Split (1)

train · 300k rows

paper_url stringlengths 36 81	paper_title stringlengths 1 242 ⌀	paper_arxiv_id stringlengths 9 16 ⌀	paper_url_abs stringlengths 18 314	paper_url_pdf stringlengths 21 935 ⌀	repo_url stringlengths 26 200	is_official bool 2 classes	mentioned_in_paper bool 2 classes	mentioned_in_github bool 2 classes	framework stringclasses 9 values
https://paperswithcode.com/paper/rebalanced-zero-shot-learning	Rebalanced Zero-shot Learning	2210.07031	https://arxiv.org/abs/2210.07031v2	https://arxiv.org/pdf/2210.07031v2.pdf	https://github.com/fouriye/rezsl-tip23	true	true	true	pytorch
https://paperswithcode.com/paper/legal-syllogism-prompting-teaching-large	Legal Syllogism Prompting: Teaching Large Language Models for Legal Judgment Prediction	2307.08321	https://arxiv.org/abs/2307.08321v1	https://arxiv.org/pdf/2307.08321v1.pdf	https://github.com/jiangcong7/legal-syllogism-prompting	true	true	true	none
https://paperswithcode.com/paper/kinetic-monte-carlo-methods-for-three	Kinetic Monte Carlo methods for three-dimensional diffusive capture problems in exterior domains	2406.13644	https://arxiv.org/abs/2406.13644v2	https://arxiv.org/pdf/2406.13644v2.pdf	https://github.com/alanlindsay/3DKMC	true	true	false	none
https://paperswithcode.com/paper/back-to-mass-square-d-one-the-neutrino-mass	Back to (Mass-)Square(d) One: The Neutrino Mass Ordering in Light of Recent Data	2007.08526	https://arxiv.org/abs/2007.08526v2	https://arxiv.org/pdf/2007.08526v2.pdf	https://github.com/speysidehep/example-neutrino	false	false	true	none
https://paperswithcode.com/paper/adaptssr-pre-training-user-model-with	AdaptSSR: Pre-training User Model with Augmentation-Adaptive Self-Supervised Ranking	2310.09706	https://arxiv.org/abs/2310.09706v2	https://arxiv.org/pdf/2310.09706v2.pdf	https://github.com/yflyl613/AdaptSSR	true	true	false	pytorch
https://paperswithcode.com/paper/multimodal-brain-age-estimation-using	Multimodal brain age estimation using interpretable adaptive population-graph learning	2307.04639	https://arxiv.org/abs/2307.04639v2	https://arxiv.org/pdf/2307.04639v2.pdf	https://github.com/bintsi/adaptive-graph-learning	true	true	true	pytorch
https://paperswithcode.com/paper/can-neural-network-memorization-be-localized	Can Neural Network Memorization Be Localized?	2307.09542	https://arxiv.org/abs/2307.09542v1	https://arxiv.org/pdf/2307.09542v1.pdf	https://github.com/pratyushmaini/localizing-memorization	true	true	false	pytorch
https://paperswithcode.com/paper/bsdm-background-suppression-diffusion-model	BSDM: Background Suppression Diffusion Model for Hyperspectral Anomaly Detection	2307.09861	https://arxiv.org/abs/2307.09861v1	https://arxiv.org/pdf/2307.09861v1.pdf	https://github.com/majitao-xd/bsdm-had	true	true	false	pytorch
https://paperswithcode.com/paper/a-deep-learning-framework-for-efficient	A deep learning framework for efficient pathology image analysis	2502.13027	https://arxiv.org/abs/2502.13027v1	https://arxiv.org/pdf/2502.13027v1.pdf	https://github.com/hms-dbmi/chief	false	false	true	pytorch
https://paperswithcode.com/paper/towards-an-ai-to-win-ghana-s-national-science	Towards an AI to Win Ghana's National Science and Maths Quiz	2308.04333	https://arxiv.org/abs/2308.04333v1	https://arxiv.org/pdf/2308.04333v1.pdf	https://github.com/nsmq-ai/nsmqai	true	true	false	none
https://paperswithcode.com/paper/can-an-ai-win-ghana-s-national-science-and	Can an AI Win Ghana's National Science and Maths Quiz? An AI Grand Challenge for Education	2301.13089	https://arxiv.org/abs/2301.13089v1	https://arxiv.org/pdf/2301.13089v1.pdf	https://github.com/nsmq-ai/nsmqai	false	false	true	none
https://paperswithcode.com/paper/how-generalizable-are-deepfake-detectors-an	How Generalizable are Deepfake Image Detectors? An Empirical Study	2308.04177	https://arxiv.org/abs/2308.04177v2	https://arxiv.org/pdf/2308.04177v2.pdf	https://github.com/boutiquelee/deepfakeempiricalstudy	true	true	false	tf
https://paperswithcode.com/paper/xflow-benchmarking-flow-behaviors-over-graphs	XFlow: Benchmarking Flow Behaviors over Graphs	2308.03819	https://arxiv.org/abs/2308.03819v1	https://arxiv.org/pdf/2308.03819v1.pdf	https://github.com/xgraphing/xflow	true	true	true	pytorch
