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/learning-rich-features-at-high-speed-for	Learning Rich Features at High-Speed for Single-Shot Object Detection	null	http://openaccess.thecvf.com/content_ICCV_2019/html/Wang_Learning_Rich_Features_at_High-Speed_for_Single-Shot_Object_Detection_ICCV_2019_paper.html	http://openaccess.thecvf.com/content_ICCV_2019/papers/Wang_Learning_Rich_Features_at_High-Speed_for_Single-Shot_Object_Detection_ICCV_2019_paper.pdf	https://github.com/vaesl/LRF-Net	true	true	false	pytorch
https://paperswithcode.com/paper/f-gan-training-generative-neural-samplers	f-GAN: Training Generative Neural Samplers using Variational Divergence Minimization	1606.00709	http://arxiv.org/abs/1606.00709v1	http://arxiv.org/pdf/1606.00709v1.pdf	https://github.com/mboudiaf/Mutual-Information-Variational-Bounds	false	false	true	tf
https://paperswithcode.com/paper/nltk-the-natural-language-toolkit	NLTK: The Natural Language Toolkit	cs/0205028	https://arxiv.org/abs/cs/0205028v1	https://arxiv.org/pdf/cs/0205028v1.pdf	https://github.com/napakalas/NLIMED	false	false	true	tf
https://paperswithcode.com/paper/an-architecture-combining-convolutional	An Architecture Combining Convolutional Neural Network (CNN) and Support Vector Machine (SVM) for Image Classification	1712.03541	http://arxiv.org/abs/1712.03541v2	http://arxiv.org/pdf/1712.03541v2.pdf	https://github.com/da-moon/classifiers-monorepo	false	false	true	tf
https://paperswithcode.com/paper/deep-forest	Deep Forest	1702.08835	https://arxiv.org/abs/1702.08835v4	https://arxiv.org/pdf/1702.08835v4.pdf	https://github.com/da-moon/classifiers-monorepo	false	false	true	tf
https://paperswithcode.com/paper/a-neural-network-architecture-combining-gated	A Neural Network Architecture Combining Gated Recurrent Unit (GRU) and Support Vector Machine (SVM) for Intrusion Detection in Network Traffic Data	1709.03082	http://arxiv.org/abs/1709.03082v8	http://arxiv.org/pdf/1709.03082v8.pdf	https://github.com/da-moon/classifiers-monorepo	false	false	true	tf
https://paperswithcode.com/paper/high-throughput-open-source-implementation-of	High Throughput Open-Source Implementation of Wi-Fi 6 and WiMAX LDPC Encoder and Decoder	2306.12063	https://arxiv.org/abs/2306.12063v1	https://arxiv.org/pdf/2306.12063v1.pdf	https://github.com/talenik/yaldpc	true	true	false	none
https://paperswithcode.com/paper/achieving-open-vocabulary-neural-machine	Achieving Open Vocabulary Neural Machine Translation with Hybrid Word-Character Models	1604.00788	http://arxiv.org/abs/1604.00788v2	http://arxiv.org/pdf/1604.00788v2.pdf	https://github.com/yurayli/stanford-cs224n-sol	false	false	true	pytorch
https://paperswithcode.com/paper/qubo-formulations-for-system-of-linear	QUBO formulations for numerical quantum computing	2106.10819	https://arxiv.org/abs/2106.10819v4	https://arxiv.org/pdf/2106.10819v4.pdf	https://github.com/ktfriends/QUBO/blob/main/Formulations.ipynb	true	false	false	none
https://paperswithcode.com/paper/continuous-dropout	Continuous Dropout	1911.12675	https://arxiv.org/abs/1911.12675v1	https://arxiv.org/pdf/1911.12675v1.pdf	https://github.com/jasonustc/caffe-multigpu	true	true	false	none
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/Dycollapsar/Attention-Based-for-Medicalimaging	false	false	true	none
https://paperswithcode.com/paper/falcon-an-accurate-real-time-monitor-for	FALCON: An accurate real-time monitor for client-based mobile network data analytics	1907.10110	https://arxiv.org/abs/1907.10110v2	https://arxiv.org/pdf/1907.10110v2.pdf	https://github.com/falkenber9/falcon	true	true	true	none
https://paperswithcode.com/paper/wavelet-convolutional-neural-networks-for	Wavelet Convolutional Neural Networks for Texture Classification	1707.07394	http://arxiv.org/abs/1707.07394v1	http://arxiv.org/pdf/1707.07394v1.pdf	https://github.com/menon92/WaveletCNN	false	false	false	tf
