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/review-highlights-opinion-mining-on-reviews-a	Review highlights: opinion mining on reviews: a hybrid model for rule selection in aspect extraction	null	https://dl.acm.org/citation.cfm?id=3158385	http://vixra.org/pdf/1910.0514v1.pdf	https://github.com/yardstick17/AspectBasedSentimentAnalysis	false	false	false	none
https://paperswithcode.com/paper/temporally-coherent-gans-for-video-super	Learning Temporal Coherence via Self-Supervision for GAN-based Video Generation	1811.09393	https://arxiv.org/abs/1811.09393v4	https://arxiv.org/pdf/1811.09393v4.pdf	https://github.com/GitHubXlong/TecoGAN	false	false	true	pytorch
https://paperswithcode.com/paper/ckmeans-and-fckmeans-two-deterministic	CKmeans and FCKmeans : Two deterministic initialization procedures for Kmeans algorithm using a modified crowding distance	2304.09989	https://arxiv.org/abs/2304.09989v2	https://arxiv.org/pdf/2304.09989v2.pdf	https://github.com/Layebuniv/fckmeans	true	false	false	none
https://paperswithcode.com/paper/1-ogc-the-first-open-gravitational-wave	1-OGC: The first open gravitational-wave catalog of binary mergers from analysis of public Advanced LIGO data	1811.01921	http://arxiv.org/abs/1811.01921v2	http://arxiv.org/pdf/1811.01921v2.pdf	https://github.com/gwastro/1-ogc	true	true	true	none
https://paperswithcode.com/paper/modeling-the-dynamics-of-online-learning	Modeling the Dynamics of Online Learning Activity	1610.05775	http://arxiv.org/abs/1610.05775v1	http://arxiv.org/pdf/1610.05775v1.pdf	https://github.com/Networks-Learning/hdhp.py	true	true	true	none
https://paperswithcode.com/paper/hierarchical-density-order-embeddings	Hierarchical Density Order Embeddings	1804.09843	http://arxiv.org/abs/1804.09843v1	http://arxiv.org/pdf/1804.09843v1.pdf	https://github.com/benathi/density-order-emb	true	true	false	pytorch
https://paperswithcode.com/paper/kernalised-multi-resolution-convnet-for	Kernalised Multi-resolution Convnet for Visual Tracking	1708.00577	http://arxiv.org/abs/1708.00577v1	http://arxiv.org/pdf/1708.00577v1.pdf	https://github.com/stevenwudi/KMC_cvprw_2017	true	true	false	tf
https://paperswithcode.com/paper/crowdsourcing-lightweight-pyramids-for-manual	Crowdsourcing Lightweight Pyramids for Manual Summary Evaluation	1904.05929	http://arxiv.org/abs/1904.05929v1	http://arxiv.org/pdf/1904.05929v1.pdf	https://github.com/OriShapira/LitePyramids	true	true	false	none
https://paperswithcode.com/paper/spatiotemporal-residual-networks-for-video	Spatiotemporal Residual Networks for Video Action Recognition	1611.02155	http://arxiv.org/abs/1611.02155v1	http://arxiv.org/pdf/1611.02155v1.pdf	https://github.com/feichtenhofer/st-resnet	true	true	false	none
https://paperswithcode.com/paper/generative-partition-networks-for-multi	Generative Partition Networks for Multi-Person Pose Estimation	1705.07422	http://arxiv.org/abs/1705.07422v2	http://arxiv.org/pdf/1705.07422v2.pdf	https://github.com/NieXC/pytorch-ppn	false	false	true	pytorch
https://paperswithcode.com/paper/traffic-graph-convolutional-recurrent-neural	Traffic Graph Convolutional Recurrent Neural Network: A Deep Learning Framework for Network-Scale Traffic Learning and Forecasting	1802.07007	https://arxiv.org/abs/1802.07007v3	https://arxiv.org/pdf/1802.07007v3.pdf	https://github.com/zhiyongc/Seattle-Loop-Data	true	true	false	none
https://paperswithcode.com/paper/surfacenet-an-end-to-end-3d-neural-network	SurfaceNet: An End-to-end 3D Neural Network for Multiview Stereopsis	1708.01749	http://arxiv.org/abs/1708.01749v1	http://arxiv.org/pdf/1708.01749v1.pdf	https://github.com/mjiUST/SurfaceNet	true	true	false	none
