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/medical-data-wrangling-with-sequential	Medical data wrangling with sequential variational autoencoders	2103.07206	https://arxiv.org/abs/2103.07206v2	https://arxiv.org/pdf/2103.07206v2.pdf	https://github.com/dbarrejon/Shi-VAE	true	true	true	pytorch
https://paperswithcode.com/paper/meta-fine-tuning-neural-language-models-for	Meta Fine-Tuning Neural Language Models for Multi-Domain Text Mining	2003.13003	https://arxiv.org/abs/2003.13003v2	https://arxiv.org/pdf/2003.13003v2.pdf	https://github.com/AntheaLi/cs224nProject	false	false	true	pytorch
https://paperswithcode.com/paper/xamg-a-library-for-solving-linear-systems	XAMG: A library for solving linear systems with multiple right-hand side vectors	2103.07329	https://arxiv.org/abs/2103.07329v1	https://arxiv.org/pdf/2103.07329v1.pdf	https://gitlab.com/xamg/xamg	true	true	false	none
https://paperswithcode.com/paper/dynamic-and-application-aware-provisioning-of	Dynamic and Application-Aware Provisioning of Chained Virtual Security Network Functions	1901.01704	https://arxiv.org/abs/1901.01704v4	https://arxiv.org/pdf/1901.01704v4.pdf	https://github.com/doriguzzi/pess-security	true	false	false	none
https://paperswithcode.com/paper/dialogpt-large-scale-generative-pre-training	DialoGPT: Large-Scale Generative Pre-training for Conversational Response Generation	1911.00536	https://arxiv.org/abs/1911.00536v3	https://arxiv.org/pdf/1911.00536v3.pdf	https://github.com/lemon234071/clean-dialog	false	false	true	none
https://paperswithcode.com/paper/right-for-the-right-concept-revising-neuro	Right for the Right Concept: Revising Neuro-Symbolic Concepts by Interacting with their Explanations	2011.12854	https://arxiv.org/abs/2011.12854v6	https://arxiv.org/pdf/2011.12854v6.pdf	https://github.com/ml-research/CLEVR-Hans	true	true	true	none
https://paperswithcode.com/paper/separation-and-concentration-in-deep-networks	Separation and Concentration in Deep Networks	2012.10424	https://arxiv.org/abs/2012.10424v2	https://arxiv.org/pdf/2012.10424v2.pdf	https://github.com/j-zarka/separation_concentration_deepnets	true	true	false	pytorch
https://paperswithcode.com/paper/neural-machine-translation-of-rare-words-with	Neural Machine Translation of Rare Words with Subword Units	1508.07909	http://arxiv.org/abs/1508.07909v5	http://arxiv.org/pdf/1508.07909v5.pdf	https://github.com/nyu-dl/dl4mt-cdec	false	false	true	none
https://paperswithcode.com/paper/incorporating-long-range-consistency-in-cnn	Incorporating long-range consistency in CNN-based texture generation	1606.01286	http://arxiv.org/abs/1606.01286v2	http://arxiv.org/pdf/1606.01286v2.pdf	https://github.com/guillaumebrg/texture_generation	true	true	false	none
https://paperswithcode.com/paper/representation-learning-for-sequence-data-1	Representation Learning for Sequence Data with Deep Autoencoding Predictive Components	2010.03135	https://arxiv.org/abs/2010.03135v2	https://arxiv.org/pdf/2010.03135v2.pdf	https://github.com/JunwenBai/DAPC	true	false	true	pytorch
https://paperswithcode.com/paper/unsupervised-pre-training-of-bidirectional	Unsupervised Pre-training of Bidirectional Speech Encoders via Masked Reconstruction	2001.10603	https://arxiv.org/abs/2001.10603v2	https://arxiv.org/pdf/2001.10603v2.pdf	https://github.com/JunwenBai/DAPC	false	false	true	pytorch
https://paperswithcode.com/paper/ultra-high-definition-image-dehazing-via	Ultra-High-Definition Image Dehazing via Multi-Guided Bilateral Learning	null	http://openaccess.thecvf.com//content/CVPR2021/html/Zheng_Ultra-High-Definition_Image_Dehazing_via_Multi-Guided_Bilateral_Learning_CVPR_2021_paper.html	http://openaccess.thecvf.com//content/CVPR2021/papers/Zheng_Ultra-High-Definition_Image_Dehazing_via_Multi-Guided_Bilateral_Learning_CVPR_2021_paper.pdf	https://github.com/zzr-idam/4KDehazing	true	false	false	pytorch
