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/benchmarking-chinese-text-recognition	Benchmarking Chinese Text Recognition: Datasets, Baselines, and an Empirical Study	2112.15093	https://arxiv.org/abs/2112.15093v2	https://arxiv.org/pdf/2112.15093v2.pdf	https://github.com/fudanvi/benchmarking-chinese-text-recognition	true	true	true	pytorch
https://paperswithcode.com/paper/fbnetgen-task-aware-gnn-based-fmri-analysis	FBNETGEN: Task-aware GNN-based fMRI Analysis via Functional Brain Network Generation	2205.12465	https://arxiv.org/abs/2205.12465v2	https://arxiv.org/pdf/2205.12465v2.pdf	https://github.com/wayfear/fbnetgen	true	true	false	pytorch
https://paperswithcode.com/paper/affect2mm-affective-analysis-of-multimedia	Affect2MM: Affective Analysis of Multimedia Content Using Emotion Causality	2103.06541	https://arxiv.org/abs/2103.06541v1	https://arxiv.org/pdf/2103.06541v1.pdf	https://github.com/mikecheninoulu/Emotional-gesture-papers	false	false	true	none
https://paperswithcode.com/paper/survey-on-emotional-body-gesture-recognition	Survey on Emotional Body Gesture Recognition	1801.07481	http://arxiv.org/abs/1801.07481v1	http://arxiv.org/pdf/1801.07481v1.pdf	https://github.com/mikecheninoulu/Emotional-gesture-papers	false	false	true	none
https://paperswithcode.com/paper/a-prototype-oriented-framework-for	A Prototype-Oriented Framework for Unsupervised Domain Adaptation	2110.12024	https://arxiv.org/abs/2110.12024v1	https://arxiv.org/pdf/2110.12024v1.pdf	https://github.com/korawat-tanwisuth/proto_da	true	true	false	pytorch
https://paperswithcode.com/paper/visually-dehallucinative-instruction-1	Visually Dehallucinative Instruction Generation: Know What You Don't Know	2402.09717	https://arxiv.org/abs/2402.09717v1	https://arxiv.org/pdf/2402.09717v1.pdf	https://github.com/ncsoft/idk	true	true	true	none
https://paperswithcode.com/paper/sub-instruction-aware-vision-and-language	Sub-Instruction Aware Vision-and-Language Navigation	2004.02707	https://arxiv.org/abs/2004.02707v2	https://arxiv.org/pdf/2004.02707v2.pdf	https://github.com/YicongHong/Fine-Grained-R2R	true	true	true	none
https://paperswithcode.com/paper/meta-learning-via-learned-loss	Meta-Learning via Learned Loss	1906.05374	https://arxiv.org/abs/1906.05374v4	https://arxiv.org/pdf/1906.05374v4.pdf	https://github.com/facebookresearch/higher	false	false	true	pytorch
https://paperswithcode.com/paper/global-convergence-and-induced-kernels-of	Global Convergence and Generalization Bound of Gradient-Based Meta-Learning with Deep Neural Nets	2006.14606	https://arxiv.org/abs/2006.14606v2	https://arxiv.org/pdf/2006.14606v2.pdf	https://github.com/facebookresearch/higher	true	true	true	pytorch
https://paperswithcode.com/paper/meta-learning-symmetries-by	Meta-Learning Symmetries by Reparameterization	2007.02933	https://arxiv.org/abs/2007.02933v3	https://arxiv.org/pdf/2007.02933v3.pdf	https://github.com/facebookresearch/higher	false	false	true	pytorch
https://paperswithcode.com/paper/fat-deepffm-field-attentive-deep-field-aware	FAT-DeepFFM: Field Attentive Deep Field-aware Factorization Machine	1905.06336	https://arxiv.org/abs/1905.06336v1	https://arxiv.org/pdf/1905.06336v1.pdf	https://github.com/mindspore-ai/models/tree/master/research/recommend/Fat-DeepFFM	false	false	false	mindspore
https://paperswithcode.com/paper/statistically-unbiased-prediction-enables	Statistically unbiased prediction enables accurate denoising of voltage imaging data	null	https://www.biorxiv.org/content/10.1101/2022.11.17.516709v1.abstract	https://www.biorxiv.org/content/10.1101/2022.11.17.516709v1.full.pdf	https://github.com/NICALab/SUPPORT	true	false	false	pytorch
https://paperswithcode.com/paper/pinto-faithful-language-reasoning-using	PINTO: Faithful Language Reasoning Using Prompt-Generated Rationales	2211.01562	https://arxiv.org/abs/2211.01562v3	https://arxiv.org/pdf/2211.01562v3.pdf	https://github.com/wangpf3/pinto-faithful-language-reasoning	true	true	true	pytorch
