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/deep-architectures-for-neural-machine | Deep Architectures for Neural Machine Translation | 1707.07631 | http://arxiv.org/abs/1707.07631v1 | http://arxiv.org/pdf/1707.07631v1.pdf | https://github.com/Avmb/deep-nmt-architectures | true | true | false | none |
https://paperswithcode.com/paper/generalization-and-equilibrium-in-generative | Generalization and Equilibrium in Generative Adversarial Nets (GANs) | 1703.00573 | http://arxiv.org/abs/1703.00573v5 | http://arxiv.org/pdf/1703.00573v5.pdf | https://github.com/PrincetonML/MIX-plus-GANs | true | true | true | none |
https://paperswithcode.com/paper/neural-factorization-machines-for-sparse | Neural Factorization Machines for Sparse Predictive Analytics | 1708.05027 | http://arxiv.org/abs/1708.05027v1 | http://arxiv.org/pdf/1708.05027v1.pdf | https://github.com/hexiangnan/neural_factorization_machine | true | true | false | tf |
https://paperswithcode.com/paper/how-intelligent-are-convolutional-neural | How intelligent are convolutional neural networks? | 1709.06126 | http://arxiv.org/abs/1709.06126v2 | http://arxiv.org/pdf/1709.06126v2.pdf | https://github.com/zhennany/synthetic | true | true | true | none |
https://paperswithcode.com/paper/learning-a-rotation-invariant-detector-with | Learning a Rotation Invariant Detector with Rotatable Bounding Box | 1711.09405 | http://arxiv.org/abs/1711.09405v1 | http://arxiv.org/pdf/1711.09405v1.pdf | https://github.com/liulei01/DRBox | true | true | true | none |
https://paperswithcode.com/paper/denoising-adversarial-autoencoders | Denoising Adversarial Autoencoders | 1703.01220 | http://arxiv.org/abs/1703.01220v4 | http://arxiv.org/pdf/1703.01220v4.pdf | https://github.com/ToniCreswell/DAAE_ | true | true | false | none |
https://paperswithcode.com/paper/texture-synthesis-with-recurrent-variational | Texture Synthesis with Recurrent Variational Auto-Encoder | 1712.08838 | http://arxiv.org/abs/1712.08838v1 | http://arxiv.org/pdf/1712.08838v1.pdf | https://github.com/MoustafaMeshry/draw | true | true | false | tf |
https://paperswithcode.com/paper/toward-controlled-generation-of-text | Toward Controlled Generation of Text | 1703.00955 | http://arxiv.org/abs/1703.00955v4 | http://arxiv.org/pdf/1703.00955v4.pdf | https://github.com/asyml/texar | true | true | false | tf |
https://paperswithcode.com/paper/deep-uq-learning-deep-neural-network | Deep UQ: Learning deep neural network surrogate models for high dimensional uncertainty quantification | 1802.00850 | http://arxiv.org/abs/1802.00850v1 | http://arxiv.org/pdf/1802.00850v1.pdf | https://github.com/rohitkt10/deep-uq-paper | true | true | false | tf |
https://paperswithcode.com/paper/tensorflow-quantum-a-software-framework-for | TensorFlow Quantum: A Software Framework for Quantum Machine Learning | 2003.02989 | https://arxiv.org/abs/2003.02989v2 | https://arxiv.org/pdf/2003.02989v2.pdf | https://github.com/tensorflow/quantum | true | true | true | tf |
https://paperswithcode.com/paper/action-segmentation-with-joint-self | Action Segmentation with Joint Self-Supervised Temporal Domain Adaptation | 2003.02824 | https://arxiv.org/abs/2003.02824v3 | https://arxiv.org/pdf/2003.02824v3.pdf | https://github.com/cmhungsteve/SSTDA | true | true | true | pytorch |
https://paperswithcode.com/paper/dancing-to-music | Dancing to Music | 1911.02001 | https://arxiv.org/abs/1911.02001v1 | https://arxiv.org/pdf/1911.02001v1.pdf | https://github.com/NVlabs/Dance2Music | true | true | true | pytorch |
https://paperswithcode.com/paper/deep-reinforcement-learning-control-of | Deep Reinforcement Learning Control of Quantum Cartpoles | 1910.09200 | https://arxiv.org/abs/1910.09200v4 | https://arxiv.org/pdf/1910.09200v4.pdf | https://github.com/Z-T-WANG/DeepReinforcementLearningControlOfQuantumCartpoles | true | true | true | pytorch |
