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 |
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