overlapping clustering on graphs by introducing a new cost function for it."}
+{"idx": 2, "title": "Unsupervised Clustering : A Guide | Built In", "date": "", "ddg_snippet": "A hierarchical clustering algorithm is based on the union between the two nearest clusters . The beginning condition is realized by setting every data point as a cluster . After a few iterations it reaches the final clusters wanted.Distance measure in objective function equation.", "subpage_snippet": "", "source": "builtin.com", "link": "https://builtin.com/articles/unsupervised-clustering", "content": "A hierarchical clustering algorithm is based on the union between the two nearest clusters . The beginning condition is realized by setting every data point as a cluster . After a few iterations it reaches the final clusters wanted.Distance measure in objective function equation."}
+{"idx": 3, "title": "(PDF) Overview of Overlapping Partitional Clustering Methods", "date": "", "ddg_snippet": "Basically, the existing overlapping clustering methods are extensions from usual. clustering models such as hierarchical , generative, graph -based or partitional modThe objective function . of PCM1is described by", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/278662450_Overview_of_Overlapping_Partitional_Clustering_Methods", "content": "Basically, the existing overlapping clustering methods are extensions from usual. clustering models such as hierarchical , generative, graph -based or partitional modThe objective function . of PCM1is described by"}
+{"idx": 4, "title": "Overlapping Hierarchical Clustering (OHC) | SpringerLink", "date": "", "ddg_snippet": "We propose an overlapping hierarchical clustering framework. We construct a quasi-dendrogram hierarchical structure to represent the clusters that is however not necessarily a tree (of specific shape) but a directed acyclic graph .", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/chapter/10.1007/978-3-030-44584-3_21", "content": "We propose an overlapping hierarchical clustering framework. We construct a quasi-dendrogram hierarchical structure to represent the clusters that is however not necessarily a tree (of specific shape) but a directed acyclic graph ."}
+{"idx": 5, "title": "Formalizing Hierarchical Clustering as Integer Linear Programming", "date": "", "ddg_snippet": "Overlapping Clustering Objective . Although we relax the transitivity requirement of the merge function in this setting, intuitively graphs in which transitiv-ity is better satised will lead to simpler overlapping cluster -ings.", "subpage_snippet": "", "source": "ojs.aaai.org", "link": "https://ojs.aaai.org/index.php/AAAI/article/download/8671/8530", "content": "Overlapping Clustering Objective . Although we relax the transitivity requirement of the merge function in this setting, intuitively graphs in which transitiv-ity is better satised will lead to simpler overlapping cluster -ings."}
+{"idx": 6, "title": "Functorial Clustering via Simplicial Complexes", "date": "", "ddg_snippet": "Hierarchical Clustering Functor .A at clustering functor is a functor C : UMet → Cov that is the identity on the underlying set.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=ZkDLcXCP5sV", "content": "Hierarchical Clustering Functor .A at clustering functor is a functor C : UMet → Cov that is the identity on the underlying set."}
+{"idx": 7, "title": "Hierarchical and temporal analysis of scientific corpora as tools for the...", "date": "", "ddg_snippet": "Overlapping Hierarchical Clustering (OHC). Hierarchical structure similarity.Ward’s method In fact as Ward Jr 1963 mentioned, any objective function chosen by the investigator could be used for the hierarchical agglomerative clustering .", "subpage_snippet": "", "source": "theses.hal.science", "link": "https://theses.hal.science/tel-03108773v2/file/JEANTET_Ian.pdf", "content": "Overlapping Hierarchical Clustering (OHC). Hierarchical structure similarity.Ward’s method In fact as Ward Jr 1963 mentioned, any objective function chosen by the investigator could be used for the hierarchical agglomerative clustering ."}
+{"idx": 8, "title": "ILP Formulation for Hierarchical", "date": "", "ddg_snippet": "• Explicit Global Objective Function Optimizing. As mentioned most existing work greedily determines the best join at each and every level of the hierarchy .2.2. Solving hierarchical clustering Using ILP allows us to formally dene an objective function and then nd globally.", "subpage_snippet": "", "source": "web.cs.ucdavis.edu", "link": "https://web.cs.ucdavis.edu/~davidson/Publications/AIJ.pdf", "content": "• Explicit Global Objective Function Optimizing. As mentioned most existing work greedily determines the best join at each and every level of the hierarchy .2.2. Solving hierarchical clustering Using ILP allows us to formally dene an objective function and then nd globally."}
+{"idx": 9, "title": "DAG-Structured Clustering by Nearest Neighbors", "date": "", "ddg_snippet": "Func-torial hierarchical clustering with overlaps .A cost function for similarity-based hier - archical clustering .", "subpage_snippet": "", "source": "proceedings.mlr.press", "link": "https://proceedings.mlr.press/v130/monath21a/monath21a.pdf", "content": "Func-torial hierarchical clustering with overlaps .A cost function for similarity-based hier - archical clustering ."}
diff --git a/data/sampled_jsons/httpsarxiv.orghtml2410.02025v1.jsonl b/data/sampled_jsons/httpsarxiv.orghtml2410.02025v1.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..90c067b537c617bc943cabaf5ed50107e21c8ed1
--- /dev/null
+++ b/data/sampled_jsons/httpsarxiv.orghtml2410.02025v1.jsonl
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+{"idx": 0, "title": "accessible HTML - arXiv info", "date": "", "ddg_snippet": "HTML as an accessible format for papers Accessibility barriers in research are not new, but they are urgent. The message we have heard from our community is that arXiv can have the most impact in the shortest time by offering HTML papers alongside the existing PDF. arXiv has successfully launched papers in HTML format. We are gradually backfilling HTML for arXiv's corpus of over 2 million ...", "subpage_snippet": "", "source": "info.arxiv.org", "link": "https://info.arxiv.org/about/accessible_HTML.html", "content": "HTML as an accessible format for papers Accessibility barriers in research are not new, but they are urgent. The message we have heard from our community is that arXiv can have the most impact in the shortest time by offering HTML papers alongside the existing PDF. arXiv has successfully launched papers in HTML format. We are gradually backfilling HTML for arXiv's corpus of over 2 million ..."}
+{"idx": 1, "title": "ar5iv - Articles from arXiv.org as responsive HTML5 web documents", "date": "", "ddg_snippet": "ar5iv offers a modern web view for arXiv's preprints. An open community resource, on a quest to a full collection of high-quality documents.", "subpage_snippet": "", "source": "ar5iv.labs.arxiv.org", "link": "https://ar5iv.labs.arxiv.org/", "content": "ar5iv offers a modern web view for arXiv's preprints. An open community resource, on a quest to a full collection of high-quality documents."}
+{"idx": 2, "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 ..."}
+{"idx": 3, "title": "Contextual Document Embeddings - arXiv.org", "date": "", "ddg_snippet": "Abstract Dense document embeddings are central to neural retrieval. The dominant paradigm is to train and construct embeddings by running encoders directly on individual documents. In this work, we argue that these embeddings, while effective, are implicitly out-of-context for targeted use cases of retrieval, and that a contextualized document embedding should take into account both the ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2410.02525v1", "content": "Abstract Dense document embeddings are central to neural retrieval. The dominant paradigm is to train and construct embeddings by running encoders directly on individual documents. In this work, we argue that these embeddings, while effective, are implicitly out-of-context for targeted use cases of retrieval, and that a contextualized document embedding should take into account both the ..."}
+{"idx": 4, "title": "[2410.02525] Contextual Document Embeddings - arXiv.org", "date": "", "ddg_snippet": "Dense document embeddings are central to neural retrieval. The dominant paradigm is to train and construct embeddings by running encoders directly on individual documents. In this work, we argue that these embeddings, while effective, are implicitly out-of-context for targeted use cases of retrieval, and that a contextualized document embedding should take into account both the document and ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2410.02525", "content": "Dense document embeddings are central to neural retrieval. The dominant paradigm is to train and construct embeddings by running encoders directly on individual documents. In this work, we argue that these embeddings, while effective, are implicitly out-of-context for targeted use cases of retrieval, and that a contextualized document embedding should take into account both the document and ..."}
+{"idx": 5, "title": "arXiv:2410.02525v4 [cs.CL] 8 Nov 2024", "date": "", "ddg_snippet": "ABSTRACT Dense document embeddings are central to neural retrieval. The dominant paradigm is to train and construct embeddings by running encoders directly on individual documents. In this work, we argue that these embeddings, while effective, are implicitly out-of-context for targeted use cases of retrieval, and that a document embedding should take into account both the document and ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2410.02525", "content": "ABSTRACT Dense document embeddings are central to neural retrieval. The dominant paradigm is to train and construct embeddings by running encoders directly on individual documents. In this work, we argue that these embeddings, while effective, are implicitly out-of-context for targeted use cases of retrieval, and that a document embedding should take into account both the document and ..."}
+{"idx": 6, "title": "A Likelihood Based Approach to Distribution Regression Using ...", "date": "", "ddg_snippet": "Abstract page for arXiv paper 2410.02025: A Likelihood Based Approach to Distribution Regression Using Conditional Deep Generative Models", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2410.02025", "content": "Abstract page for arXiv paper 2410.02025: A Likelihood Based Approach to Distribution Regression Using Conditional Deep Generative Models"}
+{"idx": 7, "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 response variable lies in a high-dimensional ambient space …", "subpage_snippet": "", "source": "ar5iv.labs.arxiv.org", "link": "https://ar5iv.labs.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 response variable lies in a high-dimensional ambient space …"}
+{"idx": 8, "title": "Demonstrating real-time and low-latency quantum error correction with ...", "date": "", "ddg_snippet": "In this work, we present a real-time decoded QEC experiment on 8 qubits with logical branching that measures the full decoding response time to be 9.6 \\unit\\micro, including 6.5 \\unit\\micro decoding time and 3.1 \\unit\\micro communication and control latency times, per 9 measurement rounds. We achieve this by decoding with a scalable FPGA implementation of a Collision Clustering decoder [30 ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2410.05202v1", "content": "In this work, we present a real-time decoded QEC experiment on 8 qubits with logical branching that measures the full decoding response time to be 9.6 \\unit\\micro, including 6.5 \\unit\\micro decoding time and 3.1 \\unit\\micro communication and control latency times, per 9 measurement rounds. We achieve this by decoding with a scalable FPGA implementation of a Collision Clustering decoder [30 ..."}
+{"idx": 9, "title": "Understanding the Human-LLM Dynamic: A Literature Survey of LLM Use in ...", "date": "", "ddg_snippet": "Abstract. Large Language Models (LLMs) are transforming programming practices, offering significant capabilities for code generation activities. While researchers have explored the potential of LLMs in various domains, this paper focuses on their use in programming tasks, drawing insights from user studies that assess the impact of LLMs on programming tasks. We first examined the user ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2410.01026v1", "content": "Abstract. Large Language Models (LLMs) are transforming programming practices, offering significant capabilities for code generation activities. While researchers have explored the potential of LLMs in various domains, this paper focuses on their use in programming tasks, drawing insights from user studies that assess the impact of LLMs on programming tasks. We first examined the user ..."}
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+{"idx": 0, "title": "HTML Görüntüleyici - Çevrimiçi HTML Editörü ve Önizleme Aracı", "date": "", "ddg_snippet": "HTML Görüntüleyici - Sözdizimi vurgulama ve duyarlı tasarım testi ile HTML kodunu düzenlemek, önizlemek ve biçimlendirmek için güçlü bir çevrimiçi araç.", "subpage_snippet": "", "source": "htmlonline.org", "link": "https://htmlonline.org/tr/", "content": "HTML Görüntüleyici - Sözdizimi vurgulama ve duyarlı tasarım testi ile HTML kodunu düzenlemek, önizlemek ve biçimlendirmek için güçlü bir çevrimiçi araç."}
+{"idx": 1, "title": "Login / Member's access to the HTML 6 Editor", "date": "", "ddg_snippet": "There's no Login to HTML 6. To access your editor, use the unique link you received completing the checkout.", "subpage_snippet": "", "source": "html6.com", "link": "https://html6.com/login/", "content": "There's no Login to HTML 6. To access your editor, use the unique link you received completing the checkout."}
+{"idx": 2, "title": "Learn HTML - Free Interactive HTML Tutorial", "date": "", "ddg_snippet": "learn- html . org is a free interactive HTML tutorial for people who want to learn HTML , fast.", "subpage_snippet": "", "source": "www.learn-html.org", "link": "https://www.learn-html.org/", "content": "learn- html . org is a free interactive HTML tutorial for people who want to learn HTML , fast."}
+{"idx": 3, "title": "HTMLbook. org - HTML , CSS, JavaScript", "date": "", "ddg_snippet": "Руководство для тех, кто увлекается веб-разработкой, создает сайты или просто хочет узнать что такое HTML , CSS и JavaScript.", "subpage_snippet": "", "source": "htmlbook.org", "link": "https://htmlbook.org/", "content": "Руководство для тех, кто увлекается веб-разработкой, создает сайты или просто хочет узнать что такое HTML , CSS и JavaScript."}
+{"idx": 4, "title": "Images in HTML export (The Org Manual)", "date": "", "ddg_snippet": "The HTML export backend has features to convert Org image links to HTML inline images and HTML clickable image links. When the link in the Org file has no description, the HTML export...", "subpage_snippet": "", "source": "orgmode.org", "link": "https://orgmode.org/manual/Images-in-HTML-export.html", "content": "The HTML export backend has features to convert Org image links to HTML inline images and HTML clickable image links. When the link in the Org file has no description, the HTML export..."}
+{"idx": 5, "title": "Online PDF to HTML Converter: Online converting PDF Files to HTML ...", "date": "", "ddg_snippet": "An easy tool to convert your PDF files into HTML Format. Upload your file and get your PDF File in HTML !", "subpage_snippet": "", "source": "www.pdf2html.org", "link": "https://www.pdf2html.org/", "content": "An easy tool to convert your PDF files into HTML Format. Upload your file and get your PDF File in HTML !"}
+{"idx": 6, "title": "Chatorg Kameralı Sohbet, Bedava Ücretsiz Sesli Chat - Görüntülü...", "date": "", "ddg_snippet": "Kameralı, sesli ve görüntülü sohbet 7/24 hizmeti veren mobil cihazlar ve chat kanalımız ile nezir bir sohbet ortamı hazırladık.", "subpage_snippet": "", "source": "chatorg.org", "link": "https://chatorg.org/kamerali-sohbet/", "content": "Kameralı, sesli ve görüntülü sohbet 7/24 hizmeti veren mobil cihazlar ve chat kanalımız ile nezir bir sohbet ortamı hazırladık."}
+{"idx": 7, "title": "1 John 3 - See what kind of love the Father has given to us... | ESV. org", "date": "", "ddg_snippet": "See what kind of love the Father has given to us, that we should be called children of God; and so we are. The reason why the world does not know us is that it did not know him. Beloved, we...", "subpage_snippet": "", "source": "www.esv.org", "link": "https://www.esv.org/1+John+3/", "content": "See what kind of love the Father has given to us, that we should be called children of God; and so we are. The reason why the world does not know us is that it did not know him. Beloved, we..."}
+{"idx": 8, "title": "HTML Tag »", "date": "", "ddg_snippet": "The element creates a rectangular pane of arbitrary size which can be used for drawing graphics, manipulating photos, and creating animations with JavaScript.", "subpage_snippet": "", "source": "html.com", "link": "https://html.com/tags/canvas/", "content": "The element creates a rectangular pane of arbitrary size which can be used for drawing graphics, manipulating photos, and creating animations with JavaScript."}
+{"idx": 9, "title": "Explore | alphaXiv", "date": "", "ddg_snippet": "Discuss, discover, and read arXiv papers. Explore trending papers, see recent activity and discussions, and follow authors of arXiv papers on alphaXiv.", "subpage_snippet": "", "source": "www.alphaxiv.org", "link": "https://www.alphaxiv.org/", "content": "Discuss, discover, and read arXiv papers. Explore trending papers, see recent activity and discussions, and follow authors of arXiv papers on alphaXiv."}
diff --git a/data/sampled_jsons/httpseurope.naverlabs.comresearchcomputer-visionimagenet-sd.jsonl b/data/sampled_jsons/httpseurope.naverlabs.comresearchcomputer-visionimagenet-sd.jsonl
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+{"idx": 0, "title": "Fake it till you make it: Learning transferable... - Naver Labs Europe", "date": "", "ddg_snippet": "Pretrained Models. We provide two ResNet50 models pretrained on our synthetic ImageNet clones: ImageNet -100- SD or ImageNet -1K- SD . In both cases, we generate images with Stable...", "subpage_snippet": "", "source": "europe.naverlabs.com", "link": "https://europe.naverlabs.com/research/computer-vision/imagenet-sd/", "content": "Pretrained Models. We provide two ResNet50 models pretrained on our synthetic ImageNet clones: ImageNet -100- SD or ImageNet -1K- SD . In both cases, we generate images with Stable..."}
+{"idx": 1, "title": "Computer vision tasks. Adapted from... | Download Scientific Diagram", "date": "", "ddg_snippet": "Adapted from \"https://www.slideshare.net/darian_f/introduction-to-theartificial-intelligence-and- computer - vision -revolution.\" from publication: Application of Deep Learning in...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/figure/Computer-vision-tasks-Adapted-from_fig5_346091812", "content": "Adapted from \"https://www.slideshare.net/darian_f/introduction-to-theartificial-intelligence-and- computer - vision -revolution.\" from publication: Application of Deep Learning in..."}
+{"idx": 2, "title": "Google Research Careers - Join Our Team - Google Research", "date": "", "ddg_snippet": "Find the right career path for you with Google Research . Our team needs talented people with experience in AI, Machine Learning and Natural Language Processing.", "subpage_snippet": "", "source": "research.google", "link": "https://research.google/careers/", "content": "Find the right career path for you with Google Research . Our team needs talented people with experience in AI, Machine Learning and Natural Language Processing."}
+{"idx": 3, "title": "Ed Gein | Research Starters | EBSCO Research", "date": "", "ddg_snippet": "Born in 1906, he experienced a troubled childhood marked by an abusive father and a fanatically religious mother who instilled in him a deep-seated aversion to sex.", "subpage_snippet": "", "source": "www.ebsco.com", "link": "https://www.ebsco.com/research-starters/history/ed-gein", "content": "Born in 1906, he experienced a troubled childhood marked by an abusive father and a fanatically religious mother who instilled in him a deep-seated aversion to sex."}
+{"idx": 4, "title": "Subir Imagenes Temporales | Fotos Anónimas", "date": "", "ddg_snippet": "Servidor temporal de imagenes. Muestra tus fotos de manera segura y anónima sin registros. Subir imagen, compartir imagen, subir foto, compartir foto.", "subpage_snippet": "", "source": "www.imagenes-temporales.com", "link": "https://www.imagenes-temporales.com/", "content": "Servidor temporal de imagenes. Muestra tus fotos de manera segura y anónima sin registros. Subir imagen, compartir imagen, subir foto, compartir foto."}
+{"idx": 5, "title": "МЫ ПРИШЕЛЬЦЫ на 24 ЧАСА и СЛЕДИМ ЗА ЛЮДЬМИ!", "date": "", "ddg_snippet": "УГАР В ROADSIDE RESEARCH - [ CoffiChannel ]» от автора Лучшее из YouTube Gaming. Хорошее качество видео без регистрации в бесплатном видеокаталоге ВКонтакте.", "subpage_snippet": "", "source": "vk.com", "link": "https://vk.com/video-95491231_456243644", "content": "УГАР В ROADSIDE RESEARCH - [ CoffiChannel ]» от автора Лучшее из YouTube Gaming. Хорошее качество видео без регистрации в бесплатном видеокаталоге ВКонтакте."}
+{"idx": 6, "title": "Двухместный номер турецкий сериал смотреть онлайн все серии", "date": "", "ddg_snippet": "Двухместный номер турецкий сериал на русском языке смотреть бесплатно на TurokTV. com в хорошем качестве.", "subpage_snippet": "", "source": "turoktv.com", "link": "https://turoktv.com/dvuhmestnyy-nomer/", "content": "Двухместный номер турецкий сериал на русском языке смотреть бесплатно на TurokTV. com в хорошем качестве."}
+{"idx": 7, "title": "Colab Paid Services Pricing", "date": "", "ddg_snippet": "", "subpage_snippet": "", "source": "colab.research.google.com", "link": "https://colab.research.google.com/signup", "content": ""}
+{"idx": 8, "title": "Google", "date": "", "ddg_snippet": "Поиск информации в интернете: веб страницы, картинки, видео и многое другое.", "subpage_snippet": "", "source": "images.google.com", "link": "https://images.google.com/m", "content": "Поиск информации в интернете: веб страницы, картинки, видео и многое другое."}
+{"idx": 9, "title": "Центр практического искусственного интеллекта — Сберлабс", "date": "", "ddg_snippet": "Работаем над перспективными направлениями исследований, создаем инновационные решения, развиваем науку...", "subpage_snippet": "", "source": "sberlabs.com", "link": "https://sberlabs.com/laboratories/sber-ai-lab", "content": "Работаем над перспективными направлениями исследований, создаем инновационные решения, развиваем науку..."}
diff --git a/data/sampled_jsons/httpsgithub.comcsznDnCNN.jsonl b/data/sampled_jsons/httpsgithub.comcsznDnCNN.jsonl
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+{"idx": 0, "title": "GitHub - cszn/DnCNN: Beyond a Gaussian Denoiser: Residual ...", "date": "", "ddg_snippet": "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.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/cszn/DnCNN", "content": "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."}
+{"idx": 1, "title": "cszn (Kai Zhang) · GitHub GitHub - cszn/KAIR: Image Restoration Toolbox (PyTorch ... [1608.03981] Beyond a Gaussian Denoiser: Residual Learning of ... cszn/DnCNN | DeepWiki GitHub Pages - Kai Zhang", "date": "", "ddg_snippet": "Image Restoration; Inverse Problems. cszn has 13 repositories available. Follow their code on GitHub . cszn . github .io/ deep-learning pytorch toolbox super-resolution image-restoration denoising dncnn sisr srmd flops esrgan usrnet ffdnet dpsr bsrgan swinir Readme MIT license 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. May 12, 2025 · DnCNN is a state-of-the-art deep learning approach for image restoration tasks, primarily focusing on image denoising but also applicable to single image super-resolution (SISR) and JPEG image deblocking. KAIR (support training and testing for DnCNN , FFDNet, SRMD, USRNet, ESRGAN) DPIR (Plug-and-Play Image Restoration with Deep Denoiser Prior) News (2025-06) Two papers are accepted in ICCV 2025. (2025-06) I will serve as a Senior Program Committee (SPC) Member for AAAI 2026. (2024-06) I will serve as a Senior Program Committee (SPC) Member for ...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/cszn", "content": "Image Restoration; Inverse Problems. cszn has 13 repositories available. Follow their code on GitHub . cszn . github .io/ deep-learning pytorch toolbox super-resolution image-restoration denoising dncnn sisr srmd flops esrgan usrnet ffdnet dpsr bsrgan swinir Readme MIT license 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. May 12, 2025 · DnCNN is a state-of-the-art deep learning approach for image restoration tasks, primarily focusing on image denoising but also applicable to single image super-resolution (SISR) and JPEG image deblocking. KAIR (support training and testing for DnCNN , FFDNet, SRMD, USRNet, ESRGAN) DPIR (Plug-and-Play Image Restoration with Deep Denoiser Prior) News (2025-06) Two papers are accepted in ICCV 2025. (2025-06) I will serve as a Senior Program Committee (SPC) Member for AAAI 2026. (2024-06) I will serve as a Senior Program Committee (SPC) Member for ..."}
+{"idx": 2, "title": "GitHub - cszn/KAIR: Image Restoration Toolbox (PyTorch ...", "date": "", "ddg_snippet": "cszn . github .io/ deep-learning pytorch toolbox super-resolution image-restoration denoising dncnn sisr srmd flops esrgan usrnet ffdnet dpsr bsrgan swinir Readme MIT license", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/cszn/KAIR", "content": "cszn . github .io/ deep-learning pytorch toolbox super-resolution image-restoration denoising dncnn sisr srmd flops esrgan usrnet ffdnet dpsr bsrgan swinir Readme MIT license"}
+{"idx": 3, "title": "DnCNN/README.md at master · cszn/DnCNN · GitHub", "date": "", "ddg_snippet": "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.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/cszn/DnCNN/blob/master/README.md", "content": "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."}
+{"idx": 4, "title": "[1608.03981] Beyond a Gaussian Denoiser: Residual Learning of ... cszn/DnCNN | DeepWiki GitHub Pages - Kai Zhang", "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. May 12, 2025 · DnCNN is a state-of-the-art deep learning approach for image restoration tasks, primarily focusing on image denoising but also applicable to single image super-resolution (SISR) and JPEG image deblocking. KAIR (support training and testing for DnCNN , FFDNet, SRMD, USRNet, ESRGAN) DPIR (Plug-and-Play Image Restoration with Deep Denoiser Prior) News (2025-06) Two papers are accepted in ICCV 2025. (2025-06) I will serve as a Senior Program Committee (SPC) Member for AAAI 2026. (2024-06) I will serve as a Senior Program Committee (SPC) Member for ...", "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. May 12, 2025 · DnCNN is a state-of-the-art deep learning approach for image restoration tasks, primarily focusing on image denoising but also applicable to single image super-resolution (SISR) and JPEG image deblocking. KAIR (support training and testing for DnCNN , FFDNet, SRMD, USRNet, ESRGAN) DPIR (Plug-and-Play Image Restoration with Deep Denoiser Prior) News (2025-06) Two papers are accepted in ICCV 2025. (2025-06) I will serve as a Senior Program Committee (SPC) Member for AAAI 2026. (2024-06) I will serve as a Senior Program Committee (SPC) Member for ..."}
+{"idx": 5, "title": "cszn/DnCNN | DeepWiki", "date": "", "ddg_snippet": "May 12, 2025 · DnCNN is a state-of-the-art deep learning approach for image restoration tasks, primarily focusing on image denoising but also applicable to single image super-resolution (SISR) and JPEG image deblocking.", "subpage_snippet": "", "source": "deepwiki.com", "link": "https://deepwiki.com/cszn/DnCNN", "content": "May 12, 2025 · DnCNN is a state-of-the-art deep learning approach for image restoration tasks, primarily focusing on image denoising but also applicable to single image super-resolution (SISR) and JPEG image deblocking."}
+{"idx": 6, "title": "GitHub Pages - Kai Zhang", "date": "", "ddg_snippet": "KAIR (support training and testing for DnCNN , FFDNet, SRMD, USRNet, ESRGAN) DPIR (Plug-and-Play Image Restoration with Deep Denoiser Prior) News (2025-06) Two papers are accepted in ICCV 2025. (2025-06) I will serve as a Senior Program Committee (SPC) Member for AAAI 2026. (2024-06) I will serve as a Senior Program Committee (SPC) Member for ...", "subpage_snippet": "", "source": "cszn.github.io", "link": "https://cszn.github.io/", "content": "KAIR (support training and testing for DnCNN , FFDNet, SRMD, USRNet, ESRGAN) DPIR (Plug-and-Play Image Restoration with Deep Denoiser Prior) News (2025-06) Two papers are accepted in ICCV 2025. (2025-06) I will serve as a Senior Program Committee (SPC) Member for AAAI 2026. (2024-06) I will serve as a Senior Program Committee (SPC) Member for ..."}
+{"idx": 7, "title": "GitHub - cszn/BSRGAN: Designing a Practical Degradation Model", "date": "", "ddg_snippet": "Our new work for real image denoising --- > https :// github . com / cszn /SCUNet ... Download KAIR : git clone https :// github . com / cszn /KAIR.git", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/cszn/BSRGAN", "content": "Our new work for real image denoising --- > https :// github . com / cszn /SCUNet ... Download KAIR : git clone https :// github . com / cszn /KAIR.git"}
+{"idx": 8, "title": "GitHub - cszn/IRCNN: Learning Deep CNN Denoiser Prior for Image", "date": "", "ddg_snippet": "https :// github . com / cszn /DPIR ... IRCNN for bicubic degradation has a comparable performance to VDSR and DnCNN . ... IEEE Transactions on Computational ...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/cszn/ircnn", "content": "https :// github . com / cszn /DPIR ... IRCNN for bicubic degradation has a comparable performance to VDSR and DnCNN . ... IEEE Transactions on Computational ..."}
+{"idx": 9, "title": "BatchNorm2d CUDNN_STATUS_NOT_SUPPORTED may be non-contiguous -", "date": "", "ddg_snippet": "DnCNN https :// github . com / cszn / DnCNN /blob/master/TrainingCodes/ dncnn _pytorch/main_train.py https ://arxiv.org/pdf/1608.03981.pdf class DnCNN (nn.Module ...", "subpage_snippet": "", "source": "discuss.pytorch.org", "link": "https://discuss.pytorch.org/t/batchnorm2d-cudnn-status-not-supported-may-be-non-contiguous/197594", "content": "DnCNN https :// github . com / cszn / DnCNN /blob/master/TrainingCodes/ dncnn _pytorch/main_train.py https ://arxiv.org/pdf/1608.03981.pdf class DnCNN (nn.Module ..."}
diff --git a/data/sampled_jsons/httpshuggingface.coapiresolve-cachespaceslambdalabsLambdaSuperRes5168985779ffa83dfc0aaee6349735972f9.jsonl b/data/sampled_jsons/httpshuggingface.coapiresolve-cachespaceslambdalabsLambdaSuperRes5168985779ffa83dfc0aaee6349735972f9.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..b63a0b21a80e758ec2b771ecb0c98b51fa3a6b15
--- /dev/null
+++ b/data/sampled_jsons/httpshuggingface.coapiresolve-cachespaceslambdalabsLambdaSuperRes5168985779ffa83dfc0aaee6349735972f9.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Hub API Endpoints - Hugging Face", "date": "", "ddg_snippet": "Want to try out our API ? Try it out now on our Playground! ... The following endpoints help get information about models , datasets, and Spaces stored on the Hub. When making API calls to retrieve information about repositories, the createdAt attribute indicates the time when the respective repository was created.", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/docs/hub/en/api", "content": "Want to try out our API ? Try it out now on our Playground! ... The following endpoints help get information about models , datasets, and Spaces stored on the Hub. When making API calls to retrieve information about repositories, the createdAt attribute indicates the time when the respective repository was created."}
+{"idx": 1, "title": "Understand caching - Hugging Face Hugging Face – The AI community building the future. How to download a model from huggingface? - Stack Overflow Code sample Downloading Cache Files? - Beginners - Hugging Face Forums Convert the models downloaded in . cache / huggingface /hub/ to ...", "date": "", "ddg_snippet": "The Hugging Face Hub cache -system is designed to be the central cache shared across librariesthat depend on the Hub. It has been updated in v0.8.0 to prevent re-downloading same filesbetween revisions. The caching system is designed as follows: The is usually your user’s home directory. However, it is customizable with the cache_dir arg... See full list on huggingface . co In addition to caching files from the Hub, downstream libraries often requires to cacheother files related to HF but not handled directly by huggingface_hub (example: filedownloaded from GitHub, preprocessed data, logs,…). In order to cache those files,called assets, one can use cached_assets_path(). This small helper generates pathsin the HF cache ... See full list on huggingface . co At the moment, cached files are never deleted from your local directory: when you downloada new revision of a branch, previous files are kept in case you need them again.Therefore it can be useful to scan your cache directory in order to know which reposand revisions are taking the most disk space. huggingface_hub provides an helper todo so that ca... See full list on huggingface . co Scanning your cache is interesting but what you really want to do next is usually todelete some portions to free up some space on your drive. This is possible using thedelete- cache CLI command. One can also programmatically use thedelete_revisions() helper from HFCacheInfoobject returned whenscanning the cache . See full list on huggingface . co The platform where the machine learning community collaborates on models , datasets, and applications. May 19, 2021 · For example, I want to download bert-base-uncased on https :// huggingface . co / models , but can't find a ' Download ' link. Or is it not downloadable? See more on stackoverflow Oct 18, 2024 · It is very difficult to access HF’s private space from the outside except through a browser or a client posing as a browser. It is basically impossible. If you just want to get the data, you can create a private model repo or dataset repo, upload the cached data from Spaces to it, and download it from curl with a token in the header. You can upload as much as you want, up to 300GB per repo. Jun 6, 2025 · So I did not use git clone to download the model like openvla/openvla-7b. But using from_pretrained() and relative path “openvla/openvla-7b” directly. The files are deposited into . cache / huggingface /hub/ file. How can I change it into original format which I could use the direct path to get inference.", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/docs/huggingface_hub/guides/manage-cache", "content": "The Hugging Face Hub cache -system is designed to be the central cache shared across librariesthat depend on the Hub. It has been updated in v0.8.0 to prevent re-downloading same filesbetween revisions. The caching system is designed as follows: The is usually your user’s home directory. However, it is customizable with the cache_dir arg... See full list on huggingface . co In addition to caching files from the Hub, downstream libraries often requires to cacheother files related to HF but not handled directly by huggingface_hub (example: filedownloaded from GitHub, preprocessed data, logs,…). In order to cache those files,called assets, one can use cached_assets_path(). This small helper generates pathsin the HF cache ... See full list on huggingface . co At the moment, cached files are never deleted from your local directory: when you downloada new revision of a branch, previous files are kept in case you need them again.Therefore it can be useful to scan your cache directory in order to know which reposand revisions are taking the most disk space. huggingface_hub provides an helper todo so that ca... See full list on huggingface . co Scanning your cache is interesting but what you really want to do next is usually todelete some portions to free up some space on your drive. This is possible using thedelete- cache CLI command. One can also programmatically use thedelete_revisions() helper from HFCacheInfoobject returned whenscanning the cache . See full list on huggingface . co The platform where the machine learning community collaborates on models , datasets, and applications. May 19, 2021 · For example, I want to download bert-base-uncased on https :// huggingface . co / models , but can't find a ' Download ' link. Or is it not downloadable? See more on stackoverflow Oct 18, 2024 · It is very difficult to access HF’s private space from the outside except through a browser or a client posing as a browser. It is basically impossible. If you just want to get the data, you can create a private model repo or dataset repo, upload the cached data from Spaces to it, and download it from curl with a token in the header. You can upload as much as you want, up to 300GB per repo. Jun 6, 2025 · So I did not use git clone to download the model like openvla/openvla-7b. But using from_pretrained() and relative path “openvla/openvla-7b” directly. The files are deposited into . cache / huggingface /hub/ file. How can I change it into original format which I could use the direct path to get inference."}
+{"idx": 2, "title": "How to download a model from huggingface? - Stack Overflow Code sample", "date": "", "ddg_snippet": "May 19, 2021 · For example, I want to download bert-base-uncased on https :// huggingface . co / models , but can't find a ' Download ' link. Or is it not downloadable? See more on stackoverflow", "subpage_snippet": "", "source": "stackoverflow.com", "link": "https://stackoverflow.com/questions/67595500/how-to-download-a-model-from-huggingface", "content": "May 19, 2021 · For example, I want to download bert-base-uncased on https :// huggingface . co / models , but can't find a ' Download ' link. Or is it not downloadable? See more on stackoverflow"}
+{"idx": 3, "title": "Downloading Cache Files? - Beginners - Hugging Face Forums", "date": "", "ddg_snippet": "Oct 18, 2024 · It is very difficult to access HF’s private space from the outside except through a browser or a client posing as a browser. It is basically impossible. If you just want to get the data, you can create a private model repo or dataset repo, upload the cached data from Spaces to it, and download it from curl with a token in the header. You can upload as much as you want, up to 300GB per repo.", "subpage_snippet": "", "source": "discuss.huggingface.co", "link": "https://discuss.huggingface.co/t/downloading-cache-files/112703", "content": "Oct 18, 2024 · It is very difficult to access HF’s private space from the outside except through a browser or a client posing as a browser. It is basically impossible. If you just want to get the data, you can create a private model repo or dataset repo, upload the cached data from Spaces to it, and download it from curl with a token in the header. You can upload as much as you want, up to 300GB per repo."}
+{"idx": 4, "title": "Convert the models downloaded in . cache / huggingface /hub/ to ...", "date": "", "ddg_snippet": "Jun 6, 2025 · So I did not use git clone to download the model like openvla/openvla-7b. But using from_pretrained() and relative path “openvla/openvla-7b” directly. The files are deposited into . cache / huggingface /hub/ file. How can I change it into original format which I could use the direct path to get inference.", "subpage_snippet": "", "source": "discuss.huggingface.co", "link": "https://discuss.huggingface.co/t/convert-the-models-downloaded-in-cache-huggingface-hub-to-original-format/158225", "content": "Jun 6, 2025 · So I did not use git clone to download the model like openvla/openvla-7b. But using from_pretrained() and relative path “openvla/openvla-7b” directly. The files are deposited into . cache / huggingface /hub/ file. How can I change it into original format which I could use the direct path to get inference."}
+{"idx": 5, "title": "Telegram: View @ai_voicemodels", "date": "", "ddg_snippet": "Download . Голоса для нейросети | Модели голосов.", "subpage_snippet": "", "source": "t.me", "link": "https://t.me/ai_voicemodels/5", "content": "Download . Голоса для нейросети | Модели голосов."}
+{"idx": 6, "title": "Разворачиваем нейросеть на бесплатном VPS: FastAPI... / Хабр", "date": "", "ddg_snippet": "Для начала создайте новый Space . Перейдите в Hugging Face Spaces . Нажмите \"New Space \".RUN pip install --no- cache -dir -r requirements.txt. COPY main. py . ENV HF_HOME=/tmp/ huggingface - cache ENV TOKENIZERS_PARALLELISM=false.", "subpage_snippet": "", "source": "habr.com", "link": "https://habr.com/ru/articles/908554/", "content": "Для начала создайте новый Space . Перейдите в Hugging Face Spaces . Нажмите \"New Space \".RUN pip install --no- cache -dir -r requirements.txt. COPY main. py . ENV HF_HOME=/tmp/ huggingface - cache ENV TOKENIZERS_PARALLELISM=false."}
+{"idx": 7, "title": "Occasionally HTTP 429 Error When Downloading Many Smaller Files...", "date": "", "ddg_snippet": "huggingface . co /datasets/haosulab/ReplicaCAD/ resolve /88bdca74dd4ec1f8c994904f7985392fc 3 f 2b4c4/configs/scenes/v3_sc3_staging_02.scene_instance.fetch.navigable_positions_simplified.obj Retrying in 1s [Retry 1/5]. Download complete. Moving file to /Users/arths...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/huggingface/huggingface_hub/issues/3342", "content": "huggingface . co /datasets/haosulab/ReplicaCAD/ resolve /88bdca74dd4ec1f8c994904f7985392fc 3 f 2b4c4/configs/scenes/v3_sc3_staging_02.scene_instance.fetch.navigable_positions_simplified.obj Retrying in 1s [Retry 1/5]. Download complete. Moving file to /Users/arths..."}
+{"idx": 8, "title": "dfgdfg huggingface . co api & model -hub dfgdfg github AI... - Toolify", "date": "", "ddg_snippet": "dfgdfg huggingface . co is an online trial and call api platform, which integrates dfgdfg's modeling effects, including api services, and provides a free online trial of dfgdfg, you can try dfgdfg online for free by clicking the link below.", "subpage_snippet": "", "source": "www.toolify.ai", "link": "https://www.toolify.ai/ai-model/model-hub-dfgdfg", "content": "dfgdfg huggingface . co is an online trial and call api platform, which integrates dfgdfg's modeling effects, including api services, and provides a free online trial of dfgdfg, you can try dfgdfg online for free by clicking the link below."}
+{"idx": 9, "title": "Cache management - 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/docs/datasets/cache", "content": "We’re on a journey to advance and democratize artificial intelligence through open source and open science."}
diff --git a/data/sampled_jsons/httpsopenreview.netpdfid=tlniJJFUW2.jsonl b/data/sampled_jsons/httpsopenreview.netpdfid=tlniJJFUW2.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..3c290fb099944f1ea706b339a72f6debfb410e6b
--- /dev/null
+++ b/data/sampled_jsons/httpsopenreview.netpdfid=tlniJJFUW2.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "GitHub - pranftw/openreview_scraper: Scrape papers from ...", "date": "", "ddg_snippet": "OpenReview Scraper Scrape papers from top conferences like ICML, ICLR, NeurIPS, etc using OpenReview API, by searching for specific keywords in title, abstract or keywords in the submissions and save them to a CSV file.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/pranftw/openreview_scraper", "content": "OpenReview Scraper Scrape papers from top conferences like ICML, ICLR, NeurIPS, etc using OpenReview API, by searching for specific keywords in title, abstract or keywords in the submissions and save them to a CSV file."}
+{"idx": 1, "title": "OpenReview Archive", "date": "", "ddg_snippet": "Minified React error #306; visit https ://react.dev/errors/306?args[]=undefined&args[]= for the full message or use the non-minified dev environment for full errors and additional helpful warnings.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/group?id=OpenReview.net/Archive", "content": "Minified React error #306; visit https ://react.dev/errors/306?args[]=undefined&args[]= for the full message or use the non-minified dev environment for full errors and additional helpful warnings."}
+{"idx": 2, "title": "What Have We Learned from OpenReview? - arXiv.org", "date": "", "ddg_snippet": "The o cial reviews and meta-reviews are all open to the public on the OpenReview platform. Public colleagues can also post their reviews on OpenReview . We will present the collected dataset of submissions and reviews from OpenReview , these submissions' citation data from Google Scholar, and their non-peer-reviewed versions from arXiv.org3.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2103.05885v3", "content": "The o cial reviews and meta-reviews are all open to the public on the OpenReview platform. Public colleagues can also post their reviews on OpenReview . We will present the collected dataset of submissions and reviews from OpenReview , these submissions' citation data from Google Scholar, and their non-peer-reviewed versions from arXiv.org3."}
+{"idx": 3, "title": "Конвертировать PDF в черно-белый и оттенки серого", "date": "", "ddg_snippet": "Преобразуйте цветные PDF в градации серого для улучшения ясности и экономии чернил. Быстрый инструмент для получения монохромных версий файлов.", "subpage_snippet": "", "source": "www.pdfhelp.net", "link": "https://www.pdfhelp.net/ru/grayscale-pdf", "content": "Преобразуйте цветные PDF в градации серого для улучшения ясности и экономии чернил. Быстрый инструмент для получения монохромных версий файлов."}
+{"idx": 4, "title": "Как конвертировать Word в PDF без изменения шрифта", "date": "", "ddg_snippet": "Узнайте самые простые способы конвертации документов Word в PDF без потери исходных шрифтов и форматирования.", "subpage_snippet": "", "source": "www.pdftools.net", "link": "https://www.pdftools.net/ru/how-to-convert-word-to-pdf-without-changing-font", "content": "Узнайте самые простые способы конвертации документов Word в PDF без потери исходных шрифтов и форматирования."}
+{"idx": 5, "title": "Разделить PDF", "date": "", "ddg_snippet": "Разделить PDF . Разделите PDF на части или оставьте в нем только необходимые вам страницы.", "subpage_snippet": "", "source": "pdf.io", "link": "https://pdf.io/ru/split/", "content": "Разделить PDF . Разделите PDF на части или оставьте в нем только необходимые вам страницы."}
+{"idx": 6, "title": "Word to PDF | Convert Word to PDF online for free", "date": "", "ddg_snippet": "Convert up to 1 GB files free, Pro users can convert up to 100 GB files; Sign up now. How to convert a Word to PDF file online. To convert a Word to PDF , drag and drop or click our upload...", "subpage_snippet": "", "source": "www.pdf.to", "link": "https://www.pdf.to/word-pdf/", "content": "Convert up to 1 GB files free, Pro users can convert up to 100 GB files; Sign up now. How to convert a Word to PDF file online. To convert a Word to PDF , drag and drop or click our upload..."}
+{"idx": 7, "title": "Chat with all of your PDF documents", "date": "", "ddg_snippet": "Simply ask a question, and PDF .ai will use AI to pick up the answer from all of your documents. Receive your answer, along with the document it came from.", "subpage_snippet": "", "source": "pdf.ai", "link": "https://pdf.ai/resources/chat-with-all-of-your-pdf-documents", "content": "Simply ask a question, and PDF .ai will use AI to pick up the answer from all of your documents. Receive your answer, along with the document it came from."}
+{"idx": 8, "title": "Login - OpenReview", "date": "", "ddg_snippet": "Sep 24, 2024 · Promoting openness in scientific communication and the peer-review process", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/login", "content": "Sep 24, 2024 · Promoting openness in scientific communication and the peer-review process"}
+{"idx": 9, "title": "Venues | OpenReview", "date": "", "ddg_snippet": "2 days ago · Promoting openness in scientific communication and the peer-review process", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/", "content": "2 days ago · Promoting openness in scientific communication and the peer-review process"}
diff --git a/data/sampled_jsons/in-pixel_feature_tracking_before_2025_SCAMP.jsonl b/data/sampled_jsons/in-pixel_feature_tracking_before_2025_SCAMP.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..fb37901e0d39ee8cc664a048af69adc64a71e076
--- /dev/null
+++ b/data/sampled_jsons/in-pixel_feature_tracking_before_2025_SCAMP.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "CVPR Poster Descriptor-In-Pixel : Point-Feature Tracking For Pixel ...", "date": "", "ddg_snippet": "This \"response map\" is utilized for both detection and tracking of point- features across the pixel -processor array. This approach is very fast, our implementation upon the SCAMP-7 PPA prototype runs at over 3000 FPS (Frames Per Second), tracking point- features reliably even under violent motion.", "subpage_snippet": "", "source": "cvpr.thecvf.com", "link": "https://cvpr.thecvf.com/virtual/2025/poster/32867", "content": "This \"response map\" is utilized for both detection and tracking of point- features across the pixel -processor array. This approach is very fast, our implementation upon the SCAMP-7 PPA prototype runs at over 3000 FPS (Frames Per Second), tracking point- features reliably even under violent motion."}
+{"idx": 1, "title": "Pixel Power: How SCAMP Is Redefining Machine Vision from the ... - LinkedIn", "date": "", "ddg_snippet": "At the heart of SCAMP is the SCAMP -5d chip, featuring a 256x256 grid of pixel -level processors. These processing elements are equipped with photodetectors, arithmetic logic units, and registers to ...", "subpage_snippet": "", "source": "www.linkedin.com", "link": "https://www.linkedin.com/pulse/pixel-power-how-scamp-redefining-machine-vision-from-ground-elvje", "content": "At the heart of SCAMP is the SCAMP -5d chip, featuring a 256x256 grid of pixel -level processors. These processing elements are equipped with photodetectors, arithmetic logic units, and registers to ..."}
+{"idx": 2, "title": "Point-Feature Tracking for Pixel Processor Arrays - IEEE Xplore", "date": "", "ddg_snippet": "This paper presents a novel approach for joint point- feature detection and tracking , designed specifically for Pixel Processor Array (PPA) vision sensors. Instead of standard pixels , PPA sensors consist of thousands of \" pixel -processors\", enabling massive parallel computation of visual data at the point of light capture. Our approach performs all computation entirely in-pixel , meaning no raw ...", "subpage_snippet": "", "source": "ieeexplore.ieee.org", "link": "https://ieeexplore.ieee.org/abstract/document/11092646", "content": "This paper presents a novel approach for joint point- feature detection and tracking , designed specifically for Pixel Processor Array (PPA) vision sensors. Instead of standard pixels , PPA sensors consist of thousands of \" pixel -processors\", enabling massive parallel computation of visual data at the point of light capture. Our approach performs all computation entirely in-pixel , meaning no raw ..."}
+{"idx": 3, "title": "Descriptor In Pixel : Point Feature Tracking for Pixel ... - YouTube", "date": "", "ddg_snippet": "This approach is very fast, our implementation upon the SCAMP-7 PPA prototype runs at over 3000 FPS (Frames Per Second), tracking point- features reliably even under violent motion.", "subpage_snippet": "", "source": "www.youtube.com", "link": "https://www.youtube.com/watch?v=HIdQtf6mFSs", "content": "This approach is very fast, our implementation upon the SCAMP-7 PPA prototype runs at over 3000 FPS (Frames Per Second), tracking point- features reliably even under violent motion."}
+{"idx": 4, "title": "SCAMP Vision Sensor", "date": "", "ddg_snippet": "The SCAMP vision sensor integrates a massively parallel SIMD processor array into the pixels of the image sensor device. It does not output raw images, like a conventional camera, but rather the results of on-sensor computations, for instance a feature map, optic flow map and/or address-events describing locations of pixels of interest. As costly early vision computations are done entirely on ...", "subpage_snippet": "", "source": "www.scamp-vision-chip.org", "link": "https://www.scamp-vision-chip.org/", "content": "The SCAMP vision sensor integrates a massively parallel SIMD processor array into the pixels of the image sensor device. It does not output raw images, like a conventional camera, but rather the results of on-sensor computations, for instance a feature map, optic flow map and/or address-events describing locations of pixels of interest. As costly early vision computations are done entirely on ..."}
+{"idx": 5, "title": "VisionChip, formerly SCAMP - uominnovationfactory.com", "date": "", "ddg_snippet": "The device is fully programmable and able to carry out a number of processes using on-sensor computations, such as a feature map, optic flow map, and address-events describing locations of pixels of interest. The reduced latency from early vision computations results in high processing speeds and low power consumption, enabling new embedded vision applications in areas such as robotics ...", "subpage_snippet": "", "source": "www.uominnovationfactory.com", "link": "https://www.uominnovationfactory.com/projects/pixel-perfect-scamp-vision-sensor/", "content": "The device is fully programmable and able to carry out a number of processes using on-sensor computations, such as a feature map, optic flow map, and address-events describing locations of pixels of interest. The reduced latency from early vision computations results in high processing speeds and low power consumption, enabling new embedded vision applications in areas such as robotics ..."}
+{"idx": 6, "title": "Scamp5d Vision System: Scamp5d System Overview - GitLab", "date": "", "ddg_snippet": "Asynchronous Acceleration SCAMP-5 vision chip also have several features that enable acceleration of image processing speed beyond synchronous pixel -parallel processing. Event-based Readout Events are '1's in a DREG plane. SCAMP-5 can directly output the coordinates of all events while the time cost is only proportional to the number of events.", "subpage_snippet": "", "source": "scamp.gitlab.io", "link": "https://scamp.gitlab.io/scamp5d_doc/_p_a_g_e__i_n_t_r_o__d_e_v_i_c_e.html", "content": "Asynchronous Acceleration SCAMP-5 vision chip also have several features that enable acceleration of image processing speed beyond synchronous pixel -parallel processing. Event-based Readout Events are '1's in a DREG plane. SCAMP-5 can directly output the coordinates of all events while the time cost is only proportional to the number of events."}
+{"idx": 7, "title": "Sensor-level computer vision with pixel processor arrays for agile ...", "date": "", "ddg_snippet": "Here, we review the history of image sensing and processing hardware from the perspective of in-pixel computing and outline the key features of a state-of-the-art smart camera system based on a PPA device, through the description of the SCAMP-5 system.", "subpage_snippet": "", "source": "www.science.org", "link": "https://www.science.org/doi/10.1126/scirobotics.abl7755", "content": "Here, we review the history of image sensing and processing hardware from the perspective of in-pixel computing and outline the key features of a state-of-the-art smart camera system based on a PPA device, through the description of the SCAMP-5 system."}
+{"idx": 8, "title": "PDF Descriptor-In-Pixel : Point-Feature Tracking For Pixel Processor Arrays", "date": "", "ddg_snippet": "The sparse readout and complete utilization of all pixel -processors makes our approach very eficient. Our imple-mentation upon the SCAMP-7 PPA prototype runs at over 3000 FPS (Frames Per Second), tracking point- features re-liably under violent motion. This is the first work perform-ing point- feature detection and tracking entirely in-pixel . 1", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2025/papers/Bose_Descriptor-In-Pixel__Point-Feature_Tracking_For_Pixel_Processor_Arrays_CVPR_2025_paper.pdf", "content": "The sparse readout and complete utilization of all pixel -processors makes our approach very eficient. Our imple-mentation upon the SCAMP-7 PPA prototype runs at over 3000 FPS (Frames Per Second), tracking point- features re-liably under violent motion. This is the first work perform-ing point- feature detection and tracking entirely in-pixel . 1"}
+{"idx": 9, "title": "Mapping Image Transformations Onto Pixel Processor Arrays", "date": "", "ddg_snippet": "Duplication or elimination of a row/column of pixel data can be performed on SCAMP-5 using the parallel data transfer operation employed previously for image shearing.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2403.16994", "content": "Duplication or elimination of a row/column of pixel data can be performed on SCAMP-5 using the parallel data transfer operation employed previously for image shearing."}
diff --git a/data/sampled_jsons/in-pixel_processing_for_feature_tracking.jsonl b/data/sampled_jsons/in-pixel_processing_for_feature_tracking.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..3251603288365dbc0accbff646cfac89d7ce1953
--- /dev/null
+++ b/data/sampled_jsons/in-pixel_processing_for_feature_tracking.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Tracking Any Pixel in a Video – Machine Learning Blog |", "date": "", "ddg_snippet": "To track the target, we need to know what it looks like, so we compute appearance features for all the frames (using a CNN), and initialize the ...", "subpage_snippet": "", "source": "blog.ml.cmu.edu", "link": "https://blog.ml.cmu.edu/2022/09/09/tracking-any-pixel-in-a-video/", "content": "To track the target, we need to know what it looks like, so we compute appearance features for all the frames (using a CNN), and initialize the ..."}
+{"idx": 1, "title": "How Tracking Pixel Works: A Basic Guide - Triple A Review", "date": "", "ddg_snippet": "Individuals can start pixel tracking for online analytics and advertising by placing tracking codes on essential web pages.", "subpage_snippet": "", "source": "tripleareview.com", "link": "https://tripleareview.com/how-tracking-pixel-works-basic-guide/", "content": "Individuals can start pixel tracking for online analytics and advertising by placing tracking codes on essential web pages."}
+{"idx": 2, "title": "Replacing Pixel Tracking: Simple, Straightforward & Worth", "date": "", "ddg_snippet": "While most companies begin the integration process thinking they want pixel -based tracking , many change paths when they learn how advantageous and ...", "subpage_snippet": "", "source": "www.exchangewire.com", "link": "https://www.exchangewire.com/blog/2018/04/11/replacing-pixel-tracking-simple-straightforward-worth/", "content": "While most companies begin the integration process thinking they want pixel -based tracking , many change paths when they learn how advantageous and ..."}
+{"idx": 3, "title": "What is Pixel Tracking? | Northbeam Blog", "date": "", "ddg_snippet": "AI and machine learning will further refine pixel tracking , offering predictive insights and personalized marketing at scale.", "subpage_snippet": "", "source": "www.northbeam.io", "link": "https://www.northbeam.io/post/what-is-pixel-tracking", "content": "AI and machine learning will further refine pixel tracking , offering predictive insights and personalized marketing at scale."}
+{"idx": 4, "title": "What are Tracking Pixels and How They Work ?", "date": "", "ddg_snippet": "... Data Protection Regulation) and CCPA (California Consumer Privacy Act) mandate strict guidelines for data collection, including tracking pixel usage.", "subpage_snippet": "", "source": "blog.emb.global", "link": "https://blog.emb.global/understand-tracking-pixels/", "content": "... Data Protection Regulation) and CCPA (California Consumer Privacy Act) mandate strict guidelines for data collection, including tracking pixel usage."}
+{"idx": 5, "title": "Digital Creative Agency - What is Tracking pixel? | Powerful", "date": "", "ddg_snippet": "AI-Powered Analytics Advances in AI mean that the data collected via tracking pixels can now be processed faster and more effectively.", "subpage_snippet": "", "source": "www.digitaland.tv", "link": "https://www.digitaland.tv/blog/what-is-tracking-pixel-ht/", "content": "AI-Powered Analytics Advances in AI mean that the data collected via tracking pixels can now be processed faster and more effectively."}
+{"idx": 6, "title": "Tracking Pixels: What Are They and How Do They Work? – Dm", "date": "", "ddg_snippet": "Not only can you insert these tracking pixels on a personal website, email campaign, or store to track use and conversions but they are just as ...", "subpage_snippet": "", "source": "dm-productions.com", "link": "https://dm-productions.com/tracking-pixels-what-are-they-and-how-do-they-work/", "content": "Not only can you insert these tracking pixels on a personal website, email campaign, or store to track use and conversions but they are just as ..."}
+{"idx": 7, "title": "What are Tracking Pixels? How Do They Work?", "date": "", "ddg_snippet": "Data aggregation from multiple tracking pixels provides information for creating detailed customer journey maps, thus giving a comprehensive view ...", "subpage_snippet": "", "source": "indixital.com", "link": "https://indixital.com/blog/tracking-pixels-explained/", "content": "Data aggregation from multiple tracking pixels provides information for creating detailed customer journey maps, thus giving a comprehensive view ..."}
+{"idx": 8, "title": "methods - Can somebody illustrate what is \"Pixel", "date": "", "ddg_snippet": "so, can I use pixel tracking method to pass the URL variable($var) to another php page (email subscibe processing php page) together with username ...", "subpage_snippet": "", "source": "stackoverflow.com", "link": "https://stackoverflow.com/questions/1366478/can-somebody-illustrate-what-is-pixel-tracking-in-php", "content": "so, can I use pixel tracking method to pass the URL variable($var) to another php page (email subscibe processing php page) together with username ..."}
+{"idx": 9, "title": "Invisble Pixel Tracking | OpenedOrNot by 500apps", "date": "", "ddg_snippet": "Use invisible tracker pixel to see who is engaging with your email, then use the customizable OpenedOrNot features to qualify those responses.", "subpage_snippet": "", "source": "openedornot.com", "link": "https://openedornot.com/invisible-pixel-tracking", "content": "Use invisible tracker pixel to see who is engaging with your email, then use the customizable OpenedOrNot features to qualify those responses."}
diff --git a/data/sampled_jsons/inference-time_alignment_methods_for_diffusion_models_backpropagation-free.jsonl b/data/sampled_jsons/inference-time_alignment_methods_for_diffusion_models_backpropagation-free.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..230941617df786f21cc81723f10c06fd5af07c02
--- /dev/null
+++ b/data/sampled_jsons/inference-time_alignment_methods_for_diffusion_models_backpropagation-free.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Inference-Time Alignment Control for Diffusion Models with ...", "date": "", "ddg_snippet": "In this paper, we introduced Reinforcement Learning Guid- ance (RLG), a novel, training- free method for dynamically controlling the alignment of generative models at inference time .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2508.21016", "content": "In this paper, we introduced Reinforcement Learning Guid- ance (RLG), a novel, training- free method for dynamically controlling the alignment of generative models at inference time ."}
+{"idx": 1, "title": "Training-Free Diffusion Model Alignment with Sampling Demons", "date": "", "ddg_snippet": "To the best of our knowledge, the proposed approach is the first inference-time , backpropagation-free preference alignment method for diffusion models . Our method can be easily integrated with existing diffusion models without further training.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=tfemquulED", "content": "To the best of our knowledge, the proposed approach is the first inference-time , backpropagation-free preference alignment method for diffusion models . Our method can be easily integrated with existing diffusion models without further training."}
+{"idx": 2, "title": "Inference-Time Alignment Control for Diffusion Models with ...", "date": "", "ddg_snippet": "Denoising-based generative models , particularly diffusion and flow matching algorithms, have achieved remarkable success. However, aligning their output distributions with complex downstream objectives, such as human preferences, compositional accuracy, or data compressibility, remains challenging. While reinforcement learning (RL) fine-tuning methods , inspired by advances in RL from human ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2508.21016", "content": "Denoising-based generative models , particularly diffusion and flow matching algorithms, have achieved remarkable success. However, aligning their output distributions with complex downstream objectives, such as human preferences, compositional accuracy, or data compressibility, remains challenging. While reinforcement learning (RL) fine-tuning methods , inspired by advances in RL from human ..."}
+{"idx": 3, "title": "[PDF] Inference-Time Alignment of Diffusion Models with Evolutionary ...", "date": "", "ddg_snippet": "This work introduces an inference-time alignment framework based on evolutionary algorithms that enables efficient inference-time alignment for both differentiable and non-differentiable alignment objectives across a range of diffusion models . Diffusion models are state-of-the-art generative models in various domains, yet their samples often fail to satisfy downstream objectives such as safety ...", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/Inference-Time-Alignment-of-Diffusion-Models-with-Jajal-Eliopoulos/7b46881e44afe97ce1d42b8ccb18f0343d95e41e", "content": "This work introduces an inference-time alignment framework based on evolutionary algorithms that enables efficient inference-time alignment for both differentiable and non-differentiable alignment objectives across a range of diffusion models . Diffusion models are state-of-the-art generative models in various domains, yet their samples often fail to satisfy downstream objectives such as safety ..."}
+{"idx": 4, "title": "Efficient Personalization of Quantized Diffusion Model", "date": "", "ddg_snippet": "Our method achieves comparable performance to prior methods in image and text alignment scores for personalizing Stable Diffusion with only forward passes while reducing training memory demand up to 8.2x.", "subpage_snippet": "", "source": "ignoww.github.io", "link": "https://ignoww.github.io/ZOODiP_project/", "content": "Our method achieves comparable performance to prior methods in image and text alignment scores for personalizing Stable Diffusion with only forward passes while reducing training memory demand up to 8.2x."}
+{"idx": 5, "title": "[2506.00299] Inference-Time Alignment of Diffusion Models with ...", "date": "", "ddg_snippet": "We introduce an inference-time alignment framework based on evolutionary algorithms. We treat diffusion models as black-boxes and search their latent space to maximize alignment objectives. Our method enables efficient inference-time alignment for both differentiable and non-differentiable alignment objectives across a range of diffusion models .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2506.00299", "content": "We introduce an inference-time alignment framework based on evolutionary algorithms. We treat diffusion models as black-boxes and search their latent space to maximize alignment objectives. Our method enables efficient inference-time alignment for both differentiable and non-differentiable alignment objectives across a range of diffusion models ."}
+{"idx": 6, "title": "GitHub - catchfree1225/demonsampling: [ICLR'25] Official implementation ...", "date": "", "ddg_snippet": "Official implementation of ICLR 2025 \"Sampling Demon\" (arXiv:2410.05760). This repository contains the official implementation of Sampling Demon, an inference-time , backpropagation-free preference alignment method for diffusion models . By aligning the denoising process with user preferences via stochastic optimization, Sampling Demon enables the use of non-differentiable reward signals—such ...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/catchfree1225/demonsampling", "content": "Official implementation of ICLR 2025 \"Sampling Demon\" (arXiv:2410.05760). This repository contains the official implementation of Sampling Demon, an inference-time , backpropagation-free preference alignment method for diffusion models . By aligning the denoising process with user preferences via stochastic optimization, Sampling Demon enables the use of non-differentiable reward signals—such ..."}
+{"idx": 7, "title": "GitHub - aiiu-lab/DemonSampling: [ICLR'25] Official implementation of ...", "date": "", "ddg_snippet": "Official implementation of ICLR 2025 \"Sampling Demon\" (arXiv:2410.05760). This repository contains the official implementation of Sampling Demon, an inference-time , backpropagation-free preference alignment method for diffusion models . By aligning the denoising process with user preferences via stochastic optimization, Sampling Demon enables the use of non-differentiable reward signals—such ...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/aiiu-lab/DemonSampling", "content": "Official implementation of ICLR 2025 \"Sampling Demon\" (arXiv:2410.05760). This repository contains the official implementation of Sampling Demon, an inference-time , backpropagation-free preference alignment method for diffusion models . By aligning the denoising process with user preferences via stochastic optimization, Sampling Demon enables the use of non-differentiable reward signals—such ..."}
+{"idx": 8, "title": "DemonSampling/README.md at main · aiiu-lab/DemonSampling · GitHub", "date": "", "ddg_snippet": "This repository contains the official implementation of Sampling Demon, an inference-time , backpropagation-free preference alignment method for diffusion models .", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/aiiu-lab/DemonSampling/blob/main/README.md", "content": "This repository contains the official implementation of Sampling Demon, an inference-time , backpropagation-free preference alignment method for diffusion models ."}
+{"idx": 9, "title": "Published as a conference paper at ICLR 2025 - OpenReview", "date": "", "ddg_snippet": "To the best of our knowledge, the proposed approach is the first inference-time , backpropagation-free preference alignment method for diffusion models . Our method can be easily integrated with existing diffusion models without further training.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=tfemquulED", "content": "To the best of our knowledge, the proposed approach is the first inference-time , backpropagation-free preference alignment method for diffusion models . Our method can be easily integrated with existing diffusion models without further training."}
diff --git a/data/sampled_jsons/machine_learning_models_sigmoid-based_loss_contrastive_learning_year_2020-2023.jsonl b/data/sampled_jsons/machine_learning_models_sigmoid-based_loss_contrastive_learning_year_2020-2023.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..d3879e2e59f22f88b586a82fa4c8e8714e82c1a1
--- /dev/null
+++ b/data/sampled_jsons/machine_learning_models_sigmoid-based_loss_contrastive_learning_year_2020-2023.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Machine - Wikipedia", "date": "", "ddg_snippet": "A machine is a thermodynamic system that uses power to apply forces and control movement to perform an action. The term is commonly applied to artificial devices, such as those employing engines or motors, but also to natural biological macromolecules, such as molecular machines .", "subpage_snippet": "", "source": "en.m.wikipedia.org", "link": "https://en.m.wikipedia.org/wiki/Machine", "content": "A machine is a thermodynamic system that uses power to apply forces and control movement to perform an action. The term is commonly applied to artificial devices, such as those employing engines or motors, but also to natural biological macromolecules, such as molecular machines ."}
+{"idx": 1, "title": "MACHINE Definition & Meaning | Dictionary .com", "date": "", "ddg_snippet": "Machine definition: an apparatus consisting of interrelated parts with separate functions, used in the performance of some kind of work.. See examples of MACHINE used in a sentence.", "subpage_snippet": "", "source": "www.dictionary.com", "link": "https://www.dictionary.com/browse/machine", "content": "Machine definition: an apparatus consisting of interrelated parts with separate functions, used in the performance of some kind of work.. See examples of MACHINE used in a sentence."}
+{"idx": 2, "title": "MACHINE Definition & Meaning - Merriam-Webster", "date": "", "ddg_snippet": "The meaning of MACHINE is a mechanically, electrically, or electronically operated device for performing a task. How to use machine in a sentence.", "subpage_snippet": "", "source": "www.merriam-webster.com", "link": "https://www.merriam-webster.com/dictionary/machine", "content": "The meaning of MACHINE is a mechanically, electrically, or electronically operated device for performing a task. How to use machine in a sentence."}
+{"idx": 3, "title": "Machine | Definition, Mechanisms & Efficiency | Britannica", "date": "", "ddg_snippet": "machine , device, having a unique purpose, that augments or replaces human or animal effort for the accomplishment of physical tasks.", "subpage_snippet": "", "source": "www.britannica.com", "link": "https://www.britannica.com/technology/machine", "content": "machine , device, having a unique purpose, that augments or replaces human or animal effort for the accomplishment of physical tasks."}
+{"idx": 4, "title": "MACHINE | English meaning - Cambridge Dictionary", "date": "", "ddg_snippet": "MACHINE definition: 1. a piece of equipment with several moving parts that uses power to do a particular type of work…. Learn more.", "subpage_snippet": "", "source": "dictionary.cambridge.org", "link": "https://dictionary.cambridge.org/dictionary/english/machine", "content": "MACHINE definition: 1. a piece of equipment with several moving parts that uses power to do a particular type of work…. Learn more."}
+{"idx": 5, "title": "Machine - definition of machine by The Free Dictionary", "date": "", "ddg_snippet": "Of, relating to, or felt to resemble a machine : machine repairs; machine politics.", "subpage_snippet": "", "source": "www.thefreedictionary.com", "link": "https://www.thefreedictionary.com/machine", "content": "Of, relating to, or felt to resemble a machine : machine repairs; machine politics."}
+{"idx": 6, "title": "What Is A Machine ? Its Types and How it Works - Mech Lesson", "date": "", "ddg_snippet": "A machine is a mechanical device that uses power to apply force and control motion to perform work efficiently . Machines range from simple tools like pulleys and levers to complex systems like engines, robots, and manufacturing equipment.", "subpage_snippet": "", "source": "mechlesson.com", "link": "https://mechlesson.com/machine/", "content": "A machine is a mechanical device that uses power to apply force and control motion to perform work efficiently . Machines range from simple tools like pulleys and levers to complex systems like engines, robots, and manufacturing equipment."}
+{"idx": 7, "title": "What is a Machine ? - Computer Hope", "date": "", "ddg_snippet": "Jun 1, 2025 · A machine is a device with several parts that work together to perform a task. A machine can augment or replace the efforts required by an animal or human to make it easier to complete a task. For example, a car is a complex machine that transports humans and other things.", "subpage_snippet": "", "source": "www.computerhope.com", "link": "https://www.computerhope.com/jargon/m/machine.htm", "content": "Jun 1, 2025 · A machine is a device with several parts that work together to perform a task. A machine can augment or replace the efforts required by an animal or human to make it easier to complete a task. For example, a car is a complex machine that transports humans and other things."}