https://paperswithcode.com/paper/joint-unsupervised-learning-of-optical-flow	Joint Unsupervised Learning of Optical Flow and Depth by Watching Stereo Videos	1810.03654	http://arxiv.org/abs/1810.03654v1	http://arxiv.org/pdf/1810.03654v1.pdf	https://github.com/baidu-research/UnDepthflow	true	true	true	tf
https://paperswithcode.com/paper/rgtsvm-support-vector-machines-on-a-gpu-in-r	Rgtsvm: Support Vector Machines on a GPU in R	1706.05544	http://arxiv.org/abs/1706.05544v1	http://arxiv.org/pdf/1706.05544v1.pdf	https://github.com/Danko-Lab/Rgtsvm	true	true	true	none
https://paperswithcode.com/paper/the-cosmic-linear-anisotropy-solving-system-1	The Cosmic Linear Anisotropy Solving System (CLASS) II: Approximation schemes	1104.2933	http://arxiv.org/abs/1104.2933v3	http://arxiv.org/pdf/1104.2933v3.pdf	https://github.com/PoulinV/class_interacting_neutrinos	false	false	true	none
https://paperswithcode.com/paper/rethinking-motion-deblurring-training-a	Rethinking Motion Deblurring Training: A Segmentation-Based Method for Simulating Non-Uniform Motion Blurred Images	2209.12675	https://arxiv.org/abs/2209.12675v1	https://arxiv.org/pdf/2209.12675v1.pdf	https://github.com/guillermocarbajal/segmentationbaseddeblurringdataset	true	true	false	tf
https://paperswithcode.com/paper/3d-manhattan-room-layout-reconstruction-from	Manhattan Room Layout Reconstruction from a Single 360 image: A Comparative Study of State-of-the-art Methods	1910.04099	https://arxiv.org/abs/1910.04099v3	https://arxiv.org/pdf/1910.04099v3.pdf	https://github.com/sunset1995/HorizonNet	false	false	true	pytorch
https://paperswithcode.com/paper/toyadmos-a-dataset-of-miniature-machine	ToyADMOS: A Dataset of Miniature-Machine Operating Sounds for Anomalous Sound Detection	1908.03299	https://arxiv.org/abs/1908.03299v1	https://arxiv.org/pdf/1908.03299v1.pdf	https://github.com/YumaKoizumi/ToyADMOS-dataset	true	true	true	none
https://paperswithcode.com/paper/gnn-explainer-a-tool-for-post-hoc-explanation	GNNExplainer: Generating Explanations for Graph Neural Networks	1903.03894	https://arxiv.org/abs/1903.03894v4	https://arxiv.org/pdf/1903.03894v4.pdf	https://github.com/anshul3899/GNNExplainer-Experiments	false	false	true	pytorch
https://paperswithcode.com/paper/collective-optimization-for-variational	Collective optimization for variational quantum eigensolvers	1910.14030	https://arxiv.org/abs/1910.14030v1	https://arxiv.org/pdf/1910.14030v1.pdf	https://github.com/QuContractor/VQE_tutorial	false	false	true	none
https://paperswithcode.com/paper/tha3aroon-at-nsurl-2019-task-8-semantic	Tha3aroon at NSURL-2019 Task 8: Semantic Question Similarity in Arabic	1912.12514	https://arxiv.org/abs/1912.12514v1	https://arxiv.org/pdf/1912.12514v1.pdf	https://github.com/AliOsm/semantic-question-similarity	true	true	true	none
https://paperswithcode.com/paper/physics-informed-deep-learning-part-ii-data	Physics Informed Deep Learning (Part II): Data-driven Discovery of Nonlinear Partial Differential Equations	1711.10566	http://arxiv.org/abs/1711.10566v1	http://arxiv.org/pdf/1711.10566v1.pdf	https://github.com/pierremtb/PINNs-TF2.0	false	false	true	tf
https://paperswithcode.com/paper/adversarial-robustness-guarantees-for	Adversarial Robustness Guarantees for Gaussian Processes	2104.03180	https://arxiv.org/abs/2104.03180v1	https://arxiv.org/pdf/2104.03180v1.pdf	https://github.com/andreapatane/check-GPclass	true	true	false	none
https://paperswithcode.com/paper/planck-2015-results-xi-cmb-power-spectra	Planck 2015 results. XI. CMB power spectra, likelihoods, and robustness of parameters	1507.02704	https://arxiv.org/abs/1507.02704v3	https://arxiv.org/pdf/1507.02704v3.pdf	https://github.com/heatherprince/cosmoped	false	false	true	none
