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/generalized-measures-of-anticipation-and	Generalized Measures of Anticipation and Responsivity in Online Language Processing	2409.10728	https://arxiv.org/abs/2409.10728v2	https://arxiv.org/pdf/2409.10728v2.pdf	https://github.com/rycolab/generalized-surprisal	true	true	true	none
https://paperswithcode.com/paper/a-simple-baseline-for-multi-object-tracking	FairMOT: On the Fairness of Detection and Re-Identification in Multiple Object Tracking	2004.01888	https://arxiv.org/abs/2004.01888v6	https://arxiv.org/pdf/2004.01888v6.pdf	https://github.com/ydhcg-bobo/stcmot	false	false	true	pytorch
https://paperswithcode.com/paper/can-graph-reordering-speed-up-graph-neural	Can Graph Reordering Speed Up Graph Neural Network Training? An Experimental Study	2409.11129	https://arxiv.org/abs/2409.11129v1	https://arxiv.org/pdf/2409.11129v1.pdf	https://github.com/nikolaimerkel/reordering	true	true	false	pytorch
https://paperswithcode.com/paper/stcmot-spatio-temporal-cohesion-learning-for	STCMOT: Spatio-Temporal Cohesion Learning for UAV-Based Multiple Object Tracking	2409.11234	https://arxiv.org/abs/2409.11234v1	https://arxiv.org/pdf/2409.11234v1.pdf	https://github.com/ydhcg-bobo/stcmot	true	true	false	pytorch
https://paperswithcode.com/paper/unsupervised-hybrid-framework-for-anomaly	Unsupervised Hybrid framework for ANomaly Detection (HAND) -- applied to Screening Mammogram	2409.11534	https://arxiv.org/abs/2409.11534v1	https://arxiv.org/pdf/2409.11534v1.pdf	https://github.com/zheminzhang96/hand_mammo	true	true	false	pytorch
https://paperswithcode.com/paper/feature-re-embedding-towards-foundation-model	Feature Re-Embedding: Towards Foundation Model-Level Performance in Computational Pathology	2402.17228	https://arxiv.org/abs/2402.17228v4	https://arxiv.org/pdf/2402.17228v4.pdf	https://github.com/dearcaat/rrt-mil	true	true	true	pytorch
https://paperswithcode.com/paper/self-supervised-diffusion-mri-denoising-via	Self-Supervised Diffusion MRI Denoising via Iterative and Stable Refinement	2501.13514	https://arxiv.org/abs/2501.13514v3	https://arxiv.org/pdf/2501.13514v3.pdf	https://github.com/fouierl/di-fusion	true	true	true	pytorch
https://paperswithcode.com/paper/revisiting-end-to-end-learning-with-slide	Revisiting End-to-End Learning with Slide-level Supervision in Computational Pathology	2506.02408	https://arxiv.org/abs/2506.02408v1	https://arxiv.org/pdf/2506.02408v1.pdf	https://github.com/dearcaat/rrt-mil	false	false	true	pytorch
https://paperswithcode.com/paper/vista3d-unravel-the-3d-darkside-of-a-single	Vista3D: Unravel the 3D Darkside of a Single Image	2409.12193	https://arxiv.org/abs/2409.12193v1	https://arxiv.org/pdf/2409.12193v1.pdf	https://github.com/florinshen/vista3d	true	true	false	none
https://paperswithcode.com/paper/volvo-discovery-challenge-at-ecml-pkdd-2024	Volvo Discovery Challenge at ECML-PKDD 2024	2409.11446	https://arxiv.org/abs/2409.11446v1	https://arxiv.org/pdf/2409.11446v1.pdf	https://github.com/mal-to/volvo-discovery-challenge-ecml-pkdd-2024	true	true	false	pytorch
https://paperswithcode.com/paper/three-dimensional-particle-in-cell	Three-Dimensional Particle-In-Cell Simulations of Two-Dimensional Bernstein-Greene-Kruskal Modes	2410.16585	https://arxiv.org/abs/2410.16585v1	https://arxiv.org/pdf/2410.16585v1.pdf	https://github.com/psc-code/psc	true	false	false	none
https://paperswithcode.com/paper/high-resolution-particle-in-cell-simulations	High-Resolution Particle-In-Cell Simulations of Two-Dimensional Bernstein-Greene-Kruskal Modes	2311.08613	https://arxiv.org/abs/2311.08613v1	https://arxiv.org/pdf/2311.08613v1.pdf	https://github.com/psc-code/psc	true	false	false	none
https://paperswithcode.com/paper/sum-of-parts-models-faithful-attributions-for	Sum-of-Parts: Faithful Attributions for Groups of Features	2310.16316	https://arxiv.org/abs/2310.16316v2	https://arxiv.org/pdf/2310.16316v2.pdf	https://github.com/debugml/sop	true	true	true	pytorch