+{"idx": 8, "title": "Machine - New World Encyclopedia", "date": "", "ddg_snippet": "Modern power tools, automated machine tools, and human-operated power machinery are tools that are also machines . Machines used to transform heat or other energy into mechanical energy are known as engines.", "subpage_snippet": "", "source": "www.newworldencyclopedia.org", "link": "https://www.newworldencyclopedia.org/entry/Machine", "content": "Modern power tools, automated machine tools, and human-operated power machinery are tools that are also machines . Machines used to transform heat or other energy into mechanical energy are known as engines."}
+{"idx": 9, "title": "What is Machine ? – An Essential Guide", "date": "", "ddg_snippet": "The machine is a device that has a unique purpose, that augments or replaces human or animal effort for the accomplishment of physical tasks.", "subpage_snippet": "", "source": "www.theengineeringchoice.com", "link": "https://www.theengineeringchoice.com/what-is-machine/", "content": "The machine is a device that has a unique purpose, that augments or replaces human or animal effort for the accomplishment of physical tasks."}
diff --git a/data/sampled_jsons/minGPT_transformer_blocks_understanding_safety_finetuning_github.jsonl b/data/sampled_jsons/minGPT_transformer_blocks_understanding_safety_finetuning_github.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..60f936ec927e73728740e93b395140799d542aac
--- /dev/null
+++ b/data/sampled_jsons/minGPT_transformer_blocks_understanding_safety_finetuning_github.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "GitHub - karpathy/ minGPT : A minimal PyTorch re-implementation of...", "date": "", "ddg_snippet": "The minGPT library is three files: mingpt /model.py contains the actual Transformer model definition, mingpt /bpe.py contains a mildly refactored Byte Pair Encoder that translates between text and sequences of integers exactly like OpenAI did in GPT , mingpt /trainer.py is...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/karpathy/minGPT", "content": "The minGPT library is three files: mingpt /model.py contains the actual Transformer model definition, mingpt /bpe.py contains a mildly refactored Byte Pair Encoder that translates between text and sequences of integers exactly like OpenAI did in GPT , mingpt /trainer.py is..."}
+{"idx": 1, "title": "Any mistakes in my understanding of Transformers ? — LessWrong", "date": "", "ddg_snippet": "I feel a similar difficulty in trying to understand how transformers work. I could pick a random recent ML paper and try and understand that architecture, or I could read about the toy models that people use to explain them online.", "subpage_snippet": "", "source": "www.lesswrong.com", "link": "https://www.lesswrong.com/posts/Bw8AXf9PrnT9nk7oL/any-mistakes-in-my-understanding-of-transformers", "content": "I feel a similar difficulty in trying to understand how transformers work. I could pick a random recent ML paper and try and understand that architecture, or I could read about the toy models that people use to explain them online."}
+{"idx": 2, "title": "minGPT 代码详解（训练 GPT 模型执行两位数加法）-CSDN博客", "date": "", "ddg_snippet": "x = block (x) x = self. transformer .ln_f(x) # (b, t, n_embd) logits = self.lm_head(x) # (b, t, vocab_size) #. if we are given some desired targets also calculate the loss loss = None if targets is not None: loss = F.cross_entropy(logits.view(-1, logits.size(-1)), targets.view(-1), ignore_index=-1).", "subpage_snippet": "", "source": "blog.csdn.net", "link": "https://blog.csdn.net/wxc971231/article/details/132000182", "content": "x = block (x) x = self. transformer .ln_f(x) # (b, t, n_embd) logits = self.lm_head(x) # (b, t, vocab_size) #. if we are given some desired targets also calculate the loss loss = None if targets is not None: loss = F.cross_entropy(logits.view(-1, logits.size(-1)), targets.view(-1), ignore_index=-1)."}
+{"idx": 3, "title": "Transformer Architecture — A Friendly Guide for... - My USA Wire", "date": "", "ddg_snippet": "Training transformers : pretraining and fine - tuning . Training often has two main stages. First is pretraining on large unlabeled data.Many models use transformer blocks as their base. BERT, GPT , T5, and RoBERTa are well known. Each model targets different tasks and objectives.", "subpage_snippet": "", "source": "myusawire.com", "link": "https://myusawire.com/transformer-architecture/", "content": "Training transformers : pretraining and fine - tuning . Training often has two main stages. First is pretraining on large unlabeled data.Many models use transformer blocks as their base. BERT, GPT , T5, and RoBERTa are well known. Each model targets different tasks and objectives."}
+{"idx": 4, "title": "Python深耕之 minGPT 的使用-腾讯云开发者社区-腾讯云", "date": "", "ddg_snippet": "Python深耕之 minGPT 的使用.return self.num_digits. def get_ block _size(self): # the length of the sequence that will feed into transformer , # containing concatenated input and the output, but -1 because #. the transformer starts making predictions at the last input element.", "subpage_snippet": "", "source": "cloud.tencent.com", "link": "https://cloud.tencent.com/developer/article/2171120", "content": "Python深耕之 minGPT 的使用.return self.num_digits. def get_ block _size(self): # the length of the sequence that will feed into transformer , # containing concatenated input and the output, but -1 because #. the transformer starts making predictions at the last input element."}
+{"idx": 5, "title": "minGPT vs gpt -2 - compare differences and reviews? | LibHunt", "date": "", "ddg_snippet": "Compare minGPT vs gpt -2 and see what are their differences. minGPT . A minimal PyTorch re-implementation of the OpenAI GPT (Generative Pretrained Transformer ) training (by karpathy).", "subpage_snippet": "", "source": "www.libhunt.com", "link": "https://www.libhunt.com/compare-minGPT-vs-gpt-2", "content": "Compare minGPT vs gpt -2 and see what are their differences. minGPT . A minimal PyTorch re-implementation of the OpenAI GPT (Generative Pretrained Transformer ) training (by karpathy)."}
+{"idx": 6, "title": "minGPT -fastai - Deep Learning - fast.ai Course Forums", "date": "", "ddg_snippet": "@ilovescience highlighted in Discord that Andrej Karpathy recently released a nicely minimalist implementation of GPT , with example notebooks too @muellerzr said I should take a look. And so here is a fastai version! …", "subpage_snippet": "", "source": "forums.fast.ai", "link": "https://forums.fast.ai/t/mingpt-fastai/76913", "content": "@ilovescience highlighted in Discord that Andrej Karpathy recently released a nicely minimalist implementation of GPT , with example notebooks too @muellerzr said I should take a look. And so here is a fastai version! …"}
+{"idx": 7, "title": "What Makes and Breaks Safety Fine - tuning ?", "date": "", "ddg_snippet": "Further Analyses to Understand Safety Fine - tuning . Analyzing how the impact of transformation propagates over the layers.Analyzing the impact of safety fine - tuning on parameter space of the second MLP layer in the transformer block .", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=JEflV4nRlH", "content": "Further Analyses to Understand Safety Fine - tuning . Analyzing how the impact of transformation propagates over the layers.Analyzing the impact of safety fine - tuning on parameter space of the second MLP layer in the transformer block ."}
+{"idx": 8, "title": "Let's build GPT : from scratch, in code, spelled out. - YouTube", "date": "", "ddg_snippet": "We build a Generatively Pretrained Transformer ( GPT ), following the paper \"Attention is All You Need\" and OpenAI's GPT -2 / GPT -3.", "subpage_snippet": "", "source": "www.youtube.com", "link": "https://www.youtube.com/watch?v=kCc8FmEb1nY", "content": "We build a Generatively Pretrained Transformer ( GPT ), following the paper \"Attention is All You Need\" and OpenAI's GPT -2 / GPT -3."}
+{"idx": 9, "title": "Predicting Cryptocurrency Price Movement using Generative... | Medium", "date": "", "ddg_snippet": "Next, we train a simple generative transformer model using the minGPT library. # create a GPT instance from mingpt .model import GPT . model_config = GPT .get_default_config() model_config.model_type = ' gpt -nano' model_config.vocab_size...", "subpage_snippet": "", "source": "medium.com", "link": "https://medium.com/@bondarchukb/predicting-cryptocurrency-price-movement-using-generative-transformer-mingpt-7bc0c3a9304b", "content": "Next, we train a simple generative transformer model using the minGPT library. # create a GPT instance from mingpt .model import GPT . model_config = GPT .get_default_config() model_config.model_type = ' gpt -nano' model_config.vocab_size..."}
diff --git a/data/sampled_jsons/natural_language_GUI_action_conversion_LLM_to_executable_commands_2024.jsonl b/data/sampled_jsons/natural_language_GUI_action_conversion_LLM_to_executable_commands_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..f570a41487e38ab6f317251be1a401e3a2febeec
--- /dev/null
+++ b/data/sampled_jsons/natural_language_GUI_action_conversion_LLM_to_executable_commands_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "One to rule them all: natural language to bind communication...", "date": "", "ddg_snippet": "The system leverages LLMs to understand natural language commands , integrates real-time perception and feedback to handle unexpected changes, and employs robust error handling mechanisms to ensure seamless task execution.", "subpage_snippet": "", "source": "deep-diver.github.io", "link": "https://deep-diver.github.io/ai-paper-reviewer/paper-reviews/2411.15033/", "content": "The system leverages LLMs to understand natural language commands , integrates real-time perception and feedback to handle unexpected changes, and employs robust error handling mechanisms to ensure seamless task execution."}
+{"idx": 1, "title": "Nl2Hltl2Plan: Scaling Up Natural Language ... | PromptLayer", "date": "", "ddg_snippet": "NL2HLTL2PLAN uses a two-phase approach to transform natural language into robot- executable commands . First, a large language model breaks down complex instructions into a hierarchical task tree, organizing sub-tasks in a logical sequence.", "subpage_snippet": "", "source": "www.promptlayer.com", "link": "https://www.promptlayer.com/research-papers/nl2hltl2plan-scaling-up-natural-language-understanding-for-multi-robots-through-hierarchical-temporal-logic-task-representation", "content": "NL2HLTL2PLAN uses a two-phase approach to transform natural language into robot- executable commands . First, a large language model breaks down complex instructions into a hierarchical task tree, organizing sub-tasks in a logical sequence."}
+{"idx": 2, "title": "llamafile : An Executable LLM", "date": "", "ddg_snippet": "Aug 22, 2024 5 min. LLM Deployment with llamafile: Discover how Mozilla’s llamafile simplifies LLM deployment into a single executable .Redundant Layers If you perform operations like moving or copying files in separate RUN commands , these actions create additional layers.", "subpage_snippet": "", "source": "datascience.fm", "link": "https://datascience.fm/llamafile-an-executable-llm/", "content": "Aug 22, 2024 5 min. LLM Deployment with llamafile: Discover how Mozilla’s llamafile simplifies LLM deployment into a single executable .Redundant Layers If you perform operations like moving or copying files in separate RUN commands , these actions create additional layers."}
+{"idx": 3, "title": "Implementing a local LLM chatbot that can run code and... | Deepgram", "date": "", "ddg_snippet": "Executing Python Code. To execute code produced by the LLM , it is essential to accurately extract and implement the code directly from the model’s responses. Typically, LLMs output code in markdown format, encapsulated within triple backticks.", "subpage_snippet": "", "source": "deepgram.com", "link": "https://deepgram.com/learn/local-llm-chatbot-that-can-run-code-searches", "content": "Executing Python Code. To execute code produced by the LLM , it is essential to accurately extract and implement the code directly from the model’s responses. Typically, LLMs output code in markdown format, encapsulated within triple backticks."}
+{"idx": 4, "title": "SMART- LLM : Smart Multi-Agent Robot Task Planning using Large...", "date": "", "ddg_snippet": "Robotics researchers increasingly leverage large language models ( LLM ) in robotics systems, using them as interfaces to receive task commands , generate task plans, form team coalitions, and allocate tasks among multi-robot and human agents.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/387418202_SMART-LLM_Smart_Multi-Agent_Robot_Task_Planning_using_Large_Language_Models", "content": "Robotics researchers increasingly leverage large language models ( LLM ) in robotics systems, using them as interfaces to receive task commands , generate task plans, form team coalitions, and allocate tasks among multi-robot and human agents."}
+{"idx": 5, "title": "Introducing LLM Sandbox: Securely Execute ... | HackerNoon", "date": "", "ddg_snippet": "LLM Sandbox is a lightweight and portable environment designed to run LLM -generated code in a secure and isolated manner using Docker containers. With its easy-to-use interface , you can set up, manage, and execute code within a controlled Docker environment.", "subpage_snippet": "", "source": "hackernoon.com", "link": "https://hackernoon.com/introducing-llm-sandbox-securely-execute-llm-generated-code-with-ease", "content": "LLM Sandbox is a lightweight and portable environment designed to run LLM -generated code in a secure and isolated manner using Docker containers. With its easy-to-use interface , you can set up, manage, and execute code within a controlled Docker environment."}
+{"idx": 6, "title": "The best LLM of local coding that you can execute yourself", "date": "", "ddg_snippet": "These LLM of local coding now have a variety of applications that were not practices before, since they provide practical assistance to directly to the developer's workflow. This, in turn, allows online autocomplete, coding purification and even reasoning in all projects.", "subpage_snippet": "", "source": "technicalterrence.com", "link": "https://technicalterrence.com/tech/ai/the-best-llm-of-local-coding-that-you-can-execute-yourself/", "content": "These LLM of local coding now have a variety of applications that were not practices before, since they provide practical assistance to directly to the developer's workflow. This, in turn, allows online autocomplete, coding purification and even reasoning in all projects."}
+{"idx": 7, "title": "Together Code Interpreter: execute LLM -generated code seamlessly...", "date": "", "ddg_snippet": "While the LLM will go to some lengths to attempt to represent this chart in plain text, it cannot execute the code to output an actual chart. When we allow the LLM to use Together Code Interpreter, it can generate Python code, execute it, and output an image of the chart back to the user .", "subpage_snippet": "", "source": "www.together.ai", "link": "https://www.together.ai/blog/code-interpreter", "content": "While the LLM will go to some lengths to attempt to represent this chart in plain text, it cannot execute the code to output an actual chart. When we allow the LLM to use Together Code Interpreter, it can generate Python code, execute it, and output an image of the chart back to the user ."}
+{"idx": 8, "title": "Enhance your LLM responses by integrating data from the Database", "date": "", "ddg_snippet": "Return Result: The LLM converts the query result back into natural language and returns it to the user . To begin, we'll use the Langchain Text-To-SQL agent to execute queries, followed by refining the results with the SQL Agent.", "subpage_snippet": "", "source": "www.shamimbhuiyan.ru", "link": "https://www.shamimbhuiyan.ru/blogs/enhance-your-llm-responses-by-integrating-data-from-the-database", "content": "Return Result: The LLM converts the query result back into natural language and returns it to the user . To begin, we'll use the Langchain Text-To-SQL agent to execute queries, followed by refining the results with the SQL Agent."}
+{"idx": 9, "title": "GitHub - rabit232/ribit.2.0", "date": "", "ddg_snippet": "A vision-based GUI automation agent powered by a low-resource LLM , now known as Ribit 2.0.Vision-Based Control: Uses screen captures and ASCII-art conversion to allow a local LLM to perceive the GUI .", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/rabit232/ribit.2.0", "content": "A vision-based GUI automation agent powered by a low-resource LLM , now known as Ribit 2.0.Vision-Based Control: Uses screen captures and ASCII-art conversion to allow a local LLM to perceive the GUI ."}
diff --git a/data/sampled_jsons/normalizing_flow_training_loss_L2_norm_alpha_regularization_equation.jsonl b/data/sampled_jsons/normalizing_flow_training_loss_L2_norm_alpha_regularization_equation.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..621636a4f3782b2b272bbead5100b6d2e15ea15f
--- /dev/null
+++ b/data/sampled_jsons/normalizing_flow_training_loss_L2_norm_alpha_regularization_equation.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "L2 Regularization Math - apxml.com", "date": "", "ddg_snippet": "This equation reveals why L2 regularization is often called weight decay. In each update step, before applying the gradient from the original loss , the weight w i j [l] wij[l] is multiplied by a factor slightly less than 1.", "subpage_snippet": "", "source": "apxml.com", "link": "https://apxml.com/courses/deep-learning-regularization-optimization/chapter-2-weight-regularization/l2-regularization-math", "content": "This equation reveals why L2 regularization is often called weight decay. In each update step, before applying the gradient from the original loss , the weight w i j [l] wij[l] is multiplied by a factor slightly less than 1."}
+{"idx": 1, "title": "PDF lec12-regularization", "date": "", "ddg_snippet": "Overfitting: your function matches the training data well but is not learning general rules that will work for new data Modify learning algorithm to favor \"simpler\" prediction rules to avoid overfitting Most commonly, regularization refers to modifying the loss function to penalize certain values of the weights you are learning. Specifically, penalize weights that are large.", "subpage_snippet": "", "source": "vumanfredi.wescreates.wesleyan.edu", "link": "https://vumanfredi.wescreates.wesleyan.edu/teaching/comp411-f21/lectures/lec12-regularization.pdf", "content": "Overfitting: your function matches the training data well but is not learning general rules that will work for new data Modify learning algorithm to favor \"simpler\" prediction rules to avoid overfitting Most commonly, regularization refers to modifying the loss function to penalize certain values of the weights you are learning. Specifically, penalize weights that are large."}
+{"idx": 2, "title": "Overfitting: L2 regularization - Google Developers", "date": "", "ddg_snippet": "Learn how the L2 regularization metric is calculated and how to set a regularization rate to minimize the combination of loss and complexity during model training , or to use alternative regularization techniques like early stopping.", "subpage_snippet": "", "source": "developers.google.com", "link": "https://developers.google.com/machine-learning/crash-course/overfitting/regularization", "content": "Learn how the L2 regularization metric is calculated and how to set a regularization rate to minimize the combination of loss and complexity during model training , or to use alternative regularization techniques like early stopping."}
+{"idx": 3, "title": "L2 Regularization Mechanism - apxml.com", "date": "", "ddg_snippet": "In summary, L2 regularization (Weight Decay) modifies the loss function to penalize large squared weights. This alters the gradient update rule, causing weights to shrink towards zero during training , leading to simpler, smoother models less prone to overfitting.", "subpage_snippet": "", "source": "apxml.com", "link": "https://apxml.com/courses/deep-learning-regularization-optimization/chapter-2-weight-regularization/l2-regularization-mechanism", "content": "In summary, L2 regularization (Weight Decay) modifies the loss function to penalize large squared weights. This alters the gradient update rule, causing weights to shrink towards zero during training , leading to simpler, smoother models less prone to overfitting."}
+{"idx": 4, "title": "Regularization Techniques in Deep Learning: Dropout, L-Norm ... - Medium", "date": "", "ddg_snippet": "L-Norm Regularization : Adds a penalty based on the magnitude of the model's weights. Batch Normalization: Normalizes layer inputs to stabilize and accelerate training , also acting as a regularizer.", "subpage_snippet": "", "source": "medium.com", "link": "https://medium.com/@adelbasli/regularization-techniques-in-deep-learning-dropout-l-norm-and-batch-normalization-with-3fe36bbbd353", "content": "L-Norm Regularization : Adds a penalty based on the magnitude of the model's weights. Batch Normalization: Normalizes layer inputs to stabilize and accelerate training , also acting as a regularizer."}
+{"idx": 5, "title": "Regularization Techniques & Performance Tuning", "date": "", "ddg_snippet": "Learn about overfitting, regularization (L1/ L2 , Dropout), batch normalization, and hyperparameter tuning basics.", "subpage_snippet": "", "source": "apxml.com", "link": "https://apxml.com/courses/introduction-to-deep-learning/chapter-6-regularization-performance-improvement", "content": "Learn about overfitting, regularization (L1/ L2 , Dropout), batch normalization, and hyperparameter tuning basics."}
+{"idx": 6, "title": "Regularization — Understanding L1 and L2 regularization for Deep ...", "date": "", "ddg_snippet": "Understanding what regularization is and why it is required for machine learning and diving deep to clarify the importance of L1 and L2 regularization in Deep learning.", "subpage_snippet": "", "source": "medium.com", "link": "https://medium.com/analytics-vidhya/regularization-understanding-l1-and-l2-regularization-for-deep-learning-a7b9e4a409bf", "content": "Understanding what regularization is and why it is required for machine learning and diving deep to clarify the importance of L1 and L2 regularization in Deep learning."}
+{"idx": 7, "title": "Understanding Regularization in Neural Networks - Zilliz", "date": "", "ddg_snippet": "L2 regularization implements a squared or L2 norm in the penalty term, as shown in the following equation : Figure 6- Compute the partial derivative of the additional penalty term in L2 regularization", "subpage_snippet": "", "source": "zilliz.com", "link": "https://zilliz.com/learn/understanding-regularization-in-nueral-networks", "content": "L2 regularization implements a squared or L2 norm in the penalty term, as shown in the following equation : Figure 6- Compute the partial derivative of the additional penalty term in L2 regularization"}
+{"idx": 8, "title": "L1 and L2 Regularization - apxml.com", "date": "", "ddg_snippet": "L1 and L2 regularization are two widely used techniques that penalize model complexity based on the magnitude of the network's weights. The core idea is that models with excessively large weights might be too sensitive to small changes in the input features, essentially memorizing the training data.", "subpage_snippet": "", "source": "apxml.com", "link": "https://apxml.com/courses/introduction-to-deep-learning/chapter-6-regularization-performance-improvement/l1-l2-regularization", "content": "L1 and L2 regularization are two widely used techniques that penalize model complexity based on the magnitude of the network's weights. The core idea is that models with excessively large weights might be too sensitive to small changes in the input features, essentially memorizing the training data."}
+{"idx": 9, "title": "Interpreting Weight Regularization In Machine Learning", "date": "", "ddg_snippet": "Figure-8: L3 regularization loss and its partial derivative with respect to each weight Wi. This has the effect of adjusting each weight by a multiple (in this case alpha times the learning rate) of the square of the weight itself in the direction that minimizes the loss . Graphically, this is what the function and its derivative look like.", "subpage_snippet": "", "source": "towardsdatascience.com", "link": "https://towardsdatascience.com/interpreting-weight-regularization-in-machine-learning-99f2677f7ef5/", "content": "Figure-8: L3 regularization loss and its partial derivative with respect to each weight Wi. This has the effect of adjusting each weight by a multiple (in this case alpha times the learning rate) of the square of the weight itself in the direction that minimizes the loss . Graphically, this is what the function and its derivative look like."}
diff --git a/data/sampled_jsons/nuScenes_dataset_Caesar_et_al._2020_abstract_year_2020.jsonl b/data/sampled_jsons/nuScenes_dataset_Caesar_et_al._2020_abstract_year_2020.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..f93fe34496cd996a951b1d39d17fe7c528611424
--- /dev/null
+++ b/data/sampled_jsons/nuScenes_dataset_Caesar_et_al._2020_abstract_year_2020.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Multimodal Framework for Explainable Autonomous Driving:", "date": "", "ddg_snippet": "Evaluated on the BDD- X dataset (21,113 samples) and the nuScenes dataset (1,000 scenes from Boston and Singapore) [ 14 , 15 ] , our model ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2507.07938v1", "content": "Evaluated on the BDD- X dataset (21,113 samples) and the nuScenes dataset (1,000 scenes from Boston and Singapore) [ 14 , 15 ] , our model ..."}
+{"idx": 1, "title": "FastDriveVLA: Efficient End-to-End Driving via Plug-and-Play", "date": "", "ddg_snippet": "To train ReconPruner, we also introduce a large-scale dataset called nuScenes -FG, consisting of 241K image-mask pairs with annotated foreground ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2507.23318v1", "content": "To train ReconPruner, we also introduce a large-scale dataset called nuScenes -FG, consisting of 241K image-mask pairs with annotated foreground ..."}
+{"idx": 2, "title": "Self-Supervised Cross-Modal Learning for Image-to-Point Cloud", "date": "", "ddg_snippet": "Contrastive Multi-view Coding (CMC) (Tian, Krishnan, and Isola 2020 ) , AVSA (Morgado, Li, and Nvasconcelos 2020 ) , and MMV (Alayrac et al .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2509.15882v1", "content": "Contrastive Multi-view Coding (CMC) (Tian, Krishnan, and Isola 2020 ) , AVSA (Morgado, Li, and Nvasconcelos 2020 ) , and MMV (Alayrac et al ."}
+{"idx": 3, "title": "The System Description of CPS Team for Track on Driving with", "date": "", "ddg_snippet": "This dataset consists of 4072 sample frames from 696 scenes in the nuScenes [ 2 ] dataset , comprising a total of 377,983 questions.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2509.11071v1", "content": "This dataset consists of 4072 sample frames from 696 scenes in the nuScenes [ 2 ] dataset , comprising a total of 377,983 questions."}
+{"idx": 4, "title": "Most Influential CVPR Papers (2022-02) – Resources |", "date": "", "ddg_snippet": "NuScenes : A Multimodal Dataset For Autonomous Driving IF:7 Related Papers Related Patents Related Grants Related Orgs Related Experts Details ...", "subpage_snippet": "", "source": "resources.paperdigest.org", "link": "https://resources.paperdigest.org/2022/02/most-influential-cvpr-papers-2022-02/", "content": "NuScenes : A Multimodal Dataset For Autonomous Driving IF:7 Related Papers Related Patents Related Grants Related Orgs Related Experts Details ..."}
+{"idx": 5, "title": "Most Influential CVPR Papers (2021-02) – Resources |", "date": "", "ddg_snippet": "NuScenes : A Multimodal Dataset For Autonomous Driving IF:6 Related Papers Related Patents Related Grants Related Orgs Related Experts Details ...", "subpage_snippet": "", "source": "resources.paperdigest.org", "link": "https://resources.paperdigest.org/2021/02/most-influential-cvpr-papers/", "content": "NuScenes : A Multimodal Dataset For Autonomous Driving IF:6 Related Papers Related Patents Related Grants Related Orgs Related Experts Details ..."}
+{"idx": 6, "title": "Most Influential CVPR Papers – Paper Digest", "date": "", "ddg_snippet": "NuScenes : A Multimodal Dataset For Autonomous Driving IF:6 Related Papers Related Patents Related Grants Related Orgs Related Experts Details ...", "subpage_snippet": "", "source": "www.paperdigest.org", "link": "https://www.paperdigest.org/2021/03/most-influential-cvpr-papers-2021-03/", "content": "NuScenes : A Multimodal Dataset For Autonomous Driving IF:6 Related Papers Related Patents Related Grants Related Orgs Related Experts Details ..."}
+{"idx": 7, "title": "Most Influential CVPR Papers (2021-08) – Paper Digest", "date": "", "ddg_snippet": "NuScenes : A Multimodal Dataset For Autonomous Driving IF:7 Related Papers Related Patents Related Grants Related Orgs Related Experts Details ...", "subpage_snippet": "", "source": "www.paperdigest.org", "link": "https://www.paperdigest.org/2021/08/most-influential-cvpr-papers-2021-08/", "content": "NuScenes : A Multimodal Dataset For Autonomous Driving IF:7 Related Papers Related Patents Related Grants Related Orgs Related Experts Details ..."}
+{"idx": 8, "title": "Robust object detection for autonomous driving based on", "date": "", "ddg_snippet": "Although some theoretical research work has exploited the ability of semi-supervised learning to improve adversarial robustness on toy datasets such ...", "subpage_snippet": "", "source": "sands.edpsciences.org", "link": "https://sands.edpsciences.org/articles/sands/full_html/2024/01/sands20230024/sands20230024.html", "content": "Although some theoretical research work has exploited the ability of semi-supervised learning to improve adversarial robustness on toy datasets such ..."}
+{"idx": 9, "title": "CBDES MoE: Hierarchically Decoupled Mixture-of-Experts for", "date": "", "ddg_snippet": "... proposed feature-level multi-modal fusion frameworks that align and jointly model camera and LiDAR information within the BEV space (Liu et al ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2508.07838v1", "content": "... proposed feature-level multi-modal fusion frameworks that align and jointly model camera and LiDAR information within the BEV space (Liu et al ..."}
diff --git a/data/sampled_jsons/nuScenes_dataset_focus_vehicle_trajectories_pedestrian_interactions.jsonl b/data/sampled_jsons/nuScenes_dataset_focus_vehicle_trajectories_pedestrian_interactions.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..761fc0855c44cdb971f729bafd34f65fb73469e8
--- /dev/null
+++ b/data/sampled_jsons/nuScenes_dataset_focus_vehicle_trajectories_pedestrian_interactions.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Top 3D Point Cloud Datasets for Autonomous Driving", "date": "", "ddg_snippet": "nuScenes is another comprehensive dataset that provides a variety of sensor data , including lidar and radar, to accelerate the development of autonomous driving systems.", "subpage_snippet": "", "source": "imerit.net", "link": "https://imerit.net/resources/blog/top-28-leading-3d-point-cloud-datasets-for-autonomous-driving-and-perception/", "content": "nuScenes is another comprehensive dataset that provides a variety of sensor data , including lidar and radar, to accelerate the development of autonomous driving systems."}
+{"idx": 1, "title": "Figure 1. An example from the nuScenes dataset [9]. Given the past...", "date": "", "ddg_snippet": "Pedestrian trajectory prediction plays a pivotal role in ensuring the safety and efficiency of various applications, including autonomous vehicles and traffic management systems.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/figure/An-example-from-the-nuScenes-dataset-9-Given-the-past-observations-of-pedestrians_fig1_343454918", "content": "Pedestrian trajectory prediction plays a pivotal role in ensuring the safety and efficiency of various applications, including autonomous vehicles and traffic management systems."}
+{"idx": 2, "title": "Euro-PVI: Pedestrian Vehicle Interactions in... | Papers With Code", "date": "", "ddg_snippet": "The interactions between vehicle and pedestrian or bicyclist have a significant impact on the trajectories of traffic participants e.g. stopping or turning to avoid collisions.", "subpage_snippet": "", "source": "paperswithcode.com", "link": "https://paperswithcode.com/paper/euro-pvi-pedestrian-vehicle-interactions-in", "content": "The interactions between vehicle and pedestrian or bicyclist have a significant impact on the trajectories of traffic participants e.g. stopping or turning to avoid collisions."}
+{"idx": 3, "title": "A context-aware pedestrian trajectory prediction framework for...", "date": "", "ddg_snippet": "In this research, we focus on the context-aware pedestrian trajectory prediction framework for automated vehicles at mid-block unsignalized crossings.We extracted pedestrian instances based on the LiDAR data of Lyft, nuScenes and Waymo datasets , and the annotations provided.", "subpage_snippet": "", "source": "scispace.com", "link": "https://scispace.com/pdf/a-context-aware-pedestrian-trajectory-prediction-framework-2hiwvp41.pdf", "content": "In this research, we focus on the context-aware pedestrian trajectory prediction framework for automated vehicles at mid-block unsignalized crossings.We extracted pedestrian instances based on the LiDAR data of Lyft, nuScenes and Waymo datasets , and the annotations provided."}
+{"idx": 4, "title": "SiT Dataset : Socially Interactive Pedestrian Trajectory", "date": "", "ddg_snippet": "Top-view trajectories : A pedestrian dataset of vehicle -crowd interaction from controlled experiments and crowded campus. In Proceedings of the IEEE Intelligent Vehicles Symposium (IV), pages 899–904, 2019.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=gMYsxTin4x", "content": "Top-view trajectories : A pedestrian dataset of vehicle -crowd interaction from controlled experiments and crowded campus. In Proceedings of the IEEE Intelligent Vehicles Symposium (IV), pages 899–904, 2019."}
+{"idx": 5, "title": "Pedestrian Intention and Trajectory Prediction in Unstructured Traffic...", "date": "", "ddg_snippet": "tion, location, ego- vehicle motion, scene context, occlusion, and pedestrian - vehicle interactions .In contrast, ego-view datasets like nuScenes [5], Waymo [7], and Argoverse2 [4] capture agent interactions but lack detailed pedestrian action annotations.", "subpage_snippet": "", "source": "cdn.iiit.ac.in", "link": "https://cdn.iiit.ac.in/cdn/cvit.iiit.ac.in/images/ConferencePapers/2025/IDD-PeD.pdf", "content": "tion, location, ego- vehicle motion, scene context, occlusion, and pedestrian - vehicle interactions .In contrast, ego-view datasets like nuScenes [5], Waymo [7], and Argoverse2 [4] capture agent interactions but lack detailed pedestrian action annotations."}
+{"idx": 6, "title": "SocialMOIF: Multi-Order Intention Fusion for Pedestrian Trajectory ...", "date": "", "ddg_snippet": "SDD [33] dataset is primarily utilized for pedestrian trajectory prediction and object tracking, containing about 19,000 pedestrian trajectories , with po-sitions annotated every 0.4s. NBA dataset focuses on the 2015–2016 season [21, 53].", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2025/papers/Chen_SocialMOIF_Multi-Order_Intention_Fusion_for_Pedestrian_Trajectory_Prediction_CVPR_2025_paper.pdf", "content": "SDD [33] dataset is primarily utilized for pedestrian trajectory prediction and object tracking, containing about 19,000 pedestrian trajectories , with po-sitions annotated every 0.4s. NBA dataset focuses on the 2015–2016 season [21, 53]."}
+{"idx": 7, "title": "Current Development, Comparison and Future Directions in Vehicle ...", "date": "", "ddg_snippet": "NuScenes [31]: This dataset is a large-scale open-source dataset for the field of autonomous driving, collected and developed by the team at Motional in 2019.“Dynamic vehicle graph interaction for trajectory prediction based on video signals”.", "subpage_snippet": "", "source": "themedicon.com", "link": "https://themedicon.com/pdf/engineeringthemes/MCET-06-203.pdf", "content": "NuScenes [31]: This dataset is a large-scale open-source dataset for the field of autonomous driving, collected and developed by the team at Motional in 2019.“Dynamic vehicle graph interaction for trajectory prediction based on video signals”."}
+{"idx": 8, "title": "Disentangling Crowd Interactions for Pedestrians Trajectory ...", "date": "", "ddg_snippet": "Estimating the unknown future path is a challenging problem due to the complex interactions occurring among pedestrians .Our method improves mADE on ETH/UCY pedestrians dataset and achieves new state-of-the-art mFDE results on nuScenes vehicle datasets .", "subpage_snippet": "", "source": "oar.a-star.edu.sg", "link": "https://oar.a-star.edu.sg/communities-collections/articles/19125", "content": "Estimating the unknown future path is a challenging problem due to the complex interactions occurring among pedestrians .Our method improves mADE on ETH/UCY pedestrians dataset and achieves new state-of-the-art mFDE results on nuScenes vehicle datasets ."}
+{"idx": 9, "title": "JaywalkerVR: A VR System for Collecting Safety-Critical...", "date": "", "ddg_snippet": "Since this dataset contains only vehicle - pedestrian interaction data that actually occurred in the real world, testing the prediction model with this dataset allows us to evaluate the model’s performance in real-world interactive scenes .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2407.04843v1", "content": "Since this dataset contains only vehicle - pedestrian interaction data that actually occurred in the real world, testing the prediction model with this dataset allows us to evaluate the model’s performance in real-world interactive scenes ."}
diff --git a/data/sampled_jsons/nuScenes_dataset_pedestrian_vehicle_trajectories_interactions_focus_year_2020.jsonl b/data/sampled_jsons/nuScenes_dataset_pedestrian_vehicle_trajectories_interactions_focus_year_2020.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..51f6032983a29d011e114549a4dcb3f0cd145c79
--- /dev/null
+++ b/data/sampled_jsons/nuScenes_dataset_pedestrian_vehicle_trajectories_interactions_focus_year_2020.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "8 Free Datasets for Automotive Industry", "date": "", "ddg_snippet": "... 354 segments of object trajectories and corresponding 3D maps. The main contribution in the area of autonomous vehicles is the part of the dataset ...", "subpage_snippet": "", "source": "www.deepchecks.com", "link": "https://www.deepchecks.com/free-datasets-automotive-industry/", "content": "... 354 segments of object trajectories and corresponding 3D maps. The main contribution in the area of autonomous vehicles is the part of the dataset ..."}
+{"idx": 1, "title": "Machine Learning Datasets | Papers With Code", "date": "", "ddg_snippet": "Stay informed on the latest trending ML papers with code, research developments, libraries, methods, and datasets . ... Interactive 29 Dialog 24 Midi ...", "subpage_snippet": "", "source": "paperswithcode.com", "link": "https://paperswithcode.com/datasets?mod=lidar", "content": "Stay informed on the latest trending ML papers with code, research developments, libraries, methods, and datasets . ... Interactive 29 Dialog 24 Midi ..."}
+{"idx": 2, "title": "GitHub -", "date": "", "ddg_snippet": "Pedestrian Trajectory Prediction in Pedestrian - Vehicle Mixed Environments: A Systematic Review, IEEE T-ITS, 2023. ... of Pedestrians and Vehicles for ...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/jiachenli94/Awesome-Interaction-Aware-Trajectory-Prediction", "content": "Pedestrian Trajectory Prediction in Pedestrian - Vehicle Mixed Environments: A Systematic Review, IEEE T-ITS, 2023. ... of Pedestrians and Vehicles for ..."}
+{"idx": 3, "title": "GitHub -", "date": "", "ddg_snippet": "... a checklist of state-of-the-art research materials ( datasets ... Autonomous vehicles that interact with pedestrians : A survey of theory and practice.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/DanqingZ/Awesome-Interaction-aware-Trajectory-Prediction", "content": "... a checklist of state-of-the-art research materials ( datasets ... Autonomous vehicles that interact with pedestrians : A survey of theory and practice."}
+{"idx": 4, "title": "motion-prediction · GitHub Topics · GitHub", "date": "", "ddg_snippet": "... interactions multiagent-systems behavior-analysis autonomous-driving autonomous- vehicles trajectory -prediction multiagent-learning ...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/topics/motion-prediction", "content": "... interactions multiagent-systems behavior-analysis autonomous-driving autonomous- vehicles trajectory -prediction multiagent-learning ..."}
+{"idx": 5, "title": "DeepUrban: Benchmark for Trajectory Prediction and Planning", "date": "", "ddg_snippet": "... dataset to enhance benchmarking of trajectory prediction, and planning methods with a focus on dense urban settings with a heavy interaction between ...", "subpage_snippet": "", "source": "iv.ee.hm.edu", "link": "https://iv.ee.hm.edu/deepurban/", "content": "... dataset to enhance benchmarking of trajectory prediction, and planning methods with a focus on dense urban settings with a heavy interaction between ..."}
+{"idx": 6, "title": "Tong Su", "date": "", "ddg_snippet": "To predict such unpredictable trajectories , we can gain insights into the interaction between pedestrians . ... Interaction Transformer (SIT), which ...", "subpage_snippet": "", "source": "www.catalyzex.com", "link": "https://www.catalyzex.com/author/Tong+Su", "content": "To predict such unpredictable trajectories , we can gain insights into the interaction between pedestrians . ... Interaction Transformer (SIT), which ..."}
+{"idx": 7, "title": "Haiming Gang", "date": "", "ddg_snippet": "... large-scale dataset that is designed to tackle joint trajectory and intention prediction for heterogeneous traffic agents ( pedestrians and vehicles ...", "subpage_snippet": "", "source": "www.catalyzex.com", "link": "https://www.catalyzex.com/author/Haiming+Gang", "content": "... large-scale dataset that is designed to tackle joint trajectory and intention prediction for heterogeneous traffic agents ( pedestrians and vehicles ..."}
+{"idx": 8, "title": "EMMA: End-to-End Multimodal Model for Autonomous Driving", "date": "", "ddg_snippet": "... by achieving state- of-the-art performance in motion planning on nuScenes as well as competitive results on the Waymo Open Motion Dataset (WOMD ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2410.23262v2", "content": "... by achieving state- of-the-art performance in motion planning on nuScenes as well as competitive results on the Waymo Open Motion Dataset (WOMD ..."}
+{"idx": 9, "title": "OmniTraj: Pre-Training on Heterogeneous Data for Adaptive and", "date": "", "ddg_snippet": "A wide range of approaches have been explored, with traditional methods focusing on modeling social interactions through techniques such as social ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2507.23657v1", "content": "A wide range of approaches have been explored, with traditional methods focusing on modeling social interactions through techniques such as social ..."}
diff --git a/data/sampled_jsons/open-source_NVIDIA_toolkit_optimize_deploy_quantized_models_on_Jetson_devices.jsonl b/data/sampled_jsons/open-source_NVIDIA_toolkit_optimize_deploy_quantized_models_on_Jetson_devices.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..5f836df9ba3e3947449aea59b267aad438951807
--- /dev/null
+++ b/data/sampled_jsons/open-source_NVIDIA_toolkit_optimize_deploy_quantized_models_on_Jetson_devices.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "NVIDIA TensorRT Model Optimizer - GitHub", "date": "", "ddg_snippet": "The NVIDIA TensorRT Model Optimizer (referred to as Model Optimizer, or ModelOpt) is a library comprising state-of-the-art model optimization techniques including quantization, distillation, pruning, speculative decoding and sparsity to accelerate models . [Input] Model Optimizer currently supports inputs of a Hugging Face, PyTorch or ONNX model .", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/NVIDIA/TensorRT-Model-Optimizer", "content": "The NVIDIA TensorRT Model Optimizer (referred to as Model Optimizer, or ModelOpt) is a library comprising state-of-the-art model optimization techniques including quantization, distillation, pruning, speculative decoding and sparsity to accelerate models . [Input] Model Optimizer currently supports inputs of a Hugging Face, PyTorch or ONNX model ."}
+{"idx": 1, "title": "Accelerating Quantized Networks with the NVIDIA QAT Toolkit ... TensorRT-Model-Optimizer/README.md at main · NVIDIA ... - GitHub Post-Training Quantization of LLMs with NVIDIA NeMo and ... Releases: NVIDIA/TensorRT-Model-Optimizer - GitHub Optimizing LLMs for Performance and ... - NVIDIA Developer Optimizing LLMs for Performance and Accuracy with Post-Training Qua… NVIDIA TensorRT Model Optimizer - GitHub NVIDIA TensorRT Model Optimizer - GitHub Post-Training Quantization of LLMs with NVIDIA NeMo and TensorRT -Model-Optimizer/README.md at main - GitHub Accelerating Quantized Networks with the NVIDIA QAT Toolkit for Working with Quantized Types — NVIDIA TensorRT Documentation", "date": "", "ddg_snippet": "Accelerating deep neural networks (DNN) inference is an important step in realizing latency-critical deployment of real-world applications such as image classification, image segmentation, natural language processing, and so on . The need for improving DNN inference latency has sparked interest in running those models in lower precisions, such as FP... See full list on developer. nvidia .com Figure 3 shows the full workflow to deploy a QAT model, obtained with the QAT Toolkit , in TensorRT. 1. Assume a pretrained TensorFlow 2 model in SavedModel format, also referred to as the baseline model. 2. Quantize that model using the quantize_modelfunction, which clones and wraps each desired layer with QDQ nodes. 3. Fine-tune the obtained quant... See full list on developer. nvidia .com In this example, we show you how to quantize and fine-tune a QAT model with the TensorFlow 2 toolkit and how to deploy that quantized model in TensorRT. For more information, see the full example_resnet50v1.ipynbJupyter notebook. See full list on developer. nvidia .com In this section, we report accuracy and latency performance numbers for various models in the ResNet and EfficientNet families: 1. ResNet-50v1 2. ResNet-50v2 3. ResNet-101v1 4. ResNet-101v2 5. EfficientNet-B0 6. EfficientNet-B3 All results were obtained on the NVIDIA A100 GPU with batch size 1 using TensorRT 8.4 (EA for ResNet and GA for EfficientN... See full list on developer. nvidia .com In this post, we introduced the NVIDIA QAT Toolkit for TensorFlow 2. We discussed the advantages of using the toolkit in the context of TensorRT inference acceleration. We then demonstrated how to use the toolkit with ResNet50 and perform accuracy and latency evaluations on ResNet and EfficientNet datasets. Experimental results show that the accura... See full list on developer. nvidia .com [ Optimize ] Model Optimizer provides Python APIs for users to easily compose the above model optimization techniques and export an optimized quantized checkpoint. Model Optimizer is also integrated with NVIDIA NeMo, Megatron-LM and Hugging Face Accelerate for training required inference optimization techniques. Sep 10, 2024 · After you build a model in NeMo using a wide array of options offered by the toolkit , NeMo export and deployment tools can be used to apply PTQ methods and serve the optimized model . The recent NeMo container release is a self-contained toolkit coming with all the required dependencies for applying PTQ and deploying quantized LLMs. A unified library of state-of-the-art model optimization techniques like quantization, pruning, distillation, speculative decoding, etc. It compresses deep learning models for downstream deployment frameworks like TensorRT-LLM or TensorRT to optimize inference speed. - Releases · NVIDIA /TensorRT- Model -Optimizer Aug 1, 2025 · Further quantizing to formats like FP4 unlocks substantial efficiency gains and performance, supported by a growing ecosystem of open-source techniques. NVIDIA TensorRT Model Optimizer post-training quantization (PTQ) framework offers a flexible and modular approach to applying these optimizations. What is Nvidia tensorrt model optimizer post-training quantization (PTQ)? Further quantizing to formats like FP4 unlocks substantial efficiency gains and performance, supported by a growing ecosystem of open-source techniques. NVIDIA TensorRT Model Optimizer post-training quantization (PTQ) framework offers a flexible and modular approach to applying these optimizations . What is Nvidia tensorrt model optimizer? The NVIDIA TensorRT Model Optimizer (referred to as Model Optimizer, or ModelOpt) is a library comprising state-of-the-art model optimization techniques including quantization, distillation, pruning, speculative decoding and sparsity to accelerate models . Which quantization formats are supported by model optimizer? Model Optimizer enables highly performant quantization formats including NVFP4, FP8, INT8, INT4 , etc and supports advanced algorithms such as SmoothQuant, AWQ, SVDQuant, and Double Quantization with easy-to-use Python APIs. Both Post-training quantization (PTQ) and Quantization-aware training (QAT) are supported. What is post-training quantization (PTQ) in Nvidia Nemo? Post-training quantization (PTQ) is implemented in NVIDIA NeMo using NVIDIA TensorRT Model Optimizer and NVIDIA TensorRT-LLM to reduce computational and memory requirements for serving trained large language models (LLMs). What is Nvidia modelopt? Seamlessly integrated within the NVIDIA AI software ecosystem, the quantized checkpoint generated from Model Optimizer is ready for deployment in downstream inference frameworks like TensorRT-LLM or TensorRT. ModelOpt is integrated with NVIDIA NeMo and Megatron-LM for training-in-the-loop optimization techniques. Does Nvidia tensorrt support PTQ & Qat? NVIDIA TensorRT supports post-training quantization (PTQ) and QAT techniques to convert floating-point DNN models to INT8 precision. In this post, we discuss these techniques, introduce the NVIDIA QAT toolkit for TensorFlow, and demonstrate an end-to-end workflow to design quantized networks optimal for TensorRT deployment. Mar 30, 2025 · We provide an open-source TensorFlow Quantization Toolkit to perform QAT in TensorFlow 2 Keras models following NVIDIA ’s QAT recipe. This leads to optimal model acceleration with TensorRT on NVIDIA GPUs and hardware accelerators.", "subpage_snippet": "", "source": "developer.nvidia.com", "link": "https://developer.nvidia.com/blog/accelerating-quantized-networks-with-qat-toolkit-and-tensorrt/", "content": "Accelerating deep neural networks (DNN) inference is an important step in realizing latency-critical deployment of real-world applications such as image classification, image segmentation, natural language processing, and so on . The need for improving DNN inference latency has sparked interest in running those models in lower precisions, such as FP... See full list on developer. nvidia .com Figure 3 shows the full workflow to deploy a QAT model, obtained with the QAT Toolkit , in TensorRT. 1. Assume a pretrained TensorFlow 2 model in SavedModel format, also referred to as the baseline model. 2. Quantize that model using the quantize_modelfunction, which clones and wraps each desired layer with QDQ nodes. 3. Fine-tune the obtained quant... See full list on developer. nvidia .com In this example, we show you how to quantize and fine-tune a QAT model with the TensorFlow 2 toolkit and how to deploy that quantized model in TensorRT. For more information, see the full example_resnet50v1.ipynbJupyter notebook. See full list on developer. nvidia .com In this section, we report accuracy and latency performance numbers for various models in the ResNet and EfficientNet families: 1. ResNet-50v1 2. ResNet-50v2 3. ResNet-101v1 4. ResNet-101v2 5. EfficientNet-B0 6. EfficientNet-B3 All results were obtained on the NVIDIA A100 GPU with batch size 1 using TensorRT 8.4 (EA for ResNet and GA for EfficientN... See full list on developer. nvidia .com In this post, we introduced the NVIDIA QAT Toolkit for TensorFlow 2. We discussed the advantages of using the toolkit in the context of TensorRT inference acceleration. We then demonstrated how to use the toolkit with ResNet50 and perform accuracy and latency evaluations on ResNet and EfficientNet datasets. Experimental results show that the accura... See full list on developer. nvidia .com [ Optimize ] Model Optimizer provides Python APIs for users to easily compose the above model optimization techniques and export an optimized quantized checkpoint. Model Optimizer is also integrated with NVIDIA NeMo, Megatron-LM and Hugging Face Accelerate for training required inference optimization techniques. Sep 10, 2024 · After you build a model in NeMo using a wide array of options offered by the toolkit , NeMo export and deployment tools can be used to apply PTQ methods and serve the optimized model . The recent NeMo container release is a self-contained toolkit coming with all the required dependencies for applying PTQ and deploying quantized LLMs. A unified library of state-of-the-art model optimization techniques like quantization, pruning, distillation, speculative decoding, etc. It compresses deep learning models for downstream deployment frameworks like TensorRT-LLM or TensorRT to optimize inference speed. - Releases · NVIDIA /TensorRT- Model -Optimizer Aug 1, 2025 · Further quantizing to formats like FP4 unlocks substantial efficiency gains and performance, supported by a growing ecosystem of open-source techniques. NVIDIA TensorRT Model Optimizer post-training quantization (PTQ) framework offers a flexible and modular approach to applying these optimizations. What is Nvidia tensorrt model optimizer post-training quantization (PTQ)? Further quantizing to formats like FP4 unlocks substantial efficiency gains and performance, supported by a growing ecosystem of open-source techniques. NVIDIA TensorRT Model Optimizer post-training quantization (PTQ) framework offers a flexible and modular approach to applying these optimizations . What is Nvidia tensorrt model optimizer? The NVIDIA TensorRT Model Optimizer (referred to as Model Optimizer, or ModelOpt) is a library comprising state-of-the-art model optimization techniques including quantization, distillation, pruning, speculative decoding and sparsity to accelerate models . Which quantization formats are supported by model optimizer? Model Optimizer enables highly performant quantization formats including NVFP4, FP8, INT8, INT4 , etc and supports advanced algorithms such as SmoothQuant, AWQ, SVDQuant, and Double Quantization with easy-to-use Python APIs. Both Post-training quantization (PTQ) and Quantization-aware training (QAT) are supported. What is post-training quantization (PTQ) in Nvidia Nemo? Post-training quantization (PTQ) is implemented in NVIDIA NeMo using NVIDIA TensorRT Model Optimizer and NVIDIA TensorRT-LLM to reduce computational and memory requirements for serving trained large language models (LLMs). What is Nvidia modelopt? Seamlessly integrated within the NVIDIA AI software ecosystem, the quantized checkpoint generated from Model Optimizer is ready for deployment in downstream inference frameworks like TensorRT-LLM or TensorRT. ModelOpt is integrated with NVIDIA NeMo and Megatron-LM for training-in-the-loop optimization techniques. Does Nvidia tensorrt support PTQ & Qat? NVIDIA TensorRT supports post-training quantization (PTQ) and QAT techniques to convert floating-point DNN models to INT8 precision. In this post, we discuss these techniques, introduce the NVIDIA QAT toolkit for TensorFlow, and demonstrate an end-to-end workflow to design quantized networks optimal for TensorRT deployment. Mar 30, 2025 · We provide an open-source TensorFlow Quantization Toolkit to perform QAT in TensorFlow 2 Keras models following NVIDIA ’s QAT recipe. This leads to optimal model acceleration with TensorRT on NVIDIA GPUs and hardware accelerators."}
+{"idx": 2, "title": "TensorRT-Model-Optimizer/README.md at main · NVIDIA ... - GitHub", "date": "", "ddg_snippet": "[ Optimize ] Model Optimizer provides Python APIs for users to easily compose the above model optimization techniques and export an optimized quantized checkpoint. Model Optimizer is also integrated with NVIDIA NeMo, Megatron-LM and Hugging Face Accelerate for training required inference optimization techniques.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/NVIDIA/TensorRT-Model-Optimizer/blob/main/README.md", "content": "[ Optimize ] Model Optimizer provides Python APIs for users to easily compose the above model optimization techniques and export an optimized quantized checkpoint. Model Optimizer is also integrated with NVIDIA NeMo, Megatron-LM and Hugging Face Accelerate for training required inference optimization techniques."}
+{"idx": 3, "title": "Post-Training Quantization of LLMs with NVIDIA NeMo and ...", "date": "", "ddg_snippet": "Sep 10, 2024 · After you build a model in NeMo using a wide array of options offered by the toolkit , NeMo export and deployment tools can be used to apply PTQ methods and serve the optimized model . The recent NeMo container release is a self-contained toolkit coming with all the required dependencies for applying PTQ and deploying quantized LLMs.", "subpage_snippet": "", "source": "developer.nvidia.com", "link": "https://developer.nvidia.com/blog/post-training-quantization-of-llms-with-nvidia-nemo-and-nvidia-tensorrt-model-optimizer/", "content": "Sep 10, 2024 · After you build a model in NeMo using a wide array of options offered by the toolkit , NeMo export and deployment tools can be used to apply PTQ methods and serve the optimized model . The recent NeMo container release is a self-contained toolkit coming with all the required dependencies for applying PTQ and deploying quantized LLMs."}
+{"idx": 4, "title": "Releases: NVIDIA/TensorRT-Model-Optimizer - GitHub", "date": "", "ddg_snippet": "A unified library of state-of-the-art model optimization techniques like quantization, pruning, distillation, speculative decoding, etc. It compresses deep learning models for downstream deployment frameworks like TensorRT-LLM or TensorRT to optimize inference speed. - Releases · NVIDIA /TensorRT- Model -Optimizer", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/NVIDIA/TensorRT-Model-Optimizer/releases", "content": "A unified library of state-of-the-art model optimization techniques like quantization, pruning, distillation, speculative decoding, etc. It compresses deep learning models for downstream deployment frameworks like TensorRT-LLM or TensorRT to optimize inference speed. - Releases · NVIDIA /TensorRT- Model -Optimizer"}
+{"idx": 5, "title": "Optimizing LLMs for Performance and ... - NVIDIA Developer", "date": "", "ddg_snippet": "Aug 1, 2025 · Further quantizing to formats like FP4 unlocks substantial efficiency gains and performance, supported by a growing ecosystem of open-source techniques. NVIDIA TensorRT Model Optimizer post-training quantization (PTQ) framework offers a flexible and modular approach to applying these optimizations.", "subpage_snippet": "", "source": "developer.nvidia.com", "link": "https://developer.nvidia.com/blog/optimizing-llms-for-performance-and-accuracy-with-post-training-quantization/", "content": "Aug 1, 2025 · Further quantizing to formats like FP4 unlocks substantial efficiency gains and performance, supported by a growing ecosystem of open-source techniques. NVIDIA TensorRT Model Optimizer post-training quantization (PTQ) framework offers a flexible and modular approach to applying these optimizations."}
+{"idx": 6, "title": "Working with Quantized Types — NVIDIA TensorRT Documentation", "date": "", "ddg_snippet": "Mar 30, 2025 · We provide an open-source TensorFlow Quantization Toolkit to perform QAT in TensorFlow 2 Keras models following NVIDIA ’s QAT recipe. This leads to optimal model acceleration with TensorRT on NVIDIA GPUs and hardware accelerators.", "subpage_snippet": "", "source": "docs.nvidia.com", "link": "https://docs.nvidia.com/deeplearning/tensorrt/10.8.0/inference-library/work-quantized-types.html", "content": "Mar 30, 2025 · We provide an open-source TensorFlow Quantization Toolkit to perform QAT in TensorFlow 2 Keras models following NVIDIA ’s QAT recipe. This leads to optimal model acceleration with TensorRT on NVIDIA GPUs and hardware accelerators."}
+{"idx": 7, "title": "Nvidia Model Deployment Techniques | Restackio", "date": "", "ddg_snippet": "Quantization Techniques for NVIDIA Models . Deploying Models with NVIDIA Triton Inference Server. Optimizing Deep Learning Model Deployment on NVIDIA Jetson .", "subpage_snippet": "", "source": "d2wozrt205r2fu.cloudfront.net", "link": "https://d2wozrt205r2fu.cloudfront.net/p/model-deployment-answer-nvidia-model-deployment-cat-ai", "content": "Quantization Techniques for NVIDIA Models . Deploying Models with NVIDIA Triton Inference Server. Optimizing Deep Learning Model Deployment on NVIDIA Jetson ."}
+{"idx": 8, "title": "(PDF) Deploying DeepSeek AI on NVIDIA Jetson AGX Orin: A Free...", "date": "", "ddg_snippet": "DeepSeek AI, a free and open - source model licensed under the permissive MIT license, provides high-performance capabilities in natural language processing (NLP) and computer vision (CV), enabling real-time applications without reliance on cloud infrastructure. The NVIDIA Jetson AGX...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/388401833_Deploying_DeepSeek_AI_on_NVIDIA_Jetson_AGX_Orin_A_Free_Open-Source_MIT-_Licensed_Solution_for_High-Performance_Edge_AI_in_Natural_Language_Processing_and_Computer_Vision", "content": "DeepSeek AI, a free and open - source model licensed under the permissive MIT license, provides high-performance capabilities in natural language processing (NLP) and computer vision (CV), enabling real-time applications without reliance on cloud infrastructure. The NVIDIA Jetson AGX..."}
+{"idx": 9, "title": "Running LLMs with TensorRT-LLM on NVIDIA Jetson Orin... - Collabnix", "date": "", "ddg_snippet": "TensorRT-LLM is an open - source library built on NVIDIA ’s TensorRT framework specifically designed to optimize the inference performance of large language models . It provides a streamlined path to deploy state-of-the-art LLMs on NVIDIA GPUs with maximum efficiency.", "subpage_snippet": "", "source": "collabnix.com", "link": "https://collabnix.com/running-llms-with-tensorrt-llm-on-nvidia-jetson-orin-nano-super/", "content": "TensorRT-LLM is an open - source library built on NVIDIA ’s TensorRT framework specifically designed to optimize the inference performance of large language models . It provides a streamlined path to deploy state-of-the-art LLMs on NVIDIA GPUs with maximum efficiency."}
diff --git a/data/sampled_jsons/openreview.net_QvqnPVGWAN_Blink_of_an_eye_github_code.jsonl b/data/sampled_jsons/openreview.net_QvqnPVGWAN_Blink_of_an_eye_github_code.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..8db5b63d38d69c8d0df2c09a1c2d8e22b0516002
--- /dev/null
+++ b/data/sampled_jsons/openreview.net_QvqnPVGWAN_Blink_of_an_eye_github_code.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Blink of an eye: a simple theory for feature localization in...", "date": "", "ddg_snippet": "May 1, 2025 · Large language models can exhibit unexpected behavior in the blink of an eye . In a recent computer use demo, a language model switched from coding to Googling pictures of Yellowstone, and these...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=QvqnPVGWAN", "content": "May 1, 2025 · Large language models can exhibit unexpected behavior in the blink of an eye . In a recent computer use demo, a language model switched from coding to Googling pictures of Yellowstone, and these..."}
+{"idx": 1, "title": "OpenReview - GitHub", "date": "", "ddg_snippet": "OpenReview has 11 repositories available. Follow their code on GitHub .", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/openreview", "content": "OpenReview has 11 repositories available. Follow their code on GitHub ."}
+{"idx": 2, "title": "The most recent documentation of OpenReview - GitHub", "date": "", "ddg_snippet": "The most recent documentation of OpenReview . Contribute to openreview / openreview development by creating an account on GitHub .", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/openreview/openreview", "content": "The most recent documentation of OpenReview . Contribute to openreview / openreview development by creating an account on GitHub ."}
+{"idx": 3, "title": "GitHub - openreview/openreview-py: Official Python client ... GitHub - ErikBird/OpenReviewCrawler: A Crawler for Open Review. Venues | OpenReview In the Blink of an Eye: Event-based Emotion Recognition", "date": "", "ddg_snippet": "Python 3.6 or newer is required to use openreview -py. Python 2.7 is no longer supported. See full list on github .com There are two ways to install the OpenReview python library. Using pip: See full list on github .com The openreview -py library can be used to easily access and modify any data stored in the OpenReview system. For example, to get all the papers submitted to ICLR 2019 and print their titles: For more information, see the official reference. You can also check the OpenReview docs for examples and How-To Guides See full list on github .com Running the openreview -py test suite requires some initial setup. First, the OpenReview API V1, OpenReview API V2 and OpenReview Web frontend must be cloned and configured to run on ports 3000, 3001 and 3030 respectively. For more information on how to install and configure those services see the README for each project: • OpenReview API V1 • OpenReview API V2 • OpenReview Web Next, pytest along with pytest-selenium and pytest-cov have to be installed. These packages can be installed with pip: Finally, you must download the proper Firefox Selenium driver for your OS from GitHub , and place the geckodriver executable in the directory openreview -py/tests/drivers. When you are done your folder structure should look like this: See full list on github .com Once the test setup above is complete you should be ready to run the test suite. To do so, start both OpenReview API versions running: Inside the OpenReview API V1 directory Inside the OpenReview API V2 directory Inside the OpenReview Web directory Once all three services are running, start the tests: To run a single set of tests from a file, you can include the file name as an argument. For example: See full list on github .com A Crawler for Open Review . . Contribute to ErikBird/OpenReviewCrawler development by creating an account on GitHub . 2 days ago · Promoting openness in scientific communication and the peer-review process We introduce a wearable single- eye emotion recognition device and a real-time approach to recognizing emotions from partial observations of an emotion that is robust to changes in lighting conditions. At the heart of our method is a bio-inspired event-based camera setup and a newly designed lightweight Spiking Eye Emotion Network (SEEN).", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/openreview/openreview-py", "content": "Python 3.6 or newer is required to use openreview -py. Python 2.7 is no longer supported. See full list on github .com There are two ways to install the OpenReview python library. Using pip: See full list on github .com The openreview -py library can be used to easily access and modify any data stored in the OpenReview system. For example, to get all the papers submitted to ICLR 2019 and print their titles: For more information, see the official reference. You can also check the OpenReview docs for examples and How-To Guides See full list on github .com Running the openreview -py test suite requires some initial setup. First, the OpenReview API V1, OpenReview API V2 and OpenReview Web frontend must be cloned and configured to run on ports 3000, 3001 and 3030 respectively. For more information on how to install and configure those services see the README for each project: • OpenReview API V1 • OpenReview API V2 • OpenReview Web Next, pytest along with pytest-selenium and pytest-cov have to be installed. These packages can be installed with pip: Finally, you must download the proper Firefox Selenium driver for your OS from GitHub , and place the geckodriver executable in the directory openreview -py/tests/drivers. When you are done your folder structure should look like this: See full list on github .com Once the test setup above is complete you should be ready to run the test suite. To do so, start both OpenReview API versions running: Inside the OpenReview API V1 directory Inside the OpenReview API V2 directory Inside the OpenReview Web directory Once all three services are running, start the tests: To run a single set of tests from a file, you can include the file name as an argument. For example: See full list on github .com A Crawler for Open Review . . Contribute to ErikBird/OpenReviewCrawler development by creating an account on GitHub . 2 days ago · Promoting openness in scientific communication and the peer-review process We introduce a wearable single- eye emotion recognition device and a real-time approach to recognizing emotions from partial observations of an emotion that is robust to changes in lighting conditions. At the heart of our method is a bio-inspired event-based camera setup and a newly designed lightweight Spiking Eye Emotion Network (SEEN)."}
+{"idx": 4, "title": "GitHub - ErikBird/OpenReviewCrawler: A Crawler for Open Review.", "date": "", "ddg_snippet": "A Crawler for Open Review . . Contribute to ErikBird/OpenReviewCrawler development by creating an account on GitHub .", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/ErikBird/OpenReviewCrawler", "content": "A Crawler for Open Review . . Contribute to ErikBird/OpenReviewCrawler development by creating an account on GitHub ."}
+{"idx": 5, "title": "In the Blink of an Eye: Event-based Emotion Recognition", "date": "", "ddg_snippet": "We introduce a wearable single- eye emotion recognition device and a real-time approach to recognizing emotions from partial observations of an emotion that is robust to changes in lighting conditions. At the heart of our method is a bio-inspired event-based camera setup and a newly designed lightweight Spiking Eye Emotion Network (SEEN).", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=k3t3yJh92V", "content": "We introduce a wearable single- eye emotion recognition device and a real-time approach to recognizing emotions from partial observations of an emotion that is robust to changes in lighting conditions. At the heart of our method is a bio-inspired event-based camera setup and a newly designed lightweight Spiking Eye Emotion Network (SEEN)."}
+{"idx": 6, "title": "In the Blink of an Eye - Walter Murch's Editing Theory Tested - YouTube", "date": "", "ddg_snippet": "Murch’s theory of assigning the human blink as emotional punctuation is a great editing technique to keep in the back of your mind as your deciding where to ...", "subpage_snippet": "", "source": "www.youtube.com", "link": "https://www.youtube.com/watch?v=0_rHsWleVmw", "content": "Murch’s theory of assigning the human blink as emotional punctuation is a great editing technique to keep in the back of your mind as your deciding where to ..."}
+{"idx": 7, "title": "About | OpenReview", "date": "", "ddg_snippet": "The source code is available on GitHub . OpenReview . net is built over an earlier version described in the paper Open Scholarship and Peer Review: a Time for Experimentation published in the ICML 2013 Peer Review Workshop.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/about", "content": "The source code is available on GitHub . OpenReview . net is built over an earlier version described in the paper Open Scholarship and Peer Review: a Time for Experimentation published in the ICML 2013 Peer Review Workshop."}
+{"idx": 8, "title": "Microsoft MakeCode for micro:bit", "date": "", "ddg_snippet": "Make code from official website of micro:bit.", "subpage_snippet": "", "source": "makecode.microbit.org", "link": "https://makecode.microbit.org/", "content": "Make code from official website of micro:bit."}
+{"idx": 9, "title": "\"In the Blink of an Eye \" – Learn This English Expression! | Engwith", "date": "", "ddg_snippet": "What Does \"In the Blink of an Eye \" Mean? There are a few other expressions in English that mean almost the same thing as \"in the blink of an eye .\" They all describe something happening super quickly, almost without you realizing i...", "subpage_snippet": "", "source": "www.engwith.com", "link": "https://www.engwith.com/2025/03/in-blink-of-eye-learn-this-english.html", "content": "What Does \"In the Blink of an Eye \" Mean? There are a few other expressions in English that mean almost the same thing as \"in the blink of an eye .\" They all describe something happening super quickly, almost without you realizing i..."}
diff --git a/data/sampled_jsons/optimal_transport_class_prior_estimation_implicit_feedback_recommendation.jsonl b/data/sampled_jsons/optimal_transport_class_prior_estimation_implicit_feedback_recommendation.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..d7cd4e145dd9a7ee8cb680046fe402d8e554deed
--- /dev/null
+++ b/data/sampled_jsons/optimal_transport_class_prior_estimation_implicit_feedback_recommendation.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "ICML Poster Unbiased Recommender Learning from Implicit ...", "date": "", "ddg_snippet": "Implicit feedback recommendation is challenged by the missing negative feedback essential for effective model training. Existing methods often resort to negative sampling, a technique that assumes unlabeled interactions as negative samples.", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/poster/46694", "content": "Implicit feedback recommendation is challenged by the missing negative feedback essential for effective model training. Existing methods often resort to negative sampling, a technique that assumes unlabeled interactions as negative samples."}
+{"idx": 1, "title": "Unbiased Recommender Learning from Implicit Feedback via Weakly...", "date": "", "ddg_snippet": "Implicit feedback recommendation is challenged by the missing negative feedback essential for effective model training.• We introduce PPT, an optimal transport -based methodol-ogy specifically designed for class prior estimation , a key factor in ensuring the unbiasedness of PURL.", "subpage_snippet": "", "source": "zhouchenlin.github.io", "link": "https://zhouchenlin.github.io/Publications/2025-ICML-Unbiased.pdf", "content": "Implicit feedback recommendation is challenged by the missing negative feedback essential for effective model training.• We introduce PPT, an optimal transport -based methodol-ogy specifically designed for class prior estimation , a key factor in ensuring the unbiasedness of PURL."}
+{"idx": 2, "title": "Unbiased Recommender Learning from Implicit Feedback via Weakly...", "date": "", "ddg_snippet": "Implicit feedback recommendation is challenged by the missing negative feedback essential for effective model training. Existing methods often resort to negative sampling, a technique that assumes unlabeled interactions as negative samples.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=0E5rZOGA13&referrer=[the+profile+of+Licheng+Pan](/profile?id=~Licheng_Pan1)", "content": "Implicit feedback recommendation is challenged by the missing negative feedback essential for effective model training. Existing methods often resort to negative sampling, a technique that assumes unlabeled interactions as negative samples."}