https://paperswithcode.com/paper/nonlinear-classifiers-for-ranking-problems | Nonlinear classifiers for ranking problems based on kernelized SVM | 2002.11436 | https://arxiv.org/abs/2002.11436v2 | https://arxiv.org/pdf/2002.11436v2.pdf | https://github.com/VaclavMacha/ClassificationOnTop_new.jl | true | true | true | none |
https://paperswithcode.com/paper/weakly-and-semi-supervised-panoptic | Weakly- and Semi-Supervised Panoptic Segmentation | 1808.03575 | http://arxiv.org/abs/1808.03575v3 | http://arxiv.org/pdf/1808.03575v3.pdf | https://github.com/qizhuli/Weakly-Supervised-Panoptic-Segmentation | true | true | true | none |
https://paperswithcode.com/paper/multi-task-self-supervised-learning-for-1 | Multi-task self-supervised learning for Robust Speech Recognition | 2001.09239 | https://arxiv.org/abs/2001.09239v2 | https://arxiv.org/pdf/2001.09239v2.pdf | https://github.com/santi-pdp/pase | true | true | true | pytorch |
https://paperswithcode.com/paper/a-model-to-search-for-synthesizable-molecules | A Model to Search for Synthesizable Molecules | 1906.05221 | https://arxiv.org/abs/1906.05221v2 | https://arxiv.org/pdf/1906.05221v2.pdf | https://github.com/john-bradshaw/molecule-chef | true | true | true | pytorch |
https://paperswithcode.com/paper/adversarial-policy-gradient-for-deep-learning | Adversarial Policy Gradient for Deep Learning Image Augmentation | 1909.04108 | https://arxiv.org/abs/1909.04108v1 | https://arxiv.org/pdf/1909.04108v1.pdf | https://github.com/victorychain/Adversarial-Policy-Gradient-Augmentation | true | true | true | pytorch |
https://paperswithcode.com/paper/improved-regularization-of-convolutional | Improved Regularization of Convolutional Neural Networks with Cutout | 1708.04552 | http://arxiv.org/abs/1708.04552v2 | http://arxiv.org/pdf/1708.04552v2.pdf | https://github.com/uoguelph-mlrg/Cutout | true | true | true | pytorch |
https://paperswithcode.com/paper/real-time-vision-based-depth-reconstruction | Real-time Vision-based Depth Reconstruction with NVidia Jetson | 1907.07210 | https://arxiv.org/abs/1907.07210v1 | https://arxiv.org/pdf/1907.07210v1.pdf | https://github.com/CnnDepth/tx2_fcnn_node | true | true | true | tf |
https://paperswithcode.com/paper/a-kernel-perspective-for-regularizing-deep | A Kernel Perspective for Regularizing Deep Neural Networks | 1810.00363 | https://arxiv.org/abs/1810.00363v4 | https://arxiv.org/pdf/1810.00363v4.pdf | https://github.com/albietz/kernel_reg | true | true | true | pytorch |
https://paperswithcode.com/paper/query-guided-end-to-end-person-search | Query-guided End-to-End Person Search | 1905.01203 | https://arxiv.org/abs/1905.01203v1 | https://arxiv.org/pdf/1905.01203v1.pdf | https://github.com/munjalbharti/Query-guided-End-to-End-Person-Search | true | true | true | none |
https://paperswithcode.com/paper/r2cnn-multi-dimensional-attention-based | SCRDet: Towards More Robust Detection for Small, Cluttered and Rotated Objects | 1811.07126 | https://arxiv.org/abs/1811.07126v4 | https://arxiv.org/pdf/1811.07126v4.pdf | https://github.com/DetectionTeamUCAS/R2CNN-Plus-Plus_Tensorflow | true | true | true | tf |
https://paperswithcode.com/paper/towards-query-efficient-black-box-attacks-an | Towards Query Efficient Black-box Attacks: An Input-free Perspective | 1809.02918 | http://arxiv.org/abs/1809.02918v1 | http://arxiv.org/pdf/1809.02918v1.pdf | https://github.com/yalidu/input-free-attack | true | true | true | tf |
https://paperswithcode.com/paper/texar-a-modularized-versatile-and-extensible-1 | Texar: A Modularized, Versatile, and Extensible Toolkit for Text Generation | 1809.00794 | https://arxiv.org/abs/1809.00794v2 | https://arxiv.org/pdf/1809.00794v2.pdf | https://github.com/asyml/texar | true | true | true | tf |
