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/up-nerf-unconstrained-pose-prior-free-neural	UP-NeRF: Unconstrained Pose-Prior-Free Neural Radiance Fields	2311.03784	https://arxiv.org/abs/2311.03784v2	https://arxiv.org/pdf/2311.03784v2.pdf	https://github.com/mlvlab/UP-NeRF	true	false	true	pytorch
https://paperswithcode.com/paper/toward-sufficient-spatial-frequency	Toward Sufficient Spatial-Frequency Interaction for Gradient-aware Underwater Image Enhancement	2309.04089	https://arxiv.org/abs/2309.04089v2	https://arxiv.org/pdf/2309.04089v2.pdf	https://github.com/zhihefang/SFGNet	true	true	true	pytorch
https://paperswithcode.com/paper/reinforcement-learning-discovers-efficient	Reinforcement Learning Discovers Efficient Decentralized Graph Path Search Strategies	2409.07932	https://arxiv.org/abs/2409.07932v2	https://arxiv.org/pdf/2409.07932v2.pdf	https://github.com/flxclxc/rl-graph-search	true	false	true	pytorch
https://paperswithcode.com/paper/affective-reasoning-at-utterance-level-in	How to Enhance Causal Discrimination of Utterances: A Case on Affective Reasoning	2305.02615	https://arxiv.org/abs/2305.02615v2	https://arxiv.org/pdf/2305.02615v2.pdf	https://github.com/zodiark-ch/mater-of-our-emnlp2023-paper	true	true	false	pytorch
https://paperswithcode.com/paper/sednet-shallow-encoder-decoder-network-for	SEDNet: Shallow Encoder-Decoder Network for Brain Tumor Segmentation	2401.13403	https://arxiv.org/abs/2401.13403v3	https://arxiv.org/pdf/2401.13403v3.pdf	https://github.com/chollette/SEDNet_Shallow-Encoder-Decoder-Network-for-Brain-Tumor-Segmentation	true	false	false	tf
https://paperswithcode.com/paper/hartley-spectral-pooling-for-deep-learning	Hartley Spectral Pooling for Deep Learning	1810.04028	https://arxiv.org/abs/1810.04028v2	https://arxiv.org/pdf/1810.04028v2.pdf	https://github.com/AlbertZhangHIT/Hartley-spectral-pooling	true	false	false	pytorch
https://paperswithcode.com/paper/beyond-automated-evaluation-metrics	Improving the TENOR of Labeling: Re-evaluating Topic Models for Content Analysis	2401.16348	https://arxiv.org/abs/2401.16348v2	https://arxiv.org/pdf/2401.16348v2.pdf	https://github.com/zli12321/tenor	true	true	true	none
https://paperswithcode.com/paper/dynamic-and-scalable-data-preparation-for	Dynamic and Scalable Data Preparation for Object-Centric Process Mining	2410.00596	https://arxiv.org/abs/2410.00596v1	https://arxiv.org/pdf/2410.00596v1.pdf	https://github.com/LienBosmans/stack-t	false	false	true	none
https://paperswithcode.com/paper/rethinking-image-mixture-for-unsupervised	Un-Mix: Rethinking Image Mixtures for Unsupervised Visual Representation Learning	2003.05438	https://arxiv.org/abs/2003.05438v5	https://arxiv.org/pdf/2003.05438v5.pdf	https://github.com/hannaiiyanggit/unicon	false	false	true	pytorch
https://paperswithcode.com/paper/a-simple-framework-for-contrastive-learning	A Simple Framework for Contrastive Learning of Visual Representations	2002.05709	https://arxiv.org/abs/2002.05709v3	https://arxiv.org/pdf/2002.05709v3.pdf	https://github.com/hannaiiyanggit/unicon	false	false	true	pytorch
https://paperswithcode.com/paper/supervised-contrastive-learning	Supervised Contrastive Learning	2004.11362	https://arxiv.org/abs/2004.11362v5	https://arxiv.org/pdf/2004.11362v5.pdf	https://github.com/hannaiiyanggit/unicon	false	false	true	pytorch
https://paperswithcode.com/paper/active-vision-reinforcement-learning-under	Active Vision Reinforcement Learning under Limited Visual Observability	2306.00975	https://arxiv.org/abs/2306.00975v2	https://arxiv.org/pdf/2306.00975v2.pdf	https://github.com/elicassion/active-gym	true	true	false	pytorch
https://paperswithcode.com/paper/improving-robustness-against-common	Improving robustness against common corruptions by covariate shift adaptation	2006.16971	https://arxiv.org/abs/2006.16971v2	https://arxiv.org/pdf/2006.16971v2.pdf	https://github.com/Claydon-Wang/OFTTA	false	false	true	pytorch