https://paperswithcode.com/paper/unsupervised-selective-rationalization-with	Unsupervised Selective Rationalization with Noise Injection	2305.17534	https://arxiv.org/abs/2305.17534v1	https://arxiv.org/pdf/2305.17534v1.pdf	https://github.com/adamstorek/noise_injection	true	true	false	pytorch
https://paperswithcode.com/paper/you-only-look-once-unified-real-time-object	You Only Look Once: Unified, Real-Time Object Detection	1506.02640	http://arxiv.org/abs/1506.02640v5	http://arxiv.org/pdf/1506.02640v5.pdf	https://github.com/Kartik-Aggarwal/Real-Time-Traffic-Sign-Detection	false	false	true	pytorch
https://paperswithcode.com/paper/or-gym-a-reinforcement-learning-library-for	OR-Gym: A Reinforcement Learning Library for Operations Research Problems	2008.06319	https://arxiv.org/abs/2008.06319v2	https://arxiv.org/pdf/2008.06319v2.pdf	https://github.com/ashwin-M-D/DM-Gym	false	false	true	none
https://paperswithcode.com/paper/caching-in-networks-without-regret	LeadCache: Regret-Optimal Caching in Networks	2009.08228	https://arxiv.org/abs/2009.08228v4	https://arxiv.org/pdf/2009.08228v4.pdf	https://github.com/abhishekmitiitm/leadcache-neurips21	true	true	false	none
https://paperswithcode.com/paper/yolov4-optimal-speed-and-accuracy-of-object	YOLOv4: Optimal Speed and Accuracy of Object Detection	2004.10934	https://arxiv.org/abs/2004.10934v1	https://arxiv.org/pdf/2004.10934v1.pdf	https://github.com/david8862/keras-YOLOv3-model-set	false	false	true	tf
https://paperswithcode.com/paper/data-engineering-for-scaling-language-models	Data Engineering for Scaling Language Models to 128K Context	2402.10171	https://arxiv.org/abs/2402.10171v1	https://arxiv.org/pdf/2402.10171v1.pdf	https://github.com/franxyao/long-context-data-engineering	true	true	true	pytorch
https://paperswithcode.com/paper/implicit-sparse-regularization-the-impact-of	Implicit Sparse Regularization: The Impact of Depth and Early Stopping	2108.05574	https://arxiv.org/abs/2108.05574v2	https://arxiv.org/pdf/2108.05574v2.pdf	https://github.com/jiangyuan2li/implicit-sparse-regularization	true	true	true	pytorch
https://paperswithcode.com/paper/discourse-aware-unsupervised-summarization	Discourse-Aware Unsupervised Summarization for Long Scientific Documents	null	https://aclanthology.org/2021.eacl-main.93	https://aclanthology.org/2021.eacl-main.93.pdf	https://github.com/mirandrom/HipoRank	true	true	false	none
https://paperswithcode.com/paper/crowd-counting-on-images-with-scale-variation	Crowd Counting on Images with Scale Variation and Isolated Clusters	1909.03839	https://arxiv.org/abs/1909.03839v1	https://arxiv.org/pdf/1909.03839v1.pdf	https://github.com/HaoyueBaiZJU/SACANet-VisDrone-Crowd	false	false	false	pytorch
https://paperswithcode.com/paper/multi-agent-variational-occlusion-inference	Multi-Agent Variational Occlusion Inference Using People as Sensors	2109.02173	https://arxiv.org/abs/2109.02173v3	https://arxiv.org/pdf/2109.02173v3.pdf	https://github.com/sisl/MultiAgentVariationalOcclusionInference	true	true	true	pytorch
https://paperswithcode.com/paper/neural-discrete-representation-learning	Neural Discrete Representation Learning	1711.00937	http://arxiv.org/abs/1711.00937v2	http://arxiv.org/pdf/1711.00937v2.pdf	https://github.com/sisl/MultiAgentVariationalOcclusionInference	false	false	true	pytorch
https://paperswithcode.com/paper/adam-a-method-for-stochastic-optimization	Adam: A Method for Stochastic Optimization	1412.6980	http://arxiv.org/abs/1412.6980v9	http://arxiv.org/pdf/1412.6980v9.pdf	https://github.com/sisl/MultiAgentVariationalOcclusionInference	false	false	true	pytorch
https://paperswithcode.com/paper/leveraging-locality-in-abstractive-text	Leveraging Locality in Abstractive Text Summarization	2205.12476	https://arxiv.org/abs/2205.12476v2	https://arxiv.org/pdf/2205.12476v2.pdf	https://github.com/yixinl7/pagesum	true	true	true	pytorch