https://paperswithcode.com/paper/field-aware-factorization-machines-in-a-real	Field-aware Factorization Machines in a Real-world Online Advertising System	1701.04099	http://arxiv.org/abs/1701.04099v3	http://arxiv.org/pdf/1701.04099v3.pdf	https://github.com/cpapadimitriou/Click-Through-Rate-prediction	false	false	true	none
https://paperswithcode.com/paper/glas-global-to-local-safe-autonomy-synthesis	GLAS: Global-to-Local Safe Autonomy Synthesis for Multi-Robot Motion Planning with End-to-End Learning	2002.11807	https://arxiv.org/abs/2002.11807v3	https://arxiv.org/pdf/2002.11807v3.pdf	https://github.com/bpriviere/glas	true	true	true	none
https://paperswithcode.com/paper/neural-machine-translation-by-jointly	Neural Machine Translation by Jointly Learning to Align and Translate	1409.0473	http://arxiv.org/abs/1409.0473v7	http://arxiv.org/pdf/1409.0473v7.pdf	https://github.com/yurayli/stanford-cs224n-sol	false	false	true	pytorch
https://paperswithcode.com/paper/words-can-shift-dynamically-adjusting-word	Words Can Shift: Dynamically Adjusting Word Representations Using Nonverbal Behaviors	1811.09362	http://arxiv.org/abs/1811.09362v2	http://arxiv.org/pdf/1811.09362v2.pdf	https://github.com/righ120/multimodal_nlp	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/ryan-perk/olympic_mining	false	false	true	none
https://paperswithcode.com/paper/constructing-metropolis-hastings-proposals	Constructing Metropolis-Hastings proposals using damped BFGS updates	1801.01243	http://arxiv.org/abs/1801.01243v2	http://arxiv.org/pdf/1801.01243v2.pdf	https://github.com/compops/qnmh-sysid2018	true	true	true	none
https://paperswithcode.com/paper/ms-marco-a-human-generated-machine-reading	MS MARCO: A Human Generated MAchine Reading COmprehension Dataset	1611.09268	http://arxiv.org/abs/1611.09268v3	http://arxiv.org/pdf/1611.09268v3.pdf	https://github.com/microsoft/MSMARCO-OpenKP	false	false	true	none
https://paperswithcode.com/paper/variational-cross-domain-natural-language	Variational Cross-domain Natural Language Generation for Spoken Dialogue Systems	1812.08879	http://arxiv.org/abs/1812.08879v1	http://arxiv.org/pdf/1812.08879v1.pdf	https://github.com/andy194673/nlg-scvae	false	false	true	pytorch
https://paperswithcode.com/paper/large-scale-study-of-curiosity-driven	Large-Scale Study of Curiosity-Driven Learning	1808.04355	http://arxiv.org/abs/1808.04355v1	http://arxiv.org/pdf/1808.04355v1.pdf	https://github.com/SPark9625/Large-Scale-Study-of-Curiosity-Driven-Learning	false	false	true	pytorch
https://paperswithcode.com/paper/unos-unified-unsupervised-optical-flow-and	UnOS: Unified Unsupervised Optical-Flow and Stereo-Depth Estimation by Watching Videos	null	http://openaccess.thecvf.com/content_CVPR_2019/html/Wang_UnOS_Unified_Unsupervised_Optical-Flow_and_Stereo-Depth_Estimation_by_Watching_Videos_CVPR_2019_paper.html	http://openaccess.thecvf.com/content_CVPR_2019/papers/Wang_UnOS_Unified_Unsupervised_Optical-Flow_and_Stereo-Depth_Estimation_by_Watching_Videos_CVPR_2019_paper.pdf	https://github.com/baidu-research/UnDepthflow	false	false	false	tf
https://paperswithcode.com/paper/microsoft-coco-common-objects-in-context	Microsoft COCO: Common Objects in Context	1405.0312	http://arxiv.org/abs/1405.0312v3	http://arxiv.org/pdf/1405.0312v3.pdf	https://github.com/vlcekl/n2n-tomo	false	false	true	pytorch
https://paperswithcode.com/paper/geometric-learning-of-the-conformational	Geometric learning of the conformational dynamics of molecules using dynamic graph neural networks	2106.13277	https://arxiv.org/abs/2106.13277v1	https://arxiv.org/pdf/2106.13277v1.pdf	https://github.com/pnnl/mol_dgnn	true	true	false	pytorch
https://paperswithcode.com/paper/asteroseismology-of-16000-kepler-red-giants	Asteroseismology of 16000 Kepler Red Giants: Global Oscillation Parameters, Masses, and Radii	1802.04455	http://arxiv.org/abs/1802.04455v2	http://arxiv.org/pdf/1802.04455v2.pdf	https://github.com/rodrigcd/Recurrent_parameter_estimation	false	false	true	tf