https://paperswithcode.com/paper/automatic-program-synthesis-of-long-programs | Automatic Program Synthesis of Long Programs with a Learned Garbage Collector | 1809.04682 | http://arxiv.org/abs/1809.04682v2 | http://arxiv.org/pdf/1809.04682v2.pdf | https://github.com/amitz25/PCCoder | true | true | true | pytorch |
https://paperswithcode.com/paper/acquisition-of-localization-confidence-for | Acquisition of Localization Confidence for Accurate Object Detection | 1807.11590 | http://arxiv.org/abs/1807.11590v1 | http://arxiv.org/pdf/1807.11590v1.pdf | https://github.com/vacancy/PreciseRoIPooling | true | true | true | pytorch |
https://paperswithcode.com/paper/a-repetition-code-of-15-qubits | A repetition code of 15 qubits | 1709.00990 | https://arxiv.org/abs/1709.00990v3 | https://arxiv.org/pdf/1709.00990v3.pdf | https://github.com/decodoku/repetition_code | true | true | true | none |
https://paperswithcode.com/paper/eddy-saturation-of-the-southern-ocean-a | Eddy saturation of the Southern Ocean: a baroclinic versus barotropic perspective | 1906.08442 | https://arxiv.org/abs/1906.08442v3 | https://arxiv.org/pdf/1906.08442v3.pdf | https://github.com/navidcy/EddySaturation-MOM6 | true | true | false | none |
https://paperswithcode.com/paper/topic-modeling-with-wasserstein-autoencoders-1 | Topic Modeling with Wasserstein Autoencoders | 1907.12374 | https://arxiv.org/abs/1907.12374v2 | https://arxiv.org/pdf/1907.12374v2.pdf | https://github.com/awslabs/w-lda | true | true | false | mxnet |
https://paperswithcode.com/paper/neural-duplicate-question-detection-without-1 | Neural Duplicate Question Detection without Labeled Training Data | 1911.05594 | https://arxiv.org/abs/1911.05594v2 | https://arxiv.org/pdf/1911.05594v2.pdf | https://github.com/UKPLab/emnlp2019-duplicate_question_detection | true | true | false | none |
https://paperswithcode.com/paper/neural-attribution-for-semantic-bug | Neural Attribution for Semantic Bug-Localization in Student Programs | null | http://papers.nips.cc/paper/9358-neural-attribution-for-semantic-bug-localization-in-student-programs | http://papers.nips.cc/paper/9358-neural-attribution-for-semantic-bug-localization-in-student-programs.pdf | https://bitbucket.org/iiscseal/nbl | true | true | false | none |
https://paperswithcode.com/paper/understanding-contrastive-representation-1 | Understanding Contrastive Representation Learning through Geometry on the Hypersphere | null | https://proceedings.icml.cc/static/paper_files/icml/2020/5503-Paper.pdf | https://proceedings.icml.cc/static/paper_files/icml/2020/5503-Paper.pdf | https://github.com/SsnL/align_uniform | true | true | false | pytorch |
https://paperswithcode.com/paper/vision-based-dynamic-offside-line-marker-for | Vision Based Dynamic Offside Line Marker for Soccer Games | 1804.06438 | http://arxiv.org/abs/1804.06438v1 | http://arxiv.org/pdf/1804.06438v1.pdf | https://github.com/surajkra/Offside_Tracker_EECS504 | true | true | true | none |
https://paperswithcode.com/paper/colors-in-context-a-pragmatic-neural-model | Colors in Context: A Pragmatic Neural Model for Grounded Language Understanding | 1703.10186 | http://arxiv.org/abs/1703.10186v2 | http://arxiv.org/pdf/1703.10186v2.pdf | https://github.com/futurulus/colors-in-context | false | false | true | none |
https://paperswithcode.com/paper/draw-a-recurrent-neural-network-for-image | DRAW: A Recurrent Neural Network For Image Generation | 1502.04623 | http://arxiv.org/abs/1502.04623v2 | http://arxiv.org/pdf/1502.04623v2.pdf | https://github.com/MoustafaMeshry/draw | false | false | true | tf |
https://paperswithcode.com/paper/conceptnet-55-an-open-multilingual-graph-of | ConceptNet 5.5: An Open Multilingual Graph of General Knowledge | 1612.03975 | http://arxiv.org/abs/1612.03975v2 | http://arxiv.org/pdf/1612.03975v2.pdf | https://github.com/LuminosoInsight/conceptnet-vector-ensemble | false | false | true | none |