https://paperswithcode.com/paper/reflection-from-a-multi-species-material-and	Reflection from a multi-species material and its transmitted effective wavenumber	1712.05427	http://arxiv.org/abs/1712.05427v3	http://arxiv.org/pdf/1712.05427v3.pdf	https://github.com/arturgower/EffectiveWaves.jl	true	true	true	none
https://paperswithcode.com/paper/intermediate-layers-matter-in-momentum	Intermediate Layers Matter in Momentum Contrastive Self Supervised Learning	2110.14805	https://arxiv.org/abs/2110.14805v1	https://arxiv.org/pdf/2110.14805v1.pdf	https://github.com/aakashrkaku/intermdiate_layer_matter_ssl	true	true	false	pytorch
https://paperswithcode.com/paper/bag-of-tricks-and-a-strong-baseline-for-image	Bag of Tricks and A Strong baseline for Image Copy Detection	2111.08004	https://arxiv.org/abs/2111.08004v2	https://arxiv.org/pdf/2111.08004v2.pdf	https://github.com/wangwenhao0716/isc-track2-submission	true	true	false	pytorch
https://paperswithcode.com/paper/domain-decomposition-for-entropy-regularized	Domain decomposition for entropy regularized optimal transport	2001.10986	https://arxiv.org/abs/2001.10986v2	https://arxiv.org/pdf/2001.10986v2.pdf	https://github.com/ismedina/DomDecOT.jl	false	false	true	none
https://paperswithcode.com/paper/computational-performance-of-deep	Computational Performance of Deep Reinforcement Learning to find Nash Equilibria	2104.12895	https://arxiv.org/abs/2104.12895v1	https://arxiv.org/pdf/2104.12895v1.pdf	https://github.com/ckrk/bidding_learning	true	true	true	pytorch
https://paperswithcode.com/paper/multimodal-knowledge-expansion	Multimodal Knowledge Expansion	2103.14431	https://arxiv.org/abs/2103.14431v3	https://arxiv.org/pdf/2103.14431v3.pdf	https://github.com/zihuixue/mke	true	true	true	none
https://paperswithcode.com/paper/accelerating-the-super-resolution	Accelerating the Super-Resolution Convolutional Neural Network	1608.00367	http://arxiv.org/abs/1608.00367v1	http://arxiv.org/pdf/1608.00367v1.pdf	https://github.com/MohammedAlkhrashi/TMA	false	false	true	none
https://paperswithcode.com/paper/learning-soccer-juggling-skills-with-layer	Learning Soccer Juggling Skills with Layer-wise Mixture-of-Experts	null	https://dl.acm.org/doi/10.1145/3528233.3530735	https://www.cs.ubc.ca/~van/papers/2022-SIGGRAPH-juggle/soccer_juggling.pdf	https://github.com/ZhaomingXie/soccer_juggle_release	false	true	false	pytorch
https://paperswithcode.com/paper/time-series-forecasting-with-llms	Time Series Forecasting with LLMs: Understanding and Enhancing Model Capabilities	2402.10835	https://arxiv.org/abs/2402.10835v5	https://arxiv.org/pdf/2402.10835v5.pdf	https://github.com/mingyuj666/time-series-forecasting-with-llms	true	true	false	jax
https://paperswithcode.com/paper/proton-probing-schema-linking-information	Proton: Probing Schema Linking Information from Pre-trained Language Models for Text-to-SQL Parsing	2206.14017	https://arxiv.org/abs/2206.14017v2	https://arxiv.org/pdf/2206.14017v2.pdf	https://github.com/AlibabaResearch/DAMO-ConvAI/tree/main/proton	true	false	false	pytorch
https://paperswithcode.com/paper/person-transfer-gan-to-bridge-domain-gap-for	Person Transfer GAN to Bridge Domain Gap for Person Re-Identification	1711.08565	http://arxiv.org/abs/1711.08565v2	http://arxiv.org/pdf/1711.08565v2.pdf	https://github.com/ucas-vg/groupsampling	false	false	true	pytorch
https://paperswithcode.com/paper/on-monte-carlo-tree-search-for-weighted	On Monte Carlo Tree Search for Weighted Vertex Coloring	2202.01665	https://arxiv.org/abs/2202.01665v2	https://arxiv.org/pdf/2202.01665v2.pdf	https://github.com/cyril-grelier/gc_wvcp_mcts	true	true	true	none