https://paperswithcode.com/paper/geometric-adaptive-monte-carlo-in-random	Geometric adaptive Monte Carlo in random environment	1608.07986	https://arxiv.org/abs/1608.07986v4	https://arxiv.org/pdf/1608.07986v4.pdf	https://github.com/scidom/MAMALASampler.jl	true	true	false	none
https://paperswithcode.com/paper/random-directions-stochastic-approximation	Random directions stochastic approximation with deterministic perturbations	1808.02871	http://arxiv.org/abs/1808.02871v2	http://arxiv.org/pdf/1808.02871v2.pdf	https://github.com/prashla/RDSA	true	true	false	none
https://paperswithcode.com/paper/manifoldnet-a-deep-network-framework-for	ManifoldNet: A Deep Network Framework for Manifold-valued Data	1809.06211	http://arxiv.org/abs/1809.06211v3	http://arxiv.org/pdf/1809.06211v3.pdf	https://github.com/jjbouza/manifold-net	true	true	false	pytorch
https://paperswithcode.com/paper/show-and-tell-a-neural-image-caption	Show and Tell: A Neural Image Caption Generator	1411.4555	http://arxiv.org/abs/1411.4555v2	http://arxiv.org/pdf/1411.4555v2.pdf	https://github.com/kirbiyik/caption-it	false	false	true	pytorch
https://paperswithcode.com/paper/disentangling-factors-of-variation-with-cycle	Disentangling Factors of Variation with Cycle-Consistent Variational Auto-Encoders	1804.10469	http://arxiv.org/abs/1804.10469v1	http://arxiv.org/pdf/1804.10469v1.pdf	https://github.com/ananyahjha93/disentangling-factors-of-variation-using-adversarial-training	false	false	true	pytorch
https://paperswithcode.com/paper/disentangling-factors-of-variation-in-deep	Disentangling factors of variation in deep representations using adversarial training	1611.03383	http://arxiv.org/abs/1611.03383v1	http://arxiv.org/pdf/1611.03383v1.pdf	https://github.com/ananyahjha93/disentangling-factors-of-variation-using-adversarial-training	false	false	true	pytorch
https://paperswithcode.com/paper/u-net-convolutional-networks-for-biomedical	U-Net: Convolutional Networks for Biomedical Image Segmentation	1505.04597	http://arxiv.org/abs/1505.04597v1	http://arxiv.org/pdf/1505.04597v1.pdf	https://github.com/neshitov/UNet	false	false	true	pytorch
https://paperswithcode.com/paper/audino-a-modern-annotation-tool-for-audio-and	audino: A Modern Annotation Tool for Audio and Speech	2006.05236	https://arxiv.org/abs/2006.05236v2	https://arxiv.org/pdf/2006.05236v2.pdf	https://github.com/midas-research/audino	true	true	true	none
https://paperswithcode.com/paper/robust-adversarial-reinforcement-learning	Robust Adversarial Reinforcement Learning	1703.02702	http://arxiv.org/abs/1703.02702v1	http://arxiv.org/pdf/1703.02702v1.pdf	https://github.com/davidsonic/robust-grasp	false	false	true	tf
https://paperswithcode.com/paper/chainercv-a-library-for-deep-learning-in	ChainerCV: a Library for Deep Learning in Computer Vision	1708.08169	http://arxiv.org/abs/1708.08169v1	http://arxiv.org/pdf/1708.08169v1.pdf	https://github.com/chainer/chainercv	false	false	true	none
https://paperswithcode.com/paper/denoising-diffusion-probabilistic-models	Denoising Diffusion Probabilistic Models	2006.11239	https://arxiv.org/abs/2006.11239v2	https://arxiv.org/pdf/2006.11239v2.pdf	https://github.com/sak-h/pytorch-Denoising-Diffusion-Probabilistic-Models	false	false	true	pytorch
https://paperswithcode.com/paper/vo-tranh-eternal-pulse-o-lumina-genesis	Vô Tranh Eternal Pulse Ω – Lumina Genesis	null	https://zenodo.org/records/15132859	https://zenodo.org/records/15132859/files/WHITEPAPER.pdf	https://github.com/vinhatson/The-Last---Lumina-genesis	false	false	false	pytorch
https://paperswithcode.com/paper/variational-dropout-sparsifies-deep-neural	Variational Dropout Sparsifies Deep Neural Networks	1701.05369	http://arxiv.org/abs/1701.05369v3	http://arxiv.org/pdf/1701.05369v3.pdf	https://github.com/ars-ashuha/sparse-vd-pytorch	false	false	true	pytorch
