Dataset Viewer
paper_url
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classes | mentioned_in_paper
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classes | mentioned_in_github
bool 2
classes | framework
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values |
|---|---|---|---|---|---|---|---|---|---|
https://paperswithcode.com/paper/odyssey-a-public-gpu-based-code-for-general
|
Odyssey: A Public GPU-Based Code for General-Relativistic Radiative Transfer in Kerr Spacetime
|
1601.02063
|
https://arxiv.org/abs/1601.02063v2
|
https://arxiv.org/pdf/1601.02063v2.pdf
|
https://github.com/LeonGeiger/Kerr
| false | false | true |
none
|
https://paperswithcode.com/paper/efficient-leave-one-out-cross-validation-for
|
Efficient leave-one-out cross-validation for Bayesian non-factorized normal and Student-t models
|
1810.10559
|
https://arxiv.org/abs/1810.10559v5
|
https://arxiv.org/pdf/1810.10559v5.pdf
|
https://github.com/paul-buerkner/psis-non-factorized-paper
| true | true | false |
none
|
https://paperswithcode.com/paper/automatic-post-editing-of-machine-translation
|
Automatic Post-Editing of Machine Translation: A Neural Programmer-Interpreter Approach
| null |
https://aclanthology.org/D18-1341
|
https://aclanthology.org/D18-1341.pdf
|
https://github.com/trangvu/ape-npi
| false | false | false |
tf
|
https://paperswithcode.com/paper/attngan-fine-grained-text-to-image-generation
|
AttnGAN: Fine-Grained Text to Image Generation with Attentional Generative Adversarial Networks
|
1711.10485
|
http://arxiv.org/abs/1711.10485v1
|
http://arxiv.org/pdf/1711.10485v1.pdf
|
https://github.com/bprabhakar/text-to-image
| false | false | false |
pytorch
|
https://paperswithcode.com/paper/photo-realistic-single-image-super-resolution
|
Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network
|
1609.04802
|
http://arxiv.org/abs/1609.04802v5
|
http://arxiv.org/pdf/1609.04802v5.pdf
|
https://github.com/2023-MindSpore-1/ms-code-210/tree/main/CSNL
| false | false | false |
mindspore
|
https://paperswithcode.com/paper/distilling-interpretable-models-into-human
|
Distilling Interpretable Models into Human-Readable Code
|
2101.08393
|
https://arxiv.org/abs/2101.08393v2
|
https://arxiv.org/pdf/2101.08393v2.pdf
|
https://github.com/google/pwlfit
| true | true | false |
none
|
https://paperswithcode.com/paper/wide-residual-networks
|
Wide Residual Networks
|
1605.07146
|
http://arxiv.org/abs/1605.07146v4
|
http://arxiv.org/pdf/1605.07146v4.pdf
|
https://github.com/epfl-ml-reproducers/subspace-attack-reproduction
| false | false | true |
pytorch
|
https://paperswithcode.com/paper/show-and-tell-lessons-learned-from-the-2015
|
Show and Tell: Lessons learned from the 2015 MSCOCO Image Captioning Challenge
|
1609.06647
|
http://arxiv.org/abs/1609.06647v1
|
http://arxiv.org/pdf/1609.06647v1.pdf
|
https://github.com/HughKu/Im2txt
| false | false | true |
tf
|
https://paperswithcode.com/paper/a-wavenet-for-speech-denoising
|
A Wavenet for Speech Denoising
|
1706.07162
|
http://arxiv.org/abs/1706.07162v3
|
http://arxiv.org/pdf/1706.07162v3.pdf
|
https://github.com/francesclluis/source-separation-wavenet
| false | false | true |
tf
|
https://paperswithcode.com/paper/stackgan-realistic-image-synthesis-with
|
StackGAN++: Realistic Image Synthesis with Stacked Generative Adversarial Networks
|
1710.10916
|
http://arxiv.org/abs/1710.10916v3
|
http://arxiv.org/pdf/1710.10916v3.pdf
|
https://github.com/Maymaher/StackGANv2
| false | false | true |
pytorch
|
https://paperswithcode.com/paper/towards-k-means-friendly-spaces-simultaneous
|
Towards K-means-friendly Spaces: Simultaneous Deep Learning and Clustering
|
1610.04794
|
http://arxiv.org/abs/1610.04794v2
|
http://arxiv.org/pdf/1610.04794v2.pdf
|
https://github.com/boyangumn/DCN
| true | true | true |
none
|
https://paperswithcode.com/paper/simulaqron-a-simulator-for-developing-quantum
|
SimulaQron - A simulator for developing quantum internet software