https://paperswithcode.com/paper/overfeat-integrated-recognition-localization	OverFeat: Integrated Recognition, Localization and Detection using Convolutional Networks	1312.6229	http://arxiv.org/abs/1312.6229v4	http://arxiv.org/pdf/1312.6229v4.pdf	https://github.com/soumith/imagenet-multiGPU.torch	false	false	true	torch
https://paperswithcode.com/paper/discovery-of-physics-and-characterization-of	Discovery of Physics and Characterization of Microstructure from Data with Bayesian Hidden Physics Models	2103.07502	https://arxiv.org/abs/2103.07502v1	https://arxiv.org/pdf/2103.07502v1.pdf	https://github.com/sdatkinson/BHPM-Ultrasound	true	false	false	jax
https://paperswithcode.com/paper/gradskip-communication-accelerated-local	GradSkip: Communication-Accelerated Local Gradient Methods with Better Computational Complexity	2210.16402	https://arxiv.org/abs/2210.16402v3	https://arxiv.org/pdf/2210.16402v3.pdf	https://github.com/artomaranjyan/GradSkip-code	true	false	false	none
https://paperswithcode.com/paper/residual-flows-for-invertible-generative	Residual Flows for Invertible Generative Modeling	1906.02735	https://arxiv.org/abs/1906.02735v6	https://arxiv.org/pdf/1906.02735v6.pdf	https://github.com/thu-ml/implicit-normalizing-flows	false	false	true	pytorch
https://paperswithcode.com/paper/graph-random-neural-network	Graph Random Neural Network for Semi-Supervised Learning on Graphs	2005.11079	https://arxiv.org/abs/2005.11079v4	https://arxiv.org/pdf/2005.11079v4.pdf	https://github.com/dmlc/dgl/tree/master/examples/pytorch/grand	false	false	false	pytorch
https://paperswithcode.com/paper/hydrodynamically-interrupted-droplet-growth	Hydrodynamically interrupted droplet growth in scalar active matter	1907.04819	https://arxiv.org/abs/1907.04819v2	https://arxiv.org/pdf/1907.04819v2.pdf	https://github.com/rajeshrinet/pyGL	false	false	true	none
https://paperswithcode.com/paper/divide-and-rule-training-context-aware-multi	Divide and Rule: Effective Pre-Training for Context-Aware Multi-Encoder Translation Models	2103.17151	https://arxiv.org/abs/2103.17151v2	https://arxiv.org/pdf/2103.17151v2.pdf	https://github.com/lorelupo/divide-and-rule	true	true	true	pytorch
https://paperswithcode.com/paper/interpreting-the-latent-space-of-gans-for	Interpreting the Latent Space of GANs for Semantic Face Editing	1907.10786	https://arxiv.org/abs/1907.10786v3	https://arxiv.org/pdf/1907.10786v3.pdf	https://github.com/genforce/interfacegan	false	false	true	tf
https://paperswithcode.com/paper/interfacegan-interpreting-the-disentangled	InterFaceGAN: Interpreting the Disentangled Face Representation Learned by GANs	2005.09635	https://arxiv.org/abs/2005.09635v2	https://arxiv.org/pdf/2005.09635v2.pdf	https://github.com/genforce/interfacegan	false	false	true	tf
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/genforce/interfacegan	false	false	true	tf
https://paperswithcode.com/paper/a-style-based-generator-architecture-for	A Style-Based Generator Architecture for Generative Adversarial Networks	1812.04948	http://arxiv.org/abs/1812.04948v3	http://arxiv.org/pdf/1812.04948v3.pdf	https://github.com/genforce/interfacegan	false	false	true	tf
https://paperswithcode.com/paper/a-unified-mrc-framework-for-named-entity	A Unified MRC Framework for Named Entity Recognition	1910.11476	https://arxiv.org/abs/1910.11476v7	https://arxiv.org/pdf/1910.11476v7.pdf	https://github.com/allenyummy/EHR_NER	false	false	true	pytorch
https://paperswithcode.com/paper/self-supervised-spatio-temporal-2	Self-Supervised Visual Learning by Variable Playback Speeds Prediction of a Video	2003.02692	https://arxiv.org/abs/2003.02692v2	https://arxiv.org/pdf/2003.02692v2.pdf	https://github.com/hyeon-jo/PSPNet	true	true	true	pytorch