https://paperswithcode.com/paper/learning-from-simulated-and-unsupervised	Learning from Simulated and Unsupervised Images through Adversarial Training	1612.07828	http://arxiv.org/abs/1612.07828v2	http://arxiv.org/pdf/1612.07828v2.pdf	https://github.com/rickyhan/SimGAN-Captcha	false	false	true	tf
https://paperswithcode.com/paper/savoias-a-diverse-multi-category-visual	SAVOIAS: A Diverse, Multi-Category Visual Complexity Dataset	1810.01771	http://arxiv.org/abs/1810.01771v1	http://arxiv.org/pdf/1810.01771v1.pdf	https://github.com/esaraee/Savoias-Dataset	true	true	false	none
https://paperswithcode.com/paper/depth-map-prediction-from-a-single-image	Depth Map Prediction from a Single Image using a Multi-Scale Deep Network	1406.2283	http://arxiv.org/abs/1406.2283v1	http://arxiv.org/pdf/1406.2283v1.pdf	https://github.com/MasazI/cnn_depth_tensorflow	false	false	true	tf
https://paperswithcode.com/paper/word-embeddings-for-the-analysis-of	Word Embeddings for the Analysis of Ideological Placement in Parliamentary Corpora	null	https://www.cambridge.org/core/journals/political-analysis/article/abs/word-embeddings-for-the-analysis-of-ideological-placement-in-parliamentary-corpora/017F0CEA9B3DB6E1B94AC36A509A8A7B	https://ludovicrheault.weebly.com/uploads/3/9/4/0/39408253/rheaultcochrane2019_pa.pdf	https://github.com/lrheault/partyembed	true	true	false	none
https://paperswithcode.com/paper/how-emotional-are-you-neural-architectures	How emotional are you? Neural Architectures for Emotion Intensity Prediction in Microblogs	null	https://aclanthology.org/C18-1247	https://aclanthology.org/C18-1247.pdf	https://github.com/Pranav-Goel/Neural_Emotion_Intensity_Prediction	true	true	false	tf
https://paperswithcode.com/paper/afet-automatic-fine-grained-entity-typing-by	AFET: Automatic Fine-Grained Entity Typing by Hierarchical Partial-Label Embedding	null	https://aclanthology.org/D16-1144	https://aclanthology.org/D16-1144.pdf	https://github.com/shanzhenren/AFET	true	true	false	none
https://paperswithcode.com/paper/the-signature-of-large-scale-turbulence	The signature of large scale turbulence driving on the structure of the interstellar medium	2206.00451	https://arxiv.org/abs/2206.00451v1	https://arxiv.org/pdf/2206.00451v1.pdf	https://bitbucket.org/rteyssie/ramses	true	false	false	none
https://paperswithcode.com/paper/the-fermilab-muon-g-2-straw-tracking	The Fermilab Muon $g-2$ straw tracking detectors, internal tracker alignment, and the muon EDM measurement	1909.12900	https://arxiv.org/abs/1909.12900v2	https://arxiv.org/pdf/1909.12900v2.pdf	https://github.com/glukicov/alignTrack	false	false	true	none
https://paperswithcode.com/paper/two-local-models-for-neural-constituent	Two Local Models for Neural Constituent Parsing	1808.04850	http://arxiv.org/abs/1808.04850v2	http://arxiv.org/pdf/1808.04850v2.pdf	https://github.com/zeeeyang/two-local-neural-conparsers	true	true	false	none
https://paperswithcode.com/paper/learning-deep-features-for-discriminative	Learning Deep Features for Discriminative Localization	1512.04150	http://arxiv.org/abs/1512.04150v1	http://arxiv.org/pdf/1512.04150v1.pdf	https://github.com/tensorpack/tensorpack/tree/master/examples/Saliency	false	false	false	tf
https://paperswithcode.com/paper/a-fast-and-scalable-joint-estimator-for-1	A Fast and Scalable Joint Estimator for Integrating Additional Knowledge in Learning Multiple Related Sparse Gaussian Graphical Models	1806.00548	http://arxiv.org/abs/1806.00548v4	http://arxiv.org/pdf/1806.00548v4.pdf	https://github.com/QData/JEEK	true	false	true	none
https://paperswithcode.com/paper/unpaired-image-to-image-translation-using	Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks	1703.10593	https://arxiv.org/abs/1703.10593v7	https://arxiv.org/pdf/1703.10593v7.pdf	https://github.com/WeiYangze/hibernate-demo	false	false	true	tf