https://paperswithcode.com/paper/sok-evaluations-in-industrial-intrusion	SoK: Evaluations in Industrial Intrusion Detection Research	2311.02929	https://arxiv.org/abs/2311.02929v1	https://arxiv.org/pdf/2311.02929v1.pdf	https://github.com/fkie-cad/ipal_evaluate	true	true	false	none
https://paperswithcode.com/paper/delivering-document-conversion-as-a-cloud	Delivering Document Conversion as a Cloud Service with High Throughput and Responsiveness	2206.00785	https://arxiv.org/abs/2206.00785v1	https://arxiv.org/pdf/2206.00785v1.pdf	https://github.com/ds4sd/deepsearch-toolkit	false	false	true	none
https://paperswithcode.com/paper/neural-networks-for-programming-quantum	Neural Networks for Programming Quantum Annealers	2308.06807	https://arxiv.org/abs/2308.06807v1	https://arxiv.org/pdf/2308.06807v1.pdf	https://github.com/boschsamuel/nnforprogrammingquantumannealers	true	true	true	pytorch
https://paperswithcode.com/paper/hierarchy-flow-for-high-fidelity-image-to	Hierarchy Flow For High-Fidelity Image-to-Image Translation	2308.06909	https://arxiv.org/abs/2308.06909v1	https://arxiv.org/pdf/2308.06909v1.pdf	https://github.com/weichenfan/hierarchyflow	true	true	true	pytorch
https://paperswithcode.com/paper/global-features-are-all-you-need-for-image	Global Features are All You Need for Image Retrieval and Reranking	2308.06954	https://arxiv.org/abs/2308.06954v2	https://arxiv.org/pdf/2308.06954v2.pdf	https://github.com/shihaoshao-gh/superglobal	true	true	true	pytorch
https://paperswithcode.com/paper/the-importance-of-being-scalable-improving	The Importance of Being Scalable: Improving the Speed and Accuracy of Neural Network Interatomic Potentials Across Chemical Domains	2410.24169	https://arxiv.org/abs/2410.24169v1	https://arxiv.org/pdf/2410.24169v1.pdf	https://github.com/ASK-Berkeley/EScAIP	true	false	false	pytorch
https://paperswithcode.com/paper/bounding-and-estimating-mcmc-convergence	Estimating MCMC convergence rates using common random number simulation	2309.15735	https://arxiv.org/abs/2309.15735v3	https://arxiv.org/pdf/2309.15735v3.pdf	https://github.com/sixter/commonrandomnumber	true	true	false	none
https://paperswithcode.com/paper/powerset-multi-class-cross-entropy-loss-for	Powerset multi-class cross entropy loss for neural speaker diarization	2310.13025	https://arxiv.org/abs/2310.13025v1	https://arxiv.org/pdf/2310.13025v1.pdf	https://github.com/frenchkrab/is2023-powerset-diarization	true	true	false	none
https://paperswithcode.com/paper/speech-recognition-and-multi-speaker	Speech Recognition and Multi-Speaker Diarization of Long Conversations	2005.08072	https://arxiv.org/abs/2005.08072v2	https://arxiv.org/pdf/2005.08072v2.pdf	https://github.com/frenchkrab/is2023-powerset-diarization	false	false	true	none
https://paperswithcode.com/paper/ava-avd-audio-visual-speaker-diarization-in	AVA-AVD: Audio-Visual Speaker Diarization in the Wild	2111.14448	https://arxiv.org/abs/2111.14448v5	https://arxiv.org/pdf/2111.14448v5.pdf	https://github.com/frenchkrab/is2023-powerset-diarization	false	false	true	none
https://paperswithcode.com/paper/understanding-and-optimizing-deep-learning	Boosting DNN Cold Inference on Edge Devices	2206.07446	https://arxiv.org/abs/2206.07446v2	https://arxiv.org/pdf/2206.07446v2.pdf	https://github.com/ubiquitouslearning/nnv12	true	true	true	none
https://paperswithcode.com/paper/primitive-geometry-segment-pre-training-for	Primitive Geometry Segment Pre-training for 3D Medical Image Segmentation	2401.03665	https://arxiv.org/abs/2401.03665v1	https://arxiv.org/pdf/2401.03665v1.pdf	https://github.com/super-tadory/primgeoseg	true	true	false	pytorch
https://paperswithcode.com/paper/nef-neural-edge-fields-for-3d-parametric	NEF: Neural Edge Fields for 3D Parametric Curve Reconstruction from Multi-view Images	2303.07653	https://arxiv.org/abs/2303.07653v2	https://arxiv.org/pdf/2303.07653v2.pdf	https://github.com/yunfan1202/NEF_code	true	false	true	pytorch