https://paperswithcode.com/paper/quantifying-the-individual-differences-of	Quantifying the Individual Differences of Driver' Risk Perception with Just Four Interpretable Parameters	2211.10907	https://arxiv.org/abs/2211.10907v1	https://arxiv.org/pdf/2211.10907v1.pdf	https://github.com/ChenChenGith/PODAR_individual_modeling_code	true	false	true	pytorch
https://paperswithcode.com/paper/covomix-advancing-zero-shot-speech-generation	CoVoMix: Advancing Zero-Shot Speech Generation for Human-like Multi-talker Conversations	2404.06690	https://arxiv.org/abs/2404.06690v3	https://arxiv.org/pdf/2404.06690v3.pdf	https://github.com/vivian556123/neurips2024-covomix	true	true	true	pytorch
https://paperswithcode.com/paper/kernel-methods-for-the-approximation-of-the	Kernel Methods for the Approximation of the Eigenfunctions of the Koopman Operator	2412.16588	https://arxiv.org/abs/2412.16588v1	https://arxiv.org/pdf/2412.16588v1.pdf	https://github.com/jonghyeon1998/koopman	true	true	false	none
https://paperswithcode.com/paper/less-is-more-a-simple-yet-effective-token	Less is More: A Simple yet Effective Token Reduction Method for Efficient Multi-modal LLMs	2409.10994	https://arxiv.org/abs/2409.10994v3	https://arxiv.org/pdf/2409.10994v3.pdf	https://github.com/freedomintelligence/trim	true	true	true	pytorch
https://paperswithcode.com/paper/scanning-tables-for-the-layer-groups	Symmetries of all lines in monolayer crystals	2410.18750	https://arxiv.org/abs/2410.18750v2	https://arxiv.org/pdf/2410.18750v2.pdf	https://github.com/Griffin-Group/scanning-tables-layer-group-data	true	false	false	none
https://paperswithcode.com/paper/reward-modeling-with-weak-supervision-for	Reward Modeling with Weak Supervision for Language Models	2410.20869	https://arxiv.org/abs/2410.20869v1	https://arxiv.org/pdf/2410.20869v1.pdf	https://github.com/DFKI-NLP/weak-supervision-rlhf	true	false	false	none
https://paperswithcode.com/paper/conditional-variational-autoencoder-with	Conditional Variational Autoencoder with Adversarial Learning for End-to-End Text-to-Speech	2106.06103	https://arxiv.org/abs/2106.06103v1	https://arxiv.org/pdf/2106.06103v1.pdf	https://github.com/pwc-1/Paper-9/tree/main/1/vits	false	false	false	mindspore
https://paperswithcode.com/paper/simple-and-fast-distillation-of-diffusion	Simple and Fast Distillation of Diffusion Models	2409.19681	https://arxiv.org/abs/2409.19681v1	https://arxiv.org/pdf/2409.19681v1.pdf	https://github.com/zhyzhouu/amed-solver	false	false	true	pytorch
https://paperswithcode.com/paper/experience-and-evidence-are-the-eyes-of-an	Experience and Evidence are the eyes of an excellent summarizer! Towards Knowledge Infused Multi-modal Clinical Conversation Summarization	2309.15739	https://arxiv.org/abs/2309.15739v1	https://arxiv.org/pdf/2309.15739v1.pdf	https://github.com/nlp-rl/mm-cliconsummation	true	true	true	pytorch
https://paperswithcode.com/paper/seeding-with-differentially-private-network	Seeding with Differentially Private Network Information	2305.16590	https://arxiv.org/abs/2305.16590v4	https://arxiv.org/pdf/2305.16590v4.pdf	https://github.com/aminrahimian/dp-inf-max	true	true	false	none
https://paperswithcode.com/paper/where-s-ben-nevis-a-2d-optimisation-benchmark	Where's Ben Nevis? A 2D optimisation benchmark with 957,174 local optima based on Great Britain terrain data	2410.02422	https://arxiv.org/abs/2410.02422v1	https://arxiv.org/pdf/2410.02422v1.pdf	https://github.com/CardiacModelling/BenNevis	true	false	false	none
https://paperswithcode.com/paper/personalized-topology-informed-12-lead-ecg	Personalized Topology-Informed Localization of Standard 12-Lead ECG Electrode Placement from Incomplete Cardiac MRIs for Efficient Cardiac Digital Twins	2408.13945	https://arxiv.org/abs/2408.13945v2	https://arxiv.org/pdf/2408.13945v2.pdf	https://github.com/lileitech/12lead_ecg_electrode_localizer	true	true	false	pytorch