|
1712.08032
|
http://arxiv.org/abs/1712.08032v2
|
http://arxiv.org/pdf/1712.08032v2.pdf
|
https://github.com/quantumprotocolzoo/protocols
| false | false | true |
none
|
https://paperswithcode.com/paper/recipenlg-a-cooking-recipes-dataset-for-semi
|
RecipeNLG: A Cooking Recipes Dataset for Semi-Structured Text Generation
| null |
https://aclanthology.org/2020.inlg-1.4
|
https://aclanthology.org/2020.inlg-1.4.pdf
|
https://github.com/Glorf/recipenlg
| false | false | false |
pytorch
|
https://paperswithcode.com/paper/online-deep-learning-learning-deep-neural
|
Online Deep Learning: Learning Deep Neural Networks on the Fly
|
1711.03705
|
http://arxiv.org/abs/1711.03705v1
|
http://arxiv.org/pdf/1711.03705v1.pdf
|
https://github.com/LIBOL/ODL
| false | false | true |
pytorch
|
https://paperswithcode.com/paper/model-rubik-s-cube-twisting-resolution-depth
|
Model Rubik's Cube: Twisting Resolution, Depth and Width for TinyNets
|
2010.14819
|
https://arxiv.org/abs/2010.14819v2
|
https://arxiv.org/pdf/2010.14819v2.pdf
|
https://github.com/leondgarse/keras_cv_attention_models/tree/main/keras_cv_attention_models/mobilenetv3_family
| false | false | false |
tf
|
https://paperswithcode.com/paper/190600133
|
ArcticNet: A Deep Learning Solution to Classify Arctic Wetlands
|
1906.00133
|
https://arxiv.org/abs/1906.00133v1
|
https://arxiv.org/pdf/1906.00133v1.pdf
|
https://github.com/geekJZY/arcticnet
| true | true | false |
pytorch
|
https://paperswithcode.com/paper/multi-label-image-classification-via
|
Multi-Label Image Classification via Knowledge Distillation from Weakly-Supervised Detection
|
1809.05884
|
http://arxiv.org/abs/1809.05884v2
|
http://arxiv.org/pdf/1809.05884v2.pdf
|
https://github.com/Yochengliu/MLIC-KD-WSD
| false | false | true |
none
|
https://paperswithcode.com/paper/few-shot-learning-with-graph-neural-networks
|
Few-Shot Learning with Graph Neural Networks
|
1711.04043
|
http://arxiv.org/abs/1711.04043v3
|
http://arxiv.org/pdf/1711.04043v3.pdf
|
https://github.com/HoganZhang/few-shot-gnn
| false | false | true |
pytorch
|
https://paperswithcode.com/paper/generative-adversarial-networks
|
Generative Adversarial Networks
|
1406.2661
|
https://arxiv.org/abs/1406.2661v1
|
https://arxiv.org/pdf/1406.2661v1.pdf
|
https://github.com/syahdeini/gan
| false | false | true |
tf
|
https://paperswithcode.com/paper/mastering-chess-and-shogi-by-self-play-with-a
|
Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm
|
1712.01815
|
http://arxiv.org/abs/1712.01815v1
|
http://arxiv.org/pdf/1712.01815v1.pdf
|
https://github.com/Neo-The1/ThinkingTicTacToe
| false | false | true |
tf
|
https://paperswithcode.com/paper/focal-loss-for-dense-object-detection
|
Focal Loss for Dense Object Detection
|
1708.02002
|
http://arxiv.org/abs/1708.02002v2
|
http://arxiv.org/pdf/1708.02002v2.pdf
|
https://github.com/trongnghia00/darknet
| false | false | true |
none
|
https://paperswithcode.com/paper/semi-supervised-learning-with-ladder-networks
|
Semi-Supervised Learning with Ladder Networks
|
1507.02672
|
http://arxiv.org/abs/1507.02672v2
|
http://arxiv.org/pdf/1507.02672v2.pdf
|
https://github.com/brandonrobertz/AcademicUrlTitles
| false | false | true |
none
|
https://paperswithcode.com/paper/co-designing-for-a-hybrid-workplace
|
Co-designing for a Hybrid Workplace Experience in Software Development
|
2212.09638
|
https://arxiv.org/abs/2212.09638v1
|
https://arxiv.org/pdf/2212.09638v1.pdf
|
https://github.com/co-design-hybrid/co-design-hybrid
| true | true | false |
none
|
https://paperswithcode.com/paper/towards-automated-deep-learning-efficient
|
Towards Automated Deep Learning: Efficient Joint Neural Architecture and Hyperparameter Search
|
1807.06906
|
http://arxiv.org/abs/1807.06906v1
|
http://arxiv.org/pdf/1807.06906v1.pdf
|
https://github.com/arberzela/EfficientNAS
| false | false | true |
pytorch
|
https://paperswithcode.com/paper/statistical-parametric-speech-synthesis-using
|