https://paperswithcode.com/paper/probabilistic-robust-linear-quadratic	Probabilistic Robust Linear Quadratic Regulators with Gaussian Processes	2105.07668	https://arxiv.org/abs/2105.07668v2	https://arxiv.org/pdf/2105.07668v2.pdf	https://github.com/Data-Science-in-Mechanical-Engineering/prlqr	true	true	true	none
https://paperswithcode.com/paper/automated-test-generation-for-rest-apis-no	Automated Test Generation for REST APIs: No Time to Rest Yet	2204.08348	https://arxiv.org/abs/2204.08348v3	https://arxiv.org/pdf/2204.08348v3.pdf	https://github.com/randomqwerqwer/issta-main	true	true	false	none
https://paperswithcode.com/paper/two-at-once-enhancing-learning-and	Two at Once: Enhancing Learning and Generalization Capacities via IBN-Net	1807.09441	https://arxiv.org/abs/1807.09441v3	https://arxiv.org/pdf/1807.09441v3.pdf	https://github.com/alibaba/cluster-contrast	false	false	true	pytorch
https://paperswithcode.com/paper/learning-to-answer-questions-in-dynamic-audio	Learning to Answer Questions in Dynamic Audio-Visual Scenarios	2203.14072	https://arxiv.org/abs/2203.14072v2	https://arxiv.org/pdf/2203.14072v2.pdf	https://github.com/GeWu-Lab/MUSIC-AVQA	true	false	false	pytorch
https://paperswithcode.com/paper/improving-the-transferability-of-speech	Improving the transferability of speech separation by meta-learning	2203.05882	https://arxiv.org/abs/2203.05882v1	https://arxiv.org/pdf/2203.05882v1.pdf	https://github.com/nobel861017/mtss	true	true	false	none
https://paperswithcode.com/paper/large-language-models-can-accurately-predict	Large language models can accurately predict searcher preferences	2309.10621	https://arxiv.org/abs/2309.10621v3	https://arxiv.org/pdf/2309.10621v3.pdf	https://github.com/RikiyaT/LARA	false	false	true	pytorch
https://paperswithcode.com/paper/direct-evaluation-of-chain-of-thought-in	Direct Evaluation of Chain-of-Thought in Multi-hop Reasoning with Knowledge Graphs	2402.11199	https://arxiv.org/abs/2402.11199v2	https://arxiv.org/pdf/2402.11199v2.pdf	https://github.com/minhvuong2000/llmreasoncert	true	true	true	none
https://paperswithcode.com/paper/a-large-scale-study-of-relevance-assessments	A Large-Scale Study of Relevance Assessments with Large Language Models: An Initial Look	2411.08275	https://arxiv.org/abs/2411.08275v1	https://arxiv.org/pdf/2411.08275v1.pdf	https://github.com/RikiyaT/LARA	false	false	true	pytorch
https://paperswithcode.com/paper/tutorial-on-deep-learning-for-human-activity	Tutorial on Deep Learning for Human Activity Recognition	2110.06663	https://arxiv.org/abs/2110.06663v1	https://arxiv.org/pdf/2110.06663v1.pdf	https://github.com/mariusbock/dl-for-har/tree/main/tutorial_notebooks	true	false	false	pytorch
https://paperswithcode.com/paper/meta-pu-an-arbitrary-scale-upsampling-network-1	Meta-PU: An Arbitrary-Scale Upsampling Network for Point Cloud	null	https://ieeexplore.ieee.org/document/9351772/	https://ieeexplore.ieee.org/document/9351772/	https://github.com/pleaseconnectwifi/Meta-PU	false	false	false	pytorch
https://paperswithcode.com/paper/unsupervised-image-translation-using	Unsupervised Image Translation using Adversarial Networks for Improved Plant Disease Recognition	1909.11915	https://arxiv.org/abs/1909.11915v1	https://arxiv.org/pdf/1909.11915v1.pdf	https://github.com/mlandcv/Auxilliary_Reconstruction_GAN	true	false	false	none
https://paperswithcode.com/paper/price-response-functions-and-spread-impact-in	Price response functions and spread impact in correlated financial markets	2010.15105	https://arxiv.org/abs/2010.15105v1	https://arxiv.org/pdf/2010.15105v1.pdf	https://github.com/juanhenao21/forex_response_spread_year	false	false	true	none