https://paperswithcode.com/paper/are-current-long-term-video-understanding	Are current long-term video understanding datasets long-term?	2308.11244	https://arxiv.org/abs/2308.11244v1	https://arxiv.org/pdf/2308.11244v1.pdf	https://github.com/ombretta/longterm_datasets	true	true	true	none
https://paperswithcode.com/paper/protein-dna-binding-sites-prediction-based-on	Protein-DNA binding sites prediction based on pre-trained protein language model and contrastive learning	2306.15912	https://arxiv.org/abs/2306.15912v1	https://arxiv.org/pdf/2306.15912v1.pdf	https://github.com/yandrewl/clape	true	true	true	pytorch
https://paperswithcode.com/paper/exposing-flaws-of-generative-model-evaluation-1	Exposing flaws of generative model evaluation metrics and their unfair treatment of diffusion models	2306.04675	https://arxiv.org/abs/2306.04675v2	https://arxiv.org/pdf/2306.04675v2.pdf	https://github.com/gmum/PALATE	false	false	true	jax
https://paperswithcode.com/paper/coarse-to-fine-amodal-segmentation-with-shape	Coarse-to-Fine Amodal Segmentation with Shape Prior	2308.16825	https://arxiv.org/abs/2308.16825v1	https://arxiv.org/pdf/2308.16825v1.pdf	https://github.com/amazon-science/c2f-seg	true	false	true	pytorch
https://paperswithcode.com/paper/large-language-model-can-transcribe-speech-in	Large Language Model Can Transcribe Speech in Multi-Talker Scenarios with Versatile Instructions	2409.08596	https://arxiv.org/abs/2409.08596v2	https://arxiv.org/pdf/2409.08596v2.pdf	https://github.com/cuhealthybrains/mt-llm	true	true	false	pytorch
https://paperswithcode.com/paper/communication-efficient-learning-of-deep	Communication-Efficient Learning of Deep Networks from Decentralized Data	1602.05629	https://arxiv.org/abs/1602.05629v4	https://arxiv.org/pdf/1602.05629v4.pdf	https://github.com/Lyhao0212/FedAvg	false	false	true	pytorch
https://paperswithcode.com/paper/ego4d-around-the-world-in-3000-hours-of	Ego4D: Around the World in 3,000 Hours of Egocentric Video	2110.07058	https://arxiv.org/abs/2110.07058v3	https://arxiv.org/pdf/2110.07058v3.pdf	https://github.com/frenchkrab/is2023-powerset-diarization	false	false	true	none
https://paperswithcode.com/paper/product-attribute-value-extraction-using	ExtractGPT: Exploring the Potential of Large Language Models for Product Attribute Value Extraction	2310.12537	https://arxiv.org/abs/2310.12537v5	https://arxiv.org/pdf/2310.12537v5.pdf	https://github.com/wbsg-uni-mannheim/extractgpt	true	true	true	none
https://paperswithcode.com/paper/voxelmorph-a-learning-framework-for	VoxelMorph: A Learning Framework for Deformable Medical Image Registration	1809.05231	https://arxiv.org/abs/1809.05231v3	https://arxiv.org/pdf/1809.05231v3.pdf	https://github.com/jw4hv/geo-sic	false	false	true	pytorch
https://paperswithcode.com/paper/stratmed-relevance-stratification-for-low	StratMed: Relevance Stratification between Biomedical Entities for Sparsity on Medication Recommendation	2308.16781	https://arxiv.org/abs/2308.16781v4	https://arxiv.org/pdf/2308.16781v4.pdf	https://github.com/lixiang-222/drug-recommendations	true	true	true	pytorch
https://paperswithcode.com/paper/a-divergence-preserving-cut-finite-element	A divergence preserving cut finite element method for Darcy flow	2205.12023	https://arxiv.org/abs/2205.12023v8	https://arxiv.org/pdf/2205.12023v8.pdf	https://github.com/cutfem/cutfem-library	true	true	false	none
https://paperswithcode.com/paper/tensorshield-safeguarding-on-device-inference	TensorShield: Safeguarding On-Device Inference by Shielding Critical DNN Tensors with TEE	2505.22735	https://arxiv.org/abs/2505.22735v1	https://arxiv.org/pdf/2505.22735v1.pdf	https://github.com/suntong30/tensorshield	true	true	false	pytorch
https://paperswithcode.com/paper/vistruct-visual-structural-knowledge	ViStruct: Visual Structural Knowledge Extraction via Curriculum Guided Code-Vision Representation	2311.13258	https://arxiv.org/abs/2311.13258v1	https://arxiv.org/pdf/2311.13258v1.pdf	https://github.com/yangyi-chen/vi-struct	true	true	true	pytorch
https://paperswithcode.com/paper/the-third-dihard-diarization-challenge	The Third DIHARD Diarization Challenge	2012.01477	https://arxiv.org/abs/2012.01477v3	https://arxiv.org/pdf/2012.01477v3.pdf	https://github.com/frenchkrab/is2023-powerset-diarization	false	false	true	none