https://paperswithcode.com/paper/do-we-really-need-to-access-the-source-data	Do We Really Need to Access the Source Data? Source Hypothesis Transfer for Unsupervised Domain Adaptation	2002.08546	https://arxiv.org/abs/2002.08546v6	https://arxiv.org/pdf/2002.08546v6.pdf	https://github.com/Claydon-Wang/OFTTA	false	false	true	pytorch
https://paperswithcode.com/paper/torchaudio-2-1-advancing-speech-recognition	TorchAudio 2.1: Advancing speech recognition, self-supervised learning, and audio processing components for PyTorch	2310.17864	https://arxiv.org/abs/2310.17864v1	https://arxiv.org/pdf/2310.17864v1.pdf	https://github.com/pytorch/audio	true	true	false	pytorch
https://paperswithcode.com/paper/2407-21024	An Autonomous GIS Agent Framework for Geospatial Data Retrieval	2407.21024	https://arxiv.org/abs/2407.21024v2	https://arxiv.org/pdf/2407.21024v2.pdf	https://github.com/teakinboyewa/autonomousgis_geodataretrieveragent	true	true	false	none
https://paperswithcode.com/paper/algebraic-proofs-of-path-disconnectedness	Algebraic Proofs of Path Disconnectedness using Time-Dependent Barrier Functions	2404.06985	https://arxiv.org/abs/2404.06985v1	https://arxiv.org/pdf/2404.06985v1.pdf	https://github.com/jarmill/set_connected	true	true	false	none
https://paperswithcode.com/paper/galapy-the-highly-optimised-c-python-spectral	GalaPy, the highly optimised C++/Python spectral modelling tool for galaxies - I - Library presentation and photometric fitting	2402.12427	https://arxiv.org/abs/2402.12427v1	https://arxiv.org/pdf/2402.12427v1.pdf	https://github.com/tommasoronconi/galapy	true	true	true	none
https://paperswithcode.com/paper/facial-expression-and-attributes-recognition-1	Facial expression and attributes recognition based on multi-task learning of lightweight neural networks	2103.17107	https://arxiv.org/abs/2103.17107v3	https://arxiv.org/pdf/2103.17107v3.pdf	https://github.com/HSE-asavchenko/face-emotion-recognition	true	true	true	tf
https://paperswithcode.com/paper/provably-convergent-stochastic-fixed-point	Provably convergent stochastic fixed-point algorithm for free-support Wasserstein barycenter of continuous non-parametric measures	2505.24384	https://arxiv.org/abs/2505.24384v1	https://arxiv.org/pdf/2505.24384v1.pdf	https://github.com/chenzeyi1101/wb_algo	true	true	false	jax
https://paperswithcode.com/paper/hse-nn-team-at-the-4th-abaw-competition-multi	HSE-NN Team at the 4th ABAW Competition: Multi-task Emotion Recognition and Learning from Synthetic Images	2207.09508	https://arxiv.org/abs/2207.09508v3	https://arxiv.org/pdf/2207.09508v3.pdf	https://github.com/HSE-asavchenko/face-emotion-recognition	true	true	true	tf
https://paperswithcode.com/paper/colrio-lidar-ranging-inertial-centralized	CoLRIO: LiDAR-Ranging-Inertial Centralized State Estimation for Robotic Swarms	2402.11790	https://arxiv.org/abs/2402.11790v2	https://arxiv.org/pdf/2402.11790v2.pdf	https://github.com/pengyu-team/co-lrio	true	true	false	none
https://paperswithcode.com/paper/improving-reliable-navigation-under	Improving Reliable Navigation under Uncertainty via Predictions Informed by Non-Local Information	2307.14501	https://arxiv.org/abs/2307.14501v1	https://arxiv.org/pdf/2307.14501v1.pdf	https://github.com/RAIL-group/RAIL-group-software/tree/main/modules/lsp_gnn	true	false	false	none
https://paperswithcode.com/paper/augmenting-automation-intent-based-user	Augmenting Automation: Intent-Based User Instruction Classification with Machine Learning	2403.01242	https://arxiv.org/abs/2403.01242v1	https://arxiv.org/pdf/2403.01242v1.pdf	https://github.com/lbasyal/Intent_classification	true	false	false	none
https://paperswithcode.com/paper/recipegpt-generative-pre-training-based	RecipeGPT: Generative Pre-training Based Cooking Recipe Generation and Evaluation System	2003.02498	https://arxiv.org/abs/2003.02498v1	https://arxiv.org/pdf/2003.02498v1.pdf	https://github.com/LARC-CMU-SMU/RecipeGPT-exp	true	true	true	tf