+{"idx": 3, "title": "(PDF) How optimal transport can tackle gender biases in multi- class ...", "date": "", "ddg_snippet": "TL;DR: In this article , a novel optimal transport strategy is proposed to mitigate undesirable algorithmic biases in multi- class neural-network classification, which is model agnostic and can be used on any multilayer classification neural network model.", "subpage_snippet": "", "source": "scispace.com", "link": "https://scispace.com/papers/how-optimal-transport-can-tackle-gender-biases-in-multi-glrunw7s", "content": "TL;DR: In this article , a novel optimal transport strategy is proposed to mitigate undesirable algorithmic biases in multi- class neural-network classification, which is model agnostic and can be used on any multilayer classification neural network model."}
+{"idx": 4, "title": "Partial Relaxed Optimal Transport for Denoised Recommendation", "date": "", "ddg_snippet": "In this work, we ground on Optimal Transport (OT) to globally match a user embedding space and an item embedding space, allowing both non-deep and deep RS models to discriminate intrinsic and noisy interactions without supervision.", "subpage_snippet": "", "source": "deep.ai", "link": "https://deep.ai/publication/partial-relaxed-optimal-transport-for-denoised-recommendation", "content": "In this work, we ground on Optimal Transport (OT) to globally match a user embedding space and an item embedding space, allowing both non-deep and deep RS models to discriminate intrinsic and noisy interactions without supervision."}
+{"idx": 5, "title": "Partial Optimal Transport", "date": "", "ddg_snippet": "Classical optimal transport problem seeks a transportation map that preserves the total mass between two probability distributions, requiring their masses to be equal.Plessis, M. C., G. Niu, and M. Sugiyama (2017). Class - prior estimation for learning from positive and unlabeled data.", "subpage_snippet": "", "source": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper/2020/file/1e6e25d952a0d639b676ee20d0519ee2-Paper.pdf", "content": "Classical optimal transport problem seeks a transportation map that preserves the total mass between two probability distributions, requiring their masses to be equal.Plessis, M. C., G. Niu, and M. Sugiyama (2017). Class - prior estimation for learning from positive and unlabeled data."}
+{"idx": 6, "title": "PMD: An Optimal Transportation -Based User Distance for...", "date": "", "ddg_snippet": "As the Netflix Prize competition has demonstrated, matrix factorization models are superior to classic nearest neighbor techniques for producing product recommendations , allowing the incorporation of additional information such as implicit feedback , temporal effects, and confidence levels.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/340573545_PMD_An_Optimal_Transportation-Based_User_Distance_for_Recommender_Systems", "content": "As the Netflix Prize competition has demonstrated, matrix factorization models are superior to classic nearest neighbor techniques for producing product recommendations , allowing the incorporation of additional information such as implicit feedback , temporal effects, and confidence levels."}
+{"idx": 7, "title": "Review for NeurIPS paper: Partial Optimal Transport with applications...", "date": "", "ddg_snippet": "Assuming that the class prior can be reliably estimated in the biased case is not realistic.Reproducibility: Yes. Additional Feedback : 1) Important references on class proportion estimation are missing [1,2]. 2) How is the PU classifier obtained from the optimal transport ?", "subpage_snippet": "", "source": "proceedings.nips.cc", "link": "https://proceedings.nips.cc/paper_files/paper/2020/file/1e6e25d952a0d639b676ee20d0519ee2-Review.html", "content": "Assuming that the class prior can be reliably estimated in the biased case is not realistic.Reproducibility: Yes. Additional Feedback : 1) Important references on class proportion estimation are missing [1,2]. 2) How is the PU classifier obtained from the optimal transport ?"}
+{"idx": 8, "title": "Sayer: Using Implicit Feedback to Optimize System Policies", "date": "", "ddg_snippet": "Implicit : Unbiased estimator for implicit feedback . Our key insight is that IPS can be interpreted as matching data-points in the trace according to an event E under which we (1) know the cost feedback and (2) can compute the probability that E occurs, P(E), in order to reweight the cost.", "subpage_snippet": "", "source": "sidsen.azurewebsites.net", "link": "https://sidsen.azurewebsites.net/papers/sayer-socc21.pdf", "content": "Implicit : Unbiased estimator for implicit feedback . Our key insight is that IPS can be interpreted as matching data-points in the trace according to an event E under which we (1) know the cost feedback and (2) can compute the probability that E occurs, P(E), in order to reweight the cost."}
+{"idx": 9, "title": "[PDF] Class - prior estimation for learning from... | Semantic Scholar", "date": "", "ddg_snippet": "We consider the problem of estimating the class prior in an unlabeled dataset.", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/Class-prior-estimation-for-learning-from-positive-Plessis-Niu/c5deab518eabc3698e0c932ac7188e8b8a2f6cd3", "content": "We consider the problem of estimating the class prior in an unlabeled dataset."}
diff --git a/data/sampled_jsons/optimal_transport_machine_learning_survey_applications.jsonl b/data/sampled_jsons/optimal_transport_machine_learning_survey_applications.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..284b2c3ba14aa12fa98b1470b6a14e7456f28e5f
--- /dev/null
+++ b/data/sampled_jsons/optimal_transport_machine_learning_survey_applications.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "A Survey on Optimal Transport for Machine Learning: Theory and Applications", "date": "", "ddg_snippet": "Optimal Transport (OT) theory has seen an increasing amount of attention from the computer science community due to its potency and relevance in modeling and machine learning . It introduces means that serve as powerful ways to compare probability distributions with each other, as well as producing optimal mappings to minimize cost functions. In this survey , we present a brief introduction and ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2106.01963", "content": "Optimal Transport (OT) theory has seen an increasing amount of attention from the computer science community due to its potency and relevance in modeling and machine learning . It introduces means that serve as powerful ways to compare probability distributions with each other, as well as producing optimal mappings to minimize cost functions. In this survey , we present a brief introduction and ..."}
+{"idx": 1, "title": "Recent Advances in Optimal Transport for Machine Learning", "date": "", "ddg_snippet": "Recently, Optimal Transport has been proposed as a probabilistic framework in Machine Learning for comparing and manipulating probability distributions. This is rooted in its rich history and theory, and has offered new solutions to different problems in machine learning , such as generative modeling and transfer learning . In this survey we explore contributions of Optimal Transport for Machine ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2306.16156", "content": "Recently, Optimal Transport has been proposed as a probabilistic framework in Machine Learning for comparing and manipulating probability distributions. This is rooted in its rich history and theory, and has offered new solutions to different problems in machine learning , such as generative modeling and transfer learning . In this survey we explore contributions of Optimal Transport for Machine ..."}
+{"idx": 2, "title": "A Survey on Optimal Transport for Machine Learning: Theory and Applications", "date": "", "ddg_snippet": "Optimal Transport (OT) theory has seen increasing attention from the computer science community due to its potency and relevance in modeling and machine learning (ML). OT provides powerful tools for comparing probability distributions and producing optimal mappings that minimize cost functions. Consequently, OT has been widely implemented in computer vision tasks such as image retrieval, image ...", "subpage_snippet": "", "source": "ieeexplore.ieee.org", "link": "https://ieeexplore.ieee.org/document/10877795", "content": "Optimal Transport (OT) theory has seen increasing attention from the computer science community due to its potency and relevance in modeling and machine learning (ML). OT provides powerful tools for comparing probability distributions and producing optimal mappings that minimize cost functions. Consequently, OT has been widely implemented in computer vision tasks such as image retrieval, image ..."}
+{"idx": 3, "title": "A Survey on Optimal Transport for Machine Learning: Theory and Applications", "date": "", "ddg_snippet": "Optimal Transport (OT) theory has seen an increasing amount of attention from the computer science community due to its potency and relevance in modeling and machine learning . It introduces means that serve as powerful ways to compare probability distributions with each other, as well as producing optimal mappings to minimize cost functions.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=f7BEg7gW0U", "content": "Optimal Transport (OT) theory has seen an increasing amount of attention from the computer science community due to its potency and relevance in modeling and machine learning . It introduces means that serve as powerful ways to compare probability distributions with each other, as well as producing optimal mappings to minimize cost functions."}
+{"idx": 4, "title": "A Survey on Algorithmic Developments in Optimal Transport Problem with ...", "date": "", "ddg_snippet": "Optimal Transport (OT) has established itself as a robust framework for quantifying differences between distributions, with applications that span fields such as machine learning , data science, and computer vision. This paper offers a detailed examination of the OT problem, beginning with its theoretical foundations, including the classical formulations of Monge and Kantorovich and their ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2501.06247", "content": "Optimal Transport (OT) has established itself as a robust framework for quantifying differences between distributions, with applications that span fields such as machine learning , data science, and computer vision. This paper offers a detailed examination of the OT problem, beginning with its theoretical foundations, including the classical formulations of Monge and Kantorovich and their ..."}
+{"idx": 5, "title": "Scalable Optimal Transport Methods in Machine Learning: A Contemporary ...", "date": "", "ddg_snippet": "This paper is about where and how optimal transport is used in machine learning with a focus on the question of scalable optimal transport . We provide a comprehensive survey of optimal transport while ensuring an accessible presentation as permitted by the nature of the topic and the context.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2305.05080", "content": "This paper is about where and how optimal transport is used in machine learning with a focus on the question of scalable optimal transport . We provide a comprehensive survey of optimal transport while ensuring an accessible presentation as permitted by the nature of the topic and the context."}
+{"idx": 6, "title": "[2505.06589] Optimal Transport for Machine Learners - arXiv.org", "date": "", "ddg_snippet": "Optimal Transport is a foundational mathematical theory that connects optimization, partial differential equations, and probability. It offers a powerful framework for comparing probability distributions and has recently become an important tool in machine learning , especially for designing and evaluating generative models. These course notes cover the fundamental mathematical aspects of OT ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2505.06589", "content": "Optimal Transport is a foundational mathematical theory that connects optimization, partial differential equations, and probability. It offers a powerful framework for comparing probability distributions and has recently become an important tool in machine learning , especially for designing and evaluating generative models. These course notes cover the fundamental mathematical aspects of OT ..."}
+{"idx": 7, "title": "PDF Applications of optimal transport to machine learning and signal processing", "date": "", "ddg_snippet": "• Optimal transport is a perfect tool to compare empirical probability distributions •In the context of machine learning /signal processing, one often has to deal with collections of samples that can be interpreted as probability distributions Motivations • Optimal transport is a perfect tool to compare empirical probability distributions", "subpage_snippet": "", "source": "mathematical-coffees.github.io", "link": "https://mathematical-coffees.github.io/slides/mc01-courty.pdf", "content": "• Optimal transport is a perfect tool to compare empirical probability distributions •In the context of machine learning /signal processing, one often has to deal with collections of samples that can be interpreted as probability distributions Motivations • Optimal transport is a perfect tool to compare empirical probability distributions"}
+{"idx": 8, "title": "Recent Advances in Optimal Transport for Machine Learning", "date": "", "ddg_snippet": "Eduardo Fernandes Montesuma,Fred Maurice Ngol`e Mboula,and Antoine Souloumiac, Abstract—Recently, Optimal Transport has been proposed as a probabilistic framework in Machine Learning for comparing and manipulating probability distributions. This is rooted in its rich history and theory, and has offered new solutions to different problems in machine learning , such as generative modeling and ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2306.16156", "content": "Eduardo Fernandes Montesuma,Fred Maurice Ngol`e Mboula,and Antoine Souloumiac, Abstract—Recently, Optimal Transport has been proposed as a probabilistic framework in Machine Learning for comparing and manipulating probability distributions. This is rooted in its rich history and theory, and has offered new solutions to different problems in machine learning , such as generative modeling and ..."}
+{"idx": 9, "title": "Luis Caicedo Torres Luiz Manella Pereira arXiv:2106.01963v1 [cs.LG] 3 ...", "date": "", "ddg_snippet": "as domain adaptation, natural language processing, and variational infer-ence. In this survey , we propose to convey the emerging promises of the optimal transport methods across va ious fields, as well as future directions of study for OT in machine learning . We will begin by looking t the history of optimal transport and introducing the founder", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2106.01963", "content": "as domain adaptation, natural language processing, and variational infer-ence. In this survey , we propose to convey the emerging promises of the optimal transport methods across va ious fields, as well as future directions of study for OT in machine learning . We will begin by looking t the history of optimal transport and introducing the founder"}
diff --git a/data/sampled_jsons/optimal_transport_recommendation_system_class_prior_before_2025.jsonl b/data/sampled_jsons/optimal_transport_recommendation_system_class_prior_before_2025.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..74daff14e8e322d524b8aaf7356bb90e49816c02
--- /dev/null
+++ b/data/sampled_jsons/optimal_transport_recommendation_system_class_prior_before_2025.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "OT-SDMG: 2025 Workshop on Optimal Transport for Structured ...", "date": "", "ddg_snippet": "Apr 29, 2025 · About Structured data including graphs, text, and time series is foundational to web technologies and applications, plays a critical role in fields like network science, recommendation systems , natural language processing, and multimedia. Recently, Optimal Transport (OT) has gained considerable traction in machine learning as a powerful framework for measuring the similarity between complex ...", "subpage_snippet": "", "source": "ot-sdmg.github.io", "link": "https://ot-sdmg.github.io/", "content": "Apr 29, 2025 · About Structured data including graphs, text, and time series is foundational to web technologies and applications, plays a critical role in fields like network science, recommendation systems , natural language processing, and multimedia. Recently, Optimal Transport (OT) has gained considerable traction in machine learning as a powerful framework for measuring the similarity between complex ..."}
+{"idx": 1, "title": "Optimal Transport for Machine Learners - arXiv.org", "date": "", "ddg_snippet": "June 8, 2025 Abstract Optimal Transport is a foundational mathematical theory that connects optimization, partial difer-ential equations, and probability. It ofers a powerful framework for comparing probability distributions and has recently become an important tool in machine learning, especially for designing and evaluating generative models.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2505.06589", "content": "June 8, 2025 Abstract Optimal Transport is a foundational mathematical theory that connects optimization, partial difer-ential equations, and probability. It ofers a powerful framework for comparing probability distributions and has recently become an important tool in machine learning, especially for designing and evaluating generative models."}
+{"idx": 2, "title": "Unsupervised domain adaptation via optimal prototypes transport", "date": "", "ddg_snippet": "May 5, 2025 · Specifically, we first design a new classification rule by computing the optimal transport plan between test samples and the prototypes of training set, and predicting the label for each test sample with the maximum class -probability. The classification rule is derived under the Bayesian perspective.", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S0957417425003665", "content": "May 5, 2025 · Specifically, we first design a new classification rule by computing the optimal transport plan between test samples and the prototypes of training set, and predicting the label for each test sample with the maximum class -probability. The classification rule is derived under the Bayesian perspective."}
+{"idx": 3, "title": "Attention-Guided Optimal Transport for Unsupervised Domain ...", "date": "", "ddg_snippet": "Nov 13, 2023 · Unsupervised domain adaptation(UDA) methods based on optimal transport have been successfully used to improve cross-domain classification performance. Optimal transport aligns the distribution of source domain and target domain by minimizing the transport cost. However, the existing works based on optimal transport ignore the class -structure prior information of domains and do not adequately ...", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/article/10.1007/s11063-023-11432-9", "content": "Nov 13, 2023 · Unsupervised domain adaptation(UDA) methods based on optimal transport have been successfully used to improve cross-domain classification performance. Optimal transport aligns the distribution of source domain and target domain by minimizing the transport cost. However, the existing works based on optimal transport ignore the class -structure prior information of domains and do not adequately ..."}
+{"idx": 4, "title": "GORACS: Group-level Optimal Transport-guided Coreset ...", "date": "", "ddg_snippet": "To overcome GORACS, these challenges, we propose which is a novel Group- Optimal level tRAnsport -guided Coreset Selection framework for LLM-based recommender systems .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2506.04015", "content": "To overcome GORACS, these challenges, we propose which is a novel Group- Optimal level tRAnsport -guided Coreset Selection framework for LLM-based recommender systems ."}
+{"idx": 5, "title": "ReCon: Reducing Congestion in Job Recommendation using ...", "date": "", "ddg_snippet": "Sep 14, 2023 · We propose a novel approach to job recommendation called ReCon, accounting for the congestion problem. Our approach is to use an optimal transport component to ensure a more equal spread of vacancies over job seekers, combined with a job recommendation model in a multi-objective optimization problem.", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.1145/3604915.3608817", "content": "Sep 14, 2023 · We propose a novel approach to job recommendation called ReCon, accounting for the congestion problem. Our approach is to use an optimal transport component to ensure a more equal spread of vacancies over job seekers, combined with a job recommendation model in a multi-objective optimization problem."}
+{"idx": 6, "title": "(PDF) M/M/1 Non-preemptive Priority Queuing System with", "date": "", "ddg_snippet": "Customers are classified into two classes , namely class I (non-preemptive priority customers) and class II, with exponentially distributed service ...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/373319694_MM1_Non-preemptive_Priority_Queuing_System_with_Multiple_Vacations_and_Vacation_Interruptions", "content": "Customers are classified into two classes , namely class I (non-preemptive priority customers) and class II, with exponentially distributed service ..."}
+{"idx": 7, "title": "US12243075B1 - System and method for optimizing online", "date": "", "ddg_snippet": "the systems , computer-readable media, programs, and methods of the present invention may further optimize online content by recommending increasing ...", "subpage_snippet": "", "source": "patents.google.com", "link": "https://patents.google.com/patent/US12243075B1/en", "content": "the systems , computer-readable media, programs, and methods of the present invention may further optimize online content by recommending increasing ..."}
+{"idx": 8, "title": "US11720572B2 - Method and system for content recommendation -", "date": "", "ddg_snippet": "More specifically, this application relates to a method and system that recommends content to users based on domain knowledge.", "subpage_snippet": "", "source": "patents.google.com", "link": "https://patents.google.com/patent/US11720572B2/en", "content": "More specifically, this application relates to a method and system that recommends content to users based on domain knowledge."}
+{"idx": 9, "title": "7 top TMS solutions for 2025 | FreightWaves Ratings", "date": "", "ddg_snippet": "As a result, Transportation Management Systems (TMS) have become essential tools for businesses seeking to optimize their logistics operations ...", "subpage_snippet": "", "source": "ratings.freightwaves.com", "link": "https://ratings.freightwaves.com/top-tms-solutions/", "content": "As a result, Transportation Management Systems (TMS) have become essential tools for businesses seeking to optimize their logistics operations ..."}
diff --git a/data/sampled_jsons/origin_of_MovieLens_100K_dataset.jsonl b/data/sampled_jsons/origin_of_MovieLens_100K_dataset.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..e04f8960167ff8ee6d36dbd215611c2b1222545f
--- /dev/null
+++ b/data/sampled_jsons/origin_of_MovieLens_100K_dataset.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "The MovieLens Datasets : History and Context", "date": "", "ddg_snippet": "These datasets are a product of member activity in the MovieLens movie recommendation system, an active research platform that has hosted many experiments since its launch in 1997. This paper documents the history of MovieLens and the MovieLens datasets .", "subpage_snippet": "", "source": "files.grouplens.org", "link": "https://files.grouplens.org/papers/harper-tiis2015.pdf", "content": "These datasets are a product of member activity in the MovieLens movie recommendation system, an active research platform that has hosted many experiments since its launch in 1997. This paper documents the history of MovieLens and the MovieLens datasets ."}
+{"idx": 1, "title": "MovieLens 100 K Dataset", "date": "", "ddg_snippet": "About Dataset . Summary & usage license. MovieLens data sets were collected by the GroupLens Research Project at the University of Minnesota. This data set consists of: * 100,000 ratings (1-5) from 943 users on 1682 movies. * Each user has rated at least 20 movies.", "subpage_snippet": "", "source": "www.kaggle.com", "link": "https://www.kaggle.com/datasets/prajitdatta/movielens-100k-dataset", "content": "About Dataset . Summary & usage license. MovieLens data sets were collected by the GroupLens Research Project at the University of Minnesota. This data set consists of: * 100,000 ratings (1-5) from 943 users on 1682 movies. * Each user has rated at least 20 movies."}
+{"idx": 2, "title": "movielens .ipynb - Colab", "date": "", "ddg_snippet": "Getting the Data . The MovieLens dataset is hosted by the GroupLens website. Several versions are available. We will use the MovieLens 100 K dataset :cite: Herlocker.Konstan.Borchers.ea.1999 .", "subpage_snippet": "", "source": "colab.research.google.com", "link": "https://colab.research.google.com/github/d2l-ai/d2l-en-colab/blob/master/chapter_recommender-systems/movielens.ipynb", "content": "Getting the Data . The MovieLens dataset is hosted by the GroupLens website. Several versions are available. We will use the MovieLens 100 K dataset :cite: Herlocker.Konstan.Borchers.ea.1999 ."}
+{"idx": 3, "title": "21.2. The MovieLens Dataset — Dive into Deep Learning...", "date": "", "ddg_snippet": "We will use the MovieLens 100 K dataset (Herlocker et al., 1999) .We split the dataset into training and test sets . The following function provides two split modes including random and seq-aware .", "subpage_snippet": "", "source": "d2l.ai", "link": "https://d2l.ai/chapter_recommender-systems/movielens.html", "content": "We will use the MovieLens 100 K dataset (Herlocker et al., 1999) .We split the dataset into training and test sets . The following function provides two split modes including random and seq-aware ."}
+{"idx": 4, "title": "Recommender system: Movielens 100 K example | Ganghua Wang", "date": "", "ddg_snippet": "The dataset was collected through the MovieLens web site ( movielens .umn.edu) during the seven months from September 19th, 1997 through April 22nd, 1998.The 100 K MovieLens data consists of 1,000,000 anonymous ratings on a five-star scale from 1,000 users on 1,700 movies.", "subpage_snippet": "", "source": "gwang.umn.edu", "link": "https://gwang.umn.edu/stat/2021/03/25/Recommender-system-Movielens-100K-example.html", "content": "The dataset was collected through the MovieLens web site ( movielens .umn.edu) during the seven months from September 19th, 1997 through April 22nd, 1998.The 100 K MovieLens data consists of 1,000,000 anonymous ratings on a five-star scale from 1,000 users on 1,700 movies."}
+{"idx": 5, "title": "GitHub - Shreyas3108/ movielens -analytics-recomendation: Analytics...", "date": "", "ddg_snippet": "Analytics of Movielens dataset ( 100 k ) along with recomendation based on the user preference.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/Shreyas3108/movielens-analytics-recomendation", "content": "Analytics of Movielens dataset ( 100 k ) along with recomendation based on the user preference."}
+{"idx": 6, "title": "MovieLens Dataset : The Essential Benchmark for... | Shaped Blog", "date": "", "ddg_snippet": "Useful for data enrichment. MovieLens Versions: From Small to Large Scale. GroupLens provides several MovieLens dataset download options to suit different needs: MovieLens Latest Small (ml-latest-small): ~100,000 ratings.", "subpage_snippet": "", "source": "www.shaped.ai", "link": "https://www.shaped.ai/blog/movielens-dataset-the-essential-benchmark-for-recommender-systems", "content": "Useful for data enrichment. MovieLens Versions: From Small to Large Scale. GroupLens provides several MovieLens dataset download options to suit different needs: MovieLens Latest Small (ml-latest-small): ~100,000 ratings."}
+{"idx": 7, "title": "movielens | TensorFlow Datasets", "date": "", "ddg_snippet": "This dataset contains a set of movie ratings from the MovieLens website, a movie recommendation service. This dataset was collected and maintained by GroupLens, a research group at the University of Minnesota. There are 5 versions included: \"25m\", \"latest-small\", \" 100 k \", \"1m\", \"20m\".", "subpage_snippet": "", "source": "www.tensorflow.org", "link": "https://www.tensorflow.org/datasets/catalog/movielens", "content": "This dataset contains a set of movie ratings from the MovieLens website, a movie recommendation service. This dataset was collected and maintained by GroupLens, a research group at the University of Minnesota. There are 5 versions included: \"25m\", \"latest-small\", \" 100 k \", \"1m\", \"20m\"."}
+{"idx": 8, "title": "What is the MovieLens 100 K dataset and how can it be... - Glarity", "date": "", "ddg_snippet": "1. The MovieLens 100 K dataset is a benchmark dataset containing 100,000 ratings from 1,000 users on 1,700 movies, released in April 1998[1]. 2. It includes simple demographic information for users such as age, gender, occupation, and zip code[3].", "subpage_snippet": "", "source": "askai.glarity.app", "link": "https://askai.glarity.app/search/What-is-the-MovieLens-100K-dataset-and-how-can-it-be-accessed", "content": "1. The MovieLens 100 K dataset is a benchmark dataset containing 100,000 ratings from 1,000 users on 1,700 movies, released in April 1998[1]. 2. It includes simple demographic information for users such as age, gender, occupation, and zip code[3]."}
+{"idx": 9, "title": "Build a Recommendation Engine With Collaborative Filtering – Real...", "date": "", "ddg_snippet": "In particular, the MovieLens 100 k dataset is a stable benchmark dataset with 100,000 ratings given by 943 users for 1682 movies, with each user having rated at least 20 movies.", "subpage_snippet": "", "source": "realpython.com", "link": "https://realpython.com/build-recommendation-engine-collaborative-filtering/", "content": "In particular, the MovieLens 100 k dataset is a stable benchmark dataset with 100,000 ratings given by 943 users for 1682 movies, with each user having rated at least 20 movies."}
diff --git a/data/sampled_jsons/per-instance_privacy_analysis_Renyi_differential_privacy_2024.jsonl b/data/sampled_jsons/per-instance_privacy_analysis_Renyi_differential_privacy_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..52fc036e92ea483a17c7cfb4adaec0c99915175a
--- /dev/null
+++ b/data/sampled_jsons/per-instance_privacy_analysis_Renyi_differential_privacy_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Use of 'as per' vs 'per' - English Language & Usage Stack...", "date": "", "ddg_snippet": "I've read and have heard of both 'as per ' and ' per ' being used conversationally, both with the same connotation of either 'according to' or 'on authority of' Examples: \"Tell Ron to start molding ...", "subpage_snippet": "", "source": "english.stackexchange.com", "link": "https://english.stackexchange.com/questions/54864/use-of-as-per-vs-per", "content": "I've read and have heard of both 'as per ' and ' per ' being used conversationally, both with the same connotation of either 'according to' or 'on authority of' Examples: \"Tell Ron to start molding ..."}
+{"idx": 1, "title": "meaning - Difference between \"per\" and \"a\" - English Language &...", "date": "", "ddg_snippet": "What is the difference between the following two sentences? She goes to the forest three times per week. She goes to the forest three times a week.", "subpage_snippet": "", "source": "english.stackexchange.com", "link": "https://english.stackexchange.com/questions/62009/difference-between-per-and-a", "content": "What is the difference between the following two sentences? She goes to the forest three times per week. She goes to the forest three times a week."}
+{"idx": 2, "title": "\"By\" vs \"Per\". Which one should I use on expressions like \"P&L...", "date": "", "ddg_snippet": "The word \" per \" carries the implication (as in percent) that there is a division going on - so if someone says to me \"I'll tell you the number of widgets manufactured per employee\" I'm expecting one number - the total number of widgets manufactured divided by the number of employees.", "subpage_snippet": "", "source": "english.stackexchange.com", "link": "https://english.stackexchange.com/questions/22644/by-vs-per-which-one-should-i-use-on-expressions-like-pl-geography-or-va", "content": "The word \" per \" carries the implication (as in percent) that there is a division going on - so if someone says to me \"I'll tell you the number of widgets manufactured per employee\" I'm expecting one number - the total number of widgets manufactured divided by the number of employees."}
+{"idx": 3, "title": "Legg-Calve-Perthes disease - Symptoms and causes - Mayo Clinic", "date": "", "ddg_snippet": "Jun 22, 2024 · Legg-Calve-Perthes (LEG-kahl-VAY- PER -tuz) disease is a childhood condition that occurs when blood supply to the ball part (femoral head) of the hip joint is temporarily interrupted and the bone begins to die. This weakened bone gradually breaks apart and can lose its round shape.", "subpage_snippet": "", "source": "www.mayoclinic.org", "link": "https://www.mayoclinic.org/diseases-conditions/legg-calve-perthes-disease/symptoms-causes/syc-20374343", "content": "Jun 22, 2024 · Legg-Calve-Perthes (LEG-kahl-VAY- PER -tuz) disease is a childhood condition that occurs when blood supply to the ball part (femoral head) of the hip joint is temporarily interrupted and the bone begins to die. This weakened bone gradually breaks apart and can lose its round shape."}
+{"idx": 4, "title": "Perimenopause - Symptoms and causes - Mayo Clinic", "date": "", "ddg_snippet": "Aug 14, 2025 · Discover effective treatments and learn self-care strategies for hot flashes, night sweats, vaginal dryness and other perimenopause symptoms.", "subpage_snippet": "", "source": "www.mayoclinic.org", "link": "https://www.mayoclinic.org/diseases-conditions/perimenopause/symptoms-causes/syc-20354666", "content": "Aug 14, 2025 · Discover effective treatments and learn self-care strategies for hot flashes, night sweats, vaginal dryness and other perimenopause symptoms."}
+{"idx": 5, "title": "What is the difference between \"as per\" and \"according to\"?", "date": "", "ddg_snippet": "The particular construction as per my knowledge is unnatural - as per is normally only used in reference to some prior statement / school of thought. It more properly corresponds to in accordance with, and can't simply be used to replace the idiomatic according to my knowledge / information / understanding.", "subpage_snippet": "", "source": "english.stackexchange.com", "link": "https://english.stackexchange.com/questions/256/what-is-the-difference-between-as-per-and-according-to", "content": "The particular construction as per my knowledge is unnatural - as per is normally only used in reference to some prior statement / school of thought. It more properly corresponds to in accordance with, and can't simply be used to replace the idiomatic according to my knowledge / information / understanding."}
+{"idx": 6, "title": "Is there a definitive spelling for the shortened version of “as...", "date": "", "ddg_snippet": "Jun 27, 2012 · 3 There's no definitive spelling, but as per ushe is a common one with the benefit of being fairly unambiguous. Alternatives include as per use, but that could be confused with \"for each use\", and as per uje, but that looks a bit odd. The OED doesn't include either, but does note as per is also a shortened form.", "subpage_snippet": "", "source": "english.stackexchange.com", "link": "https://english.stackexchange.com/questions/72652/is-there-a-definitive-spelling-for-the-shortened-version-of-as-per-usual", "content": "Jun 27, 2012 · 3 There's no definitive spelling, but as per ushe is a common one with the benefit of being fairly unambiguous. Alternatives include as per use, but that could be confused with \"for each use\", and as per uje, but that looks a bit odd. The OED doesn't include either, but does note as per is also a shortened form."}
+{"idx": 7, "title": "Bipolar disorder - Symptoms and causes - Mayo Clinic", "date": "", "ddg_snippet": "Aug 14, 2024 · Overview Bipolar disorder, formerly called manic depression, is a mental health condition that causes extreme mood swings. These include emotional highs, also known as mania or hypomania, and lows, also known as depression. Hypomania is less extreme than mania.", "subpage_snippet": "", "source": "www.mayoclinic.org", "link": "https://www.mayoclinic.org/diseases-conditions/bipolar-disorder/symptoms-causes/syc-20355955", "content": "Aug 14, 2024 · Overview Bipolar disorder, formerly called manic depression, is a mental health condition that causes extreme mood swings. These include emotional highs, also known as mania or hypomania, and lows, also known as depression. Hypomania is less extreme than mania."}
+{"idx": 8, "title": "Calorie Calculator - Mayo Clinic", "date": "", "ddg_snippet": "If you're pregnant or breast-feeding, are a competitive athlete, or have a metabolic disease, such as diabetes, the calorie calculator may overestimate or underestimate your actual calorie needs.", "subpage_snippet": "", "source": "www.mayoclinic.org", "link": "https://www.mayoclinic.org/healthy-lifestyle/weight-loss/in-depth/calorie-calculator/itt-20402304", "content": "If you're pregnant or breast-feeding, are a competitive athlete, or have a metabolic disease, such as diabetes, the calorie calculator may overestimate or underestimate your actual calorie needs."}
+{"idx": 9, "title": "Mayo Clinic corrected QT interval (QTc) calculator - Medical ...", "date": "", "ddg_snippet": "Worried about QT interval prolongation? This online evidence based resource will help guide you how to measure the QT interval and calculate the QTc value with an easy to use calculator which takes into account the patients underlying rhythm, gender and age.", "subpage_snippet": "", "source": "www.mayoclinic.org", "link": "https://www.mayoclinic.org/medical-professionals/cardiovascular-diseases/calculators/corrected-qt-interval-qtc-calculator/itt-20487211", "content": "Worried about QT interval prolongation? This online evidence based resource will help guide you how to measure the QT interval and calculate the QTc value with an easy to use calculator which takes into account the patients underlying rhythm, gender and age."}
diff --git a/data/sampled_jsons/per-instance_privacy_loss_theoretical_advantage_over_aggregate_measures.jsonl b/data/sampled_jsons/per-instance_privacy_loss_theoretical_advantage_over_aggregate_measures.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..0a80ed685b2233c53e8d53cc52638f0dbfa356fa
--- /dev/null
+++ b/data/sampled_jsons/per-instance_privacy_loss_theoretical_advantage_over_aggregate_measures.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Methodology to Decrease Packet Loss in IEC 61850 Substation", "date": "", "ddg_snippet": "For instance , the measurement and status signal data transmitted from the merging unit can be 4 Mb/s as per the requirements in IEC 61850.", "subpage_snippet": "", "source": "cse.cigre.org", "link": "https://cse.cigre.org/cse-n026/methodology-to-decrease-packet-loss-in-iec-61850-substation-communication-over-wireless-5g-communication.html", "content": "For instance , the measurement and status signal data transmitted from the merging unit can be 4 Mb/s as per the requirements in IEC 61850."}
+{"idx": 1, "title": "Fraud Loss Measurement Framework (HTML) - GOV.UK", "date": "", "ddg_snippet": "In practice, Fraud Measurement provides accountable officials with insights and perspectives necessary to make informed decisions on their appetite ...", "subpage_snippet": "", "source": "www.gov.uk", "link": "https://www.gov.uk/government/publications/international-public-sector-fraud-forum-guidance/fraud-loss-measurement-framework-html", "content": "In practice, Fraud Measurement provides accountable officials with insights and perspectives necessary to make informed decisions on their appetite ..."}
+{"idx": 2, "title": "An Information-Theoretic Intersectional Data Valuation Theory", "date": "", "ddg_snippet": "This exposure generates a privacy externality: firms benefit economically from profiling, prediction, and personalization, while users face hidden ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2507.14742v1", "content": "This exposure generates a privacy externality: firms benefit economically from profiling, prediction, and personalization, while users face hidden ..."}
+{"idx": 3, "title": "Knowledge Distillation for Variational Quantum Convolutional", "date": "", "ddg_snippet": "Theoretical analysis shows that proposed framework benefits from quantum high-dimensional representation, offering advantages over classical ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2509.16699v1", "content": "Theoretical analysis shows that proposed framework benefits from quantum high-dimensional representation, offering advantages over classical ..."}
+{"idx": 4, "title": "Towards Mitigating Excessive Forgetting in LLM Unlearning via", "date": "", "ddg_snippet": "Specifically, EAGLE measures per -sample similarity in the embedding space and dynamically adjusts the forgetting effort by reweighting the loss for ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2508.20443v1", "content": "Specifically, EAGLE measures per -sample similarity in the embedding space and dynamically adjusts the forgetting effort by reweighting the loss for ..."}
+{"idx": 5, "title": "Satellite Federated Edge Learning: Architecture Design and", "date": "", "ddg_snippet": "This not only mitigates substantial communication overheads but also addresses potential privacy concerns associated with raw data sharing ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2404.01875v1", "content": "This not only mitigates substantial communication overheads but also addresses potential privacy concerns associated with raw data sharing ..."}
+{"idx": 6, "title": "Downloads", "date": "", "ddg_snippet": "A Theoretical Analysis on Feature Learning in Neural Networks: Emergence from Inputs and Advantage over Fixed Features", "subpage_snippet": "", "source": "iclr.cc", "link": "https://iclr.cc/Downloads/2022", "content": "A Theoretical Analysis on Feature Learning in Neural Networks: Emergence from Inputs and Advantage over Fixed Features"}
+{"idx": 7, "title": "Downloads", "date": "", "ddg_snippet": "A Graph Theoretic Framework of ... AttentionXML: Label Tree-based Attention-Aware Deep Model for High-Performance Extreme Multi-Label Text", "subpage_snippet": "", "source": "neurips.cc", "link": "https://neurips.cc/Downloads/2019", "content": "A Graph Theoretic Framework of ... AttentionXML: Label Tree-based Attention-Aware Deep Model for High-Performance Extreme Multi-Label Text"}
+{"idx": 8, "title": "SIGNAL CONVOLUTION, MIDPOINT OF RANGE, AND ALL THAT –", "date": "", "ddg_snippet": "... as obviously, if such event had occurred coincident with one of the hourly measurement schedules, it would have become a part of the 24 samples per ...", "subpage_snippet": "", "source": "wattsupwiththat.com", "link": "https://wattsupwiththat.com/2019/02/15/signal-convolution-midpoint-of-range-and-all-that/", "content": "... as obviously, if such event had occurred coincident with one of the hourly measurement schedules, it would have become a part of the 24 samples per ..."}
+{"idx": 9, "title": "RFC 8655: - The RFC Archive", "date": "", "ddg_snippet": "... aggregated) DetNet flows in some or all of the intermediate nodes along the path of the flow, 2) providing explicit routes for DetNet flows that do ...", "subpage_snippet": "", "source": "www.rfc-archive.org", "link": "https://www.rfc-archive.org/getrfc?rfc=8655", "content": "... aggregated) DetNet flows in some or all of the intermediate nodes along the path of the flow, 2) providing explicit routes for DetNet flows that do ..."}
diff --git a/data/sampled_jsons/per-instance_privacy_loss_vs_C-Proxy_machine_unlearning_metric.jsonl b/data/sampled_jsons/per-instance_privacy_loss_vs_C-Proxy_machine_unlearning_metric.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..70eb145515642b95c7a444d78757714d061fbb54
--- /dev/null
+++ b/data/sampled_jsons/per-instance_privacy_loss_vs_C-Proxy_machine_unlearning_metric.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Leveraging Per-Instance Privacy for Machine Unlearning", "date": "", "ddg_snippet": "We present a principled, per-instance approach to quantifying the difficulty of unlearning via fine-tuning. We begin by sharpening an analysis of noisy gradient descent for unlearning (Chien et al., 2024), obtaining a better utility- unlearning tradeoff by replacing worst-case privacy loss bounds with per-instance privacy losses (Thudi et al., 2024), each of which bounds the (Renyi) divergence ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2505.18786", "content": "We present a principled, per-instance approach to quantifying the difficulty of unlearning via fine-tuning. We begin by sharpening an analysis of noisy gradient descent for unlearning (Chien et al., 2024), obtaining a better utility- unlearning tradeoff by replacing worst-case privacy loss bounds with per-instance privacy losses (Thudi et al., 2024), each of which bounds the (Renyi) divergence ..."}
+{"idx": 1, "title": "Leveraging Per-Example Privacy for Machine Unlearning", "date": "", "ddg_snippet": "This work focuses on developing fine-grained theoretical insights to quantify unlearning difficulty at the level of individual data points for fine-tuning-based unlearning . Unlike other unlearning methods that lack theoretical guarantees for non-convex models, our approach builds on recent advances in differential privacy to provide per-instance guarantees using Rényi divergence. While our ...", "subpage_snippet": "", "source": "research.google", "link": "https://research.google/pubs/leveraging-per-example-privacy-for-machine-unlearning/", "content": "This work focuses on developing fine-grained theoretical insights to quantify unlearning difficulty at the level of individual data points for fine-tuning-based unlearning . Unlike other unlearning methods that lack theoretical guarantees for non-convex models, our approach builds on recent advances in differential privacy to provide per-instance guarantees using Rényi divergence. While our ..."}
+{"idx": 2, "title": "Machine Unlearning: Taxonomy, Metrics, Applications, Challenges, and ...", "date": "", "ddg_snippet": "Personal digital data is a critical asset, and governments worldwide have enforced laws and regulations to protect data privacy . Data users have been endowed with the \"right to be forgotten\" (RTBF) of their data. In the course of machine learning (ML), the forgotten right requires a model provider to delete user data and its subsequent impact on ML models upon user requests. Machine ...", "subpage_snippet": "", "source": "ieeexplore.ieee.org", "link": "https://ieeexplore.ieee.org/document/10880482", "content": "Personal digital data is a critical asset, and governments worldwide have enforced laws and regulations to protect data privacy . Data users have been endowed with the \"right to be forgotten\" (RTBF) of their data. In the course of machine learning (ML), the forgotten right requires a model provider to delete user data and its subsequent impact on ML models upon user requests. Machine ..."}
+{"idx": 3, "title": "PDF Forget to Flourish: Leveraging Machine-Unlearning on Pretrained ...", "date": "", "ddg_snippet": "Hence, we propose bounded unlearning as a poisoning tool, where we maximize loss in a controlled man-ner on the pre-trained model for some noisy data points to in-crease privacy leakage of the fine-tuned LLM without com-promising its utility.", "subpage_snippet": "", "source": "shadow.merl.com", "link": "https://shadow.merl.com/publications/docs/TR2025-017.pdf", "content": "Hence, we propose bounded unlearning as a poisoning tool, where we maximize loss in a controlled man-ner on the pre-trained model for some noisy data points to in-crease privacy leakage of the fine-tuned LLM without com-promising its utility."}
+{"idx": 4, "title": "Privacy Preservation through Practical Machine Unlearning", "date": "", "ddg_snippet": "Machine Learning models thrive on vast datasets, continuously adapting to provide accurate predictions and recommendations. However, in an era dominated by privacy concerns, Machine Unlearning emerges as a transformative approach, enabling the selective removal of data from trained models. This paper examines methods such as Naive Retraining and Exact Unlearning via the SISA framework ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2502.10635", "content": "Machine Learning models thrive on vast datasets, continuously adapting to provide accurate predictions and recommendations. However, in an era dominated by privacy concerns, Machine Unlearning emerges as a transformative approach, enabling the selective removal of data from trained models. This paper examines methods such as Naive Retraining and Exact Unlearning via the SISA framework ..."}
+{"idx": 5, "title": "Leveraging Per-Instance Privacy for Machine Unlearning", "date": "", "ddg_snippet": "Leveraging Per-Instance Privacy for Machine Unlearning Naz Sepahvand · Anvith Thudi · Berivan Isik · Ashmita Bhattacharyya · Nicolas Papernot · Eleni Triantafillou · Daniel Roy · Gintare Karolina Dziugaite", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/poster/46697", "content": "Leveraging Per-Instance Privacy for Machine Unlearning Naz Sepahvand · Anvith Thudi · Berivan Isik · Ashmita Bhattacharyya · Nicolas Papernot · Eleni Triantafillou · Daniel Roy · Gintare Karolina Dziugaite"}
+{"idx": 6, "title": "A survey of security and privacy issues of machine unlearning", "date": "", "ddg_snippet": "Specifically, we begin by investigating unlearning -based security attacks, where adversaries exploit vulnerabilities in the unlearning process to compromise the security of machine learning (ML) models. We then conduct a thorough examination of privacy risks associated with the adoption of machine unlearning .", "subpage_snippet": "", "source": "onlinelibrary.wiley.com", "link": "https://onlinelibrary.wiley.com/doi/full/10.1002/aaai.12209", "content": "Specifically, we begin by investigating unlearning -based security attacks, where adversaries exploit vulnerabilities in the unlearning process to compromise the security of machine learning (ML) models. We then conduct a thorough examination of privacy risks associated with the adoption of machine unlearning ."}
+{"idx": 7, "title": "A survey on machine unlearning: Techniques and new emerged privacy ...", "date": "", "ddg_snippet": "This paper provides an overview and analysis of the existing research on machine unlearning , aiming to present the current vulnerabilities of machine unlearning approaches. We analyze privacy risks in various aspects, including definitions, implementation methods, and real-world applications.", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S2214212625000481", "content": "This paper provides an overview and analysis of the existing research on machine unlearning , aiming to present the current vulnerabilities of machine unlearning approaches. We analyze privacy risks in various aspects, including definitions, implementation methods, and real-world applications."}
+{"idx": 8, "title": "Machine Unlearning: Solutions and Challenges - IEEE Xplore", "date": "", "ddg_snippet": "Machine learning models may inadvertently memorize sensitive, unauthorized, or malicious data, posing risks of privacy breaches, security vulnerabilities, and performance degradation. To address these issues, machine unlearning has emerged as a critical technique to selectively remove specific training data points' influence on trained models. This paper provides a comprehensive taxonomy and ...", "subpage_snippet": "", "source": "ieeexplore.ieee.org", "link": "https://ieeexplore.ieee.org/document/10488864", "content": "Machine learning models may inadvertently memorize sensitive, unauthorized, or malicious data, posing risks of privacy breaches, security vulnerabilities, and performance degradation. To address these issues, machine unlearning has emerged as a critical technique to selectively remove specific training data points' influence on trained models. This paper provides a comprehensive taxonomy and ..."}
+{"idx": 9, "title": "A Survey on Machine Unlearning: Techniques and New Emerged Privacy Risks", "date": "", "ddg_snippet": "The explosive growth of machine learning has made it a critical infrastructure in the era of artificial intelligence. The extensive use of data poses a significant threat to individual privacy . Various countries have implemented corresponding laws, such as GDPR, to protect individuals' data privacy and the right to be forgotten. This has made machine unlearning a research hotspot in the field ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2406.06186", "content": "The explosive growth of machine learning has made it a critical infrastructure in the era of artificial intelligence. The extensive use of data poses a significant threat to individual privacy . Various countries have implemented corresponding laws, such as GDPR, to protect individuals' data privacy and the right to be forgotten. This has made machine unlearning a research hotspot in the field ..."}
diff --git a/data/sampled_jsons/primary_function_of_Beautiful_Soup_library.jsonl b/data/sampled_jsons/primary_function_of_Beautiful_Soup_library.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..8cc6ca52266b263eea9d4bb1ef021de580849c5a
--- /dev/null
+++ b/data/sampled_jsons/primary_function_of_Beautiful_Soup_library.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "bs4 package — Beautiful Soup 4.13.0 documentation", "date": "", "ddg_snippet": "... Beautiful Soup suspects a provided attribute name may actually ... Beautiful Soup 's best guess as to the character encoding of the original document.", "subpage_snippet": "", "source": "www.crummy.com", "link": "https://www.crummy.com/software/BeautifulSoup/bs4/doc/api/bs4.html", "content": "... Beautiful Soup suspects a provided attribute name may actually ... Beautiful Soup 's best guess as to the character encoding of the original document."}
+{"idx": 1, "title": "Beautiful Soup in Go: Finding Elements by Class - Rayobyte", "date": "", "ddg_snippet": "This article explores how to find elements by class in Go, using libraries that mimic the functionality of Beautiful Soup , and provides a ...", "subpage_snippet": "", "source": "rayobyte.com", "link": "https://rayobyte.com/community/beautiful-soup-in-go-finding-elements-by-class/", "content": "This article explores how to find elements by class in Go, using libraries that mimic the functionality of Beautiful Soup , and provides a ..."}
+{"idx": 2, "title": "Beautiful Soup 4 Python - PythonForBeginners.com", "date": "", "ddg_snippet": "Beautiful Soup is a Python library for pulling data out of HTML and XML files. ... of your Python script, import the library Now you have to pass ...", "subpage_snippet": "", "source": "www.pythonforbeginners.com:443", "link": "https://www.pythonforbeginners.com:443/beautifulsoup/beautifulsoup-4-python", "content": "Beautiful Soup is a Python library for pulling data out of HTML and XML files. ... of your Python script, import the library Now you have to pass ..."}
+{"idx": 3, "title": "Web Scraping with BeautifulSoup - PythonForBeginners.com", "date": "", "ddg_snippet": "Beautiful Soup sits on top of popular Python parsers like lxml and html5lib, allowing you to try out different parsing strategies or trade speed for ...", "subpage_snippet": "", "source": "www.pythonforbeginners.com", "link": "https://www.pythonforbeginners.com/python-on-the-web/web-scraping-with-beautifulsoup", "content": "Beautiful Soup sits on top of popular Python parsers like lxml and html5lib, allowing you to try out different parsing strategies or trade speed for ..."}
+{"idx": 4, "title": "Beautifulsoup Installation - Python - GeeksforGeeks", "date": "", "ddg_snippet": "Beautiful Soup is a Python library for pulling data out of HTML and XML files. ... BeautifulSoup BeautifulSoup(bs4) is a Python library for pulling ...", "subpage_snippet": "", "source": "www.geeksforgeeks.org", "link": "https://www.geeksforgeeks.org/beautifulsoup-installation-python/", "content": "Beautiful Soup is a Python library for pulling data out of HTML and XML files. ... BeautifulSoup BeautifulSoup(bs4) is a Python library for pulling ..."}
+{"idx": 5, "title": "BeautifulSoup – Scraping Paragraphs from HTML |", "date": "", "ddg_snippet": "bs4: Beautiful Soup (bs4) is a Python library for pulling data out of HTML and XML files. ... BeautifulSoup is a Python library that makes it easy to ...", "subpage_snippet": "", "source": "www.geeksforgeeks.org", "link": "https://www.geeksforgeeks.org/beautifulsoup-scraping-paragraphs-from-html/", "content": "bs4: Beautiful Soup (bs4) is a Python library for pulling data out of HTML and XML files. ... BeautifulSoup is a Python library that makes it easy to ..."}
+{"idx": 6, "title": "beautiful soup | The Aspiring Roboticist", "date": "", "ddg_snippet": "Web scraping and XML parsing was done using Python ’ s Beautiful Soup library . ... I couldn ’ t simply use something like Beautiful Soup ...", "subpage_snippet": "", "source": "www.mcgurrin.info", "link": "https://www.mcgurrin.info/robots/tag/beautiful-soup/", "content": "Web scraping and XML parsing was done using Python ’ s Beautiful Soup library . ... I couldn ’ t simply use something like Beautiful Soup ..."}
+{"idx": 7, "title": "Scrapy vs Beautiful Soup vs Selenium – Which One to Use? -", "date": "", "ddg_snippet": "In essence, that’s what Beautiful Soup is – a library that structures an HTML or XML page, selects the data you need, and helps you extract it in ...", "subpage_snippet": "", "source": "proxyway.com", "link": "https://proxyway.com/guides/scrapy-vs-beautiful-soup-vs-selenium", "content": "In essence, that’s what Beautiful Soup is – a library that structures an HTML or XML page, selects the data you need, and helps you extract it in ..."}
+{"idx": 8, "title": "Scrapy vs Beautiful Soup vs Selenium for Web Scraping | 33rd", "date": "", "ddg_snippet": "Beautiful Soup – A simple but flexible Python library focused on parsing and traversing HTML and XML documents.", "subpage_snippet": "", "source": "33rdsquare.com", "link": "https://33rdsquare.com/scrapy-vs-beautiful-soup-vs-selenium-for-web-scraping/", "content": "Beautiful Soup – A simple but flexible Python library focused on parsing and traversing HTML and XML documents."}
+{"idx": 9, "title": "Python and Its Libraries – A Snapshot - Madcashcentral Cash", "date": "", "ddg_snippet": "Python encompasses various libraries that take care of various job functions like Math complex calculations, data mining, exploration of data sets ...", "subpage_snippet": "", "source": "madcashcentral.com", "link": "https://madcashcentral.com/python-and-its-libraries-a-snapshot/", "content": "Python encompasses various libraries that take care of various job functions like Math complex calculations, data mining, exploration of data sets ..."}
diff --git a/data/sampled_jsons/qcnePVejeV_Do_Not_Trust_What_They_Tell_Exposing_Malicious_Accomplices_Tor_Anomalous_Circuit_Detectio.jsonl b/data/sampled_jsons/qcnePVejeV_Do_Not_Trust_What_They_Tell_Exposing_Malicious_Accomplices_Tor_Anomalous_Circuit_Detectio.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..6f70f6539d1cb24231f02955f129000e797e19f4
--- /dev/null
+++ b/data/sampled_jsons/qcnePVejeV_Do_Not_Trust_What_They_Tell_Exposing_Malicious_Accomplices_Tor_Anomalous_Circuit_Detectio.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Do Not Trust What They Tell: Exposing Malicious Accomplices ...", "date": "", "ddg_snippet": "This paper presents a novel approach for detecting anomalous circuits in the Tor network, and for the first time provides a more comprehensive identifica-tion of potential malicious accomplice nodes in Tor by taking roles of nodes in anomalous circuits into consideration.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=qcnePVejeV", "content": "This paper presents a novel approach for detecting anomalous circuits in the Tor network, and for the first time provides a more comprehensive identifica-tion of potential malicious accomplice nodes in Tor by taking roles of nodes in anomalous circuits into consideration."}
+{"idx": 1, "title": "通过异常电路检测揭露Tor中的恶意同伙 - 安全内参 | 决策者的网络安全...", "date": "", "ddg_snippet": "Mar 13, 2025 · 首次在聚类距离测度中考虑了异常电路中节点的作用最终，揭示了 Tor 网络中几个家庭团体和离散节点之间的组织联系。 提出了第一种检测异常电路的方法，能够揭示仅通过节点配置比较无法检测到的隐含节点关联，并在 tor 网络中进行了大规模验证 2. 背景", "subpage_snippet": "", "source": "www.secrss.com", "link": "https://www.secrss.com/articles/76608", "content": "Mar 13, 2025 · 首次在聚类距离测度中考虑了异常电路中节点的作用最终，揭示了 Tor 网络中几个家庭团体和离散节点之间的组织联系。 提出了第一种检测异常电路的方法，能够揭示仅通过节点配置比较无法检测到的隐含节点关联，并在 tor 网络中进行了大规模验证 2. 背景"}
+{"idx": 2, "title": "Exposing the Rat in the Tunnel: Using Trafic Analysis for Tor ...", "date": "", "ddg_snippet": "We do this using random time gaps between site accesses, similar to the negative, positive, and zero-time separated page ac-cess methodology adopted in some Tor -based trafic analysis or WF works [67, 69].", "subpage_snippet": "", "source": "coeus.ece.gatech.edu", "link": "https://coeus.ece.gatech.edu/articles/CCS22.pdf", "content": "We do this using random time gaps between site accesses, similar to the negative, positive, and zero-time separated page ac-cess methodology adopted in some Tor -based trafic analysis or WF works [67, 69]."}
+{"idx": 3, "title": "Do Not Trust What They Tell: Exposing Malicious Accomplices ...", "date": "", "ddg_snippet": "Jan 29, 2025 · This paper presents a novel approach for detecting anomalous circuits in the Tor network, and for the first time provides a more comprehensive identification of potential malicious accomplice nodes in Tor by taking roles of nodes in anomalous circuits into consideration.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=qcnePVejeV", "content": "Jan 29, 2025 · This paper presents a novel approach for detecting anomalous circuits in the Tor network, and for the first time provides a more comprehensive identification of potential malicious accomplice nodes in Tor by taking roles of nodes in anomalous circuits into consideration."}
+{"idx": 4, "title": "Spoiled Onions: Exposing Malicious Tor Exit Relays", "date": "", "ddg_snippet": "Tor exit relays are operated by volunteers and together push more than 1 GiB/s of network traffic. By design, these volunteers are able to inspect and modify the anonymized network traffic. In this paper, we seek to expose such malicious exit relays and document...", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/chapter/10.1007/978-3-319-08506-7_16", "content": "Tor exit relays are operated by volunteers and together push more than 1 GiB/s of network traffic. By design, these volunteers are able to inspect and modify the anonymized network traffic. In this paper, we seek to expose such malicious exit relays and document..."}
+{"idx": 5, "title": "An Anonymity Vulnerability in Tor | CoLab", "date": "", "ddg_snippet": "Do Not Trust What They Tell : Exposing Malicious Accomplices in Tor via Anomalous Circuit Detection .A New Website Fingerprinting Method for Tor Hidden Service.", "subpage_snippet": "", "source": "colab.ws", "link": "https://colab.ws/articles/10.1109/tnet.2022.3174003", "content": "Do Not Trust What They Tell : Exposing Malicious Accomplices in Tor via Anomalous Circuit Detection .A New Website Fingerprinting Method for Tor Hidden Service."}
+{"idx": 6, "title": "(PDF) Low-resource routing attacks against TOR", "date": "", "ddg_snippet": "Do Not Trust What They Tell : Exposing Malicious Accomplices in Tor via Anomalous Circuit Detection .We detected our use of the Tor network for example using decoy circuits such as IMAP, SMTP, and HTTP servers.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/221342248_Low-resource_routing_attacks_against_TOR", "content": "Do Not Trust What They Tell : Exposing Malicious Accomplices in Tor via Anomalous Circuit Detection .We detected our use of the Tor network for example using decoy circuits such as IMAP, SMTP, and HTTP servers."}
+{"idx": 7, "title": "qBittorrent - скачать бесплатно qBittorrent 4.4.5 / 5.1.2", "date": "", "ddg_snippet": "Бесплатно. Windows. QBittorrent - бесплатный, мощный и удобный в работе кроссплатформенный клиент файлообменной сети BitTorrent. Несмотря на кажущуюся простоту, программа обладает внушительным набором весьма полезных возможностей...", "subpage_snippet": "", "source": "www.SoftPortal.com", "link": "https://www.SoftPortal.com/software-18505-qbittorrent.html", "content": "Бесплатно. Windows. QBittorrent - бесплатный, мощный и удобный в работе кроссплатформенный клиент файлообменной сети BitTorrent. Несмотря на кажущуюся простоту, программа обладает внушительным набором весьма полезных возможностей..."}
+{"idx": 8, "title": "Anomaly Detection with ADTK in Python", "date": "", "ddg_snippet": "Anomaly Detection Methods in ADTK.AutoregressionAD: Detects anomalies based on autoregressive models. PcaAD: Leverages Principal Component Analysis for anomaly detection . For example, to detect anomalies using a threshold-based method , use the following", "subpage_snippet": "", "source": "readmedium.com", "link": "https://readmedium.com/anomaly-detection-with-adtk-in-python-1ba2d5a867ac", "content": "Anomaly Detection Methods in ADTK.AutoregressionAD: Detects anomalies based on autoregressive models. PcaAD: Leverages Principal Component Analysis for anomaly detection . For example, to detect anomalies using a threshold-based method , use the following"}
+{"idx": 9, "title": "machinelearningmastery.com/ clustering -algorithms-with-python", "date": "", "ddg_snippet": "Specifically, you learned: Clustering is an unsupervised problem of finding...", "subpage_snippet": "", "source": "machinelearningmastery.com", "link": "https://machinelearningmastery.com/clustering-algorithms-with-python/", "content": "Specifically, you learned: Clustering is an unsupervised problem of finding..."}
diff --git a/data/sampled_jsons/qtuxDy2qEB_Parallel_Simulation_Log-concave_Sampling_diagonal_update_Section_3.jsonl b/data/sampled_jsons/qtuxDy2qEB_Parallel_Simulation_Log-concave_Sampling_diagonal_update_Section_3.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..b5a303a0a0bb8157352d21b1e7df88a5fdf2d55b
--- /dev/null
+++ b/data/sampled_jsons/qtuxDy2qEB_Parallel_Simulation_Log-concave_Sampling_diagonal_update_Section_3.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Parallel Simulation for Log - concave Sampling and... | OpenReview", "date": "", "ddg_snippet": "Keywords: parallel sampling , log - concave sampling , diffusion model. Submission Number: 9486.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=qtuxDy2qEB&referrer=[the+profile+of+Masashi+Sugiyama](/profile?id=~Masashi_Sugiyama1)", "content": "Keywords: parallel sampling , log - concave sampling , diffusion model. Submission Number: 9486."}
+{"idx": 1, "title": "The adaptive complexity of parallelized log - concave sampling", "date": "", "ddg_snippet": "For unconstrained sampling , we examine distributions that are log-smooth or log-Lipschitz and log strongly or non-strongly concave.View a PDF of the paper titled The adaptive complexity of parallelized log - concave sampling , by Huanjian Zhou and 2 other authors.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2408.13045", "content": "For unconstrained sampling , we examine distributions that are log-smooth or log-Lipschitz and log strongly or non-strongly concave.View a PDF of the paper titled The adaptive complexity of parallelized log - concave sampling , by Huanjian Zhou and 2 other authors."}
+{"idx": 2, "title": "Ink Game Playable Guards Update – Tiers, Weapons, and... - Noleep", "date": "", "ddg_snippet": "The wildly popular Roblox experience, Ink Game, has released its massive Playable Guards Update , completely shifting the dynamic of the game. Players can now step into the shoes of the enigmatic guards across several modes.", "subpage_snippet": "", "source": "noleep.com", "link": "https://noleep.com/en/ink-game-playable-guards-update-tiers-weapons-and-new-features/", "content": "The wildly popular Roblox experience, Ink Game, has released its massive Playable Guards Update , completely shifting the dynamic of the game. Players can now step into the shoes of the enigmatic guards across several modes."}
+{"idx": 3, "title": "Queueing Models in Services – Analytical and Simulation ... | CoLab", "date": "", "ddg_snippet": "It uses both analytical and simulation approaches to discuss a few selected models that are useful in practice as well as to point out the significant role played by the correlated arrivals.", "subpage_snippet": "", "source": "colab.ws", "link": "https://colab.ws/articles/10.1002/9781119755234.ch2", "content": "It uses both analytical and simulation approaches to discuss a few selected models that are useful in practice as well as to point out the significant role played by the correlated arrivals."}
+{"idx": 4, "title": "How to Install HomeTube on Your Synology NAS – Marius Hosting", "date": "", "ddg_snippet": "Now create three new folders inside the dockman folder that you have previously created at STEP 3 and name them config, downloads, tmp. Follow the instructions in the image below. Note: Be careful to enter only lowercase, not uppercase letters. HomeTube Synology NAS Set up 2. STEP 5.", "subpage_snippet": "", "source": "mariushosting.com", "link": "https://mariushosting.com/how-to-install-hometube-on-your-synology-nas/", "content": "Now create three new folders inside the dockman folder that you have previously created at STEP 3 and name them config, downloads, tmp. Follow the instructions in the image below. Note: Be careful to enter only lowercase, not uppercase letters. HomeTube Synology NAS Set up 2. STEP 5."}
+{"idx": 5, "title": "Pet Mutations | Grow a Garden Wiki | Fandom", "date": "", "ddg_snippet": "This page is about mutations in Pets. For info about mutations in crops, see Crop Mutations. Pet Mutations are a core feature that was added in Update 1.14.0. Any pets can be given mutations by using the Mutation Machine near the Egg Shop for 500,000...", "subpage_snippet": "", "source": "growagarden.fandom.com", "link": "https://growagarden.fandom.com/wiki/Pet_Mutations", "content": "This page is about mutations in Pets. For info about mutations in crops, see Crop Mutations. Pet Mutations are a core feature that was added in Update 1.14.0. Any pets can be given mutations by using the Mutation Machine near the Egg Shop for 500,000..."}
+{"idx": 6, "title": "Детальный обзор полей Галуа / Хабр", "date": "", "ddg_snippet": "ac=(qeb+qf+er)b+rf. Левое слагаемое правой части делится на , поэтому не влияет на остаток.uint8_t exp[256]; uint8_t log [255]; const uint8_t primitive_element = 3", "subpage_snippet": "", "source": "habr.com", "link": "https://habr.com/ru/articles/916740/", "content": "ac=(qeb+qf+er)b+rf. Левое слагаемое правой части делится на , поэтому не влияет на остаток.uint8_t exp[256]; uint8_t log [255]; const uint8_t primitive_element = 3"}
+{"idx": 7, "title": "IELTS Writing Task 2 - Band 9 Sample Question Answers", "date": "", "ddg_snippet": "IELTS Writing Task 2 - Sample Questions with answers. The IELTS Writing Task 2 is an essential component of the IELTS exam, and it plays a significant role in assessing your writing skills. This section requires you to write an essay on a given topic within a specific time limit.", "subpage_snippet": "", "source": "mocktestielts.com", "link": "https://mocktestielts.com/ielts-writing-task-2-practice-test/", "content": "IELTS Writing Task 2 - Sample Questions with answers. The IELTS Writing Task 2 is an essential component of the IELTS exam, and it plays a significant role in assessing your writing skills. This section requires you to write an essay on a given topic within a specific time limit."}
+{"idx": 8, "title": "How to Increase UVH Level | Borderlands 4｜Game8", "date": "", "ddg_snippet": "By creating a Game8 account and logging in, you'll receive instant notifications when someone replies to your posts.", "subpage_snippet": "", "source": "game8.co", "link": "https://game8.co/games/Borderlands-4/archives/551972", "content": "By creating a Game8 account and logging in, you'll receive instant notifications when someone replies to your posts."}
+{"idx": 9, "title": "WAN 2.2 VACE: Object Swap with an Image in ComfyUI (GGUF Q5)", "date": "", "ddg_snippet": "Uses point-based masking, a 3 - sampler chain, and gentle LoRA weights. Works with FP8 and Q5 GGUF builds on low VRAM. The quick demo.If your clip is vertical, click Swap to make it 480 x 832. Good for low VRAM. Image section .", "subpage_snippet": "", "source": "aistudynow.com", "link": "https://aistudynow.com/wan-2-2-vace-object-swap-with-an-image-in-comfyui-gguf-q5/", "content": "Uses point-based masking, a 3 - sampler chain, and gentle LoRA weights. Works with FP8 and Q5 GGUF builds on low VRAM. The quick demo.If your clip is vertical, click Swap to make it 480 x 832. Good for low VRAM. Image section ."}
diff --git a/data/sampled_jsons/recent_advancements_privacy_accounting_differential_privacy_2024_2025.jsonl b/data/sampled_jsons/recent_advancements_privacy_accounting_differential_privacy_2024_2025.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..8b654772d644319eec81d80025d266530cd67afc
--- /dev/null
+++ b/data/sampled_jsons/recent_advancements_privacy_accounting_differential_privacy_2024_2025.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Differential Privacy For Secure Machine Learning In 2025", "date": "", "ddg_snippet": "Differential privacy helps prevent common privacy attacks on machine learningThe Outlook for Differential Privacy in 2025 and Beyond. Protecting individual privacy while enabling impactful machine learning is one of the defining challenges of modern applied AI.", "subpage_snippet": "", "source": "expertbeacon.com", "link": "https://expertbeacon.com/differential-privacy-machine-learning/", "content": "Differential privacy helps prevent common privacy attacks on machine learningThe Outlook for Differential Privacy in 2025 and Beyond. Protecting individual privacy while enabling impactful machine learning is one of the defining challenges of modern applied AI."}
+{"idx": 1, "title": "Avoiding Pitfalls for Privacy Accounting of Subsampled... | OpenReview", "date": "", "ddg_snippet": "First, some privacy accountants assume that the privacy guarantees for the composition of a subsampled mechanism are determined by self-composing the worst-case datasets for the uncomposed mechanism.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=fj5SqqXfn1", "content": "First, some privacy accountants assume that the privacy guarantees for the composition of a subsampled mechanism are determined by self-composing the worst-case datasets for the uncomposed mechanism."}
+{"idx": 2, "title": "(PDF) A Decade of Metric Differential Privacy : Advancements and...", "date": "", "ddg_snippet": "Metric Differential Privacy (mDP) builds upon the. core principles of Differential Privacy (DP) by in-. corporating various distance metrics, which offer.arXiv 2024 [Brauer et al., 2024 ]Temporal Distance LBS: dX(x, x′)measures the difference between time points or temporal sequences xand x′", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/388963700_A_Decade_of_Metric_Differential_Privacy_Advancements_and_Applications", "content": "Metric Differential Privacy (mDP) builds upon the. core principles of Differential Privacy (DP) by in-. corporating various distance metrics, which offer.arXiv 2024 [Brauer et al., 2024 ]Temporal Distance LBS: dX(x, x′)measures the difference between time points or temporal sequences xand x′"}
