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/conditional-neural-processes	Conditional Neural Processes	1807.01613	http://arxiv.org/abs/1807.01613v1	http://arxiv.org/pdf/1807.01613v1.pdf	https://github.com/wesselb/NeuralProcesses.jl	false	false	true	none
https://paperswithcode.com/paper/supervised-multimodal-bitransformers-for	Supervised Multimodal Bitransformers for Classifying Images and Text	1909.02950	https://arxiv.org/abs/1909.02950v2	https://arxiv.org/pdf/1909.02950v2.pdf	https://github.com/IsaacRodgz/multimodal-transformers-movies	false	false	true	pytorch
https://paperswithcode.com/paper/adversarial-audio-synthesis	Adversarial Audio Synthesis	1802.04208	http://arxiv.org/abs/1802.04208v3	http://arxiv.org/pdf/1802.04208v3.pdf	https://github.com/MaxHolmberg96/WaveGAN	false	false	true	tf
https://paperswithcode.com/paper/towards-faster-reasoners-by-using-transparent	Towards Faster Reasoners By Using Transparent Huge Pages	2004.14378	https://arxiv.org/abs/2004.14378v1	https://arxiv.org/pdf/2004.14378v1.pdf	https://github.com/daajoe/thp_docker_build	false	false	true	none
https://paperswithcode.com/paper/challenging-euclidean-topological	Challenging Euclidean Topological Autoencoders	null	https://openreview.net/forum?id=P3dZuOUnyEY	https://openreview.net/pdf?id=P3dZuOUnyEY	https://github.com/BorgwardtLab/topo-ae-distances	true	true	false	pytorch
https://paperswithcode.com/paper/yolov3-an-incremental-improvement	YOLOv3: An Incremental Improvement	1804.02767	http://arxiv.org/abs/1804.02767v1	http://arxiv.org/pdf/1804.02767v1.pdf	https://github.com/harsh2011/Yolov3-Detector	false	false	true	pytorch
https://paperswithcode.com/paper/high-quality-monocular-depth-estimation-via	High Quality Monocular Depth Estimation via Transfer Learning	1812.11941	http://arxiv.org/abs/1812.11941v2	http://arxiv.org/pdf/1812.11941v2.pdf	https://github.com/Noopuragr/DepthModel	false	false	true	tf
https://paperswithcode.com/paper/tednet-a-pytorch-toolkit-for-tensor	TedNet: A Pytorch Toolkit for Tensor Decomposition Networks	2104.05018	https://arxiv.org/abs/2104.05018v2	https://arxiv.org/pdf/2104.05018v2.pdf	https://github.com/tnbar/tednet	true	true	true	pytorch
https://paperswithcode.com/paper/annual-modulations-from-secular-variations	Annual modulations from secular variations: not relaxing DAMA?	2003.03340	https://arxiv.org/abs/2003.03340v2	https://arxiv.org/pdf/2003.03340v2.pdf	https://github.com/piacent/bayes_analysis	true	true	true	none
https://paperswithcode.com/paper/task-programming-learning-data-efficient	Task Programming: Learning Data Efficient Behavior Representations	2011.13917	https://arxiv.org/abs/2011.13917v2	https://arxiv.org/pdf/2011.13917v2.pdf	https://github.com/neuroethology/TREBA	true	true	true	pytorch
https://paperswithcode.com/paper/cubic-function-fields-with-prescribed	Cubic function fields with prescribed ramification	2003.06673	https://arxiv.org/abs/2003.06673v2	https://arxiv.org/pdf/2003.06673v2.pdf	https://github.com/JRSijsling/parshin_experiments	true	true	true	none
https://paperswithcode.com/paper/can-multi-label-classification-networks-know-1	Can multi-label classification networks know what they don’t know?	null	https://openreview.net/forum?id=enKhMfthDFS	https://openreview.net/pdf?id=enKhMfthDFS	https://github.com/deeplearning-wisc/multi-label-ood	true	true	false	pytorch
https://paperswithcode.com/paper/general-audio-tagging-with-ensembling	General audio tagging with ensembling convolutional neural network and statistical features	1810.12832	http://arxiv.org/abs/1810.12832v1	http://arxiv.org/pdf/1810.12832v1.pdf	https://github.com/r0mer0m/learning_audio_modeling	false	false	true	pytorch