https://paperswithcode.com/paper/modeling-relational-data-with-graph	Modeling Relational Data with Graph Convolutional Networks	1703.06103	http://arxiv.org/abs/1703.06103v4	http://arxiv.org/pdf/1703.06103v4.pdf	https://github.com/dmlc/dgl/tree/master/examples/tensorflow/rgcn	false	false	false	pytorch
https://paperswithcode.com/paper/date-detecting-anomalies-in-text-via-self	DATE: Detecting Anomalies in Text via Self-Supervision of Transformers	2104.05591	https://arxiv.org/abs/2104.05591v1	https://arxiv.org/pdf/2104.05591v1.pdf	https://github.com/bit-ml/date	true	true	true	pytorch
https://paperswithcode.com/paper/interval-neural-networks-as-instability	Interval Neural Networks as Instability Detectors for Image Reconstructions	2003.13471	https://arxiv.org/abs/2003.13471v1	https://arxiv.org/pdf/2003.13471v1.pdf	https://github.com/luisoala/inn	true	false	false	tf
https://paperswithcode.com/paper/interval-neural-networks-uncertainty-scores	Interval Neural Networks: Uncertainty Scores	2003.11566	https://arxiv.org/abs/2003.11566v1	https://arxiv.org/pdf/2003.11566v1.pdf	https://github.com/luisoala/inn	true	false	false	tf
https://paperswithcode.com/paper/conformative-filtering-for-implicit-feedback	Conformative Filtering for Implicit Feedback Data	1704.01889	http://arxiv.org/abs/1704.01889v2	http://arxiv.org/pdf/1704.01889v2.pdf	https://github.com/fkhawar/Conformative-Filtering	true	false	true	none
https://paperswithcode.com/paper/crystallography-companion-agent-for-high	Crystallography companion agent for high-throughput materials discovery	2008.00283	https://arxiv.org/abs/2008.00283v2	https://arxiv.org/pdf/2008.00283v2.pdf	https://github.com/bnl/pub-Maffettone_2020_08	false	false	true	tf
https://paperswithcode.com/paper/orb-slam3-an-accurate-open-source-library-for	ORB-SLAM3: An Accurate Open-Source Library for Visual, Visual-Inertial and Multi-Map SLAM	2007.11898	https://arxiv.org/abs/2007.11898v2	https://arxiv.org/pdf/2007.11898v2.pdf	https://github.com/baaixw/ORB_SLAM_test	false	false	true	none
https://paperswithcode.com/paper/180309746	Lenstronomy: multi-purpose gravitational lens modelling software package	1803.09746	http://arxiv.org/abs/1803.09746v2	http://arxiv.org/pdf/1803.09746v2.pdf	https://github.com/sibirrer/curved_arcs	false	false	true	none
https://paperswithcode.com/paper/a-guide-to-convolution-arithmetic-for-deep	A guide to convolution arithmetic for deep learning	1603.07285	http://arxiv.org/abs/1603.07285v2	http://arxiv.org/pdf/1603.07285v2.pdf	https://github.com/mrdbourke/tensorflow-deep-learning	false	false	true	tf
https://paperswithcode.com/paper/universal-language-model-fine-tuning-for-text	Universal Language Model Fine-tuning for Text Classification	1801.06146	http://arxiv.org/abs/1801.06146v5	http://arxiv.org/pdf/1801.06146v5.pdf	https://github.com/mrdbourke/tensorflow-deep-learning	false	false	true	tf
https://paperswithcode.com/paper/pubmed-200k-rct-a-dataset-for-sequential	PubMed 200k RCT: a Dataset for Sequential Sentence Classification in Medical Abstracts	1710.06071	http://arxiv.org/abs/1710.06071v1	http://arxiv.org/pdf/1710.06071v1.pdf	https://github.com/mrdbourke/tensorflow-deep-learning	false	false	true	tf
https://paperswithcode.com/paper/assessing-the-reliability-of-large-language	Assessing the Reliability of Large Language Model Knowledge	2310.09820	https://arxiv.org/abs/2310.09820v1	https://arxiv.org/pdf/2310.09820v1.pdf	https://github.com/vicky-wil/monitor	true	true	false	pytorch
https://paperswithcode.com/paper/pixel-interactive-light-system-design-based	PIXEL: Interactive Light System Design Based On Simple Gesture Recognition	2010.10180	http://arxiv.org/abs/2010.10180v1	http://arxiv.org/pdf/2010.10180v1.pdf	https://github.com/actbee/Interactive-Light-System-Design-Based-On-Simple-Gesture-Recognition-	false	false	true	none