https://paperswithcode.com/paper/conceptnet-at-semeval-2017-task-2-extending | ConceptNet at SemEval-2017 Task 2: Extending Word Embeddings with Multilingual Relational Knowledge | 1704.03560 | http://arxiv.org/abs/1704.03560v2 | http://arxiv.org/pdf/1704.03560v2.pdf | https://github.com/LuminosoInsight/conceptnet-vector-ensemble | false | false | true | none |
https://paperswithcode.com/paper/incorporating-copying-mechanism-in-sequence | Incorporating Copying Mechanism in Sequence-to-Sequence Learning | 1603.06393 | http://arxiv.org/abs/1603.06393v3 | http://arxiv.org/pdf/1603.06393v3.pdf | https://github.com/majumderb/sanskrit-ocr | false | false | true | tf |
https://paperswithcode.com/paper/transition-based-dependency-parsing-with-2 | Transition-Based Dependency Parsing with Stack Long Short-Term Memory | 1505.08075 | http://arxiv.org/abs/1505.08075v1 | http://arxiv.org/pdf/1505.08075v1.pdf | https://github.com/mstrise/dep2label | false | false | true | pytorch |
https://paperswithcode.com/paper/deconvolutional-paragraph-representation | Deconvolutional Paragraph Representation Learning | 1708.04729 | http://arxiv.org/abs/1708.04729v3 | http://arxiv.org/pdf/1708.04729v3.pdf | https://github.com/tuvuumass/SCoPE | false | false | true | tf |
https://paperswithcode.com/paper/a-hierarchical-neural-autoencoder-for | A Hierarchical Neural Autoencoder for Paragraphs and Documents | 1506.01057 | http://arxiv.org/abs/1506.01057v2 | http://arxiv.org/pdf/1506.01057v2.pdf | https://github.com/tuvuumass/SCoPE | false | false | true | tf |
https://paperswithcode.com/paper/character-level-convolutional-networks-for | Character-level Convolutional Networks for Text Classification | 1509.01626 | http://arxiv.org/abs/1509.01626v3 | http://arxiv.org/pdf/1509.01626v3.pdf | https://github.com/tuvuumass/SCoPE | false | false | true | tf |
https://paperswithcode.com/paper/191202288 | Simplified Action Decoder for Deep Multi-Agent Reinforcement Learning | 1912.02288 | https://arxiv.org/abs/1912.02288v2 | https://arxiv.org/pdf/1912.02288v2.pdf | https://github.com/facebookresearch/Hanabi_SPARTA | false | false | true | pytorch |
https://paperswithcode.com/paper/large-scale-visual-relationship-understanding | Large-Scale Visual Relationship Understanding | 1804.10660 | https://arxiv.org/abs/1804.10660v4 | https://arxiv.org/pdf/1804.10660v4.pdf | https://github.com/facebookresearch/Large-Scale-VRD | false | false | true | pytorch |
https://paperswithcode.com/paper/solving-nonlinear-and-high-dimensional | Solving Nonlinear and High-Dimensional Partial Differential Equations via Deep Learning | 1811.08782 | https://arxiv.org/abs/1811.08782v1 | https://arxiv.org/pdf/1811.08782v1.pdf | https://github.com/alialaradi/DeepGalerkinMethod | false | false | true | tf |
https://paperswithcode.com/paper/paired-open-ended-trailblazer-poet-endlessly | Paired Open-Ended Trailblazer (POET): Endlessly Generating Increasingly Complex and Diverse Learning Environments and Their Solutions | 1901.01753 | http://arxiv.org/abs/1901.01753v3 | http://arxiv.org/pdf/1901.01753v3.pdf | https://github.com/uber-research/poet | false | false | true | none |
https://paperswithcode.com/paper/deepercut-a-deeper-stronger-and-faster-multi | DeeperCut: A Deeper, Stronger, and Faster Multi-Person Pose Estimation Model | 1605.03170 | http://arxiv.org/abs/1605.03170v3 | http://arxiv.org/pdf/1605.03170v3.pdf | https://github.com/gyaansastra/DeepLab | false | false | true | tf |
https://paperswithcode.com/paper/deep-learning-tools-for-the-measurement-of | Deep learning tools for the measurement of animal behavior in neuroscience | 1909.13868 | https://arxiv.org/abs/1909.13868v2 | https://arxiv.org/pdf/1909.13868v2.pdf | https://github.com/gyaansastra/DeepLab | false | false | true | tf |