https://paperswithcode.com/paper/learning-to-benchmark-determining-best	Learning to Benchmark: Determining Best Achievable Misclassification Error from Training Data	1909.07192	https://arxiv.org/abs/1909.07192v1	https://arxiv.org/pdf/1909.07192v1.pdf	https://github.com/mrtnoshad/Bayes_Error_Estimator	false	false	true	none
https://paperswithcode.com/paper/bert-has-a-mouth-and-it-must-speak-bert-as-a	BERT has a Mouth, and It Must Speak: BERT as a Markov Random Field Language Model	1902.04094	http://arxiv.org/abs/1902.04094v2	http://arxiv.org/pdf/1902.04094v2.pdf	https://github.com/vatsal199/Obedient_BERT	false	false	true	none
https://paperswithcode.com/paper/a-unified-successive-pseudo-convex	A Unified Successive Pseudo-Convex Approximation Framework	1506.04972	https://arxiv.org/abs/1506.04972v2	https://arxiv.org/pdf/1506.04972v2.pdf	https://github.com/optyang/STELA	false	false	true	none
https://paperswithcode.com/paper/cutmix-regularization-strategy-to-train	CutMix: Regularization Strategy to Train Strong Classifiers with Localizable Features	1905.04899	https://arxiv.org/abs/1905.04899v2	https://arxiv.org/pdf/1905.04899v2.pdf	https://github.com/Kaushal28/CutMix-Regularization-using-PyTorch	false	false	true	pytorch
https://paperswithcode.com/paper/on-the-texture-bias-for-few-shot-cnn	On the Texture Bias for Few-Shot CNN Segmentation	2003.04052	https://arxiv.org/abs/2003.04052v3	https://arxiv.org/pdf/2003.04052v3.pdf	https://github.com/rezazad68/fewshot-segmentation	true	true	true	tf
https://paperswithcode.com/paper/kermit-complementing-transformer	KERMIT: Complementing Transformer Architectures with Encoders of Explicit Syntactic Interpretations	null	https://aclanthology.org/2020.emnlp-main.18	https://aclanthology.org/2020.emnlp-main.18.pdf	https://github.com/ART-Group-it/KERMIT	true	false	false	none
https://paperswithcode.com/paper/solving-even-parity-problems-using-traceless	Solving even-parity problems using traceless genetic programming	2110.02014	https://arxiv.org/abs/2110.02014v1	https://arxiv.org/pdf/2110.02014v1.pdf	https://github.com/mihaioltean/traceless-genetic-programming	true	true	false	none
https://paperswithcode.com/paper/inception-v4-inception-resnet-and-the-impact	Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning	1602.07261	http://arxiv.org/abs/1602.07261v2	http://arxiv.org/pdf/1602.07261v2.pdf	https://github.com/waynecoffee9/Traffic-Sign-Classifier	false	false	true	tf
https://paperswithcode.com/paper/the-winnability-of-klondike-and-many-other	The Winnability of Klondike Solitaire and Many Other Patience Games	1906.12314	https://arxiv.org/abs/1906.12314v5	https://arxiv.org/pdf/1906.12314v5.pdf	https://github.com/thecharlieblake/Solvitaire	true	false	false	none
https://paperswithcode.com/paper/dynaslam-tracking-mapping-and-inpainting-in	DynaSLAM: Tracking, Mapping and Inpainting in Dynamic Scenes	1806.05620	http://arxiv.org/abs/1806.05620v2	http://arxiv.org/pdf/1806.05620v2.pdf	https://github.com/linmeeka/slamProject	false	false	true	tf
https://paperswithcode.com/paper/multinet-real-time-joint-semantic-reasoning	MultiNet: Real-time Joint Semantic Reasoning for Autonomous Driving	1612.07695	http://arxiv.org/abs/1612.07695v2	http://arxiv.org/pdf/1612.07695v2.pdf	https://github.com/ziyuan400/video_segmentation	false	false	true	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/tsubasawb/DeepLearning_Paper	false	false	true	none
https://paperswithcode.com/paper/mobilenets-efficient-convolutional-neural	MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications	1704.04861	http://arxiv.org/abs/1704.04861v1	http://arxiv.org/pdf/1704.04861v1.pdf	https://github.com/tsubasawb/DeepLearning_Paper	false	false	true	none