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/simonjisu/NMT	false	false	true	pytorch
https://paperswithcode.com/paper/breaking-the-nonsmooth-barrier-a-scalable	Breaking the Nonsmooth Barrier: A Scalable Parallel Method for Composite Optimization	1707.06468	http://arxiv.org/abs/1707.06468v3	http://arxiv.org/pdf/1707.06468v3.pdf	https://github.com/fabianp/ProxASAGA	true	true	true	none
https://paperswithcode.com/paper/underground-root-tuber-sensing-via-a-wi-fi	Underground Root Tuber Sensing via a Wi-Fi Mesh Network	null	https://dl.acm.org/doi/10.1145/3715014.3724365	https://dl.acm.org/doi/pdf/10.1145/3715014.3724365	https://github.com/Data-driven-RTI/undergroud_sensing_wifi_csi	false	false	false	pytorch
https://paperswithcode.com/paper/like-what-you-like-knowledge-distill-via	Like What You Like: Knowledge Distill via Neuron Selectivity Transfer	1707.01219	http://arxiv.org/abs/1707.01219v2	http://arxiv.org/pdf/1707.01219v2.pdf	https://github.com/TuSimple/neuron-selectivity-transfer	false	false	true	tf
https://paperswithcode.com/paper/reassessing-the-goals-of-grammatical-error	Reassessing the Goals of Grammatical Error Correction: Fluency Instead of Grammaticality	null	https://aclanthology.org/Q16-1013	https://aclanthology.org/Q16-1013.pdf	https://github.com/keisks/reassess-gec	true	true	false	none
https://paperswithcode.com/paper/ctcmodel-a-keras-model-for-connectionist	CTCModel: a Keras Model for Connectionist Temporal Classification	1901.07957	http://arxiv.org/abs/1901.07957v1	http://arxiv.org/pdf/1901.07957v1.pdf	https://github.com/cyprienruffino/CTCModel	false	false	true	tf
https://paperswithcode.com/paper/alternating-direction-graph-matching	Alternating Direction Graph Matching	1611.07583	http://arxiv.org/abs/1611.07583v4	http://arxiv.org/pdf/1611.07583v4.pdf	https://github.com/netw0rkf10w/adgm	false	false	false	none
https://paperswithcode.com/paper/efficient-neural-architecture-search-via-1	Efficient Neural Architecture Search via Parameter Sharing	1802.03268	http://arxiv.org/abs/1802.03268v2	http://arxiv.org/pdf/1802.03268v2.pdf	https://github.com/Ezereal/enas	false	false	true	tf
https://paperswithcode.com/paper/forgetting-to-learn-logic-programs	Forgetting to learn logic programs	1911.06643	https://arxiv.org/abs/1911.06643v1	https://arxiv.org/pdf/1911.06643v1.pdf	https://github.com/metagol/metagol	true	true	false	none
https://paperswithcode.com/paper/yolo9000-better-faster-stronger	YOLO9000: Better, Faster, Stronger	1612.08242	http://arxiv.org/abs/1612.08242v1	http://arxiv.org/pdf/1612.08242v1.pdf	https://github.com/gpandu/Object-detection	false	false	true	tf
https://paperswithcode.com/paper/focal-loss-for-dense-object-detection	Focal Loss for Dense Object Detection	1708.02002	http://arxiv.org/abs/1708.02002v2	http://arxiv.org/pdf/1708.02002v2.pdf	https://github.com/neshitov/UNet	false	false	true	pytorch
https://paperswithcode.com/paper/unsupervised-adaptation-learning-for	Unsupervised Adaptation Learning for Hyperspectral Imagery Super-Resolution	null	http://openaccess.thecvf.com/content_CVPR_2020/html/Zhang_Unsupervised_Adaptation_Learning_for_Hyperspectral_Imagery_Super-Resolution_CVPR_2020_paper.html	http://openaccess.thecvf.com/content_CVPR_2020/papers/Zhang_Unsupervised_Adaptation_Learning_for_Hyperspectral_Imagery_Super-Resolution_CVPR_2020_paper.pdf	https://github.com/JiangtaoNie/UAL	true	true	false	pytorch
https://paperswithcode.com/paper/fast-mser	Fast MSER	null	http://openaccess.thecvf.com/content_CVPR_2020/html/Xu_Fast_MSER_CVPR_2020_paper.html	http://openaccess.thecvf.com/content_CVPR_2020/papers/Xu_Fast_MSER_CVPR_2020_paper.pdf	https://github.com/mmmn143/fast-mser	true	true	false	none