https://paperswithcode.com/paper/small-models-big-tasks-an-exploratory	Small Models, Big Tasks: An Exploratory Empirical Study on Small Language Models for Function Calling	2504.19277	https://arxiv.org/abs/2504.19277v1	https://arxiv.org/pdf/2504.19277v1.pdf	https://github.com/Raghav010/Small-Models-Big-Tasks	true	true	false	pytorch
https://paperswithcode.com/paper/controlled-evaluation-of-syntactic-knowledge	Controlled Evaluation of Syntactic Knowledge in Multilingual Language Models	2411.07474	https://arxiv.org/abs/2411.07474v2	https://arxiv.org/pdf/2411.07474v2.pdf	https://github.com/dariakryvosheieva/syntactic_generalization_multilingual	true	false	true	none
https://paperswithcode.com/paper/a-general-purpose-multimodal-foundation-model	A Multimodal Vision Foundation Model for Clinical Dermatology	2410.15038	https://arxiv.org/abs/2410.15038v2	https://arxiv.org/pdf/2410.15038v2.pdf	https://github.com/SiyuanYan1/PanDerm	true	false	true	pytorch
https://paperswithcode.com/paper/unraveling-cross-modality-knowledge-conflict	Unraveling Cross-Modality Knowledge Conflicts in Large Vision-Language Models	2410.03659	https://arxiv.org/abs/2410.03659v2	https://arxiv.org/pdf/2410.03659v2.pdf	https://github.com/luka-group/vlm-knowledge-conflict	true	true	true	pytorch
https://paperswithcode.com/paper/stacked-conditional-generative-adversarial	Stacked Conditional Generative Adversarial Networks for Jointly Learning Shadow Detection and Shadow Removal	1712.02478	http://arxiv.org/abs/1712.02478v1	http://arxiv.org/pdf/1712.02478v1.pdf	https://github.com/Param-Raval/shadow-sight	false	false	true	pytorch
https://paperswithcode.com/paper/federated-learning-with-label-masking	Federated Learning with Label-Masking Distillation	2409.13136	https://arxiv.org/abs/2409.13136v1	https://arxiv.org/pdf/2409.13136v1.pdf	https://github.com/wnma3mz/fedlmd	true	true	false	pytorch
https://paperswithcode.com/paper/policy-improvement-using-language-feedback	Policy Improvement using Language Feedback Models	2402.07876	https://arxiv.org/abs/2402.07876v6	https://arxiv.org/pdf/2402.07876v6.pdf	https://github.com/vzhong/language_feedback_models	true	false	false	pytorch
https://paperswithcode.com/paper/a-simple-image-segmentation-framework-via-in	A Simple Image Segmentation Framework via In-Context Examples	2410.04842	https://arxiv.org/abs/2410.04842v2	https://arxiv.org/pdf/2410.04842v2.pdf	https://github.com/aim-uofa/sine	true	true	true	pytorch
https://paperswithcode.com/paper/llava-prumerge-adaptive-token-reduction-for	LLaVA-PruMerge: Adaptive Token Reduction for Efficient Large Multimodal Models	2403.15388	https://arxiv.org/abs/2403.15388v5	https://arxiv.org/pdf/2403.15388v5.pdf	https://github.com/42Shawn/LLaVA-PruMerge	false	false	true	pytorch
https://paperswithcode.com/paper/swin-transformer-hierarchical-vision	Swin Transformer: Hierarchical Vision Transformer using Shifted Windows	2103.14030	https://arxiv.org/abs/2103.14030v2	https://arxiv.org/pdf/2103.14030v2.pdf	https://github.com/yangyucheng000/University/tree/main/model-3/swin	false	false	false	mindspore
https://paperswithcode.com/paper/on-the-detectability-and-parameterisation-of	On the detectability and parameterisation of binary stars through spectral energy distributions	2412.05606	https://arxiv.org/abs/2412.05606v1	https://arxiv.org/pdf/2412.05606v1.pdf	https://github.com/jikrant3/sed-analysis-tools	true	true	false	none
https://paperswithcode.com/paper/tetrahedral-diffusion-models-for-3d-shape	TetraDiffusion: Tetrahedral Diffusion Models for 3D Shape Generation	2211.13220	https://arxiv.org/abs/2211.13220v3	https://arxiv.org/pdf/2211.13220v3.pdf	https://github.com/PeterTor/TetraDiffusion	true	false	true	pytorch
https://paperswithcode.com/paper/streamgen-connecting-populations-of-streams	StreamGen: Connecting Populations of Streams and Shells to Their Host Galaxies	2409.13810	https://arxiv.org/abs/2409.13810v1	https://arxiv.org/pdf/2409.13810v1.pdf	https://github.com/adropulic/streamgen	true	true	true	jax