https://paperswithcode.com/paper/a-theory-of-continuous-generative-flow	A theory of continuous generative flow networks	2301.12594	https://arxiv.org/abs/2301.12594v2	https://arxiv.org/pdf/2301.12594v2.pdf	https://github.com/saleml/continuous-gfn	true	true	false	pytorch
https://paperswithcode.com/paper/nlatool-an-application-for-enhanced-deep-text	NLATool: an Application for Enhanced Deep Text Understanding	null	https://aclanthology.org/C18-2026	https://aclanthology.org/C18-2026.pdf	https://github.com/interactionlab/nlatool	true	true	false	none
https://paperswithcode.com/paper/sub-word-information-in-pre-trained	Sub-word information in pre-trained biomedical word representations: evaluation and hyper-parameter optimization	null	https://aclanthology.org/W18-2307	https://aclanthology.org/W18-2307.pdf	https://github.com/dterg/bionlp-embed	true	true	false	none
https://paperswithcode.com/paper/deep-polarization-reconstruction-with-pdavis	Deep Polarization Reconstruction With PDAVIS Events	null	http://openaccess.thecvf.com//content/CVPR2023/html/Mei_Deep_Polarization_Reconstruction_With_PDAVIS_Events_CVPR_2023_paper.html	http://openaccess.thecvf.com//content/CVPR2023/papers/Mei_Deep_Polarization_Reconstruction_With_PDAVIS_Events_CVPR_2023_paper.pdf	https://github.com/sensorsini/e2p	true	true	false	pytorch
https://paperswithcode.com/paper/a-perturbation-based-out-of-sample-extension	A Perturbation-Based Kernel Approximation Framework	2009.02955	https://arxiv.org/abs/2009.02955v2	https://arxiv.org/pdf/2009.02955v2.pdf	https://github.com/roymitz/perturbation_out_of_sample_extension	true	true	true	none
https://paperswithcode.com/paper/unbiased-risk-estimation-in-the-normal-means	Unbiased Risk Estimation in the Normal Means Problem via Coupled Bootstrap Techniques	2111.09447	https://arxiv.org/abs/2111.09447v3	https://arxiv.org/pdf/2111.09447v3.pdf	https://github.com/nloliveira/coupled-bootstrap-risk-estimation	true	true	false	none
https://paperswithcode.com/paper/intrinsic-dimensionality-estimation-within	Intrinsic Dimensionality Estimation within Tight Localities: A Theoretical and Experimental Analysis	2209.14475	https://arxiv.org/abs/2209.14475v1	https://arxiv.org/pdf/2209.14475v1.pdf	https://github.com/radacha/tle	true	true	false	none
https://paperswithcode.com/paper/real-time-classification-geolocation-and	Real-time Classification, Geolocation and Interactive Visualization of COVID-19 Information Shared on Social Media to Better Understand Global Developments	null	https://aclanthology.org/2020.nlpcovid19-2.37	https://aclanthology.org/2020.nlpcovid19-2.37.pdf	https://github.com/mirandrom/crisistweetmap	true	true	false	pytorch
https://paperswithcode.com/paper/dialogstitch-synthetic-deeper-and-multi	DialogStitch: Synthetic Deeper and Multi-Context Task-Oriented Dialogs	null	https://aclanthology.org/2021.sigdial-1.3	https://aclanthology.org/2021.sigdial-1.3.pdf	https://github.com/facebookresearch/dialogstitch	true	true	false	none
https://paperswithcode.com/paper/effective-approaches-to-attention-based	Effective Approaches to Attention-based Neural Machine Translation	1508.04025	http://arxiv.org/abs/1508.04025v5	http://arxiv.org/pdf/1508.04025v5.pdf	https://github.com/bplank/teaching-dl4nlp	false	false	true	none
https://paperswithcode.com/paper/deep-contextualized-word-representations	Deep contextualized word representations	1802.05365	http://arxiv.org/abs/1802.05365v2	http://arxiv.org/pdf/1802.05365v2.pdf	https://github.com/bplank/teaching-dl4nlp	false	false	true	none
https://paperswithcode.com/paper/simcse-simple-contrastive-learning-of	SimCSE: Simple Contrastive Learning of Sentence Embeddings	2104.08821	https://arxiv.org/abs/2104.08821v4	https://arxiv.org/pdf/2104.08821v4.pdf	https://github.com/shuxinyin/SimCSE-Pytorch	false	false	true	pytorch
https://paperswithcode.com/paper/panoptic-segformer	Panoptic SegFormer: Delving Deeper into Panoptic Segmentation with Transformers	2109.03814	https://arxiv.org/abs/2109.03814v4	https://arxiv.org/pdf/2109.03814v4.pdf	https://github.com/zhiqi-li/Panoptic-SegFormer	true	true	true	pytorch