https://paperswithcode.com/paper/airsim-high-fidelity-visual-and-physical	AirSim: High-Fidelity Visual and Physical Simulation for Autonomous Vehicles	1705.05065	http://arxiv.org/abs/1705.05065v2	http://arxiv.org/pdf/1705.05065v2.pdf	https://github.com/jgaleav/AirSim	false	false	true	tf
https://paperswithcode.com/paper/convolutional-neural-network-architecture-for	Convolutional neural network architecture for geometric matching	1703.05593	http://arxiv.org/abs/1703.05593v2	http://arxiv.org/pdf/1703.05593v2.pdf	https://github.com/Semanti1/cnngeometric_pytorch	false	false	true	pytorch
https://paperswithcode.com/paper/lowest-dimensional-portals-to-su-n-exotics	Lowest Dimensional Portals to SU($N$) Exotics	2010.05827	http://arxiv.org/abs/2010.05827v1	http://arxiv.org/pdf/2010.05827v1.pdf	https://github.com/jaulbric/Tesselation	true	true	false	none
https://paperswithcode.com/paper/lowresourceeval-2019-a-shared-task-on	LowResourceEval-2019: a shared task on morphological analysis for low-resource languages	2001.11285	https://arxiv.org/abs/2001.11285v1	https://arxiv.org/pdf/2001.11285v1.pdf	https://github.com/lowresource-lang-eval/morphology_scripts	true	true	false	none
https://paperswithcode.com/paper/a-simple-dynamization-of-trapezoidal-point	A Simple Dynamization of Trapezoidal Point Location in Planar Subdivisions	1912.03389	https://arxiv.org/abs/1912.03389v1	https://arxiv.org/pdf/1912.03389v1.pdf	https://github.com/milutinB/dynamic_trapezoidal_map_impl	true	true	true	none
https://paperswithcode.com/paper/pointnet-deep-learning-on-point-sets-for-3d	PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation	1612.00593	http://arxiv.org/abs/1612.00593v2	http://arxiv.org/pdf/1612.00593v2.pdf	https://github.com/GOD-GOD-Autonomous-Vehicle/self-pointnet	false	false	true	pytorch
https://paperswithcode.com/paper/rethinking-atrous-convolution-for-semantic	Rethinking Atrous Convolution for Semantic Image Segmentation	1706.05587	http://arxiv.org/abs/1706.05587v3	http://arxiv.org/pdf/1706.05587v3.pdf	https://github.com/giovanniguidi/deeplabV3_Pytorch	false	false	true	pytorch
https://paperswithcode.com/paper/speeding-up-vp9-intra-encoder-with	Speeding up VP9 Intra Encoder with Hierarchical Deep Learning Based Partition Prediction	1906.06476	https://arxiv.org/abs/1906.06476v2	https://arxiv.org/pdf/1906.06476v2.pdf	https://github.com/Somdyuti2/H-FCN	true	true	true	tf
https://paperswithcode.com/paper/encoder-decoder-with-atrous-separable	Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation	1802.02611	http://arxiv.org/abs/1802.02611v3	http://arxiv.org/pdf/1802.02611v3.pdf	https://github.com/giovanniguidi/deeplabV3_Pytorch	false	false	true	pytorch
https://paperswithcode.com/paper/transform-invariant-convolutional-neural	Transform-Invariant Convolutional Neural Networks for Image Classification and Search	1912.01447	https://arxiv.org/abs/1912.01447v1	https://arxiv.org/pdf/1912.01447v1.pdf	https://github.com/jasonustc/caffe-multigpu	true	true	false	none
https://paperswithcode.com/paper/network-trimming-a-data-driven-neuron-pruning	Network Trimming: A Data-Driven Neuron Pruning Approach towards Efficient Deep Architectures	1607.03250	http://arxiv.org/abs/1607.03250v1	http://arxiv.org/pdf/1607.03250v1.pdf	https://github.com/Mind23-2/MindCode-24	false	false	false	mindspore
https://paperswithcode.com/paper/streaming-word-embeddings-with-the-space	Streaming Word Embeddings with the Space-Saving Algorithm	1704.07463	http://arxiv.org/abs/1704.07463v1	http://arxiv.org/pdf/1704.07463v1.pdf	https://github.com/cjmay/athena	true	true	true	none