Statistical Parametric Speech Synthesis Using Generative Adversarial Networks Under A Multi-task Learning Framework
|
1707.01670
|
http://arxiv.org/abs/1707.01670v2
|
http://arxiv.org/pdf/1707.01670v2.pdf
|
https://github.com/rickyHong/GANTTS-update-repl
| false | false | true |
pytorch
|
https://paperswithcode.com/paper/perfect-sampling-with-unitary-tensor-networks
|
Perfect Sampling with Unitary Tensor Networks
|
1201.3974
|
http://arxiv.org/abs/1201.3974v3
|
http://arxiv.org/pdf/1201.3974v3.pdf
|
https://github.com/0/itensor-linear-rotors
| false | false | true |
none
|
https://paperswithcode.com/paper/real-time-single-image-and-video-super
|
Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional Neural Network
|
1609.05158
|
http://arxiv.org/abs/1609.05158v2
|
http://arxiv.org/pdf/1609.05158v2.pdf
|
https://github.com/Nhat-Thanh/ESPCN-Pytorch
| false | false | true |
pytorch
|
https://paperswithcode.com/paper/a-structured-matrix-factorization-framework
|
A structured matrix factorization framework for large scale calcium imaging data analysis
|
1409.2903
|
http://arxiv.org/abs/1409.2903v1
|
http://arxiv.org/pdf/1409.2903v1.pdf
|
https://github.com/YGUO29/FANTASIA-CaImAn
| false | false | true |
none
|
https://paperswithcode.com/paper/recent-trends-in-deep-learning-based-natural
|
Recent Trends in Deep Learning Based Natural Language Processing
|
1708.02709
|
http://arxiv.org/abs/1708.02709v8
|
http://arxiv.org/pdf/1708.02709v8.pdf
|
https://github.com/ridakadri14/AspectBasedSentimentAnalysis
| false | false | true |
tf
|
https://paperswithcode.com/paper/cvae-gan-fine-grained-image-generation
|
CVAE-GAN: Fine-Grained Image Generation through Asymmetric Training
|
1703.10155
|
http://arxiv.org/abs/1703.10155v2
|
http://arxiv.org/pdf/1703.10155v2.pdf
|
https://github.com/One-sixth/CVAE-GAN_tensorlayer
| false | false | true |
tf
|
https://paperswithcode.com/paper/liqui-a-software-design-architecture-and
|
LIQUi|>: A Software Design Architecture and Domain-Specific Language for Quantum Computing
|
1402.4467
|
http://arxiv.org/abs/1402.4467v1
|
http://arxiv.org/pdf/1402.4467v1.pdf
|
https://github.com/hhy37/Liquid
| false | false | true |
none
|
https://paperswithcode.com/paper/quantum-algorithm-for-solving-linear-systems
|
Quantum algorithm for solving linear systems of equations
|
0811.3171
|
http://arxiv.org/abs/0811.3171v3
|
http://arxiv.org/pdf/0811.3171v3.pdf
|
https://github.com/hhy37/Liquid
| false | false | true |
none
|
https://paperswithcode.com/paper/automatic-inference-of-sound-correspondence
|
Automatic Inference of Sound Correspondence Patterns across Multiple Languages
| null |
https://aclanthology.org/J19-1004
|
https://aclanthology.org/J19-1004.pdf
|
https://github.com/lingpy/correspondence-pattern-paper
| true | true | false |
none
|
https://paperswithcode.com/paper/the-temporal-event-graph
|
The Temporal Event Graph
|
1706.02128
|
http://arxiv.org/abs/1706.02128v1
|
http://arxiv.org/pdf/1706.02128v1.pdf
|
https://github.com/empiricalstateofmind/eventgraphs
| false | false | true |
none
|
https://paperswithcode.com/paper/inverse-problems-in-asteroseismology
|
Inverse Problems in Asteroseismology
|
1808.06649
|
http://arxiv.org/abs/1808.06649v1
|
http://arxiv.org/pdf/1808.06649v1.pdf
|
https://github.com/earlbellinger/thesis
| false | false | true |
none
|
https://paperswithcode.com/paper/predictive-entropy-search-for-efficient
|
Predictive Entropy Search for Efficient Global Optimization of Black-box Functions
|
1406.2541
|
http://arxiv.org/abs/1406.2541v1
|
http://arxiv.org/pdf/1406.2541v1.pdf
|
https://github.com/chongkewu/PESC-HPC
| false | false | true |
none
|
https://paperswithcode.com/paper/tencent-ml-images-a-large-scale-multi-label
|
Tencent ML-Images: A Large-Scale Multi-Label Image Database for Visual Representation Learning
|
1901.01703
|
https://arxiv.org/abs/1901.01703v7
|
https://arxiv.org/pdf/1901.01703v7.pdf
|
https://github.com/Tencent/tencent-ml-images