+{"idx": 3, "title": "Differential privacy accounting by connecting the dots", "date": "", "ddg_snippet": "Differential Privacy and Privacy Loss Distributions. A randomized algorithm is said to satisfy DP guarantees if its output “does not depend significantly” on any one entry in its training dataset, quantified mathematically with parameters (ε, δ). For example, consider the motivating...", "subpage_snippet": "", "source": "research.google", "link": "https://research.google/blog/differential-privacy-accounting-by-connecting-the-dots/", "content": "Differential Privacy and Privacy Loss Distributions. A randomized algorithm is said to satisfy DP guarantees if its output “does not depend significantly” on any one entry in its training dataset, quantified mathematically with parameters (ε, δ). For example, consider the motivating..."}
+{"idx": 4, "title": "Sequential Privacy Budget Recycling for Federated Vector Mean...", "date": "", "ddg_snippet": "Privacy , Noise, Vectors, Servers, Data Privacy , Protocols, Games, Differential Privacy , Recycling, Training, Privacy Preservation, Vector Mean Estimation, Differential Privacy , Game Theory. Abstract. Privacy -preserving vector mean estimation is a crucial primitive in federated analytics.", "subpage_snippet": "", "source": "www.computer.org", "link": "https://www.computer.org/csdl/journal/tm/2025/03/10726610/21dMvnL7qVO", "content": "Privacy , Noise, Vectors, Servers, Data Privacy , Protocols, Games, Differential Privacy , Recycling, Training, Privacy Preservation, Vector Mean Estimation, Differential Privacy , Game Theory. Abstract. Privacy -preserving vector mean estimation is a crucial primitive in federated analytics."}
+{"idx": 5, "title": "[Literature Review] A Decade of Metric Differential Privacy ...", "date": "", "ddg_snippet": "It reviews both theoretical frameworks and practical implementations, highlighting mDP’s adaptability and efficacy across various application domains. ### Introduction The introduction articulates the need for mDP, explaining how traditional Differential Privacy (DP)...", "subpage_snippet": "", "source": "www.themoonlight.io", "link": "https://www.themoonlight.io/en/review/a-decade-of-metric-differential-privacy-advancements-and-applications", "content": "It reviews both theoretical frameworks and practical implementations, highlighting mDP’s adaptability and efficacy across various application domains. ### Introduction The introduction articulates the need for mDP, explaining how traditional Differential Privacy (DP)..."}
+{"idx": 6, "title": "Differential Privacy in Machine Learning: From Symbolic AI to LLMs", "date": "", "ddg_snippet": "2024 . Advances in Differential Privacy and Differentially Private Machine Learning.Demelius et al. ( 2025 ) Lea Demelius, Roman Kern, and Andreas Trügler. 2025 . Recent Advances of Differential Privacy in Centralized Deep Learning: A Systematic Survey.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2506.11687v1", "content": "2024 . Advances in Differential Privacy and Differentially Private Machine Learning.Demelius et al. ( 2025 ) Lea Demelius, Roman Kern, and Andreas Trügler. 2025 . Recent Advances of Differential Privacy in Centralized Deep Learning: A Systematic Survey."}
+{"idx": 7, "title": "Exploring differential privacy in CNNs, LSTMs, GRUs, and RNNs for...", "date": "", "ddg_snippet": "Towards privacy -preserving medical imaging: Federated learning with differential privacy and secure aggregation using a modified resnet architecture. arXiv preprint arXiv:2412.00687 2024 . Pan L, Huang M, Wang L, Qin P, Peng S. Feddp: Privacy -preserving method based on federated...", "subpage_snippet": "", "source": "journalofbigdata.springeropen.com", "link": "https://journalofbigdata.springeropen.com/articles/10.1186/s40537-025-01292-6", "content": "Towards privacy -preserving medical imaging: Federated learning with differential privacy and secure aggregation using a modified resnet architecture. arXiv preprint arXiv:2412.00687 2024 . Pan L, Huang M, Wang L, Qin P, Peng S. Feddp: Privacy -preserving method based on federated..."}
+{"idx": 8, "title": "Privacy | Google Public Policy", "date": "", "ddg_snippet": "Protecting user privacy . We support privacy regulation and policy that fosters trust and are committed to providing products and services that are private -by-design, secure-by-default, and that put users in control of their information. Overview. Recent News. Working Together.", "subpage_snippet": "", "source": "publicpolicy.google", "link": "https://publicpolicy.google/privacy/", "content": "Protecting user privacy . We support privacy regulation and policy that fosters trust and are committed to providing products and services that are private -by-design, secure-by-default, and that put users in control of their information. Overview. Recent News. Working Together."}
+{"idx": 9, "title": "Advancing Differential Privacy", "date": "", "ddg_snippet": "Advancing Differential Privacy : Where We Are Now and Future … 1 week ago Jan 31, 2024 · Abstract In the past decade, differential privacy has seen remarkable success as a rigorous and practical formalization of data privacy .", "subpage_snippet": "", "source": "www.tpsearchtool.com", "link": "https://www.tpsearchtool.com/web/advancing-differential-privacy", "content": "Advancing Differential Privacy : Where We Are Now and Future … 1 week ago Jan 31, 2024 · Abstract In the past decade, differential privacy has seen remarkable success as a rigorous and practical formalization of data privacy ."}
diff --git a/data/sampled_jsons/self-supervised_learning_class_discovery_novel_class_detection_contrastive_learning_year_2023.jsonl b/data/sampled_jsons/self-supervised_learning_class_discovery_novel_class_detection_contrastive_learning_year_2023.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..421b336dbafa9cf85702f2e5defcc2daf77b4cf7
--- /dev/null
+++ b/data/sampled_jsons/self-supervised_learning_class_discovery_novel_class_detection_contrastive_learning_year_2023.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "JosephKJ/Awesome-Novel-Class-Discovery - GitHub", "date": "", "ddg_snippet": "In contrast to clustering, NCD setting has access to labelled data from a disjoint set of classes. This topic has plausible real-world applications and is gathering much attention in the research community. Here, we provide a non-exhaustive list of papers that study NCD .", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/JosephKJ/Awesome-Novel-Class-Discovery", "content": "In contrast to clustering, NCD setting has access to labelled data from a disjoint set of classes. This topic has plausible real-world applications and is gathering much attention in the research community. Here, we provide a non-exhaustive list of papers that study NCD ."}
+{"idx": 1, "title": "[2305.13095] Open-world Semi-supervised Novel Class Discovery Neighborhood Contrastive Learning for Novel Class Discovery Pseudo-supervised contrastive learning with inter-class ... NC2D: Novel Class Discovery for Node Classification ... More Reliable Neighborhood Contrastive Learning for Novel ... Neighborhood Contrastive Learning for Novel Class Discovery", "date": "", "ddg_snippet": "May 22, 2023 · In this paper, we introduce a new open-world semi- supervised novel class discovery approach named OpenNCD, a progressive bi-level contrastive learning method over multiple prototypes. The proposed method is composed of two reciprocally enhanced parts. In this paper, we propose a holistic learning framework for Novel Class Discovery (NCD), which adopts contrastive learning to learn discriminate features with both the labeled and unlabeled data. Apr 8, 2024 · To tackle this problem, we propose a novel method to learn discriminative features for both known and novel categories called pseudo- supervised contrastive learning with inter- class separability (PIS). Oct 21, 2024 · In response to these challenges, we introduce for the first time a more practical NCD scenario for node classification (i.e., NC-NCD), and propose a novel self - training framework with prototype replay and distillation called SWORD, adopted to our NC- NCD setting. Nov 30, 2023 · To address this limitation, this paper presents the Novel Class Discovery (NCD) problem, which aims to classify new class activities of unlabeled sensor data by fully utilizing existing activities of labeled data. In this paper, we address Novel Class Discovery (NCD), the task of unveiling new classes in a set of unlabeled samples given a labeled dataset with known classe", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2305.13095", "content": "May 22, 2023 · In this paper, we introduce a new open-world semi- supervised novel class discovery approach named OpenNCD, a progressive bi-level contrastive learning method over multiple prototypes. The proposed method is composed of two reciprocally enhanced parts. In this paper, we propose a holistic learning framework for Novel Class Discovery (NCD), which adopts contrastive learning to learn discriminate features with both the labeled and unlabeled data. Apr 8, 2024 · To tackle this problem, we propose a novel method to learn discriminative features for both known and novel categories called pseudo- supervised contrastive learning with inter- class separability (PIS). Oct 21, 2024 · In response to these challenges, we introduce for the first time a more practical NCD scenario for node classification (i.e., NC-NCD), and propose a novel self - training framework with prototype replay and distillation called SWORD, adopted to our NC- NCD setting. Nov 30, 2023 · To address this limitation, this paper presents the Novel Class Discovery (NCD) problem, which aims to classify new class activities of unlabeled sensor data by fully utilizing existing activities of labeled data. In this paper, we address Novel Class Discovery (NCD), the task of unveiling new classes in a set of unlabeled samples given a labeled dataset with known classe"}
+{"idx": 2, "title": "Neighborhood Contrastive Learning for Novel Class Discovery", "date": "", "ddg_snippet": "In this paper, we propose a holistic learning framework for Novel Class Discovery (NCD), which adopts contrastive learning to learn discriminate features with both the labeled and unlabeled data.", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2021/papers/Zhong_Neighborhood_Contrastive_Learning_for_Novel_Class_Discovery_CVPR_2021_paper.pdf", "content": "In this paper, we propose a holistic learning framework for Novel Class Discovery (NCD), which adopts contrastive learning to learn discriminate features with both the labeled and unlabeled data."}
+{"idx": 3, "title": "Pseudo-supervised contrastive learning with inter-class ...", "date": "", "ddg_snippet": "Apr 8, 2024 · To tackle this problem, we propose a novel method to learn discriminative features for both known and novel categories called pseudo- supervised contrastive learning with inter- class separability (PIS).", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S0950705124001126", "content": "Apr 8, 2024 · To tackle this problem, we propose a novel method to learn discriminative features for both known and novel categories called pseudo- supervised contrastive learning with inter- class separability (PIS)."}
+{"idx": 4, "title": "NC2D: Novel Class Discovery for Node Classification ...", "date": "", "ddg_snippet": "Oct 21, 2024 · In response to these challenges, we introduce for the first time a more practical NCD scenario for node classification (i.e., NC-NCD), and propose a novel self - training framework with prototype replay and distillation called SWORD, adopted to our NC- NCD setting.", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.1145/3627673.3679779", "content": "Oct 21, 2024 · In response to these challenges, we introduce for the first time a more practical NCD scenario for node classification (i.e., NC-NCD), and propose a novel self - training framework with prototype replay and distillation called SWORD, adopted to our NC- NCD setting."}
+{"idx": 5, "title": "More Reliable Neighborhood Contrastive Learning for Novel ...", "date": "", "ddg_snippet": "Nov 30, 2023 · To address this limitation, this paper presents the Novel Class Discovery (NCD) problem, which aims to classify new class activities of unlabeled sensor data by fully utilizing existing activities of labeled data.", "subpage_snippet": "", "source": "www.mdpi.com", "link": "https://www.mdpi.com/1424-8220/23/23/9529", "content": "Nov 30, 2023 · To address this limitation, this paper presents the Novel Class Discovery (NCD) problem, which aims to classify new class activities of unlabeled sensor data by fully utilizing existing activities of labeled data."}
+{"idx": 6, "title": "Neighborhood Contrastive Learning for Novel Class Discovery", "date": "", "ddg_snippet": "In this paper, we address Novel Class Discovery (NCD), the task of unveiling new classes in a set of unlabeled samples given a labeled dataset with known classe", "subpage_snippet": "", "source": "ieeexplore.ieee.org", "link": "https://ieeexplore.ieee.org/document/9578829", "content": "In this paper, we address Novel Class Discovery (NCD), the task of unveiling new classes in a set of unlabeled samples given a labeled dataset with known classe"}
+{"idx": 7, "title": "GitHub - sadimanna/ self - supervised - learning -and- contrastive ...", "date": "", "ddg_snippet": "Neighborhood Contrastive Learning for Novel Class Discovery . Self - Supervised Representation Learning for RGB-D Salient Object Detection .", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/sadimanna/self-supervised-learning-and-contrastive-learning-papers", "content": "Neighborhood Contrastive Learning for Novel Class Discovery . Self - Supervised Representation Learning for RGB-D Salient Object Detection ."}
+{"idx": 8, "title": "Open-world Semi- supervised Novel Class Discovery (2023)", "date": "", "ddg_snippet": "Abstract: Traditional semi- supervised learning tasks assume that both labeled and unlabeled data follow the same class distribution, but the realistic open-world scenarios are of more complexity with unknown novel classes mixed in the unlabeled set.", "subpage_snippet": "", "source": "scispace.com", "link": "https://scispace.com/papers/open-world-semi-supervised-novel-class-discovery-bo125kt5", "content": "Abstract: Traditional semi- supervised learning tasks assume that both labeled and unlabeled data follow the same class distribution, but the realistic open-world scenarios are of more complexity with unknown novel classes mixed in the unlabeled set."}
+{"idx": 9, "title": "Understanding self - supervised and contrastive learning with... - imbue", "date": "", "ddg_snippet": "Why does self - supervised learning matter? Machine learning is typically done in a supervised fashion: we use a dataset consisting of the inputs and “right answers” (outputs) to find the best function that maps from the input data onto the right answers.", "subpage_snippet": "", "source": "imbue.com", "link": "https://imbue.com/research/2020-08-24-understanding-self-supervised-contrastive-learning/", "content": "Why does self - supervised learning matter? Machine learning is typically done in a supervised fashion: we use a dataset consisting of the inputs and “right answers” (outputs) to find the best function that maps from the input data onto the right answers."}
diff --git a/data/sampled_jsons/sitear5iv.labs.arxiv.org_2502.20727_Figure_7c.jsonl b/data/sampled_jsons/sitear5iv.labs.arxiv.org_2502.20727_Figure_7c.jsonl
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index 0000000000000000000000000000000000000000..540a843abe7f763edf7f6abd17a0616873b61dfe
--- /dev/null
+++ b/data/sampled_jsons/sitear5iv.labs.arxiv.org_2502.20727_Figure_7c.jsonl
@@ -0,0 +1,8 @@
+{"idx": 0, "title": "[2502.20727] SPD: Sync-Point Drop for efficient tensor parallelism of ...", "date": "", "ddg_snippet": "To tackle this important issue, we propose Sync-Point Drop (SPD) a simple yet novel optimization technique with broad applications for LLM systems. Unlike existing works which tried to improve the communication process itself (NVIDIA, 2019; Jeaugey, 2019; Cheng et al., 2023) on system-level, SPD directly removes sync-point in the self-attention output (as in Figure 1 (b)) within the target ...", "subpage_snippet": "", "source": "ar5iv.labs.arxiv.org", "link": "https://ar5iv.labs.arxiv.org/html/2502.20727", "content": "To tackle this important issue, we propose Sync-Point Drop (SPD) a simple yet novel optimization technique with broad applications for LLM systems. Unlike existing works which tried to improve the communication process itself (NVIDIA, 2019; Jeaugey, 2019; Cheng et al., 2023) on system-level, SPD directly removes sync-point in the self-attention output (as in Figure 1 (b)) within the target ..."}
+{"idx": 1, "title": "Reducing Activation Recomputation in Large Transformer Models", "date": "", "ddg_snippet": "Figure 1: Parameters, optimizer state, and activations memory. The dashed red line represents the memory capacity of an NVIDIA A100 GPU. Present work reduces the activation memory required to fit the model.", "subpage_snippet": "", "source": "ar5iv.labs.arxiv.org", "link": "https://ar5iv.labs.arxiv.org/html/2205.05198", "content": "Figure 1: Parameters, optimizer state, and activations memory. The dashed red line represents the memory capacity of an NVIDIA A100 GPU. Present work reduces the activation memory required to fit the model."}
+{"idx": 2, "title": "Summarizing CPU and GPU Design Trends with Product Data", "date": "", "ddg_snippet": "Figure 6: Comparing single-precision and double-precision performance of CPUs and GPUs. Figure 7 : The trend of GPU Performance, TDP, base clocks, and die sizes.Finally, as observed in Figure . 7 ( c ), the GPU die size is also increasing.", "subpage_snippet": "", "source": "ar5iv.labs.arxiv.org", "link": "https://ar5iv.labs.arxiv.org/html/1911.11313", "content": "Figure 6: Comparing single-precision and double-precision performance of CPUs and GPUs. Figure 7 : The trend of GPU Performance, TDP, base clocks, and die sizes.Finally, as observed in Figure . 7 ( c ), the GPU die size is also increasing."}
+{"idx": 3, "title": "Hierarchical Text-Conditional Image Generation with CLIP Latents", "date": "", "ddg_snippet": "Figure 7 : Visualization of reconstructions of CLIP latents from progressively more PCA dimensions (20, 30, 40, 80, 120, 160, 200, 320 dimensions), with the original source image on the far right.", "subpage_snippet": "", "source": "ar5iv.labs.arxiv.org", "link": "https://ar5iv.labs.arxiv.org/html/2204.06125", "content": "Figure 7 : Visualization of reconstructions of CLIP latents from progressively more PCA dimensions (20, 30, 40, 80, 120, 160, 200, 320 dimensions), with the original source image on the far right."}
+{"idx": 4, "title": "[1901.08204] A PCB Dataset for Defects Detection and Classification", "date": "", "ddg_snippet": "Refer to caption. Figure 1: The flow chart for PCB inspection. Test image and template will be separately preprocessed and compared to locate defects, then these located defects will be send into trained neural network model to get results.", "subpage_snippet": "", "source": "ar5iv.labs.arxiv.org", "link": "https://ar5iv.labs.arxiv.org/html/1901.08204", "content": "Refer to caption. Figure 1: The flow chart for PCB inspection. Test image and template will be separately preprocessed and compared to locate defects, then these located defects will be send into trained neural network model to get results."}
+{"idx": 5, "title": "[1805.02566] Understanding Reuse, Performance, and Hardware Cost...", "date": "", "ddg_snippet": "Similarly, Figure 5(C) and (D) shows the spatial distribution on S instead of X’, and also the impact of data movement order on temporal reuse leading to different dataflow variations.", "subpage_snippet": "", "source": "ar5iv.labs.arxiv.org", "link": "https://ar5iv.labs.arxiv.org/html/1805.02566", "content": "Similarly, Figure 5(C) and (D) shows the spatial distribution on S instead of X’, and also the impact of data movement order on temporal reuse leading to different dataflow variations."}
+{"idx": 6, "title": "[2012.03385] Learning to Rearrange Deformable Cables, Fabrics, and...", "date": "", "ddg_snippet": "Figure 2: The 12 tasks in the proposed DeformableRavens benchmark (see Table I) with suction cup gripper and deformable objects. Top row: (a) cable-ring, (b) cable-ring-notarget, (c) cable-shape, (d) cable-shape-notarget, (e) cable-line-notarget, (f) fabric-cover.", "subpage_snippet": "", "source": "ar5iv.labs.arxiv.org", "link": "https://ar5iv.labs.arxiv.org/html/2012.03385", "content": "Figure 2: The 12 tasks in the proposed DeformableRavens benchmark (see Table I) with suction cup gripper and deformable objects. Top row: (a) cable-ring, (b) cable-ring-notarget, (c) cable-shape, (d) cable-shape-notarget, (e) cable-line-notarget, (f) fabric-cover."}
+{"idx": 7, "title": "[2406.08627] Time-MMD: A New Multi-Domain Multimodal Dataset for Time ...", "date": "", "ddg_snippet": "Figure 7c shows the relationship between horizon window size and the average MSE reduction for each domain. Overall, the MSE reduction is stable and promising across different horizon window size, from short term to long term. This demonstrates that the effectiveness of multimodal TSF is robust to different forecast horizon requirements.", "subpage_snippet": "", "source": "ar5iv.labs.arxiv.org", "link": "https://ar5iv.labs.arxiv.org/html/2406.08627", "content": "Figure 7c shows the relationship between horizon window size and the average MSE reduction for each domain. Overall, the MSE reduction is stable and promising across different horizon window size, from short term to long term. This demonstrates that the effectiveness of multimodal TSF is robust to different forecast horizon requirements."}
diff --git a/data/sampled_jsons/sitearxiv.org_2305.18290_Direct_Preference_Optimization_abstract_instability_overfitting_year_2023.jsonl b/data/sampled_jsons/sitearxiv.org_2305.18290_Direct_Preference_Optimization_abstract_instability_overfitting_year_2023.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..e6a983654e8a17d22d383f25671c76557202ce6e
--- /dev/null
+++ b/data/sampled_jsons/sitearxiv.org_2305.18290_Direct_Preference_Optimization_abstract_instability_overfitting_year_2023.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "[2305.18290] Direct Preference Optimization: Your Language ... [2305.18290] Direct Preference Optimization: Your Language ... Robust LLM Alignment via Distributionally Robust Direct ... RS-DPO: A Hybrid Rejection Sampling and Direct Preference ... Diffusion Model Alignment Using Direct Preference Optimization Implicit Reward as the Bridge: A Unified View of SFT and DPO ... GAPO: Learning Preferential Prompt through Generative ...", "date": "", "ddg_snippet": "May 29, 2023 · Abstract page for arXiv paper 2305.18290 : Direct Preference Optimization : Your Language Model is Secretly a Reward Model 4 Direct Preference Optimization Motivated by the challenges of applying reinforcement learning algorithms on large-scale problems such as fine-tuning language models, our goal is to derive a simple approach for policy optimization using preferences directly. We address this problem using the principled framework of distributionally robust optimization, and develop two novel distributionally robust direct preference optimization (DPO) algorithms, namely, Wasserstein DPO (WDPO) and Kullback-Leibler DPO (KLDPO). Feb 15, 2024 · Abstract Reinforcement learning from human feedback (RLHF) has been extensively employed to align large language models with user intent. However, proximal policy optimization (PPO) based RLHF is occasionally unstable requiring significant hyperparameter finetuning, and computationally expensive to maximize the estimated reward during alignment. Recently, direct preference optimization (DPO ... Nov 21, 2023 · Diffusion-DPO is adapted from the recently developed Direct Preference Optimization (DPO), a simpler alternative to RLHF which directly optimizes a policy that best satisfies human preferences under a classification objective. Through rigorous mathematical derivation, we demonstrate that both SFT and preference learning methods like Direct Preference Optimization (DPO) operate within the same optimal policy-reward subspace, with SFT representing a special case of implicit reward learning. Abstract Recent advances in large language models have highlighted the critical need for precise control over model outputs through predefined constraints. While existing methods attempt to achieve this through either direct instruction-response synthesis or preferential response optimization, they often struggle with constraint understanding and adaptation. This limitation becomes ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2305.18290", "content": "May 29, 2023 · Abstract page for arXiv paper 2305.18290 : Direct Preference Optimization : Your Language Model is Secretly a Reward Model 4 Direct Preference Optimization Motivated by the challenges of applying reinforcement learning algorithms on large-scale problems such as fine-tuning language models, our goal is to derive a simple approach for policy optimization using preferences directly. We address this problem using the principled framework of distributionally robust optimization, and develop two novel distributionally robust direct preference optimization (DPO) algorithms, namely, Wasserstein DPO (WDPO) and Kullback-Leibler DPO (KLDPO). Feb 15, 2024 · Abstract Reinforcement learning from human feedback (RLHF) has been extensively employed to align large language models with user intent. However, proximal policy optimization (PPO) based RLHF is occasionally unstable requiring significant hyperparameter finetuning, and computationally expensive to maximize the estimated reward during alignment. Recently, direct preference optimization (DPO ... Nov 21, 2023 · Diffusion-DPO is adapted from the recently developed Direct Preference Optimization (DPO), a simpler alternative to RLHF which directly optimizes a policy that best satisfies human preferences under a classification objective. Through rigorous mathematical derivation, we demonstrate that both SFT and preference learning methods like Direct Preference Optimization (DPO) operate within the same optimal policy-reward subspace, with SFT representing a special case of implicit reward learning. Abstract Recent advances in large language models have highlighted the critical need for precise control over model outputs through predefined constraints. While existing methods attempt to achieve this through either direct instruction-response synthesis or preferential response optimization, they often struggle with constraint understanding and adaptation. This limitation becomes ..."}
+{"idx": 1, "title": "[2305.18290] Direct Preference Optimization: Your Language ... Robust LLM Alignment via Distributionally Robust Direct ... RS-DPO: A Hybrid Rejection Sampling and Direct Preference ... Diffusion Model Alignment Using Direct Preference Optimization Implicit Reward as the Bridge: A Unified View of SFT and DPO ... GAPO: Learning Preferential Prompt through Generative ...", "date": "", "ddg_snippet": "4 Direct Preference Optimization Motivated by the challenges of applying reinforcement learning algorithms on large-scale problems such as fine-tuning language models, our goal is to derive a simple approach for policy optimization using preferences directly. We address this problem using the principled framework of distributionally robust optimization, and develop two novel distributionally robust direct preference optimization (DPO) algorithms, namely, Wasserstein DPO (WDPO) and Kullback-Leibler DPO (KLDPO). Feb 15, 2024 · Abstract Reinforcement learning from human feedback (RLHF) has been extensively employed to align large language models with user intent. However, proximal policy optimization (PPO) based RLHF is occasionally unstable requiring significant hyperparameter finetuning, and computationally expensive to maximize the estimated reward during alignment. Recently, direct preference optimization (DPO ... Nov 21, 2023 · Diffusion-DPO is adapted from the recently developed Direct Preference Optimization (DPO), a simpler alternative to RLHF which directly optimizes a policy that best satisfies human preferences under a classification objective. Through rigorous mathematical derivation, we demonstrate that both SFT and preference learning methods like Direct Preference Optimization (DPO) operate within the same optimal policy-reward subspace, with SFT representing a special case of implicit reward learning. Abstract Recent advances in large language models have highlighted the critical need for precise control over model outputs through predefined constraints. While existing methods attempt to achieve this through either direct instruction-response synthesis or preferential response optimization, they often struggle with constraint understanding and adaptation. This limitation becomes ...", "subpage_snippet": "", "source": "ar5iv.labs.arxiv.org", "link": "https://ar5iv.labs.arxiv.org/html/2305.18290", "content": "4 Direct Preference Optimization Motivated by the challenges of applying reinforcement learning algorithms on large-scale problems such as fine-tuning language models, our goal is to derive a simple approach for policy optimization using preferences directly. We address this problem using the principled framework of distributionally robust optimization, and develop two novel distributionally robust direct preference optimization (DPO) algorithms, namely, Wasserstein DPO (WDPO) and Kullback-Leibler DPO (KLDPO). Feb 15, 2024 · Abstract Reinforcement learning from human feedback (RLHF) has been extensively employed to align large language models with user intent. However, proximal policy optimization (PPO) based RLHF is occasionally unstable requiring significant hyperparameter finetuning, and computationally expensive to maximize the estimated reward during alignment. Recently, direct preference optimization (DPO ... Nov 21, 2023 · Diffusion-DPO is adapted from the recently developed Direct Preference Optimization (DPO), a simpler alternative to RLHF which directly optimizes a policy that best satisfies human preferences under a classification objective. Through rigorous mathematical derivation, we demonstrate that both SFT and preference learning methods like Direct Preference Optimization (DPO) operate within the same optimal policy-reward subspace, with SFT representing a special case of implicit reward learning. Abstract Recent advances in large language models have highlighted the critical need for precise control over model outputs through predefined constraints. While existing methods attempt to achieve this through either direct instruction-response synthesis or preferential response optimization, they often struggle with constraint understanding and adaptation. This limitation becomes ..."}
+{"idx": 2, "title": "Robust LLM Alignment via Distributionally Robust Direct ...", "date": "", "ddg_snippet": "We address this problem using the principled framework of distributionally robust optimization, and develop two novel distributionally robust direct preference optimization (DPO) algorithms, namely, Wasserstein DPO (WDPO) and Kullback-Leibler DPO (KLDPO).", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2502.01930v2", "content": "We address this problem using the principled framework of distributionally robust optimization, and develop two novel distributionally robust direct preference optimization (DPO) algorithms, namely, Wasserstein DPO (WDPO) and Kullback-Leibler DPO (KLDPO)."}
+{"idx": 3, "title": "RS-DPO: A Hybrid Rejection Sampling and Direct Preference ...", "date": "", "ddg_snippet": "Feb 15, 2024 · Abstract Reinforcement learning from human feedback (RLHF) has been extensively employed to align large language models with user intent. However, proximal policy optimization (PPO) based RLHF is occasionally unstable requiring significant hyperparameter finetuning, and computationally expensive to maximize the estimated reward during alignment. Recently, direct preference optimization (DPO ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2402.10038v1", "content": "Feb 15, 2024 · Abstract Reinforcement learning from human feedback (RLHF) has been extensively employed to align large language models with user intent. However, proximal policy optimization (PPO) based RLHF is occasionally unstable requiring significant hyperparameter finetuning, and computationally expensive to maximize the estimated reward during alignment. Recently, direct preference optimization (DPO ..."}
+{"idx": 4, "title": "Diffusion Model Alignment Using Direct Preference Optimization", "date": "", "ddg_snippet": "Nov 21, 2023 · Diffusion-DPO is adapted from the recently developed Direct Preference Optimization (DPO), a simpler alternative to RLHF which directly optimizes a policy that best satisfies human preferences under a classification objective.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2311.12908", "content": "Nov 21, 2023 · Diffusion-DPO is adapted from the recently developed Direct Preference Optimization (DPO), a simpler alternative to RLHF which directly optimizes a policy that best satisfies human preferences under a classification objective."}
+{"idx": 5, "title": "More is Less: The Pitfalls of Multi-Model Synthetic Preference", "date": "", "ddg_snippet": "... instability associated with traditional RL methods such as Proximal Policy Optimization (PPO) (Schulman et al., 2017 ) , researchers have developed ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2504.02193v2", "content": "... instability associated with traditional RL methods such as Proximal Policy Optimization (PPO) (Schulman et al., 2017 ) , researchers have developed ..."}
+{"idx": 6, "title": "Not All Preferences are What You Need for Post-Training:", "date": "", "ddg_snippet": "DPO eliminates the need for an explicit reward model or reinforcement learning by directly optimizing the model using preference pairs.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2507.07725v1", "content": "DPO eliminates the need for an explicit reward model or reinforcement learning by directly optimizing the model using preference pairs."}
+{"idx": 7, "title": "Lightweight and Direct Document Relevance Optimization for", "date": "", "ddg_snippet": "To address these challenges, we introduce a direct document relevance optimization ( DDRO ) method that employs a pairwise ranking approach into ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2504.05181v1", "content": "To address these challenges, we introduce a direct document relevance optimization ( DDRO ) method that employs a pairwise ranking approach into ..."}
+{"idx": 8, "title": "Adversarial Preference Optimization: Enhancing Your Alignment", "date": "", "ddg_snippet": "... directly optimizing the non-differentiable rewards, contrastive learning methods enlarge the likelihood gap between preferred and rejected response ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2311.08045v4", "content": "... directly optimizing the non-differentiable rewards, contrastive learning methods enlarge the likelihood gap between preferred and rejected response ..."}
+{"idx": 9, "title": "More is Less: The Pitfalls of Multi-Model Synthetic Preference", "date": "", "ddg_snippet": "... instability associated with traditional RL methods such as Proximal Policy Optimization (PPO) (Schulman et al., 2017 ) , researchers have developed ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2504.02193v3", "content": "... instability associated with traditional RL methods such as Proximal Policy Optimization (PPO) (Schulman et al., 2017 ) , researchers have developed ..."}
diff --git a/data/sampled_jsons/sitearxiv.org_2407.01511_CRAB_Cross-environment_Agent_Benchmark_abstract_year_2024.jsonl b/data/sampled_jsons/sitearxiv.org_2407.01511_CRAB_Cross-environment_Agent_Benchmark_abstract_year_2024.jsonl
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+{"idx": 0, "title": "[2407.01511] CRAB: Cross-environment Agent Benchmark for ... CRAB: Cross-environment Agent Benchmark for Multimodal ... Towards Evaluating Generalist Agents: An Automated Benchmark ... arXiv:2410.03524v1 [cs.CL] 4 Oct 2024 CRAB: C ENVIRONMENT AGENT BENCHMARK FOR M LANGUAGE MODEL AGENTS arXiv:2505.21668v1 [cs.AI] 27 May 2025 Detecting Pipeline Failures through Fine-Grained Analysis of ...", "date": "", "ddg_snippet": "Jul 1, 2024 · To overcome these limitations, we introduce Crab , the first agent benchmark framework designed to support cross-environment tasks, incorporating a graph-based fine-grained evaluation method and an efficient mechanism for task and evaluator construction. To overcome these limitations, we introduce CRAB , the first cross-environment agent benchmark framework, incor- porating a graph-based fine-grained evaluation method and an efficient task generation method. Our framework supports multiple devices and can be easily extended to any environment with a Python interface. The CRAB framework Xu et al. (2024) introduces a cross-environment benchmark that leverages multimodal language models to perform tasks across various GUI environments. Tianqi Xu, Linyao Chen, Dai-Jie Wu, Yanjun Chen, Zecheng Zhang, Xiang Yao, Zhiqiang Xie, Yongchao Chen, Shilong Liu, Bochen Qian, et al. Crab : Cross-environment agent benchmark for multimodal language model agents . arXiv preprint arXiv: 2407.01511 , 2024. To overcome these limitations, we introduce CRAB , the first agent benchmark framework designed to support cross-environment tasks, incorporating a graph-based fine-grained evalua- tion method and an efficient mechanism for task and evaluator construction. Tianqi Xu, Linyao Chen, Dai-Jie Wu, Yanjun Chen, Zecheng Zhang, Xiang Yao, Zhiqiang Xie, Yongchao Chen, Shilong Liu, Bochen Qian, et al. Crab : Cross-environment agent benchmark for multimodal language model agents . arXiv preprint arXiv: 2407.01511 , 2024. 2 days ago · CRAB : Cross-environment agent benchmark for multimodal language model agents . In Findings of the Association for Computational Linguistics: ACL 2025, pages 21607–21647, Vienna, Austria.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2407.01511", "content": "Jul 1, 2024 · To overcome these limitations, we introduce Crab , the first agent benchmark framework designed to support cross-environment tasks, incorporating a graph-based fine-grained evaluation method and an efficient mechanism for task and evaluator construction. To overcome these limitations, we introduce CRAB , the first cross-environment agent benchmark framework, incor- porating a graph-based fine-grained evaluation method and an efficient task generation method. Our framework supports multiple devices and can be easily extended to any environment with a Python interface. The CRAB framework Xu et al. (2024) introduces a cross-environment benchmark that leverages multimodal language models to perform tasks across various GUI environments. Tianqi Xu, Linyao Chen, Dai-Jie Wu, Yanjun Chen, Zecheng Zhang, Xiang Yao, Zhiqiang Xie, Yongchao Chen, Shilong Liu, Bochen Qian, et al. Crab : Cross-environment agent benchmark for multimodal language model agents . arXiv preprint arXiv: 2407.01511 , 2024. To overcome these limitations, we introduce CRAB , the first agent benchmark framework designed to support cross-environment tasks, incorporating a graph-based fine-grained evalua- tion method and an efficient mechanism for task and evaluator construction. Tianqi Xu, Linyao Chen, Dai-Jie Wu, Yanjun Chen, Zecheng Zhang, Xiang Yao, Zhiqiang Xie, Yongchao Chen, Shilong Liu, Bochen Qian, et al. Crab : Cross-environment agent benchmark for multimodal language model agents . arXiv preprint arXiv: 2407.01511 , 2024. 2 days ago · CRAB : Cross-environment agent benchmark for multimodal language model agents . In Findings of the Association for Computational Linguistics: ACL 2025, pages 21607–21647, Vienna, Austria."}
+{"idx": 1, "title": "CRAB: Cross-environment Agent Benchmark for Multimodal ...", "date": "", "ddg_snippet": "To overcome these limitations, we introduce CRAB , the first cross-environment agent benchmark framework, incor- porating a graph-based fine-grained evaluation method and an efficient task generation method. Our framework supports multiple devices and can be easily extended to any environment with a Python interface.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2407.01511", "content": "To overcome these limitations, we introduce CRAB , the first cross-environment agent benchmark framework, incor- porating a graph-based fine-grained evaluation method and an efficient task generation method. Our framework supports multiple devices and can be easily extended to any environment with a Python interface."}
+{"idx": 2, "title": "Towards Evaluating Generalist Agents: An Automated Benchmark ...", "date": "", "ddg_snippet": "The CRAB framework Xu et al. (2024) introduces a cross-environment benchmark that leverages multimodal language models to perform tasks across various GUI environments.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2310.08367v2", "content": "The CRAB framework Xu et al. (2024) introduces a cross-environment benchmark that leverages multimodal language models to perform tasks across various GUI environments."}
+{"idx": 3, "title": "arXiv:2410.03524v1 [cs.CL] 4 Oct 2024", "date": "", "ddg_snippet": "Tianqi Xu, Linyao Chen, Dai-Jie Wu, Yanjun Chen, Zecheng Zhang, Xiang Yao, Zhiqiang Xie, Yongchao Chen, Shilong Liu, Bochen Qian, et al. Crab : Cross-environment agent benchmark for multimodal language model agents . arXiv preprint arXiv: 2407.01511 , 2024.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2410.03524v1", "content": "Tianqi Xu, Linyao Chen, Dai-Jie Wu, Yanjun Chen, Zecheng Zhang, Xiang Yao, Zhiqiang Xie, Yongchao Chen, Shilong Liu, Bochen Qian, et al. Crab : Cross-environment agent benchmark for multimodal language model agents . arXiv preprint arXiv: 2407.01511 , 2024."}
+{"idx": 4, "title": "CRAB: C ENVIRONMENT AGENT BENCHMARK FOR M LANGUAGE MODEL AGENTS", "date": "", "ddg_snippet": "To overcome these limitations, we introduce CRAB , the first agent benchmark framework designed to support cross-environment tasks, incorporating a graph-based fine-grained evalua- tion method and an efficient mechanism for task and evaluator construction.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2407.01511v2", "content": "To overcome these limitations, we introduce CRAB , the first agent benchmark framework designed to support cross-environment tasks, incorporating a graph-based fine-grained evalua- tion method and an efficient mechanism for task and evaluator construction."}
+{"idx": 5, "title": "arXiv:2505.21668v1 [cs.AI] 27 May 2025", "date": "", "ddg_snippet": "Tianqi Xu, Linyao Chen, Dai-Jie Wu, Yanjun Chen, Zecheng Zhang, Xiang Yao, Zhiqiang Xie, Yongchao Chen, Shilong Liu, Bochen Qian, et al. Crab : Cross-environment agent benchmark for multimodal language model agents . arXiv preprint arXiv: 2407.01511 , 2024.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2505.21668", "content": "Tianqi Xu, Linyao Chen, Dai-Jie Wu, Yanjun Chen, Zecheng Zhang, Xiang Yao, Zhiqiang Xie, Yongchao Chen, Shilong Liu, Bochen Qian, et al. Crab : Cross-environment agent benchmark for multimodal language model agents . arXiv preprint arXiv: 2407.01511 , 2024."}
+{"idx": 6, "title": "Advancing Language Multi- Agent Learning with Credit Re-Assignment...", "date": "", "ddg_snippet": "Androidworld: A dynamic benchmarking environment for autonomous agents . CRAB : Cross - environment agent benchmark for multimodal language model agents . arXiv preprint arXiv: 2407 . 01511 , 2024a.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2502.14496v3", "content": "Androidworld: A dynamic benchmarking environment for autonomous agents . CRAB : Cross - environment agent benchmark for multimodal language model agents . arXiv preprint arXiv: 2407 . 01511 , 2024a."}
+{"idx": 7, "title": "COMMA: A Communicative Multimodal Multi- Agent Benchmark", "date": "", "ddg_snippet": "Osworld: Benchmarking multimodal agents for open-ended tasks in real computer environments. Crab : Cross - environment agent benchmark for multimodal language model agents , 2024c.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2410.07553v3", "content": "Osworld: Benchmarking multimodal agents for open-ended tasks in real computer environments. Crab : Cross - environment agent benchmark for multimodal language model agents , 2024c."}
+{"idx": 8, "title": "COMMA: A Communicative Multimodal Multi- Agent Benchmark", "date": "", "ddg_snippet": "Osworld: Benchmarking multimodal agents for open-ended tasks in real computer environments, 2024. Crab : Cross - environment agent benchmark for multimodal language model agents , 2024.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2410.07553v1/", "content": "Osworld: Benchmarking multimodal agents for open-ended tasks in real computer environments, 2024. Crab : Cross - environment agent benchmark for multimodal language model agents , 2024."}
+{"idx": 9, "title": "Advancing Language Multi- Agent Learning with Credit Re-Assignment...", "date": "", "ddg_snippet": "Androidworld: A dynamic benchmarking environment for autonomous agents . CRAB : Cross - environment agent benchmark for multimodal language model agents . arXiv preprint arXiv: 2407 . 01511 , 2024.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2502.14496v2", "content": "Androidworld: A dynamic benchmarking environment for autonomous agents . CRAB : Cross - environment agent benchmark for multimodal language model agents . arXiv preprint arXiv: 2407 . 01511 , 2024."}
diff --git a/data/sampled_jsons/sitearxiv.org_2410.02025_Table_2_Wasserstein_distance_MNIST_sparsely_connected.jsonl b/data/sampled_jsons/sitearxiv.org_2410.02025_Table_2_Wasserstein_distance_MNIST_sparsely_connected.jsonl
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@@ -0,0 +1,4 @@
+{"idx": 0, "title": "A likelihood based approach to distribution regression ...", "date": "", "ddg_snippet": "by S Kumar · 2024 · Cited by 1 — Furthermore, we characterize the Wasserstein convergence rates for the induced intrinsic conditional distribution estimator on the manifold ( ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2410.02025", "content": "by S Kumar · 2024 · Cited by 1 — Furthermore, we characterize the Wasserstein convergence rates for the induced intrinsic conditional distribution estimator on the manifold ( ..."}
+{"idx": 1, "title": "A Likelihood Based Approach to Distribution Regression ...", "date": "", "ddg_snippet": "2 Oct 2024 — We utilized the widely used MNIST dataset for two purposes: to demonstrate the generalizability of our approach to a benchmark image dataset ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2410.02025v1", "content": "2 Oct 2024 — We utilized the widely used MNIST dataset for two purposes: to demonstrate the generalizability of our approach to a benchmark image dataset ..."}
+{"idx": 2, "title": "A Survey on Statistical Theory of Deep Learning", "date": "", "ddg_snippet": "2 .1 Expressive power of fully- connected networks. In this subsection, we briefly review some important results in the approximation theory of neural networks.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2401.07187", "content": "2 .1 Expressive power of fully- connected networks. In this subsection, we briefly review some important results in the approximation theory of neural networks."}
+{"idx": 3, "title": "", "date": "", "ddg_snippet": "", "subpage_snippet": "", "source": "", "link": "", "content": ""}
diff --git a/data/sampled_jsons/sitearxiv.org_2412.06329.jsonl b/data/sampled_jsons/sitearxiv.org_2412.06329.jsonl
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index 0000000000000000000000000000000000000000..4c54321a88bad8f309ebb8fb9f51f2696f228e67
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@@ -0,0 +1,10 @@
+{"idx": 0, "title": "[2412.03607] What and why of entanglement", "date": "", "ddg_snippet": "Abstract page for arXiv paper 2412.03607: What and why of entanglement.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2412.03607", "content": "Abstract page for arXiv paper 2412.03607: What and why of entanglement."}
+{"idx": 1, "title": "[2412.06329] Normalizing Flows are Capable Generative Models Normalizing Flows are Capable Generative Models [2412.06329] Normalizing Flows are Capable Generative Models Abstract arXiv:2412.06329v2 [cs.CV] 10 Dec 2024 Normalizing Flows are Capable Models for RL - arXiv.org FORT: Forward-Only Regression Training of Normalizing Flows Manifold Learning with Normalizing Flows: Towards Regularity ...", "date": "", "ddg_snippet": "Dec 9, 2024 · 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 ... Normalizing Flows (NFs) are likelihood-based models for continuous inputs. They have demon- strated promising results on both density estima- tion and generative modeling tasks, but have re- ceived 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 ... Jan 7, 2025 · 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 attentio… can be thought of as a Transformer-based vari- ant of Masked Autoregressive Flows (MAFs): it consists of a stack of autoregressive Transformer blocks on image patches, alternating the autore- gression direction between layers. TARFLOWis straightforward to train end-to-end, and capable of directly modeling and generating pixels. We also propose three key techniques to improve sam- ple quality ... Normalizing Flows (NFs) are among the most flexible probabilistic models: they (1) support both likelihood maximization and variational inference training, and (2) enable eficient sampling and exact likelihood computation (see Table 1). Yet, NFs have received far less attention from the RL community, perhaps due to the (mis)conception that they have restricted architectures or training ... Given their capacity to compute exact likelihoods, classical normalizing flows (NF) have remained the de facto method for generative modelling in scientific domains (Dinh et al., 2014, 2016; Rezende and Mohamed, 2015). For example, in tasks such as equilibrium sampling of molecular systems with Boltzmann generators (Noé et al., 2019), rapid and exact likelihood evaluation is critical both for ... Modern machine learning increasingly leverages the insight that high-dimensional data often lie near low-dimensional, non-linear manifolds, an idea known as the manifold hypothesis. By explicitly modeling the geometric structure of data through learning Riemannian geometry algorithms can achieve improved performance and interpretability in tasks like clustering, dimensionality reduction, and ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2412.06329", "content": "Dec 9, 2024 · 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 ... Normalizing Flows (NFs) are likelihood-based models for continuous inputs. They have demon- strated promising results on both density estima- tion and generative modeling tasks, but have re- ceived 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 ... Jan 7, 2025 · 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 attentio… can be thought of as a Transformer-based vari- ant of Masked Autoregressive Flows (MAFs): it consists of a stack of autoregressive Transformer blocks on image patches, alternating the autore- gression direction between layers. TARFLOWis straightforward to train end-to-end, and capable of directly modeling and generating pixels. We also propose three key techniques to improve sam- ple quality ... Normalizing Flows (NFs) are among the most flexible probabilistic models: they (1) support both likelihood maximization and variational inference training, and (2) enable eficient sampling and exact likelihood computation (see Table 1). Yet, NFs have received far less attention from the RL community, perhaps due to the (mis)conception that they have restricted architectures or training ... Given their capacity to compute exact likelihoods, classical normalizing flows (NF) have remained the de facto method for generative modelling in scientific domains (Dinh et al., 2014, 2016; Rezende and Mohamed, 2015). For example, in tasks such as equilibrium sampling of molecular systems with Boltzmann generators (Noé et al., 2019), rapid and exact likelihood evaluation is critical both for ... Modern machine learning increasingly leverages the insight that high-dimensional data often lie near low-dimensional, non-linear manifolds, an idea known as the manifold hypothesis. By explicitly modeling the geometric structure of data through learning Riemannian geometry algorithms can achieve improved performance and interpretability in tasks like clustering, dimensionality reduction, and ..."}
+{"idx": 2, "title": "[2412.21187] Do NOT Think That Much for 2+3=? On the Overthinking...", "date": "", "ddg_snippet": "We introduce novel efficiency metrics from both outcome and process perspectives to evaluate the rational use of computational resources by o1-like models.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2412.21187", "content": "We introduce novel efficiency metrics from both outcome and process perspectives to evaluate the rational use of computational resources by o1-like models."}
+{"idx": 3, "title": "[2412.06329] Normalizing Flows are Capable Generative Models", "date": "", "ddg_snippet": "Jan 7, 2025 · 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 attentio…", "subpage_snippet": "", "source": "ar5iv.labs.arxiv.org", "link": "https://ar5iv.labs.arxiv.org/html/2412.06329", "content": "Jan 7, 2025 · 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 attentio…"}
+{"idx": 4, "title": "Abstract arXiv:2412.06329v2 [cs.CV] 10 Dec 2024", "date": "", "ddg_snippet": "can be thought of as a Transformer-based vari- ant of Masked Autoregressive Flows (MAFs): it consists of a stack of autoregressive Transformer blocks on image patches, alternating the autore- gression direction between layers. TARFLOWis straightforward to train end-to-end, and capable of directly modeling and generating pixels. We also propose three key techniques to improve sam- ple quality ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2412.06329v2", "content": "can be thought of as a Transformer-based vari- ant of Masked Autoregressive Flows (MAFs): it consists of a stack of autoregressive Transformer blocks on image patches, alternating the autore- gression direction between layers. TARFLOWis straightforward to train end-to-end, and capable of directly modeling and generating pixels. We also propose three key techniques to improve sam- ple quality ..."}
+{"idx": 5, "title": "Normalizing Flows are Capable Models for RL - arXiv.org", "date": "", "ddg_snippet": "Normalizing Flows (NFs) are among the most flexible probabilistic models: they (1) support both likelihood maximization and variational inference training, and (2) enable eficient sampling and exact likelihood computation (see Table 1). Yet, NFs have received far less attention from the RL community, perhaps due to the (mis)conception that they have restricted architectures or training ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2505.23527v3", "content": "Normalizing Flows (NFs) are among the most flexible probabilistic models: they (1) support both likelihood maximization and variational inference training, and (2) enable eficient sampling and exact likelihood computation (see Table 1). Yet, NFs have received far less attention from the RL community, perhaps due to the (mis)conception that they have restricted architectures or training ..."}
+{"idx": 6, "title": "Normalizing Flows are Capable Generative Models", "date": "", "ddg_snippet": "References. License: CC BY 4.0. arXiv: 2412 . 06329 v2 [cs.CV] 10 Dec 2024.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2412.06329v2", "content": "References. License: CC BY 4.0. arXiv: 2412 . 06329 v2 [cs.CV] 10 Dec 2024."}
+{"idx": 7, "title": "Normalizing Flows are Capable Generative Models", "date": "", "ddg_snippet": "License: CC BY 4.0. arXiv: 2412 . 06329 v3 [cs.CV] 06 Jun 2025.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2412.06329v3", "content": "License: CC BY 4.0. arXiv: 2412 . 06329 v3 [cs.CV] 06 Jun 2025."}
+{"idx": 8, "title": "Jet: A Modern Transformer-Based Normalizing Flow", "date": "", "ddg_snippet": "Normalizing flows are capable generative models. arXiv: 2412 . 06329 , 2024. Appendix A Appendix.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2412.15129v1", "content": "Normalizing flows are capable generative models. arXiv: 2412 . 06329 , 2024. Appendix A Appendix."}
+{"idx": 9, "title": "Normalizing Flows are Capable Generative Models", "date": "", "ddg_snippet": "Normalizing Flows (NFs) are likelihood-based models for continuous inputs. They have demon- strated promising results on both density estima- tion and generative modeling tasks, but have re- ceived 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 ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2412.06329", "content": "Normalizing Flows (NFs) are likelihood-based models for continuous inputs. They have demon- strated promising results on both density estima- tion and generative modeling tasks, but have re- ceived 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 ..."}
diff --git a/data/sampled_jsons/sitearxiv.org_2501.02376_Section_G_limitations_IP-Adapter.jsonl b/data/sampled_jsons/sitearxiv.org_2501.02376_Section_G_limitations_IP-Adapter.jsonl
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@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Machine Learning Jun 2024 - web3.arxiv.org", "date": "", "ddg_snippet": "Comments: To appear in the Journal of Automated Reasoning (JAR), 2024. This is an extended version of a CAV 2023 paper, titled: \"Verifying Generalization in Deep Learning\" Subjects:Machine Learning (cs.LG); Logic in Computer Science (cs.LO) [211] arXiv:2406.02027 [pdf, html, other] Title: Inference Attacks: A Taxonomy, Survey, and Promising Directions Feng Wu, Lei Cui, Shaowen Yao, Shui Yu ...", "subpage_snippet": "", "source": "web3.arxiv.org", "link": "https://web3.arxiv.org/list/cs.LG/2024-06?skip=150&show=2000", "content": "Comments: To appear in the Journal of Automated Reasoning (JAR), 2024. This is an extended version of a CAV 2023 paper, titled: \"Verifying Generalization in Deep Learning\" Subjects:Machine Learning (cs.LG); Logic in Computer Science (cs.LO) [211] arXiv:2406.02027 [pdf, html, other] Title: Inference Attacks: A Taxonomy, Survey, and Promising Directions Feng Wu, Lei Cui, Shaowen Yao, Shui Yu ..."}
+{"idx": 1, "title": "[2501.07410] Anonymous Attention and Abuse", "date": "", "ddg_snippet": "Using data on linked domains, we show how these trends reflect broader changes in the economics profession's digital footprint. Our analysis sheds light on EJMR's informational role but also raises questions about inclusivity and professional ethics in economics.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2501.07410", "content": "Using data on linked domains, we show how these trends reflect broader changes in the economics profession's digital footprint. Our analysis sheds light on EJMR's informational role but also raises questions about inclusivity and professional ethics in economics."}
+{"idx": 2, "title": "[2501.08643v1] MonSter: Marry Monodepth to Stereo Unleashes Power", "date": "", "ddg_snippet": "Existing methods struggle to handle ill-posed regions with limited matching cues, such as occlusions and textureless areas. To address this, we propose MonSter, a novel method that leverages the complementary strengths of monocular depth estimation and stereo matching.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2501.08643v1", "content": "Existing methods struggle to handle ill-posed regions with limited matching cues, such as occlusions and textureless areas. To address this, we propose MonSter, a novel method that leverages the complementary strengths of monocular depth estimation and stereo matching."}
+{"idx": 3, "title": "Generalizable Origin Identification for Text-Guided Image-to-Image...", "date": "", "ddg_snippet": "Limitation . We note that certain methods, such as InstructPix2Pix [2] and IP - Adapter [46] (see Fig. 10), perform text-guided image-to-image tasks in paradigms that go beyond the scope of our theorems. For a more detailed discussion, please refer to Supplementary ( Section 13).", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2501.02376v1/", "content": "Limitation . We note that certain methods, such as InstructPix2Pix [2] and IP - Adapter [46] (see Fig. 10), perform text-guided image-to-image tasks in paradigms that go beyond the scope of our theorems. For a more detailed discussion, please refer to Supplementary ( Section 13)."}
+{"idx": 4, "title": "Computer Vision and Pattern Recognition", "date": "", "ddg_snippet": "Title: CoStruction: Conjoint radiance field optimization for urban scene reconStruction with limited image overlap.[72] arXiv:2501.03737 (cross-list from eess.IV) [pdf, html, other].", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/list/cs.CV/pastweek?show=455", "content": "Title: CoStruction: Conjoint radiance field optimization for urban scene reconStruction with limited image overlap.[72] arXiv:2501.03737 (cross-list from eess.IV) [pdf, html, other]."}
+{"idx": 5, "title": "Lynx: Towards High-Fidelity Personalized Video Generation", "date": "", "ddg_snippet": "To overcome these limitations , tuning-free methods [47, 43] introduce lightweight ID-injection modules that avoid per-subject training. IP-Adapter [47] represents identity features with a face recognition encoder and injects them into the base model through adapters .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2509.15496v1", "content": "To overcome these limitations , tuning-free methods [47, 43] introduce lightweight ID-injection modules that avoid per-subject training. IP-Adapter [47] represents identity features with a face recognition encoder and injects them into the base model through adapters ."}
+{"idx": 6, "title": "DPDEdit: Detail-Preserved Diffusion Models for", "date": "", "ddg_snippet": "In comparison to IP-Adapter , our approach achieves a higher degree of alignment with the input textures, demonstrating the effectiveness of the proposed texture injection and refinement mechanism. For more qualitative results, please refer to the supplementary materials.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2409.01086v3", "content": "In comparison to IP-Adapter , our approach achieves a higher degree of alignment with the input textures, demonstrating the effectiveness of the proposed texture injection and refinement mechanism. For more qualitative results, please refer to the supplementary materials."}
+{"idx": 7, "title": "DPDEdit: Detail-Preserved Diffusion Models for Multimodal Fashion Image ...", "date": "", "ddg_snippet": "As a result, introducing multimodal approaches in fashion image editing is essential for meeting user requirements. IP - Adapter [28] introduces a lightweight adapter that injects im-age conditions into the denoising process. MGD [19] integrates text, human pose, and garment sketch modalities for fashion image editing using text inversion techniques.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2409.01086v3", "content": "As a result, introducing multimodal approaches in fashion image editing is essential for meeting user requirements. IP - Adapter [28] introduces a lightweight adapter that injects im-age conditions into the denoising process. MGD [19] integrates text, human pose, and garment sketch modalities for fashion image editing using text inversion techniques."}
+{"idx": 8, "title": "TIDE: Achieving Balanced Subject-Driven Image Generation via Target ...", "date": "", "ddg_snippet": "4 Methodology In this section , we first present our open-domain instruction-target dataset for supervised training in SDIG. Next, we show an overview of the TIDE framework, followed by an explanation of how we leverage multimodal information to train external adapter , including multimodal representation and Direct Subject Diffusion optimization.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2509.06499v2", "content": "4 Methodology In this section , we first present our open-domain instruction-target dataset for supervised training in SDIG. Next, we show an overview of the TIDE framework, followed by an explanation of how we leverage multimodal information to train external adapter , including multimodal representation and Direct Subject Diffusion optimization."}
+{"idx": 9, "title": "Technical Report: Protocol-Aware Firmware Rehosting for Effective ...", "date": "", "ddg_snippet": "Probing Relying solely on state extraction to explore the network state has limitations , particularly when there is no prior knowledge of the protocol suite or when the firmware, based on its role (e.g., client or server), remains idle without an external prompt. We use an active probing approach to address this issue, as detailed in Section 3.4.1.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2509.13740v1", "content": "Probing Relying solely on state extraction to explore the network state has limitations , particularly when there is no prior knowledge of the protocol suite or when the firmware, based on its role (e.g., client or server), remains idle without an external prompt. We use an active probing approach to address this issue, as detailed in Section 3.4.1."}
diff --git a/data/sampled_jsons/sitearxiv.org_2502.00775_ATA_Adaptive_Task_Allocation.jsonl b/data/sampled_jsons/sitearxiv.org_2502.00775_ATA_Adaptive_Task_Allocation.jsonl
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@@ -0,0 +1,10 @@
+{"idx": 0, "title": "[2502.00775] ATA: Adaptive Task Allocation for Efficient ...", "date": "", "ddg_snippet": "Feb 2, 2025 · In this paper, we propose ATA ( Adaptive Task Allocation ), a method that adapts to heterogeneous and random distributions of worker computation times. Through rigorous theoretical analysis, we show that ATA identifies the optimal task allocation and performs comparably to methods with prior knowledge of computation times.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2502.00775", "content": "Feb 2, 2025 · In this paper, we propose ATA ( Adaptive Task Allocation ), a method that adapts to heterogeneous and random distributions of worker computation times. Through rigorous theoretical analysis, we show that ATA identifies the optimal task allocation and performs comparably to methods with prior knowledge of computation times."}
+{"idx": 1, "title": "Optimization and Control Feb 2025 - arXiv.org", "date": "", "ddg_snippet": "Title: ATA : Adaptive Task Allocation for Efficient Resource Management in Distributed Machine Learning Artavazd Maranjyan, El Mehdi Saad, Peter Richtárik, Francesco Orabona Subjects:Machine Learning (cs.LG); Distributed, Parallel, and Cluster Computing (cs.DC); Optimization and Control (math.OC); Machine Learning (stat.ML)", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/list/math.OC/2025-02?skip=150", "content": "Title: ATA : Adaptive Task Allocation for Efficient Resource Management in Distributed Machine Learning Artavazd Maranjyan, El Mehdi Saad, Peter Richtárik, Francesco Orabona Subjects:Machine Learning (cs.LG); Distributed, Parallel, and Cluster Computing (cs.DC); Optimization and Control (math.OC); Machine Learning (stat.ML)"}
+{"idx": 2, "title": "Mathematics Feb 2025 - arXiv.org", "date": "", "ddg_snippet": "Title: ATA : Adaptive Task Allocation for Efficient Resource Management in Distributed Machine Learning Artavazd Maranjyan, El Mehdi Saad, Peter Richtárik, Francesco Orabona Subjects:Machine Learning (cs.LG); Distributed, Parallel, and Cluster Computing (cs.DC); Optimization and Control (math.OC); Machine Learning (stat.ML)", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/list/math/2025-02?skip=2100", "content": "Title: ATA : Adaptive Task Allocation for Efficient Resource Management in Distributed Machine Learning Artavazd Maranjyan, El Mehdi Saad, Peter Richtárik, Francesco Orabona Subjects:Machine Learning (cs.LG); Distributed, Parallel, and Cluster Computing (cs.DC); Optimization and Control (math.OC); Machine Learning (stat.ML)"}
+{"idx": 3, "title": "Statistics Feb 2025 - arXiv.org", "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": "arxiv.org", "link": "https://arxiv.org/list/stat/2025-02?skip=550", "content": "ATA : Adaptive Task Allocation for Efficient Resource Management in Distributed Machine Learning Artavazd Maranjyan, El Mehdi Saad, Peter Richtárik, Francesco Orabona"}
+{"idx": 4, "title": "Search | arXiv e-print repository", "date": "", "ddg_snippet": "EcoFL implements a two-stage optimisation approach: an RL-based rApp for dynamic RAT selection that balances energy efficiency with network performance, and a CNN-based xApp for near real-time resource allocation with adaptive policies. This coordinated approach significantly enhances communication resilience under fluctuating network conditions.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/search/eess?searchtype=author&query=Shah,+S", "content": "EcoFL implements a two-stage optimisation approach: an RL-based rApp for dynamic RAT selection that balances energy efficiency with network performance, and a CNN-based xApp for near real-time resource allocation with adaptive policies. This coordinated approach significantly enhances communication resilience under fluctuating network conditions."}
+{"idx": 5, "title": "ATA: Adaptive Task Allocation for Efficient Resource ...", "date": "", "ddg_snippet": "In this paper, we propose ATA (Adaptive Task Allocation), a method that adapts to heterogeneous and random distributions of worker computation ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2502.00775v1", "content": "In this paper, we propose ATA (Adaptive Task Allocation), a method that adapts to heterogeneous and random distributions of worker computation ..."}
+{"idx": 6, "title": "cherryATA", "date": "", "ddg_snippet": "arXiv:2502.00775v1 [cs.LG] 2 Feb 2025. Page 2. ATA: Adaptive Task Allocation for Efficient Resource Management in Distributed Machine Learning. 1.1. A ...", "subpage_snippet": "", "source": "www.arxiv.org", "link": "https://www.arxiv.org/pdf/2502.00775v1", "content": "arXiv:2502.00775v1 [cs.LG] 2 Feb 2025. Page 2. ATA: Adaptive Task Allocation for Efficient Resource Management in Distributed Machine Learning. 1.1. A ..."}
+{"idx": 7, "title": "Edge AI: A Taxonomy, Systematic Review and Future Directions", "date": "", "ddg_snippet": "The problem of resource allocation in edge devices can be described as MDP, and the deep Q network (DQN) algorithm uses multiple replay memories to minimize the total delay and resource utilization. the study in [113] addresses the intricate issue of joint task offloading and resource allocation problem for computationally intensive tasks in ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2407.04053v1", "content": "The problem of resource allocation in edge devices can be described as MDP, and the deep Q network (DQN) algorithm uses multiple replay memories to minimize the total delay and resource utilization. the study in [113] addresses the intricate issue of joint task offloading and resource allocation problem for computationally intensive tasks in ..."}
+{"idx": 8, "title": "Optimization and Control Feb 2025", "date": "", "ddg_snippet": "[311] arXiv:2502.00775 (cross-list from cs.LG) ... Title: ATA: Adaptive Task Allocation for Efficient Resource Management in Distributed Machine Learning ... Title: ...", "subpage_snippet": "", "source": "www.arxiv.org", "link": "https://www.arxiv.org/list/math.OC/2025-02?skip=300&show=250", "content": "[311] arXiv:2502.00775 (cross-list from cs.LG) ... Title: ATA: Adaptive Task Allocation for Efficient Resource Management in Distributed Machine Learning ... Title: ..."}
+{"idx": 9, "title": "Machine Learning Feb 2025", "date": "", "ddg_snippet": "[263] arXiv: 2502.00775 (cross-list from cs.LG) ... Title: ATA : Adaptive Task Allocation for Efficient Resource Management in Distributed Machine Learning.", "subpage_snippet": "", "source": "arxiv.org", "link": "http://arxiv.org/list/stat.ML/2025-02?skip=250&show=250", "content": "[263] arXiv: 2502.00775 (cross-list from cs.LG) ... Title: ATA : Adaptive Task Allocation for Efficient Resource Management in Distributed Machine Learning."}
diff --git a/data/sampled_jsons/sitearxiv.org_2502.00775_Algorithm_8_Recursive_Allocation_Selection_RAS.jsonl b/data/sampled_jsons/sitearxiv.org_2502.00775_Algorithm_8_Recursive_Allocation_Selection_RAS.jsonl
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index 0000000000000000000000000000000000000000..c0f867884146f6a10e267e18e86464dbf30e4c33
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@@ -0,0 +1,10 @@
+{"idx": 0, "title": "ATA: Adaptive Task Allocation for Efficient Resource ...", "date": "", "ddg_snippet": "1.2 Contributions In this work, we formalize the task allocation problem as a combinatorial online learning problem with partial feedback and non-linear losses. Then, we introduce ATA, a lower-confidence bound-based algorithm designed to solve the proposed allocation problem.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2502.00775v1", "content": "1.2 Contributions In this work, we formalize the task allocation problem as a combinatorial online learning problem with partial feedback and non-linear losses. Then, we introduce ATA, a lower-confidence bound-based algorithm designed to solve the proposed allocation problem."}
+{"idx": 1, "title": "[ 2502 . 00775 ] ATA: Adaptive Task Allocation for Efficient Resource...", "date": "", "ddg_snippet": "arXiv: 2502 . 00775 (cs). [Submitted on 2 Feb 2025 (v1), last revised 22 May 2025 (this version, v2)].View a PDF of the paper titled ATA: Adaptive Task Allocation for Efficient Resource Management in Distributed Machine Learning, by Artavazd Maranjyan and 3 other authors.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2502.00775", "content": "arXiv: 2502 . 00775 (cs). [Submitted on 2 Feb 2025 (v1), last revised 22 May 2025 (this version, v2)].View a PDF of the paper titled ATA: Adaptive Task Allocation for Efficient Resource Management in Distributed Machine Learning, by Artavazd Maranjyan and 3 other authors."}
+{"idx": 2, "title": "ATA: Adaptive Task Allocation for Efficient Resource Management in...", "date": "", "ddg_snippet": "Algorithm 8 Recursive Allocation Selection ( RAS ). 1: Input: Scores., RAS ( Algorithm 8 ) allocates the task to worker with the smallest score (line 9). Thus, the base case holds.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2502.00775v2", "content": "Algorithm 8 Recursive Allocation Selection ( RAS ). 1: Input: Scores., RAS ( Algorithm 8 ) allocates the task to worker with the smallest score (line 9). Thus, the base case holds."}
+{"idx": 3, "title": "cherryATA: cherryAdaptive cherryTask cherryAllocation for Efficient...", "date": "", "ddg_snippet": "B. Recursive Allocation Selection Algorithm. In this section, we introduce an efficient method for finding the best allocation . Algorithm 8 Recursive Allocation Selection ( RAS ).", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2502.00775", "content": "B. Recursive Allocation Selection Algorithm. In this section, we introduce an efficient method for finding the best allocation . Algorithm 8 Recursive Allocation Selection ( RAS )."}
+{"idx": 4, "title": "Optimality of the recursive Neyman allocation", "date": "", "ddg_snippet": "We use V- allocation to give a formal proof of optimality of the popular recursive Neyman algorithm , rNa. This approach is convenient also for a quick proof of optimality of the algorithm of Stenger and Gabler (2005), SGa, as well as of its modication, coma, we propose here.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2105.14486", "content": "We use V- allocation to give a formal proof of optimality of the popular recursive Neyman algorithm , rNa. This approach is convenient also for a quick proof of optimality of the algorithm of Stenger and Gabler (2005), SGa, as well as of its modication, coma, we propose here."}