https://paperswithcode.com/paper/transverse-confinement-of-electron-beams-in-a	Transverse Confinement of Electron Beams in a 2D Optical Lattice for Compact Coherent X-Ray Sources	2104.11586	https://arxiv.org/abs/2104.11586v2	https://arxiv.org/pdf/2104.11586v2.pdf	https://github.com/aryafallahi/mithra	true	true	false	none
https://paperswithcode.com/paper/multipoint-cross-spectral-registration-of	MultiPoint: Cross-spectral registration of thermal and optical aerial imagery	null	https://proceedings.mlr.press/v155/achermann21a	https://proceedings.mlr.press/v155/achermann21a/achermann21a.pdf	https://github.com/ethz-asl/multipoint	false	true	false	pytorch
https://paperswithcode.com/paper/skin-lesion-analysis-toward-melanoma	Skin Lesion Analysis Toward Melanoma Detection: A Challenge at the 2017 International Symposium on Biomedical Imaging (ISBI), Hosted by the International Skin Imaging Collaboration (ISIC)	1710.05006	http://arxiv.org/abs/1710.05006v3	http://arxiv.org/pdf/1710.05006v3.pdf	https://github.com/datascisteven/Melanoma-Image-Classification	false	false	true	tf
https://paperswithcode.com/paper/skin-lesion-analysis-toward-melanoma-1	Skin Lesion Analysis Toward Melanoma Detection 2018: A Challenge Hosted by the International Skin Imaging Collaboration (ISIC)	1902.03368	http://arxiv.org/abs/1902.03368v2	http://arxiv.org/pdf/1902.03368v2.pdf	https://github.com/datascisteven/Melanoma-Image-Classification	false	false	true	tf
https://paperswithcode.com/paper/mmdetection-open-mmlab-detection-toolbox-and	MMDetection: Open MMLab Detection Toolbox and Benchmark	1906.07155	https://arxiv.org/abs/1906.07155v1	https://arxiv.org/pdf/1906.07155v1.pdf	https://github.com/SiriusKY/SceneTextDetector	false	false	true	pytorch
https://paperswithcode.com/paper/end-to-end-asr-system-with-automatic	End to End ASR System with Automatic Punctuation Insertion	2012.02012	https://arxiv.org/abs/2012.02012v1	https://arxiv.org/pdf/2012.02012v1.pdf	https://github.com/GavinGuan95/Punctuator.Pytorch	true	false	false	pytorch
https://paperswithcode.com/paper/teaching-agents-how-to-map-spatial-reasoning	Teaching Agents how to Map: Spatial Reasoning for Multi-Object Navigation	2107.06011	https://arxiv.org/abs/2107.06011v4	https://arxiv.org/pdf/2107.06011v4.pdf	https://github.com/PierreMarza/teaching_agents_how_to_map	true	true	true	pytorch
https://paperswithcode.com/paper/sufficient-dimension-reduction-for	Sufficient dimension reduction for classification using principal optimal transport direction	2010.09921	https://arxiv.org/abs/2010.09921v4	https://arxiv.org/pdf/2010.09921v4.pdf	https://github.com/ChengzijunAixiaoli/POTD	true	false	false	none
https://paperswithcode.com/paper/tabtransformer-tabular-data-modeling-using	TabTransformer: Tabular Data Modeling Using Contextual Embeddings	2012.06678	https://arxiv.org/abs/2012.06678v1	https://arxiv.org/pdf/2012.06678v1.pdf	https://github.com/timeseriesAI/tsai/blob/main/tsai/models/TabTransformer.py	false	false	false	pytorch
https://paperswithcode.com/paper/opt-open-pre-trained-transformer-language	OPT: Open Pre-trained Transformer Language Models	2205.01068	https://arxiv.org/abs/2205.01068v4	https://arxiv.org/pdf/2205.01068v4.pdf	https://github.com/2023-MindSpore-1/ms-code-218/tree/main/opt	false	false	false	mindspore
https://paperswithcode.com/paper/wasserstein-gan	Wasserstein GAN	1701.07875	http://arxiv.org/abs/1701.07875v3	http://arxiv.org/pdf/1701.07875v3.pdf	https://github.com/2023-MindSpore-1/ms-code-7/tree/main/WGAN_GP	false	false	false	mindspore
https://paperswithcode.com/paper/recursive-contour-saliency-blending-network	Recursive Contour Saliency Blending Network for Accurate Salient Object Detection	2105.13865	https://arxiv.org/abs/2105.13865v3	https://arxiv.org/pdf/2105.13865v3.pdf	https://github.com/BarCodeReader/RCSB-PyTorch	true	true	false	pytorch