| true | true | true |
tf
|
https://paperswithcode.com/paper/visual-relationship-detection-with-language-1
|
Visual Relationship Detection with Language prior and Softmax
|
1904.07798
|
http://arxiv.org/abs/1904.07798v1
|
http://arxiv.org/pdf/1904.07798v1.pdf
|
https://github.com/Jungjaewon/Visual-Relationship-Detection
| false | false | true |
caffe2
|
https://paperswithcode.com/paper/end-to-end-memory-networks
|
End-To-End Memory Networks
|
1503.08895
|
http://arxiv.org/abs/1503.08895v5
|
http://arxiv.org/pdf/1503.08895v5.pdf
|
https://github.com/dare0021/MemN2N_Bench
| false | false | true |
none
|
https://paperswithcode.com/paper/towards-high-performance-video-object
|
Towards High Performance Video Object Detection for Mobiles
|
1804.05830
|
http://arxiv.org/abs/1804.05830v1
|
http://arxiv.org/pdf/1804.05830v1.pdf
|
https://github.com/stanlee321/LightFlow-TensorFlow
| false | false | true |
tf
|
https://paperswithcode.com/paper/multimodal-word-distributions
|
Multimodal Word Distributions
|
1704.08424
|
https://arxiv.org/abs/1704.08424v2
|
https://arxiv.org/pdf/1704.08424v2.pdf
|
https://github.com/benathi/multisense-prob-fasttext
| false | false | true |
none
|
https://paperswithcode.com/paper/feature-importance-measure-for-non-linear
|
Feature Importance Measure for Non-linear Learning Algorithms
|
1611.07567
|
http://arxiv.org/abs/1611.07567v1
|
http://arxiv.org/pdf/1611.07567v1.pdf
|
https://github.com/mcvidomi/MFI
| false | false | true |
none
|
https://paperswithcode.com/paper/inference-of-stellar-parameters-from
|
Inference of stellar parameters from brightness variations
|
1805.04519
|
http://arxiv.org/abs/1805.04519v1
|
http://arxiv.org/pdf/1805.04519v1.pdf
|
https://github.com/mkness/ACFCannon
| true | true | false |
none
|
https://paperswithcode.com/paper/event-graphs-advances-and-applications-of
|
Event Graphs: Advances and Applications of Second-Order Time-Unfolded Temporal Network Models
|
1809.03457
|
http://arxiv.org/abs/1809.03457v1
|
http://arxiv.org/pdf/1809.03457v1.pdf
|
https://github.com/empiricalstateofmind/eventgraphs
| true | true | true |
none
|
https://paperswithcode.com/paper/separating-the-signal-from-the-noise-evidence
|
Separating the signal from the noise: Evidence for deceleration in old-age death rates
|
1707.09433
|
http://arxiv.org/abs/1707.09433v2
|
http://arxiv.org/pdf/1707.09433v2.pdf
|
https://github.com/dfeehan/oldage-paper-code-released
| false | false | true |
none
|
https://paperswithcode.com/paper/cnncnn-convolutional-decoders-for-image
|
CNN+CNN: Convolutional Decoders for Image Captioning
|
1805.09019
|
http://arxiv.org/abs/1805.09019v1
|
http://arxiv.org/pdf/1805.09019v1.pdf
|
https://github.com/qingzwang/GHA-ImageCaptioning
| false | false | true |
pytorch
|
https://paperswithcode.com/paper/spnets-differentiable-fluid-dynamics-for-deep
|
SPNets: Differentiable Fluid Dynamics for Deep Neural Networks
|
1806.06094
|
http://arxiv.org/abs/1806.06094v2
|
http://arxiv.org/pdf/1806.06094v2.pdf
|
https://github.com/cschenck/SmoothParticleNets
| true | true | false |
pytorch
|
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/MindSpore-paper-code-3/code7/tree/main/FaceAttribute
| false | false | false |
mindspore
|
https://paperswithcode.com/paper/efficient-estimation-of-word-representations
|
Efficient Estimation of Word Representations in Vector Space
|
1301.3781
|
http://arxiv.org/abs/1301.3781v3
|
http://arxiv.org/pdf/1301.3781v3.pdf
|
https://github.com/palmagro/gg2vec
| false | false | true |
none
|
https://paperswithcode.com/paper/a-structured-self-attentive-sentence
|
A Structured Self-attentive Sentence Embedding
|
1703.03130
|
http://arxiv.org/abs/1703.03130v1
|
http://arxiv.org/pdf/1703.03130v1.pdf
|
https://github.com/hantek/SelfAttentiveSentEmbed
| false | false | true |
pytorch
|
https://paperswithcode.com/paper/focal-loss-for-dense-object-detection
|
Focal Loss for Dense Object Detection
|
1708.02002
|