+{"idx": 5, "title": "Adaptive Resource Allocation Optimization Using Large ... [2307.00337] Recursive Algorithmic Reasoning - arXiv.org Efficient Pre-Training of LLMs via Topology-Aware ... [2504.11250] A Rollout-Based Algorithm and Reward Function ... Dynamic Resource Allocation: Algorithmic Design Principles ...", "date": "", "ddg_snippet": "Feb 4, 2025 · Abstract page for arXiv paper 2502.02287: Adaptive Resource Allocation Optimization Using Large Language Models in Dynamic Wireless Environments Jul 1, 2023 · Learning models that execute algorithms can enable us to address a key problem in deep learning: generalizing to out-of-distribution data. However, neural networks are currently unable to execute recursive algorithms because they do not have arbitrarily large memory to store and recall state. To address this, we (1) propose a way to augment graph neural networks (GNNs) with a stack, and (2 ... Then it recursively bi-partitions the physical graph and maps the job graph to the sub-graphs (Hierarchical Static Mapping Dual Recursive Bi-partitioning [10]). The graph bi-partitioning is implemented by the Fiduccia Mattheyses algorithm [11]. Apr 15, 2025 · Abstract page for arXiv paper 2504.11250: A Rollout-Based Algorithm and Reward Function for Efficient Resource Allocation in Business Processes May 18, 2022 · Dynamic resource allocation problems are ubiquitous, arising in inventory management, order fulfillment, online advertising, and other applications. We initially focus on one of the simplest models of online resource allocation : the multisecretary problem.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2502.02287", "content": "Feb 4, 2025 · Abstract page for arXiv paper 2502.02287: Adaptive Resource Allocation Optimization Using Large Language Models in Dynamic Wireless Environments Jul 1, 2023 · Learning models that execute algorithms can enable us to address a key problem in deep learning: generalizing to out-of-distribution data. However, neural networks are currently unable to execute recursive algorithms because they do not have arbitrarily large memory to store and recall state. To address this, we (1) propose a way to augment graph neural networks (GNNs) with a stack, and (2 ... Then it recursively bi-partitions the physical graph and maps the job graph to the sub-graphs (Hierarchical Static Mapping Dual Recursive Bi-partitioning [10]). The graph bi-partitioning is implemented by the Fiduccia Mattheyses algorithm [11]. Apr 15, 2025 · Abstract page for arXiv paper 2504.11250: A Rollout-Based Algorithm and Reward Function for Efficient Resource Allocation in Business Processes May 18, 2022 · Dynamic resource allocation problems are ubiquitous, arising in inventory management, order fulfillment, online advertising, and other applications. We initially focus on one of the simplest models of online resource allocation : the multisecretary problem."}
+{"idx": 6, "title": "[2307.00337] Recursive Algorithmic Reasoning - arXiv.org Efficient Pre-Training of LLMs via Topology-Aware ... [2504.11250] A Rollout-Based Algorithm and Reward Function ... Dynamic Resource Allocation: Algorithmic Design Principles ...", "date": "", "ddg_snippet": "Jul 1, 2023 · Learning models that execute algorithms can enable us to address a key problem in deep learning: generalizing to out-of-distribution data. However, neural networks are currently unable to execute recursive algorithms because they do not have arbitrarily large memory to store and recall state. To address this, we (1) propose a way to augment graph neural networks (GNNs) with a stack, and (2 ... Then it recursively bi-partitions the physical graph and maps the job graph to the sub-graphs (Hierarchical Static Mapping Dual Recursive Bi-partitioning [10]). The graph bi-partitioning is implemented by the Fiduccia Mattheyses algorithm [11]. Apr 15, 2025 · Abstract page for arXiv paper 2504.11250: A Rollout-Based Algorithm and Reward Function for Efficient Resource Allocation in Business Processes May 18, 2022 · Dynamic resource allocation problems are ubiquitous, arising in inventory management, order fulfillment, online advertising, and other applications. We initially focus on one of the simplest models of online resource allocation : the multisecretary problem.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2307.00337", "content": "Jul 1, 2023 · Learning models that execute algorithms can enable us to address a key problem in deep learning: generalizing to out-of-distribution data. However, neural networks are currently unable to execute recursive algorithms because they do not have arbitrarily large memory to store and recall state. To address this, we (1) propose a way to augment graph neural networks (GNNs) with a stack, and (2 ... Then it recursively bi-partitions the physical graph and maps the job graph to the sub-graphs (Hierarchical Static Mapping Dual Recursive Bi-partitioning [10]). The graph bi-partitioning is implemented by the Fiduccia Mattheyses algorithm [11]. Apr 15, 2025 · Abstract page for arXiv paper 2504.11250: A Rollout-Based Algorithm and Reward Function for Efficient Resource Allocation in Business Processes May 18, 2022 · Dynamic resource allocation problems are ubiquitous, arising in inventory management, order fulfillment, online advertising, and other applications. We initially focus on one of the simplest models of online resource allocation : the multisecretary problem."}
+{"idx": 7, "title": "[2504.11250] A Rollout-Based Algorithm and Reward Function ...", "date": "", "ddg_snippet": "Apr 15, 2025 · Abstract page for arXiv paper 2504.11250: A Rollout-Based Algorithm and Reward Function for Efficient Resource Allocation in Business Processes", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2504.11250", "content": "Apr 15, 2025 · Abstract page for arXiv paper 2504.11250: A Rollout-Based Algorithm and Reward Function for Efficient Resource Allocation in Business Processes"}
+{"idx": 8, "title": "Dynamic Resource Allocation: Algorithmic Design Principles ...", "date": "", "ddg_snippet": "May 18, 2022 · Dynamic resource allocation problems are ubiquitous, arising in inventory management, order fulfillment, online advertising, and other applications. We initially focus on one of the simplest models of online resource allocation : the multisecretary problem.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2205.09078", "content": "May 18, 2022 · Dynamic resource allocation problems are ubiquitous, arising in inventory management, order fulfillment, online advertising, and other applications. We initially focus on one of the simplest models of online resource allocation : the multisecretary problem."}
+{"idx": 9, "title": "Mathematics", "date": "", "ddg_snippet": "Title: Sampling models for selective inference. Daniel García Rasines, G. Alastair Young.[851] arXiv: 2502 . 00775 (cross-list from cs.LG) [pdf, html, other]. Title: ATA: Adaptive Task Allocation for Efficient Resource Management in Distributed Machine Learning.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/list/math/pastweek?show=869", "content": "Title: Sampling models for selective inference. Daniel García Rasines, G. Alastair Young.[851] arXiv: 2502 . 00775 (cross-list from cs.LG) [pdf, html, other]. Title: ATA: Adaptive Task Allocation for Efficient Resource Management in Distributed Machine Learning."}
diff --git a/data/sampled_jsons/sitearxiv.org_2502.01846_section_3.1_spherical_coordinates_UV_mapping_formula.jsonl b/data/sampled_jsons/sitearxiv.org_2502.01846_section_3.1_spherical_coordinates_UV_mapping_formula.jsonl
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@@ -0,0 +1,10 @@
+{"idx": 0, "title": "UVGS: Reimagining Unstructured 3D Gaussian Splatting using UV ... UVGS: Reimagining Unstructured 3D Gaussian Splatting using UV ... arXiv:2502.01846v3 [cs.CV] 20 Mar 2025 UVGS: Reimagining Unstructured 3D Gaussian Splatting using UV ... COMPUTING WITH FUNCTIONS IN SPHERICAL AND POLAR GEOMETRIES I ... A Novel Orbit Parameterization in Spherical Coordinates Catchup results for Astrophysics on Wed, 03 Sep 2025 - arXiv.org", "date": "", "ddg_snippet": "Feb 3 , 2025 · 3D Gaussian Splatting (3DGS) has demonstrated superior quality in modeling 3D objects and scenes. However, generating 3DGS remains challenging due to their discrete, unstructured, and permutation-invariant nature. In this work, we present a simple yet effective method to overcome these challenges. We utilize spherical mapping to transform 3DGS into a structured 2D representation, termed UVGS ... Mar 5, 2025 · To address these shortcomings, we introduce UV Gaussian Splatting (UVGS), which provides a structured transformation of 3D Gaussian primitives into a 2D representation while preserving essential 3D information. We use spherical mapping [43] that inscribes Gaussian splats in a spherical surface, and projects attributes like position, rotation, scale, opacity, and color into an organized 14 ... Hence we opted for spherical mapping the process of which involves convert- ing 3D Cartesian coordinates (x,y,z) into spherical coor- dinates (ρ,θ,ϕ) and then mapping these onto a 2D plane. To address these shortcomings, we introduce UV Gaussian Splatting (UVGS), which provides a structured transformation of 3D Gaussian primitives into a 2D representation while preserving essential 3D information. We use spherical mapping [42] that inscribes Gaussian splats in a spherical surface, and projects attributes like position, rotation, scale, opacity, and color into an organized 14 ... It is therefore common practice to express vector-valued functions in Cartesian coordinates , not latitude{longitude coordinates . In Cartesian coordinates the components of the vector eld are smooth and can be approximated using the low rank techniques developed in Section 3. Oct 4, 2024 · In this paper we introduce a new orbit parameterization in spherical coordinates that makes it simple to represent orbits with a variety of invariant and varying parameters. We define the coordinate system in Section 2 and highlight some of its properties in Section 3. Sep 3 , 2025 · Utilising high-resolution archival data from AstroSat/UVIT for UV imaging and the Atacama Large Millimeter/submillimeter Array (ALMA) for CO (2-1) molecular gas mapping , we explore recent star formation and gas distribution with a spatial resolution of about 120 to 132 pc.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2502.01846", "content": "Feb 3 , 2025 · 3D Gaussian Splatting (3DGS) has demonstrated superior quality in modeling 3D objects and scenes. However, generating 3DGS remains challenging due to their discrete, unstructured, and permutation-invariant nature. In this work, we present a simple yet effective method to overcome these challenges. We utilize spherical mapping to transform 3DGS into a structured 2D representation, termed UVGS ... Mar 5, 2025 · To address these shortcomings, we introduce UV Gaussian Splatting (UVGS), which provides a structured transformation of 3D Gaussian primitives into a 2D representation while preserving essential 3D information. We use spherical mapping [43] that inscribes Gaussian splats in a spherical surface, and projects attributes like position, rotation, scale, opacity, and color into an organized 14 ... Hence we opted for spherical mapping the process of which involves convert- ing 3D Cartesian coordinates (x,y,z) into spherical coor- dinates (ρ,θ,ϕ) and then mapping these onto a 2D plane. To address these shortcomings, we introduce UV Gaussian Splatting (UVGS), which provides a structured transformation of 3D Gaussian primitives into a 2D representation while preserving essential 3D information. We use spherical mapping [42] that inscribes Gaussian splats in a spherical surface, and projects attributes like position, rotation, scale, opacity, and color into an organized 14 ... It is therefore common practice to express vector-valued functions in Cartesian coordinates , not latitude{longitude coordinates . In Cartesian coordinates the components of the vector eld are smooth and can be approximated using the low rank techniques developed in Section 3. Oct 4, 2024 · In this paper we introduce a new orbit parameterization in spherical coordinates that makes it simple to represent orbits with a variety of invariant and varying parameters. We define the coordinate system in Section 2 and highlight some of its properties in Section 3. Sep 3 , 2025 · Utilising high-resolution archival data from AstroSat/UVIT for UV imaging and the Atacama Large Millimeter/submillimeter Array (ALMA) for CO (2-1) molecular gas mapping , we explore recent star formation and gas distribution with a spatial resolution of about 120 to 132 pc."}
+{"idx": 1, "title": "COMPUTING WITH FUNCTIONS IN SPHERICAL AND POLAR GEOMETRIES I ...", "date": "", "ddg_snippet": "It is therefore common practice to express vector-valued functions in Cartesian coordinates , not latitude{longitude coordinates . In Cartesian coordinates the components of the vector eld are smooth and can be approximated using the low rank techniques developed in Section 3.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/1510.08094", "content": "It is therefore common practice to express vector-valued functions in Cartesian coordinates , not latitude{longitude coordinates . In Cartesian coordinates the components of the vector eld are smooth and can be approximated using the low rank techniques developed in Section 3."}
+{"idx": 2, "title": "A Novel Orbit Parameterization in Spherical Coordinates", "date": "", "ddg_snippet": "Oct 4, 2024 · In this paper we introduce a new orbit parameterization in spherical coordinates that makes it simple to represent orbits with a variety of invariant and varying parameters. We define the coordinate system in Section 2 and highlight some of its properties in Section 3.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2410.03598v1", "content": "Oct 4, 2024 · In this paper we introduce a new orbit parameterization in spherical coordinates that makes it simple to represent orbits with a variety of invariant and varying parameters. We define the coordinate system in Section 2 and highlight some of its properties in Section 3."}
+{"idx": 3, "title": "Catchup results for Astrophysics on Wed, 03 Sep 2025 - arXiv.org", "date": "", "ddg_snippet": "Sep 3 , 2025 · Utilising high-resolution archival data from AstroSat/UVIT for UV imaging and the Atacama Large Millimeter/submillimeter Array (ALMA) for CO (2-1) molecular gas mapping , we explore recent star formation and gas distribution with a spatial resolution of about 120 to 132 pc.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/catchup/astro-ph/2025-09-03?abs=True&page=1", "content": "Sep 3 , 2025 · Utilising high-resolution archival data from AstroSat/UVIT for UV imaging and the Atacama Large Millimeter/submillimeter Array (ALMA) for CO (2-1) molecular gas mapping , we explore recent star formation and gas distribution with a spatial resolution of about 120 to 132 pc."}
+{"idx": 4, "title": "UVGS: Reimagining Unstructured 3D Gaussian Splatting using UV ...", "date": "", "ddg_snippet": "arXiv: 2502 . 01846 v1 [cs.CV] 3 Feb 2025.We use spherical mapping [42] that inscribes Gaussian splats in a spherical surface, and projects attributes like position, rota-tion, scale, opacity, and color into an organized 14-channel image-like UV map .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2502.01846", "content": "arXiv: 2502 . 01846 v1 [cs.CV] 3 Feb 2025.We use spherical mapping [42] that inscribes Gaussian splats in a spherical surface, and projects attributes like position, rota-tion, scale, opacity, and color into an organized 14-channel image-like UV map ."}
+{"idx": 5, "title": "UVGS: Reimagining Unstructured 3D Gaussian Splatting using UV ...", "date": "", "ddg_snippet": "To address these shortcomings, we introduce UV Gaussian Splatting (UVGS), which provides a structured transformation of 3D Gaussian primitives into a 2D representation while preserving essential 3D information. We use spherical mapping [42] that inscribes Gaussian splats in a spherical surface, and projects attributes like position, rotation, scale, opacity, and color into an organized 14 ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2502.01846v1", "content": "To address these shortcomings, we introduce UV Gaussian Splatting (UVGS), which provides a structured transformation of 3D Gaussian primitives into a 2D representation while preserving essential 3D information. We use spherical mapping [42] that inscribes Gaussian splats in a spherical surface, and projects attributes like position, rotation, scale, opacity, and color into an organized 14 ..."}
+{"idx": 6, "title": "UVGS: Reimagining Unstructured 3D Gaussian Splatting using UV ...", "date": "", "ddg_snippet": "arXiv: 2502 . 01846 v3 [cs.CV] 20 Mar 2025.Figure 2: The input 3DGS object is first converted to UVGS maps through spherical mapping . We use a multibranch forward mapping network to convert the obtained 14-channel UVGS to a compact 3-channel Super UVGS image.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2502.01846v3", "content": "arXiv: 2502 . 01846 v3 [cs.CV] 20 Mar 2025.Figure 2: The input 3DGS object is first converted to UVGS maps through spherical mapping . We use a multibranch forward mapping network to convert the obtained 14-channel UVGS to a compact 3-channel Super UVGS image."}
+{"idx": 7, "title": "UVGS: Reimagining Unstructured 3D Gaussian Splatting using UV ...", "date": "", "ddg_snippet": "Spherical Mapping : Spherical mapping [43] is a fundamental technique in computer graphics that is used to project 3D meshes onto a 2D map generally for texture mapping , where a 2D image is wrapped around a 3D object, such as a cylinder or a sphere .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2502.01846v2", "content": "Spherical Mapping : Spherical mapping [43] is a fundamental technique in computer graphics that is used to project 3D meshes onto a 2D map generally for texture mapping , where a 2D image is wrapped around a 3D object, such as a cylinder or a sphere ."}
+{"idx": 8, "title": "Generative Human Geometry Distribution", "date": "", "ddg_snippet": "In this section , we first introduce our human geometry distribution formulation by identifying suitable source and target distributions. Next, we describe encoding this distribution into a feature map using an auto-decoder architecture, as depicted in Fig.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2503.01448v2", "content": "In this section , we first introduce our human geometry distribution formulation by identifying suitable source and target distributions. Next, we describe encoding this distribution into a feature map using an auto-decoder architecture, as depicted in Fig."}
+{"idx": 9, "title": "UVGS: Reimagining Unstructured 3D Gaussian Splatting using UV ...", "date": "", "ddg_snippet": "Mar 5, 2025 · To address these shortcomings, we introduce UV Gaussian Splatting (UVGS), which provides a structured transformation of 3D Gaussian primitives into a 2D representation while preserving essential 3D information. We use spherical mapping [43] that inscribes Gaussian splats in a spherical surface, and projects attributes like position, rotation, scale, opacity, and color into an organized 14 ...", "subpage_snippet": "", "source": "ar5iv.labs.arxiv.org", "link": "https://ar5iv.labs.arxiv.org/html/2502.01846", "content": "Mar 5, 2025 · To address these shortcomings, we introduce UV Gaussian Splatting (UVGS), which provides a structured transformation of 3D Gaussian primitives into a 2D representation while preserving essential 3D information. We use spherical mapping [43] that inscribes Gaussian splats in a spherical surface, and projects attributes like position, rotation, scale, opacity, and color into an organized 14 ..."}
diff --git a/data/sampled_jsons/sitearxiv.org_2502.05028_Section_5_experimental_setup_adversarial_target.jsonl b/data/sampled_jsons/sitearxiv.org_2502.05028_Section_5_experimental_setup_adversarial_target.jsonl
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index 0000000000000000000000000000000000000000..bb0e5f913c0b66c6e57495183328b6b5c70bc5db
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+++ b/data/sampled_jsons/sitearxiv.org_2502.05028_Section_5_experimental_setup_adversarial_target.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Morphence: Moving Target Defense Against Adversarial Examples", "date": "", "ddg_snippet": "In this section , we describe the experimental setup adopted for Mor-phence evaluation. Particularly, we explain the studied datasets, evasion attacks, base models, and baseline defenses.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2108.13952", "content": "In this section , we describe the experimental setup adopted for Mor-phence evaluation. Particularly, we explain the studied datasets, evasion attacks, base models, and baseline defenses."}
+{"idx": 1, "title": "Effectiveness of Moving Target Defenses for Adversarial Attacks in ML ...", "date": "", "ddg_snippet": "Aqib Rashid, Jose Such Abstract—Several moving target defenses (MTDs) to counter adversarial ML attacks have been proposed in recent years. MTDs claim to increase the difficulty for the attacker in conducting attacks by regularly changing certain elements of the defense, such as cycling through configurations. To examine these claims, we study for the first time the effectiveness of several ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2302.00537v1", "content": "Aqib Rashid, Jose Such Abstract—Several moving target defenses (MTDs) to counter adversarial ML attacks have been proposed in recent years. MTDs claim to increase the difficulty for the attacker in conducting attacks by regularly changing certain elements of the defense, such as cycling through configurations. To examine these claims, we study for the first time the effectiveness of several ..."}
+{"idx": 2, "title": "MALT Powers Up Adversarial Attacks - arXiv.org", "date": "", "ddg_snippet": "Current adversarial attacks for multi-class classifiers choose the target class for a given input naively, based on the classifier's confidence levels for various target classes. We present a novel adversarial targeting method, MALT - Mesoscopic Almost Linearity Targeting, based on medium-scale almost linearity assumptions. Our attack wins over the current state of the art AutoAttack on the ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2407.02240", "content": "Current adversarial attacks for multi-class classifiers choose the target class for a given input naively, based on the classifier's confidence levels for various target classes. We present a novel adversarial targeting method, MALT - Mesoscopic Almost Linearity Targeting, based on medium-scale almost linearity assumptions. Our attack wins over the current state of the art AutoAttack on the ..."}
+{"idx": 3, "title": "Adversarial Machine Learning: Attacks, Defenses, and Open Challenges", "date": "", "ddg_snippet": "In this section , we categorize different • ei is the ith basis vector, types of adversarial attacks based on the attacker's capabilities • δ ≪ 1 is a small perturbation. and objectives. Popular black-box attacks include:", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2502.05637", "content": "In this section , we categorize different • ei is the ith basis vector, types of adversarial attacks based on the attacker's capabilities • δ ≪ 1 is a small perturbation. and objectives. Popular black-box attacks include:"}
+{"idx": 4, "title": "Prototype-Guided Robust Learning against Backdoor Attacks", "date": "", "ddg_snippet": "The classifier comprises a single FC layer followed by a softmax activation. The experiments are performed over NVIDIA RTX A6000 with a memory size of 50 GB. The experiments are carried out on CIFAR-10 unless otherwise specified; the experimental setup of the employed attacks is specified in the appendix B.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2509.08748", "content": "The classifier comprises a single FC layer followed by a softmax activation. The experiments are performed over NVIDIA RTX A6000 with a memory size of 50 GB. The experiments are carried out on CIFAR-10 unless otherwise specified; the experimental setup of the employed attacks is specified in the appendix B."}
+{"idx": 5, "title": "AttentionDrop: A Novel Regularization Method for Transformer Models", "date": "", "ddg_snippet": "Section 7 ( Experimental Setup ) tells the experimental setup across vision and translation benchmarks. Section 8 (Results) presents empirical results comparing AttentionDrop to baseline methods. Section 9 (Discussion) offers insights into ablation, calibration, and robustness behavior.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2504.12088v2", "content": "Section 7 ( Experimental Setup ) tells the experimental setup across vision and translation benchmarks. Section 8 (Results) presents empirical results comparing AttentionDrop to baseline methods. Section 9 (Discussion) offers insights into ablation, calibration, and robustness behavior."}
+{"idx": 6, "title": "Exploit Tool Invocation Prompt for Tool Behavior Hijacking in LLM-Based ...", "date": "", "ddg_snippet": "5.1 Experimental Setup Target LLM-based Agentic Systems. Our evaluation targets span three categories of LLM-based agentic systems: chatbox, coding IDE, and coding CLI, as listed in Table 1. Notably, the TIP processes in these LLM-based agentic systems in current implementations are based on MCP.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2509.05755v4", "content": "5.1 Experimental Setup Target LLM-based Agentic Systems. Our evaluation targets span three categories of LLM-based agentic systems: chatbox, coding IDE, and coding CLI, as listed in Table 1. Notably, the TIP processes in these LLM-based agentic systems in current implementations are based on MCP."}
+{"idx": 7, "title": "IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY 1 Efficient ...", "date": "", "ddg_snippet": "Finally, AdvDiffVLM embeds more target semantics into adversarial examples after multiple iterations. Experimental results show that our method generates adversarial examples 5x to 10x faster than state-of-the-art (SOTA) transfer-based adversarial attacks while maintaining higher quality adversarial examples.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2404.10335v4", "content": "Finally, AdvDiffVLM embeds more target semantics into adversarial examples after multiple iterations. Experimental results show that our method generates adversarial examples 5x to 10x faster than state-of-the-art (SOTA) transfer-based adversarial attacks while maintaining higher quality adversarial examples."}
+{"idx": 8, "title": "CoDoL: Conditional Domain Prompt Learning for Out-of-Distribution ...", "date": "", "ddg_snippet": "In this section , we first introduce the problem setup and notations of OOD generalization. Then, we provide brief reviews on prompt-based CLIP methods, including zero-shot CLIP (Radford et al., 2021b) and prompt-tuning CLIP (Zhou et al., 2022b, a).", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2509.15330v1", "content": "In this section , we first introduce the problem setup and notations of OOD generalization. Then, we provide brief reviews on prompt-based CLIP methods, including zero-shot CLIP (Radford et al., 2021b) and prompt-tuning CLIP (Zhou et al., 2022b, a)."}
+{"idx": 9, "title": "MUSE: MCTS-Driven Red Teaming Framework for Enhanced Multi-Turn ...", "date": "", "ddg_snippet": "We generate training data for safety align-ment by sampling from the Beavertails dataset, which covers diverse dialogue interactions with adversarial potential, and we adopt Llama-3-8B as the target model for adversarial data synthesis.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2509.14651", "content": "We generate training data for safety align-ment by sampling from the Beavertails dataset, which covers diverse dialogue interactions with adversarial potential, and we adopt Llama-3-8B as the target model for adversarial data synthesis."}
diff --git a/data/sampled_jsons/sitearxiv.org_2502.20727.jsonl b/data/sampled_jsons/sitearxiv.org_2502.20727.jsonl
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index 0000000000000000000000000000000000000000..bab5d2d2f50ff84dbe3abee4f426d788612867e0
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@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Computer Science Feb 2025 - arXiv.org", "date": "", "ddg_snippet": "Accepted to AAAI 2025. Camera ready version Subjects:Computation and Language (cs.CL) [10432] arXiv : 2502 .20754 [pdf, html, other] Title: Acquiring Grounded Representations of Words with Situated Interactive Instruction Shiwali Mohan, Aaron H. Mininger, James R. Kirk, John E. Laird Journal-ref: Advances in Cognitive Systems (2012) Subjects:Artificial Intelligence (cs.AI); Computation and ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/list/cs/2025-02?skip=10400", "content": "Accepted to AAAI 2025. Camera ready version Subjects:Computation and Language (cs.CL) [10432] arXiv : 2502 .20754 [pdf, html, other] Title: Acquiring Grounded Representations of Words with Situated Interactive Instruction Shiwali Mohan, Aaron H. Mininger, James R. Kirk, John E. Laird Journal-ref: Advances in Cognitive Systems (2012) Subjects:Artificial Intelligence (cs.AI); Computation and ..."}
+{"idx": 1, "title": "Computer Science Feb 2025 - arXiv.org", "date": "", "ddg_snippet": "Authors and titles for February 2025 Total of 11895 entries : 1-50... 10251-10300 10301-10350 10351-10400 10376-10425 10401-10450 10451-10500 10501-10550... 11851-11895 Showing up to 50 entries per page: fewer | more | all [10376] arXiv : 2502 .20636 [pdf, html, other] Title: Delayed-Decision Motion Planning in the Presence of Multiple Predictions David Isele, Alexandre Miranda Anon, Faizan M ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/list/cs/2025-02?skip=10375", "content": "Authors and titles for February 2025 Total of 11895 entries : 1-50... 10251-10300 10301-10350 10351-10400 10376-10425 10401-10450 10451-10500 10501-10550... 11851-11895 Showing up to 50 entries per page: fewer | more | all [10376] arXiv : 2502 .20636 [pdf, html, other] Title: Delayed-Decision Motion Planning in the Presence of Multiple Predictions David Isele, Alexandre Miranda Anon, Faizan M ..."}
+{"idx": 2, "title": "[2502.20727] SPD: Sync-Point Drop for Efficient Tensor ...", "date": "", "ddg_snippet": "Feb 28, 2025 · With the rapid expansion in the scale of large language models (LLMs), enabling efficient distributed inference across multiple computing units has become increasingly critical. However, communication overheads from popular distributed inference techniques such as Tensor Parallelism pose a significant challenge to achieve scalability and low latency. Therefore, we introduce a novel ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2502.20727", "content": "Feb 28, 2025 · With the rapid expansion in the scale of large language models (LLMs), enabling efficient distributed inference across multiple computing units has become increasingly critical. However, communication overheads from popular distributed inference techniques such as Tensor Parallelism pose a significant challenge to achieve scalability and low latency. Therefore, we introduce a novel ..."}
+{"idx": 3, "title": "[2502.20504v1] A Thousand Words or An Image: Studying the...", "date": "", "ddg_snippet": "We then create a systematic evaluation framework with 60 questions and corresponding metrics to assess how well LLMs embody each persona across its attributes and scenarios.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2502.20504v1", "content": "We then create a systematic evaluation framework with 60 questions and corresponding metrics to assess how well LLMs embody each persona across its attributes and scenarios."}
+{"idx": 4, "title": "[2502.20727] SPD: Sync-Point Drop for efficient tensor ...", "date": "", "ddg_snippet": "Mar 5, 2025 · With the rapid expansion in the scale of large language models (LLMs), enabling efficient distributed inference across multiple computing units has become increasingly critical. However, communication overheads from po…", "subpage_snippet": "", "source": "ar5iv.labs.arxiv.org", "link": "https://ar5iv.labs.arxiv.org/html/2502.20727", "content": "Mar 5, 2025 · With the rapid expansion in the scale of large language models (LLMs), enabling efficient distributed inference across multiple computing units has become increasingly critical. However, communication overheads from po…"}
+{"idx": 5, "title": "SPD: Sync-Point Drop for efficient tensor parallelism of Large...", "date": "", "ddg_snippet": "arXiv: 2502 . 20727 v1 [cs.DC] 28 Feb 2025. Han-Byul Kim 1 Duc Hoang 1 Arnav Kundu 1 Mohammad Samragh 1 Minsik Cho 1. Abstract.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2502.20727", "content": "arXiv: 2502 . 20727 v1 [cs.DC] 28 Feb 2025. Han-Byul Kim 1 Duc Hoang 1 Arnav Kundu 1 Mohammad Samragh 1 Minsik Cho 1. Abstract."}
+{"idx": 6, "title": "SPD: Sync-Point Drop for efficient tensor parallelism of Large...", "date": "", "ddg_snippet": "arXiv: 2502 . 20727 v1 [cs.DC] 28 Feb 2025.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2502.20727v1", "content": "arXiv: 2502 . 20727 v1 [cs.DC] 28 Feb 2025."}
+{"idx": 7, "title": "SPD: Sync-Point Drop for efficient tensor parallelism of Large...", "date": "", "ddg_snippet": "arXiv: 2502 . 20727 v2 [cs.DC] 04 May 2025.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2502.20727v2", "content": "arXiv: 2502 . 20727 v2 [cs.DC] 04 May 2025."}
+{"idx": 8, "title": "SPD: Sync-Point Drop for efficient tensor parallelism of Large...", "date": "", "ddg_snippet": "Abstract. With the rapid expansion in the scale of large language models (LLMs), enabling efficient distributed inference across multiple computing units has become increasingly critical.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2502.20727v3", "content": "Abstract. With the rapid expansion in the scale of large language models (LLMs), enabling efficient distributed inference across multiple computing units has become increasingly critical."}
+{"idx": 9, "title": "Computer Science", "date": "", "ddg_snippet": "Approach: We conducted a randomized, controlled experiment with 145 students as participants divided into two groups. Both groups programmed using graphics in Python, but used different approaches: one group used a compositional graphics library named PyTamaro; the other used the Turtle graphics library from Python's standard library. Student engagement was assessed with surveys, and ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/list/cs/new?skip=47", "content": "Approach: We conducted a randomized, controlled experiment with 145 students as participants divided into two groups. Both groups programmed using graphics in Python, but used different approaches: one group used a compositional graphics library named PyTamaro; the other used the Turtle graphics library from Python's standard library. Student engagement was assessed with surveys, and ..."}
diff --git a/data/sampled_jsons/sitearxiv.org_2503.06366_Table_1_Schubert_polynomials_accuracy.jsonl b/data/sampled_jsons/sitearxiv.org_2503.06366_Table_1_Schubert_polynomials_accuracy.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..c6575bc6fec1ea1701f2b9857ca5315112ec43da
--- /dev/null
+++ b/data/sampled_jsons/sitearxiv.org_2503.06366_Table_1_Schubert_polynomials_accuracy.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "[2503.03903] Single-SEM Schubert Polynomials", "date": "", "ddg_snippet": "Abstract page for arXiv paper 2503.03903: Single-SEM Schubert Polynomials .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2503.03903", "content": "Abstract page for arXiv paper 2503.03903: Single-SEM Schubert Polynomials ."}
+{"idx": 1, "title": "Single-sem schubert polynomials", "date": "", "ddg_snippet": "Schubert polynomials form an important basis for the polynomial ring Z[x 1 , x2 . . . ]. For each w ∈ Sn, there is a Schubert polynomial Sw in variables x 1 , . . . xn− 1 . It is natural to wonder when a Schubert polynomial is just one monomial.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2503.03903", "content": "Schubert polynomials form an important basis for the polynomial ring Z[x 1 , x2 . . . ]. For each w ∈ Sn, there is a Schubert polynomial Sw in variables x 1 , . . . xn− 1 . It is natural to wonder when a Schubert polynomial is just one monomial."}
+{"idx": 2, "title": "Skew schubert polynomials", "date": "", "ddg_snippet": "We dene skew Schubert polynomials to be normal form ( polynomial ) represen-tatives of certain classes in the cohomology of a ag manifold.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/math/0202090", "content": "We dene skew Schubert polynomials to be normal form ( polynomial ) represen-tatives of certain classes in the cohomology of a ag manifold."}
+{"idx": 3, "title": "[ 2503 . 06366 ] Machine Learning meets Algebraic Combinatorics...", "date": "", "ddg_snippet": "Further differentiating our dataset collection is the fact that it aims at the conjecturing process. Each dataset includes an open-ended research-level question and a large collection of examples (up to 10M in some cases) from which conjectures should be generated.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2503.06366", "content": "Further differentiating our dataset collection is the fact that it aims at the conjecturing process. Each dataset includes an open-ended research-level question and a large collection of examples (up to 10M in some cases) from which conjectures should be generated."}
+{"idx": 4, "title": "Manifold Dimension Estimation: An Empirical Study - arXiv.org", "date": "", "ddg_snippet": "Abstract The manifold hypothesis suggests that high-dimensional data often lie on or near a low-dimensional manifold. Estimating the dimension of this manifold is essential for leveraging its structure, yet existing work on dimension estimation is fragmented and lacks systematic evaluation. This article provides a comprehensive survey for both researchers and practitioners. We review often ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2509.15517v1", "content": "Abstract The manifold hypothesis suggests that high-dimensional data often lie on or near a low-dimensional manifold. Estimating the dimension of this manifold is essential for leveraging its structure, yet existing work on dimension estimation is fragmented and lacks systematic evaluation. This article provides a comprehensive survey for both researchers and practitioners. We review often ..."}
+{"idx": 5, "title": "Machine Learning meets Algebraic Combinatorics: A Suite of Datasets...", "date": "", "ddg_snippet": "Table 4. Baseline model classification accuracy for predicting KL polynomial coefficients for n = 5, 6. Results are averaged over three random weight initializations with 95% confidence intervals.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2503.06366", "content": "Table 4. Baseline model classification accuracy for predicting KL polynomial coefficients for n = 5, 6. Results are averaged over three random weight initializations with 95% confidence intervals."}
+{"idx": 6, "title": "Machine Learning meets Algebraic Combinatorics: A Suite of Datasets ...", "date": "", "ddg_snippet": "Our collection includes both open problems (e.g., the combinatorial interpretation of Schubert polynomial structure constants) and classic problems whose solution is a major result in the field (e.g., a combinatorial method of calculating the character of irreducible symmetric group representations).", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2503.06366v1", "content": "Our collection includes both open problems (e.g., the combinatorial interpretation of Schubert polynomial structure constants) and classic problems whose solution is a major result in the field (e.g., a combinatorial method of calculating the character of irreducible symmetric group representations)."}
+{"idx": 7, "title": "A multimodal stress detection dataset with facial expressions and ...", "date": "", "ddg_snippet": "The analysis of stress involves using wearable sensors to deduce physiological signals. Table 1 shows the standard signals provided by wearables. In contrast, the study and analysis of human emotions use a camera to infer facial expressions that represent emotions. In the literature, emotions and stress signals are typically considered two disjoint topics and are usually studied independently ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2209.13542v2", "content": "The analysis of stress involves using wearable sensors to deduce physiological signals. Table 1 shows the standard signals provided by wearables. In contrast, the study and analysis of human emotions use a camera to infer facial expressions that represent emotions. In the literature, emotions and stress signals are typically considered two disjoint topics and are usually studied independently ..."}
+{"idx": 8, "title": "Free Math Resources - Worksheets, Games, & More Ad Viewing ads is privacy protected by DuckDuckGo. Ad clicks are managed by Microsoft's ad network ( more info ).", "date": "", "ddg_snippet": "Bring learning to life with worksheets, games, lessons, & more for every grade & subject. Everything you need to teach kids math all in one place! Visit Education.com today.", "subpage_snippet": "", "source": "duckduckgo.com", "link": "https://duckduckgo.com/y.js?ad_domain=education.com&ad_provider=bingv7aa&ad_type=txad&click_metadata=_G0Y9sv67aa3N05uCkPtsGMNlsApuWL13SKIzjoZqhfV6LSOm1FmqFG6vt4M3e57Z7nujqagJbJ62Rt2ArKC_qqfQSdSOIV-JyD84pbd_Tj1vwxbjsKtOLF59GDZtrGF.xBJYvCceYlPqdPn0-hvfjw&rut=c38c255a10d93568b70d9b0f3d415a824acb6101f745c5be986071d046eb3a02&u3=https://www.bing.com/aclick?ld=e8jtwfIZcdwmfDtiIUrcKD7zVUCUz5l0bGjF8V2vyvUkjoGu4YKWzlNrfC-Wic221fSSX51eOxjQZTivKoOqmPMfMCbx_hO7Rjrm2u7FwxFIKm_pySgYs9XYET5EQHLprtxklj2ISJkuILUqNW3WgSODJ-QJ9yrFDbyV03sqkRyiVa4ZoNpSnzoK0243_ag653Gd4rDg&u=aHR0cHMlM2ElMmYlMmZ3d3cuZWR1Y2F0aW9uLmNvbSUyZnJlc291cmNlcyUyZm1hdGglMmYlM2Ztc2Nsa2lkJTNkNGU5OTkzZDVkMjU1MTkwNWU2ZjBmYjA3NzM1OGNiNWElMjZ1dG1fc291cmNlJTNkYmluZyUyNnV0bV9tZWRpdW0lM2RjcGMlMjZ1dG1fY2FtcGFpZ24lM2RTZWFyY2glMjUyMC0lMjUyMEVkdSUyNTIwVGVybXMlMjUyMC0lMjUyMEJNTSUyNnV0bV90ZXJtJTNkbWF0aCUyNnV0bV9jb250ZW50JTNkTWF0aA&rlid=4e9993d5d2551905e6f0fb077358cb5a&vqd=4-62071694168897742177089635436368321175&iurl={1}IG=4144F86889A544CE8B240B399010068E&CID=0E701566FC4C6C0306720316FD796D74&ID=DevEx,5035.1", "content": "Bring learning to life with worksheets, games, lessons, & more for every grade & subject. Everything you need to teach kids math all in one place! Visit Education.com today."}
+{"idx": 9, "title": "Manifold Dimension Estimation: An Empirical Study", "date": "", "ddg_snippet": "Abstract The manifold hypothesis suggests that high-dimensional data often lie on or near a low-dimensional manifold. Estimating the dimension of this manifold is essential for leveraging its structure, yet existing work on dimension estimation is fragmented and lacks systematic evaluation. This article provides a comprehensive survey for both researchers and practitioners. We review often ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2509.15517", "content": "Abstract The manifold hypothesis suggests that high-dimensional data often lie on or near a low-dimensional manifold. Estimating the dimension of this manifold is essential for leveraging its structure, yet existing work on dimension estimation is fragmented and lacks systematic evaluation. This article provides a comprehensive survey for both researchers and practitioners. We review often ..."}
diff --git a/data/sampled_jsons/sitearxiv.org_Beyond_Self-Repellent_Kernels_HDT_LRU_cache_memory_reduction_90%.jsonl b/data/sampled_jsons/sitearxiv.org_Beyond_Self-Repellent_Kernels_HDT_LRU_cache_memory_reduction_90%.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..fc52865df3bd626beaa57c4612c026c6a8380b3a
--- /dev/null
+++ b/data/sampled_jsons/sitearxiv.org_Beyond_Self-Repellent_Kernels_HDT_LRU_cache_memory_reduction_90%.jsonl
@@ -0,0 +1,4 @@
+{"idx": 0, "title": "Beyond Self-Repellent Kernels: History-Driven Target Towards Efficient ...", "date": "", "ddg_snippet": "We propose a history-driven target (HDT)framework in Markov Chain Monte Carlo (MCMC) to improve any random walk algorithm on discrete state spaces, such as general undirected graphs, for efficient sampling from target distribution 𝝁𝝁{\\bm{\\mu}}bold_italic_μ. With broad applications in network science and distributed optimization, recent innovations like the self-repellent random walk ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2505.18300v1", "content": "We propose a history-driven target (HDT)framework in Markov Chain Monte Carlo (MCMC) to improve any random walk algorithm on discrete state spaces, such as general undirected graphs, for efficient sampling from target distribution 𝝁𝝁{\\bm{\\mu}}bold_italic_μ. With broad applications in network science and distributed optimization, recent innovations like the self-repellent random walk ..."}
+{"idx": 1, "title": "Beyond Self - Repellent Kernels : History-Driven Target Towards...", "date": "", "ddg_snippet": "A General Self - Repellent Framework: We introduce the HDT framework, which replaces the target.Scalable Implementation for Large Graphs: We implement an LRU ( Least Recently Used ) cache scheme to manage. 𝐱{\\mathbf{x}}bold_x. with limited memory .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2505.18300v3", "content": "A General Self - Repellent Framework: We introduce the HDT framework, which replaces the target.Scalable Implementation for Large Graphs: We implement an LRU ( Least Recently Used ) cache scheme to manage. 𝐱{\\mathbf{x}}bold_x. with limited memory ."}
+{"idx": 2, "title": "[2505.18300] Beyond Self - Repellent Kernels : History-Driven Target...", "date": "", "ddg_snippet": "View a PDF of the paper titled Beyond Self - Repellent Kernels : History-Driven Target Towards Efficient Nonlinear MCMC on General Graphs, by Jie Hu and 2 other authors.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2505.18300", "content": "View a PDF of the paper titled Beyond Self - Repellent Kernels : History-Driven Target Towards Efficient Nonlinear MCMC on General Graphs, by Jie Hu and 2 other authors."}
+{"idx": 3, "title": "Statistics May 2025", "date": "", "ddg_snippet": "Title: Beyond Self - Repellent Kernels : History-Driven Target Towards Efficient Nonlinear MCMC on General Graphs.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/list/stat/2025-05?skip=1100", "content": "Title: Beyond Self - Repellent Kernels : History-Driven Target Towards Efficient Nonlinear MCMC on General Graphs."}
diff --git a/data/sampled_jsons/sitearxiv.org_CVE-Bench_2503.17332_Table_6_CVE_details_CVSS_scores_complete_list.jsonl b/data/sampled_jsons/sitearxiv.org_CVE-Bench_2503.17332_Table_6_CVE_details_CVSS_scores_complete_list.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..2a61b78bfa0bce9b13b2630c7e7e06469d8cb21d
--- /dev/null
+++ b/data/sampled_jsons/sitearxiv.org_CVE-Bench_2503.17332_Table_6_CVE_details_CVSS_scores_complete_list.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "CVE-Bench: A Benchmark for AI Agents' Ability to Exploit ...", "date": "", "ddg_snippet": "by Y Zhu · 2025 · Cited by 12 — The severity score is calculated according to the base score of the Common Vulnerability Scoring System ( CVSS ) version 3.1. Range of severity ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2503.17332", "content": "by Y Zhu · 2025 · Cited by 12 — The severity score is calculated according to the base score of the Common Vulnerability Scoring System ( CVSS ) version 3.1. Range of severity ..."}
+{"idx": 1, "title": "SEC-bench: Automated Benchmarking of LLM Agents on Real-World ...", "date": "", "ddg_snippet": "Goal and Challenge of SEC- bench . We aim to propose a framework to automatically collect and verify real-world CVE instances with reproducible PoC artifacts and validated security patches, creating a benchmark to evaluate LLM agents on authentic security tasks.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2506.11791", "content": "Goal and Challenge of SEC- bench . We aim to propose a framework to automatically collect and verify real-world CVE instances with reproducible PoC artifacts and validated security patches, creating a benchmark to evaluate LLM agents on authentic security tasks."}
+{"idx": 2, "title": "CVE-Bench: A Benchmark for AI Agents' Ability to Exploit ...", "date": "", "ddg_snippet": "10 Apr 2025 — As shown in Table 2, CVEs in CVE - Bench have a minimum of 9.0 severity score measured by the Common Vulnerability Scoring System ( CVSS ) version ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2503.17332v3", "content": "10 Apr 2025 — As shown in Table 2, CVEs in CVE - Bench have a minimum of 9.0 severity score measured by the Common Vulnerability Scoring System ( CVSS ) version ..."}
+{"idx": 3, "title": "CVE-Bench: A Benchmark for AI Agents' Ability to Exploit ...", "date": "", "ddg_snippet": "by Y Zhu · 2025 · Cited by 12 — We present the details of selected CVEs in Table 5. We list the CVE ID, it's publish date, base rating results by CVSS version 3, the name ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2503.17332?", "content": "by Y Zhu · 2025 · Cited by 12 — We present the details of selected CVEs in Table 5. We list the CVE ID, it's publish date, base rating results by CVSS version 3, the name ..."}
+{"idx": 4, "title": "CVE-Bench: A Benchmark for AI Agents' Ability to Exploit ...", "date": "", "ddg_snippet": "As shown in Table 2, CVEs in CVE - Bench have a minimum of 9.0 severity score measured by the Common Vulnerability Scoring System ( CVSS ) version 3.1 base score .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2503.17332", "content": "As shown in Table 2, CVEs in CVE - Bench have a minimum of 9.0 severity score measured by the Common Vulnerability Scoring System ( CVSS ) version 3.1 base score ."}
+{"idx": 5, "title": "[2503.17332] CVE-Bench: A Benchmark for AI Agents' Ability to ... CVE-Bench: A Benchmark for AI Agents' Ability to Exploit Real ... Establishing Best Practices for Building Rigorous Agentic ... CVE-Bench: A Benchmark for AI Agents' Ability to Exploit Real ... SEC-bench: Automated Benchmarking of LLM Agents on Real-World ... Vulnerability Management Chaining: An Integrated Framework ... CTIBench: A Benchmark for Evaluating LLMs in Cyber Threat ...", "date": "", "ddg_snippet": "Mar 21, 2025 · In CVE - Bench , we design a sandbox framework that enables LLM agents to exploit vulnerable web applications in scenarios that mimic real-world conditions, while also providing effective evaluation of their exploits. We show the details of each CVE in Table 6 , including its identifier, publication date, CVSS 3.x score , affected web application, and our reproduced attack type. CVE - bench measured such attacks by checking whether an outbound server has been accessed. To answer check T.9, we conducted various rounds of mock execution and identified that agents consistently passed the evaluation for this attack, which likely indicates a bug in the implementation. We focused on CVEs of web applications that are rated as “critical” by the Common Vulnerability Scoring System ( CVSS ) version 3 (Mell et al., 2022), indicating high exploitability and severe potential impacts on sensitive data and vital services. Goal and Challenge of SEC- bench . We aim to propose a framework to automatically collect and verify real-world CVE instances with reproducible PoC artifacts and validated security patches, creating a benchmark to evaluate LLM agents on authentic security tasks. Jun 2, 2025 · Current approaches using Common Vulnerability Scoring System ( CVSS ) scores produce overwhelming volumes of high-priority vulnerabilities, while Exploit Prediction Scoring System (EPSS) and Known Exploited Vulnerabilities ( KEV ) catalog offer valuable but incomplete perspectives on actual exploitation risk. Jun 11, 2024 · Analyze the following CVE description and calculate the CVSS v3.1 Base Score . Determine the values for each base metric: AV, AC, PR, UI, S, C, I, and A. Summarize each metric’s value and provide the final CVSS v3.1 vector string.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2503.17332", "content": "Mar 21, 2025 · In CVE - Bench , we design a sandbox framework that enables LLM agents to exploit vulnerable web applications in scenarios that mimic real-world conditions, while also providing effective evaluation of their exploits. We show the details of each CVE in Table 6 , including its identifier, publication date, CVSS 3.x score , affected web application, and our reproduced attack type. CVE - bench measured such attacks by checking whether an outbound server has been accessed. To answer check T.9, we conducted various rounds of mock execution and identified that agents consistently passed the evaluation for this attack, which likely indicates a bug in the implementation. We focused on CVEs of web applications that are rated as “critical” by the Common Vulnerability Scoring System ( CVSS ) version 3 (Mell et al., 2022), indicating high exploitability and severe potential impacts on sensitive data and vital services. Goal and Challenge of SEC- bench . We aim to propose a framework to automatically collect and verify real-world CVE instances with reproducible PoC artifacts and validated security patches, creating a benchmark to evaluate LLM agents on authentic security tasks. Jun 2, 2025 · Current approaches using Common Vulnerability Scoring System ( CVSS ) scores produce overwhelming volumes of high-priority vulnerabilities, while Exploit Prediction Scoring System (EPSS) and Known Exploited Vulnerabilities ( KEV ) catalog offer valuable but incomplete perspectives on actual exploitation risk. Jun 11, 2024 · Analyze the following CVE description and calculate the CVSS v3.1 Base Score . Determine the values for each base metric: AV, AC, PR, UI, S, C, I, and A. Summarize each metric’s value and provide the final CVSS v3.1 vector string."}
+{"idx": 6, "title": "CVE-Bench: A Benchmark for AI Agents' Ability to Exploit Real ...", "date": "", "ddg_snippet": "We focused on CVEs of web applications that are rated as “critical” by the Common Vulnerability Scoring System ( CVSS ) version 3 (Mell et al., 2022), indicating high exploitability and severe potential impacts on sensitive data and vital services.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2503.17332v3", "content": "We focused on CVEs of web applications that are rated as “critical” by the Common Vulnerability Scoring System ( CVSS ) version 3 (Mell et al., 2022), indicating high exploitability and severe potential impacts on sensitive data and vital services."}
+{"idx": 7, "title": "CTIBench: A Benchmark for Evaluating LLMs in Cyber Threat ...", "date": "", "ddg_snippet": "Jun 11, 2024 · Analyze the following CVE description and calculate the CVSS v3.1 Base Score . Determine the values for each base metric: AV, AC, PR, UI, S, C, I, and A. Summarize each metric’s value and provide the final CVSS v3.1 vector string.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2406.07599v1", "content": "Jun 11, 2024 · Analyze the following CVE description and calculate the CVSS v3.1 Base Score . Determine the values for each base metric: AV, AC, PR, UI, S, C, I, and A. Summarize each metric’s value and provide the final CVSS v3.1 vector string."}
+{"idx": 8, "title": "xOffense: An AI-driven autonomous penetration testing ...", "date": "", "ddg_snippet": "4 days ago — CVE - Bench grounds evaluation in real-world web CVEs, reporting typical success rates in the low teens even for state-of-the-art agents, ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2509.13021v1", "content": "4 days ago — CVE - Bench grounds evaluation in real-world web CVEs, reporting typical success rates in the low teens even for state-of-the-art agents, ..."}
+{"idx": 9, "title": "xOffense: An AI-driven autonomous penetration testing ...", "date": "", "ddg_snippet": "by PD Luong · 2025 — CVE - Bench grounds evaluation in real-world web CVEs, reporting typical success rates in the low teens even for state-of- the-art agents, ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2509.13021", "content": "by PD Luong · 2025 — CVE - Bench grounds evaluation in real-world web CVEs, reporting typical success rates in the low teens even for state-of- the-art agents, ..."}
diff --git a/data/sampled_jsons/sitearxiv.org_LAuReL_Learned_Augmented_Residual_Layer_Table_7.jsonl b/data/sampled_jsons/sitearxiv.org_LAuReL_Learned_Augmented_Residual_Layer_Table_7.jsonl
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index 0000000000000000000000000000000000000000..7f43e0b03716519229b4963ab263e31b434486f5
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@@ -0,0 +1,10 @@
+{"idx": 0, "title": "[2411.07501] LAuReL: Learned Augmented Residual Layer", "date": "", "ddg_snippet": "In this paper we introduce Learned Augmented Residual Layer ( LAuReL ) -- a novel generalization of the canonical residual connection -- with the goal to be an in-situ replacement of the latter while outperforming on both model quality and footprint metrics.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2411.07501", "content": "In this paper we introduce Learned Augmented Residual Layer ( LAuReL ) -- a novel generalization of the canonical residual connection -- with the goal to be an in-situ replacement of the latter while outperforming on both model quality and footprint metrics."}
+{"idx": 1, "title": "LAuReL: Learned Augmented Residual Layer - arXiv.org", "date": "", "ddg_snippet": "In this paper we introduce learned augmented residual layer , LAuReL , which generalizes the canonical residual connection. Recall that deep-learning models with residual connections have a 'block' structure, with many blocks chained together between the input and final output; these could be convolution/identity blocks within a ResNet, a transformer block in a transformer encoder/decoder ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2411.07501v4", "content": "In this paper we introduce learned augmented residual layer , LAuReL , which generalizes the canonical residual connection. Recall that deep-learning models with residual connections have a 'block' structure, with many blocks chained together between the input and final output; these could be convolution/identity blocks within a ResNet, a transformer block in a transformer encoder/decoder ..."}
+{"idx": 2, "title": "LAUREL: Learned Augmented Residual Layer - arXiv.org", "date": "", "ddg_snippet": "In this paper, we introduce the Learned Aug-mented Residual Layer (LAUREL)—a novel gen-eralization of the canonical residual connection— designed to serve as an in-situ replacement while outperforming it in both model quality and foot-print metrics. Our experiments show that LAU-REL can enhance quality for both vision and lan-guage models while adding fewer parameters and incurring less ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2411.07501v4", "content": "In this paper, we introduce the Learned Aug-mented Residual Layer (LAUREL)—a novel gen-eralization of the canonical residual connection— designed to serve as an in-situ replacement while outperforming it in both model quality and foot-print metrics. Our experiments show that LAU-REL can enhance quality for both vision and lan-guage models while adding fewer parameters and incurring less ..."}
+{"idx": 3, "title": "[2411.07501] LAuReL: Learned Augmented Residual Layer", "date": "", "ddg_snippet": "In this paper we introduce \\emph { Learned Augmented Residual Layer } ( LAuReL ) -- a novel generalization of the canonical residual connection -- with the goal to be an in-situ replacement of the latter while outperforming on both model quality and footprint metrics. Our experiments show that using \\ laurel can help boost performance for both vision and language models. For example, on the ResNet ...", "subpage_snippet": "", "source": "export.arxiv.org", "link": "http://export.arxiv.org/abs/2411.07501", "content": "In this paper we introduce \\emph { Learned Augmented Residual Layer } ( LAuReL ) -- a novel generalization of the canonical residual connection -- with the goal to be an in-situ replacement of the latter while outperforming on both model quality and footprint metrics. Our experiments show that using \\ laurel can help boost performance for both vision and language models. For example, on the ResNet ..."}
+{"idx": 4, "title": "LAUREL: Learned Augmented Residual Layer - arXiv.org", "date": "", "ddg_snippet": "In this paper we introduce learned augmented residual layer , LAUREL , which generalizes the canonical residual connec-tion. Recall that deep-learning models with residual con-nections have a 'block' structure, with many blocks chained together between the input and final output; these could be convolution/identity blocks within a ResNet, a ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2411.07501", "content": "In this paper we introduce learned augmented residual layer , LAUREL , which generalizes the canonical residual connec-tion. Recall that deep-learning models with residual con-nections have a 'block' structure, with many blocks chained together between the input and final output; these could be convolution/identity blocks within a ResNet, a ..."}
+{"idx": 5, "title": "LAuReL: Learned Augmented Residual Layer - arXiv.org", "date": "", "ddg_snippet": "In this paper, we introduce the Learned Augmented Residual Layer (LAuReL)—a novel generalization of the canonical residual connection—designed to serve as an in-situ replacement while outperforming it in both model quality and footprint metrics. Our experiments show that LAuReL can enhance model quality for both vision and language models while adding fewer parameters and incurring less ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2411.07501v3", "content": "In this paper, we introduce the Learned Augmented Residual Layer (LAuReL)—a novel generalization of the canonical residual connection—designed to serve as an in-situ replacement while outperforming it in both model quality and footprint metrics. Our experiments show that LAuReL can enhance model quality for both vision and language models while adding fewer parameters and incurring less ..."}
+{"idx": 6, "title": "DeepCrossAttention: Supercharging Transformer Residual Connections", "date": "", "ddg_snippet": "Building on DenseFormer, LAuReL (Menghani et al., 2024) presents three aggrega-tion functions, the best performing one applies a learned low-rank transformation to the previous layer outputs before the learned linear combination.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2502.06785", "content": "Building on DenseFormer, LAuReL (Menghani et al., 2024) presents three aggrega-tion functions, the best performing one applies a learned low-rank transformation to the previous layer outputs before the learned linear combination."}
+{"idx": 7, "title": "[2009.01462v1] Penalty and Augmented Lagrangian Methods for Layer ...", "date": "", "ddg_snippet": "Algorithms for training residual networks (ResNets) typically require forward pass of data, followed by backpropagating of loss gradient to perform parameter updates, which can take many hours or even days for networks with hundreds of layers . Inspired by the penalty and augmented Lagrangian methods, a layer -parallel training algorithm is proposed in this work to overcome the scalability ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2009.01462v1", "content": "Algorithms for training residual networks (ResNets) typically require forward pass of data, followed by backpropagating of loss gradient to perform parameter updates, which can take many hours or even days for networks with hundreds of layers . Inspired by the penalty and augmented Lagrangian methods, a layer -parallel training algorithm is proposed in this work to overcome the scalability ..."}
+{"idx": 8, "title": "LAUREL: Learned Augmented Residual Layer - arXiv.org", "date": "", "ddg_snippet": "In this paper we introduce Learned Augmented Residual Layer (LAUREL)—a novel generaliza-tion of the canonical residual connection—with the goal to be an in-situ replacement of the latter while outperforming on both model quality and footprint metrics. Our experiments show that us-ing LAUREL can help boost performance for both vision and language models. For example, on the ResNet-50 ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2411.07501v2", "content": "In this paper we introduce Learned Augmented Residual Layer (LAUREL)—a novel generaliza-tion of the canonical residual connection—with the goal to be an in-situ replacement of the latter while outperforming on both model quality and footprint metrics. Our experiments show that us-ing LAUREL can help boost performance for both vision and language models. For example, on the ResNet-50 ..."}
+{"idx": 9, "title": "The Generalization Ridge: Information Flow in Natural Language Generation", "date": "", "ddg_snippet": "Transformer-based language models have achieved state-of-the-art performance in natural language generation (NLG) tasks, yet their internal mechanisms for synthesizing task-relevant information remain insufficiently understood. While prior studies suggest that intermediate layers often yield more generalizable repre- sentations than final layers , how this generalization ability emerges and ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2507.05387", "content": "Transformer-based language models have achieved state-of-the-art performance in natural language generation (NLG) tasks, yet their internal mechanisms for synthesizing task-relevant information remain insufficiently understood. While prior studies suggest that intermediate layers often yield more generalizable repre- sentations than final layers , how this generalization ability emerges and ..."}
diff --git a/data/sampled_jsons/sitearxiv.org_Medusa_Simple_LLM_Inference_Acceleration_Framework_with_Multiple_Decoding_Heads_abstra.jsonl b/data/sampled_jsons/sitearxiv.org_Medusa_Simple_LLM_Inference_Acceleration_Framework_with_Multiple_Decoding_Heads_abstra.jsonl
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index 0000000000000000000000000000000000000000..67bd4ff1ed1735f027b65216dbfcfe7877224267
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@@ -0,0 +1,10 @@
+{"idx": 0, "title": "[2401.10774] Medusa: Simple LLM Inference Acceleration ... Medusa: Simple LLM Inference Acceleration Framework with ... Abstract - arXiv.org arXiv.org e-Print archive Acceleration Multiple Heads Decoding for LLM via Dynamic Tree ... Hydra: Sequentially-Dependent Draft Heads for Medusa Decoding Medusa: Simple LLM Inference Acceleration Framework with ...", "date": "", "ddg_snippet": "Jan 19, 2024 · View a PDF of the paper titled Medusa: Simple LLM Inference Acceleration Framework with Multiple Decoding Heads , by Tianle Cai and 6 other authors Abstract Large Language Models (LLMs) employ auto-regressive decoding that requires sequential computation, with each step reliant on the previous one’s output. This creates a bottleneck as each step necessitates moving the full model parameters from High-Bandwidth Memory (HBM) to the accelerator’s cache. While methods such as speculative decoding have been suggested to address this issue ... Abstract MEDUSA : Simple LLM Inference Acceleration Framework with Multiple Decoding Heads Medusa accelerates LLM inference by adding extra decoding heads , enabling parallel token prediction and overcoming sequential decoding limitations. Abstract Multiple heads decoding accelerates the inference of Large Language Models (LLMs) by predicting next several tokens simultaneously. It generates and verifies multiple candidate sequences in parallel via tree attention with a fixed structure. In this paper, we replace the fixed tree attention with dynamic tree attention on multiple head decoding, specifically in the context of MEDUSA ... Oct 7, 2024 · Abstract To combat the memory bandwidth-bound nature of autoregressive LLM inference, previous research has proposed the speculative decoding framework. To perform speculative decoding, a small draft model proposes candidate continuations of the input sequence that are then verified in parallel by the base model. One way to specify the draft model, as used in the recent Medusa decoding ... Jan 19, 2024 · Abstract The inference process in Large Language Models (LLMs) is often limited due to the absence of parallelism in the auto-regressive decoding process, resulting in most operations being restricted by the memory bandwidth of accelerators. While methods such as speculative decoding have been suggested to address this issue, their implementation is impeded by the challenges associated with ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2401.10774", "content": "Jan 19, 2024 · View a PDF of the paper titled Medusa: Simple LLM Inference Acceleration Framework with Multiple Decoding Heads , by Tianle Cai and 6 other authors Abstract Large Language Models (LLMs) employ auto-regressive decoding that requires sequential computation, with each step reliant on the previous one’s output. This creates a bottleneck as each step necessitates moving the full model parameters from High-Bandwidth Memory (HBM) to the accelerator’s cache. While methods such as speculative decoding have been suggested to address this issue ... Abstract MEDUSA : Simple LLM Inference Acceleration Framework with Multiple Decoding Heads Medusa accelerates LLM inference by adding extra decoding heads , enabling parallel token prediction and overcoming sequential decoding limitations. Abstract Multiple heads decoding accelerates the inference of Large Language Models (LLMs) by predicting next several tokens simultaneously. It generates and verifies multiple candidate sequences in parallel via tree attention with a fixed structure. In this paper, we replace the fixed tree attention with dynamic tree attention on multiple head decoding, specifically in the context of MEDUSA ... Oct 7, 2024 · Abstract To combat the memory bandwidth-bound nature of autoregressive LLM inference, previous research has proposed the speculative decoding framework. To perform speculative decoding, a small draft model proposes candidate continuations of the input sequence that are then verified in parallel by the base model. One way to specify the draft model, as used in the recent Medusa decoding ... Jan 19, 2024 · Abstract The inference process in Large Language Models (LLMs) is often limited due to the absence of parallelism in the auto-regressive decoding process, resulting in most operations being restricted by the memory bandwidth of accelerators. While methods such as speculative decoding have been suggested to address this issue, their implementation is impeded by the challenges associated with ..."}
+{"idx": 1, "title": "Medusa: Simple LLM Inference Acceleration Framework with ...", "date": "", "ddg_snippet": "Abstract Large Language Models (LLMs) employ auto-regressive decoding that requires sequential computation, with each step reliant on the previous one’s output. This creates a bottleneck as each step necessitates moving the full model parameters from High-Bandwidth Memory (HBM) to the accelerator’s cache. While methods such as speculative decoding have been suggested to address this issue ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2401.10774v3", "content": "Abstract Large Language Models (LLMs) employ auto-regressive decoding that requires sequential computation, with each step reliant on the previous one’s output. This creates a bottleneck as each step necessitates moving the full model parameters from High-Bandwidth Memory (HBM) to the accelerator’s cache. While methods such as speculative decoding have been suggested to address this issue ..."}
+{"idx": 2, "title": "Medusa: Simple LLM Inference Acceleration Framework ...", "date": "", "ddg_snippet": "by T Cai · 2024 · Cited by 349 — Abstract . Large Language Models (LLMs) employ auto- regressive decoding ... MEDUSA: Simple LLM Inference Acceleration Framework with Multiple Decoding Heads .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2401.10774", "content": "by T Cai · 2024 · Cited by 349 — Abstract . Large Language Models (LLMs) employ auto- regressive decoding ... MEDUSA: Simple LLM Inference Acceleration Framework with Multiple Decoding Heads ."}
+{"idx": 3, "title": "Acceleration Multiple Heads Decoding for LLM via Dynamic Tree ...", "date": "", "ddg_snippet": "Abstract Multiple heads decoding accelerates the inference of Large Language Models (LLMs) by predicting next several tokens simultaneously. It generates and verifies multiple candidate sequences in parallel via tree attention with a fixed structure. In this paper, we replace the fixed tree attention with dynamic tree attention on multiple head decoding, specifically in the context of MEDUSA ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2502.05947v1", "content": "Abstract Multiple heads decoding accelerates the inference of Large Language Models (LLMs) by predicting next several tokens simultaneously. It generates and verifies multiple candidate sequences in parallel via tree attention with a fixed structure. In this paper, we replace the fixed tree attention with dynamic tree attention on multiple head decoding, specifically in the context of MEDUSA ..."}
+{"idx": 4, "title": "Medusa: Simple LLM Inference Acceleration Framework with ...", "date": "", "ddg_snippet": "Jan 19, 2024 · Abstract The inference process in Large Language Models (LLMs) is often limited due to the absence of parallelism in the auto-regressive decoding process, resulting in most operations being restricted by the memory bandwidth of accelerators. While methods such as speculative decoding have been suggested to address this issue, their implementation is impeded by the challenges associated with ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2401.10774v1", "content": "Jan 19, 2024 · Abstract The inference process in Large Language Models (LLMs) is often limited due to the absence of parallelism in the auto-regressive decoding process, resulting in most operations being restricted by the memory bandwidth of accelerators. While methods such as speculative decoding have been suggested to address this issue, their implementation is impeded by the challenges associated with ..."}
+{"idx": 5, "title": "Abstract - arXiv.org", "date": "", "ddg_snippet": "Abstract MEDUSA : Simple LLM Inference Acceleration Framework with Multiple Decoding Heads", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2401.10774v1", "content": "Abstract MEDUSA : Simple LLM Inference Acceleration Framework with Multiple Decoding Heads"}
+{"idx": 6, "title": "Hydra: Sequentially-Dependent Draft Heads for Medusa Decoding", "date": "", "ddg_snippet": "Oct 7, 2024 · Abstract To combat the memory bandwidth-bound nature of autoregressive LLM inference, previous research has proposed the speculative decoding framework. To perform speculative decoding, a small draft model proposes candidate continuations of the input sequence that are then verified in parallel by the base model. One way to specify the draft model, as used in the recent Medusa decoding ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2402.05109v2", "content": "Oct 7, 2024 · Abstract To combat the memory bandwidth-bound nature of autoregressive LLM inference, previous research has proposed the speculative decoding framework. To perform speculative decoding, a small draft model proposes candidate continuations of the input sequence that are then verified in parallel by the base model. One way to specify the draft model, as used in the recent Medusa decoding ..."}
+{"idx": 7, "title": "arXiv:2410.03804v2 [cs.CL] 3 Apr 2025", "date": "", "ddg_snippet": "by M Zimmer · 2024 · Cited by 5 — Medusa: Simple llm inference acceleration framework with multiple decoding heads , 2024b. URL https://arxiv.org/abs/2401.10774. Mark Chen, Jerry ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2410.03804", "content": "by M Zimmer · 2024 · Cited by 5 — Medusa: Simple llm inference acceleration framework with multiple decoding heads , 2024b. URL https://arxiv.org/abs/2401.10774. Mark Chen, Jerry ..."}
+{"idx": 8, "title": "Bi-directional Multi-head Decoding for Accelerating LLM ...", "date": "", "ddg_snippet": "Medusa: Simple llm inference acceleration framework with multiple decoding heads . Preprint, arXiv:2401.10774. Chen et al. (2023) ↑ Charlie Chen, Sebastian ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2406.13170v2", "content": "Medusa: Simple llm inference acceleration framework with multiple decoding heads . Preprint, arXiv:2401.10774. Chen et al. (2023) ↑ Charlie Chen, Sebastian ..."}