https://paperswithcode.com/paper/policyspace-a-modeling-platform	PolicySpace: a modeling platform	1801.00259	http://arxiv.org/abs/1801.00259v1	http://arxiv.org/pdf/1801.00259v1.pdf	https://github.com/IpeaDISET/PolicySpace	false	false	true	none
https://paperswithcode.com/paper/self-critical-sequence-training-for-image	Self-critical Sequence Training for Image Captioning	1612.00563	http://arxiv.org/abs/1612.00563v2	http://arxiv.org/pdf/1612.00563v2.pdf	https://github.com/xiaobai714/image_caption	false	false	true	pytorch
https://paperswithcode.com/paper/flexible-marginal-models-for-dependent-data	Flexible Marginal Models for Dependent Data	2204.07188	https://arxiv.org/abs/2204.07188v1	https://arxiv.org/pdf/2204.07188v1.pdf	https://github.com/awstringer1/mam	true	true	false	none
https://paperswithcode.com/paper/unsupervised-representation-learning-with-1	Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks	1511.06434	http://arxiv.org/abs/1511.06434v2	http://arxiv.org/pdf/1511.06434v2.pdf	https://github.com/gagan16/DcGan-Tensorflow	false	false	true	tf
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/leon-liangwu/py-caffe-yolo	false	false	true	caffe2
https://paperswithcode.com/paper/ask-me-anything-dynamic-memory-networks-for	Ask Me Anything: Dynamic Memory Networks for Natural Language Processing	1506.07285	http://arxiv.org/abs/1506.07285v5	http://arxiv.org/pdf/1506.07285v5.pdf	https://github.com/scakc/QAwiki	false	false	true	none
https://paperswithcode.com/paper/video-to-video-synthesis	Video-to-Video Synthesis	1808.06601	http://arxiv.org/abs/1808.06601v2	http://arxiv.org/pdf/1808.06601v2.pdf	https://github.com/divyanshpuri02/divyansh.github.io	false	false	true	pytorch
https://paperswithcode.com/paper/diversity-in-valuing-social-contact-and-risk	Diversity in Valuing Social Contact and Risk Tolerance Lead to the Emergence of Homophily in Populations Facing Infectious Threats	2111.11362	https://arxiv.org/abs/2111.11362v1	https://arxiv.org/pdf/2111.11362v1.pdf	https://github.com/kazarraha/socdistmodel	true	true	false	none
https://paperswithcode.com/paper/bag-of-tricks-for-efficient-text	Bag of Tricks for Efficient Text Classification	1607.01759	http://arxiv.org/abs/1607.01759v3	http://arxiv.org/pdf/1607.01759v3.pdf	https://github.com/FengJiaChunFromSYSU/fastText	false	false	true	none
https://paperswithcode.com/paper/fcos-fully-convolutional-one-stage-object	FCOS: Fully Convolutional One-Stage Object Detection	1904.01355	https://arxiv.org/abs/1904.01355v5	https://arxiv.org/pdf/1904.01355v5.pdf	https://github.com/abcxs/maskrcnn-contest	false	false	true	pytorch
https://paperswithcode.com/paper/retinamask-learning-to-predict-masks-improves	RetinaMask: Learning to predict masks improves state-of-the-art single-shot detection for free	1901.03353	http://arxiv.org/abs/1901.03353v1	http://arxiv.org/pdf/1901.03353v1.pdf	https://github.com/abcxs/maskrcnn-contest	false	false	true	pytorch
https://paperswithcode.com/paper/how-to-make-chord-correct	How to Make Chord Correct	1502.06461	http://arxiv.org/abs/1502.06461v2	http://arxiv.org/pdf/1502.06461v2.pdf	https://github.com/kratikagupta-developer/CHORD-Protocol-Implementation	false	false	true	none
https://paperswithcode.com/paper/cross-domain-ensemble-distillation-for-domain-2	Cross-Domain Ensemble Distillation for Domain Generalization	2211.14058	https://arxiv.org/abs/2211.14058v1	https://arxiv.org/pdf/2211.14058v1.pdf	https://github.com/leekyungmoon/XDED	true	true	false	pytorch
https://paperswithcode.com/paper/190600722	Topological Autoencoders	1906.00722	https://arxiv.org/abs/1906.00722v5	https://arxiv.org/pdf/1906.00722v5.pdf	https://github.com/BorgwardtLab/topo-ae-distances	false	false	true	pytorch