http://arxiv.org/abs/1708.02002v2
|
http://arxiv.org/pdf/1708.02002v2.pdf
|
https://github.com/fizyr/keras-retinanet
| false | false | true |
tf
|
https://paperswithcode.com/paper/yolo9000-better-faster-stronger
|
YOLO9000: Better, Faster, Stronger
|
1612.08242
|
http://arxiv.org/abs/1612.08242v1
|
http://arxiv.org/pdf/1612.08242v1.pdf
|
https://github.com/vantupham/darknet
| false | false | true |
none
|
https://paperswithcode.com/paper/u-net-convolutional-networks-for-biomedical
|
U-Net: Convolutional Networks for Biomedical Image Segmentation
|
1505.04597
|
http://arxiv.org/abs/1505.04597v1
|
http://arxiv.org/pdf/1505.04597v1.pdf
|
https://github.com/muramasa8191/DeepLearning
| false | false | true |
tf
|
https://paperswithcode.com/paper/sgdr-stochastic-gradient-descent-with-warm
|
SGDR: Stochastic Gradient Descent with Warm Restarts
|
1608.03983
|
http://arxiv.org/abs/1608.03983v5
|
http://arxiv.org/pdf/1608.03983v5.pdf
|
https://github.com/Harshvardhan1/cyclic-learning-schedulers-pytorch
| false | false | true |
pytorch
|
https://paperswithcode.com/paper/spatiotemporal-multiplier-networks-for-video
|
Spatiotemporal Multiplier Networks for Video Action Recognition
| null |
http://openaccess.thecvf.com/content_cvpr_2017/html/Feichtenhofer_Spatiotemporal_Multiplier_Networks_CVPR_2017_paper.html
|
http://openaccess.thecvf.com/content_cvpr_2017/papers/Feichtenhofer_Spatiotemporal_Multiplier_Networks_CVPR_2017_paper.pdf
|
https://github.com/feichtenhofer/st-resnet
| true | true | false |
none
|
https://paperswithcode.com/paper/knowing-when-to-look-adaptive-attention-via-a
|
Knowing When to Look: Adaptive Attention via A Visual Sentinel for Image Captioning
|
1612.01887
|
http://arxiv.org/abs/1612.01887v2
|
http://arxiv.org/pdf/1612.01887v2.pdf
|
https://github.com/miroblog/AdaptiveAttention
| false | false | true |
pytorch
|
https://paperswithcode.com/paper/salient-object-detection-driven-by-fixation
|
Salient Object Detection Driven by Fixation Prediction
| null |
http://openaccess.thecvf.com/content_cvpr_2018/html/Wang_Salient_Object_Detection_CVPR_2018_paper.html
|
http://openaccess.thecvf.com/content_cvpr_2018/papers/Wang_Salient_Object_Detection_CVPR_2018_paper.pdf
|
https://github.com/wenguanwang/ASNet
| true | true | false |
none
|
https://paperswithcode.com/paper/supervised-learning-of-universal-sentence
|
Supervised Learning of Universal Sentence Representations from Natural Language Inference Data
|
1705.02364
|
http://arxiv.org/abs/1705.02364v5
|
http://arxiv.org/pdf/1705.02364v5.pdf
|
https://github.com/facebookresearch/InferSent
| true | true | true |
pytorch
|
https://paperswithcode.com/paper/a-high-coverage-method-for-automatic-false
|
A High Coverage Method for Automatic False Friends Detection for Spanish and Portuguese
| null |
https://aclanthology.org/W18-3903
|
https://aclanthology.org/W18-3903.pdf
|
https://github.com/pln-fing-udelar/false-friends
| true | true | false |
none
|
https://paperswithcode.com/paper/the-chefs-hat-simulation-environment-for
|
The Chef's Hat Simulation Environment for Reinforcement-Learning-Based Agents
|
2003.05861
|
https://arxiv.org/abs/2003.05861v1
|
https://arxiv.org/pdf/2003.05861v1.pdf
|
https://github.com/pablovin/MoodyFramework
| false | false | true |
none
|
https://paperswithcode.com/paper/deep-video-deblurring
|
Deep Video Deblurring
|
1611.08387
|
http://arxiv.org/abs/1611.08387v1
|
http://arxiv.org/pdf/1611.08387v1.pdf
|
https://github.com/susomena/DeepSlowMotion
| false | false | true |
tf
|
https://paperswithcode.com/paper/adaptive-system-optimization-using-random
|
Adaptive system optimization using random directions stochastic approximation
|
1502.05577
|
http://arxiv.org/abs/1502.05577v2
|
http://arxiv.org/pdf/1502.05577v2.pdf
|
https://github.com/prashla/RDSA
| true | true | false |
none
|
https://paperswithcode.com/paper/identification-of-emergency-blood-donation
|
Identification of Emergency Blood Donation Request on Twitter