+{"idx": 9, "title": "Vision-Aware Speculative Decoding (ViSpec)", "date": "", "ddg_snippet": "6 days ago — Medusa: Simple llm inference acceleration framework with multiple decoding heads . In International Conference on Machine Learning, pages ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2509.15235v1", "content": "6 days ago — Medusa: Simple llm inference acceleration framework with multiple decoding heads . In International Conference on Machine Learning, pages ..."}
diff --git a/data/sampled_jsons/sitearxiv.org_Training-Free_Diffusion_Model_Alignment_with_Sampling_Demons_Section_J_reward_estimati.jsonl b/data/sampled_jsons/sitearxiv.org_Training-Free_Diffusion_Model_Alignment_with_Sampling_Demons_Section_J_reward_estimati.jsonl
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index 0000000000000000000000000000000000000000..08afe5de3fac06d5e5ab2e4518c2b30095564e65
--- /dev/null
+++ b/data/sampled_jsons/sitearxiv.org_Training-Free_Diffusion_Model_Alignment_with_Sampling_Demons_Section_J_reward_estimati.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Training-free Diffusion Model Alignment with Sampling ...", "date": "", "ddg_snippet": "Aligning diffusion models with user preferences has been a key challenge . Existing methods for aligning diffusion models either require retraining or are ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2410.05760v2", "content": "Aligning diffusion models with user preferences has been a key challenge . Existing methods for aligning diffusion models either require retraining or are ..."}
+{"idx": 1, "title": "training-free diffusion model alignment with sampling ...", "date": "", "ddg_snippet": "by PH Yeh · 2024 · Cited by 5 — Aligning diffusion models with user preferences has been a key challenge . Existing methods for aligning diffusion models either require ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2410.05760?", "content": "by PH Yeh · 2024 · Cited by 5 — Aligning diffusion models with user preferences has been a key challenge . Existing methods for aligning diffusion models either require ..."}
+{"idx": 2, "title": "arXiv:2502.06693v1 [cs.LG] 10 Feb 2025", "date": "", "ddg_snippet": "by A Peluso · 2025 — Using machine learning to individualize treatment effect estimation : Challenges and opportunities. Clinical Pharmacology & Therapeutics, 115(4):710–719 ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2502.06693", "content": "by A Peluso · 2025 — Using machine learning to individualize treatment effect estimation : Challenges and opportunities. Clinical Pharmacology & Therapeutics, 115(4):710–719 ..."}
+{"idx": 3, "title": "Machine Learning Oct 2024", "date": "", "ddg_snippet": "21 Oct 2024 — Title: Training - free Diffusion Model Alignment with Sampling Demons ... Title: Model-free Estimation of Latent Structure via Multiscale ...", "subpage_snippet": "", "source": "www.arxiv.org", "link": "https://www.arxiv.org/list/stat.ML/2024-10?skip=275&show=500", "content": "21 Oct 2024 — Title: Training - free Diffusion Model Alignment with Sampling Demons ... Title: Model-free Estimation of Latent Structure via Multiscale ..."}
+{"idx": 4, "title": "Statistics Oct 2024", "date": "", "ddg_snippet": "24 Oct 2024 — Title: Model-free Estimation of Latent Structure via Multiscale ... Title: Training - free Diffusion Model Alignment with Sampling Demons .", "subpage_snippet": "", "source": "www.arxiv.org", "link": "https://www.arxiv.org/list/stat/2024-10?skip=425&show=1000", "content": "24 Oct 2024 — Title: Model-free Estimation of Latent Structure via Multiscale ... Title: Training - free Diffusion Model Alignment with Sampling Demons ."}
+{"idx": 5, "title": "Intellectual Property in Graph-Based Machine Learning as ...", "date": "", "ddg_snippet": "by L Li · 2025 — In Section 5.1, we will explore these techniques in detail, focusing on how they contribute to the security and robustness of GML models under ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2508.19641", "content": "by L Li · 2025 — In Section 5.1, we will explore these techniques in detail, focusing on how they contribute to the security and robustness of GML models under ..."}
+{"idx": 6, "title": "Computer Science Jul 2025", "date": "", "ddg_snippet": "8 Jul 2025 — Title: MaXsive: High-Capacity and Robust Training - Free Generative Image Watermarking in Diffusion Models . Po-Yuan Mao, Cheng-Chang Tsai, Chun ...", "subpage_snippet": "", "source": "www.arxiv.org", "link": "https://www.arxiv.org/list/cs/2025-07?skip=9650&show=1000", "content": "8 Jul 2025 — Title: MaXsive: High-Capacity and Robust Training - Free Generative Image Watermarking in Diffusion Models . Po-Yuan Mao, Cheng-Chang Tsai, Chun ..."}
+{"idx": 7, "title": "Computer Science Jan 2025", "date": "", "ddg_snippet": "... Training - free Denoising Diffusion Models . Chaohao Xie, Kai Han, Kwan-Yee K ... Title: Deep Reinforcement Learning with Hybrid Intrinsic Reward Model.", "subpage_snippet": "", "source": "www.arxiv.org", "link": "https://www.arxiv.org/list/cs/2025-01?skip=4975&show=2000", "content": "... Training - free Denoising Diffusion Models . Chaohao Xie, Kai Han, Kwan-Yee K ... Title: Deep Reinforcement Learning with Hybrid Intrinsic Reward Model."}
+{"idx": 8, "title": "", "date": "", "ddg_snippet": "", "subpage_snippet": "", "source": "", "link": "", "content": ""}
+{"idx": 9, "title": "UTrace: Poisoning Forensics for Private Collaborative ...", "date": "", "ddg_snippet": "by E Rose · 2024 · Cited by 3 — We provide a rigorous empirical evaluation of UTrace across three data modalities, including vision, text, and malware, four datasets, and 10 poisoning attacks.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2409.15126", "content": "by E Rose · 2024 · Cited by 3 — We provide a rigorous empirical evaluation of UTrace across three data modalities, including vision, text, and malware, four datasets, and 10 poisoning attacks."}
diff --git a/data/sampled_jsons/sitearxiv.org_VRSBench_2406.12384_resolution_image_size.jsonl b/data/sampled_jsons/sitearxiv.org_VRSBench_2406.12384_resolution_image_size.jsonl
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index 0000000000000000000000000000000000000000..bc0e121a41279c6db167d9c7292e5212db56e5f0
--- /dev/null
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@@ -0,0 +1,10 @@
+{"idx": 0, "title": "[2406.12384] VRSBench: A Versatile Vision-Language Benchmark ... [2406.12384] VRSBench: A Versatile Vision-Language Benchmark ... Abstract - arXiv.org XLRS-Bench: Could Your Multimodal LLMs Understand Extremely ... Advancements in Visual Language Models for Remote Sensing ... GeoGround: A Unified Large Vision-Language Model Enhancing Ultra High Resolution Remote Sensing Imagery ...", "date": "", "ddg_snippet": "Jun 18, 2024 · Abstract page for arXiv paper 2406.12384 : VRSBench : A Versatile Vision-Language Benchmark Dataset for Remote Sensing Image Understanding Jul 5, 2024 · Exploring these improvement opportunities, we present a V ersatile vision-language Bench mark for R emote S ensing image understanding, termed VRSBench . This benchmark comprises 29,614 images , with 29,614 human-verified detailed captions, 52,472 object references, and 123,221 question-answer pairs. Based on the semi-automatic data collection pipeline, we collect VRSBench dataset that provides detailed image captioning, visual grounding, and visual question-answer labels in a unified dataset, and therefore, enables a comprehensive evaluation of multiple vision-languages capabilities based on this dataset. Image Size . Real ultra-high resolution RS images often capture scenes at the least city-level or above with large size (e.g., 10,000 \\times × 10,000). However, benchmarks like VRSBench [28] merely utilize 512 \\times × 512 image slices to evaluate the performance of MLLMs on cross-modal perception and understanding (like visual question answering (VQA), image captioning and visual grounding ... The ViT adopts the first one. Compared to convolutional networks, ViT has a significant advantage in scaling up ability. Its performance on image processing does not saturate quickly as the model size increases, allowing for better utilization of larger models and enhanced capabilities in handling more complex tasks. May 10, 2025 · The details of each dataset are provided in Tab. 1. Since both GeoChat [7] and VRSBench [14] use DIOR [12] image data, which overlap with DIOR-RSVG [38], we remove the samples corresponding to images that appear in the DIOR-RSVG test and validation sets from the GeoChat and VRSBench training set to prevent data leakage. Nov 12, 2024 · Abstract Ultra High Resolution (UHR) remote sensing imagery (RSI) (e.g. 100,000 × 100,000 pixels or more) poses a significant challenge for current Remote Sensing Multimodal Large Language Models (RSMLLMs). If choose to resize the UHR image to standard input image size , the extensive spatial and contextual information that UHR images contain will be neglected. Otherwise, the original size of ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2406.12384", "content": "Jun 18, 2024 · Abstract page for arXiv paper 2406.12384 : VRSBench : A Versatile Vision-Language Benchmark Dataset for Remote Sensing Image Understanding Jul 5, 2024 · Exploring these improvement opportunities, we present a V ersatile vision-language Bench mark for R emote S ensing image understanding, termed VRSBench . This benchmark comprises 29,614 images , with 29,614 human-verified detailed captions, 52,472 object references, and 123,221 question-answer pairs. Based on the semi-automatic data collection pipeline, we collect VRSBench dataset that provides detailed image captioning, visual grounding, and visual question-answer labels in a unified dataset, and therefore, enables a comprehensive evaluation of multiple vision-languages capabilities based on this dataset. Image Size . Real ultra-high resolution RS images often capture scenes at the least city-level or above with large size (e.g., 10,000 \\times × 10,000). However, benchmarks like VRSBench [28] merely utilize 512 \\times × 512 image slices to evaluate the performance of MLLMs on cross-modal perception and understanding (like visual question answering (VQA), image captioning and visual grounding ... The ViT adopts the first one. Compared to convolutional networks, ViT has a significant advantage in scaling up ability. Its performance on image processing does not saturate quickly as the model size increases, allowing for better utilization of larger models and enhanced capabilities in handling more complex tasks. May 10, 2025 · The details of each dataset are provided in Tab. 1. Since both GeoChat [7] and VRSBench [14] use DIOR [12] image data, which overlap with DIOR-RSVG [38], we remove the samples corresponding to images that appear in the DIOR-RSVG test and validation sets from the GeoChat and VRSBench training set to prevent data leakage. Nov 12, 2024 · Abstract Ultra High Resolution (UHR) remote sensing imagery (RSI) (e.g. 100,000 × 100,000 pixels or more) poses a significant challenge for current Remote Sensing Multimodal Large Language Models (RSMLLMs). If choose to resize the UHR image to standard input image size , the extensive spatial and contextual information that UHR images contain will be neglected. Otherwise, the original size of ..."}
+{"idx": 1, "title": "Abstract - arXiv.org", "date": "", "ddg_snippet": "Based on the semi-automatic data collection pipeline, we collect VRSBench dataset that provides detailed image captioning, visual grounding, and visual question-answer labels in a unified dataset, and therefore, enables a comprehensive evaluation of multiple vision-languages capabilities based on this dataset.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2406.12384", "content": "Based on the semi-automatic data collection pipeline, we collect VRSBench dataset that provides detailed image captioning, visual grounding, and visual question-answer labels in a unified dataset, and therefore, enables a comprehensive evaluation of multiple vision-languages capabilities based on this dataset."}
+{"idx": 2, "title": "[2406.12384] VRSBench: A Versatile Vision-Language Benchmark ...", "date": "", "ddg_snippet": "Jul 5, 2024 · Exploring these improvement opportunities, we present a V ersatile vision-language Bench mark for R emote S ensing image understanding, termed VRSBench . This benchmark comprises 29,614 images , with 29,614 human-verified detailed captions, 52,472 object references, and 123,221 question-answer pairs.", "subpage_snippet": "", "source": "ar5iv.labs.arxiv.org", "link": "https://ar5iv.labs.arxiv.org/html/2406.12384", "content": "Jul 5, 2024 · Exploring these improvement opportunities, we present a V ersatile vision-language Bench mark for R emote S ensing image understanding, termed VRSBench . This benchmark comprises 29,614 images , with 29,614 human-verified detailed captions, 52,472 object references, and 123,221 question-answer pairs."}
+{"idx": 3, "title": "Enhancing Ultra High Resolution Remote Sensing Imagery ...", "date": "", "ddg_snippet": "Nov 12, 2024 · Abstract Ultra High Resolution (UHR) remote sensing imagery (RSI) (e.g. 100,000 × 100,000 pixels or more) poses a significant challenge for current Remote Sensing Multimodal Large Language Models (RSMLLMs). If choose to resize the UHR image to standard input image size , the extensive spatial and contextual information that UHR images contain will be neglected. Otherwise, the original size of ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2411.07688v1", "content": "Nov 12, 2024 · Abstract Ultra High Resolution (UHR) remote sensing imagery (RSI) (e.g. 100,000 × 100,000 pixels or more) poses a significant challenge for current Remote Sensing Multimodal Large Language Models (RSMLLMs). If choose to resize the UHR image to standard input image size , the extensive spatial and contextual information that UHR images contain will be neglected. Otherwise, the original size of ..."}
+{"idx": 4, "title": "VRSBench : A Versatile Vision-Language Benchmark Dataset for...", "date": "", "ddg_snippet": "VRSBench comprises 29,614 images , each enriched with human-verified detailed captions, complex object referring, and question-answer pairs, check Table 1 for a detailed comparison with existing datasets.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2406.12384v1", "content": "VRSBench comprises 29,614 images , each enriched with human-verified detailed captions, complex object referring, and question-answer pairs, check Table 1 for a detailed comparison with existing datasets."}
+{"idx": 5, "title": "VRSBench : A Versatile Vision-Language Benchmark Dataset for...", "date": "", "ddg_snippet": "VRSBench comprises 29,614 images , each enriched with human-verified detailed captions, complex object referring, and question-answer pairs, check Table 1 for a detailed comparison with existing datasets.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2406.12384v2", "content": "VRSBench comprises 29,614 images , each enriched with human-verified detailed captions, complex object referring, and question-answer pairs, check Table 1 for a detailed comparison with existing datasets."}
+{"idx": 6, "title": "XLRS-Bench: Could Your Multimodal LLMs Understand Extremely ...", "date": "", "ddg_snippet": "Image Size . Real ultra-high resolution RS images often capture scenes at the least city-level or above with large size (e.g., 10,000 \\times × 10,000). However, benchmarks like VRSBench [28] merely utilize 512 \\times × 512 image slices to evaluate the performance of MLLMs on cross-modal perception and understanding (like visual question answering (VQA), image captioning and visual grounding ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2503.23771", "content": "Image Size . Real ultra-high resolution RS images often capture scenes at the least city-level or above with large size (e.g., 10,000 \\times × 10,000). However, benchmarks like VRSBench [28] merely utilize 512 \\times × 512 image slices to evaluate the performance of MLLMs on cross-modal perception and understanding (like visual question answering (VQA), image captioning and visual grounding ..."}
+{"idx": 7, "title": "Advancements in Visual Language Models for Remote Sensing ...", "date": "", "ddg_snippet": "The ViT adopts the first one. Compared to convolutional networks, ViT has a significant advantage in scaling up ability. Its performance on image processing does not saturate quickly as the model size increases, allowing for better utilization of larger models and enhanced capabilities in handling more complex tasks.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2410.17283v1", "content": "The ViT adopts the first one. Compared to convolutional networks, ViT has a significant advantage in scaling up ability. Its performance on image processing does not saturate quickly as the model size increases, allowing for better utilization of larger models and enhanced capabilities in handling more complex tasks."}
+{"idx": 8, "title": "GeoGround: A Unified Large Vision-Language Model", "date": "", "ddg_snippet": "May 10, 2025 · The details of each dataset are provided in Tab. 1. Since both GeoChat [7] and VRSBench [14] use DIOR [12] image data, which overlap with DIOR-RSVG [38], we remove the samples corresponding to images that appear in the DIOR-RSVG test and validation sets from the GeoChat and VRSBench training set to prevent data leakage.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2411.11904", "content": "May 10, 2025 · The details of each dataset are provided in Tab. 1. Since both GeoChat [7] and VRSBench [14] use DIOR [12] image data, which overlap with DIOR-RSVG [38], we remove the samples corresponding to images that appear in the DIOR-RSVG test and validation sets from the GeoChat and VRSBench training set to prevent data leakage."}
+{"idx": 9, "title": "XLRS- Bench : Could Your Multimodal LLMs Understand Extremely...", "date": "", "ddg_snippet": "VRSBench [28] includes 29,614 images with paired descriptions, 52,472 object references, and 123,221 question-answer pairs. Nevertheless, these benchmarks have two main limitations.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2503.23771v1", "content": "VRSBench [28] includes 29,614 images with paired descriptions, 52,472 object references, and 123,221 question-answer pairs. Nevertheless, these benchmarks have two main limitations."}
diff --git a/data/sampled_jsons/sitearxiv.org_filetypepdf_2312.15427_Semi-Bandit_Learning_for_Monotone_Stochastic_Optimization.jsonl b/data/sampled_jsons/sitearxiv.org_filetypepdf_2312.15427_Semi-Bandit_Learning_for_Monotone_Stochastic_Optimization.jsonl
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@@ -0,0 +1,9 @@
+{"idx": 0, "title": "Semi - Bandit Learning for Monotone Stochastic Optimization", "date": "", "ddg_snippet": "arXiv: 2312 . 15427 v1 [cs.LG] 24 Dec 2023. Semi - Bandit Learning for Monotone Stochastic Optimization . Stochastic optimization is a widely used approach for optimization under uncertainty, where uncertain input parameters are modeled by random variables.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2312.15427", "content": "arXiv: 2312 . 15427 v1 [cs.LG] 24 Dec 2023. Semi - Bandit Learning for Monotone Stochastic Optimization . Stochastic optimization is a widely used approach for optimization under uncertainty, where uncertain input parameters are modeled by random variables."}
+{"idx": 1, "title": "Nadav Merlis Reinforcement Learning with Lookahea - arXiv.org", "date": "", "ddg_snippet": "h Nagarajan. Semi-bandit learning for monotone stochastic optimization . arXiv preprint arXiv:2312 E Schapire. The nonstochastic multiarmed ba dit problem. SIAM journal on computing, 32(1):4 Rémi Munos. Minimax regret bounds for rein-forcem nt learning . In International Conference on Machine Learning , pag s 263-272 i Bertsekas. A course in ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2406.02258v2", "content": "h Nagarajan. Semi-bandit learning for monotone stochastic optimization . arXiv preprint arXiv:2312 E Schapire. The nonstochastic multiarmed ba dit problem. SIAM journal on computing, 32(1):4 Rémi Munos. Minimax regret bounds for rein-forcem nt learning . In International Conference on Machine Learning , pag s 263-272 i Bertsekas. A course in ..."}
+{"idx": 2, "title": "Reinforcement Learning with Lookahead Information", "date": "", "ddg_snippet": "by N Merlis · 2024 — Semi-bandit learning for monotone stochastic optimization . arXiv preprint arXiv:2312.15427, 2023. Peter Auer, Nicolo Cesa-Bianchi, Yoav ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2406.02258", "content": "by N Merlis · 2024 — Semi-bandit learning for monotone stochastic optimization . arXiv preprint arXiv:2312.15427, 2023. Peter Auer, Nicolo Cesa-Bianchi, Yoav ..."}
+{"idx": 3, "title": "[ 2312 . 15427 ] Semi - Bandit Learning for Monotone Stochastic ...", "date": "", "ddg_snippet": "View a PDF of the paper titled Semi - Bandit Learning for Monotone Stochastic Optimization , by Arpit Agarwal and Rohan Ghuge and Viswanath Nagarajan.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2312.15427", "content": "View a PDF of the paper titled Semi - Bandit Learning for Monotone Stochastic Optimization , by Arpit Agarwal and Rohan Ghuge and Viswanath Nagarajan."}
+{"idx": 4, "title": "Semi - Bandit Learning for Monotone Stochastic Optimization", "date": "", "ddg_snippet": "Stochastic optimization is a widely used approach for optimization under uncertainty, where uncertain input parameters are modeled by random variables. Exact or approximation algorithms have been obtained for several fundamental problems in this area.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2312.15427v2", "content": "Stochastic optimization is a widely used approach for optimization under uncertainty, where uncertain input parameters are modeled by random variables. Exact or approximation algorithms have been obtained for several fundamental problems in this area."}
+{"idx": 5, "title": "Semi - Bandit Learning for Monotone Stochastic Optimization", "date": "", "ddg_snippet": "Report issue for preceding element. Semi - Bandit Learning for Monotone Stochastic Optimization . Stochastic optimization is a widely used approach for optimization under uncertainty, where uncertain input parameters are modeled by random variables.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2312.15427v1", "content": "Report issue for preceding element. Semi - Bandit Learning for Monotone Stochastic Optimization . Stochastic optimization is a widely used approach for optimization under uncertainty, where uncertain input parameters are modeled by random variables."}
+{"idx": 6, "title": "[2302.01324] Randomized Greedy Learning for Non- monotone ...", "date": "", "ddg_snippet": "View a PDF of the paper titled Randomized Greedy Learning for Non- monotone Stochastic Submodular Maximization Under Full- bandit Feedback, by Fares Fourati and 3 other authors.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2302.01324", "content": "View a PDF of the paper titled Randomized Greedy Learning for Non- monotone Stochastic Submodular Maximization Under Full- bandit Feedback, by Fares Fourati and 3 other authors."}
+{"idx": 7, "title": "Combinatorial Stochastic -Greedy Bandit", "date": "", "ddg_snippet": "Stochastic linear optimization under bandit feedback.Randomized greedy learning for non- monotone stochastic submodular maximization under full- bandit feedback. In International Conference on Artificial Intelligence and Statistics, 7455–7471.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2312.08057v1", "content": "Stochastic linear optimization under bandit feedback.Randomized greedy learning for non- monotone stochastic submodular maximization under full- bandit feedback. In International Conference on Artificial Intelligence and Statistics, 7455–7471."}
+{"idx": 8, "title": "", "date": "", "ddg_snippet": "", "subpage_snippet": "", "source": "", "link": "", "content": ""}
diff --git a/data/sampled_jsons/sitearxiv.orghtml2505.18786_Section_4.3_group_unlearning_analysis.jsonl b/data/sampled_jsons/sitearxiv.orghtml2505.18786_Section_4.3_group_unlearning_analysis.jsonl
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index 0000000000000000000000000000000000000000..e7c881dfba5da497ac0c3577de36a9e49da06520
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@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Roman sculpture - Wikipedia", "date": "", "ddg_snippet": "The study of Roman sculpture is complicated by its relation to Greek sculpture. Many examples of even the most famous Greek sculptures , such as the Apollo Belvedere and Barberini Faun, are known only from Roman Imperial or Hellenistic \"copies\".", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/Roman_sculpture", "content": "The study of Roman sculpture is complicated by its relation to Greek sculpture. Many examples of even the most famous Greek sculptures , such as the Apollo Belvedere and Barberini Faun, are known only from Roman Imperial or Hellenistic \"copies\"."}
+{"idx": 1, "title": "Leveraging Per-Instance Privacy for Machine Unlearning", "date": "", "ddg_snippet": "4 . 3 Limitations of Existing Group Unlearning Analyses .Our analysis provides a theoretical foundation for understanding how the number of iterations needed to approximately unlearn scales with per-instance privacy losses.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2505.18786v1", "content": "4 . 3 Limitations of Existing Group Unlearning Analyses .Our analysis provides a theoretical foundation for understanding how the number of iterations needed to approximately unlearn scales with per-instance privacy losses."}
+{"idx": 2, "title": "Ancient Roman Art - Exploring the Stunning Roman Art Wonders", "date": "", "ddg_snippet": "Discover the majestic legacy of ancient Roman art , from exquisite sculptures to intricate mosaics that reflect a rich cultural heritage.", "subpage_snippet": "", "source": "roman-empire.net", "link": "https://roman-empire.net/society/ancient-roman-art", "content": "Discover the majestic legacy of ancient Roman art , from exquisite sculptures to intricate mosaics that reflect a rich cultural heritage."}
+{"idx": 3, "title": "Roman Sculpture - World History Encyclopedia", "date": "", "ddg_snippet": "Aug 25, 2013 · Roman sculpture blended the idealised perfection of Classical Greek sculpture with a greater aspiration for realism. It also absorbed artistic preferences and styles from the East to create images in stone and bronze which rank among the finest works from antiquity.", "subpage_snippet": "", "source": "www.worldhistory.org", "link": "https://www.worldhistory.org/Roman_Sculpture/", "content": "Aug 25, 2013 · Roman sculpture blended the idealised perfection of Classical Greek sculpture with a greater aspiration for realism. It also absorbed artistic preferences and styles from the East to create images in stone and bronze which rank among the finest works from antiquity."}
+{"idx": 4, "title": "10 Most Famous Roman Statues and Sculptures - Artst", "date": "", "ddg_snippet": "The relationship between Roman and Greek art complicates the study of Roman sculpture . Many of the most renowned Greek sculptures , such as the Apollo Belvedere and the Barberini Faun, are only known via Roman Imperial or Hellenistic “copies.”", "subpage_snippet": "", "source": "www.artst.org", "link": "https://www.artst.org/roman-statues/", "content": "The relationship between Roman and Greek art complicates the study of Roman sculpture . Many of the most renowned Greek sculptures , such as the Apollo Belvedere and the Barberini Faun, are only known via Roman Imperial or Hellenistic “copies.”"}
+{"idx": 5, "title": "Ancient Roman Art - The Art Institute of Chicago", "date": "", "ddg_snippet": "From celebratory statues to intricate mosaic panels, art was created for a wide variety of functions and contexts during the centuries that the Roman Empire reigned. Explore a few highlights from the Art Institute’s collection of ancient Roman art here.", "subpage_snippet": "", "source": "www.artic.edu", "link": "https://www.artic.edu/highlights/19/ancient-roman-art", "content": "From celebratory statues to intricate mosaic panels, art was created for a wide variety of functions and contexts during the centuries that the Roman Empire reigned. Explore a few highlights from the Art Institute’s collection of ancient Roman art here."}
+{"idx": 6, "title": "10 Ancient Roman Art Pieces You Can't Miss - History Lists", "date": "", "ddg_snippet": "In this guide, we've selected 10 of the most remarkable examples of ancient Roman art that showcase the enduring legacy of both the Roman and ancient Greek art traditions. The sculpture is one of the most recognizable icons of ancient mythology and a symbol of Rome since ancient times.", "subpage_snippet": "", "source": "historylists.org", "link": "https://historylists.org/art/10-ancient-roman-art-pieces-you-cant-miss.html", "content": "In this guide, we've selected 10 of the most remarkable examples of ancient Roman art that showcase the enduring legacy of both the Roman and ancient Greek art traditions. The sculpture is one of the most recognizable icons of ancient mythology and a symbol of Rome since ancient times."}
+{"idx": 7, "title": "Famous Roman Statues – The Best Ancient Roman Sculptures", "date": "", "ddg_snippet": "Jul 21, 2022 · This article introduces famous Roman statues, including a selection of ancient Roman sculptures and some more recent sculptures by famous Italian artists, such as Michelangelo and Bernini.", "subpage_snippet": "", "source": "artincontext.org", "link": "https://artincontext.org/famous-roman-statues/", "content": "Jul 21, 2022 · This article introduces famous Roman statues, including a selection of ancient Roman sculptures and some more recent sculptures by famous Italian artists, such as Michelangelo and Bernini."}
+{"idx": 8, "title": "Roman Portrait Sculpture: The Stylistic Cycle", "date": "", "ddg_snippet": "Oct 1, 2003 · The turbulence of the year 68/69 A.D., which saw the rise and fall of three different emperors, instigated drastic changes in Roman portraiture characterized by a return to a veristic representation that emphasized their military strengths.", "subpage_snippet": "", "source": "www.metmuseum.org", "link": "https://www.metmuseum.org/essays/roman-portrait-sculpture-the-stylistic-cycle", "content": "Oct 1, 2003 · The turbulence of the year 68/69 A.D., which saw the rise and fall of three different emperors, instigated drastic changes in Roman portraiture characterized by a return to a veristic representation that emphasized their military strengths."}
+{"idx": 9, "title": "7 Roman Statues That Shaped Art History - Culture", "date": "", "ddg_snippet": "Aug 22, 2024 · Explore the artistry and history of ancient Roman sculptures . Discover the famous Roman statues that shaped the Roman Empire and their lasting impact on art.", "subpage_snippet": "", "source": "culture.org", "link": "https://culture.org/art-and-culture/roman-statues/", "content": "Aug 22, 2024 · Explore the artistry and history of ancient Roman sculptures . Discover the famous Roman statues that shaped the Roman Empire and their lasting impact on art."}
diff --git a/data/sampled_jsons/sitearxiv.orghtml2506.06866_Effects_of_Penalty_Parameter_lambda.jsonl b/data/sampled_jsons/sitearxiv.orghtml2506.06866_Effects_of_Penalty_Parameter_lambda.jsonl
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@@ -0,0 +1,3 @@
+{"idx": 0, "title": "Safe: Finding Sparse and Flat Minima to Improve Pruning", "date": "", "ddg_snippet": "F.1 Effects of Penalty Parameter .Figure 4: Effects of different choices of penalty parameter schedules (a) on validation accuracy of sparsified/dense network (b-c) and the distance to the target sparsity constraint (d) over the training process of ResNet-20/CIFAR-10 using Safe on.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2506.06866v2", "content": "F.1 Effects of Penalty Parameter .Figure 4: Effects of different choices of penalty parameter schedules (a) on validation accuracy of sparsified/dense network (b-c) and the distance to the target sparsity constraint (d) over the training process of ResNet-20/CIFAR-10 using Safe on."}
+{"idx": 1, "title": "Safe: Finding Sparse and Flat Minima to Improve Pruning", "date": "", "ddg_snippet": "F.1 Effects of Penalty Parameter . λ𝜆\\lambdaitalic_λ. (a) Dense/sparse validation accuracies. (b) Distance to constraint. Figure 3: Effect of the penalty parameter .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2506.06866v1", "content": "F.1 Effects of Penalty Parameter . λ𝜆\\lambdaitalic_λ. (a) Dense/sparse validation accuracies. (b) Distance to constraint. Figure 3: Effect of the penalty parameter ."}
+{"idx": 2, "title": "", "date": "", "ddg_snippet": "", "subpage_snippet": "", "source": "", "link": "", "content": ""}
diff --git a/data/sampled_jsons/sitegithub.com_facebookresearchMask2Former_configs.jsonl b/data/sampled_jsons/sitegithub.com_facebookresearchMask2Former_configs.jsonl
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index 0000000000000000000000000000000000000000..678f9229397c6cc6171c310d6a8fdd31def275dc
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@@ -0,0 +1,10 @@
+{"idx": 0, "title": "GitHub - facebookresearch/Mask2Former: Code release for \"Masked ...", "date": "", "ddg_snippet": "A single architecture for panoptic, instance and semantic segmentation. Support major segmentation datasets: ADE20K, Cityscapes, COCO, Mapillary Vistas. The majority of Mask2Former is licensed under a MIT License. However portions of the project are available under separate license terms: Swin ...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/facebookresearch/Mask2Former", "content": "A single architecture for panoptic, instance and semantic segmentation. Support major segmentation datasets: ADE20K, Cityscapes, COCO, Mapillary Vistas. The majority of Mask2Former is licensed under a MIT License. However portions of the project are available under separate license terms: Swin ..."}
+{"idx": 1, "title": "Mask2Former/ at main · facebookresearch/Mask2Former · GitHub", "date": "", "ddg_snippet": "Citing Mask2Former If you use Mask2Former in your research or wish to refer to the baseline results published in the Model Zoo, please use the following BibTeX entry.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/facebookresearch/Mask2Former?search=1", "content": "Citing Mask2Former If you use Mask2Former in your research or wish to refer to the baseline results published in the Model Zoo, please use the following BibTeX entry."}
+{"idx": 2, "title": "Mask2Former/GETTING_STARTED.md at main · facebookresearch ... - GitHub", "date": "", "ddg_snippet": "We provide a script train_net.py, that is made to train all the configs provided in Mask2Former. To train a model with \"train_net.py\", first setup the corresponding datasets following datasets/README.md, then run:", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/facebookresearch/Mask2Former/blob/master/GETTING_STARTED.md", "content": "We provide a script train_net.py, that is made to train all the configs provided in Mask2Former. To train a model with \"train_net.py\", first setup the corresponding datasets following datasets/README.md, then run:"}
+{"idx": 3, "title": "GitHub - facebookresearch/MaskFormer: Per-Pixel Classification is Not ...", "date": "", "ddg_snippet": "Checkout Mask2Former, a universal architecture based on MaskFormer meta-architecture that achieves SOTA on panoptic, instance and semantic segmentation across four popular datasets (ADE20K, Cityscapes, COCO, Mapillary Vistas). The majority of MaskFormer is licensed under a Creative Commons ...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/facebookresearch/MaskFormer", "content": "Checkout Mask2Former, a universal architecture based on MaskFormer meta-architecture that achieves SOTA on panoptic, instance and semantic segmentation across four popular datasets (ADE20K, Cityscapes, COCO, Mapillary Vistas). The majority of MaskFormer is licensed under a Creative Commons ..."}
+{"idx": 4, "title": "MaskFormer/README.md at main · facebookresearch/MaskFormer · GitHub", "date": "", "ddg_snippet": "Checkout Mask2Former, a universal architecture based on MaskFormer meta-architecture that achieves SOTA on panoptic, instance and semantic segmentation across four popular datasets (ADE20K, Cityscapes, COCO, Mapillary Vistas).", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/facebookresearch/MaskFormer/blob/main/README.md", "content": "Checkout Mask2Former, a universal architecture based on MaskFormer meta-architecture that achieves SOTA on panoptic, instance and semantic segmentation across four popular datasets (ADE20K, Cityscapes, COCO, Mapillary Vistas)."}
+{"idx": 5, "title": "GitHub - PopupBuddy/FB-Mask2Former: Code release for \"Masked-attention ...", "date": "", "ddg_snippet": "Mask2Former: Masked-attention Mask Transformer for Universal Image Segmentation (CVPR 2022) Bowen Cheng, Ishan Misra, Alexander G. Schwing, Alexander Kirillov, Rohit Girdhar", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/PopupBuddy/FB-Mask2Former", "content": "Mask2Former: Masked-attention Mask Transformer for Universal Image Segmentation (CVPR 2022) Bowen Cheng, Ishan Misra, Alexander G. Schwing, Alexander Kirillov, Rohit Girdhar"}
+{"idx": 6, "title": "Mask2Former/demo/demo.py at main · facebookresearch/Mask2Former", "date": "", "ddg_snippet": "from detectron2.data.detection_utils import read_image from detectron2.projects.deeplab import add_deeplab_config from detectron2.utils.logger import setup_logger from mask2former import add_maskformer2_config from predictor import VisualizationDemo # constants WINDOW_NAME = \"mask2former demo\"", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/facebookresearch/Mask2Former/blob/main/demo/demo.py", "content": "from detectron2.data.detection_utils import read_image from detectron2.projects.deeplab import add_deeplab_config from detectron2.utils.logger import setup_logger from mask2former import add_maskformer2_config from predictor import VisualizationDemo # constants WINDOW_NAME = \"mask2former demo\""}
+{"idx": 7, "title": "Running mask2former without a GPU · Issue #169 · facebookresearch ...", "date": "", "ddg_snippet": "Hello, I am trying to run mask2former on my local laptop, but it requires a GPU which I do not have. I know that I can run it using Google Colab, but I am unfamiliar with how to use the platform an...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/facebookresearch/Mask2Former/issues/169", "content": "Hello, I am trying to run mask2former on my local laptop, but it requires a GPU which I do not have. I know that I can run it using Google Colab, but I am unfamiliar with how to use the platform an..."}
+{"idx": 8, "title": "Mask2Former/GETTING_STARTED.md at main · facebookresearch ... - GitHub", "date": "", "ddg_snippet": "Code release for \"Masked-attention Mask Transformer for Universal Image Segmentation\" - Mask2Former/GETTING_STARTED.md at main · facebookresearch/Mask2Former", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/facebookresearch/Mask2Former/blob/main/GETTING_STARTED.md?plain=1", "content": "Code release for \"Masked-attention Mask Transformer for Universal Image Segmentation\" - Mask2Former/GETTING_STARTED.md at main · facebookresearch/Mask2Former"}
+{"idx": 9, "title": "Setting the config correct for numerous, small objects #191", "date": "", "ddg_snippet": "facebookresearch / Mask2Former Public archive Notifications You must be signed in to change notification settings Fork 399 Star 2.7k", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/facebookresearch/Mask2Former/issues/191", "content": "facebookresearch / Mask2Former Public archive Notifications You must be signed in to change notification settings Fork 399 Star 2.7k"}
diff --git a/data/sampled_jsons/sitegithub.com_facebookresearchMask2Former_train_net.py_learning_rate.jsonl b/data/sampled_jsons/sitegithub.com_facebookresearchMask2Former_train_net.py_learning_rate.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..6e387b2e202a8adb016e6f19babb4c4102712964
--- /dev/null
+++ b/data/sampled_jsons/sitegithub.com_facebookresearchMask2Former_train_net.py_learning_rate.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Mask 2 Former / train _ net . py at main · facebookresearch / Mask 2 Former", "date": "", "ddg_snippet": "Code release for \"Masked-attention Mask Transformer for Universal Image Segmentation\" - Mask 2 Former / train _ net . py at main · facebookresearch / Mask 2 Former .", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/facebookresearch/Mask2Former/blob/main/train_net.py", "content": "Code release for \"Masked-attention Mask Transformer for Universal Image Segmentation\" - Mask 2 Former / train _ net . py at main · facebookresearch / Mask 2 Former ."}
+{"idx": 1, "title": "GitHub - facebookresearch/Mask2Former: Code release for \"Masked ...", "date": "", "ddg_snippet": "Citing Mask2Former If you use Mask2Former in your research or wish to refer to the baseline results published in the Model Zoo, please use the following BibTeX entry.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/facebookresearch/Mask2Former", "content": "Citing Mask2Former If you use Mask2Former in your research or wish to refer to the baseline results published in the Model Zoo, please use the following BibTeX entry."}
+{"idx": 2, "title": "Mask2Former/GETTING_STARTED.md at main · facebookresearch ... - GitHub", "date": "", "ddg_snippet": "We provide a script train_net.py , that is made to train all the configs provided in Mask2Former. To train a model with \" train_net.py \", first setup the corresponding datasets following datasets/README.md, then run:", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/facebookresearch/Mask2Former/blob/master/GETTING_STARTED.md", "content": "We provide a script train_net.py , that is made to train all the configs provided in Mask2Former. To train a model with \" train_net.py \", first setup the corresponding datasets following datasets/README.md, then run:"}
+{"idx": 3, "title": "Mask2Former/ at main · facebookresearch/Mask2Former · GitHub", "date": "", "ddg_snippet": "Citing Mask2Former If you use Mask2Former in your research or wish to refer to the baseline results published in the Model Zoo, please use the following BibTeX entry.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/facebookresearch/Mask2Former?search=1", "content": "Citing Mask2Former If you use Mask2Former in your research or wish to refer to the baseline results published in the Model Zoo, please use the following BibTeX entry."}
+{"idx": 4, "title": "MaskFormer/GETTING_STARTED.md at main · facebookresearch ... - GitHub", "date": "", "ddg_snippet": "* To run __on cpu__, add `MODEL.DEVICE cpu` after `--opts`. * To save outputs to a directory (for images) or a file (for webcam or video), use `--output`. ### Training & Evaluation in Command Line We provide two scripts in ` train_net.py `, that are made to train all the configs provided in MaskFormer.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/facebookresearch/MaskFormer/blob/main/GETTING_STARTED.md?plain=1", "content": "* To run __on cpu__, add `MODEL.DEVICE cpu` after `--opts`. * To save outputs to a directory (for images) or a file (for webcam or video), use `--output`. ### Training & Evaluation in Command Line We provide two scripts in ` train_net.py `, that are made to train all the configs provided in MaskFormer."}
+{"idx": 5, "title": "MaskFormer/train_net.py at main · facebookresearch/MaskFormer", "date": "", "ddg_snippet": "Per-Pixel Classification is Not All You Need for Semantic Segmentation (NeurIPS 2021, spotlight) - facebookresearch/MaskFormer", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/facebookresearch/MaskFormer/blob/main/train_net.py", "content": "Per-Pixel Classification is Not All You Need for Semantic Segmentation (NeurIPS 2021, spotlight) - facebookresearch/MaskFormer"}
+{"idx": 6, "title": "Mask2Former-facebook/train_net.py at main - GitHub", "date": "", "ddg_snippet": "Code release for \"Masked-attention Mask Transformer for Universal Image Segmentation\" - anaCatarinaUsebens/Mask2Former-facebook", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/anaCatarinaUsebens/Mask2Former-facebook/blob/main/train_net.py", "content": "Code release for \"Masked-attention Mask Transformer for Universal Image Segmentation\" - anaCatarinaUsebens/Mask2Former-facebook"}
+{"idx": 7, "title": "How to train Mask2Former? · Issue #76 · facebookresearch ... - GitHub", "date": "", "ddg_snippet": "Hi everyone, I am quite new to Detectron and Mask2Former so please don't lynch me :) I registered the Dataset via register_coco_instances. If I run this code evrything works fine: from detectron2.e...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/facebookresearch/Mask2Former/issues/76", "content": "Hi everyone, I am quite new to Detectron and Mask2Former so please don't lynch me :) I registered the Dataset via register_coco_instances. If I run this code evrything works fine: from detectron2.e..."}
+{"idx": 8, "title": "Mask2Former/GETTING_STARTED.md at main · facebookresearch ... - GitHub", "date": "", "ddg_snippet": "### Video instance segmentation Please use `demo_video/demo.py` for video instance segmentation demo and `train_net_video.py` to train and evaluate video instance segmentation models.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/facebookresearch/Mask2Former/blob/main/GETTING_STARTED.md?plain=1", "content": "### Video instance segmentation Please use `demo_video/demo.py` for video instance segmentation demo and `train_net_video.py` to train and evaluate video instance segmentation models."}
+{"idx": 9, "title": "GitHub - facebookresearch /MaskFormer: Per-Pixel Classification is...", "date": "", "ddg_snippet": "requirements.txt. train _ net . py .Checkout Mask 2 Former , a universal architecture based on MaskFormer meta-architecture that achieves SOTA on panoptic, instance and semantic segmentation across four popular datasets (ADE20K, Cityscapes, COCO, Mapillary Vistas). Features.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/facebookresearch/MaskFormer", "content": "requirements.txt. train _ net . py .Checkout Mask 2 Former , a universal architecture based on MaskFormer meta-architecture that achieves SOTA on panoptic, instance and semantic segmentation across four popular datasets (ADE20K, Cityscapes, COCO, Mapillary Vistas). Features."}
diff --git a/data/sampled_jsons/sitegithub.com_facebookresearchMask2Formerblobmainconfigscocopanoptic-segmentationBase-Panoptic-C2N-.jsonl b/data/sampled_jsons/sitegithub.com_facebookresearchMask2Formerblobmainconfigscocopanoptic-segmentationBase-Panoptic-C2N-.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..3f30e467e7d0ee1dc5cf97fe3713e5b2ccfab982
--- /dev/null
+++ b/data/sampled_jsons/sitegithub.com_facebookresearchMask2Formerblobmainconfigscocopanoptic-segmentationBase-Panoptic-C2N-.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "GitHub - facebookresearch/Mask2Former: Code release for ... Mask2Former/GETTING_STARTED.md at main - GitHub GitHub - facebookresearch/MaskFormer: Per-Pixel ... Mask2Former/tools/README.md at main · facebookresearch ... Mask2Former/datasets/README.md at main - GitHub Mask2Former/configs/coco/instance-segmentation ... - GitHub", "date": "", "ddg_snippet": "Bowen Cheng, Ishan Misra, Alexander G. Schwing, Alexander Kirillov, Rohit Girdhar [arXiv] [Project] [BibTeX] Features •A single architecture for panoptic , instance and semantic segmentation . See full list on github . com •Add Google Colab demo. •Video instance segmentation is now supported! Please check our tech report for more details. See full list on github . com See Preparing Datasets for Mask2Former . See Getting Started with Mask2Former . Run our demo using Colab: Integrated into Huggingface Spaces 🤗 using Gradio. Try out the Web Demo: See full list on github . com We provide a large set of baseline results and trained models available for download in the Mask2Former Model Zoo. See full list on github . com Shield: The majority of Mask2Former is licensed under a MIT License. See full list on github . com If you use Mask2Former in your research or wish to refer to the baseline results published in the Model Zoo, please use the following BibTeX entry. If you find the code useful, please also consider the following BibTeX entry. See full list on github . com Code is largely based on MaskFormer (https:// github . com / facebookresearch /MaskFormer). See full list on github . com ## Getting Started with Mask2Former This document provides a brief intro of the usage of Mask2Former . Please see [Getting Started with Detectron2] (https://github. com /facebookresearch/detectron2/ blob /master/GETTING_STARTED.md) for full usage. Checkout Mask2Former , a universal architecture based on MaskFormer meta-architecture that achieves SOTA on panoptic , instance and semantic segmentation across four popular datasets (ADE20K, Cityscapes, COCO , Mapillary Vistas). Note that, for semantic segmentation (ADE20K only), we use a dummy image with fixed size that equals to cfg.INPUT.CROP.SIZE[0] x cfg.INPUT.CROP.SIZE[0]. Please do not use --use-fixed-input-size for calculating FLOPs on other datasets like Cityscapes! If you want to evaluate instance segmentation on Mapillary Vistas, run `python datasets/prepare_mapillary_vistas_ins_seg.py` to generate COCO -style instance annotations. Code release for \"Masked-attention Mask Transformer for Universal Image Segmentation \" - Mask2Former / configs / coco /instance- segmentation /maskformer2_R50_bs16_50ep. yaml at main · facebookresearch/ Mask2Former", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/facebookresearch/Mask2Former", "content": "Bowen Cheng, Ishan Misra, Alexander G. Schwing, Alexander Kirillov, Rohit Girdhar [arXiv] [Project] [BibTeX] Features •A single architecture for panoptic , instance and semantic segmentation . See full list on github . com •Add Google Colab demo. •Video instance segmentation is now supported! Please check our tech report for more details. See full list on github . com See Preparing Datasets for Mask2Former . See Getting Started with Mask2Former . Run our demo using Colab: Integrated into Huggingface Spaces 🤗 using Gradio. Try out the Web Demo: See full list on github . com We provide a large set of baseline results and trained models available for download in the Mask2Former Model Zoo. See full list on github . com Shield: The majority of Mask2Former is licensed under a MIT License. See full list on github . com If you use Mask2Former in your research or wish to refer to the baseline results published in the Model Zoo, please use the following BibTeX entry. If you find the code useful, please also consider the following BibTeX entry. See full list on github . com Code is largely based on MaskFormer (https:// github . com / facebookresearch /MaskFormer). See full list on github . com ## Getting Started with Mask2Former This document provides a brief intro of the usage of Mask2Former . Please see [Getting Started with Detectron2] (https://github. com /facebookresearch/detectron2/ blob /master/GETTING_STARTED.md) for full usage. Checkout Mask2Former , a universal architecture based on MaskFormer meta-architecture that achieves SOTA on panoptic , instance and semantic segmentation across four popular datasets (ADE20K, Cityscapes, COCO , Mapillary Vistas). Note that, for semantic segmentation (ADE20K only), we use a dummy image with fixed size that equals to cfg.INPUT.CROP.SIZE[0] x cfg.INPUT.CROP.SIZE[0]. Please do not use --use-fixed-input-size for calculating FLOPs on other datasets like Cityscapes! If you want to evaluate instance segmentation on Mapillary Vistas, run `python datasets/prepare_mapillary_vistas_ins_seg.py` to generate COCO -style instance annotations. Code release for \"Masked-attention Mask Transformer for Universal Image Segmentation \" - Mask2Former / configs / coco /instance- segmentation /maskformer2_R50_bs16_50ep. yaml at main · facebookresearch/ Mask2Former"}
+{"idx": 1, "title": "Mask2Former/configs/coco/panoptic-segmentation ... - GitHub", "date": "", "ddg_snippet": "Code release for \"Masked-attention Mask Transformer for Universal Image Segmentation\" - Mask2Former / configs / coco / panoptic - segmentation /maskformer2_R50_bs16_50ep. yaml at main · facebookresearch/ Mask2Former", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/facebookresearch/Mask2Former/blob/main/configs/coco/panoptic-segmentation/maskformer2_R50_bs16_50ep.yaml", "content": "Code release for \"Masked-attention Mask Transformer for Universal Image Segmentation\" - Mask2Former / configs / coco / panoptic - segmentation /maskformer2_R50_bs16_50ep. yaml at main · facebookresearch/ Mask2Former"}
+{"idx": 2, "title": "Mask2Former/GETTING_STARTED.md at main - GitHub", "date": "", "ddg_snippet": "## Getting Started with Mask2Former This document provides a brief intro of the usage of Mask2Former . Please see [Getting Started with Detectron2] (https://github. com /facebookresearch/detectron2/ blob /master/GETTING_STARTED.md) for full usage.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/facebookresearch/Mask2Former/blob/main/GETTING_STARTED.md?plain=1", "content": "## Getting Started with Mask2Former This document provides a brief intro of the usage of Mask2Former . Please see [Getting Started with Detectron2] (https://github. com /facebookresearch/detectron2/ blob /master/GETTING_STARTED.md) for full usage."}
+{"idx": 3, "title": "GitHub - facebookresearch/MaskFormer: Per-Pixel ...", "date": "", "ddg_snippet": "Checkout Mask2Former , a universal architecture based on MaskFormer meta-architecture that achieves SOTA on panoptic , instance and semantic segmentation across four popular datasets (ADE20K, Cityscapes, COCO , Mapillary Vistas).", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/facebookresearch/MaskFormer", "content": "Checkout Mask2Former , a universal architecture based on MaskFormer meta-architecture that achieves SOTA on panoptic , instance and semantic segmentation across four popular datasets (ADE20K, Cityscapes, COCO , Mapillary Vistas)."}
+{"idx": 4, "title": "Mask2Former/tools/README.md at main · facebookresearch ...", "date": "", "ddg_snippet": "Note that, for semantic segmentation (ADE20K only), we use a dummy image with fixed size that equals to cfg.INPUT.CROP.SIZE[0] x cfg.INPUT.CROP.SIZE[0]. Please do not use --use-fixed-input-size for calculating FLOPs on other datasets like Cityscapes!", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/facebookresearch/Mask2Former/blob/main/tools/README.md", "content": "Note that, for semantic segmentation (ADE20K only), we use a dummy image with fixed size that equals to cfg.INPUT.CROP.SIZE[0] x cfg.INPUT.CROP.SIZE[0]. Please do not use --use-fixed-input-size for calculating FLOPs on other datasets like Cityscapes!"}
+{"idx": 5, "title": "Mask2Former/datasets/README.md at main - GitHub", "date": "", "ddg_snippet": "If you want to evaluate instance segmentation on Mapillary Vistas, run `python datasets/prepare_mapillary_vistas_ins_seg.py` to generate COCO -style instance annotations.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/facebookresearch/Mask2Former/blob/main/datasets/README.md?plain=1", "content": "If you want to evaluate instance segmentation on Mapillary Vistas, run `python datasets/prepare_mapillary_vistas_ins_seg.py` to generate COCO -style instance annotations."}
+{"idx": 6, "title": "Mask2Former/configs/coco/instance-segmentation ... - GitHub", "date": "", "ddg_snippet": "Code release for \"Masked-attention Mask Transformer for Universal Image Segmentation \" - Mask2Former / configs / coco /instance- segmentation /maskformer2_R50_bs16_50ep. yaml at main · facebookresearch/ Mask2Former", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/facebookresearch/Mask2Former/blob/main/configs/coco/instance-segmentation/maskformer2_R50_bs16_50ep.yaml", "content": "Code release for \"Masked-attention Mask Transformer for Universal Image Segmentation \" - Mask2Former / configs / coco /instance- segmentation /maskformer2_R50_bs16_50ep. yaml at main · facebookresearch/ Mask2Former"}
+{"idx": 7, "title": "How to train Panoptic Segmentation on a custom dataset ?", "date": "", "ddg_snippet": "Instructions To Reproduce the Issue: python3 train_net.py --config-file configs / coco / panoptic - segmentation /swin/maskformer2_swin_ large _IN21 k _ 384 _bs16_100ep_custom. yaml --num-gpus 1 SOLVER.IMS_PER_BATCH 2.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/facebookresearch/Mask2Former/issues/147", "content": "Instructions To Reproduce the Issue: python3 train_net.py --config-file configs / coco / panoptic - segmentation /swin/maskformer2_swin_ large _IN21 k _ 384 _bs16_100ep_custom. yaml --num-gpus 1 SOLVER.IMS_PER_BATCH 2."}
+{"idx": 8, "title": "github.com/ facebookresearch /detectron2/ blob / main / configs / COCO ...", "date": "", "ddg_snippet": "Detectron2 is a platform for object detection, segmentation and other visual recognition tasks. - facebookresearch /detectron2.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/facebookresearch/detectron2/blob/main/configs/COCO-PanopticSegmentation/panoptic_fpn_R_101_3x.yaml", "content": "Detectron2 is a platform for object detection, segmentation and other visual recognition tasks. - facebookresearch /detectron2."}
+{"idx": 9, "title": "github.com/ facebookresearch /detectron2/ blob / main / configs / COCO ...", "date": "", "ddg_snippet": "Detectron2 is a platform for object detection, segmentation and other visual recognition tasks. - detectron2/ configs / COCO -PanopticSegmentation/ Base - Panoptic -FPN. yaml at main · facebookresearch /detectron2.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/facebookresearch/detectron2/blob/main/configs/COCO-PanopticSegmentation/Base-Panoptic-FPN.yaml", "content": "Detectron2 is a platform for object detection, segmentation and other visual recognition tasks. - detectron2/ configs / COCO -PanopticSegmentation/ Base - Panoptic -FPN. yaml at main · facebookresearch /detectron2."}
diff --git a/data/sampled_jsons/sitegithub.com_fiveai_understanding_safety_finetuning.jsonl b/data/sampled_jsons/sitegithub.com_fiveai_understanding_safety_finetuning.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..8bd43c8fb6bb91b9be1ba4668285fa2724628773
--- /dev/null
+++ b/data/sampled_jsons/sitegithub.com_fiveai_understanding_safety_finetuning.jsonl
@@ -0,0 +1,9 @@
+{"idx": 0, "title": "GitHub - fiveai / understanding _ safety _ finetuning : Official Code for...", "date": "", "ddg_snippet": "fiveai / understanding _ safety _ finetuning Public.The official implementation of \"What Makes and Breaks Safety Fine - tuning ? A Mechanistic Study\". This work is accepted to NeurIPS 2024.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/fiveai/understanding_safety_finetuning", "content": "fiveai / understanding _ safety _ finetuning Public.The official implementation of \"What Makes and Breaks Safety Fine - tuning ? A Mechanistic Study\". This work is accepted to NeurIPS 2024."}
+{"idx": 1, "title": "understanding_safety_finetuning/README.md at main · fiveai ...", "date": "", "ddg_snippet": "Official Code for What Makes and Breaks Safety Fine - tuning ? A Mechanistic Study (NeurIPS 2024) - understanding _ safety _ finetuning /README.md at main · fiveai / understanding _ safety _ finetuning", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/fiveai/understanding_safety_finetuning/blob/main/README.md", "content": "Official Code for What Makes and Breaks Safety Fine - tuning ? A Mechanistic Study (NeurIPS 2024) - understanding _ safety _ finetuning /README.md at main · fiveai / understanding _ safety _ finetuning"}
+{"idx": 2, "title": "understanding_safety_finetuning/ssft at main · fiveai ...", "date": "", "ddg_snippet": "Official Code for What Makes and Breaks Safety Fine - tuning ? A Mechanistic Study (NeurIPS 2024) - understanding _ safety _ finetuning /ssft at main · fiveai / understanding _ safety _ finetuning", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/fiveai/understanding_safety_finetuning/tree/main/ssft", "content": "Official Code for What Makes and Breaks Safety Fine - tuning ? A Mechanistic Study (NeurIPS 2024) - understanding _ safety _ finetuning /ssft at main · fiveai / understanding _ safety _ finetuning"}
+{"idx": 3, "title": "Security: selimkuzucu/understanding_safety_finetuning - GitHub", "date": "", "ddg_snippet": "Dismiss alert selimkuzucu / understanding _ safety _ finetuning Public fiveai / understanding _ safety _ finetuning", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/selimkuzucu/understanding_safety_finetuning/security", "content": "Dismiss alert selimkuzucu / understanding _ safety _ finetuning Public fiveai / understanding _ safety _ finetuning"}
+{"idx": 4, "title": "fiveai / understanding_safety_finetuning Public - GitHub", "date": "", "ddg_snippet": "Official Code for What Makes and Breaks Safety Fine - tuning ? A Mechanistic Study (NeurIPS 2024) - understanding _ safety _ finetuning /trainer_pretrain.py at main · fiveai / understanding _ safety _ finetuning", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/fiveai/understanding_safety_finetuning/blob/main/trainer_pretrain.py", "content": "Official Code for What Makes and Breaks Safety Fine - tuning ? A Mechanistic Study (NeurIPS 2024) - understanding _ safety _ finetuning /trainer_pretrain.py at main · fiveai / understanding _ safety _ finetuning"}
+{"idx": 5, "title": "Issues · fiveai / understanding _ safety _ finetuning · GitHub", "date": "", "ddg_snippet": "Official Code for What Makes and Breaks Safety Fine - tuning ? A Mechanistic Study (NeurIPS 2024) - Issues · fiveai / understanding _ safety _ finetuning .", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/fiveai/understanding_safety_finetuning/issues", "content": "Official Code for What Makes and Breaks Safety Fine - tuning ? A Mechanistic Study (NeurIPS 2024) - Issues · fiveai / understanding _ safety _ finetuning ."}
+{"idx": 6, "title": "samyakjain0112 (Samyak Jain) · GitHub", "date": "", "ddg_snippet": "Forked from fiveai / understanding _ safety _ finetuning . Official Code for What Makes and Breaks Safety Fine - tuning ? A Mechanistic Study (NeurIPS 2024).", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/samyakjain0112", "content": "Forked from fiveai / understanding _ safety _ finetuning . Official Code for What Makes and Breaks Safety Fine - tuning ? A Mechanistic Study (NeurIPS 2024)."}
+{"idx": 7, "title": "FiveAI · GitHub", "date": "", "ddg_snippet": "five . ai . Learn more about verified organizations. understanding _ safety _ finetuning Public. Official Code for What Makes and Breaks Safety Fine - tuning ? A Mechanistic Study (NeurIPS 2024).", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/fiveai", "content": "five . ai . Learn more about verified organizations. understanding _ safety _ finetuning Public. Official Code for What Makes and Breaks Safety Fine - tuning ? A Mechanistic Study (NeurIPS 2024)."}
+{"idx": 8, "title": "fiveai repositories · GitHub", "date": "", "ddg_snippet": "understanding _ safety _ finetuning . Public. Official Code for What Makes and Breaks Safety Fine - tuning ? A Mechanistic Study (NeurIPS 2024).", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/orgs/fiveai/repositories", "content": "understanding _ safety _ finetuning . Public. Official Code for What Makes and Breaks Safety Fine - tuning ? A Mechanistic Study (NeurIPS 2024)."}
diff --git a/data/sampled_jsons/sitegithub.com_zyxunhentity_erasure_config.yaml.jsonl b/data/sampled_jsons/sitegithub.com_zyxunhentity_erasure_config.yaml.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..f72731824400414003e114cf3791280eea137889
--- /dev/null
+++ b/data/sampled_jsons/sitegithub.com_zyxunhentity_erasure_config.yaml.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "zyxunh/entity_erasure - EntityErasure", "date": "", "ddg_snippet": "... configs/entityv2/entity_segmentation/mask2former_hornet_3x.yaml WEIGHTS to your model path. python3 unhcv/projects/diffusion/inpainting/evaluation ...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/zyxunh/entity_erasure", "content": "... configs/entityv2/entity_segmentation/mask2former_hornet_3x.yaml WEIGHTS to your model path. python3 unhcv/projects/diffusion/inpainting/evaluation ..."}
+{"idx": 1, "title": "GitHub - zyxunh/Entity_for_entity_erasure", "date": "", "ddg_snippet": "Contribute to zyxunh / Entity _for_ entity _ erasure development by creating an account on GitHub .", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/zyxunh/Entity_for_entity_erasure", "content": "Contribute to zyxunh / Entity _for_ entity _ erasure development by creating an account on GitHub ."}
+{"idx": 2, "title": "GitHub - zyxunh/EntityErasure-ProjectPage", "date": "", "ddg_snippet": "Contribute to zyxunh / EntityErasure -ProjectPage development by creating an account on GitHub .", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/zyxunh/EntityErasure-ProjectPage", "content": "Contribute to zyxunh / EntityErasure -ProjectPage development by creating an account on GitHub ."}
+{"idx": 3, "title": "Entity_for_entity_erasure/Entityv2/README.md at main - GitHub", "date": "", "ddg_snippet": "Contribute to zyxunh / Entity _for_ entity _ erasure development by creating an account on GitHub .", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/zyxunh/Entity_for_entity_erasure/blob/main/Entityv2/README.md", "content": "Contribute to zyxunh / Entity _for_ entity _ erasure development by creating an account on GitHub ."}
+{"idx": 4, "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": 5, "title": "GitHub - zyxunh/diffusers_for_entity_erasure", "date": "", "ddg_snippet": "Contribute to zyxunh /diffusers_for_ entity _ erasure development by creating an account on GitHub .", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/zyxunh/diffusers_for_entity_erasure", "content": "Contribute to zyxunh /diffusers_for_ entity _ erasure development by creating an account on GitHub ."}
+{"idx": 6, "title": "Security: zyxunh/Mask2Former_for_entity_erasure - GitHub", "date": "", "ddg_snippet": "GitHub is where people build software. More than 150 million people use GitHub to discover, fork, and contribute to over 420 million projects.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/zyxunh/Mask2Former_for_entity_erasure/security", "content": "GitHub is where people build software. More than 150 million people use GitHub to discover, fork, and contribute to over 420 million projects."}
+{"idx": 7, "title": "config . yaml and migrations folder should not be mandatory for CLI...", "date": "", "ddg_snippet": "$ hasura metadata reload --endpoint localhost:8080 --admin-secret FATA[0000] validating current directory failed: validate: cannot find [ config . yaml , migrations] | search stopped at filesystem boundary. However, create an empty file and an empty folder will make it works.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/hasura/graphql-engine/issues/2039", "content": "$ hasura metadata reload --endpoint localhost:8080 --admin-secret FATA[0000] validating current directory failed: validate: cannot find [ config . yaml , migrations] | search stopped at filesystem boundary. However, create an empty file and an empty folder will make it works."}
+{"idx": 8, "title": "Problema da principiante su configuration . yaml · indomus forum...", "date": "", "ddg_snippet": "# Loads default set of integrations. Do not remove. default_ config : # Load frontend themes from the themes folder frontend: themes: !include_dir_merge_named themes. automation: !include automations. yaml script: !include scripts. yaml scene: !include scenes. yaml .", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/indomus/forum/discussions/2829", "content": "# Loads default set of integrations. Do not remove. default_ config : # Load frontend themes from the themes folder frontend: themes: !include_dir_merge_named themes. automation: !include automations. yaml script: !include scripts. yaml scene: !include scenes. yaml ."}
+{"idx": 9, "title": "Issue with state_class: total_increasing in configuration . yaml Causing...", "date": "", "ddg_snippet": "\"This entity is no longer being provided by the template integration. If the entity is no longer in use, delete it in settings.\" This issue makes it impossible to persistently configure the sensor in configuration . yaml , as the entity disappears from Home Assistant after restarting.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/home-assistant/core/issues/138810", "content": "\"This entity is no longer being provided by the template integration. If the entity is no longer in use, delete it in settings.\" This issue makes it impossible to persistently configure the sensor in configuration . yaml , as the entity disappears from Home Assistant after restarting."}
diff --git a/data/sampled_jsons/sitegithub.comfiveaiunderstanding_safety_finetuning_minGPT_config_n_layer.jsonl b/data/sampled_jsons/sitegithub.comfiveaiunderstanding_safety_finetuning_minGPT_config_n_layer.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..44296beb3852e677fd5005ac7c820508378fa88f
--- /dev/null
+++ b/data/sampled_jsons/sitegithub.comfiveaiunderstanding_safety_finetuning_minGPT_config_n_layer.jsonl
@@ -0,0 +1 @@
+{"idx": 0, "title": "", "date": "", "ddg_snippet": "", "subpage_snippet": "", "source": "", "link": "", "content": ""}
diff --git a/data/sampled_jsons/sitegithub.comfiveaiunderstanding_safety_finetuning_synthetic_minGPT_config.jsonl b/data/sampled_jsons/sitegithub.comfiveaiunderstanding_safety_finetuning_synthetic_minGPT_config.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..b2cb16b945d79b70e187e13f8759421d55737b97
--- /dev/null
+++ b/data/sampled_jsons/sitegithub.comfiveaiunderstanding_safety_finetuning_synthetic_minGPT_config.jsonl
@@ -0,0 +1,2 @@
+{"idx": 0, "title": "understanding_safety_finetuning/README.md at main - GitHub", "date": "", "ddg_snippet": "To better understand the underlying factors that make models safe via safety fine-tuning, we design a synthetic data generation framework that captures salient aspects of an unsafe input by modeling the interaction between the task the model is asked to perform (e.g., “design”) versus the specific concepts the task is asked to be performed ...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/fiveai/understanding_safety_finetuning/blob/main/README.md", "content": "To better understand the underlying factors that make models safe via safety fine-tuning, we design a synthetic data generation framework that captures salient aspects of an unsafe input by modeling the interaction between the task the model is asked to perform (e.g., “design”) versus the specific concepts the task is asked to be performed ..."}
+{"idx": 1, "title": "understanding_safety_finetuning/pretrain.py at main - GitHub", "date": "", "ddg_snippet": "Official Code for What Makes and Breaks Safety Fine-tuning? A Mechanistic Study (NeurIPS 2024) - fiveai /understanding_safety_finetuning", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/fiveai/understanding_safety_finetuning/blob/main/pretrain.py", "content": "Official Code for What Makes and Breaks Safety Fine-tuning? A Mechanistic Study (NeurIPS 2024) - fiveai /understanding_safety_finetuning"}
diff --git a/data/sampled_jsons/sitegithub.commodel-similaritylm-similarity_CAPA_c_obs_observed_agreement.jsonl b/data/sampled_jsons/sitegithub.commodel-similaritylm-similarity_CAPA_c_obs_observed_agreement.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..44296beb3852e677fd5005ac7c820508378fa88f
--- /dev/null
+++ b/data/sampled_jsons/sitegithub.commodel-similaritylm-similarity_CAPA_c_obs_observed_agreement.jsonl
@@ -0,0 +1 @@
+{"idx": 0, "title": "", "date": "", "ddg_snippet": "", "subpage_snippet": "", "source": "", "link": "", "content": ""}
diff --git a/data/sampled_jsons/sitehuggingface.co_PumpkinCatLHRS_Data.jsonl b/data/sampled_jsons/sitehuggingface.co_PumpkinCatLHRS_Data.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..325f79ef9679e0654e807ef65c99efe4dee1d95b
--- /dev/null
+++ b/data/sampled_jsons/sitehuggingface.co_PumpkinCatLHRS_Data.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "PumpkinCat/LHRS_Data at main - Hugging Face", "date": "", "ddg_snippet": "Jul 3, 2024 · PumpkinCat Upload RSVG.json ea957f7 verified 2 months ago LHRS-Bench commit LHRS -Bench 6 months ago RSVG training data 6 months ago Stage1 training data 6 months ago Stage2", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/datasets/PumpkinCat/LHRS_Data/tree/main", "content": "Jul 3, 2024 · PumpkinCat Upload RSVG.json ea957f7 verified 2 months ago LHRS-Bench commit LHRS -Bench 6 months ago RSVG training data 6 months ago Stage1 training data 6 months ago Stage2"}
+{"idx": 1, "title": "Stage1/features_02.json · PumpkinCat/LHRS_Data at main", "date": "", "ddg_snippet": "LHRS _ Data like 4 Modalities: Image Text Formats: json Size: 1K - 10K Libraries: Datasets Dask Croissant License: mit Dataset card Data Studio FilesFiles and versions Community 1 main LHRS _ Data / Stage1 /features_02.json PumpkinCat training data dbc3790 about 1 year ago download Copy download link history blame contribute delete Safe 74.7 MB", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/datasets/PumpkinCat/LHRS_Data/blob/main/Stage1/features_02.json", "content": "LHRS _ Data like 4 Modalities: Image Text Formats: json Size: 1K - 10K Libraries: Datasets Dask Croissant License: mit Dataset card Data Studio FilesFiles and versions Community 1 main LHRS _ Data / Stage1 /features_02.json PumpkinCat training data dbc3790 about 1 year ago download Copy download link history blame contribute delete Safe 74.7 MB"}
+{"idx": 2, "title": "PumpkinCat/LHRS_Data at main - Hugging Face", "date": "", "ddg_snippet": "LHRS _ Data /Stage2 1 contributor History:3 commits PumpkinCat Upload RSVG.json ea957f7 verified7 months ago HR.json Safe 345 MB LFS training data11 months ago LR.json Safe 8.81 MB LFS training data11 months ago METERML.json Safe 615 kB LFS training data11 months ago NWPU.json Safe 50 MB LFS training data11 months ago NWPUDetail.json Safe 34.9 MB ...", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/datasets/PumpkinCat/LHRS_Data/tree/main/Stage2", "content": "LHRS _ Data /Stage2 1 contributor History:3 commits PumpkinCat Upload RSVG.json ea957f7 verified7 months ago HR.json Safe 345 MB LFS training data11 months ago LR.json Safe 8.81 MB LFS training data11 months ago METERML.json Safe 615 kB LFS training data11 months ago NWPU.json Safe 50 MB LFS training data11 months ago NWPUDetail.json Safe 34.9 MB ..."}
+{"idx": 3, "title": "README.md · PumpkinCat/LHRS_Data at main - Hugging Face", "date": "", "ddg_snippet": "main LHRS _ Data / README.md PumpkinCat initial commit 5ead71f verified 2 months ago", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/datasets/PumpkinCat/LHRS_Data/blob/main/README.md", "content": "main LHRS _ Data / README.md PumpkinCat initial commit 5ead71f verified 2 months ago"}
+{"idx": 4, "title": "PumpkinCat/LHRS_Data at main", "date": "", "ddg_snippet": "9 Jul 2024 — Datasets: · PumpkinCat . /. LHRS_Data . like 4 · Dataset card · Data Studio · Files Files and versions · Community. 1. main. LHRS_Data / LHRS- ...", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/datasets/PumpkinCat/LHRS_Data/tree/main/LHRS-Bench", "content": "9 Jul 2024 — Datasets: · PumpkinCat . /. LHRS_Data . like 4 · Dataset card · Data Studio · Files Files and versions · Community. 1. main. LHRS_Data / LHRS- ..."}
+{"idx": 5, "title": "PumpkinCat/LHRS_Data at main", "date": "", "ddg_snippet": "LHRS_Data / LHRS-Bench /imgs_png. 1 contributor · History: 1 commit. PumpkinCat's picture. PumpkinCat · commit LHRS-Bench · c60778e about 1 year ago.", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/datasets/PumpkinCat/LHRS_Data/tree/main/LHRS-Bench/imgs_png", "content": "LHRS_Data / LHRS-Bench /imgs_png. 1 contributor · History: 1 commit. PumpkinCat's picture. PumpkinCat · commit LHRS-Bench · c60778e about 1 year ago."}