https://paperswithcode.com/paper/sentence-bert-sentence-embeddings-using	Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks	1908.10084	https://arxiv.org/abs/1908.10084v1	https://arxiv.org/pdf/1908.10084v1.pdf	https://github.com/aneesha/SiameseBERT-Notebook	false	false	true	none
https://paperswithcode.com/paper/darts-differentiable-architecture-search	DARTS: Differentiable Architecture Search	1806.09055	http://arxiv.org/abs/1806.09055v2	http://arxiv.org/pdf/1806.09055v2.pdf	https://github.com/google-research/google-research/tree/master/enas_lm	false	false	true	tf
https://paperswithcode.com/paper/regularizing-and-optimizing-lstm-language	Regularizing and Optimizing LSTM Language Models	1708.02182	http://arxiv.org/abs/1708.02182v1	http://arxiv.org/pdf/1708.02182v1.pdf	https://github.com/google-research/google-research/tree/master/enas_lm	false	false	true	tf
https://paperswithcode.com/paper/mect-multi-metadata-embedding-based-cross	MECT: Multi-Metadata Embedding based Cross-Transformer for Chinese Named Entity Recognition	2107.05418	https://arxiv.org/abs/2107.05418v1	https://arxiv.org/pdf/2107.05418v1.pdf	https://github.com/CoderMusou/MECT4CNER	true	true	true	pytorch
https://paperswithcode.com/paper/image-to-image-translation-with-conditional	Image-to-Image Translation with Conditional Adversarial Networks	1611.07004	http://arxiv.org/abs/1611.07004v3	http://arxiv.org/pdf/1611.07004v3.pdf	https://github.com/vaibhavjindal/pix2pix-pytorch	false	false	true	pytorch
https://paperswithcode.com/paper/analyzing-machine-learning-workloads-using-a	Analyzing Machine Learning Workloads Using a Detailed GPU Simulator	1811.08933	http://arxiv.org/abs/1811.08933v1	http://arxiv.org/pdf/1811.08933v1.pdf	https://github.com/prdalmia/gpgpu-sim-tlb	false	false	true	pytorch
https://paperswithcode.com/paper/pixel-wise-motion-deblurring-of-thermal	Pixel-Wise Motion Deblurring of Thermal Videos	2006.04973	https://arxiv.org/abs/2006.04973v1	https://arxiv.org/pdf/2006.04973v1.pdf	https://github.com/umautobots/pixelwise-deblurring	false	false	true	none
https://paperswithcode.com/paper/limitations-of-lazy-training-of-two-layers	Limitations of Lazy Training of Two-layers Neural Networks	1906.08899	https://arxiv.org/abs/1906.08899v1	https://arxiv.org/pdf/1906.08899v1.pdf	https://github.com/bGhorbani/Lazy-Training-Neural-Nets	false	false	true	tf
https://paperswithcode.com/paper/a-multimodal-deep-learning-framework-for	A multimodal deep learning framework for scalable content based visual media retrieval	2105.08665	https://arxiv.org/abs/2105.08665v1	https://arxiv.org/pdf/2105.08665v1.pdf	https://github.com/ambareeshravi/media_retrieval	true	true	true	none
https://paperswithcode.com/paper/a-general-and-adaptive-robust-loss-function	A General and Adaptive Robust Loss Function	1701.03077	http://arxiv.org/abs/1701.03077v10	http://arxiv.org/pdf/1701.03077v10.pdf	https://github.com/jonbarron/robust_loss_pytorch	false	false	true	pytorch
https://paperswithcode.com/paper/an-entropy-stable-discontinuous-galerkin	An entropy stable discontinuous Galerkin method for the two-layer shallow water equations on curvilinear meshes	2306.12699	https://arxiv.org/abs/2306.12699v1	https://arxiv.org/pdf/2306.12699v1.pdf	https://github.com/trixi-framework/paper-2023-es_two_layer	true	true	false	none
https://paperswithcode.com/paper/cullnet-calibrated-and-pose-aware-confidence	CullNet: Calibrated and Pose Aware Confidence Scores for Object Pose Estimation	1909.13476	https://arxiv.org/abs/1909.13476v1	https://arxiv.org/pdf/1909.13476v1.pdf	https://github.com/kartikgupta-at-anu/CullNet	true	true	true	pytorch
https://paperswithcode.com/paper/semantic-image-synthesis-with-spatially	Semantic Image Synthesis with Spatially-Adaptive Normalization	1903.07291	https://arxiv.org/abs/1903.07291v2	https://arxiv.org/pdf/1903.07291v2.pdf	https://github.com/Kokonut133/frame2frame	false	false	true	tf