https://paperswithcode.com/paper/to-glue-or-not-to-glue-classical-vs-learned	To Glue or Not to Glue? Classical vs Learned Image Matching for Mobile Mapping Cameras to Textured Semantic 3D Building Models	2505.17973	https://arxiv.org/abs/2505.17973v1	https://arxiv.org/pdf/2505.17973v1.pdf	https://github.com/simbauer/to_glue_or_not_to_glue	true	true	false	pytorch
https://paperswithcode.com/paper/simulating-the-two-dimensional-t-j-model-at	Simulating the two-dimensional $t-J$ model at finite doping with neural quantum states	2411.10430	https://arxiv.org/abs/2411.10430v2	https://arxiv.org/pdf/2411.10430v2.pdf	https://github.com/HannahLange/HFDSfortJ	true	false	true	jax
https://paperswithcode.com/paper/flame-financial-large-language-model	FLAME: Financial Large-Language Model Assessment and Metrics Evaluation	2501.06211	https://arxiv.org/abs/2501.06211v1	https://arxiv.org/pdf/2501.06211v1.pdf	https://github.com/flame-ruc/flame	true	true	false	none
https://paperswithcode.com/paper/first-experiments-with-neural-cvc5	First Experiments with Neural cvc5	2501.09379	https://arxiv.org/abs/2501.09379v1	https://arxiv.org/pdf/2501.09379v1.pdf	https://github.com/jellepiepenbrock/mlcvc5-lpar	true	true	false	none
https://paperswithcode.com/paper/scaling-up-your-kernels-large-kernel-design	Scaling Up Your Kernels: Large Kernel Design in ConvNets towards Universal Representations	2410.08049	https://arxiv.org/abs/2410.08049v1	https://arxiv.org/pdf/2410.08049v1.pdf	https://github.com/ailab-cvc/unireplknet	true	true	true	pytorch
https://paperswithcode.com/paper/optimal-state-dynamics-estimation-for-physics	Optimal-state Dynamics Estimation for Physics-based Human Motion Capture from Videos	2410.07795	https://arxiv.org/abs/2410.07795v4	https://arxiv.org/pdf/2410.07795v4.pdf	https://github.com/cuongle1206/osdcap	true	true	true	pytorch
https://paperswithcode.com/paper/memsduino-an-arduino-based-mems-switch	MEMSDuino: An Arduino-Based MEMS Switch Controller	2501.03340	https://arxiv.org/abs/2501.03340v1	https://arxiv.org/pdf/2501.03340v1.pdf	https://github.com/lafefspietz/memsduino	true	true	true	none
https://paperswithcode.com/paper/query-enhanced-knowledge-intensive	Query Enhanced Knowledge-Intensive Conversation via Unsupervised Joint Modeling	2212.09588	https://arxiv.org/abs/2212.09588v2	https://arxiv.org/pdf/2212.09588v2.pdf	https://github.com/MindCode-4/code-12/tree/main/model-conversion-via	false	false	false	mindspore
https://paperswithcode.com/paper/pyramidal-flow-matching-for-efficient-video	Pyramidal Flow Matching for Efficient Video Generative Modeling	2410.05954	https://arxiv.org/abs/2410.05954v1	https://arxiv.org/pdf/2410.05954v1.pdf	https://github.com/jy0205/Pyramid-Flow	true	false	true	pytorch
https://paperswithcode.com/paper/aligning-few-step-diffusion-models-with-dense	Aligning Few-Step Diffusion Models with Dense Reward Difference Learning	2411.11727	https://arxiv.org/abs/2411.11727v1	https://arxiv.org/pdf/2411.11727v1.pdf	https://github.com/ziyizhang27/sdpo	true	true	true	pytorch
https://paperswithcode.com/paper/identifying-memorization-of-diffusion-models	Identifying Memorization of Diffusion Models through p-Laplace Analysis	2505.08246	https://arxiv.org/abs/2505.08246v1	https://arxiv.org/pdf/2505.08246v1.pdf	https://github.com/jonathanbrok/identifying-memorization-of-diffusion-models-through-p-laplace-analysis	true	true	false	pytorch
https://paperswithcode.com/paper/extreme-values-of-the-mass-distribution	Extreme values of the mass distribution associated with $d$-quasi-copulas via linear programming	2410.19339	https://arxiv.org/abs/2410.19339v2	https://arxiv.org/pdf/2410.19339v2.pdf	https://gitlab.com/mrcinv/quasicopula.jl	true	true	true	none