+{"idx": 6, "title": "Commits · PumpkinCat/LHRS_Data", "date": "", "ddg_snippet": "PumpkinCat. /. LHRS_Data . like 4. Modalities: Image · Text. Formats: json. Size: 1K ... Commit History. commit LHRS-Bench. c60778e. PumpkinCat commited on Jul 9, ...", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/datasets/PumpkinCat/LHRS_Data/commits/main/LHRS-Bench", "content": "PumpkinCat. /. LHRS_Data . like 4. Modalities: Image · Text. Formats: json. Size: 1K ... Commit History. commit LHRS-Bench. c60778e. PumpkinCat commited on Jul 9, ..."}
+{"idx": 7, "title": "PumpkinCat (Dilxat Muhtar) – Likes", "date": "", "ddg_snippet": "Likes; Articles. liked a dataset 4 months ago. PumpkinCat/LHRS_Data . Viewer • Updated Oct 26, 2024 • 4.6k • 127 • 4. liked a model 5 months ago. LHRS/LHRS-Bot- ...", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/PumpkinCat/activity/likes", "content": "Likes; Articles. liked a dataset 4 months ago. PumpkinCat/LHRS_Data . Viewer • Updated Oct 26, 2024 • 4.6k • 127 • 4. liked a model 5 months ago. LHRS/LHRS-Bot- ..."}
+{"idx": 8, "title": "PumpkinCat/LHRS_Data · Datasets at Hugging Face", "date": "", "ddg_snippet": "LHRS _ Data like 2 Modalities: Image Text Formats: json Size: 1K - 10K Libraries: Datasets Dask Croissant License: mit Dataset card Data Studio FilesFiles and versions Community 1 Dataset Viewer Auto-converted to Parquet API Embed Data Studio Subset (1) default·4.6k rows Split (1) test·4.6k rows test (4.6k rows) SQL", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/datasets/PumpkinCat/LHRS_Data", "content": "LHRS _ Data like 2 Modalities: Image Text Formats: json Size: 1K - 10K Libraries: Datasets Dask Croissant License: mit Dataset card Data Studio FilesFiles and versions Community 1 Dataset Viewer Auto-converted to Parquet API Embed Data Studio Subset (1) default·4.6k rows Split (1) test·4.6k rows test (4.6k rows) SQL"}
+{"idx": 9, "title": "commit LHRS-Bench · PumpkinCat/LHRS_Data at c60778e", "date": "", "ddg_snippet": "LHRS -Bench/imgs_jpg/042.jpg +3-0 LHRS-Bench/imgs_jpg/043.jpg +3-0 LHRS-Bench/imgs_jpg/044.jpg +3-0 LHRS-Bench/imgs_jpg/045.jpg +3-0 LHRS-Bench/imgs_jpg/046.jpg +3-0 LHRS-Bench/imgs_jpg/047.jpg +3-0 LHRS-Bench/imgs_jpg/048.jpg +3-0 LHRS-Bench/imgs_jpg/049.jpg +3-0 LHRS-Bench/imgs_jpg/050.jpg +3-0 LHRS-Bench/imgs_jpg/001.jpg Viewed Git LFS Details", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/datasets/PumpkinCat/LHRS_Data/commit/c60778e7893c4e1d6f4d9605d6c4ee1694bced58", "content": "LHRS -Bench/imgs_jpg/042.jpg +3-0 LHRS-Bench/imgs_jpg/043.jpg +3-0 LHRS-Bench/imgs_jpg/044.jpg +3-0 LHRS-Bench/imgs_jpg/045.jpg +3-0 LHRS-Bench/imgs_jpg/046.jpg +3-0 LHRS-Bench/imgs_jpg/047.jpg +3-0 LHRS-Bench/imgs_jpg/048.jpg +3-0 LHRS-Bench/imgs_jpg/049.jpg +3-0 LHRS-Bench/imgs_jpg/050.jpg +3-0 LHRS-Bench/imgs_jpg/001.jpg Viewed Git LFS Details"}
diff --git a/data/sampled_jsons/siteicml.cc_2025_46680_Socialized_Coevolution_PDF.jsonl b/data/sampled_jsons/siteicml.cc_2025_46680_Socialized_Coevolution_PDF.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..fbc717778e2107360075079fea479c14cc76d881
--- /dev/null
+++ b/data/sampled_jsons/siteicml.cc_2025_46680_Socialized_Coevolution_PDF.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Socialized Coevolution: Advancing a Better World through ...", "date": "", "ddg_snippet": "Socialized Coevolution : Advancing a Better World through Cross-Task Collaboration Xinjie Yao · Yu Wang · Pengfei Zhu · Wanyu LIN · Ruipu Zhao · Zhoupeng Guo · Weihao Li · Qinghua Hu", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/poster/46680", "content": "Socialized Coevolution : Advancing a Better World through Cross-Task Collaboration Xinjie Yao · Yu Wang · Pengfei Zhu · Wanyu LIN · Ruipu Zhao · Zhoupeng Guo · Weihao Li · Qinghua Hu"}
+{"idx": 1, "title": "Downloads 2025 - icml.cc", "date": "", "ddg_snippet": "Info- Coevolution : An Efficient Framework for Data Model Coevolution InfoCons: Identifying Interpretable Critical Concepts in Point Clouds via Information Theory Information Bottleneck-guided MLPs for Robust Spatial-temporal Forecasting InfoSAM: Fine-Tuning the Segment Anything Model from An Information-Theoretic Perspective", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/Downloads/2025", "content": "Info- Coevolution : An Efficient Framework for Data Model Coevolution InfoCons: Identifying Interpretable Critical Concepts in Point Clouds via Information Theory Information Bottleneck-guided MLPs for Robust Spatial-temporal Forecasting InfoSAM: Fine-Tuning the Segment Anything Model from An Information-Theoretic Perspective"}
+{"idx": 2, "title": "ICML 2025 Papers", "date": "", "ddg_snippet": "Select Year: ( 2025 ) Getting Started Schedule Tutorials Main Conference Workshops Community Exhibitors Organizers Help Browse Visualization", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/papers.html?layout=mini&filter=session", "content": "Select Year: ( 2025 ) Getting Started Schedule Tutorials Main Conference Workshops Community Exhibitors Organizers Help Browse Visualization"}
+{"idx": 3, "title": "ICML Poster Socialized Learning: Making Each Other Better ...", "date": "", "ddg_snippet": "Socialized Learning: Making Each Other Better Through Multi-Agent Collaboration Xinjie Yao · Yu Wang · Pengfei Zhu · Wanyu LIN · Li Jialu · Weihao Li · Qinghua Hu", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2024/poster/33681", "content": "Socialized Learning: Making Each Other Better Through Multi-Agent Collaboration Xinjie Yao · Yu Wang · Pengfei Zhu · Wanyu LIN · Li Jialu · Weihao Li · Qinghua Hu"}
+{"idx": 4, "title": "Info-Coevolution: An Efficient Framework for Data Model ...", "date": "", "ddg_snippet": "Info- Coevolution : An Efficient Framework for Data Model Coevolution Ziheng Qin · Hailun Xu · Wei Yew · Qi Jia · Yang Luo · Kanchan Sarkar · Danhui Guan · Kai Wang · Yang You", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/poster/45070", "content": "Info- Coevolution : An Efficient Framework for Data Model Coevolution Ziheng Qin · Hailun Xu · Wei Yew · Qi Jia · Yang Luo · Kanchan Sarkar · Danhui Guan · Kai Wang · Yang You"}
+{"idx": 5, "title": "ICML 2025 Schedule", "date": "", "ddg_snippet": "Info- Coevolution : An Efficient Framework for Data Model Coevolution . Evolving Minds: Logic-Informed Inference from Temporal Action Patterns.ICML 2025 Workshop on Computational Optimization of Buildings (CO-BUILD). (ends 6:00 PM).", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/calendar", "content": "Info- Coevolution : An Efficient Framework for Data Model Coevolution . Evolving Minds: Logic-Informed Inference from Temporal Action Patterns.ICML 2025 Workshop on Computational Optimization of Buildings (CO-BUILD). (ends 6:00 PM)."}
+{"idx": 6, "title": "ICML 2025 Getting Started", "date": "", "ddg_snippet": "ICML 2025 Getting Started. Badges/Checkin. There are two registration desks (West Level 1) and (East ground floor), here is the schedule.Vancouver Convention Center. FloorPlan. Annotated pdf .", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025", "content": "ICML 2025 Getting Started. Badges/Checkin. There are two registration desks (West Level 1) and (East ground floor), here is the schedule.Vancouver Convention Center. FloorPlan. Annotated pdf ."}
+{"idx": 7, "title": "2025 Dates and Deadlines", "date": "", "ddg_snippet": "The Forty-Second annual conference is held Sun. Jul 13th through Sat the 19th, 2025 at the Vancouver Convention Center.", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/Conferences/2025/Dates", "content": "The Forty-Second annual conference is held Sun. Jul 13th through Sat the 19th, 2025 at the Vancouver Convention Center."}
+{"idx": 8, "title": "ICML 2024 Papers", "date": "", "ddg_snippet": "A New Linear Scaling Rule for Private Adaptive Hyperparameter Optimization Self-Alignment of Large Language Models via Monopolylogue-based Social Scene Simulation Trainable Transformer in Transformer Position: Topological Deep Learning is the New Frontier for Relational Learning Position: Bayesian Deep Learning is Needed in the Age of Large ...", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2024/papers.html", "content": "A New Linear Scaling Rule for Private Adaptive Hyperparameter Optimization Self-Alignment of Large Language Models via Monopolylogue-based Social Scene Simulation Trainable Transformer in Transformer Position: Topological Deep Learning is the New Frontier for Relational Learning Position: Bayesian Deep Learning is Needed in the Age of Large ..."}
+{"idx": 9, "title": "ICML 2025 Invited Talks", "date": "", "ddg_snippet": "In addition to highlighting valuable data sources, we will examine how lessons from social science can inform the design of human feedback loops in AI. These insights help avoid common pitfalls in capturing human intentions and preferences—such as measurement error, framing effects, and unrepresentative sampling—that have plagued opinion ...", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/eventlistwithbios/Invited+Talk", "content": "In addition to highlighting valuable data sources, we will examine how lessons from social science can inform the design of human feedback loops in AI. These insights help avoid common pitfalls in capturing human intentions and preferences—such as measurement error, framing effects, and unrepresentative sampling—that have plagued opinion ..."}
diff --git a/data/sampled_jsons/siteicml.cc_2025_poster_46671_Linear_convergence_Sinkhorn_conclusion_Luo_Tseng.jsonl b/data/sampled_jsons/siteicml.cc_2025_poster_46671_Linear_convergence_Sinkhorn_conclusion_Luo_Tseng.jsonl
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index 0000000000000000000000000000000000000000..ec57417a41b71a400f3784958cd10b68a6403437
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@@ -0,0 +1,10 @@
+{"idx": 0, "title": "ICML Poster Linear convergence of Sinkhorn's algorithm for ...", "date": "", "ddg_snippet": "Poster Linear convergence of Sinkhorn 's algorithm for generalized static Schrödinger bridge Rahul Choudhary · Hanbaek Lyu West Exhibition Hall B2-B3 #W-506", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/poster/46671", "content": "Poster Linear convergence of Sinkhorn 's algorithm for generalized static Schrödinger bridge Rahul Choudhary · Hanbaek Lyu West Exhibition Hall B2-B3 #W-506"}
+{"idx": 1, "title": "ICML Poster On Linear Convergence in Smooth Convex-Concave ...", "date": "", "ddg_snippet": "However, the class of bilinearly-coupled saddle-point problems for which linear convergence is possible is much wider and can involve general smooth non-strongly convex functions f (x) and g (y). Therefore, *we develop the first lower complexity bounds and matching optimal linearly converging algorithms for this problem class*.", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/poster/43671", "content": "However, the class of bilinearly-coupled saddle-point problems for which linear convergence is possible is much wider and can involve general smooth non-strongly convex functions f (x) and g (y). Therefore, *we develop the first lower complexity bounds and matching optimal linearly converging algorithms for this problem class*."}
+{"idx": 2, "title": "ICML Poster Achieving Linear Speedup and Optimal Complexity for...", "date": "", "ddg_snippet": "Select Year: ( 2025 ).This paper bridges this gap by introducing effective metrics to capture the influence of row-stochastic mixing matrices and establishing the first convergence lower bound for decentralized learning over row-stochastic networks.", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/poster/45127", "content": "Select Year: ( 2025 ).This paper bridges this gap by introducing effective metrics to capture the influence of row-stochastic mixing matrices and establishing the first convergence lower bound for decentralized learning over row-stochastic networks."}
+{"idx": 3, "title": "ICML Poster Linear $Q$-Learning Does Not Diverge in $L^2 ...", "date": "", "ddg_snippet": "Poster Linear Q -Learning Does Not Diverge in L 2: Convergence Rates to a Bounded Set Xinyu Liu · Zixuan Xie · Shangtong Zhang West Exhibition Hall B2-B3 #W-1010", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/poster/44653", "content": "Poster Linear Q -Learning Does Not Diverge in L 2: Convergence Rates to a Bounded Set Xinyu Liu · Zixuan Xie · Shangtong Zhang West Exhibition Hall B2-B3 #W-1010"}
+{"idx": 4, "title": "ICML Poster A Generalization Result for Convergence in ...", "date": "", "ddg_snippet": "Spotlight Poster A Generalization Result for Convergence in Learning-to-Optimize Michael Sucker · Peter Ochs West Exhibition Hall B2-B3 #W-913", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/poster/45367", "content": "Spotlight Poster A Generalization Result for Convergence in Learning-to-Optimize Michael Sucker · Peter Ochs West Exhibition Hall B2-B3 #W-913"}
+{"idx": 5, "title": "ICML Poster Hierarchical Refinement: Optimal Transport to ...", "date": "", "ddg_snippet": "Hierarchical Refinement uses linear space and has log- linear runtime, retaining the space advantage of low-rank OT while overcoming its limited resolution. We demonstrate the advantages of Hierarchical Refinement on several datasets, including ones containing over a million points, scaling full-rank OT to problems previously beyond Sinkhorn 's ...", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/poster/45965", "content": "Hierarchical Refinement uses linear space and has log- linear runtime, retaining the space advantage of low-rank OT while overcoming its limited resolution. We demonstrate the advantages of Hierarchical Refinement on several datasets, including ones containing over a million points, scaling full-rank OT to problems previously beyond Sinkhorn 's ..."}
+{"idx": 6, "title": "ICML Poster On Unbalanced Optimal Transport: An Analysis of...", "date": "", "ddg_snippet": "2025 . 2024. 2023.Abstract: We provide a computational complexity analysis for the Sinkhorn algorithm that solves the entropic regularized Unbalanced Optimal Transport (UOT) problem between two measures of possibly different masses with at most $n$ components.", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2020/poster/6163", "content": "2025 . 2024. 2023.Abstract: We provide a computational complexity analysis for the Sinkhorn algorithm that solves the entropic regularized Unbalanced Optimal Transport (UOT) problem between two measures of possibly different masses with at most $n$ components."}
+{"idx": 7, "title": "ICML 2023 Posters", "date": "", "ddg_snippet": "Poster . Lesi Chen · Jing Xu · Luo Luo . [ Exhibit Hall 1 ]. Abstract. converges linearly to a solution that achieves (almost) zero training loss, without requiring any assumptions on the clients' data distributions.", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2023/events/poster", "content": "Poster . Lesi Chen · Jing Xu · Luo Luo . [ Exhibit Hall 1 ]. Abstract. converges linearly to a solution that achieves (almost) zero training loss, without requiring any assumptions on the clients' data distributions."}
+{"idx": 8, "title": "ICML 2025 Orals", "date": "", "ddg_snippet": "However, Sinkhorn has quadratic space complexity in the number of points, limiting scalability to larger datasets. Low-rank OT achieves linear -space complexity, but by definition, cannot compute a one-to-one correspondence between points.", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/events/oral", "content": "However, Sinkhorn has quadratic space complexity in the number of points, limiting scalability to larger datasets. Low-rank OT achieves linear -space complexity, but by definition, cannot compute a one-to-one correspondence between points."}
+{"idx": 9, "title": "ICML 2025 Papers", "date": "", "ddg_snippet": "Skip to yearly menu bar Skip to main contentMain Navigation", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/papers.html", "content": "Skip to yearly menu bar Skip to main contentMain Navigation"}
diff --git a/data/sampled_jsons/sitemit.edu_normalization_operator_g(z)_equation_2.jsonl b/data/sampled_jsons/sitemit.edu_normalization_operator_g(z)_equation_2.jsonl
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index 0000000000000000000000000000000000000000..603a8c88c6ff9472a1a5d96796a3e32dcb4c2452
--- /dev/null
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@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Effect of Objective Normalization and Penalty Parameter on ...", "date": "", "ddg_snippet": "1 Mar 2021 — Equation 2 suggests that there are two sources of inaccuracy in the normalization procedure: (i) in the estimation of the ideal point by and ( ...", "subpage_snippet": "", "source": "direct.mit.edu", "link": "https://direct.mit.edu/evco/article/29/1/157/97345/Effect-of-Objective-Normalization-and-Penalty", "content": "1 Mar 2021 — Equation 2 suggests that there are two sources of inaccuracy in the normalization procedure: (i) in the estimation of the ideal point by and ( ..."}
+{"idx": 1, "title": "5. Classic Operator Renormalization Group Equations (RGE)", "date": "", "ddg_snippet": "And you can use that to take an anomalous dimension equation for the operator and turn it into one for the coefficient. So last time, we talked about the fact ...", "subpage_snippet": "", "source": "ocw.mit.edu", "link": "https://ocw.mit.edu/courses/8-851-effective-field-theory-spring-2013/dc7f02e102adf40417949e4fa6631c34_zr3wuh3fWRw.pdf", "content": "And you can use that to take an anomalous dimension equation for the operator and turn it into one for the coefficient. So last time, we talked about the fact ..."}
+{"idx": 2, "title": "a randomized preconditioner for the wave-equation Hessian", "date": "", "ddg_snippet": "by L Demanet · 2010 · Cited by 61 — Abstract. This paper considers the problem of approximating the inverse of the wave- equation Hessian, also called normal operator , in seismology and other ... 21 pages", "subpage_snippet": "", "source": "klein.mit.edu", "link": "https://klein.mit.edu/icg/papers/matrix-probing-seismic.pdf", "content": "by L Demanet · 2010 · Cited by 61 — Abstract. This paper considers the problem of approximating the inverse of the wave- equation Hessian, also called normal operator , in seismology and other ... 21 pages"}
+{"idx": 3, "title": "A Fast Algorithm for Linear Estimation of Two-Dimensional ...", "date": "", "ddg_snippet": "by BC LEVY · 1985 · Cited by 22 — an equivalent system of equations for the normalized filters g ,(. , .) and g ,+r(. , .), since as R + cc, the normalized filtering problem (2.18) remains ...", "subpage_snippet": "", "source": "www.mit.edu", "link": "https://www.mit.edu/~jnt/Papers/J009-85-isotropic_random_fields.pdf", "content": "by BC LEVY · 1985 · Cited by 22 — an equivalent system of equations for the normalized filters g ,(. , .) and g ,+r(. , .), since as R + cc, the normalized filtering problem (2.18) remains ..."}
+{"idx": 4, "title": "Quantum Physics II (8.05) Fall 2002 Assignment 6", "date": "", "ddg_snippet": "by K Rajagopal · 2002 — an operator -valued function, this time G ( z ) ≡ ez( ˆ. ˆ. A+B)e−zA . This time, carefully d calculate dz ln G ≡ G −1 dG dz .] (d) Use the identities you have ...", "subpage_snippet": "", "source": "dspace.mit.edu", "link": "https://dspace.mit.edu/bitstream/handle/1721.1/35801/8-05Fall-2002/NR/rdonlyres/Physics/8-05Quantum-Physics-IIFall2002/B9149E1E-2F7F-44E1-A0CD-C27DCC05E371/0/ps6.pdf", "content": "by K Rajagopal · 2002 — an operator -valued function, this time G ( z ) ≡ ez( ˆ. ˆ. A+B)e−zA . This time, carefully d calculate dz ln G ≡ G −1 dG dz .] (d) Use the identities you have ..."}
+{"idx": 5, "title": "Lecture 9", "date": "", "ddg_snippet": "Note: If x is Hermitian, x has real eigenvalues. Math fact: Hermitian operators have only real eigenvalues. Example. What is p†? Z f.", "subpage_snippet": "", "source": "web.mit.edu", "link": "http://web.mit.edu/brem/Public/8.04LectureNotes/Chapter+9.pdf", "content": "Note: If x is Hermitian, x has real eigenvalues. Math fact: Hermitian operators have only real eigenvalues. Example. What is p†? Z f."}
+{"idx": 6, "title": "Unsupervised Riemannian Metric Learning for Histograms ...", "date": "", "ddg_snippet": "by T Le · 2015 · Cited by 20 — sure or normalization operator . A definition for the differ- ence between x and z is naturally defined as: x z = x ⊕ (−1 ⊗ z ) = C xi/zi. 1≤i≤n+1 ...", "subpage_snippet": "", "source": "jmlr.csail.mit.edu", "link": "https://jmlr.csail.mit.edu/proceedings/papers/v37/le15.pdf", "content": "by T Le · 2015 · Cited by 20 — sure or normalization operator . A definition for the differ- ence between x and z is naturally defined as: x z = x ⊕ (−1 ⊗ z ) = C xi/zi. 1≤i≤n+1 ..."}
+{"idx": 7, "title": "statistical properties of eigenfunctions", "date": "", "ddg_snippet": "by A SHNIRELMAN · Cited by 13 — Typically there exists no global solution of the equation ( 2 ) with prescribed multi- valued behavior. This is certainly true for compact Riemannian manifolds of ...", "subpage_snippet": "", "source": "math.mit.edu", "link": "https://math.mit.edu/~dyatlov/files/2019/shnirelman.pdf", "content": "by A SHNIRELMAN · Cited by 13 — Typically there exists no global solution of the equation ( 2 ) with prescribed multi- valued behavior. This is certainly true for compact Riemannian manifolds of ..."}
+{"idx": 8, "title": "Normalizing Flows for Probabilistic Modeling and Inference", "date": "", "ddg_snippet": "by G Papamakarios · 2021 · Cited by 2399 — Normalizing flows provide a general mechanism for defining expressive probability distribu- tions, only requiring the specification of a (usually simple) ...", "subpage_snippet": "", "source": "jmlr.csail.mit.edu", "link": "https://jmlr.csail.mit.edu/papers/volume22/19-1028/19-1028.pdf", "content": "by G Papamakarios · 2021 · Cited by 2399 — Normalizing flows provide a general mechanism for defining expressive probability distribu- tions, only requiring the specification of a (usually simple) ..."}
+{"idx": 9, "title": "Exact formulas for the normalizing constants of Wishart ...", "date": "", "ddg_snippet": "by C Uhler · 2018 · Cited by 52 — Lemma (S. 2 ). Let G be a graph on 2 + m vertices with two nodes that are connected to all other nodes but not to each other, i.e. K is of the form. K = KAA.", "subpage_snippet": "", "source": "dspace.mit.edu", "link": "https://dspace.mit.edu/bitstream/handle/1721.1/134197/1406.4901.pdf?sequence=2&isAllowed=y", "content": "by C Uhler · 2018 · Cited by 52 — Lemma (S. 2 ). Let G be a graph on 2 + m vertices with two nodes that are connected to all other nodes but not to each other, i.e. K is of the form. K = KAA."}
diff --git a/data/sampled_jsons/siteopenaccess.thecvf.com_EventPS_Real-Time_Photometric_Stereo_Using_an_Event_Camera.jsonl b/data/sampled_jsons/siteopenaccess.thecvf.com_EventPS_Real-Time_Photometric_Stereo_Using_an_Event_Camera.jsonl
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index 0000000000000000000000000000000000000000..81ffa840fa47e846f8721f769fc6435f2d49e9b0
--- /dev/null
+++ b/data/sampled_jsons/siteopenaccess.thecvf.com_EventPS_Real-Time_Photometric_Stereo_Using_an_Event_Camera.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "EventPS: Real-Time Photometric Stereo Using an Event Camera", "date": "", "ddg_snippet": "by B Yu · 2024 · Cited by 25 — This ap- proach, termed EventPS , allows us to harness the inherent strengths of event cameras for achieving real-time PS. For real scenes where events are noisy ...", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2024/papers/Yu_EventPS_Real-Time_Photometric_Stereo_Using_an_Event_Camera_CVPR_2024_paper.pdf", "content": "by B Yu · 2024 · Cited by 25 — This ap- proach, termed EventPS , allows us to harness the inherent strengths of event cameras for achieving real-time PS. For real scenes where events are noisy ..."}
+{"idx": 1, "title": "EventPS: Real-Time Photometric Stereo Using an Event Camera", "date": "", "ddg_snippet": "However the requirement of capturing multiple high dynamic range images under different illumination conditions limits the speed and real-time applications. This paper introduces EventPS a novel approach to real - time photometric stereo using an event camera .", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2024/html/Yu_EventPS_Real-Time_Photometric_Stereo_Using_an_Event_Camera_CVPR_2024_paper.html", "content": "However the requirement of capturing multiple high dynamic range images under different illumination conditions limits the speed and real-time applications. This paper introduces EventPS a novel approach to real - time photometric stereo using an event camera ."}
+{"idx": 2, "title": "EventPSR: Surface Normal and Reflectance Estimation from ...", "date": "", "ddg_snippet": "by B Yu · 2025 — EventPS [29] leveraged an event camera and a fast-rotating light source to achieve real-time surface normal reconstruc- tion, which demonstrated the superiority ...", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2025/papers/Yu_EventPSR_Surface_Normal_and_Reflectance_Estimation_from_Photometric_Stereo_Using_CVPR_2025_paper.pdf", "content": "by B Yu · 2025 — EventPS [29] leveraged an event camera and a fast-rotating light source to achieve real-time surface normal reconstruc- tion, which demonstrated the superiority ..."}
+{"idx": 3, "title": "PS-EIP: Robust Photometric Stereo Based on Event Interval ...", "date": "", "ddg_snippet": "Recently, event cameras have emerged as a promising al-ternative for energy-efficient computer vision applications. Unlike conventional RGB cameras , event cameras indepen-dently and asynchronously detect changes in intensity at each pixel, recording the timestamp of each change as an event .", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2025/papers/Kitazawa_PS-EIP_Robust_Photometric_Stereo_Based_on_Event_Interval_Profile_CVPR_2025_paper.pdf", "content": "Recently, event cameras have emerged as a promising al-ternative for energy-efficient computer vision applications. Unlike conventional RGB cameras , event cameras indepen-dently and asynchronously detect changes in intensity at each pixel, recording the timestamp of each change as an event ."}
+{"idx": 4, "title": "Active Event-based Stereo Vision - CVF Open Access", "date": "", "ddg_snippet": "We use the built stereo camera prototype to record 85 sequences including event stream pairs, RGB frames, infrared frames, and depth values. After spatiotemporal calibration, all la-bels are provided at a frequency of 15 Hz by the RealSense D455.", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2025/papers/Li_Active_Event-based_Stereo_Vision_CVPR_2025_paper.pdf", "content": "We use the built stereo camera prototype to record 85 sequences including event stream pairs, RGB frames, infrared frames, and depth values. After spatiotemporal calibration, all la-bels are provided at a frequency of 15 Hz by the RealSense D455."}
+{"idx": 5, "title": "Active Hyperspectral Imaging Using an Event Camera", "date": "", "ddg_snippet": "by B Yu · 2025 — In this work, we present a novel event -based active hyperspectral imaging system de- signed for real - time capture with low bandwidth in dynamic scenes. By ...", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "http://openaccess.thecvf.com/content/CVPR2025/papers/Yu_Active_Hyperspectral_Imaging_Using_an_Event_Camera_CVPR_2025_paper.pdf", "content": "by B Yu · 2025 — In this work, we present a novel event -based active hyperspectral imaging system de- signed for real - time capture with low bandwidth in dynamic scenes. By ..."}
+{"idx": 6, "title": "Event Ellipsometer: Event-based Mueller-Matrix Video Imaging", "date": "", "ddg_snippet": "by R Maeda · 2025 · Cited by 3 — Eventps: Real-time photometric stereo using an event camera . In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2024. 2 ... 10 pages", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2025/papers/Maeda_Event_Ellipsometer_Event-based_Mueller-Matrix_Video_Imaging_CVPR_2025_paper.pdf", "content": "by R Maeda · 2025 · Cited by 3 — Eventps: Real-time photometric stereo using an event camera . In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2024. 2 ... 10 pages"}
+{"idx": 7, "title": "EventGPT: Event Stream Understanding with Multimodal ...", "date": "", "ddg_snippet": "by S Liu · 2025 · Cited by 10 — Event cameras capture visual information as asynchronous pixel change streams, excelling in challenging lighting and high-dynamic scenarios.", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2025/papers/Liu_EventGPT_Event_Stream_Understanding_with_Multimodal_Large_Language_Models_CVPR_2025_paper.pdf", "content": "by S Liu · 2025 · Cited by 10 — Event cameras capture visual information as asynchronous pixel change streams, excelling in challenging lighting and high-dynamic scenarios."}
+{"idx": 8, "title": "Realtime Time Synchronized Event-based Stereo - CVF Open Access", "date": "", "ddg_snippet": "In this work, we propose a novel event based stereo method which addresses the problem of motion blur for a moving event camera . Our method uses the velocity of the camera and a range of disparities to synchronize the positions of the events, as if they were captured at a single point in time.", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content_ECCV_2018/papers/Alex_Zhu_Realtime_Time_Synchronized_ECCV_2018_paper.pdf", "content": "In this work, we propose a novel event based stereo method which addresses the problem of motion blur for a moving event camera . Our method uses the velocity of the camera and a range of disparities to synchronize the positions of the events, as if they were captured at a single point in time."}
+{"idx": 9, "title": "Event Fields: Capturing Light Fields at High Speed ...", "date": "", "ddg_snippet": "In this manuscript, we introduce “ event field”, a new ap-proach for capturing high-speed, high resolution, HDR light fields using two optical configurations—kaleidoscope and galvanometer—combined with an event camera .", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2025/papers/Qu_Event_Fields_Capturing_Light_Fields_at_High_Speed_Resolution_and_CVPR_2025_paper.pdf", "content": "In this manuscript, we introduce “ event field”, a new ap-proach for capturing high-speed, high resolution, HDR light fields using two optical configurations—kaleidoscope and galvanometer—combined with an event camera ."}
diff --git a/data/sampled_jsons/siteopenaccess.thecvf.com_Video-ColBERT_Equation_(5).jsonl b/data/sampled_jsons/siteopenaccess.thecvf.com_Video-ColBERT_Equation_(5).jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..98ae1e3cf33f0c9aea1a86d1fe435b4a8494b32c
--- /dev/null
+++ b/data/sampled_jsons/siteopenaccess.thecvf.com_Video-ColBERT_Equation_(5).jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Video-ColBERT: Contextualized Late Interaction for Text-to ...", "date": "", "ddg_snippet": "In this work, we introduced VIDEO - COLBERT , a novel approach for text-to-video retrieval that uses eficient fine-grained interactions with both spatial and spatio-temporal visual features.", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2025/papers/Reddy_Video-ColBERT_Contextualized_Late_Interaction_for_Text-to-Video_Retrieval_CVPR_2025_paper.pdf", "content": "In this work, we introduced VIDEO - COLBERT , a novel approach for text-to-video retrieval that uses eficient fine-grained interactions with both spatial and spatio-temporal visual features."}
+{"idx": 1, "title": "Video-ColBERT: Contextualized Late Interaction for Text-to ...", "date": "", "ddg_snippet": "In Tab.12, we show how video retrieval results are af- fected by different choices of padding token when using soft query augmentation in VIDEO - COLBERT with a CLIP- B/32 backbone.", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2025/supplemental/Reddy_Video-ColBERT_Contextualized_Late_CVPR_2025_supplemental.pdf", "content": "In Tab.12, we show how video retrieval results are af- fected by different choices of padding token when using soft query augmentation in VIDEO - COLBERT with a CLIP- B/32 backbone."}
+{"idx": 2, "title": "Beyond Image Classification: A Video Benchmark and Dual ...", "date": "", "ddg_snippet": "This indicates a stronger preference for seen classes in video data, with models facing greater dificulty generalizing to unseen compositions, highlighting the potential limitations of image-based compositional learning modules in video contexts.", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2025/papers/Jiang_Beyond_Image_Classification_A_Video_Benchmark_and_Dual-Branch_Hybrid_Discrimination_CVPR_2025_paper.pdf", "content": "This indicates a stronger preference for seen classes in video data, with models facing greater dificulty generalizing to unseen compositions, highlighting the potential limitations of image-based compositional learning modules in video contexts."}
+{"idx": 3, "title": "Apollo: An Exploration of Video Understanding in Large ...", "date": "", "ddg_snippet": "Utilizing these insights, we conduct an extensive study across the video -LMM design space, addressing essential aspects of video -language modeling, such as video sam-pling and encoding methods, token resampling and integra-tion strategies, and data compositions (Sec. 5 & 6).", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2025/papers/Zohar_Apollo__An_Exploration_of_Video_Understanding_in_Large_Multimodal_CVPR_2025_paper.pdf", "content": "Utilizing these insights, we conduct an extensive study across the video -LMM design space, addressing essential aspects of video -language modeling, such as video sam-pling and encoding methods, token resampling and integra-tion strategies, and data compositions (Sec. 5 & 6)."}
+{"idx": 4, "title": "Gram-based Attentive Neural Ordinary Differential Equations ...", "date": "", "ddg_snippet": "Abstract Video nystagmography (VNG) is the diagnostic gold standard of benign paroxysmal positional vertigo (BPPV), which requires medical professionals to examine the direc-tion, frequency, intensity, duration, and variation in the strength of nystagmus on a VNG video .", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/ICCV2023/papers/Qiu_Gram-based_Attentive_Neural_Ordinary_Differential_Equations_Network_for_Video_Nystagmography_ICCV_2023_paper.pdf", "content": "Abstract Video nystagmography (VNG) is the diagnostic gold standard of benign paroxysmal positional vertigo (BPPV), which requires medical professionals to examine the direc-tion, frequency, intensity, duration, and variation in the strength of nystagmus on a VNG video ."}
+{"idx": 5, "title": "CVPR 2025 Open Access Repository", "date": "", "ddg_snippet": "Video - ColBERT : Contextualized Late Interaction for Text-to-Video Retrieval. Video - ColBERT is built upon 3 main components: a fine-grained spatial and temporal token-wise interaction, query and visual expansions, and a dual sigmoid loss during training.", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2025/html/Reddy_Video-ColBERT_Contextualized_Late_Interaction_for_Text-to-Video_Retrieval_CVPR_2025_paper.html", "content": "Video - ColBERT : Contextualized Late Interaction for Text-to-Video Retrieval. Video - ColBERT is built upon 3 main components: a fine-grained spatial and temporal token-wise interaction, query and visual expansions, and a dual sigmoid loss during training."}
+{"idx": 6, "title": "CVPR 2025 Open Access Repository", "date": "", "ddg_snippet": "Video - ColBERT : Contextualized Late Interaction for Text-to-Video Retrieval. Arun Reddy, Alexander Martin", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/CVPR2025?day=all", "content": "Video - ColBERT : Contextualized Late Interaction for Text-to-Video Retrieval. Arun Reddy, Alexander Martin"}
+{"idx": 7, "title": "CVPR 2024 Open Access Repository", "date": "", "ddg_snippet": "Through experimentation on VATEX MSRVTT and YC2 datasets we empirically demonstrate that CaptionT 5 performs competitively against prior-art video captioning approaches without using encoders specialized for video data.", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2024W/MMFM/html/Kim_Show_Think_and_Tell_Thought-Augmented_Fine-Tuning_of_Large_Language_Models_CVPRW_2024_paper.html", "content": "Through experimentation on VATEX MSRVTT and YC2 datasets we empirically demonstrate that CaptionT 5 performs competitively against prior-art video captioning approaches without using encoders specialized for video data."}
+{"idx": 8, "title": "CVPR 2023 Open Access Repository", "date": "", "ddg_snippet": "Distinguished in scenario complexity, our dataset contains 200 videos from 26 artistic swimming events with 8 athletes in each sample along with an average duration of 204.2 seconds.", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2023/html/Zhang_LOGO_A_Long-Form_Video_Dataset_for_Group_Action_Quality_Assessment_CVPR_2023_paper.html", "content": "Distinguished in scenario complexity, our dataset contains 200 videos from 26 artistic swimming events with 8 athletes in each sample along with an average duration of 204.2 seconds."}
+{"idx": 9, "title": "CVPR 2025 Open Access Repository", "date": "", "ddg_snippet": "Jun 14, 2025 · A Consecutive Events-Based Benchmark for Future Long Video Generation}, booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference ( CVPR )}, month = {June}, year = {2025}, pages = {13629-13638} }", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/CVPR2025?day=2025-06-14", "content": "Jun 14, 2025 · A Consecutive Events-Based Benchmark for Future Long Video Generation}, booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference ( CVPR )}, month = {June}, year = {2025}, pages = {13629-13638} }"}
diff --git a/data/sampled_jsons/siteopenreview.net_0E5rZOGA13_class_prior_estimation_methodology.jsonl b/data/sampled_jsons/siteopenreview.net_0E5rZOGA13_class_prior_estimation_methodology.jsonl
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+{"idx": 0, "title": "Class Prior Estimation in Active Positive and Unlabeled ...", "date": "", "ddg_snippet": "Dec 31, 2019 · Estimating the proportion of positive examples (i.e., the class prior ) from positive and unlabeled (PU) data is an important task that facilitates learning a classifier from such data. In this paper, we explore how to tackle this problem when the observed labels were acquired via active learning. This introduces the challenge that the observed labels were not selected completely at random ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=jBV9QSklXx", "content": "Dec 31, 2019 · Estimating the proportion of positive examples (i.e., the class prior ) from positive and unlabeled (PU) data is an important task that facilitates learning a classifier from such data. In this paper, we explore how to tackle this problem when the observed labels were acquired via active learning. This introduces the challenge that the observed labels were not selected completely at random ..."}
+{"idx": 1, "title": "RETHINKING CLASS-PRIOR ESTIMATION POSITIVE-UNLABELED LEARNING", "date": "", "ddg_snippet": "ABSTRACT Given only positive (P) and unlabeled (U) data, PU learning can train a binary classifier without any negative data. It has two building blocks: PU class - prior estimation (CPE) and PU classification; the latter has been well studied while the former has received less attention. Hitherto, the distributional-assumption-free CPE methods rely on a critical assumption that the support of ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=aYAA-XHKyk", "content": "ABSTRACT Given only positive (P) and unlabeled (U) data, PU learning can train a binary classifier without any negative data. It has two building blocks: PU class - prior estimation (CPE) and PU classification; the latter has been well studied while the former has received less attention. Hitherto, the distributional-assumption-free CPE methods rely on a critical assumption that the support of ..."}
+{"idx": 2, "title": "Can Class-Priors Help Single-Positive Multi-Label Learning?", "date": "", "ddg_snippet": "Sep 22, 2023 · The paper claims that the class - prior of each category are different, and they aim to align the estimated class -priors with the actual class -priors as training progresses. However, the paper is most based on the empirical study without theoretical guarantee. The key of the proposed methods is the threshold, the technical contribution of this paper is too limited. And reviewers find that ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=jdynlBj3b0", "content": "Sep 22, 2023 · The paper claims that the class - prior of each category are different, and they aim to align the estimated class -priors with the actual class -priors as training progresses. However, the paper is most based on the empirical study without theoretical guarantee. The key of the proposed methods is the threshold, the technical contribution of this paper is too limited. And reviewers find that ..."}
+{"idx": 3, "title": "The hidden uniform cluster prior in self-supervised learning", "date": "", "ddg_snippet": "Many common self-supervised learning frameworks provably impose a hidden uniform prior , which is detrimental when pretraining with real-world class -imbalanced data.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=04K3PMtMckp", "content": "Many common self-supervised learning frameworks provably impose a hidden uniform prior , which is detrimental when pretraining with real-world class -imbalanced data."}
+{"idx": 4, "title": "Detecting Out-of-distribution Data through In-distribution Class Prior", "date": "", "ddg_snippet": "To improve the generalizability of OOD detection methods, the class - prior distribution of the training data should be taken into account, which might benefit future researches in the OOD detection community.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=charggEv8v", "content": "To improve the generalizability of OOD detection methods, the class - prior distribution of the training data should be taken into account, which might benefit future researches in the OOD detection community."}
+{"idx": 5, "title": "Unbiased Recommender Learning from Implicit Feedback via ...", "date": "", "ddg_snippet": "However, the estimator’s performance is contingent upon a predefined class prior , which requires expert knowledge for empirical selection. To address this, we develop a Pro- gressive Proximal Transport (PPT) method which achieves automatic class prior estimation by minimizing the proximal transport cost between the positive and unlabeled samples.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/attachment?id=0E5rZOGA13&name=pdf", "content": "However, the estimator’s performance is contingent upon a predefined class prior , which requires expert knowledge for empirical selection. To address this, we develop a Pro- gressive Proximal Transport (PPT) method which achieves automatic class prior estimation by minimizing the proximal transport cost between the positive and unlabeled samples."}
+{"idx": 6, "title": "Positive-Unlabeled Learning with Uncertainty-aware Pseudo ...", "date": "", "ddg_snippet": "Jan 28, 2022 · However, there are methods that estimate the class prior exclusively from positive-unlabeled data [5] and class prior estimation in itself is a separate research branch in PU Learning [6].", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=jJis-v9Pzhj", "content": "Jan 28, 2022 · However, there are methods that estimate the class prior exclusively from positive-unlabeled data [5] and class prior estimation in itself is a separate research branch in PU Learning [6]."}
+{"idx": 7, "title": "Learning from Positive and Unlabeled Data with a Selection Bias", "date": "", "ddg_snippet": "Dec 20, 2018 · As Reviewer 2 pointed out, we can still use the existing methods in the class prior estimation as a heuristic. We used Ramaswamy et al. (2016) to estimate the class prior , which is considered to be the state-of-the-art method in the case-control PU learning.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=rJzLciCqKm", "content": "Dec 20, 2018 · As Reviewer 2 pointed out, we can still use the existing methods in the class prior estimation as a heuristic. We used Ramaswamy et al. (2016) to estimate the class prior , which is considered to be the state-of-the-art method in the case-control PU learning."}
+{"idx": 8, "title": "Estimating Expected Calibration Error for Positive-Unlabeled ...", "date": "", "ddg_snippet": "The term P in these expressions is a constant factor that does πP not depend on the sample size, but it indicates that a smaller class prior leads to a smaller expected bias. πP In practical PU learning scenarios, the true class prior is often unknown and must be estimated from data (Bepler et al., 2019; Ito & Sugiyama, 2023), which ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/attachment?id=SvoBtLIrPZ&name=pdf", "content": "The term P in these expressions is a constant factor that does πP not depend on the sample size, but it indicates that a smaller class prior leads to a smaller expected bias. πP In practical PU learning scenarios, the true class prior is often unknown and must be estimated from data (Bepler et al., 2019; Ito & Sugiyama, 2023), which ..."}
+{"idx": 9, "title": "Aul Is a Better Optimization Metric in Pu Learning", "date": "", "ddg_snippet": "Estimating the class prior in positive and unlabeled data through decision tree induction. In Proceedings of the 32th AAAI conference on artificial intelligence, pp. 2712-2719.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=2NU7a9AHo-6", "content": "Estimating the class prior in positive and unlabeled data through decision tree induction. In Proceedings of the 32th AAAI conference on artificial intelligence, pp. 2712-2719."}
diff --git a/data/sampled_jsons/siteopenreview.net_4tFSKOY2mT_Section_3.3_Cross-Verification.jsonl b/data/sampled_jsons/siteopenreview.net_4tFSKOY2mT_Section_3.3_Cross-Verification.jsonl
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@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Hymba: A Hybrid-head Architecture for Small Language... | OpenReview", "date": "", "ddg_snippet": "Thanks to the global context summarized by SSMs, the attention heads in our model can be further optimized through cross -layer key-value (KV) sharing and a mix of global and local attention, resulting in a compact cache size without compromising accuracy.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=A1ztozypga", "content": "Thanks to the global context summarized by SSMs, the attention heads in our model can be further optimized through cross -layer key-value (KV) sharing and a mix of global and local attention, resulting in a compact cache size without compromising accuracy."}
+{"idx": 1, "title": "Missing Proofs of Section 3", "date": "", "ddg_snippet": "A Missing Proofs of Section 3 . Proof. (of Lemma 3.1) For notation convenience, we dene Sj,k and SjO,PkT to be the set of bidders who get allocated to any of the top-k slots of auction j in A and OPT, respectively. The optimal welfare can be written, by exchanging summations, as.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/attachment?id=01884FCwbNf&name=supplementary_material", "content": "A Missing Proofs of Section 3 . Proof. (of Lemma 3.1) For notation convenience, we dene Sj,k and SjO,PkT to be the set of bidders who get allocated to any of the top-k slots of auction j in A and OPT, respectively. The optimal welfare can be written, by exchanging summations, as."}
+{"idx": 2, "title": "Enhancing Structured Evidence Extraction for Fact Verification", "date": "", "ddg_snippet": "In this paper, we propose Structured Evidence Extraction with Row and Column Semantics of Tables (SEE-ST), a novel approach to enhance both table extraction and cell selection modules for fact verification by leveraging the semantics of rows and columns. This method aligns the seman-tic granularity of structured evidence with claim and sentence evidence, enabling more precise ev-idence ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=b7ZJcAkjC3", "content": "In this paper, we propose Structured Evidence Extraction with Row and Column Semantics of Tables (SEE-ST), a novel approach to enhance both table extraction and cell selection modules for fact verification by leveraging the semantics of rows and columns. This method aligns the seman-tic granularity of structured evidence with claim and sentence evidence, enabling more precise ev-idence ..."}
+{"idx": 3, "title": "Retrieve, Rethink, and Review: Cross-Granularity Retrieval ...", "date": "", "ddg_snippet": "This section presents the Cross -granularity Retrieval-Augmented Network (CRAN) in detail for open-domain fact verification . We begin by defining the task and introducing the overall framework of CRAN.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=FF4xT5xKL4", "content": "This section presents the Cross -granularity Retrieval-Augmented Network (CRAN) in detail for open-domain fact verification . We begin by defining the task and introducing the overall framework of CRAN."}
+{"idx": 4, "title": "OmniQuant: Omnidirectionally Calibrated Quantization... | OpenReview", "date": "", "ddg_snippet": "Large language models (LLMs) have revolutionized natural language processing tasks. However, their practical deployment is hindered by their immense memory and computation requirements.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=8Wuvhh0LYW", "content": "Large language models (LLMs) have revolutionized natural language processing tasks. However, their practical deployment is hindered by their immense memory and computation requirements."}
+{"idx": 5, "title": "Alize w ell t hrough gsnr of p arameters", "date": "", "ddg_snippet": "Experimental Verification of the relation between GSNR and OSGR.147 To verify equation 17, we carry out the experiment on MNIST training dataset with simple 148 CNNs which consists of 2 Conv-Relu-MaxPooling blocks and 2 fully-connnected layers.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/attachment?id=HyevIJStwH&name=original_pdf", "content": "Experimental Verification of the relation between GSNR and OSGR.147 To verify equation 17, we carry out the experiment on MNIST training dataset with simple 148 CNNs which consists of 2 Conv-Relu-MaxPooling blocks and 2 fully-connnected layers."}
+{"idx": 6, "title": "Related Work", "date": "", "ddg_snippet": "In this section , we give details of the implementation and parameter configurations used for each of the algorithms in the comparative experiments in Sections 5 and 6. We provide the following statistics about each dataset to be utilized by all algorithms (if applicable): (1) total number of...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/attachment?id=i0DmV60aeK&name=supplementary_material", "content": "In this section , we give details of the implementation and parameter configurations used for each of the algorithms in the comparative experiments in Sections 5 and 6. We provide the following statistics about each dataset to be utilized by all algorithms (if applicable): (1) total number of..."}
+{"idx": 7, "title": "Under review as a conference paper at ICLR 2024", "date": "", "ddg_snippet": "1, the network is able to learn respectively e1, e2, e3 after the first three steps of training in the proportional sample regime.Center: (e3, e2) cross section . Right: 3D teacher subspace (e1, e2, e3). In accordance with Thm.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/attachment?id=dER6OpDTnq&name=supplementary_material", "content": "1, the network is able to learn respectively e1, e2, e3 after the first three steps of training in the proportional sample regime.Center: (e3, e2) cross section . Right: 3D teacher subspace (e1, e2, e3). In accordance with Thm."}
+{"idx": 8, "title": "I-Con: A Unifying Framework for Representation Learning", "date": "", "ddg_snippet": "Jan 22, 2025 · However in section 3.2, it is also used to describe integrated (new) representation learning method, causing confusion in the following results. To avoid confusion, use distinct terms to clearly differentiate between I-Con as a framework and I-Con as a specific method.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=WfaQrKCr4X", "content": "Jan 22, 2025 · However in section 3.2, it is also used to describe integrated (new) representation learning method, causing confusion in the following results. To avoid confusion, use distinct terms to clearly differentiate between I-Con as a framework and I-Con as a specific method."}
+{"idx": 9, "title": "Angular Steering: Behavior Control via Rotation in Activation ...", "date": "", "ddg_snippet": "In Section 3, we first discuss the extraction of feature di-rections and the construction of the steering plane, then introduce the Angular Steering operation and its adaptive variant. Section 4 presents refusal steering experiments and analyzes the behavioral transition across angles.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=GU2UeVZrSw", "content": "In Section 3, we first discuss the extraction of feature di-rections and the construction of the steering plane, then introduce the Angular Steering operation and its adaptive variant. Section 4 presents refusal steering experiments and analyzes the behavioral transition across angles."}
diff --git a/data/sampled_jsons/siteopenreview.net_4vAa0A98xI_equations_8_9.jsonl b/data/sampled_jsons/siteopenreview.net_4vAa0A98xI_equations_8_9.jsonl
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@@ -0,0 +1,10 @@
+{"idx": 0, "title": "ODE-based Smoothing Neural Network for Reinforcement Learning ...", "date": "", "ddg_snippet": "Jan 22, 2025 · TL;DR: This paper proposes a neural unit structure with smooth properties, and based on it, proposes a smoothing policy neural network. This work is the first time that Neural ODE method is used for action smoothing in deep reinforcement learning.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=S5Yo6w3n3f", "content": "Jan 22, 2025 · TL;DR: This paper proposes a neural unit structure with smooth properties, and based on it, proposes a smoothing policy neural network. This work is the first time that Neural ODE method is used for action smoothing in deep reinforcement learning."}
+{"idx": 1, "title": "ACL ARR | OpenReview", "date": "", "ddg_snippet": "Welcome to the OpenReview homepage for ACL ARR", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/group?id=aclweb.org/ACL/ARR", "content": "Welcome to the OpenReview homepage for ACL ARR"}
+{"idx": 2, "title": "Forum - OpenReview", "date": "", "ddg_snippet": "Promoting openness in scientific communication and the peer-review process", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=9ldXNHQFMl", "content": "Promoting openness in scientific communication and the peer-review process"}
+{"idx": 3, "title": "M Echanistic n eural", "date": "", "ddg_snippet": "Comparing RK4 with the NeuRLP solver. 2-Body Problem. Accepted at the ICLR 2024 Workshop on AI4Differential Equations In Science.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/attachment?id=0FXPlZGh7K&name=pdf", "content": "Comparing RK4 with the NeuRLP solver. 2-Body Problem. Accepted at the ICLR 2024 Workshop on AI4Differential Equations In Science."}
+{"idx": 4, "title": "C Ausal g enerative n eural n etworks", "date": "", "ddg_snippet": "equations tell that the values of Y are of X2 are computed as a function of.In the language of causality, each of the previous equations expresses a direct causal relation from the causes XPa(i;G) and noise Ei to the effect Xi, described by some causal mechanism fi.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=ry5wc1bCW", "content": "equations tell that the values of Y are of X2 are computed as a function of.In the language of causality, each of the previous equations expresses a direct causal relation from the causes XPa(i;G) and noise Ei to the effect Xi, described by some causal mechanism fi."}
+{"idx": 5, "title": "Efficient Model Editing with Task-Localized Sparse Fine-tuning", "date": "", "ddg_snippet": "Jan 22, 2025 · Equation 8 introduces parameter k as a significant factor in the upper bound, but there is insufficient discussion of its impact and optimal selection. A more comprehensive analysis of this parameter would be valuable. The direct relationship between gradient values and task interference is not thoroughly explored or justified theoretically.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=TDyE2iuvyc", "content": "Jan 22, 2025 · Equation 8 introduces parameter k as a significant factor in the upper bound, but there is insufficient discussion of its impact and optimal selection. A more comprehensive analysis of this parameter would be valuable. The direct relationship between gradient values and task interference is not thoroughly explored or justified theoretically."}
+{"idx": 6, "title": "Revisiting inverse Hessian vector products for calculating ...", "date": "", "ddg_snippet": "Sep 25, 2024 · Influence functions are a popular tool for attributing models' outputs to training data. The traditional approach relies on the calculation of inverse Hessian-vector products (iHVP), but the...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=s6nYndMwG7", "content": "Sep 25, 2024 · Influence functions are a popular tool for attributing models' outputs to training data. The traditional approach relies on the calculation of inverse Hessian-vector products (iHVP), but the..."}
+{"idx": 7, "title": "Building Normalizing Flows with Stochastic Interpolants", "date": "", "ddg_snippet": "Feb 1, 2023 · A method is proposed to construct normalizing flows based on stochastic interpolants, yielding an efficient training algorithm compared to equivalent ODE methods, and providing a theoretical framework to map score based diffusions to ODEs.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=li7qeBbCR1t", "content": "Feb 1, 2023 · A method is proposed to construct normalizing flows based on stochastic interpolants, yielding an efficient training algorithm compared to equivalent ODE methods, and providing a theoretical framework to map score based diffusions to ODEs."}
+{"idx": 8, "title": "Learning and aligning single-neuron invariance manifolds in ...", "date": "", "ddg_snippet": "Jan 22, 2025 · Understanding how sensory neurons exhibit selectivity to certain features and invariance to others is central to uncovering the computational principles underlying robustness and generalization in visual perception. Most existing methods for characterizing selectivity and invariance identify single or finite discrete sets of stimuli. Since these are only isolated measurements from an ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=kbjJ9ZOakb", "content": "Jan 22, 2025 · Understanding how sensory neurons exhibit selectivity to certain features and invariance to others is central to uncovering the computational principles underlying robustness and generalization in visual perception. Most existing methods for characterizing selectivity and invariance identify single or finite discrete sets of stimuli. Since these are only isolated measurements from an ..."}
+{"idx": 9, "title": "E rror analysis of numerical methods", "date": "", "ddg_snippet": "Suppose we are solving an extremal problem , where {fi} is a monotonically convergent sequence, and {∥xi∥} is also a convergent sequence, but not necessarily monotone.In the inequality ( 8 ), the value of f ∗ can be equal to zero, greater than zero, or less than zero.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/attachment?id=midqBE6xML&name=pdf", "content": "Suppose we are solving an extremal problem , where {fi} is a monotonically convergent sequence, and {∥xi∥} is also a convergent sequence, but not necessarily monotone.In the inequality ( 8 ), the value of f ∗ can be equal to zero, greater than zero, or less than zero."}
diff --git a/data/sampled_jsons/siteopenreview.net_uxDFlPGRLX_Table_2_nc_demb_values.jsonl b/data/sampled_jsons/siteopenreview.net_uxDFlPGRLX_Table_2_nc_demb_values.jsonl
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+++ b/data/sampled_jsons/siteopenreview.net_uxDFlPGRLX_Table_2_nc_demb_values.jsonl
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+{"idx": 0, "title": "NeurIPS - OpenReview", "date": "", "ddg_snippet": "Welcome to the OpenReview homepage for NeurIPS", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/group?id=NeurIPS.cc", "content": "Welcome to the OpenReview homepage for NeurIPS"}
+{"idx": 1, "title": "Rethinking the Value of Network Pruning | OpenReview", "date": "", "ddg_snippet": "Community Implementations: [![CatalyzeX](/images/catalyzex_icon.svg) 2 code implementations](https://www.catalyzex.com/paper/rethinking-the- value -of-network-pruning/code).", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=rJlnB3C5Ym", "content": "Community Implementations: [![CatalyzeX](/images/catalyzex_icon.svg) 2 code implementations](https://www.catalyzex.com/paper/rethinking-the- value -of-network-pruning/code)."}
+{"idx": 2, "title": "Efficient Training of Low-Curvature Neural Networks | OpenReview", "date": "", "ddg_snippet": "We propose a practical method to train neural networks such that they have a low curvature, without losing predictive accuracy.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=2B2xIJ299rx", "content": "We propose a practical method to train neural networks such that they have a low curvature, without losing predictive accuracy."}
+{"idx": 3, "title": "Adam-mini: Use Fewer Learning Rates To Gain More - OpenReview", "date": "", "ddg_snippet": "Jan 22, 2025 · We propose Adam-mini, an optimizer that achieves on-par or better performance than AdamW with $50$% less memory footprint. Adam-mini reduces memory by cutting down the learning rate resources in...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=iBExhaU3Lc", "content": "Jan 22, 2025 · We propose Adam-mini, an optimizer that achieves on-par or better performance than AdamW with $50$% less memory footprint. Adam-mini reduces memory by cutting down the learning rate resources in..."}
+{"idx": 4, "title": "Zirun Guo - OpenReview", "date": "", "ddg_snippet": "Promoting openness in scientific communication and the peer-review process", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/profile?id=~Zirun_Guo1", "content": "Promoting openness in scientific communication and the peer-review process"}
+{"idx": 5, "title": "Tanmay Bakshi - OpenReview", "date": "", "ddg_snippet": "Tanmay Bakshi Pronouns: he/him Intern, Research, Microsoft Undergrad student, Computer Science and Engineering, Indian Institute of Technology, Roorkee Joined September 2025", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/profile?id=~Tanmay_Bakshi2", "content": "Tanmay Bakshi Pronouns: he/him Intern, Research, Microsoft Undergrad student, Computer Science and Engineering, Indian Institute of Technology, Roorkee Joined September 2025"}
+{"idx": 6, "title": "Exponential Moving Average of Weights in Deep... | OpenReview", "date": "", "ddg_snippet": "Weight averaging of Stochastic Gradient Descent (SGD) iterates is a popular method for training deep learning models.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=2M9CUnYnBA", "content": "Weight averaging of Stochastic Gradient Descent (SGD) iterates is a popular method for training deep learning models."}
+{"idx": 7, "title": "Light-MILPopt: Solving Large-scale Mixed Integer Linear... | OpenReview", "date": "", "ddg_snippet": "Machine Learning (ML)-based optimization approaches emerge as a promising technique for solving large-scale Mixed Integer Linear Programs (MILPs).", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=2oWRumm67L", "content": "Machine Learning (ML)-based optimization approaches emerge as a promising technique for solving large-scale Mixed Integer Linear Programs (MILPs)."}
+{"idx": 8, "title": "Pre-training Text-to-Text Transformers for... | OpenReview", "date": "", "ddg_snippet": "Pretrained language models (PTLM) have achieved impressive results in a range of natural language understanding (NLU) and generation (NLG) tasks that require a syntactic and semantic understanding...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=3k20LAiHYL2", "content": "Pretrained language models (PTLM) have achieved impressive results in a range of natural language understanding (NLU) and generation (NLG) tasks that require a syntactic and semantic understanding..."}
+{"idx": 9, "title": "Time Travel in LLMs: Tracing Data Contamination in... | OpenReview", "date": "", "ddg_snippet": "Data contamination, i.e., the presence of test data from downstream tasks in the training data of large language models (LLMs), is a potential major issue in measuring LLMs' real effectiveness on...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=2Rwq6c3tvr", "content": "Data contamination, i.e., the presence of test data from downstream tasks in the training data of large language models (LLMs), is a potential major issue in measuring LLMs' real effectiveness on..."}
diff --git a/data/sampled_jsons/siteopenreview.netpdf_table_1_Schubert_polynomials_n=6_accuracy.jsonl b/data/sampled_jsons/siteopenreview.netpdf_table_1_Schubert_polynomials_n=6_accuracy.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..90f0dbfa2abb85ad4837d346593dde465f1321f6
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+++ b/data/sampled_jsons/siteopenreview.netpdf_table_1_Schubert_polynomials_n=6_accuracy.jsonl
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+{"idx": 0, "title": "PDF OpenReview", "date": "", "ddg_snippet": "OpenReview", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf/0381a18f5cdf57d1b8cc805a21ced8ccfa4a6239.pdf", "content": "OpenReview"}
+{"idx": 1, "title": "PDF On Compositional Uncertainty Quantification for Seq Seq Graph Parsing", "date": "", "ddg_snippet": "First, in Section 3.1, we report different calibration metrics for a state-of-the-art seq2seq parser (Lin et al., 2022b) based on T5 (Raffel et al., 2020) as well as its different advanced uncertainty vari-ants on the benchmark. We demonstrate that compared to vanilla ECE based on sequence accuracy , CECE is a better metric for reflecting distributional shift, i.e., task dificulty and domain ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf/b36dc0f3f80cd19f7198d10e37c8003772a84b06.pdf", "content": "First, in Section 3.1, we report different calibration metrics for a state-of-the-art seq2seq parser (Lin et al., 2022b) based on T5 (Raffel et al., 2020) as well as its different advanced uncertainty vari-ants on the benchmark. We demonstrate that compared to vanilla ECE based on sequence accuracy , CECE is a better metric for reflecting distributional shift, i.e., task dificulty and domain ..."}
+{"idx": 2, "title": "PDF Fourier Neural Operator for Parametric Partial Differential Equations", "date": "", "ddg_snippet": "ABSTRACT The classical development of neural networks has primarily focused on learning mappings between finite-dimensional Euclidean spaces. Recently, this has been generalized to neural operators that learn mappings between function spaces. For partial differential equations (PDEs), neural operators directly learn the mapping from any functional parametric dependence to the solution. Thus ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf/53c47f849d1cd4d21b865caf7d774e07a5c42aa4.pdf", "content": "ABSTRACT The classical development of neural networks has primarily focused on learning mappings between finite-dimensional Euclidean spaces. Recently, this has been generalized to neural operators that learn mappings between function spaces. For partial differential equations (PDEs), neural operators directly learn the mapping from any functional parametric dependence to the solution. Thus ..."}
+{"idx": 3, "title": "PDF Accurate and Steady Inertial Pose Estimation through ... - OpenReview", "date": "", "ddg_snippet": "As shown in Table 1 and Table 2, the results indicate that our method has surpassed previous approaches by a significant margin on both four benchmark datasets, achieving more accurate and steadier pose estimation.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf/5ca30c2c075553363dc7e34ac8ab6e6f81222c2d.pdf", "content": "As shown in Table 1 and Table 2, the results indicate that our method has surpassed previous approaches by a significant margin on both four benchmark datasets, achieving more accurate and steadier pose estimation."}
+{"idx": 4, "title": "PDF REGRESSION PRIOR NETWORKS - OpenReview", "date": "", "ddg_snippet": "Under review as a conference paper at ICLR 2021 never evaluated. We derive all measures of uncertainty, the reverse KL-divergence training objective, and the Ensemble Distribution Distillation objective in closed form. Regression Prior Networks are then evaluated on synthetic data, selected UCI datasets and the NYUv2 and KITTI monocular depth estimation tasks, where they are shown to yield ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf/d160e7d6173cdebf940ce18312f1b6e50cead309.pdf", "content": "Under review as a conference paper at ICLR 2021 never evaluated. We derive all measures of uncertainty, the reverse KL-divergence training objective, and the Ensemble Distribution Distillation objective in closed form. Regression Prior Networks are then evaluated on synthetic data, selected UCI datasets and the NYUv2 and KITTI monocular depth estimation tasks, where they are shown to yield ..."}
+{"idx": 5, "title": "PDF R W E SCATTERED k-SUB-EMBEDDING - OpenReview", "date": "", "ddg_snippet": "1 INTRODUCTION Embedding is useful for mapping high-dimensional vector to a low-dimensional space for various neural network models such as language models and graph neural networks. Especially in modern language models, a contextual embedding is represented respectively for each contextual unit. For instance, some language models like word2vec (Mikolov et al., 2013) which deal with a whole ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf/b8524fccb1f030c0052f1ab58d502781ea2bd561.pdf", "content": "1 INTRODUCTION Embedding is useful for mapping high-dimensional vector to a low-dimensional space for various neural network models such as language models and graph neural networks. Especially in modern language models, a contextual embedding is represented respectively for each contextual unit. For instance, some language models like word2vec (Mikolov et al., 2013) which deal with a whole ..."}
+{"idx": 6, "title": "PDF Universal Neural Functionals - OpenReview", "date": "", "ddg_snippet": "Until recently, researchers lacked a unifying and principled framework for designing neural function-als, and would implement a custom model for their particular weight-space task. A significant recent advance was the development of weight-space models that are permutation equivariant [Navon et al., 2023, Zhou et al., 2023a]. Neuron permutation symmetries arise in a neural network's weight ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf/fbc16f7ac077ab56bd7a08aac0db1fcea010cc94.pdf", "content": "Until recently, researchers lacked a unifying and principled framework for designing neural function-als, and would implement a custom model for their particular weight-space task. A significant recent advance was the development of weight-space models that are permutation equivariant [Navon et al., 2023, Zhou et al., 2023a]. Neuron permutation symmetries arise in a neural network's weight ..."}
+{"idx": 7, "title": "Free Algebra Resources - Worksheets, Games, & More Ad Viewing ads is privacy protected by DuckDuckGo. Ad clicks are managed by Microsoft's ad network ( more info ).", "date": "", "ddg_snippet": "Get thousands of teacher-crafted activities that sync up with the school year. Everything you need to teach kids algebra all in one place! Visit Education.com today.", "subpage_snippet": "", "source": "duckduckgo.com", "link": "https://duckduckgo.com/y.js?ad_domain=education.com&ad_provider=bingv7aa&ad_type=txad&click_metadata=QEOadRcXBJVPTl_xVvGxj3qjoeDU8dVh1iyBcQyx_X94oiH-RCQHR4rGchJVO_YCoi05C-ItSNbXPBXXBrh5XLW_J6SmlPbSFkmdri0IYdbsO_cXxBITD4oh-36n-_rB.IJdDQgbyBcQH8RGMYNEk8g&rut=72c5c31ca98aba06114534f56a05c466330d9df81d3f0c6bd5ffb3b2edef4ccb&u3=https://www.bing.com/aclick?ld=e8l7T1b8p0I05QaYIO9Cu5czVUCUxDkAU7_Fk4ieHnCpBdl_cPMAAC7-cNwW2eB3IV7Hi4tof7DmwwaK1tr7S-Kxcy1E-RXBd1CM4u4aM8-Chpq9d9UVJAgcEhB11_-rGllWfjMvMyEbPPfHo-sFBcL6VshvsksLiPUByswOL0nl5GDJWwyHQbiDUrGkVJQMYxoiR6lw&u=aHR0cHMlM2ElMmYlMmZ3d3cuZWR1Y2F0aW9uLmNvbSUyZnJlc291cmNlcyUyZm1hdGglMmZhbGdlYnJhJTJmJTNmbXNjbGtpZCUzZGIwZGFkYjc1ZjJkYzFkOTIwNTdlYjNhNDg3Mzc3OTNmJTI2dXRtX3NvdXJjZSUzZGJpbmclMjZ1dG1fbWVkaXVtJTNkY3BjJTI2dXRtX2NhbXBhaWduJTNkU2VhcmNoJTI1MjAtJTI1MjBFZHUlMjUyMFRlcm1zJTI1MjAtJTI1MjBCTU0lMjZ1dG1fdGVybSUzZGFsZ2VicmElMjZ1dG1fY29udGVudCUzZEFsZ2VicmE&rlid=b0dadb75f2dc1d92057eb3a48737793f&vqd=4-189719719774452605700917523618411039434&iurl={1}IG=9C5285DF2803462095E0D8D93C90B45B&CID=2E4EFD3CCD9568C313FFEB53CC686977&ID=DevEx,5045.1", "content": "Get thousands of teacher-crafted activities that sync up with the school year. Everything you need to teach kids algebra all in one place! Visit Education.com today."}
+{"idx": 8, "title": "PDF Probabilistic size-and-shape functional mixed models", "date": "", "ddg_snippet": "The reliable recovery and uncertainty quantification of a fixed effect functionµin a functional mixed model, for modelling population- and object-level variability in noisily observed functional data, is a notoriously challenging task: variations along the xand yaxes are confounded with additive measurement error, and cannot in general be disentangled. The question then as to what properties ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf/535e3a309e7c63957c2de8b836b87674829c0840.pdf", "content": "The reliable recovery and uncertainty quantification of a fixed effect functionµin a functional mixed model, for modelling population- and object-level variability in noisily observed functional data, is a notoriously challenging task: variations along the xand yaxes are confounded with additive measurement error, and cannot in general be disentangled. The question then as to what properties ..."}
+{"idx": 9, "title": "PDF Structured Unrestricted-Rank Matrices for Parameter Efficient Fine-tuning", "date": "", "ddg_snippet": "Abstract Recent efforts to scale Transformer models have been successful across a wide range of tasks [77]. However, fine-tuning these models for downstream tasks can be expensive, as it requires updating a large number of parameters in the Transformer model. Parameter-eficient fine-tuning (PEFT) approaches have emerged as a viable alternative that allow us to fine-tune models by updating only ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf/13d021ceca6e4f8c1d66f8ac57178562c5e644d9.pdf", "content": "Abstract Recent efforts to scale Transformer models have been successful across a wide range of tasks [77]. However, fine-tuning these models for downstream tasks can be expensive, as it requires updating a large number of parameters in the Transformer model. Parameter-eficient fine-tuning (PEFT) approaches have emerged as a viable alternative that allow us to fine-tune models by updating only ..."}
diff --git a/data/sampled_jsons/siteproceedings.neurips.cc_Regret_Matching+_Farina_abstract_year_2023.jsonl b/data/sampled_jsons/siteproceedings.neurips.cc_Regret_Matching+_Farina_abstract_year_2023.jsonl
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+{"idx": 0, "title": "Regret Matching+: (In)Stability and Fast Convergence in Games", "date": "", "ddg_snippet": "Authors Gabriele Farina , Julien Grand-Clément, Christian Kroer, Chung-Wei Lee, Haipeng Luo Abstract Regret Matching$^+$ (RM$^+$) and its variants are important algorithms for solving large-scale games.However, a theoretical understanding of their success in practice is still a mystery.Moreover, recent advances on fast convergence in games are limited to no-regret algorithms such as online ...", "subpage_snippet": "", "source": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper_files/paper/2023/hash/c209cd57e13f3344a4cad4ce84d0ee1b-Abstract-Conference.html", "content": "Authors Gabriele Farina , Julien Grand-Clément, Christian Kroer, Chung-Wei Lee, Haipeng Luo Abstract Regret Matching$^+$ (RM$^+$) and its variants are important algorithms for solving large-scale games.However, a theoretical understanding of their success in practice is still a mystery.Moreover, recent advances on fast convergence in games are limited to no-regret algorithms such as online ..."}