https://paperswithcode.com/paper/squeeze-excite-guided-few-shot-segmentation | 'Squeeze & Excite' Guided Few-Shot Segmentation of Volumetric Images | 1902.01314 | https://arxiv.org/abs/1902.01314v2 | https://arxiv.org/pdf/1902.01314v2.pdf | https://github.com/CSCYQJ/LOCATION-SENSITIVE-LOCAL-PROTOTYPE-NETWORK | false | false | true | pytorch |
https://paperswithcode.com/paper/large-covariance-estimation-by-thresholding | Large Covariance Estimation by Thresholding Principal Orthogonal Complements | 1201.0175 | https://arxiv.org/abs/1201.0175v2 | https://arxiv.org/pdf/1201.0175v2.pdf | https://github.com/brucewuquant/POET | false | false | true | none |
https://paperswithcode.com/paper/model-of-spin-liquids-with-and-without-time | Model of spin liquids with and without time-reversal symmetry | 1810.09858 | https://arxiv.org/abs/1810.09858v1 | https://arxiv.org/pdf/1810.09858v1.pdf | https://github.com/gonghour/DMRG_for_spin-ladder_systems | false | false | true | none |
https://paperswithcode.com/paper/pannuke-dataset-extension-insights-and | PanNuke Dataset Extension, Insights and Baselines | 2003.10778 | https://arxiv.org/abs/2003.10778v7 | https://arxiv.org/pdf/2003.10778v7.pdf | https://github.com/aaparna/UNet-Image-Segmentation | false | false | true | none |
https://paperswithcode.com/paper/safe-by-design-control-for-euler-lagrange | Safe-by-Design Control for Euler-Lagrange Systems | 2009.03767 | https://arxiv.org/abs/2009.03767v2 | https://arxiv.org/pdf/2009.03767v2.pdf | https://github.com/shawcortez/safe-control-euler-lagrange | true | true | true | none |
https://paperswithcode.com/paper/deep-learning-with-differential-privacy | Deep Learning with Differential Privacy | 1607.00133 | http://arxiv.org/abs/1607.00133v2 | http://arxiv.org/pdf/1607.00133v2.pdf | https://github.com/zzzer1019/FL_DP | false | false | true | tf |
https://paperswithcode.com/paper/learning-to-pivot-with-adversarial-networks | Learning to Pivot with Adversarial Networks | 1611.01046 | http://arxiv.org/abs/1611.01046v3 | http://arxiv.org/pdf/1611.01046v3.pdf | https://github.com/faroukmokhtar/GradProject | false | false | true | none |
https://paperswithcode.com/paper/search-for-supersymmetry-in-events-with-one | Search for supersymmetry in events with one lepton and multiple jets in proton-proton collisions at sqrt(s) = 13 TeV | 1609.09386 | https://arxiv.org/abs/1609.09386v2 | https://arxiv.org/pdf/1609.09386v2.pdf | https://github.com/faroukmokhtar/GradProject | false | false | true | none |
https://paperswithcode.com/paper/autoencoder-by-forest | AutoEncoder by Forest | 1709.09018 | http://arxiv.org/abs/1709.09018v1 | http://arxiv.org/pdf/1709.09018v1.pdf | https://github.com/AntoinePassemiers/Encoder-Forest | false | false | true | none |
https://paperswithcode.com/paper/semantic-segmentation-of-underwater-imagery | Semantic Segmentation of Underwater Imagery: Dataset and Benchmark | 2004.01241 | https://arxiv.org/abs/2004.01241v3 | https://arxiv.org/pdf/2004.01241v3.pdf | https://github.com/xahidbuffon/SUIM | false | false | true | none |
https://paperswithcode.com/paper/traditional-and-accelerated-gradient-descent | Traditional and accelerated gradient descent for neural architecture search | 2006.15218 | https://arxiv.org/abs/2006.15218v3 | https://arxiv.org/pdf/2006.15218v3.pdf | https://github.com/bibliotecadebabel/EvAI | false | false | true | pytorch |
https://paperswithcode.com/paper/segnet-a-deep-convolutional-encoder-decoder-1 | SegNet: A Deep Convolutional Encoder-Decoder Architecture for Robust Semantic Pixel-Wise Labelling | 1505.07293 | http://arxiv.org/abs/1505.07293v1 | http://arxiv.org/pdf/1505.07293v1.pdf | https://github.com/xahidbuffon/SUIM | false | false | true | none |
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/xahidbuffon/SUIM | false | false | true | none |
