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/wasserstein-gan
|
Wasserstein GAN
|
1701.07875
|
http://arxiv.org/abs/1701.07875v3
|
http://arxiv.org/pdf/1701.07875v3.pdf
|
https://github.com/Mohammad-Rahmdel/WassersteinGAN-Tensorflow
| false | false | true |
tf
|
https://paperswithcode.com/paper/learning-speaker-representations-with-mutual
|
Learning Speaker Representations with Mutual Information
|
1812.00271
|
http://arxiv.org/abs/1812.00271v2
|
http://arxiv.org/pdf/1812.00271v2.pdf
|
https://github.com/Js-Mim/rl_singing_voice
| false | false | true |
pytorch
|
https://paperswithcode.com/paper/infogan-interpretable-representation-learning
|
InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets
|
1606.03657
|
http://arxiv.org/abs/1606.03657v1
|
http://arxiv.org/pdf/1606.03657v1.pdf
|
https://github.com/openai/InfoGAN
| false | false | true |
tf
|
https://paperswithcode.com/paper/qt-opt-scalable-deep-reinforcement-learning
|
QT-Opt: Scalable Deep Reinforcement Learning for Vision-Based Robotic Manipulation
|
1806.10293
|
http://arxiv.org/abs/1806.10293v3
|
http://arxiv.org/pdf/1806.10293v3.pdf
|
https://github.com/hyecheol123/Summary_of_QT-Opt
| false | false | true |
none
|
https://paperswithcode.com/paper/simulation-based-lidar-super-resolution-for-1
|
Simulation-based Lidar Super-resolution for Ground Vehicles
|
2004.05242
|
https://arxiv.org/abs/2004.05242v1
|
https://arxiv.org/pdf/2004.05242v1.pdf
|
https://github.com/RobustFieldAutonomyLab/lidar_super_resolution
| true | true | false |
tf
|
https://paperswithcode.com/paper/integrating-multi-view-analysis-multi-view
|
Integrating Multi-view Analysis: Multi-view Mixture-of-Expert for Textual Personality Detection
|
2408.08551
|
https://arxiv.org/abs/2408.08551v1
|
https://arxiv.org/pdf/2408.08551v1.pdf
|
https://github.com/Hugo-Zhu/MvP
| true | false | false |
pytorch
|
https://paperswithcode.com/paper/the-many-faces-of-robustness-a-critical
|
The Many Faces of Robustness: A Critical Analysis of Out-of-Distribution Generalization
|
2006.16241
|
https://arxiv.org/abs/2006.16241v3
|
https://arxiv.org/pdf/2006.16241v3.pdf
|
https://github.com/hendrycks/imagenet-r
| true | true | true |
pytorch
|
https://paperswithcode.com/paper/answering-questions-on-covid-19-in-real-time
|
Answering Questions on COVID-19 in Real-Time
|
2006.15830
|
https://arxiv.org/abs/2006.15830v2
|
https://arxiv.org/pdf/2006.15830v2.pdf
|
https://github.com/dmis-lab/covidAsk
| true | true | false |
pytorch
|
https://paperswithcode.com/paper/influencers-identification-in-complex
|
Influencers identification in complex networks through reaction-diffusion dynamics
|
1803.01212
|
http://arxiv.org/abs/1803.01212v3
|
http://arxiv.org/pdf/1803.01212v3.pdf
|
https://github.com/kunda00/viralrank_centrality
| true | true | true |
none
|
https://paperswithcode.com/paper/segicp-integrated-deep-semantic-segmentation
|
SegICP: Integrated Deep Semantic Segmentation and Pose Estimation
|
1703.01661
|
http://arxiv.org/abs/1703.01661v2
|
http://arxiv.org/pdf/1703.01661v2.pdf
|
https://github.com/Pacific-cyber/KUKA_Catch_Project
| false | false | true |
tf
|
https://paperswithcode.com/paper/photospheric-prompt-emission-from-long-gamma
|