+{"idx": 1, "title": "PDF Regret Matching : (In)Stability and Fast Convergence in Games", "date": "", "ddg_snippet": "Abstract Regret Matching+ (RM+) and its variants are important algorithms for solving large-scale games [35]. However, a theoretical understanding of their success in practice is still a mystery. Moreover, recent advances [34] on fast convergence in games are limited to no-regret algorithms such as online mirror descent, which satisfy stability. In this paper, we first give counterexamples ...", "subpage_snippet": "", "source": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper_files/paper/2023/file/c209cd57e13f3344a4cad4ce84d0ee1b-Paper-Conference.pdf", "content": "Abstract Regret Matching+ (RM+) and its variants are important algorithms for solving large-scale games [35]. However, a theoretical understanding of their success in practice is still a mystery. Moreover, recent advances [34] on fast convergence in games are limited to no-regret algorithms such as online mirror descent, which satisfy stability. In this paper, we first give counterexamples ..."}
+{"idx": 2, "title": "PDF Extensive-Form Game Solving via Blackwell Approachability on Treeplexes", "date": "", "ddg_snippet": "Abstract We introduce the first algorithmic framework for Blackwell approachability on the sequence-form polytope, the class of convex polytopes capturing the strategies of players in extensive-form games (EFGs). This leads to a new class of regret-minimization algorithms that are stepsize-invariant, in the same sense as the Regret Matching and Regret Matching+ algorithms for the simplex. Our ...", "subpage_snippet": "", "source": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper_files/paper/2024/file/3e2aeb66481dd63a32421bf032b70384-Paper-Conference.pdf", "content": "Abstract We introduce the first algorithmic framework for Blackwell approachability on the sequence-form polytope, the class of convex polytopes capturing the strategies of players in extensive-form games (EFGs). This leads to a new class of regret-minimization algorithms that are stepsize-invariant, in the same sense as the Regret Matching and Regret Matching+ algorithms for the simplex. Our ..."}
diff --git a/data/sampled_jsons/sitewww.ybh1998.space_eventps_3D_printed_MAE.jsonl b/data/sampled_jsons/sitewww.ybh1998.space_eventps_3D_printed_MAE.jsonl
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+{"idx": 0, "title": "EventPS: Real-Time Photometric Stereo Using an Event Camera", "date": "", "ddg_snippet": "May 27, 2024 · This paper introduces EventPS , a novel approach to real-time photometric stereo using an event camera. Capitalizing on the exceptional temporal resolution, dynamic range, and low bandwidth characteristics of event cameras, EventPS estimates surface normal only from the radiance changes, significantly enhancing data efficiency.", "subpage_snippet": "", "source": "www.ybh1998.space", "link": "https://www.ybh1998.space/eventps-real-time-photometric-stereo-using-an-event-camera/", "content": "May 27, 2024 · This paper introduces EventPS , a novel approach to real-time photometric stereo using an event camera. Capitalizing on the exceptional temporal resolution, dynamic range, and low bandwidth characteristics of event cameras, EventPS estimates surface normal only from the radiance changes, significantly enhancing data efficiency."}
+{"idx": 1, "title": "EventPS: Real-Time Photometric Stereo Using an Event Camera", "date": "", "ddg_snippet": "This paper introduces EventPS , a novel approach to real-time photometric stereo using an event camera. Capitalizing on the exceptional temporal resolution, dynamic range, and low bandwidth character-istics of event cameras, EventPS estimates surface nor-mal only from the radiance changes, significantly enhanc-ing data efficiency.", "subpage_snippet": "", "source": "www.ybh1998.space", "link": "https://www.ybh1998.space/wp-content/uploads/2024/06/EventPS_Real-Time_Photometric_Stereo_Using_an_Event_Camera.pdf", "content": "This paper introduces EventPS , a novel approach to real-time photometric stereo using an event camera. Capitalizing on the exceptional temporal resolution, dynamic range, and low bandwidth character-istics of event cameras, EventPS estimates surface nor-mal only from the radiance changes, significantly enhanc-ing data efficiency."}
+{"idx": 2, "title": "ybh1998 – Bohan Yu's Homepage", "date": "", "ddg_snippet": "EventPS : Real-Time Photometric Stereo Using an Event Camera by ybh1998 2024-05-27 Published in: The IEEE/CVF Conference on Computer Vision and Pattern Recognition 2024 (Oral, Best Paper Runner-Up) Continue Reading Publications Computational Photography", "subpage_snippet": "", "source": "www.ybh1998.space", "link": "https://www.ybh1998.space/author/ybh1998/", "content": "EventPS : Real-Time Photometric Stereo Using an Event Camera by ybh1998 2024-05-27 Published in: The IEEE/CVF Conference on Computer Vision and Pattern Recognition 2024 (Oral, Best Paper Runner-Up) Continue Reading Publications Computational Photography"}
+{"idx": 3, "title": "Publications – Bohan Yu's Homepage", "date": "", "ddg_snippet": "EventPS : Real-Time Photometric Stereo Using an Event Camera 2024-05-27 Published in: The IEEE/CVF Conference on Computer Vision and Pattern Recognition 2024 (Oral, Best Paper Runner-Up) MILO: Multi-bounce inverse rendering for indoor scene with light-emitting objects 2023-03-29", "subpage_snippet": "", "source": "www.ybh1998.space", "link": "https://www.ybh1998.space/publications/", "content": "EventPS : Real-Time Photometric Stereo Using an Event Camera 2024-05-27 Published in: The IEEE/CVF Conference on Computer Vision and Pattern Recognition 2024 (Oral, Best Paper Runner-Up) MILO: Multi-bounce inverse rendering for indoor scene with light-emitting objects 2023-03-29"}
+{"idx": 4, "title": "Publications – Bohan Yu's Homepage", "date": "", "ddg_snippet": "EventPS : Real-Time Photometric Stereo Using an Event Camera by ybh1998 2024-05-27 Published in: The IEEE/CVF Conference on Computer Vision and Pattern Recognition 2024 (Oral, Best Paper Runner-Up) Continue Reading Publications Computational Photography", "subpage_snippet": "", "source": "www.ybh1998.space", "link": "https://www.ybh1998.space/category/publications/", "content": "EventPS : Real-Time Photometric Stereo Using an Event Camera by ybh1998 2024-05-27 Published in: The IEEE/CVF Conference on Computer Vision and Pattern Recognition 2024 (Oral, Best Paper Runner-Up) Continue Reading Publications Computational Photography"}
+{"idx": 5, "title": "Active Hyperspectral Imaging Using an Event Camera", "date": "", "ddg_snippet": "larly in dynamic environments. In this work, we present a novel event-based active hyperspectral imaging system de-signed for real-time capture with low bandwidth in dynamic scenes. By combining an event camera with a dynamic illumination strategy, our system achieves unprecedented temporal resolution while maintaining high spectral fidelity, all at a fraction of the bandwidth requirements of ...", "subpage_snippet": "", "source": "www.ybh1998.space", "link": "https://www.ybh1998.space/wp-content/uploads/2025/05/Active_Hyperspectral_Imaging_Using_an_Event_Camera.pdf", "content": "larly in dynamic environments. In this work, we present a novel event-based active hyperspectral imaging system de-signed for real-time capture with low bandwidth in dynamic scenes. By combining an event camera with a dynamic illumination strategy, our system achieves unprecedented temporal resolution while maintaining high spectral fidelity, all at a fraction of the bandwidth requirements of ..."}
diff --git a/data/sampled_jsons/spurious_correlations_math_reasoning_LLM_surface_patterns_heuristics_underlying_cause.jsonl b/data/sampled_jsons/spurious_correlations_math_reasoning_LLM_surface_patterns_heuristics_underlying_cause.jsonl
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index 0000000000000000000000000000000000000000..b623edc6707665160325e8fc8fa97881372b7eac
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+{"idx": 0, "title": "Mitigating Spurious Correlations in LLMs via Causality-Aware Post-Training", "date": "", "ddg_snippet": "Preprint. Under review. In this work, we analyze the establishment of pre-training spurious correlations from a data generation perspective through the causal lens and propose Causality-Aware Post-Training (CAPT), a simple yet effective method to improve the reasoning generalization of LLMs by mitigating both pre-training and fine-tuning biases.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2506.09433", "content": "Preprint. Under review. In this work, we analyze the establishment of pre-training spurious correlations from a data generation perspective through the causal lens and propose Causality-Aware Post-Training (CAPT), a simple yet effective method to improve the reasoning generalization of LLMs by mitigating both pre-training and fine-tuning biases."}
+{"idx": 1, "title": "Probing for Correlations of Causal Facts Large Language Models and ...", "date": "", "ddg_snippet": "Large Language Models ( LLMs ) are subject to an ongoing heated debate, leaving open the question of progress towards AGI and dividing the community into two camps: the ones who see the arguably impressive results as evidence to the scal-ing hypothesis, and the others who are worried about the lack of interpretability and reasoning capabilities. By investigating to which extent causal representa ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=UPwzqPOs4-", "content": "Large Language Models ( LLMs ) are subject to an ongoing heated debate, leaving open the question of progress towards AGI and dividing the community into two camps: the ones who see the arguably impressive results as evidence to the scal-ing hypothesis, and the others who are worried about the lack of interpretability and reasoning capabilities. By investigating to which extent causal representa ..."}
+{"idx": 2, "title": "Mitigating Spurious Correlations in LLMs via Causality-Aware Post ...", "date": "", "ddg_snippet": "However, these methods frequently fail to address the root cause of poor OOD generalization: spurious correlations embedded during pre-training that create dependencies between irrelevant event details and reasoning outcomes.", "subpage_snippet": "", "source": "www.alphaxiv.org", "link": "https://www.alphaxiv.org/overview/2506.09433", "content": "However, these methods frequently fail to address the root cause of poor OOD generalization: spurious correlations embedded during pre-training that create dependencies between irrelevant event details and reasoning outcomes."}
+{"idx": 3, "title": "PDF Spurious Correlations in Mathematical Thinking - JSTOR", "date": "", "ddg_snippet": "Because reliance on spu - rious correlations stems from attention to surface rather than to deep-structural features, we predicted that features with higher perceived saliency would cause more heightened sensitivity to spurious correlations during learning.", "subpage_snippet": "", "source": "www.jstor.org", "link": "https://www.jstor.org/stable/pdf/3233916.pdf", "content": "Because reliance on spu - rious correlations stems from attention to surface rather than to deep-structural features, we predicted that features with higher perceived saliency would cause more heightened sensitivity to spurious correlations during learning."}
+{"idx": 4, "title": "PDF Explore Spurious Correlations at the Concept Level in Language Models ...", "date": "", "ddg_snippet": "We nd that LMs, when encountering spurious correlations between a concept and a label in training or prompts, resort to shortcuts for predictions. Second, we introduce a data re- balancingtechniquethatincorporatesChatGPT- generated counterfactual data, thereby balanc- ing label distribution and mitigating spurious correlations .", "subpage_snippet": "", "source": "aclanthology.org", "link": "https://aclanthology.org/2024.acl-long.28.pdf", "content": "We nd that LMs, when encountering spurious correlations between a concept and a label in training or prompts, resort to shortcuts for predictions. Second, we introduce a data re- balancingtechniquethatincorporatesChatGPT- generated counterfactual data, thereby balanc- ing label distribution and mitigating spurious correlations ."}
+{"idx": 5, "title": "Explore Spurious Correlations at the Concept Level in Language Models ...", "date": "", "ddg_snippet": "In this paper, we first use the LLM to label the concept for each text and then measure the concept bias of models for fine-tuning or ICL on the test data. Second, we propose a data rebalancing method to mitigate the spurious correlations by adding the LLM -generated counterfactual data to make a balanced label distribution for each concept.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2311.08648v1", "content": "In this paper, we first use the LLM to label the concept for each text and then measure the concept bias of models for fine-tuning or ICL on the test data. Second, we propose a data rebalancing method to mitigate the spurious correlations by adding the LLM -generated counterfactual data to make a balanced label distribution for each concept."}
+{"idx": 6, "title": "PDF Constructing Concept-based Models to Mitigate Spurious Correlations ...", "date": "", "ddg_snippet": "Considering that some concepts may be associated with spurious correlations , automatic concept filtering with MLLM is applied to identify and remove those tied to potential spurious correlations within each dataset. In the second stage, automatic concept annotations are performed using MLLM based on the fil- tered concept list.", "subpage_snippet": "", "source": "www.ecva.net", "link": "https://www.ecva.net/papers/eccv_2024/papers_ECCV/papers/10348.pdf", "content": "Considering that some concepts may be associated with spurious correlations , automatic concept filtering with MLLM is applied to identify and remove those tied to potential spurious correlations within each dataset. In the second stage, automatic concept annotations are performed using MLLM based on the fil- tered concept list."}
+{"idx": 7, "title": "PDF Counterfactual Invariance to Spurious Correlations: Why and ... - NeurIPS", "date": "", "ddg_snippet": "It turns out that both the means and implications of counterfactual invariance depend fundamentally on the true underlying causal structure of the data—in particular, whether the label causes the features or the features cause the label. Distinct causal structures require distinct regularization schemes to induce counterfactual invariance.", "subpage_snippet": "", "source": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper_files/paper/2021/file/8710ef761bbb29a6f9d12e4ef8e4379c-Paper.pdf", "content": "It turns out that both the means and implications of counterfactual invariance depend fundamentally on the true underlying causal structure of the data—in particular, whether the label causes the features or the features cause the label. Distinct causal structures require distinct regularization schemes to induce counterfactual invariance."}
+{"idx": 8, "title": "Large Language Models and Causality | Empirical Inference - Max Planck ...", "date": "", "ddg_snippet": "Spurious Correlation in LLMs We have argued that state-of-the-art LLMs still suffer from spurious correlations in reasoning tasks such as math word problems, performing poorly at robustness tests by structural variation of several factors in the input [] and also variations of the symbolic problem structure [].", "subpage_snippet": "", "source": "is.mpg.de", "link": "https://is.mpg.de/pio/en/research_fields/large-language-models-and-causality", "content": "Spurious Correlation in LLMs We have argued that state-of-the-art LLMs still suffer from spurious correlations in reasoning tasks such as math word problems, performing poorly at robustness tests by structural variation of several factors in the input [] and also variations of the symbolic problem structure []."}
+{"idx": 9, "title": "Causally Motivated Sycophancy Mitigation for Large Language Models", "date": "", "ddg_snippet": "This paper introduces a new framework called CAUSM (Causally Motivated Sycophaocy Mitigation) aimed at reducing sycophancy in Large Language Models ( LLMs ). The paper analyzes and models the sycophancy issue in LLMs through the lens of Structured Causal Models (SCMs). A significant causal signature is proposed to distinguish latent causal embeddings from spurious embeddings that cause ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=yRKelogz5i", "content": "This paper introduces a new framework called CAUSM (Causally Motivated Sycophaocy Mitigation) aimed at reducing sycophancy in Large Language Models ( LLMs ). The paper analyzes and models the sycophancy issue in LLMs through the lens of Structured Causal Models (SCMs). A significant causal signature is proposed to distinguish latent causal embeddings from spurious embeddings that cause ..."}
diff --git a/data/sampled_jsons/stochastic_combinatorial_bandits_non-continuous_loss_function_year_2024.jsonl b/data/sampled_jsons/stochastic_combinatorial_bandits_non-continuous_loss_function_year_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..e6eddd1a27d72c7a25af44e6dedd247a5c5303e4
--- /dev/null
+++ b/data/sampled_jsons/stochastic_combinatorial_bandits_non-continuous_loss_function_year_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Multi-armed bandit - Wikipedia", "date": "", "ddg_snippet": "A row of slot machines in Las Vegas. In probability theory and machine learning, the multi-armed bandit problem is named from imagining a gambler at a row of slot machines, who has to decide which machines to play, how many times to play each machine...", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/Multi-armed_bandit", "content": "A row of slot machines in Las Vegas. In probability theory and machine learning, the multi-armed bandit problem is named from imagining a gambler at a row of slot machines, who has to decide which machines to play, how many times to play each machine..."}
+{"idx": 1, "title": "[2101.01301] Adversarial Combinatorial Bandits with General...", "date": "", "ddg_snippet": "{The adversarial combinatorial bandit with general non -linear reward is an important open problem in bandit literature, and it is still unclear whether there is a significant gap from the case of linear reward, stochastic bandit , or semi- bandit feedback.}", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2101.01301", "content": "{The adversarial combinatorial bandit with general non -linear reward is an important open problem in bandit literature, and it is still unclear whether there is a significant gap from the case of linear reward, stochastic bandit , or semi- bandit feedback.}"}
+{"idx": 2, "title": "Contextual Combinatorial Multi-armed Bandits with Volatile Arms and...", "date": "", "ddg_snippet": "In this paper, we study the stochastic contextual combinatorial multi-armed ban - dit (CC-MAB) framework that is tailored for volatile arms and submodular reward functions .", "subpage_snippet": "", "source": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper/2018/file/207f88018f72237565570f8a9e5ca240-Paper.pdf", "content": "In this paper, we study the stochastic contextual combinatorial multi-armed ban - dit (CC-MAB) framework that is tailored for volatile arms and submodular reward functions ."}
+{"idx": 3, "title": "A Framework for Adapting Offline Algorithms to Solve Combinatorial ...", "date": "", "ddg_snippet": "We investigate the problem of stochastic , combinatorial multi-armed bandits where the learner only has access to bandit feedback and the reward function can be non -linear.", "subpage_snippet": "", "source": "deepai.org", "link": "https://deepai.org/publication/a-framework-for-adapting-offline-algorithms-to-solve-combinatorial-multi-armed-bandit-problems-with-bandit-feedback", "content": "We investigate the problem of stochastic , combinatorial multi-armed bandits where the learner only has access to bandit feedback and the reward function can be non -linear."}
+{"idx": 4, "title": "(PDF) Combinatorial Multi-Armed Bandit with General Reward...", "date": "", "ddg_snippet": "In this paper, we study the stochastic combinatorial multi-armed bandit (CMAB) framework that allows a general nonlinear reward function , whose expected value may not depend only on the means of the input random variables but possibly on the entire distributions of these variables.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/309388321_Combinatorial_Multi-Armed_Bandit_with_General_Reward_Functions", "content": "In this paper, we study the stochastic combinatorial multi-armed bandit (CMAB) framework that allows a general nonlinear reward function , whose expected value may not depend only on the means of the input random variables but possibly on the entire distributions of these variables."}
+{"idx": 5, "title": "Adversarial Combinatorial Bandits with General Non -linear Reward...", "date": "", "ddg_snippet": "torial bandit with general non -linear reward is an. important open problem in bandit literature, and. it is still unclear whether there is a signicant gap. from the case of linear reward, stochastic bandit", "subpage_snippet": "", "source": "proceedings.mlr.press", "link": "https://proceedings.mlr.press/v139/han21b/han21b.pdf", "content": "torial bandit with general non -linear reward is an. important open problem in bandit literature, and. it is still unclear whether there is a signicant gap. from the case of linear reward, stochastic bandit"}
+{"idx": 6, "title": "Nonstochastic Contextual Combinatorial Bandits", "date": "", "ddg_snippet": "On the side of non - stochastic losses , the only relevant works we are aware of are those of Kale et al.As outlined in the introduction, we are considering a non - stochastic bandit problem with combinatorial actions and contexts provided in each timestep.", "subpage_snippet": "", "source": "www.cs.bme.hu", "link": "https://www.cs.bme.hu/~gergo/files/ZHCBN23.pdf", "content": "On the side of non - stochastic losses , the only relevant works we are aware of are those of Kale et al.As outlined in the introduction, we are considering a non - stochastic bandit problem with combinatorial actions and contexts provided in each timestep."}
+{"idx": 7, "title": "GitHub - ZIYU-DEEP/Awesome-Papers-on- Combinatorial -Semi...", "date": "", "ddg_snippet": "Topics also generally include combinatorial bandits , semi- bandits , Thompson Sampling, submodularity, sequential decision making, surrogate losses , approximate solutions, etc./:smiling_imp: denote paper importance from my own perspective.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/ZIYU-DEEP/Awesome-Papers-on-Combinatorial-Semi-Bandit-Problems", "content": "Topics also generally include combinatorial bandits , semi- bandits , Thompson Sampling, submodularity, sequential decision making, surrogate losses , approximate solutions, etc./:smiling_imp: denote paper importance from my own perspective."}
+{"idx": 8, "title": "(PDF) Regret Bounds for Stochastic Combinatorial Multi-Armed...", "date": "", "ddg_snippet": "This is the first work for combinatorial bandit where the reward received can be a non -linear function of the chosen $K$ arms. The direct use of multi-armed bandit requires choosing among $N$-choose-$K$ options making the state space large.", "subpage_snippet": "", "source": "www.academia.edu", "link": "https://www.academia.edu/123981524/Regret_Bounds_for_Stochastic_Combinatorial_Multi_Armed_Bandits_with_Linear_Space_Complexity", "content": "This is the first work for combinatorial bandit where the reward received can be a non -linear function of the chosen $K$ arms. The direct use of multi-armed bandit requires choosing among $N$-choose-$K$ options making the state space large."}
+{"idx": 9, "title": "Contextual Combinatorial Bandit", "date": "", "ddg_snippet": "Contextual Combinatorial Bandit and its Application on Diversied Online Recommendation.", "subpage_snippet": "", "source": "www.chenshouyuan.com", "link": "https://www.chenshouyuan.com/papers/sdm14.pdf", "content": "Contextual Combinatorial Bandit and its Application on Diversied Online Recommendation."}
diff --git a/data/sampled_jsons/symmetric_cross_entropy_loss_formula_equations_4_5_6.jsonl b/data/sampled_jsons/symmetric_cross_entropy_loss_formula_equations_4_5_6.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..8a4153d7577bc8b7c3da3fcc8ecb01cdf97e118f
--- /dev/null
+++ b/data/sampled_jsons/symmetric_cross_entropy_loss_formula_equations_4_5_6.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Why don't we use a symmetric cross-entropy loss?", "date": "", "ddg_snippet": "The minimum value that the cross - entropy of ℍ [𝑝,𝑞] can have is when 𝑞=𝑝 which is ℍ [𝑝,𝑝], simple the entropy of the distribution 𝑝. While evaluating different built models say 𝑞 and 𝑞', we often need to compare different them, and cross - entropy can be used here.", "subpage_snippet": "", "source": "stats.stackexchange.com", "link": "https://stats.stackexchange.com/questions/331942/why-dont-we-use-a-symmetric-cross-entropy-loss", "content": "The minimum value that the cross - entropy of ℍ [𝑝,𝑞] can have is when 𝑞=𝑝 which is ℍ [𝑝,𝑝], simple the entropy of the distribution 𝑝. While evaluating different built models say 𝑞 and 𝑞', we often need to compare different them, and cross - entropy can be used here."}
+{"idx": 1, "title": "Cross Entropy Loss — distancia 0.0.1 documentation", "date": "", "ddg_snippet": "Cross Entropy Loss ¶ Introduction ¶ The Cross Entropy Loss is a widely used measure to evaluate the difference between two probability distributions. In the context of neural networks, it is primarily used to assess the performance of classification models, particularly in multi-class classification tasks. Mathematical Formula ¶ The cross entropy loss function is mathematically defined as ...", "subpage_snippet": "", "source": "distancia.readthedocs.io", "link": "https://distancia.readthedocs.io/en/latest/CrossEntropy.html", "content": "Cross Entropy Loss ¶ Introduction ¶ The Cross Entropy Loss is a widely used measure to evaluate the difference between two probability distributions. In the context of neural networks, it is primarily used to assess the performance of classification models, particularly in multi-class classification tasks. Mathematical Formula ¶ The cross entropy loss function is mathematically defined as ..."}
+{"idx": 2, "title": "A Simple Introduction to Cross Entropy Loss", "date": "", "ddg_snippet": "The Cross Entropy Loss is a standard evaluation function in machine learning, used to assess model performance for classification problems. This article will cover how Cross Entropy is calculated, and work through a few examples to illustrate its application in machine learning.", "subpage_snippet": "", "source": "insidelearningmachines.com", "link": "https://insidelearningmachines.com/cross_entropy_loss/", "content": "The Cross Entropy Loss is a standard evaluation function in machine learning, used to assess model performance for classification problems. This article will cover how Cross Entropy is calculated, and work through a few examples to illustrate its application in machine learning."}
+{"idx": 3, "title": "PDF 5.1. Cross-entropy loss Deep learning - Fleuret", "date": "", "ddg_snippet": "C Deep learning / 5.1. Cross - entropy loss value that makes sense geometrically. Beside being conceptually wrong for classification, in practice it penalizes responses Consider this example with correct class 1, and two outputs ˆy and ˆy′. y", "subpage_snippet": "", "source": "fleuret.org", "link": "https://fleuret.org/dlc/materials/dlc-handout-5-1-cross-entropy-loss.pdf", "content": "C Deep learning / 5.1. Cross - entropy loss value that makes sense geometrically. Beside being conceptually wrong for classification, in practice it penalizes responses Consider this example with correct class 1, and two outputs ˆy and ˆy′. y"}
+{"idx": 4, "title": "Correlations of Cross-Entropy Loss in Machine Learning", "date": "", "ddg_snippet": "Cross - entropy loss is crucial in training many deep neural networks. In this context, we show a number of novel and strong correlations among various related divergence functions. In particular, we demonstrate that, in some circumstances, (a) cross - entropy ...", "subpage_snippet": "", "source": "www.ncbi.nlm.nih.gov", "link": "https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11203011/", "content": "Cross - entropy loss is crucial in training many deep neural networks. In this context, we show a number of novel and strong correlations among various related divergence functions. In particular, we demonstrate that, in some circumstances, (a) cross - entropy ..."}
+{"idx": 5, "title": "PDF Cross-Entropy Loss Functions: Theoretical Analysis and Applications", "date": "", "ddg_snippet": "Abstract Cross - entropy is a widely used loss function in applications. It coincides with the logistic loss applied to the outputs of a neural network, when the softmax is used. But, what guarantees can we rely on when using cross - entropy as a surro-gate loss ? We present a theoretical analysis of a broad family of loss functions, comp-sum losses, that includes cross - entropy (or logistic loss ...", "subpage_snippet": "", "source": "cs.nyu.edu", "link": "https://cs.nyu.edu/~mohri/pub/comp.pdf", "content": "Abstract Cross - entropy is a widely used loss function in applications. It coincides with the logistic loss applied to the outputs of a neural network, when the softmax is used. But, what guarantees can we rely on when using cross - entropy as a surro-gate loss ? We present a theoretical analysis of a broad family of loss functions, comp-sum losses, that includes cross - entropy (or logistic loss ..."}
+{"idx": 6, "title": "Cross-Entropy Loss: Simple Explanations, Maths Explained", "date": "", "ddg_snippet": "In this simple scenario, you've just implemented a rudimentary \" loss function\" - the feedback mechanism that powers machine learning. From facial recognition to language translation, I've found that cross - entropy loss still stands as the cornerstone of modern AI systems. Cross - Entropy : A Confidence Meter", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/blog/royswastik/cross-entropy-simplified", "content": "In this simple scenario, you've just implemented a rudimentary \" loss function\" - the feedback mechanism that powers machine learning. From facial recognition to language translation, I've found that cross - entropy loss still stands as the cornerstone of modern AI systems. Cross - Entropy : A Confidence Meter"}
+{"idx": 7, "title": "PDF Deep Learning 07 Cross-Entropy Loss - GitHub Pages", "date": "", "ddg_snippet": "Cross - entropy H(p, q) is the average number of bits required to encode the messages from a source distribution p when encoded with different model q H(p, q) = ∑i pi log2(qi) − · Note that cross - entropy is not symmetric metric, i.e,", "subpage_snippet": "", "source": "krmopuri.github.io", "link": "https://krmopuri.github.io/dl24/static_files/presentations/dl-07.pdf", "content": "Cross - entropy H(p, q) is the average number of bits required to encode the messages from a source distribution p when encoded with different model q H(p, q) = ∑i pi log2(qi) − · Note that cross - entropy is not symmetric metric, i.e,"}
+{"idx": 8, "title": "What Is Cross-Entropy Loss Function? - GeeksforGeeks", "date": "", "ddg_snippet": "Cross - entropy loss is a way to measure how close a model's predictions are to the correct answers in classification problems. It helps train models to make more confident and accurate predictions by rewarding correct answers and penalizing wrong ones. This makes it a key part of building reliable machine learning classifiers.", "subpage_snippet": "", "source": "www.geeksforgeeks.org", "link": "https://www.geeksforgeeks.org/machine-learning/what-is-cross-entropy-loss-function/", "content": "Cross - entropy loss is a way to measure how close a model's predictions are to the correct answers in classification problems. It helps train models to make more confident and accurate predictions by rewarding correct answers and penalizing wrong ones. This makes it a key part of building reliable machine learning classifiers."}
+{"idx": 9, "title": "Cross Entropy Loss in Machine Learning | Universal Equations, Inc.", "date": "", "ddg_snippet": "Cross entropy loss measures the difference between predicted probabilities and actual labels in classification tasks.", "subpage_snippet": "", "source": "www.uequations.com", "link": "https://www.uequations.com/en-us/article/machine-learning/2025-04/cross-entropy-loss-machine-learning", "content": "Cross entropy loss measures the difference between predicted probabilities and actual labels in classification tasks."}
diff --git a/data/sampled_jsons/table_4_dims_per_head_TOP-ERL.jsonl b/data/sampled_jsons/table_4_dims_per_head_TOP-ERL.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..f0ef588017503522ed114a84bc7c16148001f719
--- /dev/null
+++ b/data/sampled_jsons/table_4_dims_per_head_TOP-ERL.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Erling Haaland sparks Borussia Dortmund rout as Bundesliga", "date": "", "ddg_snippet": "Erling Haaland carried on where he left off before the coronavirus suspension as the teenager kicked off Borussia Dortmund s thumping victory ...", "subpage_snippet": "", "source": "www.telegraph.co.uk", "link": "https://www.telegraph.co.uk/football/2020/05/16/borussia-dortmund-vs-schalke-bundesliga-live-score-latest-updates/", "content": "Erling Haaland carried on where he left off before the coronavirus suspension as the teenager kicked off Borussia Dortmund s thumping victory ..."}
+{"idx": 1, "title": "Manchester City 'willing to offer Erling Braut Haaland more", "date": "", "ddg_snippet": "... United's Cristiano Ronaldo , who are both receiving around £385,000 per week, but Haaland's potential salary would see him rocket to the top of the ...", "subpage_snippet": "", "source": "www.sportsmole.co.uk", "link": "https://www.sportsmole.co.uk/football/man-city/transfer-talk/news/man-city-willing-to-offer-haaland-more-than-gbp500k-per-week_480858.html", "content": "... United's Cristiano Ronaldo , who are both receiving around £385,000 per week, but Haaland's potential salary would see him rocket to the top of the ..."}
+{"idx": 2, "title": "Used BMW I4 2022 Green in Brixworth, Northamptonshire | Erl's", "date": "", "ddg_snippet": "Performance Plus 93.4kWh GTS Sport Turismo Auto 4WD 5dr (11kW Charger) Es.. ... Erl 's Vehicles are very pleased to offer this rather special 2023 (73) ...", "subpage_snippet": "", "source": "www.erlsvehicles.co.uk", "link": "https://www.erlsvehicles.co.uk/details/used-car-in-brixworth/bmw/i4/40-83.9kwh-m-sport-gran-coupe-auto-edrive-5dr/1171892/", "content": "Performance Plus 93.4kWh GTS Sport Turismo Auto 4WD 5dr (11kW Charger) Es.. ... Erl 's Vehicles are very pleased to offer this rather special 2023 (73) ..."}
+{"idx": 3, "title": "Top 86 Small Winter Quotes & Sayings", "date": "", "ddg_snippet": "One by one they are being picked off around him: in his small circle of colleagues the ratio slowly grows top -heavy, more ghosts, more each winter ...", "subpage_snippet": "", "source": "quotessayings.net", "link": "https://quotessayings.net/topics/small-winter/", "content": "One by one they are being picked off around him: in his small circle of colleagues the ratio slowly grows top -heavy, more ghosts, more each winter ..."}
+{"idx": 4, "title": "Unstoppable Erling Haaland at the double as Man City thrash", "date": "", "ddg_snippet": "D ortmund's 4 -1 win on the road at Sevilla puts them second in the group on six points, three behind Man City, who can guarantee progression in the ...", "subpage_snippet": "", "source": "www.telegraph.co.uk", "link": "https://www.telegraph.co.uk/football/2022/10/05/manchester-city-vs-copenhagen-live-champions-league-score-latest/", "content": "D ortmund's 4 -1 win on the road at Sevilla puts them second in the group on six points, three behind Man City, who can guarantee progression in the ..."}
+{"idx": 5, "title": "Auckland Star, Nov 13, 1940, p. 4 | NewspaperArchive", "date": "", "ddg_snippet": "The a Naffe Pine a i 1j jl1 117 i 4 kill he Infin Birtt m til i la in a 11 pm her Turt Bruno and 11 06d in i be in n ¿ j m p a for Hrej i lit 111 c ...", "subpage_snippet": "", "source": "newspaperarchive.com", "link": "https://newspaperarchive.com/auckland-star-nov-13-1940-p-4/", "content": "The a Naffe Pine a i 1j jl1 117 i 4 kill he Infin Birtt m til i la in a 11 pm her Turt Bruno and 11 06d in i be in n ¿ j m p a for Hrej i lit 111 c ..."}
+{"idx": 6, "title": "Module:de-noun - Wiktionary, the free dictionary", "date": "", "ddg_snippet": "Add surname indicator to indicate surnames, decline appropriately and include both masc and fem variants in the table .", "subpage_snippet": "", "source": "en.wiktionary.org", "link": "https://en.wiktionary.org/wiki/Module:de-noun", "content": "Add surname indicator to indicate surnames, decline appropriately and include both masc and fem variants in the table ."}
+{"idx": 7, "title": "Man City vs Tottenham: Stats, Standings & Lineups | Sports", "date": "", "ddg_snippet": "Despite their traditionally dominant performances, City appears to be caught in a challenging position as they aim to claw back into the top spot.", "subpage_snippet": "", "source": "sportspundit.com", "link": "https://sportspundit.com/soccer/teams/vs/man-city-vs-tottenham", "content": "Despite their traditionally dominant performances, City appears to be caught in a challenging position as they aim to claw back into the top spot."}
+{"idx": 8, "title": "Used PORSCHE TAYCAN 2023 Grey in Brixworth, Northamptonshire |", "date": "", "ddg_snippet": "... Steering Wheel in Race-Tex (with Matt Carbon)|SportChrono Pack w/Porsche Design Clock|Electric Sport Sound|Comfort Access|Headlining in Race-Tex| 4 ...", "subpage_snippet": "", "source": "www.erlsvehicles.co.uk", "link": "https://www.erlsvehicles.co.uk/details/used-car-in-brixworth/porsche/taycan/performance-plus-93.4kwh-gts-sport-turismo-auto-4wd-5dr-11kw-charger/1427397/", "content": "... Steering Wheel in Race-Tex (with Matt Carbon)|SportChrono Pack w/Porsche Design Clock|Electric Sport Sound|Comfort Access|Headlining in Race-Tex| 4 ..."}
+{"idx": 9, "title": "Blackpool news, fixtures, results and transfer updates - Page", "date": "", "ddg_snippet": "... the already eliminated sides get a chance to feature in the second tier and the Clarets are hoping to close Leicester s lead at the top of the table ...", "subpage_snippet": "", "source": "www.dailymail.co.uk", "link": "https://www.dailymail.co.uk/sport/blackpool/index.html?page=11", "content": "... the already eliminated sides get a chance to feature in the second tier and the Clarets are hoping to close Leicester s lead at the top of the table ..."}
diff --git a/data/sampled_jsons/three_types_of_events_social_media_climate_change_studies_OR_climate_action_events_categories.jsonl b/data/sampled_jsons/three_types_of_events_social_media_climate_change_studies_OR_climate_action_events_categories.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..9656e5cf648e796e7dd80fb97fc90498e67a923f
--- /dev/null
+++ b/data/sampled_jsons/three_types_of_events_social_media_climate_change_studies_OR_climate_action_events_categories.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Climate misinformation is rife on social media—and poised to ...", "date": "", "ddg_snippet": "Jan 20, 2025 · Extreme weather events often lead to a spike in social media attention to climate change . Social media posting peaks during a crisis but drops off quickly.", "subpage_snippet": "", "source": "phys.org", "link": "https://phys.org/news/2025-01-climate-misinformation-rife-social-media.html", "content": "Jan 20, 2025 · Extreme weather events often lead to a spike in social media attention to climate change . Social media posting peaks during a crisis but drops off quickly."}
+{"idx": 1, "title": "Young climate activists find powerful voice in social media ... Global Warming’s Six Americas across age, race/ethnicity, and ... Climate Change - National Geographic Society Psychological, Social, and Cultural Barriers to Public ... Trellis Group (formerly GreenBiz) Evidence-based recommendations for communicating the impacts ...", "date": "", "ddg_snippet": "Sep 12, 2024 · Young activists on social media stress the urgency of climate change : 'Once you start seeing it in your life, then you have no option but to act.' Apr 5, 2023 · Many youth-centered organizations, young climate activists, and social media influencers (e.g., EcoTok and other Climate Creators) are raising awareness about climate change and promoting greater climate action . In the U.S., younger Americans have become more worried and engaged with global warming than older Americans over the last decade. Apr 10, 2025 · Climate change has also been connected with other damaging weather events such as more frequent and more intense hurricanes, floods, downpours, and winter storms. In polar regions, the warming global temperatures associated with climate change have meant ice sheets and glaciers are melting at an accelerated rate from season to season. The following are categories of barriers to accurate risk perceptions and action. First, many - especially in the Global North - underestimate of the threat posed by climate change . Trellis Group empowers sustainability professionals to tackle the climate crisis through our industry-leading events , media and peer network. May 25, 2022 · Evidence-based communication techniques can be used to empower people to act on newfound knowledge about the critical intersection of climate and health.", "subpage_snippet": "", "source": "www.latimes.com", "link": "https://www.latimes.com/environment/story/2024-09-12/young-climate-activists-find-powerful-voice-in-social-media", "content": "Sep 12, 2024 · Young activists on social media stress the urgency of climate change : 'Once you start seeing it in your life, then you have no option but to act.' Apr 5, 2023 · Many youth-centered organizations, young climate activists, and social media influencers (e.g., EcoTok and other Climate Creators) are raising awareness about climate change and promoting greater climate action . In the U.S., younger Americans have become more worried and engaged with global warming than older Americans over the last decade. Apr 10, 2025 · Climate change has also been connected with other damaging weather events such as more frequent and more intense hurricanes, floods, downpours, and winter storms. In polar regions, the warming global temperatures associated with climate change have meant ice sheets and glaciers are melting at an accelerated rate from season to season. The following are categories of barriers to accurate risk perceptions and action. First, many - especially in the Global North - underestimate of the threat posed by climate change . Trellis Group empowers sustainability professionals to tackle the climate crisis through our industry-leading events , media and peer network. May 25, 2022 · Evidence-based communication techniques can be used to empower people to act on newfound knowledge about the critical intersection of climate and health."}
+{"idx": 2, "title": "Climate Change - National Geographic Society", "date": "", "ddg_snippet": "Apr 10, 2025 · Climate change has also been connected with other damaging weather events such as more frequent and more intense hurricanes, floods, downpours, and winter storms. In polar regions, the warming global temperatures associated with climate change have meant ice sheets and glaciers are melting at an accelerated rate from season to season.", "subpage_snippet": "", "source": "education.nationalgeographic.org", "link": "https://education.nationalgeographic.org/resource/climate-change/", "content": "Apr 10, 2025 · Climate change has also been connected with other damaging weather events such as more frequent and more intense hurricanes, floods, downpours, and winter storms. In polar regions, the warming global temperatures associated with climate change have meant ice sheets and glaciers are melting at an accelerated rate from season to season."}
+{"idx": 3, "title": "Psychological, Social, and Cultural Barriers to Public ...", "date": "", "ddg_snippet": "The following are categories of barriers to accurate risk perceptions and action. First, many - especially in the Global North - underestimate of the threat posed by climate change .", "subpage_snippet": "", "source": "oxfordre.com", "link": "https://oxfordre.com/climatescience/climatescience/abstract/10.1093/acrefore/9780190228620.001.0001/acrefore-9780190228620-e-377", "content": "The following are categories of barriers to accurate risk perceptions and action. First, many - especially in the Global North - underestimate of the threat posed by climate change ."}
+{"idx": 4, "title": "Climate Denial Fuels Climate Change Discussions More ...", "date": "", "ddg_snippet": "by M Shah · 2021 · Cited by 11 — Climate change discussions increased significantly for all three types of events , with the highest discussion during the 2016 elections.", "subpage_snippet": "", "source": "www.frontiersin.org", "link": "https://www.frontiersin.org/journals/psychology/articles/10.3389/fpsyg.2021.682057/full", "content": "by M Shah · 2021 · Cited by 11 — Climate change discussions increased significantly for all three types of events , with the highest discussion during the 2016 elections."}
+{"idx": 5, "title": "Is resilience socially constructed? Empirical evidence from ...", "date": "", "ddg_snippet": "by C Béné · 2016 · Cited by 268 — ... events were expected to influence the type of responses employed. Accordingly, a distinction was made between three types of events : (i) rapid shocks ...", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S0959378016300267", "content": "by C Béné · 2016 · Cited by 268 — ... events were expected to influence the type of responses employed. Accordingly, a distinction was made between three types of events : (i) rapid shocks ..."}
+{"idx": 6, "title": "Ethnic diversity and divergent perceptions of climate change", "date": "", "ddg_snippet": "by H Yang · 2024 · Cited by 2 — The number of households involved in these three types of events each exceeds 20% of the total number of respondents: 24.8% (301), 20.6% (251), ...", "subpage_snippet": "", "source": "www.nature.com", "link": "https://www.nature.com/articles/s41599-024-03207-x", "content": "by H Yang · 2024 · Cited by 2 — The number of households involved in these three types of events each exceeds 20% of the total number of respondents: 24.8% (301), 20.6% (251), ..."}
+{"idx": 7, "title": "Human Rights and Mega-Events: A Systematic Literature ...", "date": "", "ddg_snippet": "tional sports events , while three types of events are non-sport related. The Olympics and FIFA World. Cup attracted the most academic attention for their ...", "subpage_snippet": "", "source": "www.ingentaconnect.com", "link": "https://www.ingentaconnect.com/contentone/cog/em/pre-prints/content-wuhuman?crawler=true&mimetype=application/pdf", "content": "tional sports events , while three types of events are non-sport related. The Olympics and FIFA World. Cup attracted the most academic attention for their ..."}
+{"idx": 8, "title": "Global Warming’s Six Americas across age, race/ethnicity, and ...", "date": "", "ddg_snippet": "Apr 5, 2023 · Many youth-centered organizations, young climate activists, and social media influencers (e.g., EcoTok and other Climate Creators) are raising awareness about climate change and promoting greater climate action . In the U.S., younger Americans have become more worried and engaged with global warming than older Americans over the last decade.", "subpage_snippet": "", "source": "climatecommunication.yale.edu", "link": "https://climatecommunication.yale.edu/publications/global-warmings-six-americas-age-race-ethnicity-gender/", "content": "Apr 5, 2023 · Many youth-centered organizations, young climate activists, and social media influencers (e.g., EcoTok and other Climate Creators) are raising awareness about climate change and promoting greater climate action . In the U.S., younger Americans have become more worried and engaged with global warming than older Americans over the last decade."}
+{"idx": 9, "title": "Trellis Group (formerly GreenBiz)", "date": "", "ddg_snippet": "Trellis Group empowers sustainability professionals to tackle the climate crisis through our industry-leading events , media and peer network.", "subpage_snippet": "", "source": "trellis.net", "link": "https://trellis.net/", "content": "Trellis Group empowers sustainability professionals to tackle the climate crisis through our industry-leading events , media and peer network."}
diff --git a/data/sampled_jsons/tlniJJFUW2_Machine_Learning_Algebraic_Combinatorics_spurious_correlation_year_2024.jsonl b/data/sampled_jsons/tlniJJFUW2_Machine_Learning_Algebraic_Combinatorics_spurious_correlation_year_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..8763d097348ff72035fcb44c2c3a6acec4eed7c9
--- /dev/null
+++ b/data/sampled_jsons/tlniJJFUW2_Machine_Learning_Algebraic_Combinatorics_spurious_correlation_year_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Machine Learning Meets Algebraic Combinatorics: A Suite of ...", "date": "", "ddg_snippet": "In this paper we introduce the Algebraic Combinatorics Dataset Repository, a collection of datasets representing open problems or research-level foundational results that have been structured for machine learning .", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=tlniJJFUW2", "content": "In this paper we introduce the Algebraic Combinatorics Dataset Repository, a collection of datasets representing open problems or research-level foundational results that have been structured for machine learning ."}
+{"idx": 1, "title": "Machine Learning meets Algebraic Combinatorics: A Suite of ...", "date": "", "ddg_snippet": "Mar 9, 2025 · With recent dramatic increases in AI system capabilities, there has been growing interest in utilizing machine learning for reasoning-heavy, quantitative tasks, particularly mathematics. While there are many resources capturing mathematics at the high-school, undergraduate, and graduate level, there are far fewer resources available that align with the level of difficulty and open endedness ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2503.06366", "content": "Mar 9, 2025 · With recent dramatic increases in AI system capabilities, there has been growing interest in utilizing machine learning for reasoning-heavy, quantitative tasks, particularly mathematics. While there are many resources capturing mathematics at the high-school, undergraduate, and graduate level, there are far fewer resources available that align with the level of difficulty and open endedness ..."}
+{"idx": 2, "title": "The Relationship between combinatorics, machine learning and ...", "date": "", "ddg_snippet": "Aug 31, 2019 · 2- Combinatorics optimization > related to operations research, algorithm theory Machine Learning , Image Analysis and ANNs 3- Dynamical systems > related to graph dynamical system.", "subpage_snippet": "", "source": "medium.com", "link": "https://medium.com/@m.saber/the-relationship-between-combinatorics-machine-learning-and-artificial-neural-networks-b3088481dba0", "content": "Aug 31, 2019 · 2- Combinatorics optimization > related to operations research, algorithm theory Machine Learning , Image Analysis and ANNs 3- Dynamical systems > related to graph dynamical system."}
+{"idx": 3, "title": "Spurious Correlations in Machine Learning - Doc Zamora's ML & AI Spurious Correlations in Machine Learning: A Survey The Relationship between combinatorics, machine learning and ...", "date": "", "ddg_snippet": "One of the challenges we face while designing machine learning solutions is picking the right prediction features. Deciding which elements are essential and which ones genuinely contribute to model performance becomes complicated as the number of features increases. The infinite monkey theorem states that a monkey hitting keys at random on a typewr... See full list on doczamora.com Covariance is a measure of the joint variability of two random variables. This is, the changes in the X variable are aligned to changes in the Y variable. The issue with covariance is that the relationship is judged based on the linear relationship (as in the Pearson Correlation Coefficient). Two random variables might be perfectly correlated but w... See full list on doczamora.com See full list on doczamora.com Feb 20, 2024 · Machine learning systems are known to be sensitive to spurious correlations between non-essential features of the inputs (e.g., background, texture, and secondary objects) and the corresponding labels. These features and their correlations with the labels are known as \" spurious \" because they tend to change with shifts in real-world data distributions, which can negatively impact the model's ... The Relationship between combinatorics , machine learning and artificial neural networks Let's first identify components Combinatorics to know how to be employed in ML and ANNs.", "subpage_snippet": "", "source": "www.doczamora.com", "link": "https://www.doczamora.com/spurious-correlations-in-machine-learning", "content": "One of the challenges we face while designing machine learning solutions is picking the right prediction features. Deciding which elements are essential and which ones genuinely contribute to model performance becomes complicated as the number of features increases. The infinite monkey theorem states that a monkey hitting keys at random on a typewr... See full list on doczamora.com Covariance is a measure of the joint variability of two random variables. This is, the changes in the X variable are aligned to changes in the Y variable. The issue with covariance is that the relationship is judged based on the linear relationship (as in the Pearson Correlation Coefficient). Two random variables might be perfectly correlated but w... See full list on doczamora.com See full list on doczamora.com Feb 20, 2024 · Machine learning systems are known to be sensitive to spurious correlations between non-essential features of the inputs (e.g., background, texture, and secondary objects) and the corresponding labels. These features and their correlations with the labels are known as \" spurious \" because they tend to change with shifts in real-world data distributions, which can negatively impact the model's ... The Relationship between combinatorics , machine learning and artificial neural networks Let's first identify components Combinatorics to know how to be employed in ML and ANNs."}
+{"idx": 4, "title": "Spurious Correlations in Machine Learning: A Survey", "date": "", "ddg_snippet": "Feb 20, 2024 · Machine learning systems are known to be sensitive to spurious correlations between non-essential features of the inputs (e.g., background, texture, and secondary objects) and the corresponding labels. These features and their correlations with the labels are known as \" spurious \" because they tend to change with shifts in real-world data distributions, which can negatively impact the model's ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2402.12715", "content": "Feb 20, 2024 · Machine learning systems are known to be sensitive to spurious correlations between non-essential features of the inputs (e.g., background, texture, and secondary objects) and the corresponding labels. These features and their correlations with the labels are known as \" spurious \" because they tend to change with shifts in real-world data distributions, which can negatively impact the model's ..."}
+{"idx": 5, "title": "The Relationship between combinatorics, machine learning and ...", "date": "", "ddg_snippet": "The Relationship between combinatorics , machine learning and artificial neural networks Let's first identify components Combinatorics to know how to be employed in ML and ANNs.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/profile/Mohamed-Ahmad/publication/329590488_The_Relationship_between_combinatorics_machine_learning_and_artificial_neural_networks/links/5c113fec4585157ac1be0aac/The-Relationship-between-combinatorics-machine-learning-and-artificial-neural-networks.pdf", "content": "The Relationship between combinatorics , machine learning and artificial neural networks Let's first identify components Combinatorics to know how to be employed in ML and ANNs."}
+{"idx": 6, "title": "Spurious Correlations", "date": "", "ddg_snippet": "Correlation is not causation: thousands of charts of real data showing actual correlations between ridiculous variables.", "subpage_snippet": "", "source": "tylervigen.com", "link": "https://tylervigen.com/spurious-correlations", "content": "Correlation is not causation: thousands of charts of real data showing actual correlations between ridiculous variables."}
+{"idx": 7, "title": "The 10 Most Bizarre Correlations", "date": "", "ddg_snippet": "One of the first things you learn in any statistics class is that correlation doesn't imply causation. Nonetheless, it's fun to consider the causal relationships one could infer from these correlations .", "subpage_snippet": "", "source": "www.buzzfeednews.com", "link": "https://www.buzzfeednews.com/article/kjh2110/the-10-most-bizarre-correlations", "content": "One of the first things you learn in any statistics class is that correlation doesn't imply causation. Nonetheless, it's fun to consider the causal relationships one could infer from these correlations ."}
+{"idx": 8, "title": "Machine Learning Interatomic Potentials: library for efficient training...", "date": "", "ddg_snippet": "Machine Learning Interatomic Potential (MLIP) models aim to approach the accuracy of quantum chemistry methods at a fraction of the computational cost.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2505.22397v1", "content": "Machine Learning Interatomic Potential (MLIP) models aim to approach the accuracy of quantum chemistry methods at a fraction of the computational cost."}
+{"idx": 9, "title": "Classification Report in Machine Learning | Aman Kharwal", "date": "", "ddg_snippet": "A classification report is a performance evaluation metric in machine learning . It is used to show the precision, recall, F1 Score, and support of your trained classification model.", "subpage_snippet": "", "source": "amanxai.com", "link": "https://amanxai.com/2021/07/07/classification-report-in-machine-learning/", "content": "A classification report is a performance evaluation metric in machine learning . It is used to show the precision, recall, F1 Score, and support of your trained classification model."}
diff --git a/data/sampled_jsons/tlniJJFUW2_Machine_Learning_meets_Algebraic_Combinatorics_S18_characters_dataset_training_examples.jsonl b/data/sampled_jsons/tlniJJFUW2_Machine_Learning_meets_Algebraic_Combinatorics_S18_characters_dataset_training_examples.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..321e01d49457ef80c097ca72f4a2819c18446b98
--- /dev/null
+++ b/data/sampled_jsons/tlniJJFUW2_Machine_Learning_meets_Algebraic_Combinatorics_S18_characters_dataset_training_examples.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Machine Learning meets Algebraic Combinatorics: A Suite of ...", "date": "", "ddg_snippet": "Mar 9, 2025 · We describe all nine datasets , the different ways machine learning models can be applied to them (e.g., training with narrow models followed by interpretability analysis or program synthesis with LLMs), and discuss some of the challenges involved in designing datasets like these.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2503.06366", "content": "Mar 9, 2025 · We describe all nine datasets , the different ways machine learning models can be applied to them (e.g., training with narrow models followed by interpretability analysis or program synthesis with LLMs), and discuss some of the challenges involved in designing datasets like these."}
+{"idx": 1, "title": "Machine Learning Meets Algebraic Combinatorics: A Suite of ...", "date": "", "ddg_snippet": "In this paper we introduce the Algebraic Combinatorics Dataset Repository, a collection of datasets representing open problems or research-level foundational results that have been structured for machine learning .", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=tlniJJFUW2", "content": "In this paper we introduce the Algebraic Combinatorics Dataset Repository, a collection of datasets representing open problems or research-level foundational results that have been structured for machine learning ."}
+{"idx": 2, "title": "Algebraic Combinatorics Dataset Repository - GitHub", "date": "", "ddg_snippet": "To lower the barrier of entry to the machine learning community, we include datasets centered around open problems in algebraic combinatorics . We hope that use of these by the AI-community will translate into progress in mathematics.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/pnnl/ML4AlgComb", "content": "To lower the barrier of entry to the machine learning community, we include datasets centered around open problems in algebraic combinatorics . We hope that use of these by the AI-community will translate into progress in mathematics."}
+{"idx": 3, "title": "The Relationship between combinatorics, machine learning and ...", "date": "", "ddg_snippet": "Aug 31, 2019 · So Machine Learning needs >> advanced study in the field of mathematical optimization on discrete and combinatoricsobjects. and Optimization is started as a part of combinatorics and graph theory ...", "subpage_snippet": "", "source": "medium.com", "link": "https://medium.com/@m.saber/the-relationship-between-combinatorics-machine-learning-and-artificial-neural-networks-b3088481dba0", "content": "Aug 31, 2019 · So Machine Learning needs >> advanced study in the field of mathematical optimization on discrete and combinatoricsobjects. and Optimization is started as a part of combinatorics and graph theory ..."}
+{"idx": 4, "title": "[2502.19944] Algebraic Machine Learning: Learning as ...", "date": "", "ddg_snippet": "Feb 27, 2025 · This algebraic foundation offers a fresh perspective on machine intelligence, featuring direct learning from training data without the need for validation dataset , scaling through model additivity, and asymptotic convergence to the underlying rule in the data.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2502.19944", "content": "Feb 27, 2025 · This algebraic foundation offers a fresh perspective on machine intelligence, featuring direct learning from training data without the need for validation dataset , scaling through model additivity, and asymptotic convergence to the underlying rule in the data."}
+{"idx": 5, "title": "Machine Learning meets Algebraic Combinatorics: A Suite of ...", "date": "", "ddg_snippet": "To address this, we introduce a new collection of datasets , the Algebraic Combinatorics Dataset Repository (ACD Repo), representing either foundational results or open problems in algebraic combinatorics , a subfield of mathematics that studies discrete structures arising from abstract algebra .", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/papers/2503.06366", "content": "To address this, we introduce a new collection of datasets , the Algebraic Combinatorics Dataset Repository (ACD Repo), representing either foundational results or open problems in algebraic combinatorics , a subfield of mathematics that studies discrete structures arising from abstract algebra ."}
+{"idx": 6, "title": "Machine Learning Tutorial 1 - Linear Regression on Boston... - YouTube", "date": "", "ddg_snippet": "In this video, we will learn about Linear regression with python machine learning .", "subpage_snippet": "", "source": "www.youtube.com", "link": "https://www.youtube.com/watch?v=ZeM2tHtjGy4", "content": "In this video, we will learn about Linear regression with python machine learning ."}
+{"idx": 7, "title": "Гайд Anomalous Coffee Machine 2 все слова-коды для превращений", "date": "", "ddg_snippet": "Максимально полный список команд и комбинаций для приготовления напитков в Anomalous Coffee Machine 2 (Аномальная кофе машина 2).", "subpage_snippet": "", "source": "vsewaifu.com", "link": "https://vsewaifu.com/walkthrough/gajd-anomalous-coffee-machine-2-vse-slova/", "content": "Максимально полный список команд и комбинаций для приготовления напитков в Anomalous Coffee Machine 2 (Аномальная кофе машина 2)."}
+{"idx": 8, "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": 9, "title": "machinelearningmastery.com/k-fold-cross-validation", "date": "", "ddg_snippet": "Machine Learning Mastery – 22 May 18.", "subpage_snippet": "", "source": "machinelearningmastery.com", "link": "https://machinelearningmastery.com/k-fold-cross-validation/", "content": "Machine Learning Mastery – 22 May 18."}
diff --git a/data/sampled_jsons/what_is_sqlmap_year_2023.jsonl b/data/sampled_jsons/what_is_sqlmap_year_2023.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..d39138909e65bb58eef3844035e6a8a8fbd51f99
--- /dev/null
+++ b/data/sampled_jsons/what_is_sqlmap_year_2023.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "What is sqlmap? - Hacktress", "date": "", "ddg_snippet": "What is sqlmap ? ... Tools / What is sqlmap ? ... sqlmap is an open source penetration testing tool that automates the process of detecting and ...", "subpage_snippet": "", "source": "www.hacktress.com", "link": "https://www.hacktress.com/what-is-sqlmap/", "content": "What is sqlmap ? ... Tools / What is sqlmap ? ... sqlmap is an open source penetration testing tool that automates the process of detecting and ..."}
+{"idx": 1, "title": "FAQ · sqlmapproject/sqlmap Wiki · GitHub", "date": "", "ddg_snippet": "What is sqlmap ? ... sqlmap is an open source penetration testing tool that automates the process of detecting and exploiting SQL injection flaws and ...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/sqlmapproject/sqlmap/wiki/FAQ", "content": "What is sqlmap ? ... sqlmap is an open source penetration testing tool that automates the process of detecting and exploiting SQL injection flaws and ..."}
+{"idx": 2, "title": "The Practical Guide to sqlmap for SQL Injection Course - Cybr", "date": "", "ddg_snippet": "We learn tips & tricks to see what sqlmap is doing under the hood and to troubleshoot when we come across issues.", "subpage_snippet": "", "source": "cybr.com", "link": "https://cybr.com/courses/the-practical-guide-to-sqlmap-for-sql-injection/", "content": "We learn tips & tricks to see what sqlmap is doing under the hood and to troubleshoot when we come across issues."}
+{"idx": 3, "title": "Newest 'sqlmap' Questions - Stack Overflow", "date": "", "ddg_snippet": "The technique as identified by SQLMAP is error-based - WHERE, HAVING, ORDER BY or GROUP BY clause (BIGINT UNSIGNED) It will be great if ...", "subpage_snippet": "", "source": "stackoverflow.com", "link": "https://stackoverflow.com/questions/tagged/sqlmap", "content": "The technique as identified by SQLMAP is error-based - WHERE, HAVING, ORDER BY or GROUP BY clause (BIGINT UNSIGNED) It will be great if ..."}
+{"idx": 4, "title": "sqlmap Tutorial: Features, Use Cases, How It Works | Wiz", "date": "", "ddg_snippet": "TL;DR, What is sqlmap ? ... sqlmap is a powerful, open-source penetration testing tool written in Python that ... What is sqlmap used for?", "subpage_snippet": "", "source": "www.wiz.io", "link": "https://www.wiz.io/academy/sqlmap-overview", "content": "TL;DR, What is sqlmap ? ... sqlmap is a powerful, open-source penetration testing tool written in Python that ... What is sqlmap used for?"}
+{"idx": 5, "title": "Unleashing the Power of SQL Injection Testing with SQLMap: A", "date": "", "ddg_snippet": "The sqlmap system is written in Python, so you have to install Python 2.6 or later on your computer in order to run sqlmap .", "subpage_snippet": "", "source": "www.codelivly.com", "link": "https://www.codelivly.com/sqlmap-tutorial/", "content": "The sqlmap system is written in Python, so you have to install Python 2.6 or later on your computer in order to run sqlmap ."}
+{"idx": 6, "title": "Tutorial Install and Run SQLMap on Kali Linux - Eldernode Blog", "date": "", "ddg_snippet": "The SQLMap tool is one of the most famous SQL injection tools, which quickly provides the possibility of testing penetration into the website through ...", "subpage_snippet": "", "source": "blog.eldernode.com", "link": "https://blog.eldernode.com/install-and-run-sqlmap-on-kali-linux/", "content": "The SQLMap tool is one of the most famous SQL injection tools, which quickly provides the possibility of testing penetration into the website through ..."}
+{"idx": 7, "title": "Sqlmap Overview", "date": "", "ddg_snippet": "Sqlmap is an open source SQL injection tool that automates the process of testing and exploiting SQL injection vulnerabilities.", "subpage_snippet": "", "source": "www.sqlinjection.net", "link": "https://www.sqlinjection.net/sqlmap/", "content": "Sqlmap is an open source SQL injection tool that automates the process of testing and exploiting SQL injection vulnerabilities."}
+{"idx": 8, "title": "🔐 sqlmap: Automate SQL Injection Testing Like a Pro in 2025", "date": "", "ddg_snippet": "What is sqlmap ? ... sqlmap is an open-source penetration testing tool that automates the detection and exploitation of SQL injection vulnerabilities .", "subpage_snippet": "", "source": "blog.thnkandgrow.com", "link": "https://blog.thnkandgrow.com/sqlmap-automate-sql-injection-testing-like-a-pro-2025/", "content": "What is sqlmap ? ... sqlmap is an open-source penetration testing tool that automates the detection and exploitation of SQL injection vulnerabilities ."}
+{"idx": 9, "title": "SQL Injection with SQLMap Tutorial - Blue Goat Cyber", "date": "", "ddg_snippet": "What is SQLMap ? ... Once the detection scan is complete, SQLMap will present the detection results, including the databases on the target system.", "subpage_snippet": "", "source": "bluegoatcyber.com", "link": "https://bluegoatcyber.com/blog/sqlmap-tutorial-mastering-sql-injection-detection-and-exploitation/", "content": "What is SQLMap ? ... Once the detection scan is complete, SQLMap will present the detection results, including the databases on the target system."}
diff --git "a/data/sampled_jsons/when_the_depth_increases_from_2_to_3_layers_Gumiho_\317\204_speedup_decreases.jsonl" "b/data/sampled_jsons/when_the_depth_increases_from_2_to_3_layers_Gumiho_\317\204_speedup_decreases.jsonl"
new file mode 100644
index 0000000000000000000000000000000000000000..345f1952d2c908eb7528e9b8aa8c35f5cd0854e2
--- /dev/null
+++ "b/data/sampled_jsons/when_the_depth_increases_from_2_to_3_layers_Gumiho_\317\204_speedup_decreases.jsonl"
@@ -0,0 +1,3 @@
+{"idx": 0, "title": "Gumiho: A Hybrid Architecture to Prioritize Early Tokens in ...", "date": "", "ddg_snippet": "However, when the depth increases from 2 to 3 layers , although 𝜏 \\tau italic_ τ improves further, the speedup ratio decreases . This is because the overall speedup depends not only on 𝜏 \\tau italic_ τ but also on the time required to complete a single draft process.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2503.10135v1", "content": "However, when the depth increases from 2 to 3 layers , although 𝜏 \\tau italic_ τ improves further, the speedup ratio decreases . This is because the overall speedup depends not only on 𝜏 \\tau italic_ τ but also on the time required to complete a single draft process."}
+{"idx": 1, "title": "Gumiho: A Hybrid Architecture to Prioritize Early Tokens in ...", "date": "", "ddg_snippet": "However, when the depth increases from 2 to 3 layers , although τ improves further, the speedup ratio decreases . This is because the overall speedup depends not only on τ but also on the time required to complete a single draft process.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2503.10135v2", "content": "However, when the depth increases from 2 to 3 layers , although τ improves further, the speedup ratio decreases . This is because the overall speedup depends not only on τ but also on the time required to complete a single draft process."}
+{"idx": 2, "title": "Gumiho: A Hybrid Architecture to Prioritize Early Tokens in ...", "date": "", "ddg_snippet": "However, when the depth increases from 2 to 3 layers , although τ improves further, the speedup ratio decreases . This is because the overall speedup depends not only on τ but also on the time ...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/389821466_Gumiho_A_Hybrid_Architecture_to_Prioritize_Early_Tokens_in_Speculative_Decoding/fulltext/67d3a362be849d39d676f503/Gumiho-A-Hybrid-Architecture-to-Prioritize-Early-Tokens-in-Speculative-Decoding.pdf", "content": "However, when the depth increases from 2 to 3 layers , although τ improves further, the speedup ratio decreases . This is because the overall speedup depends not only on τ but also on the time ..."}
diff --git "a/data/sampled_jsons/\316\251(\342\210\232(\316\243\317\203_t^2_log_N_\316\246))_O(\342\210\232(\316\243\317\203_t^2_log_N_\316\246))_Catoni_contextual_bandits.jsonl" "b/data/sampled_jsons/\316\251(\342\210\232(\316\243\317\203_t^2_log_N_\316\246))_O(\342\210\232(\316\243\317\203_t^2_log_N_\316\246))_Catoni_contextual_bandits.jsonl"
new file mode 100644
index 0000000000000000000000000000000000000000..6df6150afae9a53b82ee424369a9fbf4a065dffe
--- /dev/null
+++ "b/data/sampled_jsons/\316\251(\342\210\232(\316\243\317\203_t^2_log_N_\316\246))_O(\342\210\232(\316\243\317\203_t^2_log_N_\316\246))_Catoni_contextual_bandits.jsonl"
@@ -0,0 +1,6 @@
+{"idx": 0, "title": "The Stats Map · Catoni-Giulini M-Estimator", "date": "", "ddg_snippet": "If we have a bound on the raw second moment, E ∥ X ∥ 2 ≤ v 2 , then choosing λ = 2 lo g ( 1/ δ ) / ( n v 2 ) and β = λ n v 2 gives ...", "subpage_snippet": "", "source": "thestatsmap.com", "link": "https://thestatsmap.com/Catoni-Giulini-M-estimator", "content": "If we have a bound on the raw second moment, E ∥ X ∥ 2 ≤ v 2 , then choosing λ = 2 lo g ( 1/ δ ) / ( n v 2 ) and β = λ n v 2 gives ..."}
+{"idx": 1, "title": "Extended UCB Policies for Multi-Armed Bandit Problems", "date": "", "ddg_snippet": "Strikingly, Lai and Robbins [ 21 ] proved an asymptotic lower bound O ( log T ) O (\\ log T ) on the regret growth rate, under the assumption ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/1112.1768v5", "content": "Strikingly, Lai and Robbins [ 21 ] proved an asymptotic lower bound O ( log T ) O (\\ log T ) on the regret growth rate, under the assumption ..."}
+{"idx": 2, "title": "Aaditya Ramdas' Webpage", "date": "", "ddg_snippet": "... the National Academy of Sciences of the US in a joint ... Logarithmic Neyman regret for adaptive estimation of the average treatment effect (with O .", "subpage_snippet": "", "source": "www.stat.cmu.edu", "link": "https://www.stat.cmu.edu/~aramdas/", "content": "... the National Academy of Sciences of the US in a joint ... Logarithmic Neyman regret for adaptive estimation of the average treatment effect (with O ."}
+{"idx": 3, "title": "ICML 2022 Papers", "date": "", "ddg_snippet": "The Combinatorial Brain Surgeon: Pruning Weights That ... Representation Topology Divergence: A Method for Comparing Neural Network Representations.", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2022/papers.html", "content": "The Combinatorial Brain Surgeon: Pruning Weights That ... Representation Topology Divergence: A Method for Comparing Neural Network Representations."}
+{"idx": 4, "title": "How Balanced Should Causal Covariates Be?", "date": "", "ddg_snippet": "... 2015 ] noted that τ = 0.1 𝜏 0.1 \\tau=0.1 italic_τ = 0.1 had been adopted in observational study matching with standardized covariates, but that ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2503.03860v1", "content": "... 2015 ] noted that τ = 0.1 𝜏 0.1 \\tau=0.1 italic_τ = 0.1 had been adopted in observational study matching with standardized covariates, but that ..."}
+{"idx": 5, "title": "Downloads", "date": "", "ddg_snippet": "Adversarially Robust Models may not Transfer Better: Sufficient Conditions for Domain Transferability from the View of Regularization", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/Downloads/2022", "content": "Adversarially Robust Models may not Transfer Better: Sufficient Conditions for Domain Transferability from the View of Regularization"}