https://paperswithcode.com/paper/echomamba4rec-harmonizing-bidirectional-state	EchoMamba4Rec: Harmonizing Bidirectional State Space Models with Spectral Filtering for Advanced Sequential Recommendation	2406.02638	https://arxiv.org/abs/2406.02638v2	https://arxiv.org/pdf/2406.02638v2.pdf	https://github.com/wyd0042/EchoMamba4Rec	true	false	false	pytorch
https://paperswithcode.com/paper/a-physical-model-guided-framework-for	A Physical Model-Guided Framework for Underwater Image Enhancement and Depth Estimation	2407.04230	https://arxiv.org/abs/2407.04230v1	https://arxiv.org/pdf/2407.04230v1.pdf	https://github.com/ddz16/UWEnhancer	true	false	true	pytorch
https://paperswithcode.com/paper/post-ocr-document-correction-with-large	Post-OCR Document Correction with large Ensembles of Character Sequence-to-Sequence Models	2109.06264	https://arxiv.org/abs/2109.06264v3	https://arxiv.org/pdf/2109.06264v3.pdf	https://github.com/jarobyte91/post_ocr_correction	true	true	true	pytorch
https://paperswithcode.com/paper/distributed-representations-of-words-and-1	Distributed Representations of Words and Phrases and their Compositionality	1310.4546	http://arxiv.org/abs/1310.4546v1	http://arxiv.org/pdf/1310.4546v1.pdf	https://github.com/Robotmurlock/Deepwalk-and-Node2vec	false	false	true	pytorch
https://paperswithcode.com/paper/self-distillation-regularized-connectionist	Self-distillation Regularized Connectionist Temporal Classification Loss for Text Recognition: A Simple Yet Effective Approach	2308.08806	https://arxiv.org/abs/2308.08806v4	https://arxiv.org/pdf/2308.08806v4.pdf	https://github.com/zzyhlyoko/DCTC	true	true	true	pytorch
https://paperswithcode.com/paper/roboss-a-robust-bounded-sparse-and-smooth	RoBoSS: A Robust, Bounded, Sparse, and Smooth Loss Function for Supervised Learning	2309.02250	https://arxiv.org/abs/2309.02250v1	https://arxiv.org/pdf/2309.02250v1.pdf	https://github.com/mtanveer1/RoBoSS	true	true	false	none
https://paperswithcode.com/paper/mask-of-truth-model-sensitivity-to-unexpected	Mask of truth: model sensitivity to unexpected regions of medical images	2412.04030	https://arxiv.org/abs/2412.04030v2	https://arxiv.org/pdf/2412.04030v2.pdf	https://github.com/theosourget/mmc_masking_eyefundus	true	true	false	pytorch
https://paperswithcode.com/paper/aggregating-capacity-in-fl-through-successive	Aggregating Capacity in FL through Successive Layer Training for Computationally-Constrained Devices	2305.17005	https://arxiv.org/abs/2305.17005v2	https://arxiv.org/pdf/2305.17005v2.pdf	https://github.com/k1l1/SLT	true	true	false	pytorch
https://paperswithcode.com/paper/event-based-dynamic-graph-representation	Event-based Dynamic Graph Representation Learning for Patent Application Trend Prediction	2308.09780	https://arxiv.org/abs/2308.09780v2	https://arxiv.org/pdf/2308.09780v2.pdf	https://github.com/Hope-Rita/EDGPAT	true	false	false	pytorch
https://paperswithcode.com/paper/graph-neural-networks-use-graphs-when-they	Graph Neural Networks Use Graphs When They Shouldn't	2309.04332	https://arxiv.org/abs/2309.04332v2	https://arxiv.org/pdf/2309.04332v2.pdf	https://github.com/mayabechlerspeicher/Graph_Neural_Networks_Overfit_Graphs	true	true	false	pytorch
https://paperswithcode.com/paper/how-susceptible-are-large-language-models-to	How Susceptible are Large Language Models to Ideological Manipulation?	2402.11725	https://arxiv.org/abs/2402.11725v3	https://arxiv.org/pdf/2402.11725v3.pdf	https://github.com/kaichen23/llm_ideo_manipulate	true	true	true	none
https://paperswithcode.com/paper/mxt-mamba-x-transformer-for-image-inpainting	MxT: Mamba x Transformer for Image Inpainting	2407.16126	https://arxiv.org/abs/2407.16126v3	https://arxiv.org/pdf/2407.16126v3.pdf	https://github.com/chrischen1023/mxt	true	true	false	pytorch
https://paperswithcode.com/paper/omnisearchsage-multi-task-multi-entity	OmniSearchSage: Multi-Task Multi-Entity Embeddings for Pinterest Search	2404.16260	https://arxiv.org/abs/2404.16260v1	https://arxiv.org/pdf/2404.16260v1.pdf	https://github.com/pinterest/atg-research	true	true	false	pytorch