https://paperswithcode.com/paper/finite-element-hybridization-of-port	Finite element hybridization of port-Hamiltonian systems	2302.06239	https://arxiv.org/abs/2302.06239v4	https://arxiv.org/pdf/2302.06239v4.pdf	https://github.com/a-brugnoli/ph_hybridization	true	true	true	none
https://paperswithcode.com/paper/robustness-of-graph-embedding-methods-for	Robustness of graph embedding methods for community detection	2405.00636	https://arxiv.org/abs/2405.00636v2	https://arxiv.org/pdf/2405.00636v2.pdf	https://github.com/zf-wei/Robustness-of-Graph-Embeddings-for-Community-Detection	true	false	true	none
https://paperswithcode.com/paper/deelema-missing-information-search-with-deep	DeeLeMa: Missing information search with Deep Learning for Mass estimation	2212.12836	https://arxiv.org/abs/2212.12836v3	https://arxiv.org/pdf/2212.12836v3.pdf	https://github.com/Yonsei-HEP-COSMO/DeeLeMa	true	true	true	pytorch
https://paperswithcode.com/paper/corrective-machine-unlearning	Corrective Machine Unlearning	2402.14015	https://arxiv.org/abs/2402.14015v2	https://arxiv.org/pdf/2402.14015v2.pdf	https://github.com/drimpossible/corrective-unlearning-bench	true	true	true	pytorch
https://paperswithcode.com/paper/a-goal-driven-approach-to-systems	A Goal-Driven Approach to Systems Neuroscience	2311.02704	https://arxiv.org/abs/2311.02704v1	https://arxiv.org/pdf/2311.02704v1.pdf	https://github.com/HZhongLab/melander_nayebi_2021	false	false	false	none
https://paperswithcode.com/paper/3d-vessel-reconstruction-from-sparse-view	3D Vessel Reconstruction from Sparse-View Dynamic DSA Images via Vessel Probability Guided Attenuation Learning	2405.10705	https://arxiv.org/abs/2405.10705v1	https://arxiv.org/pdf/2405.10705v1.pdf	https://github.com/Zhentao-Liu/VPAL	true	false	true	none
https://paperswithcode.com/paper/cmdag-a-chinese-metaphor-dataset-with	CMDAG: A Chinese Metaphor Dataset with Annotated Grounds as CoT for Boosting Metaphor Generation	2402.13145	https://arxiv.org/abs/2402.13145v2	https://arxiv.org/pdf/2402.13145v2.pdf	https://github.com/xingweiqu/NLPCC-2024-Shared-Task-9	false	false	true	none
https://paperswithcode.com/paper/the-media-bias-taxonomy-a-systematic	The Media Bias Taxonomy: A Systematic Literature Review on the Forms and Automated Detection of Media Bias	2312.16148	https://arxiv.org/abs/2312.16148v3	https://arxiv.org/pdf/2312.16148v3.pdf	https://github.com/media-bias-group/media-bias-taxonomy	true	true	false	none
https://paperswithcode.com/paper/a-novel-and-accurate-bilstm-configuration	A Novel and Accurate BiLSTM Configuration Controller for Modular Soft Robots with Module Number Adaptability	2401.10997	https://arxiv.org/abs/2401.10997v1	https://arxiv.org/pdf/2401.10997v1.pdf	https://github.com/zixichen007115/23zcd	true	true	false	none
https://paperswithcode.com/paper/unsupervised-video-summarization	Unsupervised Video Summarization via Iterative Training and Simplified GAN	2311.03745	https://arxiv.org/abs/2311.03745v2	https://arxiv.org/pdf/2311.03745v2.pdf	https://github.com/hanklee97121/SUM-SR-5iter	true	false	false	pytorch
https://paperswithcode.com/paper/an-explicit-version-of-chen-s-theorem	An explicit version of Chen's theorem and the linear sieve	2207.09452	https://arxiv.org/abs/2207.09452v6	https://arxiv.org/pdf/2207.09452v6.pdf	https://github.com/Valeriia57/Chen-s-theorem	true	false	false	none
https://paperswithcode.com/paper/a-pattern-to-align-them-all-integrating	A Pattern to Align Them All: Integrating Different Modalities to Define Multi-Modal Entities	2410.13803	https://arxiv.org/abs/2410.13803v1	https://arxiv.org/pdf/2410.13803v1.pdf	https://github.com/ida-fbk/multimodalpattern	true	true	true	none