https://paperswithcode.com/paper/qanet-combining-local-convolution-with-global	QANet: Combining Local Convolution with Global Self-Attention for Reading Comprehension	1804.09541	http://arxiv.org/abs/1804.09541v1	http://arxiv.org/pdf/1804.09541v1.pdf	https://github.com/shikhar1sharma/NLP-Resources	false	false	true	none
https://paperswithcode.com/paper/on-the-fly-aligned-data-augmentation-for	On-the-Fly Aligned Data Augmentation for Sequence-to-Sequence ASR	2104.01393	https://arxiv.org/abs/2104.01393v2	https://arxiv.org/pdf/2104.01393v2.pdf	https://github.com/StatNLP/ada4asr	true	true	false	pytorch
https://paperswithcode.com/paper/towards-the-automatic-anime-characters	Towards the Automatic Anime Characters Creation with Generative Adversarial Networks	1708.05509	http://arxiv.org/abs/1708.05509v1	http://arxiv.org/pdf/1708.05509v1.pdf	https://github.com/MasayaGit/AnimeGAN	false	false	true	pytorch
https://paperswithcode.com/paper/video-captioning-with-recurrent-networks	Video captioning with recurrent networks based on frame- and video-level features and visual content classification	1512.02949	http://arxiv.org/abs/1512.02949v1	http://arxiv.org/pdf/1512.02949v1.pdf	https://github.com/rakshithShetty/captionGAN	false	false	true	none
https://paperswithcode.com/paper/fasttrack-an-open-source-software-for	FastTrack: an open-source software for tracking varying numbers of deformable objects	2011.06837	https://arxiv.org/abs/2011.06837v1	https://arxiv.org/pdf/2011.06837v1.pdf	https://github.com/FastTrackOrg/FastTrack	true	true	false	none
https://paperswithcode.com/paper/learning-semantically-enhanced-feature-for	Learning Semantically Enhanced Feature for Fine-Grained Image Classification	2006.13457	https://arxiv.org/abs/2006.13457v3	https://arxiv.org/pdf/2006.13457v3.pdf	https://github.com/YNCao/mysef	false	false	true	pytorch
https://paperswithcode.com/paper/online-abuse-detection-the-value-of	Online abuse detection: the value of preprocessing and neural attention models	null	https://aclanthology.org/W19-1303	https://aclanthology.org/W19-1303.pdf	https://github.com/ddhruvkr/Online_Abuse_Detection	true	true	false	pytorch
https://paperswithcode.com/paper/debface-de-biasing-face-recognition	Jointly De-biasing Face Recognition and Demographic Attribute Estimation	1911.08080	https://arxiv.org/abs/1911.08080v4	https://arxiv.org/pdf/1911.08080v4.pdf	https://github.com/gongsixue/DebFace	true	true	false	pytorch
https://paperswithcode.com/paper/explaining-anomalies-detected-by-autoencoders	Explaining Anomalies Detected by Autoencoders Using SHAP	1903.02407	https://arxiv.org/abs/1903.02407v2	https://arxiv.org/pdf/1903.02407v2.pdf	https://github.com/ronniemi/explainAnomaliesUsingSHAP	true	true	false	tf
https://paperswithcode.com/paper/painting-with-baryons-augmenting-n-body	Painting with baryons: augmenting N-body simulations with gas using deep generative models	1903.12173	https://arxiv.org/abs/1903.12173v2	https://arxiv.org/pdf/1903.12173v2.pdf	https://github.com/tilmantroester/baryon_painter	true	true	true	none
https://paperswithcode.com/paper/teaching-temporal-logics-to-neural-networks	Teaching Temporal Logics to Neural Networks	2003.04218	https://arxiv.org/abs/2003.04218v3	https://arxiv.org/pdf/2003.04218v3.pdf	https://github.com/reactive-systems/deepltl	true	true	true	tf
https://paperswithcode.com/paper/smart-mc-sparse-matrix-estimation-with	SMART-MC: Characterizing the Dynamics of Multiple Sclerosis Therapy Transitions Using a Covariate-Based Markov Model	2412.03596	https://arxiv.org/abs/2412.03596v2	https://arxiv.org/pdf/2412.03596v2.pdf	https://github.com/priyamdas2/SMART-MC-MSCOR	true	false	false	none