https://paperswithcode.com/paper/aggregated-residual-transformations-for-deep	Aggregated Residual Transformations for Deep Neural Networks	1611.05431	http://arxiv.org/abs/1611.05431v2	http://arxiv.org/pdf/1611.05431v2.pdf	https://github.com/TuSimple/resnet.mxnet	false	false	true	tf
https://paperswithcode.com/paper/backpack-packing-more-into-backprop	BackPACK: Packing more into backprop	1912.10985	https://arxiv.org/abs/1912.10985v2	https://arxiv.org/pdf/1912.10985v2.pdf	https://github.com/f-dangel/backpack	false	false	false	pytorch
https://paperswithcode.com/paper/linear-colour-segmentation-revisited	Linear colour segmentation revisited	1901.00534	http://arxiv.org/abs/1901.00534v1	http://arxiv.org/pdf/1901.00534v1.pdf	https://github.com/visillect/colorsegdataset	true	true	false	none
https://paperswithcode.com/paper/guided-image-generation-with-conditional	Guided Image Generation with Conditional Invertible Neural Networks	1907.02392	https://arxiv.org/abs/1907.02392v3	https://arxiv.org/pdf/1907.02392v3.pdf	https://github.com/5yearsKim/Conditional-Normalizing-Flow	false	false	true	pytorch
https://paperswithcode.com/paper/glow-generative-flow-with-invertible-1x1	Glow: Generative Flow with Invertible 1x1 Convolutions	1807.03039	http://arxiv.org/abs/1807.03039v2	http://arxiv.org/pdf/1807.03039v2.pdf	https://github.com/5yearsKim/Conditional-Normalizing-Flow	false	false	true	pytorch
https://paperswithcode.com/paper/the-simple-essence-of-automatic	The simple essence of automatic differentiation	1804.00746	http://arxiv.org/abs/1804.00746v2	http://arxiv.org/pdf/1804.00746v2.pdf	https://github.com/conal/essence-of-ad	false	false	true	none
https://paperswithcode.com/paper/an-ensemble-model-of-word-based-and-character	An Ensemble Model of Word-based and Character-based Models for Japanese and Chinese Input Method	null	https://aclanthology.org/W12-4802	https://aclanthology.org/W12-4802.pdf	https://github.com/nokuno/jsc	true	true	false	none
https://paperswithcode.com/paper/meta-transfer-networks-for-zero-shot-learning	Episode-based Prototype Generating Network for Zero-Shot Learning	1909.03360	https://arxiv.org/abs/1909.03360v2	https://arxiv.org/pdf/1909.03360v2.pdf	https://github.com/yunlongyu/EPGN	true	true	false	tf
https://paperswithcode.com/paper/spin-orientations-of-merging-black-holes	Spin orientations of merging black holes formed from the evolution of stellar binaries	1808.02491	http://arxiv.org/abs/1808.02491v1	http://arxiv.org/pdf/1808.02491v1.pdf	https://github.com/dgerosa/spops	true	true	false	none
https://paperswithcode.com/paper/constrained-size-tensorflow-models-for	Constrained-size Tensorflow Models for YouTube-8M Video Understanding Challenge	1808.06739	http://arxiv.org/abs/1808.06739v3	http://arxiv.org/pdf/1808.06739v3.pdf	https://github.com/boliu61/youtube-8m	true	true	true	tf
https://paperswithcode.com/paper/a-neural-algorithm-of-artistic-style	A Neural Algorithm of Artistic Style	1508.06576	http://arxiv.org/abs/1508.06576v2	http://arxiv.org/pdf/1508.06576v2.pdf	https://github.com/Gaurav927/Neural_Style_Transfer	false	false	true	pytorch
https://paperswithcode.com/paper/easy-transfer-learning-by-exploiting-intra	Easy Transfer Learning By Exploiting Intra-domain Structures	1904.01376	http://arxiv.org/abs/1904.01376v2	http://arxiv.org/pdf/1904.01376v2.pdf	https://github.com/jindongwang/transferlearning	false	false	true	pytorch
https://paperswithcode.com/paper/convex-space-learning-for-tabular-synthetic	Convex space learning for tabular synthetic data generation	2407.09789	https://arxiv.org/abs/2407.09789v1	https://arxiv.org/pdf/2407.09789v1.pdf	https://github.com/manjunath-mahendra/NextConvGeN	true	false	false	tf