https://paperswithcode.com/paper/memory-efficient-meta-learning-with-large	Memory Efficient Meta-Learning with Large Images	2107.01105	https://arxiv.org/abs/2107.01105v2	https://arxiv.org/pdf/2107.01105v2.pdf	https://github.com/cambridge-mlg/LITE	true	true	true	pytorch
https://paperswithcode.com/paper/reduced-operator-inference-for-nonlinear	Reduced operator inference for nonlinear partial differential equations	2102.00083	https://arxiv.org/abs/2102.00083v2	https://arxiv.org/pdf/2102.00083v2.pdf	https://github.com/elizqian/operator-inference	true	true	true	none
https://paperswithcode.com/paper/learning-to-compose-with-professional	Learning to Compose with Professional Photographs on the Web	1702.00503	http://arxiv.org/abs/1702.00503v2	http://arxiv.org/pdf/1702.00503v2.pdf	https://github.com/yiling-chen/view-finding-network	true	true	true	tf
https://paperswithcode.com/paper/lift-learn-physics-informed-machine-learning	Lift & Learn: Physics-informed machine learning for large-scale nonlinear dynamical systems	1912.08177	https://arxiv.org/abs/1912.08177v5	https://arxiv.org/pdf/1912.08177v5.pdf	https://github.com/elizqian/operator-inference	false	false	true	none
https://paperswithcode.com/paper/ucc-uncertainty-guided-cross-head-co-training	UCC: Uncertainty guided Cross-head Co-training for Semi-Supervised Semantic Segmentation	2205.10334	https://arxiv.org/abs/2205.10334v2	https://arxiv.org/pdf/2205.10334v2.pdf	https://github.com/voldemortX/DST-CBC	true	false	false	pytorch
https://paperswithcode.com/paper/towards-gradient-based-bilevel-optimization	Towards Gradient-based Bilevel Optimization with Non-convex Followers and Beyond	2110.00455	https://arxiv.org/abs/2110.00455v2	https://arxiv.org/pdf/2110.00455v2.pdf	https://github.com/vis-opt-group/iaptt-gm	true	true	true	pytorch
https://paperswithcode.com/paper/adam-a-method-for-stochastic-optimization	Adam: A Method for Stochastic Optimization	1412.6980	http://arxiv.org/abs/1412.6980v9	http://arxiv.org/pdf/1412.6980v9.pdf	https://github.com/mirzaevinom/data_science_bowl_2018	false	false	true	tf
https://paperswithcode.com/paper/mask-r-cnn	Mask R-CNN	1703.06870	http://arxiv.org/abs/1703.06870v3	http://arxiv.org/pdf/1703.06870v3.pdf	https://github.com/mirzaevinom/data_science_bowl_2018	false	false	true	tf
https://paperswithcode.com/paper/learning-prototype-representations-across-few	Learning Prototype Representations Across Few-Shot Tasks for Event Detection	null	https://aclanthology.org/2021.emnlp-main.427	https://aclanthology.org/2021.emnlp-main.427.pdf	https://github.com/laiviet/fsl-proact	true	true	false	pytorch
https://paperswithcode.com/paper/generative-planning-for-temporally-1	Generative Planning for Temporally Coordinated Exploration in Reinforcement Learning	2201.09765	https://arxiv.org/abs/2201.09765v2	https://arxiv.org/pdf/2201.09765v2.pdf	https://github.com/Haichao-Zhang/generative-planning	true	false	false	pytorch
https://paperswithcode.com/paper/metric-learning-cross-entropy-vs-pairwise	A unifying mutual information view of metric learning: cross-entropy vs. pairwise losses	2003.08983	https://arxiv.org/abs/2003.08983v3	https://arxiv.org/pdf/2003.08983v3.pdf	https://github.com/jeromerony/dml_cross_entropy	true	true	false	pytorch
https://paperswithcode.com/paper/fully-convolutional-siamese-neural-networks	Fully convolutional Siamese neural networks for buildings damage assessment from satellite images	2111.00508	https://arxiv.org/abs/2111.00508v1	https://arxiv.org/pdf/2111.00508v1.pdf	https://github.com/bloodaxe/xview2-solution	true	true	true	pytorch
https://paperswithcode.com/paper/progressive-growing-of-gans-for-improved	Progressive Growing of GANs for Improved Quality, Stability, and Variation	1710.10196	http://arxiv.org/abs/1710.10196v3	http://arxiv.org/pdf/1710.10196v3.pdf	https://github.com/valentingol/GANJax	false	false	true	jax