https://paperswithcode.com/paper/item-graph2vec-a-efficient-and-effective	Item-Graph2vec: a Efficient and Effective Approach using Item Co-occurrence Graph Embedding for Collaborative Filtering	2310.14215	https://arxiv.org/abs/2310.14215v1	https://arxiv.org/pdf/2310.14215v1.pdf	https://github.com/cpu135/item-graph2vec	true	true	false	none
https://paperswithcode.com/paper/end-to-end-user-behavior-retrieval-in-click	End-to-End User Behavior Retrieval in Click-Through RatePrediction Model	2108.04468	https://arxiv.org/abs/2108.04468v1	https://arxiv.org/pdf/2108.04468v1.pdf	https://github.com/reczoo/FuxiCTR	false	false	true	pytorch
https://paperswithcode.com/paper/imface-a-sophisticated-nonlinear-3d-morphable	ImFace++: A Sophisticated Nonlinear 3D Morphable Face Model with Implicit Neural Representations	2312.04028	https://arxiv.org/abs/2312.04028v3	https://arxiv.org/pdf/2312.04028v3.pdf	https://github.com/mingwuzheng/imface	true	true	true	pytorch
https://paperswithcode.com/paper/deep-interest-network-for-click-through-rate	Deep Interest Network for Click-Through Rate Prediction	1706.06978	http://arxiv.org/abs/1706.06978v4	http://arxiv.org/pdf/1706.06978v4.pdf	https://github.com/reczoo/FuxiCTR	false	false	false	pytorch
https://paperswithcode.com/paper/affective-and-dynamic-beam-search-for-story	Affective and Dynamic Beam Search for Story Generation	2310.15079	https://arxiv.org/abs/2310.15079v1	https://arxiv.org/pdf/2310.15079v1.pdf	https://github.com/tenghaohuang/affgen	true	true	false	pytorch
https://paperswithcode.com/paper/impala-scalable-distributed-deep-rl-with	IMPALA: Scalable Distributed Deep-RL with Importance Weighted Actor-Learner Architectures	1802.01561	http://arxiv.org/abs/1802.01561v3	http://arxiv.org/pdf/1802.01561v3.pdf	https://github.com/seolhokim/SimpleDistributedRL	false	false	false	pytorch
https://paperswithcode.com/paper/seamless-integration-of-tactile-sensors-for	Seamless Integration of Tactile Sensors for Cobots	2309.05792	https://arxiv.org/abs/2309.05792v1	https://arxiv.org/pdf/2309.05792v1.pdf	https://github.com/remkopr/airo-halberd	true	true	false	none
https://paperswithcode.com/paper/conversational-recommender-system-and-large	Conversational Recommender System and Large Language Model Are Made for Each Other in E-commerce Pre-sales Dialogue	2310.14626	https://arxiv.org/abs/2310.14626v2	https://arxiv.org/pdf/2310.14626v2.pdf	https://github.com/leeeeoliu/llm-crs	true	true	false	pytorch
https://paperswithcode.com/paper/normdial-a-comparable-bilingual-synthetic	NormDial: A Comparable Bilingual Synthetic Dialog Dataset for Modeling Social Norm Adherence and Violation	2310.14563	https://arxiv.org/abs/2310.14563v2	https://arxiv.org/pdf/2310.14563v2.pdf	https://github.com/aochong-li/normdial	true	true	true	none
https://paperswithcode.com/paper/qonfusion-quantum-approaches-to-gaussian	QonFusion -- Quantum Approaches to Gaussian Random Variables: Applications in Stable Diffusion and Brownian Motion	2309.16258	https://arxiv.org/abs/2309.16258v1	https://arxiv.org/pdf/2309.16258v1.pdf	https://github.com/BoltzmannEntropy/QonFusion	true	false	true	pytorch
https://paperswithcode.com/paper/w2v-bert-combining-contrastive-learning-and	W2v-BERT: Combining Contrastive Learning and Masked Language Modeling for Self-Supervised Speech Pre-Training	2108.06209	https://arxiv.org/abs/2108.06209v2	https://arxiv.org/pdf/2108.06209v2.pdf	https://github.com/wenet-e2e/wenet	false	false	false	pytorch
https://paperswithcode.com/paper/doclayout-yolo-enhancing-document-layout	DocLayout-YOLO: Enhancing Document Layout Analysis through Diverse Synthetic Data and Global-to-Local Adaptive Perception	2410.12628	https://arxiv.org/abs/2410.12628v1	https://arxiv.org/pdf/2410.12628v1.pdf	https://github.com/opendatalab/PDF-Extract-Kit	false	false	true	pytorch
https://paperswithcode.com/paper/tree-prompting-efficient-task-adaptation	Tree Prompting: Efficient Task Adaptation without Fine-Tuning	2310.14034	https://arxiv.org/abs/2310.14034v1	https://arxiv.org/pdf/2310.14034v1.pdf	https://github.com/csinva/tree-prompt	true	false	true	pytorch