https://paperswithcode.com/paper/giraffe-using-deep-reinforcement-learning-to	Giraffe: Using Deep Reinforcement Learning to Play Chess	1509.01549	http://arxiv.org/abs/1509.01549v2	http://arxiv.org/pdf/1509.01549v2.pdf	https://github.com/saikrishna-1996/deep_pepper_chess	false	false	true	pytorch
https://paperswithcode.com/paper/13013666	Zero-Shot Learning Through Cross-Modal Transfer	1301.3666	http://arxiv.org/abs/1301.3666v2	http://arxiv.org/pdf/1301.3666v2.pdf	https://github.com/mganjoo/zslearning	false	false	false	none
https://paperswithcode.com/paper/semantic-image-synthesis-via-adversarial	Semantic Image Synthesis via Adversarial Learning	1707.06873	http://arxiv.org/abs/1707.06873v1	http://arxiv.org/pdf/1707.06873v1.pdf	https://github.com/vtddggg/BilinearGAN_for_LBIE	false	false	true	pytorch
https://paperswithcode.com/paper/composition-based-crystal-materials-symmetry	Composition based crystal materials symmetry prediction using machine learning with enhanced descriptors	2105.07303	https://arxiv.org/abs/2105.07303v1	https://arxiv.org/pdf/2105.07303v1.pdf	https://github.com/Yuxinya/SG_predict	true	true	false	none
https://paperswithcode.com/paper/generative-adversarial-text-to-image	Generative Adversarial Text to Image Synthesis	1605.05396	http://arxiv.org/abs/1605.05396v2	http://arxiv.org/pdf/1605.05396v2.pdf	https://github.com/vtddggg/BilinearGAN_for_LBIE	false	false	true	pytorch
https://paperswithcode.com/paper/mixup-beyond-empirical-risk-minimization	mixup: Beyond Empirical Risk Minimization	1710.09412	http://arxiv.org/abs/1710.09412v2	http://arxiv.org/pdf/1710.09412v2.pdf	https://github.com/simongrest/kaggle-freesound-audio-tagging-2019	false	false	true	none
https://paperswithcode.com/paper/metasci-scalable-and-adaptive-reconstruction	MetaSCI: Scalable and Adaptive Reconstruction for Video Compressive Sensing	2103.01786	https://arxiv.org/abs/2103.01786v1	https://arxiv.org/pdf/2103.01786v1.pdf	https://github.com/xyvirtualgroup/MetaSCI-CVPR2021	true	true	false	tf
https://paperswithcode.com/paper/squeeze-and-excitation-networks	Squeeze-and-Excitation Networks	1709.01507	https://arxiv.org/abs/1709.01507v4	https://arxiv.org/pdf/1709.01507v4.pdf	https://github.com/simongrest/kaggle-freesound-audio-tagging-2019	false	false	true	none
https://paperswithcode.com/paper/open3d-a-modern-library-for-3d-data	Open3D: A Modern Library for 3D Data Processing	1801.09847	http://arxiv.org/abs/1801.09847v1	http://arxiv.org/pdf/1801.09847v1.pdf	https://github.com/IntelVCL/Open3D	false	false	true	tf
https://paperswithcode.com/paper/trainable-frontend-for-robust-and-far-field	Trainable Frontend For Robust and Far-Field Keyword Spotting	1607.05666	http://arxiv.org/abs/1607.05666v1	http://arxiv.org/pdf/1607.05666v1.pdf	https://github.com/simongrest/kaggle-freesound-audio-tagging-2019	false	false	true	none
https://paperswithcode.com/paper/xdeepfm-combining-explicit-and-implicit	xDeepFM: Combining Explicit and Implicit Feature Interactions for Recommender Systems	1803.05170	http://arxiv.org/abs/1803.05170v3	http://arxiv.org/pdf/1803.05170v3.pdf	https://github.com/bettenW/Tencent2019_Finals_Rank1st	false	false	true	tf
https://paperswithcode.com/paper/real-time-localization-and-tracking-of	Real-Time Localization and Tracking of Multiple Radio-Tagged Animals with an Autonomous UAV	1712.01491	http://arxiv.org/abs/1712.01491v4	http://arxiv.org/pdf/1712.01491v4.pdf	https://github.com/AdelaideAuto-IDLab/TrackerBots/tree/master/JoFR_2019	true	false	false	none
https://paperswithcode.com/paper/adversarial-autoencoders	Adversarial Autoencoders	1511.05644	http://arxiv.org/abs/1511.05644v2	http://arxiv.org/pdf/1511.05644v2.pdf	https://github.com/santi-pdp/pase	false	false	true	pytorch
https://paperswithcode.com/paper/accelerating-deep-unsupervised-domain	Accelerating Deep Unsupervised Domain Adaptation with Transfer Channel Pruning	1904.02654	http://arxiv.org/abs/1904.02654v1	http://arxiv.org/pdf/1904.02654v1.pdf	https://github.com/jindongwang/transferlearning	true	true	true	pytorch