https://paperswithcode.com/paper/supervised-learning-of-universal-sentence	Supervised Learning of Universal Sentence Representations from Natural Language Inference Data	1705.02364	http://arxiv.org/abs/1705.02364v5	http://arxiv.org/pdf/1705.02364v5.pdf	https://github.com/menajosep/AleatoricSent	false	false	true	tf
https://paperswithcode.com/paper/yolact-better-real-time-instance-segmentation	YOLACT++: Better Real-time Instance Segmentation	1912.06218	https://arxiv.org/abs/1912.06218v2	https://arxiv.org/pdf/1912.06218v2.pdf	https://github.com/2023-MindSpore-1/ms-code-7/tree/main/WGAN_GP	false	false	false	mindspore
https://paperswithcode.com/paper/asking-and-answering-questions-to-evaluate	Asking and Answering Questions to Evaluate the Factual Consistency of Summaries	2004.04228	https://arxiv.org/abs/2004.04228v1	https://arxiv.org/pdf/2004.04228v1.pdf	https://github.com/W4ngatang/qags	true	true	true	pytorch
https://paperswithcode.com/paper/augmenting-sequential-recommendation-with	Augmenting Sequential Recommendation with Pseudo-Prior Items via Reversely Pre-training Transformer	2105.00522	https://arxiv.org/abs/2105.00522v1	https://arxiv.org/pdf/2105.00522v1.pdf	https://github.com/DyGRec/ASReP	true	true	true	tf
https://paperswithcode.com/paper/quartile-based-prediction-of-event-types-and	A Comparison of Deep-Learning Methods for Analysing and Predicting Business Processes	2102.07838	https://arxiv.org/abs/2102.07838v2	https://arxiv.org/pdf/2102.07838v2.pdf	https://github.com/ishwarvenugopal/GCN-ProcessPrediction	true	true	false	pytorch
https://paperswithcode.com/paper/eec-learning-to-encode-and-regenerate-images-1	EEC: Learning to Encode and Regenerate Images for Continual Learning	2101.04904	https://arxiv.org/abs/2101.04904v4	https://arxiv.org/pdf/2101.04904v4.pdf	https://github.com/aliayub7/EEC	true	true	true	pytorch
https://paperswithcode.com/paper/edge-enhanced-feature-distillation-network	Edge-enhanced Feature Distillation Network for Efficient Super-Resolution	2204.08759	https://arxiv.org/abs/2204.08759v2	https://arxiv.org/pdf/2204.08759v2.pdf	https://github.com/icandle/efdn	true	true	true	pytorch
https://paperswithcode.com/paper/bart-denoising-sequence-to-sequence-pre	BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension	1910.13461	https://arxiv.org/abs/1910.13461v1	https://arxiv.org/pdf/1910.13461v1.pdf	https://github.com/W4ngatang/qags	false	false	true	pytorch
https://paperswithcode.com/paper/newsqa-a-machine-comprehension-dataset	NewsQA: A Machine Comprehension Dataset	1611.09830	http://arxiv.org/abs/1611.09830v3	http://arxiv.org/pdf/1611.09830v3.pdf	https://github.com/W4ngatang/qags	false	false	true	pytorch
https://paperswithcode.com/paper/bottom-up-abstractive-summarization	Bottom-Up Abstractive Summarization	1808.10792	http://arxiv.org/abs/1808.10792v2	http://arxiv.org/pdf/1808.10792v2.pdf	https://github.com/W4ngatang/qags	false	false	true	pytorch
https://paperswithcode.com/paper/hasco-towards-agile-hardware-and-software-co	HASCO: Towards Agile HArdware and Software CO-design for Tensor Computation	2105.01585	https://arxiv.org/abs/2105.01585v1	https://arxiv.org/pdf/2105.01585v1.pdf	https://github.com/pku-liang/HASCO	true	true	true	none
https://paperswithcode.com/paper/neural-ray-tracing-learning-surfaces-and	Neural Ray-Tracing: Learning Surfaces and Reflectance for Relighting and View Synthesis	2104.13562	https://arxiv.org/abs/2104.13562v2	https://arxiv.org/pdf/2104.13562v2.pdf	https://github.com/princeton-computational-imaging/neural_raytracing	true	true	true	pytorch
https://paperswithcode.com/paper/towards-making-deep-learning-based	Towards Making Deep Learning-based Vulnerability Detectors Robust	2108.00669	https://arxiv.org/abs/2108.00669v2	https://arxiv.org/pdf/2108.00669v2.pdf	https://github.com/ZigZagframework/zigzag_framework	true	true	false	none