https://paperswithcode.com/paper/tackling-the-abstraction-and-reasoning-corpus-1	Tackling the Abstraction and Reasoning Corpus with Vision Transformers: the Importance of 2D Representation, Positions, and Objects	2410.06405	https://arxiv.org/abs/2410.06405v1	https://arxiv.org/pdf/2410.06405v1.pdf	https://github.com/khalil-research/ViTARC	true	false	true	pytorch
https://paperswithcode.com/paper/revisiting-rcnn-on-awakening-the	Revisiting RCNN: On Awakening the Classification Power of Faster RCNN	1803.06799	http://arxiv.org/abs/1803.06799v3	http://arxiv.org/pdf/1803.06799v3.pdf	https://github.com/MindSpore-paper-code-3/code10/tree/main/faster_rcnn_ssod	false	false	false	mindspore
https://paperswithcode.com/paper/evolution-strategies-as-a-scalable	Evolution Strategies as a Scalable Alternative to Reinforcement Learning	1703.03864	http://arxiv.org/abs/1703.03864v2	http://arxiv.org/pdf/1703.03864v2.pdf	https://github.com/cesch97/NeuroEvolution	false	false	true	none
https://paperswithcode.com/paper/improving-continuous-time-conflict-based	Improving Continuous-time Conflict Based Search	2101.09723	https://arxiv.org/abs/2101.09723v2	https://arxiv.org/pdf/2101.09723v2.pdf	https://github.com/thaynewalker/ccbs	false	false	true	none
https://paperswithcode.com/paper/varformer-adapting-var-s-generative-prior-for	Varformer: Adapting VAR's Generative Prior for Image Restoration	2412.21063	https://arxiv.org/abs/2412.21063v1	https://arxiv.org/pdf/2412.21063v1.pdf	https://github.com/siywang541/Varformer	true	false	true	pytorch
https://paperswithcode.com/paper/improved-yolov5-network-for-real-time-multi	Improved YOLOv5 network for real-time multi-scale traffic sign detection	2112.08782	https://arxiv.org/abs/2112.08782v2	https://arxiv.org/pdf/2112.08782v2.pdf	https://github.com/NWPU-Li/AF_FPN	false	false	true	pytorch
https://paperswithcode.com/paper/160600942	Approximating the Spectral Sums of Large-scale Matrices using Chebyshev Approximations	1606.00942	http://arxiv.org/abs/1606.00942v2	http://arxiv.org/pdf/1606.00942v2.pdf	https://github.com/EiffL/SpectralFlow	false	false	true	tf
https://paperswithcode.com/paper/backpropagation-for-implicit-spectral	Backpropagation for Implicit Spectral Densities	1806.00499	http://arxiv.org/abs/1806.00499v1	http://arxiv.org/pdf/1806.00499v1.pdf	https://github.com/EiffL/SpectralFlow	false	false	true	tf
https://paperswithcode.com/paper/mixed-integer-optimal-control-via	Mixed-Integer Optimal Control via Reinforcement Learning: A Case Study on Hybrid Electric Vehicle Energy Management	2305.01461	https://arxiv.org/abs/2305.01461v3	https://arxiv.org/pdf/2305.01461v3.pdf	https://github.com/xujinming01/td3aq	true	true	true	pytorch
https://paperswithcode.com/paper/unsupervised-learning-of-phylogenetic-trees	Unsupervised Learning of Phylogenetic Trees via Split-Weight Embedding	2312.16074	https://arxiv.org/abs/2312.16074v2	https://arxiv.org/pdf/2312.16074v2.pdf	https://github.com/solislemuslab/phyloclustering.jl	true	true	true	none
https://paperswithcode.com/paper/multi-eup-the-multilingual-european	Multi-EuP: The Multilingual European Parliament Dataset for Analysis of Bias in Information Retrieval	2311.01870	https://arxiv.org/abs/2311.01870v1	https://arxiv.org/pdf/2311.01870v1.pdf	https://github.com/jrnlp/multi-eup	true	true	false	none
https://paperswithcode.com/paper/diversified-outlier-exposure-for-out-of	Diversified Outlier Exposure for Out-of-Distribution Detection via Informative Extrapolation	null	https://openreview.net/forum?id=RuxBLfiEqI	https://openreview.net/pdf?id=RuxBLfiEqI	https://github.com/zfancy/divoe	true	true	false	pytorch
https://paperswithcode.com/paper/temporal-3d-shape-modeling-for-video-based	Temporal 3D Shape Modeling for Video-Based Cloth-Changing Person Re-Identification	null	https://openaccess.thecvf.com/content/WACV2024W/RWS/html/Nguyen_Temporal_3D_Shape_Modeling_for_Video-Based_Cloth-Changing_Person_Re-Identification_WACVW_2024_paper.html	https://openaccess.thecvf.com/content/WACV2024W/RWS/papers/Nguyen_Temporal_3D_Shape_Modeling_for_Video-Based_Cloth-Changing_Person_Re-Identification_WACVW_2024_paper.pdf	https://github.com/dustin-nguyen-qil/Videobased-ClothChanging-ReID-Baseline	false	false	false	pytorch