https://paperswithcode.com/paper/faster-r-cnn-towards-real-time-object	Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks	1506.01497	http://arxiv.org/abs/1506.01497v3	http://arxiv.org/pdf/1506.01497v3.pdf	https://github.com/zacks417/faster-rcnn-tf	false	false	true	tf
https://paperswithcode.com/paper/stein-variational-gradient-descent-a-general	Stein Variational Gradient Descent: A General Purpose Bayesian Inference Algorithm	1608.04471	https://arxiv.org/abs/1608.04471v3	https://arxiv.org/pdf/1608.04471v3.pdf	https://github.com/activatedgeek/stein-gradient	false	false	false	pytorch
https://paperswithcode.com/paper/focal-loss-for-dense-object-detection	Focal Loss for Dense Object Detection	1708.02002	http://arxiv.org/abs/1708.02002v2	http://arxiv.org/pdf/1708.02002v2.pdf	https://github.com/vantupham/darknet	false	false	true	none
https://paperswithcode.com/paper/context-aware-attentive-knowledge-tracing	Context-Aware Attentive Knowledge Tracing	2007.12324	https://arxiv.org/abs/2007.12324v1	https://arxiv.org/pdf/2007.12324v1.pdf	https://github.com/arghosh/AKT	true	true	true	pytorch
https://paperswithcode.com/paper/efficient-estimation-of-word-representations	Efficient Estimation of Word Representations in Vector Space	1301.3781	http://arxiv.org/abs/1301.3781v3	http://arxiv.org/pdf/1301.3781v3.pdf	https://github.com/rohith2506/word_embeddings	false	false	true	none
https://paperswithcode.com/paper/compressing-physical-properties-of-atomic	Compressing physical properties of atomic species for improving predictive chemistry	1811.00123	http://arxiv.org/abs/1811.00123v1	http://arxiv.org/pdf/1811.00123v1.pdf	https://github.com/jeherr/element-encoder	false	false	true	tf
https://paperswithcode.com/paper/contour-knowledge-transfer-for-salient-object	Contour Knowledge Transfer for Salient Object Detection	null	http://openaccess.thecvf.com/content_ECCV_2018/html/Xin_Li_Contour_Knowledge_Transfer_ECCV_2018_paper.html	http://openaccess.thecvf.com/content_ECCV_2018/papers/Xin_Li_Contour_Knowledge_Transfer_ECCV_2018_paper.pdf	https://github.com/lixin666/C2SNet	true	true	false	none
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/comicencyclo/TransferLearning_DiscriminativeFineTuning	false	false	true	none
https://paperswithcode.com/paper/restoring-negative-information-in-few-shot	Restoring Negative Information in Few-Shot Object Detection	2010.11714	https://arxiv.org/abs/2010.11714v2	https://arxiv.org/pdf/2010.11714v2.pdf	https://github.com/yang-yk/NP-RepMet	true	true	false	mxnet
https://paperswithcode.com/paper/predictive-entropy-search-for-bayesian	Predictive Entropy Search for Bayesian Optimization with Unknown Constraints	1502.05312	http://arxiv.org/abs/1502.05312v2	http://arxiv.org/pdf/1502.05312v2.pdf	https://github.com/chongkewu/PESC-HPC	false	false	true	none
https://paperswithcode.com/paper/real-time-air-pollution-prediction-model	Real-time Air Pollution prediction model based on Spatiotemporal Big data	1805.00432	http://arxiv.org/abs/1805.00432v3	http://arxiv.org/pdf/1805.00432v3.pdf	https://github.com/vanduc103/air_analysis_v1	true	true	false	tf
https://paperswithcode.com/paper/focal-loss-for-dense-object-detection	Focal Loss for Dense Object Detection	1708.02002	http://arxiv.org/abs/1708.02002v2	http://arxiv.org/pdf/1708.02002v2.pdf	https://github.com/yhenon/pytorch-retinanet	false	false	true	pytorch
https://paperswithcode.com/paper/learning-spatiotemporal-features-with-3d	Learning Spatiotemporal Features with 3D Convolutional Networks	1412.0767	http://arxiv.org/abs/1412.0767v4	http://arxiv.org/pdf/1412.0767v4.pdf	https://github.com/AKASH2907/Content-based-Video-Recommendation	false	false	true	tf