https://paperswithcode.com/paper/physics-informed-neural-networks-for-22	Physics-informed Neural Networks for Functional Differential Equations: Cylindrical Approximation and Its Convergence Guarantees	2410.18153	https://arxiv.org/abs/2410.18153v1	https://arxiv.org/pdf/2410.18153v1.pdf	https://github.com/taikimiyagawa/functionalpinn	true	true	true	pytorch
https://paperswithcode.com/paper/a-joint-learning-framework-with-feature	A Joint Learning Framework with Feature Reconstruction and Prediction for Incomplete Satellite Image Time Series in Agricultural Semantic Segmentation	2505.19159	https://arxiv.org/abs/2505.19159v1	https://arxiv.org/pdf/2505.19159v1.pdf	https://github.com/wangyuze-csu/joint_frp	true	true	false	none
https://paperswithcode.com/paper/a-survey-of-medical-vision-and-language	A Survey of Medical Vision-and-Language Applications and Their Techniques	2411.12195	https://arxiv.org/abs/2411.12195v1	https://arxiv.org/pdf/2411.12195v1.pdf	https://github.com/ytongxie/medical-vision-and-language-tasks-and-methodologies-a-survey	true	true	true	pytorch
https://paperswithcode.com/paper/local-and-global-decoding-in-text-generation	Local and Global Decoding in Text Generation	2410.10810	https://arxiv.org/abs/2410.10810v1	https://arxiv.org/pdf/2410.10810v1.pdf	https://github.com/lowlypalace/global-decoding	true	true	false	pytorch
https://paperswithcode.com/paper/bbsea-an-exploration-of-brain-body	BBSEA: An Exploration of Brain-Body Synchronization for Embodied Agents	2402.08212	https://arxiv.org/abs/2402.08212v1	https://arxiv.org/pdf/2402.08212v1.pdf	https://github.com/yangsizhe/bbsea	false	false	true	pytorch
https://paperswithcode.com/paper/multi-type-preference-learning-empowering	Multi-Type Preference Learning: Empowering Preference-Based Reinforcement Learning with Equal Preferences	2409.07268	https://arxiv.org/abs/2409.07268v2	https://arxiv.org/pdf/2409.07268v2.pdf	https://github.com/feicuilengmmbb/paper_mtpl	true	true	true	none
https://paperswithcode.com/paper/towards-better-multi-head-attention-via	Towards Better Multi-head Attention via Channel-wise Sample Permutation	2410.10914	https://arxiv.org/abs/2410.10914v1	https://arxiv.org/pdf/2410.10914v1.pdf	https://github.com/dashenzi721/csp	true	true	false	pytorch
https://paperswithcode.com/paper/automato-a-parameter-free-persistence-based	AuToMATo: An Out-Of-The-Box Persistence-Based Clustering Algorithm	2408.06958	https://arxiv.org/abs/2408.06958v2	https://arxiv.org/pdf/2408.06958v2.pdf	https://github.com/m-a-huber/AuToMATo	true	false	false	none
https://paperswithcode.com/paper/denial-of-service-poisoning-attacks-against	Denial-of-Service Poisoning Attacks against Large Language Models	2410.10760	https://arxiv.org/abs/2410.10760v1	https://arxiv.org/pdf/2410.10760v1.pdf	https://github.com/sail-sg/p-dos	true	true	true	pytorch
https://paperswithcode.com/paper/ar-tta-a-simple-method-for-real-world	AR-TTA: A Simple Method for Real-World Continual Test-Time Adaptation	2309.10109	https://arxiv.org/abs/2309.10109v2	https://arxiv.org/pdf/2309.10109v2.pdf	https://github.com/dmn-sjk/ar-tta	true	true	true	none
https://paperswithcode.com/paper/v2m-visual-2-dimensional-mamba-for-image	V2M: Visual 2-Dimensional Mamba for Image Representation Learning	2410.10382	https://arxiv.org/abs/2410.10382v1	https://arxiv.org/pdf/2410.10382v1.pdf	https://github.com/wangck20/v2m	true	true	true	pytorch
https://paperswithcode.com/paper/finetuning-pretrained-transformers-into-rnns	Finetuning Pretrained Transformers into RNNs	2103.13076	https://arxiv.org/abs/2103.13076v2	https://arxiv.org/pdf/2103.13076v2.pdf	https://github.com/hazyresearch/lolcats	false	false	true	pytorch
https://paperswithcode.com/paper/the-hedgehog-the-porcupine-expressive-linear	The Hedgehog & the Porcupine: Expressive Linear Attentions with Softmax Mimicry	2402.04347	https://arxiv.org/abs/2402.04347v1	https://arxiv.org/pdf/2402.04347v1.pdf	https://github.com/hazyresearch/lolcats	false	false	true	pytorch