https://paperswithcode.com/paper/repvgg-making-vgg-style-convnets-great-again | RepVGG: Making VGG-style ConvNets Great Again | 2101.03697 | https://arxiv.org/abs/2101.03697v3 | https://arxiv.org/pdf/2101.03697v3.pdf | https://github.com/mindspore-ecosystem/mindcv/blob/main/mindcv/models/repvgg.py | false | false | false | mindspore |
https://paperswithcode.com/paper/mastering-2048-with-delayed-temporal | Mastering 2048 with Delayed Temporal Coherence Learning, Multi-Stage Weight Promotion, Redundant Encoding and Carousel Shaping | 1604.05085 | http://arxiv.org/abs/1604.05085v3 | http://arxiv.org/pdf/1604.05085v3.pdf | https://github.com/abachurin/2048 | false | false | true | tf |
https://paperswithcode.com/paper/blockwise-self-attention-for-long-document | Blockwise Self-Attention for Long Document Understanding | 1911.02972 | https://arxiv.org/abs/1911.02972v2 | https://arxiv.org/pdf/1911.02972v2.pdf | https://github.com/xptree/BlockBERT | true | true | false | none |
https://paperswithcode.com/paper/automatic-discrete-differentiation-and-its | Deep Energy-Based Modeling of Discrete-Time Physics | 1905.08604 | https://arxiv.org/abs/1905.08604v3 | https://arxiv.org/pdf/1905.08604v3.pdf | https://github.com/tksmatsubara/discrete-autograd | true | true | true | pytorch |
https://paperswithcode.com/paper/understanding-and-improving-interpolation-in | Understanding and Improving Interpolation in Autoencoders via an Adversarial Regularizer | 1807.07543 | http://arxiv.org/abs/1807.07543v2 | http://arxiv.org/pdf/1807.07543v2.pdf | https://github.com/baohq1595/aae-experiment | false | false | true | tf |
https://paperswithcode.com/paper/adversarial-autoencoders | Adversarial Autoencoders | 1511.05644 | http://arxiv.org/abs/1511.05644v2 | http://arxiv.org/pdf/1511.05644v2.pdf | https://github.com/baohq1595/aae-experiment | false | false | true | tf |
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/sadicLiu/yolov3 | 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/Intoxillectual/Monocular-Depth-Estimation-using-DenseNet169 | false | false | true | tf |
https://paperswithcode.com/paper/deep-complex-networks | Deep Complex Networks | 1705.09792 | http://arxiv.org/abs/1705.09792v4 | http://arxiv.org/pdf/1705.09792v4.pdf | https://github.com/ypeleg/komplex | false | false | true | tf |
https://paperswithcode.com/paper/on-complex-valued-convolutional-neural | On Complex Valued Convolutional Neural Networks | 1602.09046 | http://arxiv.org/abs/1602.09046v1 | http://arxiv.org/pdf/1602.09046v1.pdf | https://github.com/ypeleg/komplex | false | false | true | tf |
https://paperswithcode.com/paper/complex-valued-neural-networks-with-non | Complex-valued Neural Networks with Non-parametric Activation Functions | 1802.08026 | http://arxiv.org/abs/1802.08026v1 | http://arxiv.org/pdf/1802.08026v1.pdf | https://github.com/ypeleg/komplex | false | false | true | tf |
https://paperswithcode.com/paper/one-shot-visual-imitation-learning-via-meta | One-Shot Visual Imitation Learning via Meta-Learning | 1709.04905 | http://arxiv.org/abs/1709.04905v1 | http://arxiv.org/pdf/1709.04905v1.pdf | https://github.com/ErickRosete/MAML-Imitation-Learning | false | false | true | pytorch |
https://paperswithcode.com/paper/physical-layer-encryption-using-a-vernam | Physical Layer Encryption using a Vernam Cipher | 1910.08262 | https://arxiv.org/abs/1910.08262v1 | https://arxiv.org/pdf/1910.08262v1.pdf | https://github.com/ymirsky/VPSC-py | false | false | true | none |
https://paperswithcode.com/paper/wide-deep-learning-for-recommender-systems | Wide & Deep Learning for Recommender Systems | 1606.07792 | http://arxiv.org/abs/1606.07792v1 | http://arxiv.org/pdf/1606.07792v1.pdf | https://github.com/sandeepnair2812/Deep-Learning-Based-Search-and-Recommendation-System | false | false | true | tf |