https://paperswithcode.com/paper/cross-domain-cross-architecture-black-box	Cross-domain Cross-architecture Black-box Attacks on Fine-tuned Models with Transferred Evolutionary Strategies	2208.13182	https://arxiv.org/abs/2208.13182v1	https://arxiv.org/pdf/2208.13182v1.pdf	https://github.com/hkust-knowcomp/tes	true	true	false	pytorch
https://paperswithcode.com/paper/visual-reasoning-strategies-for-effect-size	Visual Reasoning Strategies for Effect Size Judgments and Decisions	2007.14516	https://arxiv.org/abs/2007.14516v3	https://arxiv.org/pdf/2007.14516v3.pdf	https://github.com/fredhohman/awesome-mathematical-notation-design	false	false	true	none
https://paperswithcode.com/paper/sciencemeter-tracking-scientific-knowledge	ScienceMeter: Tracking Scientific Knowledge Updates in Language Models	2505.24302	https://arxiv.org/abs/2505.24302v1	https://arxiv.org/pdf/2505.24302v1.pdf	https://github.com/yikee/sciencemeter	true	true	true	pytorch
https://paperswithcode.com/paper/fast-3d-registration-with-accurate	Fast 3D registration with accurate optimisation and little learning for Learn2Reg 2021	2112.03053	https://arxiv.org/abs/2112.03053v1	https://arxiv.org/pdf/2112.03053v1.pdf	https://github.com/multimodallearning/convexadam	true	true	false	pytorch
https://paperswithcode.com/paper/melgan-generative-adversarial-networks-for	MelGAN: Generative Adversarial Networks for Conditional Waveform Synthesis	1910.06711	https://arxiv.org/abs/1910.06711v3	https://arxiv.org/pdf/1910.06711v3.pdf	https://github.com/jaywalnut310/melgan-pytorch	false	false	true	pytorch
https://paperswithcode.com/paper/mcmi-multi-cycle-image-translation-with	MCMI: Multi-Cycle Image Translation with Mutual Information Constraints	2007.02919	https://arxiv.org/abs/2007.02919v1	https://arxiv.org/pdf/2007.02919v1.pdf	https://github.com/yuzhenmao/MI_P2V	false	false	true	pytorch
https://paperswithcode.com/paper/pix2vox-multi-scale-context-aware-3d-object	Pix2Vox++: Multi-scale Context-aware 3D Object Reconstruction from Single and Multiple Images	2006.12250	https://arxiv.org/abs/2006.12250v2	https://arxiv.org/pdf/2006.12250v2.pdf	https://github.com/yuzhenmao/MI_P2V	false	false	true	pytorch
https://paperswithcode.com/paper/a-unified-view-on-graph-neural-networks-as-1	A Unified View on Graph Neural Networks as Graph Signal Denoising	2010.01777	https://arxiv.org/abs/2010.01777v2	https://arxiv.org/pdf/2010.01777v2.pdf	https://github.com/alge24/ADA-UGNN	true	true	true	pytorch
https://paperswithcode.com/paper/systematic-analysis-of-programming-languages	Systematic Analysis of Programming Languages and Their Execution Environments for Spectre Attacks	2111.12528	https://arxiv.org/abs/2111.12528v1	https://arxiv.org/pdf/2111.12528v1.pdf	https://github.com/misc0110/pteditor	false	false	true	none
https://paperswithcode.com/paper/merger-rate-density-of-binary-black-holes-1	Merger rate density of binary black holes through isolated Population I, II, III and extremely metal-poor binary star evolution	2110.10846	https://arxiv.org/abs/2110.10846v4	https://arxiv.org/pdf/2110.10846v4.pdf	https://github.com/atrtnkw/bseemp	true	true	false	none
https://paperswithcode.com/paper/physics-based-model-to-predict-the-acoustic	Physics-based model to predict the acoustic detection distance of terrestrial autonomous recording units over the diel cycle and across seasons: insights from an Alpine and a Neotropical forest	2211.16077	https://arxiv.org/abs/2211.16077v1	https://arxiv.org/pdf/2211.16077v1.pdf	https://github.com/shaupert/haupert_mee_2022	true	true	false	none