https://paperswithcode.com/paper/identifiable-cognitive-diagnosis-with-encoder	Towards the Identifiability and Explainability for Personalized Learner Modeling: An Inductive Paradigm	2309.00300	https://arxiv.org/abs/2309.00300v4	https://arxiv.org/pdf/2309.00300v4.pdf	https://github.com/cslijt/id-cdf	true	true	false	pytorch
https://paperswithcode.com/paper/the-convergence-of-stochastic-differential	The convergence of stochastic differential equations to their linearisation in small noise limits	2309.16334	https://arxiv.org/abs/2309.16334v2	https://arxiv.org/pdf/2309.16334v2.pdf	https://github.com/liamblake/explicit-characterisation-sde-linearisation	true	true	true	none
https://paperswithcode.com/paper/mapping-bias-in-vision-language-models	debiaSAE: Benchmarking and Mitigating Vision-Language Model Bias	2410.13146	https://arxiv.org/abs/2410.13146v2	https://arxiv.org/pdf/2410.13146v2.pdf	https://github.com/kuleens/vlmbiaseval	true	true	false	jax
https://paperswithcode.com/paper/accelerating-ilp-solvers-for-minimum-flow	Accelerating ILP solvers for Minimum Flow Decompositions through search space and dimensionality reductions	2311.10563	https://arxiv.org/abs/2311.10563v1	https://arxiv.org/pdf/2311.10563v1.pdf	https://github.com/algbio/optimized-fd	true	true	false	none
https://paperswithcode.com/paper/second-order-group-knockoffs-with	Second-order group knockoffs with applications to GWAS	2310.15069	https://arxiv.org/abs/2310.15069v2	https://arxiv.org/pdf/2310.15069v2.pdf	https://github.com/biona001/knockoffspy	true	true	true	none
https://paperswithcode.com/paper/visualwebbench-how-far-have-multimodal-llms	VisualWebBench: How Far Have Multimodal LLMs Evolved in Web Page Understanding and Grounding?	2404.05955	https://arxiv.org/abs/2404.05955v1	https://arxiv.org/pdf/2404.05955v1.pdf	https://github.com/visualwebbench/visualwebbench	false	false	true	pytorch
https://paperswithcode.com/paper/fully-differentiable-ransac	Generalized Differentiable RANSAC	2212.13185	https://arxiv.org/abs/2212.13185v3	https://arxiv.org/pdf/2212.13185v3.pdf	https://github.com/weitong8591/ars_magsac	false	false	true	pytorch
https://paperswithcode.com/paper/consensus-guided-correspondence-denoising	Progressive Correspondence Pruning by Consensus Learning	2101.00591	https://arxiv.org/abs/2101.00591v2	https://arxiv.org/pdf/2101.00591v2.pdf	https://github.com/weitong8591/ars_magsac	false	false	true	pytorch
https://paperswithcode.com/paper/risk-of-bias-in-chest-x-ray-foundation-models	Risk of Bias in Chest Radiography Deep Learning Foundation Models	2209.02965	https://arxiv.org/abs/2209.02965v3	https://arxiv.org/pdf/2209.02965v3.pdf	https://github.com/biomedia-mira/cxr-foundation-bias	true	true	false	pytorch
https://paperswithcode.com/paper/an-application-driven-method-for-assembling	An Application Driven Method for Assembling Numerical Schemes for the Solution of Complex Multiphysics Problems	2309.17055	https://arxiv.org/abs/2309.17055v2	https://arxiv.org/pdf/2309.17055v2.pdf	https://github.com/pzimbrod/multiphysics-pde-methods	true	true	false	none
https://paperswithcode.com/paper/alma-lensing-cluster-survey-average-dust-gas	ALMA Lensing Cluster Survey: average dust, gas, and star formation properties of cluster and field galaxies from stacking analysis	2309.16832	https://arxiv.org/abs/2309.16832v1	https://arxiv.org/pdf/2309.16832v1.pdf	https://github.com/guerrero-andrea/stacking_codes	true	true	true	none
https://paperswithcode.com/paper/an-image-is-worth-16x16-words-transformers-1	An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale	2010.11929	https://arxiv.org/abs/2010.11929v2	https://arxiv.org/pdf/2010.11929v2.pdf	https://github.com/rayanramoul/Visual-Transformer-PyTorch	false	false	true	pytorch
https://paperswithcode.com/paper/improved-anisotropic-gaussian-filters	Improved Anisotropic Gaussian Filters	2303.13278	https://arxiv.org/abs/2303.13278v2	https://arxiv.org/pdf/2303.13278v2.pdf	https://github.com/akeilmann/anigauss	true	true	true	none