https://paperswithcode.com/paper/flashlight-cnn-image-denoising	Flashlight CNN Image Denoising	2003.00762	https://arxiv.org/abs/2003.00762v2	https://arxiv.org/pdf/2003.00762v2.pdf	https://github.com/binhpht/flashlightCNN	true	true	true	none
https://paperswithcode.com/paper/first-exit-time-analysis-of-stochastic	First Exit Time Analysis of Stochastic Gradient Descent Under Heavy-Tailed Gradient Noise	1906.09069	https://arxiv.org/abs/1906.09069v1	https://arxiv.org/pdf/1906.09069v1.pdf	https://github.com/umutsimsekli/sgd_first_exit_time	true	false	false	pytorch
https://paperswithcode.com/paper/expressive-power-of-tensor-network-1	Expressive power of tensor-network factorizations for probabilistic modeling	null	http://papers.nips.cc/paper/8429-expressive-power-of-tensor-network-factorizations-for-probabilistic-modeling	http://papers.nips.cc/paper/8429-expressive-power-of-tensor-network-factorizations-for-probabilistic-modeling.pdf	https://github.com/glivan/tensor_networks_for_probabilistic_modeling	true	true	false	none
https://paperswithcode.com/paper/importance-resampling-for-off-policy	Importance Resampling for Off-policy Prediction	1906.04328	https://arxiv.org/abs/1906.04328v2	https://arxiv.org/pdf/1906.04328v2.pdf	https://github.com/mkschleg/Resampling.jl	true	false	false	none
https://paperswithcode.com/paper/metaquant-learning-to-quantize-by-learning-to	MetaQuant: Learning to Quantize by Learning to Penetrate Non-differentiable Quantization	null	http://papers.nips.cc/paper/8647-metaquant-learning-to-quantize-by-learning-to-penetrate-non-differentiable-quantization	http://papers.nips.cc/paper/8647-metaquant-learning-to-quantize-by-learning-to-penetrate-non-differentiable-quantization.pdf	https://github.com/csyhhu/MetaQuant	true	true	false	pytorch
https://paperswithcode.com/paper/phyre-a-new-benchmark-for-physical-reasoning	PHYRE: A New Benchmark for Physical Reasoning	1908.05656	https://arxiv.org/abs/1908.05656v1	https://arxiv.org/pdf/1908.05656v1.pdf	https://github.com/facebookresearch/phyre	true	false	false	none
https://paperswithcode.com/paper/towards-a-zero-one-law-for-entrywise-low-rank	Towards a Zero-One Law for Column Subset Selection	1811.01442	https://arxiv.org/abs/1811.01442v2	https://arxiv.org/pdf/1811.01442v2.pdf	https://github.com/zpl7840/general_loss_column_subset_selection	true	false	false	none
https://paperswithcode.com/paper/semantically-regularized-logic-graph	Embedding Symbolic Knowledge into Deep Networks	1909.01161	https://arxiv.org/abs/1909.01161v4	https://arxiv.org/pdf/1909.01161v4.pdf	https://github.com/ZiweiXU/LENSR	true	true	false	pytorch
https://paperswithcode.com/paper/limitations-of-lazy-training-of-two-layers-1	Limitations of Lazy Training of Two-layers Neural Network	null	http://papers.nips.cc/paper/9111-limitations-of-lazy-training-of-two-layers-neural-network	http://papers.nips.cc/paper/9111-limitations-of-lazy-training-of-two-layers-neural-network.pdf	https://github.com/bGhorbani/Lazy-Training-Neural-Nets	true	false	false	tf
https://paperswithcode.com/paper/neural-discrete-representation-learning	Neural Discrete Representation Learning	1711.00937	http://arxiv.org/abs/1711.00937v2	http://arxiv.org/pdf/1711.00937v2.pdf	https://github.com/iomanker/VQVAE-TF2	false	false	true	tf
https://paperswithcode.com/paper/reinforcement-learning-with-convex	Reinforcement Learning with Convex Constraints	1906.09323	https://arxiv.org/abs/1906.09323v2	https://arxiv.org/pdf/1906.09323v2.pdf	https://github.com/xkianteb/ApproPO	true	false	false	pytorch
https://paperswithcode.com/paper/surfing-iterative-optimization-over	Surfing: Iterative optimization over incrementally trained deep networks	1907.08653	https://arxiv.org/abs/1907.08653v1	https://arxiv.org/pdf/1907.08653v1.pdf	https://github.com/jdlafferty/surfing	true	false	false	tf