https://paperswithcode.com/paper/multidimensional-representations-in-late-life	Multidimensional representations in late-life depression: convergence in neuroimaging, cognition, clinical symptomatology and genetics	2110.11347	https://arxiv.org/abs/2110.11347v2	https://arxiv.org/pdf/2110.11347v2.pdf	https://github.com/anbai106/mlni	false	false	true	pytorch
https://paperswithcode.com/paper/reproducible-evaluation-of-diffusion-mri	Reproducible evaluation of diffusion MRI features for automatic classification of patients with Alzheimers disease	1812.11183	https://arxiv.org/abs/1812.11183v4	https://arxiv.org/pdf/1812.11183v4.pdf	https://github.com/anbai106/mlni	false	false	true	pytorch
https://paperswithcode.com/paper/are-transformers-more-robust-than-cnns	Are Transformers More Robust Than CNNs?	2111.05464	https://arxiv.org/abs/2111.05464v1	https://arxiv.org/pdf/2111.05464v1.pdf	https://github.com/ytongbai/ViTs-vs-CNNs	true	true	true	pytorch
https://paperswithcode.com/paper/controlled-text-generation-as-continuous	Controlled Text Generation as Continuous Optimization with Multiple Constraints	2108.01850	https://arxiv.org/abs/2108.01850v1	https://arxiv.org/pdf/2108.01850v1.pdf	https://github.com/sachin19/mucoco	false	false	true	pytorch
https://paperswithcode.com/paper/texttt-express-extensible-high-level	$\texttt{express}$: extensible, high-level workflows for swifter $\textit{ab initio}$ materials modeling	2109.11724	https://arxiv.org/abs/2109.11724v1	https://arxiv.org/pdf/2109.11724v1.pdf	https://github.com/MineralsCloud/Express.jl	true	true	true	none
https://paperswithcode.com/paper/let-each-quantum-bit-choose-its-basis-gates	Let Each Quantum Bit Choose Its Basis Gates	2208.13380	https://arxiv.org/abs/2208.13380v2	https://arxiv.org/pdf/2208.13380v2.pdf	https://github.com/sophlin/nonstandard_2qbasis_gates	true	true	false	none
https://paperswithcode.com/paper/issafe-improving-semantic-segmentation-in	ISSAFE: Improving Semantic Segmentation in Accidents by Fusing Event-based Data	2008.08974	https://arxiv.org/abs/2008.08974v2	https://arxiv.org/pdf/2008.08974v2.pdf	https://github.com/jamycheung/ISSAFE	true	true	true	pytorch
https://paperswithcode.com/paper/exploring-event-driven-dynamic-context-for	Exploring Event-driven Dynamic Context for Accident Scene Segmentation	2112.05006	https://arxiv.org/abs/2112.05006v1	https://arxiv.org/pdf/2112.05006v1.pdf	https://github.com/jamycheung/ISSAFE	true	true	true	pytorch
https://paperswithcode.com/paper/antipodal-robotic-grasping-using-generative	Antipodal Robotic Grasping using Generative Residual Convolutional Neural Network	1909.04810	https://arxiv.org/abs/1909.04810v4	https://arxiv.org/pdf/1909.04810v4.pdf	https://github.com/SteveHao74/shahao_GR-ConvNet	false	false	true	pytorch
https://paperswithcode.com/paper/physics-guided-deep-learning-for-data	Utilising physics-guided deep learning to overcome data scarcity	2211.15664	https://arxiv.org/abs/2211.15664v3	https://arxiv.org/pdf/2211.15664v3.pdf	https://github.com/jinshuaibai/pgdl_review	true	true	true	tf
https://paperswithcode.com/paper/yake-keyword-extraction-from-single-documents	YAKE! Keyword extraction from single documents using multiple local features	null	https://repositorio.inesctec.pt/server/api/core/bitstreams/ef121a01-a0a6-4be8-945d-3324a58fc944/content	https://repositorio.inesctec.pt/server/api/core/bitstreams/ef121a01-a0a6-4be8-945d-3324a58fc944/content	https://github.com/LIAAD/yake	false	false	false	tf
https://paperswithcode.com/paper/denoising-of-3d-mr-images-using-a-voxel-wise	Denoising of 3D MR images using a voxel-wise hybrid residual MLP-CNN model to improve small lesion diagnostic confidence	2209.13818	https://arxiv.org/abs/2209.13818v1	https://arxiv.org/pdf/2209.13818v1.pdf	https://github.com/laowangbobo/residual_mlp_cnn_mixer	true	true	true	pytorch
https://paperswithcode.com/paper/detect-consolidate-delineate-scalable-mapping	Detect, consolidate, delineate: scalable mapping of field boundaries using satellite images	null	https://www.mdpi.com/2072-4292/13/11/2197	https://www.mdpi.com/2072-4292/13/11/2197/pdf	https://github.com/waldnerf/decode	false	true	false	mxnet