| null |
https://aclanthology.org/W18-5907
|
https://aclanthology.org/W18-5907.pdf
|
https://github.com/pmathur5k10/EBDR
| true | true | false |
none
|
https://paperswithcode.com/paper/rethinking-on-multi-stage-networks-for-human
|
Rethinking on Multi-Stage Networks for Human Pose Estimation
|
1901.00148
|
https://arxiv.org/abs/1901.00148v4
|
https://arxiv.org/pdf/1901.00148v4.pdf
|
https://github.com/chenyilun95/tf-cpn
| false | false | true |
tf
|
https://paperswithcode.com/paper/semantic-visual-navigation-by-watching
|
Semantic Visual Navigation by Watching YouTube Videos
|
2006.10034
|
https://arxiv.org/abs/2006.10034v2
|
https://arxiv.org/pdf/2006.10034v2.pdf
|
https://github.com/MatthewChang/video-dqn
| true | false | false |
pytorch
|
https://paperswithcode.com/paper/sound-event-detection-and-time-frequency
|
Sound Event Detection and Time-Frequency Segmentation from Weakly Labelled Data
|
1804.04715
|
http://arxiv.org/abs/1804.04715v2
|
http://arxiv.org/pdf/1804.04715v2.pdf
|
https://github.com/qiuqiangkong/sed_time_freq_segmentation
| true | true | false |
pytorch
|
https://paperswithcode.com/paper/dueling-network-architectures-for-deep
|
Dueling Network Architectures for Deep Reinforcement Learning
|
1511.06581
|
http://arxiv.org/abs/1511.06581v3
|
http://arxiv.org/pdf/1511.06581v3.pdf
|
https://github.com/prajwalgatti/DRL-Continuous-Control
| false | false | true |
none
|
https://paperswithcode.com/paper/very-deep-convolutional-networks-for-large
|
Very Deep Convolutional Networks for Large-Scale Image Recognition
|
1409.1556
|
http://arxiv.org/abs/1409.1556v6
|
http://arxiv.org/pdf/1409.1556v6.pdf
|
https://github.com/Tools4Project/4501Project
| false | false | true |
tf
|
https://paperswithcode.com/paper/efficient-training-of-energy-based-models-via
|
Efficient training of energy-based models via spin-glass control
|
1910.01592
|
https://arxiv.org/abs/1910.01592v4
|
https://arxiv.org/pdf/1910.01592v4.pdf
|
https://github.com/apozas/rapid
| true | true | true |
pytorch
|
https://paperswithcode.com/paper/context-dependent-fine-grained-entity-type
|
Context-Dependent Fine-Grained Entity Type Tagging
|
1412.1820
|
http://arxiv.org/abs/1412.1820v2
|
http://arxiv.org/pdf/1412.1820v2.pdf
|
https://github.com/shanzhenren/AFET
| false | false | true |
none
|
https://paperswithcode.com/paper/sampling-generative-networks
|
Sampling Generative Networks
|
1609.04468
|
http://arxiv.org/abs/1609.04468v3
|
http://arxiv.org/pdf/1609.04468v3.pdf
|
https://github.com/ptrblck/prog_gans_pytorch_inference
| false | false | true |
pytorch
|
https://paperswithcode.com/paper/breaking-the-curse-of-space-explosion-towards
|
Breaking the Curse of Space Explosion: Towards Efficient NAS with Curriculum Search
|
2007.07197
|
https://arxiv.org/abs/2007.07197v2
|
https://arxiv.org/pdf/2007.07197v2.pdf
|
https://github.com/guoyongcs/CNAS
| true | true | false |
pytorch
|
https://paperswithcode.com/paper/learning-imbalanced-datasets-with-label
|
Learning Imbalanced Datasets with Label-Distribution-Aware Margin Loss
|
1906.07413
|
https://arxiv.org/abs/1906.07413v2
|
https://arxiv.org/pdf/1906.07413v2.pdf
|
https://github.com/feidfoe/AdjustBnd4Imbalance
| false | false | true |
pytorch
|
https://paperswithcode.com/paper/learning-2d-temporal-adjacent-networks-for
|
Learning 2D Temporal Adjacent Networks for Moment Localization with Natural Language
|
1912.03590
|
https://arxiv.org/abs/1912.03590v3
|
https://arxiv.org/pdf/1912.03590v3.pdf
|
https://github.com/researchmm/2D-TAN
| false | false | true |
pytorch
|
https://paperswithcode.com/paper/multigrid-predictive-filter-flow-for
|
Multigrid Predictive Filter Flow for Unsupervised Learning on Videos
|
1904.01693
|
http://arxiv.org/abs/1904.01693v1
|
http://arxiv.org/pdf/1904.01693v1.pdf
|
https://github.com/bestaar/predictiveFilterFlow
| false | false | true |
pytorch
|
https://paperswithcode.com/paper/image-reconstruction-with-predictive-filter