https://paperswithcode.com/paper/a-neurally-plausible-model-learns-successor	A neurally plausible model learns successor representations in partially observable environments	1906.09480	https://arxiv.org/abs/1906.09480v1	https://arxiv.org/pdf/1906.09480v1.pdf	https://github.com/evertes/distributional_SF	true	false	false	none
https://paperswithcode.com/paper/compositional-plan-vectors	Compositional Plan Vectors	null	http://papers.nips.cc/paper/9636-compositional-plan-vectors	http://papers.nips.cc/paper/9636-compositional-plan-vectors.pdf	https://github.com/cdevin/cpv	true	false	false	pytorch
https://paperswithcode.com/paper/compiler-auto-vectorization-with-imitation	Compiler Auto-Vectorization with Imitation Learning	null	http://papers.nips.cc/paper/9604-compiler-auto-vectorization-with-imitation-learning	http://papers.nips.cc/paper/9604-compiler-auto-vectorization-with-imitation-learning.pdf	https://github.com/ithemal/vemal	true	false	false	none
https://paperswithcode.com/paper/integrating-semantics-and-neighborhood	Integrating Semantics and Neighborhood Information with Graph-Driven Generative Models for Document Retrieval	2105.13066	https://arxiv.org/abs/2105.13066v1	https://arxiv.org/pdf/2105.13066v1.pdf	https://github.com/MindSpore-paper-code-3/code9/tree/main/snuh	false	false	false	mindspore
https://paperswithcode.com/paper/deep-residual-learning-for-image-recognition	Deep Residual Learning for Image Recognition	1512.03385	http://arxiv.org/abs/1512.03385v1	http://arxiv.org/pdf/1512.03385v1.pdf	https://github.com/MegEngine/Models/tree/master/official/vision/classification/resnet	false	false	false	none
https://paperswithcode.com/paper/an-information-theoretic-framework-for-the	An Information-theoretic Framework for the Lossy Compression of Link Streams	1807.06874	http://arxiv.org/abs/1807.06874v1	http://arxiv.org/pdf/1807.06874v1.pdf	https://github.com/Lamarche-Perrin/greedy-graph-compression	false	false	true	none
https://paperswithcode.com/paper/matrix-product-states-and-the-nonabelian	Matrix product states and the nonabelian rotor model	1507.06624	http://arxiv.org/abs/1507.06624v2	http://arxiv.org/pdf/1507.06624v2.pdf	https://github.com/amilsted/mps-rotors	false	false	true	none
https://paperswithcode.com/paper/capsules-with-inverted-dot-product-attention-1	Capsules with Inverted Dot-Product Attention Routing	2002.04764	https://arxiv.org/abs/2002.04764v2	https://arxiv.org/pdf/2002.04764v2.pdf	https://github.com/yaohungt/Capsules-Inverted-Attention-Routing	true	true	false	pytorch
https://paperswithcode.com/paper/learning-to-predict-without-looking-ahead	Learning to Predict Without Looking Ahead: World Models Without Forward Prediction	1910.13038	https://arxiv.org/abs/1910.13038v2	https://arxiv.org/pdf/1910.13038v2.pdf	https://github.com/google/brain-tokyo-workshop	false	false	true	none
https://paperswithcode.com/paper/designing-network-design-spaces	Designing Network Design Spaces	2003.13678	https://arxiv.org/abs/2003.13678v1	https://arxiv.org/pdf/2003.13678v1.pdf	https://github.com/tuggeluk/pycls	false	false	true	pytorch
https://paperswithcode.com/paper/stochastic-variational-video-prediction	Stochastic Variational Video Prediction	1710.11252	http://arxiv.org/abs/1710.11252v2	http://arxiv.org/pdf/1710.11252v2.pdf	https://github.com/StanfordVL/roboturk_real_dataset	false	false	true	tf
https://paperswithcode.com/paper/convolutional-neural-networks-for-sentence	Convolutional Neural Networks for Sentence Classification	1408.5882	http://arxiv.org/abs/1408.5882v2	http://arxiv.org/pdf/1408.5882v2.pdf	https://github.com/threelittlemonkeys/cnn-text-classification-pytorch	false	false	true	pytorch
https://paperswithcode.com/paper/super-low-resolution-rf-powered	Super Low Resolution RF Powered Accelerometers for Alerting on Hospitalized Patient Bed Exits	2003.08530	https://arxiv.org/abs/2003.08530v1	https://arxiv.org/pdf/2003.08530v1.pdf	https://github.com/AdelaideAuto-IDLab/ID-Sensor	true	true	false	tf