https://paperswithcode.com/paper/size-limits-sensitivity-in-all-kinetic	Size limits sensitivity in all kinetic schemes	2112.07777	https://arxiv.org/abs/2112.07777v1	https://arxiv.org/pdf/2112.07777v1.pdf	https://github.com/jaowen/nested-hysteresis	true	true	true	none
https://paperswithcode.com/paper/generalization-bounds-for-meta-learning-an	Generalization Bounds For Meta-Learning: An Information-Theoretic Analysis	2109.14595	https://arxiv.org/abs/2109.14595v2	https://arxiv.org/pdf/2109.14595v2.pdf	https://github.com/livreq/meta-sgld	true	true	false	pytorch
https://paperswithcode.com/paper/optab-public-code-for-generating-gas-opacity	Optab: Public code for generating gas opacity tables for radiation hydrodynamics simulations	2112.05689	https://arxiv.org/abs/2112.05689v1	https://arxiv.org/pdf/2112.05689v1.pdf	https://github.com/nombac/optab	true	true	true	none
https://paperswithcode.com/paper/automatically-learning-compact-quality-aware	Automatically Learning Compact Quality-aware Surrogates for Optimization Problems	2006.10815	https://arxiv.org/abs/2006.10815v2	https://arxiv.org/pdf/2006.10815v2.pdf	https://github.com/PredOptwithSoftConstraint/PredOptwithSoftConstraint	false	false	true	pytorch
https://paperswithcode.com/paper/an-imaging-search-for-post-main-sequence	An Imaging Search for Post-Main-Sequence Planets of Sirius B	2112.05234	https://arxiv.org/abs/2112.05234v1	https://arxiv.org/pdf/2112.05234v1.pdf	https://github.com/mileslucas/sirius-b	true	true	true	none
https://paperswithcode.com/paper/online-mixed-integer-optimization-in	Online Mixed-Integer Optimization in Milliseconds	1907.02206	https://arxiv.org/abs/1907.02206v4	https://arxiv.org/pdf/1907.02206v4.pdf	https://github.com/bstellato/mlopt	true	true	true	pytorch
https://paperswithcode.com/paper/learning-mixed-integer-convex-optimization	Learning Mixed-Integer Convex Optimization Strategies for Robot Planning and Control	2004.03736	https://arxiv.org/abs/2004.03736v2	https://arxiv.org/pdf/2004.03736v2.pdf	https://github.com/bstellato/mlopt	false	false	true	pytorch
https://paperswithcode.com/paper/a-twin-decoder-structure-for-incompressible	A twin-decoder structure for incompressible laminar flow reconstruction with uncertainty estimation around 2D obstacles	2104.03619	https://arxiv.org/abs/2104.03619v2	https://arxiv.org/pdf/2104.03619v2.pdf	https://github.com/jviquerat/twin_autoencoder	true	true	true	tf
https://paperswithcode.com/paper/a-deep-knowledge-distillation-framework-for	A Deep Knowledge Distillation framework for EEG assisted enhancement of single-lead ECG based sleep staging	2112.07252	https://arxiv.org/abs/2112.07252v2	https://arxiv.org/pdf/2112.07252v2.pdf	https://github.com/acrophase/sleep_staging_kd	true	true	false	pytorch
https://paperswithcode.com/paper/efficient-convnet-for-real-time-semantic	Efficient ConvNet for Real-time Semantic Segmentation	null	https://ieeexplore.ieee.org/document/7995966	https://ieeexplore.ieee.org/document/7995966	https://github.com/yangyucheng000/erfnet	false	false	false	mindspore
https://paperswithcode.com/paper/u-time-a-fully-convolutional-network-for-time	U-Time: A Fully Convolutional Network for Time Series Segmentation Applied to Sleep Staging	1910.11162	https://arxiv.org/abs/1910.11162v1	https://arxiv.org/pdf/1910.11162v1.pdf	https://github.com/acrophase/sleep_staging_kd	false	false	true	pytorch
https://paperswithcode.com/paper/product1m-towards-weakly-supervised-instance	Product1M: Towards Weakly Supervised Instance-Level Product Retrieval via Cross-modal Pretraining	2107.14572	https://arxiv.org/abs/2107.14572v2	https://arxiv.org/pdf/2107.14572v2.pdf	https://github.com/zhanxlin/product1m	true	true	true	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.