https://paperswithcode.com/paper/a-reinforcement-learning-environment-for-job	A Reinforcement Learning Environment For Job-Shop Scheduling	2104.03760	https://arxiv.org/abs/2104.03760v1	https://arxiv.org/pdf/2104.03760v1.pdf	https://github.com/prosysscience/JSS	true	true	true	tf
https://paperswithcode.com/paper/multipole-graph-neural-operator-for	Multipole Graph Neural Operator for Parametric Partial Differential Equations	2006.09535	https://arxiv.org/abs/2006.09535v2	https://arxiv.org/pdf/2006.09535v2.pdf	https://github.com/zongyi-li/graph-pde	true	false	true	pytorch
https://paperswithcode.com/paper/ac-dc-alternating-compressed-decompressed	AC/DC: Alternating Compressed/DeCompressed Training of Deep Neural Networks	2106.12379	https://arxiv.org/abs/2106.12379v2	https://arxiv.org/pdf/2106.12379v2.pdf	https://github.com/IST-DASLab/ACDC	true	true	true	pytorch
https://paperswithcode.com/paper/logistic-regression-through-the-veil-of	Logistic Regression Through the Veil of Imprecise Data	2106.00492	https://arxiv.org/abs/2106.00492v2	https://arxiv.org/pdf/2106.00492v2.pdf	https://github.com/ngg1995/LR-python	true	true	false	none
https://paperswithcode.com/paper/homomorphic-payment-addresses-and-the-pay-to	Homomorphic Payment Addresses and the Pay-to-Contract Protocol	1212.3257	http://arxiv.org/abs/1212.3257v1	http://arxiv.org/pdf/1212.3257v1.pdf	https://github.com/bitcoinjs/bitcoinjs-lib	false	false	false	none
https://paperswithcode.com/paper/soft-attention-improves-skin-cancer	Soft-Attention Improves Skin Cancer Classification Performance	2105.03358	https://arxiv.org/abs/2105.03358v3	https://arxiv.org/pdf/2105.03358v3.pdf	https://github.com/skrantidatta/Attention-based-Skin-Cancer-Classification	true	true	true	tf
https://paperswithcode.com/paper/conversations-are-not-flat-modeling-the	Conversations Are Not Flat: Modeling the Dynamic Information Flow across Dialogue Utterances	2106.02227	https://arxiv.org/abs/2106.02227v1	https://arxiv.org/pdf/2106.02227v1.pdf	https://github.com/ictnlp/DialoFlow	true	true	true	pytorch
https://paperswithcode.com/paper/fine-grained-angular-contrastive-learning	Fine-grained Angular Contrastive Learning with Coarse Labels	2012.03515	https://arxiv.org/abs/2012.03515v3	https://arxiv.org/pdf/2012.03515v3.pdf	https://github.com/guybuk/ANCOR	true	true	true	pytorch
https://paperswithcode.com/paper/group-cam-group-score-weighted-visual	Group-CAM: Group Score-Weighted Visual Explanations for Deep Convolutional Networks	2103.13859	https://arxiv.org/abs/2103.13859v4	https://arxiv.org/pdf/2103.13859v4.pdf	https://github.com/wofmanaf/Group-CAM	true	true	true	pytorch
https://paperswithcode.com/paper/distance-matters-in-human-object-interaction	Distance Matters in Human-Object Interaction Detection	2207.01869	https://arxiv.org/abs/2207.01869v1	https://arxiv.org/pdf/2207.01869v1.pdf	https://github.com/daoyuan98/sdt-hoi	true	true	false	pytorch
https://paperswithcode.com/paper/spectral-embedding-for-dynamic-networks-with	Spectral embedding for dynamic networks with stability guarantees	2106.01282	https://arxiv.org/abs/2106.01282v2	https://arxiv.org/pdf/2106.01282v2.pdf	https://github.com/iggallagher/Dynamic-Network-Embedding	true	true	false	none
https://paperswithcode.com/paper/attentivenas-improving-neural-architecture	AttentiveNAS: Improving Neural Architecture Search via Attentive Sampling	2011.09011	https://arxiv.org/abs/2011.09011v2	https://arxiv.org/pdf/2011.09011v2.pdf	https://github.com/facebookresearch/AlphaNet	false	false	true	pytorch