|
Image Reconstruction with Predictive Filter Flow
|
1811.11482
|
http://arxiv.org/abs/1811.11482v1
|
http://arxiv.org/pdf/1811.11482v1.pdf
|
https://github.com/bestaar/predictiveFilterFlow
| false | false | true |
pytorch
|
https://paperswithcode.com/paper/revisiting-unreasonable-effectiveness-of-data
|
Revisiting Unreasonable Effectiveness of Data in Deep Learning Era
|
1707.02968
|
http://arxiv.org/abs/1707.02968v2
|
http://arxiv.org/pdf/1707.02968v2.pdf
|
https://github.com/Tencent/tencent-ml-images
| false | false | true |
tf
|
https://paperswithcode.com/paper/general-purpose-atomic-crosschain
|
General Purpose Atomic Crosschain Transactions
|
2011.12783
|
https://arxiv.org/abs/2011.12783v4
|
https://arxiv.org/pdf/2011.12783v4.pdf
|
https://github.com/ConsenSys/gpact
| true | true | true |
none
|
https://paperswithcode.com/paper/ms-dpps-multi-source-determinantal-point
|
MS-DPPs: Multi-Source Determinantal Point Processes for Contextual Diversity Refinement of Composite Attributes in Text to Image Retrieval
|
2507.06654
|
https://arxiv.org/abs/2507.06654v1
|
https://arxiv.org/pdf/2507.06654v1.pdf
|
https://github.com/nec-n-sogi/msdpp
| true | true | true |
pytorch
|
https://paperswithcode.com/paper/atlas-end-to-end-3d-scene-reconstruction-from
|
Atlas: End-to-End 3D Scene Reconstruction from Posed Images
|
2003.10432
|
https://arxiv.org/abs/2003.10432v3
|
https://arxiv.org/pdf/2003.10432v3.pdf
|
https://github.com/magicleap/Atlas
| false | false | true |
pytorch
|
https://paperswithcode.com/paper/nimbro-op2x-adult-sized-open-source-3d
|
NimbRo-OP2X: Adult-sized Open-source 3D Printed Humanoid Robot
|
1810.08395
|
http://arxiv.org/abs/1810.08395v1
|
http://arxiv.org/pdf/1810.08395v1.pdf
|
https://github.com/iswariyam/Mini-semantic-segmentation-network-pytorch
| false | false | true |
pytorch
|
https://paperswithcode.com/paper/invariance-analysis-of-saliency-models-versus
|
Invariance Analysis of Saliency Models versus Human Gaze During Scene Free Viewing
|
1810.04456
|
http://arxiv.org/abs/1810.04456v1
|
http://arxiv.org/pdf/1810.04456v1.pdf
|
https://github.com/CZHQuality/Sal-CFS-GAN
| false | false | true |
tf
|
https://paperswithcode.com/paper/unpaired-image-to-image-translation-using
|
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
|
1703.10593
|
https://arxiv.org/abs/1703.10593v7
|
https://arxiv.org/pdf/1703.10593v7.pdf
|
https://github.com/Shumway82/CycleGAN
| false | false | true |
tf
|
https://paperswithcode.com/paper/semi-supervised-learning-with-ladder-networks
|
Semi-Supervised Learning with Ladder Networks
|
1507.02672
|
http://arxiv.org/abs/1507.02672v2
|
http://arxiv.org/pdf/1507.02672v2.pdf
|
https://github.com/CuriousAI/ladder
| false | false | true |
none
|
https://paperswithcode.com/paper/bayesian-optimization-of-hyper-parameters-in
|
Bayesian optimization of hyper-parameters in reservoir computing
|
1611.05193
|
http://arxiv.org/abs/1611.05193v3
|
http://arxiv.org/pdf/1611.05193v3.pdf
|
https://github.com/rednotion/parallel_esn_web
| false | false | true |
none
|
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/ignacio-rocco/cnngeometric_pytorch
| false | false | true |
pytorch
|
https://paperswithcode.com/paper/neural-audio-synthesis-of-musical-notes-with
|
Neural Audio Synthesis of Musical Notes with WaveNet Autoencoders
|
1704.01279
|
http://arxiv.org/abs/1704.01279v1
|
http://arxiv.org/pdf/1704.01279v1.pdf
|
https://github.com/NoaCahan/WavenetAutoEncoder
| false | false | true |
pytorch
|
https://paperswithcode.com/paper/tips-and-tricks-for-visual-question-answering
|
Tips and Tricks for Visual Question Answering: Learnings from the 2017 Challenge
|
1708.02711
|
http://arxiv.org/abs/1708.02711v1
|
http://arxiv.org/pdf/1708.02711v1.pdf
|
https://github.com/feifengwhu/question_attention
| false | false | true |
pytorch
|
https://paperswithcode.com/paper/retinal-vessel-segmentation-based-on-fully
|