https://paperswithcode.com/paper/band-selection-with-higher-order-multivariate	Band selection with Higher Order Multivariate Cumulants for small target detection in hyperspectral images	1808.03513	http://arxiv.org/abs/1808.03513v1	http://arxiv.org/pdf/1808.03513v1.pdf	https://github.com/ZKSI/CumulantsFeatures.jl	false	false	true	none
https://paperswithcode.com/paper/the-use-of-the-higher-order-singular-value	The use of the Higher Order Singular Value Decomposition of the 4-cumulant's tensors in features selection and outlier detection	1804.00541	http://arxiv.org/abs/1804.00541v3	http://arxiv.org/pdf/1804.00541v3.pdf	https://github.com/ZKSI/CumulantsFeatures.jl	true	true	true	none
https://paperswithcode.com/paper/3d-shape-temporal-aggregation-for-video-based	3D Shape Temporal Aggregation for Video-Based Clothing-Change Person Re-Identication	null	https://link.springer.com/chapter/10.1007/978-3-031-26348-4_5	https://openaccess.thecvf.com/content/ACCV2022/papers/Han_3D_Shape_Temporal_Aggregation_for_Video-Based_Clothing-Change_Person_Re-identification_ACCV_2022_paper.pdf	https://github.com/dustin-nguyen-qil/Videobased-ClothChanging-ReID-Baseline	false	false	false	pytorch
https://paperswithcode.com/paper/reference-based-restoration-of-digitized	Reference-based Restoration of Digitized Analog Videotapes	2310.14926	https://arxiv.org/abs/2310.14926v2	https://arxiv.org/pdf/2310.14926v2.pdf	https://github.com/miccunifi/analog-video-restoration	false	false	true	pytorch
https://paperswithcode.com/paper/be-careful-when-evaluating-explanations	Be Careful When Evaluating Explanations Regarding Ground Truth	2311.04813	https://arxiv.org/abs/2311.04813v1	https://arxiv.org/pdf/2311.04813v1.pdf	https://github.com/mi2datalab/be-careful-evaluating-explanations	true	true	false	pytorch
https://paperswithcode.com/paper/uni-moe-scaling-unified-multimodal-llms-with	Uni-MoE: Scaling Unified Multimodal LLMs with Mixture of Experts	2405.11273	https://arxiv.org/abs/2405.11273v1	https://arxiv.org/pdf/2405.11273v1.pdf	https://github.com/hitsz-tmg/umoe-scaling-unified-multimodal-llms	true	true	true	pytorch
https://paperswithcode.com/paper/multi-order-graph-clustering-with-adaptive	Multi-order Graph Clustering with Adaptive Node-level Weight Learning	2405.12183	https://arxiv.org/abs/2405.12183v1	https://arxiv.org/pdf/2405.12183v1.pdf	https://github.com/scutft-ml/mogc	true	true	true	none
https://paperswithcode.com/paper/a-comparative-study-of-deep-reinforcement-1	A Comparative Study of Deep Reinforcement Learning Models: DQN vs PPO vs A2C	2407.14151	https://arxiv.org/abs/2407.14151v1	https://arxiv.org/pdf/2407.14151v1.pdf	https://github.com/neilus03/drl_comparative_study	true	true	false	pytorch
https://paperswithcode.com/paper/iclr-2021-challenge-for-computational	ICLR 2021 Challenge for Computational Geometry & Topology: Design and Results	2108.09810	https://arxiv.org/abs/2108.09810v2	https://arxiv.org/pdf/2108.09810v2.pdf	https://github.com/oxcsml/geomstats	false	false	true	jax
https://paperswithcode.com/paper/an-iterative-conditional-dispatch-algorithm	An iterative sample scenario approach for the dynamic dispatch waves problem	2308.14476	https://arxiv.org/abs/2308.14476v3	https://arxiv.org/pdf/2308.14476v3.pdf	https://github.com/leonlan/dynamic-dispatch-waves	true	true	true	none
https://paperswithcode.com/paper/grounding-dino-marrying-dino-with-grounded	Grounding DINO: Marrying DINO with Grounded Pre-Training for Open-Set Object Detection	2303.05499	https://arxiv.org/abs/2303.05499v5	https://arxiv.org/pdf/2303.05499v5.pdf	https://github.com/camenduru/grounded-segment-anything-colab	false	false	true	none