https://paperswithcode.com/paper/geometry-informed-neural-networks	Geometry-Informed Neural Networks	2402.14009	https://arxiv.org/abs/2402.14009v3	https://arxiv.org/pdf/2402.14009v3.pdf	https://github.com/ml-jku/ginns-geometry-informed-neural-networks	true	true	true	pytorch
https://paperswithcode.com/paper/implicit-multi-spectral-transformer-an	Implicit Multi-Spectral Transformer: An Lightweight and Effective Visible to Infrared Image Translation Model	2404.07072	https://arxiv.org/abs/2404.07072v2	https://arxiv.org/pdf/2404.07072v2.pdf	https://github.com/CXH-Research/IRFormer	true	false	true	pytorch
https://paperswithcode.com/paper/a-quick-primer-on-machine-learning-in	A Quick Primer on Machine Learning in Wireless Communications	2312.17713	https://arxiv.org/abs/2312.17713v6	https://arxiv.org/pdf/2312.17713v6.pdf	https://github.com/farismismar/eesc7v86-fall22	true	true	true	tf
https://paperswithcode.com/paper/subjective-and-objective-analysis-of-indian	Subjective and Objective Analysis of Indian Social Media Video Quality	2401.02794	https://arxiv.org/abs/2401.02794v1	https://arxiv.org/pdf/2401.02794v1.pdf	https://github.com/sandeep-sm/live-sc	true	true	true	none
https://paperswithcode.com/paper/inflation-of-test-accuracy-due-to-data	Inflation of test accuracy due to data leakage in deep learning-based classification of OCT images	2202.12267	https://arxiv.org/abs/2202.12267v2	https://arxiv.org/pdf/2202.12267v2.pdf	https://github.com/iulianemiltampu/split_properly_oct_data	true	true	false	tf
https://paperswithcode.com/paper/topa-extend-large-language-models-for-video	TOPA: Extending Large Language Models for Video Understanding via Text-Only Pre-Alignment	2405.13911	https://arxiv.org/abs/2405.13911v2	https://arxiv.org/pdf/2405.13911v2.pdf	https://github.com/dhg-wei/topa	true	true	true	pytorch
https://paperswithcode.com/paper/unsupervised-homography-estimation-on	Unsupervised Homography Estimation on Multimodal Image Pair via Alternating Optimization	2411.13036	https://arxiv.org/abs/2411.13036v1	https://arxiv.org/pdf/2411.13036v1.pdf	https://github.com/songsang7/alto	true	true	true	pytorch
https://paperswithcode.com/paper/vista-dataset-do-vision-language-models	ViSTa Dataset: Do vision-language models understand sequential tasks?	2411.13211	https://arxiv.org/abs/2411.13211v2	https://arxiv.org/pdf/2411.13211v2.pdf	https://github.com/eugleo/vista-dataset	true	true	true	pytorch
https://paperswithcode.com/paper/whales-a-multi-agent-scheduling-dataset-for	WHALES: A Multi-agent Scheduling Dataset for Enhanced Cooperation in Autonomous Driving	2411.13340	https://arxiv.org/abs/2411.13340v1	https://arxiv.org/pdf/2411.13340v1.pdf	https://github.com/chensiweithu/whales	true	true	false	pytorch
https://paperswithcode.com/paper/teaching-vlms-to-localize-specific-objects	Teaching VLMs to Localize Specific Objects from In-context Examples	2411.13317	https://arxiv.org/abs/2411.13317v1	https://arxiv.org/pdf/2411.13317v1.pdf	https://github.com/sivandoveh/iploc	true	true	false	pytorch
https://paperswithcode.com/paper/joint-vision-language-social-bias-removal-for	Joint Vision-Language Social Bias Removal for CLIP	2411.12785	https://arxiv.org/abs/2411.12785v1	https://arxiv.org/pdf/2411.12785v1.pdf	https://github.com/haoyusimon/VL_Debiasing	true	false	true	pytorch
https://paperswithcode.com/paper/fg-dfpn-flow-guided-deformable-frame	FG-DFPN: Flow Guided Deformable Frame Prediction Network	2503.11343	https://arxiv.org/abs/2503.11343v1	https://arxiv.org/pdf/2503.11343v1.pdf	https://github.com/KUIS-AI-Tekalp-Research-Group/frame-prediction	true	false	false	pytorch
https://paperswithcode.com/paper/consistency-models	Consistency Models	2303.01469	https://arxiv.org/abs/2303.01469v2	https://arxiv.org/pdf/2303.01469v2.pdf	https://github.com/cloneofsimo/consistency_models	false	false	true	pytorch