Retinal vessel segmentation based on Fully Convolutional Neural Networks
|
1812.07110
|
http://arxiv.org/abs/1812.07110v2
|
http://arxiv.org/pdf/1812.07110v2.pdf
|
https://github.com/americofmoliveira/VesselSegmentation_ESWA
| false | false | true |
none
|
https://paperswithcode.com/paper/randomized-matrix-decompositions-using-r
|
Randomized Matrix Decompositions using R
|
1608.02148
|
http://arxiv.org/abs/1608.02148v4
|
http://arxiv.org/pdf/1608.02148v4.pdf
|
https://github.com/Benli11/ristretto
| false | false | true |
none
|
https://paperswithcode.com/paper/modified-shallow-water-equations-for
|
Modified Shallow Water Equations for significantly varying seabeds
|
1202.6542
|
http://arxiv.org/abs/1202.6542v6
|
http://arxiv.org/pdf/1202.6542v6.pdf
|
https://github.com/huwb/crest-oceanrender
| false | false | true |
none
|
https://paperswithcode.com/paper/semantic-document-distance-measures-and
|
Semantic Document Distance Measures and Unsupervised Document Revision Detection
|
1709.01256
|
http://arxiv.org/abs/1709.01256v2
|
http://arxiv.org/pdf/1709.01256v2.pdf
|
https://github.com/XiaofengZhu/wDTW-wTED
| true | true | true |
none
|
https://paperswithcode.com/paper/revisiting-decomposable-submodular-function
|
Revisiting Decomposable Submodular Function Minimization with Incidence Relations
|
1803.03851
|
http://arxiv.org/abs/1803.03851v3
|
http://arxiv.org/pdf/1803.03851v3.pdf
|
https://github.com/lipan00123/DSFM-with-incidence-relations
| true | true | false |
none
|
https://paperswithcode.com/paper/learning-deep-representations-of-fine-grained
|
Learning Deep Representations of Fine-grained Visual Descriptions
|
1605.05395
|
http://arxiv.org/abs/1605.05395v1
|
http://arxiv.org/pdf/1605.05395v1.pdf
|
https://github.com/Maymaher/StackGANv2
| false | false | true |
pytorch
|
https://paperswithcode.com/paper/an-end-to-end-trainable-neural-network-for
|
An End-to-End Trainable Neural Network for Image-based Sequence Recognition and Its Application to Scene Text Recognition
|
1507.05717
|
http://arxiv.org/abs/1507.05717v1
|
http://arxiv.org/pdf/1507.05717v1.pdf
|
https://github.com/bai-shang/crnn_ctc_ocr.Tensorflow
| false | false | true |
tf
|
https://paperswithcode.com/paper/learning-to-learn-without-forgetting-by
|
Learning to Learn without Forgetting by Maximizing Transfer and Minimizing Interference
|
1810.11910
|
https://arxiv.org/abs/1810.11910v3
|
https://arxiv.org/pdf/1810.11910v3.pdf
|
https://github.com/mattriemer/mer
| true | true | false |
pytorch
|
https://paperswithcode.com/paper/pythia-v01-the-winning-entry-to-the-vqa
|
Pythia v0.1: the Winning Entry to the VQA Challenge 2018
|
1807.09956
|
http://arxiv.org/abs/1807.09956v2
|
http://arxiv.org/pdf/1807.09956v2.pdf
|
https://github.com/songhe17/pythia-clone
| false | false | true |
pytorch
|
https://paperswithcode.com/paper/generative-adversarial-text-to-image
|
Generative Adversarial Text to Image Synthesis
|
1605.05396
|
http://arxiv.org/abs/1605.05396v2
|
http://arxiv.org/pdf/1605.05396v2.pdf
|
https://github.com/Maymaher/StackGANv2
| false | false | true |
pytorch
|
https://paperswithcode.com/paper/attngan-fine-grained-text-to-image-generation
|
AttnGAN: Fine-Grained Text to Image Generation with Attentional Generative Adversarial Networks
|
1711.10485
|
http://arxiv.org/abs/1711.10485v1
|
http://arxiv.org/pdf/1711.10485v1.pdf
|
https://github.com/Maymaher/StackGANv2
| false | false | true |
pytorch
|
https://paperswithcode.com/paper/benchmarking-machine-learning-models-on-eicu
|
Benchmarking machine learning models on multi-centre eICU critical care dataset
|
1910.00964
|
https://arxiv.org/abs/1910.00964v3
|
https://arxiv.org/pdf/1910.00964v3.pdf
|
https://github.com/mostafaalishahi/eICU_Benchmark
| true | true | true |
none
|
End of preview. Expand
in Data Studio
This dataset will not be updated. It corresponds to the last available public snapshot of the data, retrieved on July 28th, 2025.
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