https://paperswithcode.com/paper/ttt-a-temporal-refinement-heuristic-for	TTT: A Temporal Refinement Heuristic for Tenuously Tractable Discrete Time Reachability Problems	2407.14394	https://arxiv.org/abs/2407.14394v2	https://arxiv.org/pdf/2407.14394v2.pdf	https://github.com/sisl/OVERTVerify.jl	true	false	false	none
https://paperswithcode.com/paper/deep-learning-for-portfolio-optimisation	Deep Learning for Portfolio Optimization	2005.13665	https://arxiv.org/abs/2005.13665v3	https://arxiv.org/pdf/2005.13665v3.pdf	https://github.com/thinklab-sjtu/linsatnet	false	false	true	pytorch
https://paperswithcode.com/paper/jkonet-proximal-optimal-transport-modeling-of	Proximal Optimal Transport Modeling of Population Dynamics	2106.06345	https://arxiv.org/abs/2106.06345v4	https://arxiv.org/pdf/2106.06345v4.pdf	https://github.com/gjhuizing/wsingular	false	false	true	pytorch
https://paperswithcode.com/paper/joint-or-disjoint-mixing-training-regimes-for	Joint or Disjoint: Mixing Training Regimes for Early-Exit Models	2407.14320	https://arxiv.org/abs/2407.14320v1	https://arxiv.org/pdf/2407.14320v1.pdf	https://github.com/kamadforge/early-exit-benchmark	true	false	false	pytorch
https://paperswithcode.com/paper/renderme-360-a-large-digital-asset-library-1	RenderMe-360: A Large Digital Asset Library and Benchmarks Towards High-fidelity Head Avatars	2305.13353	https://arxiv.org/abs/2305.13353v1	https://arxiv.org/pdf/2305.13353v1.pdf	https://github.com/renderme-360/renderme-360	true	true	true	pytorch
https://paperswithcode.com/paper/moleculargpt-open-large-language-model-llm	MolecularGPT: Open Large Language Model (LLM) for Few-Shot Molecular Property Prediction	2406.12950	https://arxiv.org/abs/2406.12950v2	https://arxiv.org/pdf/2406.12950v2.pdf	https://github.com/nyushcs/moleculargpt	true	true	true	pytorch
https://paperswithcode.com/paper/wasserstein-k-centres-clustering-for	Wasserstein $k$-Centers Clustering for Distributional Data	2407.08228	https://arxiv.org/abs/2407.08228v4	https://arxiv.org/pdf/2407.08228v4.pdf	https://github.com/RyoOkano21/kCentresDIstributionalClustering	true	false	false	none
https://paperswithcode.com/paper/can-llms-speak-for-diverse-people-tuning-llms	Can LLMs Speak For Diverse People? Tuning LLMs via Debate to Generate Controllable Controversial Statements	2402.10614	https://arxiv.org/abs/2402.10614v2	https://arxiv.org/pdf/2402.10614v2.pdf	https://github.com/tianyi-lab/debatune	true	true	true	none
https://paperswithcode.com/paper/diffusion-self-guidance-for-controllable-1	Diffusion Self-Guidance for Controllable Image Generation	2306.00986	https://arxiv.org/abs/2306.00986v3	https://arxiv.org/pdf/2306.00986v3.pdf	https://github.com/Sainzerjj/Free-Guidance-Diffusion	false	false	true	pytorch
https://paperswithcode.com/paper/pareto-actor-critic-for-equilibrium-selection	Pareto Actor-Critic for Equilibrium Selection in Multi-Agent Reinforcement Learning	2209.14344	https://arxiv.org/abs/2209.14344v3	https://arxiv.org/pdf/2209.14344v3.pdf	https://github.com/uoe-agents/epymarl	true	true	true	pytorch
https://paperswithcode.com/paper/progressive-distillation-for-fast-sampling-of-1	Progressive Distillation for Fast Sampling of Diffusion Models	2202.00512	https://arxiv.org/abs/2202.00512v2	https://arxiv.org/pdf/2202.00512v2.pdf	https://github.com/deepxuan/dn-dp	false	false	true	pytorch
https://paperswithcode.com/paper/improving-diffusion-model-efficiency-through	Improving Diffusion Model Efficiency Through Patching	2207.04316	https://arxiv.org/abs/2207.04316v1	https://arxiv.org/pdf/2207.04316v1.pdf	https://github.com/deepxuan/dn-dp	false	false	true	pytorch
https://paperswithcode.com/paper/differentiable-all-pole-filters-for-time	Differentiable All-pole Filters for Time-varying Audio Systems	2404.07970	https://arxiv.org/abs/2404.07970v4	https://arxiv.org/pdf/2404.07970v4.pdf	https://github.com/DiffAPF/web	true	false	false	none