https://paperswithcode.com/paper/deepfm-a-factorization-machine-based-neural | DeepFM: A Factorization-Machine based Neural Network for CTR Prediction | 1703.04247 | http://arxiv.org/abs/1703.04247v1 | http://arxiv.org/pdf/1703.04247v1.pdf | https://github.com/sandeepnair2812/Deep-Learning-Based-Search-and-Recommendation-System | false | false | true | tf |
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/Sezoir/DCGAN-Dog-Generator | false | false | true | tf |
https://paperswithcode.com/paper/oriented-point-sampling-for-plane-detection | Oriented Point Sampling for Plane Detection in Unorganized Point Clouds | 1905.02553 | https://arxiv.org/abs/1905.02553v1 | https://arxiv.org/pdf/1905.02553v1.pdf | https://github.com/bsun7/Oriented-Point-Sampling | false | false | true | none |
https://paperswithcode.com/paper/multistep-inverse-is-not-all-you-need | Multistep Inverse Is Not All You Need | 2403.11940 | https://arxiv.org/abs/2403.11940v2 | https://arxiv.org/pdf/2403.11940v2.pdf | https://github.com/midi-lab/acdf | true | true | false | pytorch |
https://paperswithcode.com/paper/160803828 | Prutor: A System for Tutoring CS1 and Collecting Student Programs for Analysis | 1608.03828 | http://arxiv.org/abs/1608.03828v1 | http://arxiv.org/pdf/1608.03828v1.pdf | https://github.com/umairzahmed/seet2020 | false | false | true | none |
https://paperswithcode.com/paper/pypsa-eur-an-open-optimisation-model-of-the | PyPSA-Eur: An Open Optimisation Model of the European Transmission System | 1806.01613 | http://arxiv.org/abs/1806.01613v1 | http://arxiv.org/pdf/1806.01613v1.pdf | https://github.com/pz-max/energyworld | false | false | true | none |
https://paperswithcode.com/paper/language-agnostic-bert-sentence-embedding | Language-agnostic BERT Sentence Embedding | 2007.01852 | https://arxiv.org/abs/2007.01852v2 | https://arxiv.org/pdf/2007.01852v2.pdf | https://github.com/bojone/labse | false | false | true | tf |
https://paperswithcode.com/paper/vulnerability-of-deep-reinforcement-learning | Vulnerability of Deep Reinforcement Learning to Policy Induction Attacks | 1701.04143 | http://arxiv.org/abs/1701.04143v1 | http://arxiv.org/pdf/1701.04143v1.pdf | https://github.com/coderatwork7/attack | false | false | true | tf |
https://paperswithcode.com/paper/model-free-bounds-for-multi-asset-options | Model-free bounds for multi-asset options using option-implied information and their exact computation | 2006.14288 | https://arxiv.org/abs/2006.14288v3 | https://arxiv.org/pdf/2006.14288v3.pdf | https://github.com/qikunxiang/ModelFreePriceBounds | true | true | true | none |
https://paperswithcode.com/paper/detecting-persuasive-atypicality-by-modeling | Detecting Persuasive Atypicality by Modeling Contextual Compatibility | null | http://openaccess.thecvf.com//content/ICCV2021/html/Guo_Detecting_Persuasive_Atypicality_by_Modeling_Contextual_Compatibility_ICCV_2021_paper.html | http://openaccess.thecvf.com//content/ICCV2021/papers/Guo_Detecting_Persuasive_Atypicality_by_Modeling_Contextual_Compatibility_ICCV_2021_paper.pdf | https://github.com/meiqiguo/iccv2021-atypicalitydetection | true | true | false | pytorch |
https://paperswithcode.com/paper/ubermag-towards-more-effective-micromagnetic | Ubermag: Towards more effective micromagnetic workflows | 2105.08355 | https://arxiv.org/abs/2105.08355v1 | https://arxiv.org/pdf/2105.08355v1.pdf | https://github.com/marijanbeg/2021-paper-ubermag | true | true | false | none |
https://paperswithcode.com/paper/conditional-image-synthesis-with-auxiliary | Conditional Image Synthesis With Auxiliary Classifier GANs | 1610.09585 | http://arxiv.org/abs/1610.09585v4 | http://arxiv.org/pdf/1610.09585v4.pdf | https://github.com/kushalpatil1997/text_to_image_synthesis | false | false | true | tf |
https://paperswithcode.com/paper/skip-thought-vectors | Skip-Thought Vectors | 1506.06726 | http://arxiv.org/abs/1506.06726v1 | http://arxiv.org/pdf/1506.06726v1.pdf | https://github.com/kushalpatil1997/text_to_image_synthesis | false | false | true | tf |