https://paperswithcode.com/paper/developing-a-top-tier-framework-in	Developing a Top-tier Framework in Naturalistic Conditions Challenge for Categorized Emotion Prediction: From Speech Foundation Models and Learning Objective to Data Augmentation and Engineering Choices	2505.22133	https://arxiv.org/abs/2505.22133v2	https://arxiv.org/pdf/2505.22133v2.pdf	https://github.com/tiantiaf0627/vox-profile-release	true	true	false	pytorch
https://paperswithcode.com/paper/gaze-guided-learning-avoiding-shortcut-bias	Gaze-Guided Learning: Avoiding Shortcut Bias in Visual Classification	2504.05583	https://arxiv.org/abs/2504.05583v1	https://arxiv.org/pdf/2504.05583v1.pdf	https://github.com/rekkles2/Gaze-CIFAR-10	true	true	true	pytorch
https://paperswithcode.com/paper/trustworthy-deep-learning-via-proper	Better Uncertainty Calibration via Proper Scores for Classification and Beyond	2203.07835	https://arxiv.org/abs/2203.07835v4	https://arxiv.org/pdf/2203.07835v4.pdf	https://github.com/MLO-lab/better_uncertainty_calibration_via_proper_scores_for_classification_and_beyond	true	false	true	pytorch
https://paperswithcode.com/paper/flipsketch-flipping-static-drawings-to-text	FlipSketch: Flipping Static Drawings to Text-Guided Sketch Animations	2411.10818	https://arxiv.org/abs/2411.10818v1	https://arxiv.org/pdf/2411.10818v1.pdf	https://github.com/hmrishavbandy/flipsketch	true	true	true	pytorch
https://paperswithcode.com/paper/intruding-with-words-towards-understanding	Intruding with Words: Towards Understanding Graph Injection Attacks at the Text Level	2405.16405	https://arxiv.org/abs/2405.16405v2	https://arxiv.org/pdf/2405.16405v2.pdf	https://github.com/leirunlin/text-level-graph-attack	true	true	true	pytorch
https://paperswithcode.com/paper/a-llm-based-ranking-method-for-the-evaluation	A LLM-Based Ranking Method for the Evaluation of Automatic Counter-Narrative Generation	2406.15227	https://arxiv.org/abs/2406.15227v3	https://arxiv.org/pdf/2406.15227v3.pdf	https://github.com/hitz-zentroa/cn-eval	true	true	true	pytorch
https://paperswithcode.com/paper/adjointdeis-efficient-gradients-for-diffusion	AdjointDEIS: Efficient Gradients for Diffusion Models	2405.15020	https://arxiv.org/abs/2405.15020v3	https://arxiv.org/pdf/2405.15020v3.pdf	https://github.com/zblasingame/adjointdeis	true	true	true	pytorch
https://paperswithcode.com/paper/simulation-of-nanorobots-with-artificial	Simulation of Nanorobots with Artificial Intelligence and Reinforcement Learning for Advanced Cancer Cell Detection and Tracking	2411.02345	https://arxiv.org/abs/2411.02345v1	https://arxiv.org/pdf/2411.02345v1.pdf	https://github.com/shahab-k93/cancer-and-smart-nanorobot	true	true	false	none
https://paperswithcode.com/paper/generative-ai-aided-optimization-for-ai	Diffusion-based Reinforcement Learning for Edge-enabled AI-Generated Content Services	2303.13052	https://arxiv.org/abs/2303.13052v3	https://arxiv.org/pdf/2303.13052v3.pdf	https://github.com/lizonghang/agod	true	true	true	pytorch
https://paperswithcode.com/paper/neural-audio-synthesis-of-musical-notes-with	Neural Audio Synthesis of Musical Notes with WaveNet Autoencoders	1704.01279	http://arxiv.org/abs/1704.01279v1	http://arxiv.org/pdf/1704.01279v1.pdf	https://github.com/MindSpore-scientific/code-6/tree/main/neural-audio-synthesis-wavenet	false	false	false	mindspore
https://paperswithcode.com/paper/normalization-layer-per-example-gradients-are	Normalization Layer Per-Example Gradients are Sufficient to Predict Gradient Noise Scale in Transformers	2411.00999	https://arxiv.org/abs/2411.00999v1	https://arxiv.org/pdf/2411.00999v1.pdf	https://github.com/cerebrasresearch/nanogns	true	true	true	pytorch
https://paperswithcode.com/paper/interpreting-clip-with-sparse-linear-concept	Interpreting CLIP with Sparse Linear Concept Embeddings (SpLiCE)	2402.10376	https://arxiv.org/abs/2402.10376v2	https://arxiv.org/pdf/2402.10376v2.pdf	https://github.com/ai4life-group/splice	true	true	true	pytorch