https://paperswithcode.com/paper/removing-biases-from-molecular	Removing Biases from Molecular Representations via Information Maximization	2312.00718	https://arxiv.org/abs/2312.00718v1	https://arxiv.org/pdf/2312.00718v1.pdf	https://github.com/uhlerlab/infocore	true	true	true	pytorch
https://paperswithcode.com/paper/famo-fast-adaptive-multitask-optimization-1	FAMO: Fast Adaptive Multitask Optimization	2306.03792	https://arxiv.org/abs/2306.03792v3	https://arxiv.org/pdf/2306.03792v3.pdf	https://github.com/cranial-xix/famo	true	true	true	pytorch
https://paperswithcode.com/paper/improving-the-astrometric-solution-of-the	Improving the astrometric solution of the Hyper Suprime-Cam with anisotropic Gaussian processes	2103.09881	https://arxiv.org/abs/2103.09881v1	https://arxiv.org/pdf/2103.09881v1.pdf	https://github.com/PFLeget/treegp	true	true	true	none
https://paperswithcode.com/paper/averitec-a-dataset-for-real-world-claim-1	AVeriTeC: A Dataset for Real-world Claim Verification with Evidence from the Web	2305.13117	https://arxiv.org/abs/2305.13117v3	https://arxiv.org/pdf/2305.13117v3.pdf	https://github.com/ssu-humane/hero	false	false	false	pytorch
https://paperswithcode.com/paper/the-positioning-of-stress-fibers-in	The positioning of stress fibers in contractile cells minimizes internal mechanical stress	2407.07797	https://arxiv.org/abs/2407.07797v2	https://arxiv.org/pdf/2407.07797v2.pdf	https://github.com/usschwarz/dune-structures	true	false	false	none
https://paperswithcode.com/paper/caseformer-pre-training-for-legal-case	Caseformer: Pre-training for Legal Case Retrieval Based on Inter-Case Distinctions	2311.00333	https://arxiv.org/abs/2311.00333v2	https://arxiv.org/pdf/2311.00333v2.pdf	https://github.com/oneal2000/caseformer	true	true	false	pytorch
https://paperswithcode.com/paper/automated-detection-of-label-errors-in	Automated Detection of Label Errors in Semantic Segmentation Datasets via Deep Learning and Uncertainty Quantification	2207.06104	https://arxiv.org/abs/2207.06104v2	https://arxiv.org/pdf/2207.06104v2.pdf	https://github.com/mrcoee/automatic-label-error-detection	true	true	true	none
https://paperswithcode.com/paper/occamy-a-preemptive-buffer-management-for-on	Occamy: A Preemptive Buffer Management for On-chip Shared-memory Switches	2501.13570	https://arxiv.org/abs/2501.13570v1	https://arxiv.org/pdf/2501.13570v1.pdf	https://github.com/ants-xjtu/occamy	true	true	false	none
https://paperswithcode.com/paper/does-it-chug-towards-a-data-driven	Does it Chug? Towards a Data-Driven Understanding of Guitar Tone Description	2412.11769	https://arxiv.org/abs/2412.11769v1	https://arxiv.org/pdf/2412.11769v1.pdf	https://github.com/pratikstar/doesitchug	true	true	false	none
https://paperswithcode.com/paper/global-transformer-overheating-from	Global transformer overheating from geomagnetic storms	2403.18070	https://arxiv.org/abs/2403.18070v2	https://arxiv.org/pdf/2403.18070v2.pdf	https://github.com/allfed/geomagneticmodel	true	true	false	tf
https://paperswithcode.com/paper/llara-aligning-large-language-models-with	LLaRA: Large Language-Recommendation Assistant	2312.02445	https://arxiv.org/abs/2312.02445v4	https://arxiv.org/pdf/2312.02445v4.pdf	https://github.com/ljy0ustc/llara	true	true	false	pytorch
https://paperswithcode.com/paper/embedding-neighborhoods-simultaneously-t-sne	ENS-t-SNE: Embedding Neighborhoods Simultaneously t-SNE	2205.11720	https://arxiv.org/abs/2205.11720v3	https://arxiv.org/pdf/2205.11720v3.pdf	https://github.com/enggiqbal/mpse-tsne	true	true	false	none
https://paperswithcode.com/paper/muse-text-to-image-generation-via-masked	Muse: Text-To-Image Generation via Masked Generative Transformers	2301.00704	https://arxiv.org/abs/2301.00704v1	https://arxiv.org/pdf/2301.00704v1.pdf	https://github.com/baaivision/muse-pytorch	false	false	true	pytorch