https://paperswithcode.com/paper/mask-shadowgan-learning-to-remove-shadows | Mask-ShadowGAN: Learning to Remove Shadows from Unpaired Data | 1903.10683 | https://arxiv.org/abs/1903.10683v3 | https://arxiv.org/pdf/1903.10683v3.pdf | https://github.com/mducducd/ghost-free-shadow-removal | false | false | true | tf |
https://paperswithcode.com/paper/tac-gan-text-conditioned-auxiliary-classifier | TAC-GAN - Text Conditioned Auxiliary Classifier Generative Adversarial Network | 1703.06412 | http://arxiv.org/abs/1703.06412v2 | http://arxiv.org/pdf/1703.06412v2.pdf | https://github.com/kushalpatil1997/text_to_image_synthesis | false | false | true | tf |
https://paperswithcode.com/paper/towards-ghost-free-shadow-removal-via-dual | Towards Ghost-free Shadow Removal via Dual Hierarchical Aggregation Network and Shadow Matting GAN | 1911.08718 | https://arxiv.org/abs/1911.08718v2 | https://arxiv.org/pdf/1911.08718v2.pdf | https://github.com/mducducd/ghost-free-shadow-removal | false | false | true | tf |
https://paperswithcode.com/paper/single-image-reflection-separation-with | Single Image Reflection Separation with Perceptual Losses | 1806.05376 | http://arxiv.org/abs/1806.05376v1 | http://arxiv.org/pdf/1806.05376v1.pdf | https://github.com/mducducd/ghost-free-shadow-removal | false | false | true | tf |
https://paperswithcode.com/paper/estimating-or-propagating-gradients-through-1 | Estimating or Propagating Gradients Through Stochastic Neurons for Conditional Computation | 1308.3432 | http://arxiv.org/abs/1308.3432v1 | http://arxiv.org/pdf/1308.3432v1.pdf | https://github.com/georgeretsi/SparsityLoss | false | false | true | pytorch |
https://paperswithcode.com/paper/bias-correction-of-learned-generative-models | Bias Correction of Learned Generative Models using Likelihood-Free Importance Weighting | 1906.09531 | https://arxiv.org/abs/1906.09531v2 | https://arxiv.org/pdf/1906.09531v2.pdf | https://github.com/kevtran23/autoregressive_bias_correction | false | false | true | pytorch |
https://paperswithcode.com/paper/pano-avqa-grounded-audio-visual-question | Pano-AVQA: Grounded Audio-Visual Question Answering on 360deg Videos | null | http://openaccess.thecvf.com//content/ICCV2021/html/Yun_Pano-AVQA_Grounded_Audio-Visual_Question_Answering_on_360deg_Videos_ICCV_2021_paper.html | http://openaccess.thecvf.com//content/ICCV2021/papers/Yun_Pano-AVQA_Grounded_Audio-Visual_Question_Answering_on_360deg_Videos_ICCV_2021_paper.pdf | https://github.com/hs-yn/panoavqa | true | true | false | pytorch |
https://paperswithcode.com/paper/deep-evidential-regression | Deep Evidential Regression | 1910.02600 | https://arxiv.org/abs/1910.02600v2 | https://arxiv.org/pdf/1910.02600v2.pdf | https://github.com/deebuls/deep_evidential_regression_loss_pytorch | false | false | true | pytorch |
https://paperswithcode.com/paper/automatic-fault-detection-for-deep-learning | Automatic Fault Detection for Deep Learning Programs Using Graph Transformations | 2105.08095 | https://arxiv.org/abs/2105.08095v2 | https://arxiv.org/pdf/2105.08095v2.pdf | https://github.com/neuralint/neuralint | true | true | false | tf |
https://paperswithcode.com/paper/eegnet-a-compact-convolutional-network-for | EEGNet: A Compact Convolutional Network for EEG-based Brain-Computer Interfaces | 1611.08024 | http://arxiv.org/abs/1611.08024v4 | http://arxiv.org/pdf/1611.08024v4.pdf | https://github.com/adwaykanhere/FYP | false | false | true | pytorch |
https://paperswithcode.com/paper/lewis-levenshtein-editing-for-unsupervised | LEWIS: Levenshtein Editing for Unsupervised Text Style Transfer | 2105.08206 | https://arxiv.org/abs/2105.08206v1 | https://arxiv.org/pdf/2105.08206v1.pdf | https://github.com/machelreid/lewis | true | true | false | pytorch |
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