https://paperswithcode.com/paper/the-partial-entropy-decomposition-decomposing	The Partial Entropy Decomposition: Decomposing multivariate entropy and mutual information via pointwise common surprisal	1702.01591	http://arxiv.org/abs/1702.01591v2	http://arxiv.org/pdf/1702.01591v2.pdf	https://github.com/robince/partial-info-decomp	true	true	true	none
https://paperswithcode.com/paper/measuring-multivariate-redundant-information	Measuring multivariate redundant information with pointwise common change in surprisal	1602.05063	http://arxiv.org/abs/1602.05063v3	http://arxiv.org/pdf/1602.05063v3.pdf	https://github.com/robince/partial-info-decomp	true	true	true	none
https://paperswithcode.com/paper/engineering-serendipity-through	Engineering Serendipity through Recommendations of Items with Atypical Aspects	2505.23580	https://arxiv.org/abs/2505.23580v1	https://arxiv.org/pdf/2505.23580v1.pdf	https://github.com/ramituncc49er/atars	true	true	true	none
https://paperswithcode.com/paper/object-aware-adaptive-positivity-learning-for	Object-aware Adaptive-Positivity Learning for Audio-Visual Question Answering	2312.12816	https://arxiv.org/abs/2312.12816v1	https://arxiv.org/pdf/2312.12816v1.pdf	https://github.com/zhangbin-ai/apl	true	true	true	pytorch
https://paperswithcode.com/paper/manipulation-robust-regression-discontinuity	Manipulation-Robust Regression Discontinuity Designs	2009.07551	https://arxiv.org/abs/2009.07551v7	https://arxiv.org/pdf/2009.07551v7.pdf	https://github.com/smasa11/rdtest	false	false	true	none
https://paperswithcode.com/paper/linear-recurrent-units-for-sequential	Linear Recurrent Units for Sequential Recommendation	2310.02367	https://arxiv.org/abs/2310.02367v2	https://arxiv.org/pdf/2310.02367v2.pdf	https://github.com/yueqirex/lrurec	true	true	true	pytorch
https://paperswithcode.com/paper/nearest-neighbor-machine-translation-is-meta	Nearest Neighbor Machine Translation is Meta-Optimizer on Output Projection Layer	2305.13034	https://arxiv.org/abs/2305.13034v2	https://arxiv.org/pdf/2305.13034v2.pdf	https://github.com/ruizgao/knnmt-meta-optimizer	true	true	false	pytorch
https://paperswithcode.com/paper/eden-multimodal-synthetic-dataset-of-enclosed	EDEN: Multimodal Synthetic Dataset of Enclosed GarDEN Scenes	2011.04389	https://arxiv.org/abs/2011.04389v2	https://arxiv.org/pdf/2011.04389v2.pdf	https://github.com/lhoangan/eden-generation	false	false	true	none
https://paperswithcode.com/paper/vision-language-pseudo-labels-for-single	Vision-Language Pseudo-Labels for Single-Positive Multi-Label Learning	2310.15985	https://arxiv.org/abs/2310.15985v1	https://arxiv.org/pdf/2310.15985v1.pdf	https://github.com/mvrl/vlpl	true	true	false	pytorch
https://paperswithcode.com/paper/rrhf-v-ranking-responses-to-mitigate	RRHF-V: Ranking Responses to Mitigate Hallucinations in Multimodal Large Language Models with Human Feedback	null	https://aclanthology.org/2025.coling-main.454/	https://aclanthology.org/2025.coling-main.454.pdf	https://github.com/MindSpore-scientific-2/code-7/tree/main/RRHF-V_mindformers	false	false	false	mindspore
https://paperswithcode.com/paper/pruning-filters-for-efficient-convnets	Pruning Filters for Efficient ConvNets	1608.08710	http://arxiv.org/abs/1608.08710v3	http://arxiv.org/pdf/1608.08710v3.pdf	https://github.com/VainF/Torch-Pruning	false	false	true	pytorch
https://paperswithcode.com/paper/few-shot-generative-model-adaption-via	Few Shot Generative Model Adaption via Relaxed Spatial Structural Alignment	2203.04121	https://arxiv.org/abs/2203.04121v3	https://arxiv.org/pdf/2203.04121v3.pdf	https://github.com/2023-MindSpore-4/Code12/tree/main/liliang/RSSA_mds-main	false	false	false	mindspore
https://paperswithcode.com/paper/quark-controllable-text-generation-with	Quark: Controllable Text Generation with Reinforced Unlearning	2205.13636	https://arxiv.org/abs/2205.13636v2	https://arxiv.org/pdf/2205.13636v2.pdf	https://github.com/gximinglu/quark	true	true	true	pytorch
https://paperswithcode.com/paper/fingerspelling-recognition-in-the-wild-with	Fingerspelling recognition in the wild with iterative visual attention	1908.10546	https://arxiv.org/abs/1908.10546v1	https://arxiv.org/pdf/1908.10546v1.pdf	https://github.com/fmahoudeau/MiCT-RANet-ASL-FingerSpelling	false	false	true	pytorch