https://paperswithcode.com/paper/leveraging-vision-centric-multi-modal-1	Leveraging Vision-Centric Multi-Modal Expertise for 3D Object Detection	2310.15670	https://arxiv.org/abs/2310.15670v1	https://arxiv.org/pdf/2310.15670v1.pdf	https://github.com/opendrivelab/birds-eye-view-perception	true	true	true	pytorch
https://paperswithcode.com/paper/shape-iou-more-accurate-metric-considering	Shape-IoU: More Accurate Metric considering Bounding Box Shape and Scale	2312.17663	https://arxiv.org/abs/2312.17663v2	https://arxiv.org/pdf/2312.17663v2.pdf	https://github.com/malagoutou/shape-iou	true	true	true	none
https://paperswithcode.com/paper/svft-parameter-efficient-fine-tuning-with	SVFT: Parameter-Efficient Fine-Tuning with Singular Vectors	2405.19597	https://arxiv.org/abs/2405.19597v1	https://arxiv.org/pdf/2405.19597v1.pdf	https://github.com/vijaylingam95/svft	true	true	false	pytorch
https://paperswithcode.com/paper/attention-beats-linear-for-fast-implicit	Attention Beats Linear for Fast Implicit Neural Representation Generation	2407.15355	https://arxiv.org/abs/2407.15355v1	https://arxiv.org/pdf/2407.15355v1.pdf	https://github.com/roninton/anr	true	true	true	pytorch
https://paperswithcode.com/paper/growing-urban-bicycle-networks	Growing Urban Bicycle Networks	2107.02185	https://arxiv.org/abs/2107.02185v3	https://arxiv.org/pdf/2107.02185v3.pdf	https://github.com/mszell/bikenwgrowth	true	true	true	none
https://paperswithcode.com/paper/distance-guided-generative-adversarial	Distance Guided Generative Adversarial Network for Explainable Binary Classifications	2312.17538	https://arxiv.org/abs/2312.17538v1	https://arxiv.org/pdf/2312.17538v1.pdf	https://github.com/yxiangxiong/disgan	true	true	false	pytorch
https://paperswithcode.com/paper/towards-efficient-and-effective-deep	Towards Efficient and Effective Deep Clustering with Dynamic Grouping and Prototype Aggregation	2401.13581	https://arxiv.org/abs/2401.13581v2	https://arxiv.org/pdf/2401.13581v2.pdf	https://github.com/regan-zhang/digpro	true	true	true	pytorch
https://paperswithcode.com/paper/mint-evaluating-llms-in-multi-turn	MINT: Evaluating LLMs in Multi-turn Interaction with Tools and Language Feedback	2309.10691	https://arxiv.org/abs/2309.10691v3	https://arxiv.org/pdf/2309.10691v3.pdf	https://github.com/xingyaoww/mint-bench	false	false	true	none
https://paperswithcode.com/paper/iroyinspeech-a-multi-purpose-yoruba-speech	ÌròyìnSpeech: A multi-purpose Yorùbá Speech Corpus	2307.16071	https://arxiv.org/abs/2307.16071v2	https://arxiv.org/pdf/2307.16071v2.pdf	https://github.com/niger-volta-lti/yoruba-voice	true	true	true	none
https://paperswithcode.com/paper/gifsplanation-via-latent-shift-a-simple	Gifsplanation via Latent Shift: A Simple Autoencoder Approach to Counterfactual Generation for Chest X-rays	2102.09475	https://arxiv.org/abs/2102.09475v2	https://arxiv.org/pdf/2102.09475v2.pdf	https://github.com/ieee8023/latentshift	false	false	true	pytorch
https://paperswithcode.com/paper/codex-a-cluster-based-method-for-explainable	CODEX: A Cluster-Based Method for Explainable Reinforcement Learning	2312.04216	https://arxiv.org/abs/2312.04216v1	https://arxiv.org/pdf/2312.04216v1.pdf	https://github.com/ainfosec/codex	true	true	false	none
https://paperswithcode.com/paper/domain-private-transformers	Domain Private Transformers for Multi-Domain Dialog Systems	2305.14208	https://arxiv.org/abs/2305.14208v2	https://arxiv.org/pdf/2305.14208v2.pdf	https://github.com/asappresearch/domain-private-transformers	true	true	true	pytorch
https://paperswithcode.com/paper/weakly-supervised-video-individual	Weakly Supervised Video Individual CountingWeakly Supervised Video Individual Counting	2312.05923	https://arxiv.org/abs/2312.05923v1	https://arxiv.org/pdf/2312.05923v1.pdf	https://github.com/streamer-ap/cgnet	true	true	false	pytorch

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.