https://paperswithcode.com/paper/ssd-single-shot-multibox-detector	SSD: Single Shot MultiBox Detector	1512.02325	http://arxiv.org/abs/1512.02325v5	http://arxiv.org/pdf/1512.02325v5.pdf	https://github.com/victordibia/handtracking	false	false	true	tf
https://paperswithcode.com/paper/corresponding-projections-for-orphan	Corresponding Projections for Orphan Screening	1812.00058	http://arxiv.org/abs/1812.00058v1	http://arxiv.org/pdf/1812.00058v1.pdf	https://github.com/diogofbraga/OrphanPrincipalComponentAnalysis	false	false	true	none
https://paperswithcode.com/paper/robustness-quantification-for-classification	Adversarial Robustness Guarantees for Classification with Gaussian Processes	1905.11876	https://arxiv.org/abs/1905.11876v3	https://arxiv.org/pdf/1905.11876v3.pdf	https://github.com/andreapatane/check-GPclass	true	true	true	none
https://paperswithcode.com/paper/real-time-and-accurate-object-detection-in	Real-Time and Accurate Object Detection in Compressed Video by Long Short-term Feature Aggregation	2103.14529	https://arxiv.org/abs/2103.14529v1	https://arxiv.org/pdf/2103.14529v1.pdf	https://github.com/hustvl/LSFA	true	true	false	mxnet
https://paperswithcode.com/paper/on-catastrophic-interference-in-atari-2600	On Catastrophic Interference in Atari 2600 Games	2002.12499	https://arxiv.org/abs/2002.12499v2	https://arxiv.org/pdf/2002.12499v2.pdf	https://github.com/google-research/google-research/tree/master/memento	true	false	false	tf
https://paperswithcode.com/paper/topological-control-of-synchronization	Topological Control of Synchronization Patterns: Trading Symmetry for Stability	1902.03255	https://arxiv.org/abs/1902.03255v1	https://arxiv.org/pdf/1902.03255v1.pdf	https://github.com/y-z-zhang/optimize_sym_cluster	true	true	false	none
https://paperswithcode.com/paper/knowledge-tracing-for-complex-problem-solving	Knowledge Tracing for Complex Problem Solving: Granular Rank-Based Tensor Factorization	2210.09013	https://arxiv.org/abs/2210.09013v1	https://arxiv.org/pdf/2210.09013v1.pdf	https://github.com/persai-lab/umap2021-grate	true	true	false	none
https://paperswithcode.com/paper/optimization-of-molecules-via-deep	Optimization of Molecules via Deep Reinforcement Learning	1810.08678	http://arxiv.org/abs/1810.08678v3	http://arxiv.org/pdf/1810.08678v3.pdf	https://github.com/google-research/google-research/tree/master/mol_dqn	true	false	false	tf
https://paperswithcode.com/paper/drop-an-octave-reducing-spatial-redundancy-in	Drop an Octave: Reducing Spatial Redundancy in Convolutional Neural Networks with Octave Convolution	1904.05049	https://arxiv.org/abs/1904.05049v3	https://arxiv.org/pdf/1904.05049v3.pdf	https://github.com/SharadGitHub/OctaveUnet	false	false	true	pytorch
https://paperswithcode.com/paper/digging-into-self-supervised-monocular-depth	Digging Into Self-Supervised Monocular Depth Estimation	1806.01260	https://arxiv.org/abs/1806.01260v4	https://arxiv.org/pdf/1806.01260v4.pdf	https://github.com/FangGet/tf-monodepth2	false	false	true	tf
https://paperswithcode.com/paper/a-fully-differentiable-beam-search-decoder	A Fully Differentiable Beam Search Decoder	1902.06022	http://arxiv.org/abs/1902.06022v1	http://arxiv.org/pdf/1902.06022v1.pdf	https://github.com/johnhw/differentiable_sorting	false	false	true	tf
https://paperswithcode.com/paper/statistical-models-for-the-analysis-of	Statistical Models for the Analysis of Optimization Algorithms with Benchmark Functions	2010.03783	https://arxiv.org/abs/2010.03783v4	https://arxiv.org/pdf/2010.03783v4.pdf	https://github.com/davidissamattos/statscomp	true	false	false	none
