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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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