https://paperswithcode.com/paper/improving-fairness-of-graph-neural-networks-a	Towards Fair Graph Neural Networks via Graph Counterfactual	2307.04937	https://arxiv.org/abs/2307.04937v2	https://arxiv.org/pdf/2307.04937v2.pdf	https://github.com/timelovercc/caf-gnn	true	true	true	pytorch
https://paperswithcode.com/paper/quantum-clustering-and-gaussian-mixtures	Quantum Clustering and Gaussian Mixtures	1612.09199	http://arxiv.org/abs/1612.09199v1	http://arxiv.org/pdf/1612.09199v1.pdf	https://github.com/mrpintime/Quantum_Gaussian_Mixtures_Clustering	false	false	true	none
https://paperswithcode.com/paper/revisiting-semidefinite-programming	Revisiting semidefinite programming approaches to options pricing: complexity and computational perspectives	2111.07701	https://arxiv.org/abs/2111.07701v3	https://arxiv.org/pdf/2111.07701v3.pdf	https://github.com/informsjoc/2022.0328	false	false	true	none
https://paperswithcode.com/paper/out-of-distribution-detection-by-leveraging	Out-of-Distribution Detection by Leveraging Between-Layer Transformation Smoothness	2310.02832	https://arxiv.org/abs/2310.02832v2	https://arxiv.org/pdf/2310.02832v2.pdf	https://github.com/fjelenic/between-layer-ood	true	true	true	pytorch
https://paperswithcode.com/paper/docreal-robust-document-dewarping-of-real	DocReal: Robust Document Dewarping of Real-Life Images via Attention-Enhanced Control Point Prediction	null	https://openaccess.thecvf.com/content/WACV2024/html/Yu_DocReal_Robust_Document_Dewarping_of_Real-Life_Images_via_Attention-Enhanced_Control_WACV_2024_paper.html	https://openaccess.thecvf.com/content/WACV2024/papers/Yu_DocReal_Robust_Document_Dewarping_of_Real-Life_Images_via_Attention-Enhanced_Control_WACV_2024_paper.pdf	https://github.com/SciYu/DocReal	false	false	false	none
https://paperswithcode.com/paper/stella-nera-achieving-161-top-s-w-with	Stella Nera: Achieving 161 TOp/s/W with Multiplier-free DNN Acceleration based on Approximate Matrix Multiplication	2311.10207	https://arxiv.org/abs/2311.10207v1	https://arxiv.org/pdf/2311.10207v1.pdf	https://github.com/joennlae/halutmatmul	true	true	true	pytorch
https://paperswithcode.com/paper/sums-of-hurwitz-class-numbers-cm-modular	Sums of Hurwitz class numbers, CM modular forms, and primes of the form $x^2+ny^2$	2405.07565	https://arxiv.org/abs/2405.07565v1	https://arxiv.org/pdf/2405.07565v1.pdf	https://github.com/zinmik/Hurwitz-class-numbers	true	true	false	none

Subsets and Splits

Framework Repo Connectivity Analysis

Reveals the number of official and unofficial repositories and papers associated with different frameworks, highlighting the most connected ones.

Deduplicated Paper-Code Links

This query provides a detailed and organized list of repositories linked to single papers, highlighting official status and mention sources, which is useful for understanding the relationship between papers and their corresponding repositories.

Paper Repo Counts & Distribution

Provides detailed statistics on the distribution of papers across different numbers of repositories, highlighting the percentage of papers with multiple repositories.

Quantum Papers with Code Links

Lists quantum-related papers with their titles, arXiv IDs, frameworks, and code repository links, providing a valuable resource for researchers interested in quantum computing.

Financial Stock Price Prediction

Finds papers related to stock prices, financial markets, and predictions, providing a focused subset for further analysis.

SQL Console for pwc-archive/links-between-paper-and-code

Retrieves specific details about a single paper by its arXiv ID, providing limited insight into the dataset.

Search for YOLO Links

Retrieves a limited set of records related to YOLO, providing basic information about papers and repositories but without deeper analysis.

Prompt Optimization and Personalization

Retrieves a limited set of papers with titles containing specific keywords related to prompt optimization and personalization, providing basic filtering of the dataset.