https://paperswithcode.com/paper/automatically-designing-cnn-architectures	Automatically designing CNN architectures using genetic algorithm for image classification	1808.03818	https://arxiv.org/abs/1808.03818v3	https://arxiv.org/pdf/1808.03818v3.pdf	https://github.com/Marius-Juston/AutoCNN	false	false	true	tf
https://paperswithcode.com/paper/an-application-of-paraexp-to-electromagnetic	An Application of ParaExp to Electromagnetic Wave Problems	1607.00368	https://arxiv.org/abs/1607.00368v2	https://arxiv.org/pdf/1607.00368v2.pdf	https://github.com/temf/paraexp	true	false	false	none
https://paperswithcode.com/paper/paraexp-using-leapfrog-as-integrator-for-high	ParaExp using Leapfrog as Integrator for High-Frequency Electromagnetic Simulations	1705.08019	https://arxiv.org/abs/1705.08019v2	https://arxiv.org/pdf/1705.08019v2.pdf	https://github.com/temf/paraexp	true	false	false	none
https://paperswithcode.com/paper/asymmetric-statistical-errors	Asymmetric Statistical Errors	physics/0406120	https://arxiv.org/abs/physics/0406120v1	https://arxiv.org/pdf/physics/0406120v1.pdf	https://github.com/muryelgp/asymmetric_uncertainties	false	false	true	none
https://paperswithcode.com/paper/are-undocumented-workers-the-same-as-illegal	Are "Undocumented Workers" the Same as "Illegal Aliens"? Disentangling Denotation and Connotation in Vector Spaces	2010.02976	https://arxiv.org/abs/2010.02976v2	https://arxiv.org/pdf/2010.02976v2.pdf	https://github.com/awebson/congressional_adversary	true	true	true	pytorch
https://paperswithcode.com/paper/sample-efficient-actor-critic-with-experience	Sample Efficient Actor-Critic with Experience Replay	1611.01224	http://arxiv.org/abs/1611.01224v2	http://arxiv.org/pdf/1611.01224v2.pdf	https://github.com/Kaixhin/ACER	false	false	true	pytorch
https://paperswithcode.com/paper/fedhca2-towards-hetero-client-federated-multi	FedHCA2: Towards Hetero-Client Federated Multi-Task Learning	null	http://openaccess.thecvf.com//content/CVPR2024/html/Lu_FedHCA2_Towards_Hetero-Client_Federated_Multi-Task_Learning_CVPR_2024_paper.html	http://openaccess.thecvf.com//content/CVPR2024/papers/Lu_FedHCA2_Towards_Hetero-Client_Federated_Multi-Task_Learning_CVPR_2024_paper.pdf	https://github.com/innovator-zero/fedhca2	true	true	false	pytorch
https://paperswithcode.com/paper/naturalization-of-text-by-the-insertion-of	Naturalization of Text by the Insertion of Pauses and Filler Words	2011.03713	https://arxiv.org/abs/2011.03713v1	https://arxiv.org/pdf/2011.03713v1.pdf	https://github.com/parthvshah/naturalization	true	false	false	none
https://paperswithcode.com/paper/probabilistic-event-calculus-for-event	Probabilistic Event Calculus for Event Recognition	1207.3270	http://arxiv.org/abs/1207.3270v2	http://arxiv.org/pdf/1207.3270v2.pdf	https://github.com/koo5/notes2	false	false	true	none
https://paperswithcode.com/paper/mean-subtraction-and-mode-selection-in	Clarifying the effect of mean subtraction on Dynamic Mode Decomposition	2105.03607	https://arxiv.org/abs/2105.03607v6	https://arxiv.org/pdf/2105.03607v6.pdf	https://github.com/gowtham-ss-ragavan/msub_mdselect_dmd	true	true	false	none
https://paperswithcode.com/paper/deeperforensics-10-a-large-scale-dataset-for	DeeperForensics-1.0: A Large-Scale Dataset for Real-World Face Forgery Detection	2001.03024	https://arxiv.org/abs/2001.03024v2	https://arxiv.org/pdf/2001.03024v2.pdf	https://github.com/EndlessSora/DeeperForensics-1.0	true	false	true	none
https://paperswithcode.com/paper/3d-object-reconstruction-from-hand-object	3D Object Reconstruction from Hand-Object Interactions	1704.00529	http://arxiv.org/abs/1704.00529v1	http://arxiv.org/pdf/1704.00529v1.pdf	https://github.com/dimtziwnas/InHandScanningICCV15_Reconstruction	true	false	false	none