https://paperswithcode.com/paper/towards-efficient-unconstrained-palmprint	Towards Efficient Unconstrained Palmprint Recognition via Deep Distillation Hashing	2004.03303	https://arxiv.org/abs/2004.03303v1	https://arxiv.org/pdf/2004.03303v1.pdf	https://github.com/HuikaiShao/DDH	false	false	true	tf
https://paperswithcode.com/paper/sum-of-ranked-range-loss-for-supervised	Sum of Ranked Range Loss for Supervised Learning	2106.03300	https://arxiv.org/abs/2106.03300v2	https://arxiv.org/pdf/2106.03300v2.pdf	https://github.com/discovershu/SoRR	true	true	false	pytorch
https://paperswithcode.com/paper/anomalous-thermal-expansion-in-ising-like	Anomalous thermal expansion in Ising-like puckered sheets	2105.10015	https://arxiv.org/abs/2105.10015v2	https://arxiv.org/pdf/2105.10015v2.pdf	https://github.com/phanakata/programmable-matter	true	false	false	none
https://paperswithcode.com/paper/efficientnet-rethinking-model-scaling-for	EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks	1905.11946	https://arxiv.org/abs/1905.11946v5	https://arxiv.org/pdf/1905.11946v5.pdf	https://github.com/houstonsantos/CassavaLeafDisease	false	false	true	none
https://paperswithcode.com/paper/probabilistic-transformers	Probabilistic Transformers	2010.15583	https://arxiv.org/abs/2010.15583v3	https://arxiv.org/pdf/2010.15583v3.pdf	https://github.com/apple/ml-probabilistic-attention	false	false	true	pytorch
https://paperswithcode.com/paper/shape-modeling-with-spline-partitions	Shape Modeling with Spline Partitions	2108.02507	https://arxiv.org/abs/2108.02507v2	https://arxiv.org/pdf/2108.02507v2.pdf	https://github.com/ShufeiGe/Shape-Modeling-with-Spline-Partitions	true	true	true	none
https://paperswithcode.com/paper/junction-tree-variational-autoencoder-for	Junction Tree Variational Autoencoder for Molecular Graph Generation	1802.04364	http://arxiv.org/abs/1802.04364v4	http://arxiv.org/pdf/1802.04364v4.pdf	https://github.com/LiamWilbraham/jtnnencoder	false	false	true	pytorch
https://paperswithcode.com/paper/prognet-a-transferable-deep-network-for	ProgNet: A Transferable Deep Network for Aircraft Engine Damage Propagation Prognosis under Real Flight Conditions	null	https://www.mdpi.com/2226-4310/10/1/10	https://www.mdpi.com/2226-4310/10/1/10	https://github.com/TBdevellopper/NEW_CMAPSS_Dataset-2021-	false	false	false	none
https://paperswithcode.com/paper/region-aware-adaptive-instance-normalization	Region-aware Adaptive Instance Normalization for Image Harmonization	2106.02853	https://arxiv.org/abs/2106.02853v1	https://arxiv.org/pdf/2106.02853v1.pdf	https://github.com/junleen/RainNet	true	true	true	pytorch
https://paperswithcode.com/paper/once-for-all-train-one-network-and-specialize	Once-for-All: Train One Network and Specialize it for Efficient Deployment	1908.09791	https://arxiv.org/abs/1908.09791v5	https://arxiv.org/pdf/1908.09791v5.pdf	https://github.com/twice154/ofa-for-super-resolution	false	false	true	pytorch
https://paperswithcode.com/paper/vitae-vision-transformer-advanced-by	ViTAE: Vision Transformer Advanced by Exploring Intrinsic Inductive Bias	2106.03348	https://arxiv.org/abs/2106.03348v4	https://arxiv.org/pdf/2106.03348v4.pdf	https://github.com/Annbless/ViTAE	true	true	true	pytorch
https://paperswithcode.com/paper/professional-differences-a-comparative-study	Professional Differences: A Comparative Study of Visualization Task Performance and Spatial Ability Across Disciplines	2108.02333	https://arxiv.org/abs/2108.02333v1	https://arxiv.org/pdf/2108.02333v1.pdf	https://github.com/vialab/spatial-abilities-public	true	true	false	none
https://paperswithcode.com/paper/decoding-the-protein-ligand-interactions	Decoding the Protein-ligand Interactions Using Parallel Graph Neural Networks	2111.15144	https://arxiv.org/abs/2111.15144v1	https://arxiv.org/pdf/2111.15144v1.pdf	https://github.com/nkkchem/pf-gnn_pli	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.