Photospheric Prompt Emission From Long Gamma Ray Burst Simulations -- I. Optical Emission
|
2105.06505
|
https://arxiv.org/abs/2105.06505v2
|
https://arxiv.org/pdf/2105.06505v2.pdf
|
https://github.com/parsotat/ProcessMCRaT
| true | true | false |
none
|
https://paperswithcode.com/paper/fade-a-task-agnostic-upsampling-operator-for
|
FADE: A Task-Agnostic Upsampling Operator for Encoder-Decoder Architectures
|
2407.13500
|
https://arxiv.org/abs/2407.13500v1
|
https://arxiv.org/pdf/2407.13500v1.pdf
|
https://github.com/poppinace/fade
| true | true | false |
pytorch
|
https://paperswithcode.com/paper/photospheric-prompt-emission-from-long-gamma
|
Photospheric Prompt Emission From Long Gamma Ray Burst Simulations -- I. Optical Emission
|
2105.06505
|
https://arxiv.org/abs/2105.06505v2
|
https://arxiv.org/pdf/2105.06505v2.pdf
|
https://github.com/lazzati-astro/MCRaT
| true | true | false |
none
|
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/Media-Smart/vedastr
| false | false | true |
pytorch
|
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/fepegar/unet
| false | false | true |
pytorch
|
https://paperswithcode.com/paper/combining-stochastic-adaptive-cubic
|
Combining Stochastic Adaptive Cubic Regularization with Negative Curvature for Nonconvex Optimization
|
1906.11417
|
https://arxiv.org/abs/1906.11417v1
|
https://arxiv.org/pdf/1906.11417v1.pdf
|
https://github.com/seonho-park/Stochastic-Adaptive-cubic-regularization-with-Negative-Curvature
| false | false | true |
tf
|
https://paperswithcode.com/paper/geometry-aware-generation-of-adversarial-and-1
|
Geometry-Aware Generation of Adversarial Point Clouds
|
1912.11171
|
https://arxiv.org/abs/1912.11171v3
|
https://arxiv.org/pdf/1912.11171v3.pdf
|
https://github.com/Yuxin-Wen/GeoA3
| true | true | true |
pytorch
|
https://paperswithcode.com/paper/variational-deep-embedding-an-unsupervised
|
Variational Deep Embedding: An Unsupervised and Generative Approach to Clustering
|
1611.05148
|
http://arxiv.org/abs/1611.05148v3
|
http://arxiv.org/pdf/1611.05148v3.pdf
|
https://github.com/gudgud96/piano-synthesis
| false | false | true |
pytorch
|
https://paperswithcode.com/paper/does-causal-coherence-predict-online-spread
|
Does Causal Coherence Predict Online Spread of Social Media?
| null |
https://link.springer.com/chapter/10.1007/978-3-030-21741-9_19
|
https://link.springer.com/chapter/10.1007/978-3-030-21741-9_19
|
https://github.com/phosseini/SBP-BRiMS2019
| true | false | false |
none
|
https://paperswithcode.com/paper/generative-modelling-for-controllable-audio
|
Generative Modelling for Controllable Audio Synthesis of Expressive Piano Performance
|
2006.09833
|
https://arxiv.org/abs/2006.09833v2
|
https://arxiv.org/pdf/2006.09833v2.pdf
|
https://github.com/gudgud96/piano-synthesis
| true | true | true |
pytorch
|
https://paperswithcode.com/paper/recsim-a-configurable-simulation-platform-for
|
RecSim: A Configurable Simulation Platform for Recommender Systems
|
1909.04847
|
https://arxiv.org/abs/1909.04847v2
|
https://arxiv.org/pdf/1909.04847v2.pdf
|
https://github.com/google-research/recsim
| true | true | true |
tf
|
https://paperswithcode.com/paper/multi-domain-learning-and-identity-mining-for
|
Multi-Domain Learning and Identity Mining for Vehicle Re-Identification
|