https://paperswithcode.com/paper/flexchunk-enabling-100mx100m-out-of-core-spmv	FlexChunk: Enabling 100M×100M Out-of-Core SpMV (~1.8 min, ~1.7 GB RAM) with Near-Linear Scaling	null	https://www.lesswrong.com/posts/zpRhsdDkWygTDScxb/flexchunk-enabling-100m-100m-out-of-core-spmv-1-8-min-1-7-gb	https://github.com/DanielSwift1992/FlexChunk/blob/main/docs/lesswrong.com-FlexChunk.pdf	https://github.com/DanielSwift1992/FlexChunk	false	false	false	none
https://paperswithcode.com/paper/gateformer-advancing-multivariate-time-series	Gateformer: Advancing Multivariate Time Series Forecasting through Temporal and Variate-Wise Attention with Gated Representations	2505.00307	https://arxiv.org/abs/2505.00307v2	https://arxiv.org/pdf/2505.00307v2.pdf	https://github.com/nyuolab/gateformer	true	true	true	pytorch
https://paperswithcode.com/paper/c-pmi-conditional-pointwise-mutual	C-PMI: Conditional Pointwise Mutual Information for Turn-level Dialogue Evaluation	2306.15245	https://arxiv.org/abs/2306.15245v3	https://arxiv.org/pdf/2306.15245v3.pdf	https://github.com/renll/c-pmi	true	true	true	none
https://paperswithcode.com/paper/tip-of-the-tongue-query-elicitation-for	Tip of the Tongue Query Elicitation for Simulated Evaluation	2502.17776	https://arxiv.org/abs/2502.17776v1	https://arxiv.org/pdf/2502.17776v1.pdf	https://github.com/kimdanny/human-tot-query-elicitation-mturk	true	false	false	none
https://paperswithcode.com/paper/luminance-attentive-networks-for-hdr-image	Luminance Attentive Networks for HDR Image and Panorama Reconstruction	2109.06688	https://arxiv.org/abs/2109.06688v1	https://arxiv.org/pdf/2109.06688v1.pdf	https://github.com/MindSpore-scientific/code-13/tree/main/Luminance-Guided-Chrominance-Enhancement-for-HEVC-Intra-Coding	false	false	false	mindspore
https://paperswithcode.com/paper/in-context-learning-with-hypothesis-class	In-Context Learning with Hypothesis-Class Guidance	2502.19787	https://arxiv.org/abs/2502.19787v1	https://arxiv.org/pdf/2502.19787v1.pdf	https://github.com/uw-madison-lee-lab/icl-hcg	true	true	false	none
https://paperswithcode.com/paper/steerable-conditional-diffusion-for-out-of	Steerable Conditional Diffusion for Out-of-Distribution Adaptation in Medical Image Reconstruction	2308.14409	https://arxiv.org/abs/2308.14409v3	https://arxiv.org/pdf/2308.14409v3.pdf	https://github.com/alexdenker/SteerableConditionalDiffusion	true	true	true	pytorch
https://paperswithcode.com/paper/deft-efficient-finetuning-of-conditional	DEFT: Efficient Fine-Tuning of Diffusion Models by Learning the Generalised $h$-transform	2406.01781	https://arxiv.org/abs/2406.01781v5	https://arxiv.org/pdf/2406.01781v5.pdf	https://github.com/alexdenker/deft	true	true	true	pytorch
https://paperswithcode.com/paper/strategic-learning-and-trading-in-broker	Strategic Learning and Trading in Broker-Mediated Markets	2412.20847	https://arxiv.org/abs/2412.20847v1	https://arxiv.org/pdf/2412.20847v1.pdf	https://github.com/muhammadalifaqsha/broker_informed_noise_filtering_game	true	false	false	none
https://paperswithcode.com/paper/the-bigger-the-better-accurate-molecular	The Bigger the Better? Accurate Molecular Potential Energy Surfaces from Minimalist Neural Networks	2411.18121	https://arxiv.org/abs/2411.18121v1	https://arxiv.org/pdf/2411.18121v1.pdf	https://github.com/MMunibas/KerNN	true	false	true	pytorch
https://paperswithcode.com/paper/model-x-ray-detect-backdoored-models-via	Model X-ray:Detecting Backdoored Models via Decision Boundary	2402.17465	https://arxiv.org/abs/2402.17465v2	https://arxiv.org/pdf/2402.17465v2.pdf	https://github.com/SuYanghao/Model_X-ray	true	false	true	pytorch
https://paperswithcode.com/paper/pruning-in-the-face-of-adversaries	Pruning in the Face of Adversaries	2108.08560	https://arxiv.org/abs/2108.08560v1	https://arxiv.org/pdf/2108.08560v1.pdf	https://github.com/FlorianMerkle/network-pruning-and-robustness	false	false	true	tf

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.