https://paperswithcode.com/paper/simple-and-effective-vae-training-with	Simple and Effective VAE Training with Calibrated Decoders	2006.13202	https://arxiv.org/abs/2006.13202v3	https://arxiv.org/pdf/2006.13202v3.pdf	https://github.com/orybkin/sigma-vae	true	false	false	tf
https://paperswithcode.com/paper/multi-agent-generative-adversarial-imitation	Multi-Agent Generative Adversarial Imitation Learning	1807.09936	http://arxiv.org/abs/1807.09936v1	http://arxiv.org/pdf/1807.09936v1.pdf	https://github.com/ermongroup/multiagent-gail	false	false	true	none
https://paperswithcode.com/paper/where-s-crypto-automated-identification-and	Where's Crypto?: Automated Identification and Classification of Proprietary Cryptographic Primitives in Binary Code	2009.04274	https://arxiv.org/abs/2009.04274v1	https://arxiv.org/pdf/2009.04274v1.pdf	https://github.com/wheres-crypto/wheres-crypto	true	true	false	none
https://paperswithcode.com/paper/scalable-learning-of-non-decomposable	Scalable Learning of Non-Decomposable Objectives	1608.04802	http://arxiv.org/abs/1608.04802v2	http://arxiv.org/pdf/1608.04802v2.pdf	https://github.com/tensorflow/models/tree/master/research/global_objectives	false	false	true	tf
https://paperswithcode.com/paper/you-only-derive-once-yodo-automatic	You Only Derive Once (YODO): Automatic Differentiation for Efficient Sensitivity Analysis in Bayesian Networks	2206.08687	https://arxiv.org/abs/2206.08687v1	https://arxiv.org/pdf/2206.08687v1.pdf	https://github.com/rballester/yodo	true	true	true	pytorch
https://paperswithcode.com/paper/mri-to-ct-translation-with-gans	MRI to CT Translation with GANs	1901.05259	http://arxiv.org/abs/1901.05259v1	http://arxiv.org/pdf/1901.05259v1.pdf	https://github.com/bodokaiser/mrtoct-pytorch	true	false	false	pytorch
https://paperswithcode.com/paper/music-genre-classification-using-machine	Music Genre Classification using Machine Learning Techniques	1804.01149	http://arxiv.org/abs/1804.01149v1	http://arxiv.org/pdf/1804.01149v1.pdf	https://github.com/HareeshBahuleyan/music-genre-classification	true	true	true	tf
https://paperswithcode.com/paper/rainfall-runoff-prediction-at-multiple	Rainfall-Runoff Prediction at Multiple Timescales with a Single Long Short-Term Memory Network	2010.07921	https://arxiv.org/abs/2010.07921v1	https://arxiv.org/pdf/2010.07921v1.pdf	https://github.com/gauchm/mts-lstm	true	true	true	pytorch
https://paperswithcode.com/paper/empty-cities-a-dynamic-object-invariant-space	Empty Cities: a Dynamic-Object-Invariant Space for Visual SLAM	2010.07646	https://arxiv.org/abs/2010.07646v1	https://arxiv.org/pdf/2010.07646v1.pdf	https://github.com/bertabescos/EmptyCities_SLAM	true	true	false	pytorch
https://paperswithcode.com/paper/new-evolutionary-computation-models-and-their	New Evolutionary Computation Models and their Applications to Machine Learning	2110.00468	https://arxiv.org/abs/2110.00468v1	https://arxiv.org/pdf/2110.00468v1.pdf	https://github.com/mihaioltean/traceless-genetic-programming	true	false	false	none
https://paperswithcode.com/paper/vehicle-predictive-trajectory-patterns-from	Vehicle predictive trajectory patterns from isochronous data	2010.05026	https://arxiv.org/abs/2010.05026v2	https://arxiv.org/pdf/2010.05026v2.pdf	https://github.com/Seeker3000/AUD	true	false	false	none
https://paperswithcode.com/paper/a-run-and-tumble-model-with-autochemotaxis	A Run-and-Tumble Model with Autochemotaxis	2009.03221	https://arxiv.org/abs/2009.03221v1	https://arxiv.org/pdf/2009.03221v1.pdf	https://github.com/Louminator/Plankton_Signal_RT	true	true	false	none