https://paperswithcode.com/paper/increasingly-packing-multiple-facial	Increasingly Packing Multiple Facial-Informatics Modules in A Unified Deep-Learning Model via Lifelong Learning	null	https://dl.acm.org/doi/10.1145/3323873.3325053	https://dl.acm.org/doi/pdf/10.1145/3323873.3325053	https://github.com/ivclab/CPG	false	false	false	pytorch
https://paperswithcode.com/paper/integralaction-pose-driven-feature	IntegralAction: Pose-driven Feature Integration for Robust Human Action Recognition in Videos	2007.06317	https://arxiv.org/abs/2007.06317v2	https://arxiv.org/pdf/2007.06317v2.pdf	https://github.com/arunos728/arunos728.github.io	false	false	true	none
https://paperswithcode.com/paper/fully-convolutional-pixel-adaptive-image	Fully Convolutional Pixel Adaptive Image Denoiser	1807.07569	https://arxiv.org/abs/1807.07569v4	https://arxiv.org/pdf/1807.07569v4.pdf	https://github.com/csm9493/FC-AIDE	false	false	true	tf
https://paperswithcode.com/paper/joint-3d-face-reconstruction-and-dense	Joint 3D Face Reconstruction and Dense Alignment with Position Map Regression Network	1803.07835	http://arxiv.org/abs/1803.07835v1	http://arxiv.org/pdf/1803.07835v1.pdf	https://github.com/jimmy0087/faceai-master	false	false	true	tf
https://paperswithcode.com/paper/implicit-self-consistent-description-of	Implicit self-consistent description of electrolyte in plane-wave density-functional theory	1601.03346	http://arxiv.org/abs/1601.03346v1	http://arxiv.org/pdf/1601.03346v1.pdf	https://github.com/henniggroup/VASPsol	true	true	true	none
https://paperswithcode.com/paper/news-headline-grouping-as-a-challenging-nlu-1	News Headline Grouping as a Challenging NLU Task	2105.05391	https://arxiv.org/abs/2105.05391v1	https://arxiv.org/pdf/2105.05391v1.pdf	https://github.com/tingofurro/headline_grouping	true	true	false	pytorch
https://paperswithcode.com/paper/end-to-end-learning-of-lda-by-mirror-descent	End-to-end Learning of LDA by Mirror-Descent Back Propagation over a Deep Architecture	1508.03398	http://arxiv.org/abs/1508.03398v2	http://arxiv.org/pdf/1508.03398v2.pdf	https://github.com/jvking/bp-lda	true	true	false	none
https://paperswithcode.com/paper/an-optimization-approach-to-learning-falling	An Optimization Approach to Learning Falling Rule Lists	1710.02572	http://arxiv.org/abs/1710.02572v3	http://arxiv.org/pdf/1710.02572v3.pdf	https://github.com/cfchen-duke/FRLOptimization	true	true	false	none
https://paperswithcode.com/paper/dueling-network-architectures-for-deep	Dueling Network Architectures for Deep Reinforcement Learning	1511.06581	http://arxiv.org/abs/1511.06581v3	http://arxiv.org/pdf/1511.06581v3.pdf	https://github.com/wtingda/DeepRLBreakout	false	false	true	tf
https://paperswithcode.com/paper/asynchronous-methods-for-deep-reinforcement	Asynchronous Methods for Deep Reinforcement Learning	1602.01783	http://arxiv.org/abs/1602.01783v2	http://arxiv.org/pdf/1602.01783v2.pdf	https://github.com/wtingda/DeepRLBreakout	false	false	true	tf

Subsets and Splits

Framework Repo Connectivity Analysis

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

Deduplicated Paper-Code Links

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

Paper Repo Counts & Distribution

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

Quantum Papers with Code Links

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

Financial Stock Price Prediction

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

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

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

Search for YOLO Links

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

Prompt Optimization and Personalization

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