https://paperswithcode.com/paper/k-space-deep-learning-for-accelerated-mri	k-Space Deep Learning for Accelerated MRI	1805.03779	https://arxiv.org/abs/1805.03779v3	https://arxiv.org/pdf/1805.03779v3.pdf	https://github.com/hanyoseob/k-space-deep-learning	false	false	true	none
https://paperswithcode.com/paper/optimal-routing-for-constant-function-market	Optimal Routing for Constant Function Market Makers	2204.05238	https://arxiv.org/abs/2204.05238v1	https://arxiv.org/pdf/2204.05238v1.pdf	https://github.com/angeris/cfmm-routing-code	true	true	false	none
https://paperswithcode.com/paper/reachability-analysis-for-feed-forward-neural	Reachability Analysis for Feed-Forward Neural Networks using Face Lattices	2003.01226	https://arxiv.org/abs/2003.01226v1	https://arxiv.org/pdf/2003.01226v1.pdf	https://github.com/verivital/FaceLattice	true	true	false	none
https://paperswithcode.com/paper/deep-learning-with-convolutional-neural	Deep learning with convolutional neural networks for EEG decoding and visualization	1703.05051	http://arxiv.org/abs/1703.05051v5	http://arxiv.org/pdf/1703.05051v5.pdf	https://github.com/rczhen/Movement-Classification-based-on-Electroencephalography-EEG-Signals	false	false	true	none
https://paperswithcode.com/paper/factorization-tricks-for-lstm-networks	Factorization tricks for LSTM networks	1703.10722	http://arxiv.org/abs/1703.10722v3	http://arxiv.org/pdf/1703.10722v3.pdf	https://github.com/rdspring1/PyTorch_GBW_LM	false	false	true	pytorch
https://paperswithcode.com/paper/contrastive-adaptation-network-for	Contrastive Adaptation Network for Unsupervised Domain Adaptation	1901.00976	http://arxiv.org/abs/1901.00976v2	http://arxiv.org/pdf/1901.00976v2.pdf	https://github.com/kgl-prml/Contrastive-Adaptation-Network-for-Unsupervised-Domain-Adaptation	false	false	false	pytorch
https://paperswithcode.com/paper/learning-to-generalize-meta-learning-for	Learning to Generalize: Meta-Learning for Domain Generalization	1710.03463	http://arxiv.org/abs/1710.03463v1	http://arxiv.org/pdf/1710.03463v1.pdf	https://github.com/HAHA-DL/MLDG	false	false	true	pytorch
https://paperswithcode.com/paper/kervolutional-neural-networks	Kervolutional Neural Networks	1904.03955	https://arxiv.org/abs/1904.03955v2	https://arxiv.org/pdf/1904.03955v2.pdf	https://github.com/ryanaleksander/kernel-convolution	false	false	true	pytorch
https://paperswithcode.com/paper/probably-approximately-correct-vision-based	Probably Approximately Correct Vision-Based Planning using Motion Primitives	2002.12852	https://arxiv.org/abs/2002.12852v2	https://arxiv.org/pdf/2002.12852v2.pdf	https://github.com/irom-lab/PAC-Vision-Planning	true	true	true	pytorch
https://paperswithcode.com/paper/practical-calibration-of-the-temperature	Practical calibration of the temperature parameter in Gibbs posteriors	2004.10522	https://arxiv.org/abs/2004.10522v1	https://arxiv.org/pdf/2004.10522v1.pdf	https://github.com/lucieperrotta/temperature_calibration	true	true	true	none
https://paperswithcode.com/paper/constraint-answer-set-programming-without	Constraint Answer Set Programming without Grounding	1804.11162	https://arxiv.org/abs/1804.11162v2	https://arxiv.org/pdf/1804.11162v2.pdf	https://github.com/sarat-chandra-varanasi/pysasp	false	false	true	none
https://paperswithcode.com/paper/definition-of-static-and-dynamic-load-models	Definition of Static and Dynamic Load Models for Grid Studies of Electric Vehicles Connected to Fast Charging Stations	2302.03943	https://arxiv.org/abs/2302.03943v1	https://arxiv.org/pdf/2302.03943v1.pdf	https://github.com/davide-del-giudice/electric_vehicle_models	true	true	false	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.