https://paperswithcode.com/paper/zinet-linking-chinese-characters-spanning	ZiNet: Linking Chinese Characters Spanning Three Thousand Years	null	https://aclanthology.org/2022.findings-acl.242	https://aclanthology.org/2022.findings-acl.242.pdf	https://github.com/yangchijlu/ancientchinesecharsim	true	true	false	pytorch
https://paperswithcode.com/paper/low-dose-ct-image-denoising-using-a	Low Dose CT Image Denoising Using a Generative Adversarial Network with Wasserstein Distance and Perceptual Loss	1708.00961	http://arxiv.org/abs/1708.00961v2	http://arxiv.org/pdf/1708.00961v2.pdf	https://github.com/SSinyu/WGAN-VGG	false	false	true	pytorch
https://paperswithcode.com/paper/190807906	PCRNet: Point Cloud Registration Network using PointNet Encoding	1908.07906	https://arxiv.org/abs/1908.07906v2	https://arxiv.org/pdf/1908.07906v2.pdf	https://github.com/vinits5/pcrnet	true	true	true	tf
https://paperswithcode.com/paper/human-and-automatic-detection-of-generated	Automatic Detection of Generated Text is Easiest when Humans are Fooled	1911.00650	https://arxiv.org/abs/1911.00650v2	https://arxiv.org/pdf/1911.00650v2.pdf	https://github.com/kirubarajan/roft	false	false	true	none
https://paperswithcode.com/paper/correlation-aware-deep-generative-model-for	Correlation-aware Deep Generative Model for Unsupervised Anomaly Detection	2002.07349	https://arxiv.org/abs/2002.07349v3	https://arxiv.org/pdf/2002.07349v3.pdf	https://github.com/haoyfan/CADGMM	true	false	true	tf
https://paperswithcode.com/paper/towards-ai-complete-question-answering-a-set	Towards AI-Complete Question Answering: A Set of Prerequisite Toy Tasks	1502.05698	http://arxiv.org/abs/1502.05698v10	http://arxiv.org/pdf/1502.05698v10.pdf	https://github.com/kirubarajan/roft	false	false	true	none
https://paperswithcode.com/paper/verification-of-hierarchical-artifact-systems	Verification of Hierarchical Artifact Systems	1604.00967	http://arxiv.org/abs/1604.00967v1	http://arxiv.org/pdf/1604.00967v1.pdf	https://github.com/oi02lyl/has-verifier	false	false	true	none
https://paperswithcode.com/paper/discontinuous-transition-of-molecular	Discontinuous transition of molecular-hydrogen chain to the quasi-atomic state: Exact diagonalization - ab initio approach	1506.03356	https://arxiv.org/abs/1506.03356v2	https://arxiv.org/pdf/1506.03356v2.pdf	https://bitbucket.org/azja/qmt	true	true	false	none
https://paperswithcode.com/paper/metallization-of-solid-molecular-hydrogen-in	Metallization of solid molecular hydrogen in two dimensions: Mott-Hubbard-type transition	1702.06575	https://arxiv.org/abs/1702.06575v1	https://arxiv.org/pdf/1702.06575v1.pdf	https://bitbucket.org/azja/qmt	true	false	false	none
https://paperswithcode.com/paper/combined-shared-and-distributed-memory-ab	Combined shared and distributed memory ab-initio computations of molecular-hydrogen systems in the correlated state: process pool solution and two-level parallelism	1504.00500	https://arxiv.org/abs/1504.00500v3	https://arxiv.org/pdf/1504.00500v3.pdf	https://bitbucket.org/azja/qmt	true	true	false	none
https://paperswithcode.com/paper/dot-ring-nanostructure-rigorous-analysis-of	Dot-ring nanostructure: Rigorous analysis of many-electron effects	1605.01195	https://arxiv.org/abs/1605.01195v1	https://arxiv.org/pdf/1605.01195v1.pdf	https://bitbucket.org/azja/qmt	true	true	false	none
https://paperswithcode.com/paper/automatic-design-of-mechanical-metamaterial	Automatic Design of Mechanical Metamaterial Actuators	2002.03032	https://arxiv.org/abs/2002.03032v1	https://arxiv.org/pdf/2002.03032v1.pdf	https://github.com/ComplexityBiosystems/metamech_datasets	false	false	true	none

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.