2004.10547
|
https://arxiv.org/abs/2004.10547v2
|
https://arxiv.org/pdf/2004.10547v2.pdf
|
https://github.com/heshuting555/AICITY2020_DMT_VehicleReID
| true | true | true |
pytorch
|
https://paperswithcode.com/paper/saccadenet-a-fast-and-accurate-object
|
SaccadeNet: A Fast and Accurate Object Detector
|
2003.12125
|
https://arxiv.org/abs/2003.12125v1
|
https://arxiv.org/pdf/2003.12125v1.pdf
|
https://github.com/voidrank/SaccadeNet
| false | false | true |
pytorch
|
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/chikalabouka/INF8225-TP4
| false | false | true |
none
|
https://paperswithcode.com/paper/unsupervised-domain-attention-adaptation
|
Unsupervised Domain Attention Adaptation Network for Caricature Attribute Recognition
|
2007.09344
|
https://arxiv.org/abs/2007.09344v1
|
https://arxiv.org/pdf/2007.09344v1.pdf
|
https://github.com/KeleiHe/DAAN
| true | true | true |
pytorch
|
https://paperswithcode.com/paper/mutan-multimodal-tucker-fusion-for-visual
|
MUTAN: Multimodal Tucker Fusion for Visual Question Answering
|
1705.06676
|
http://arxiv.org/abs/1705.06676v1
|
http://arxiv.org/pdf/1705.06676v1.pdf
|
https://github.com/vuhoangminh/vqa_medical
| false | false | true |
pytorch
|
https://paperswithcode.com/paper/hadamard-product-for-low-rank-bilinear
|
Hadamard Product for Low-rank Bilinear Pooling
|
1610.04325
|
http://arxiv.org/abs/1610.04325v4
|
http://arxiv.org/pdf/1610.04325v4.pdf
|
https://github.com/vuhoangminh/vqa_medical
| false | false | true |
pytorch
|
https://paperswithcode.com/paper/multimodal-compact-bilinear-pooling-for
|
Multimodal Compact Bilinear Pooling for Visual Question Answering and Visual Grounding
|
1606.01847
|
http://arxiv.org/abs/1606.01847v3
|
http://arxiv.org/pdf/1606.01847v3.pdf
|
https://github.com/vuhoangminh/vqa_medical
| false | false | true |
pytorch
|
https://paperswithcode.com/paper/generating-sequences-with-recurrent-neural
|
Generating Sequences With Recurrent Neural Networks
|
1308.0850
|
http://arxiv.org/abs/1308.0850v5
|
http://arxiv.org/pdf/1308.0850v5.pdf
|
https://github.com/swechhachoudhary/Handwriting-synthesis
| false | false | true |
pytorch
|
https://paperswithcode.com/paper/bidirectional-lstm-crf-models-for-sequence
|
Bidirectional LSTM-CRF Models for Sequence Tagging
|
1508.01991
|
http://arxiv.org/abs/1508.01991v1
|
http://arxiv.org/pdf/1508.01991v1.pdf
|
https://github.com/Akshayanti/supersense-sequence-labelling
| false | false | true |
none
|
https://paperswithcode.com/paper/bert-based-multi-head-selection-for-joint
|
BERT-Based Multi-Head Selection for Joint Entity-Relation Extraction
|
1908.05908
|
https://arxiv.org/abs/1908.05908v2
|
https://arxiv.org/pdf/1908.05908v2.pdf
|
https://github.com/jaykay233/EventExtraction
| false | false | true |
pytorch
|
https://paperswithcode.com/paper/linknet-exploiting-encoder-representations
|
LinkNet: Exploiting Encoder Representations for Efficient Semantic Segmentation
|
1707.03718
|
http://arxiv.org/abs/1707.03718v1
|
http://arxiv.org/pdf/1707.03718v1.pdf
|
https://github.com/fourmi1995/IronSegExperiment-LinkNet
| false | false | true |
tf
|
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/ajoshi944/Segmentation-severstal-steel
| false | false | true |
none
|
https://paperswithcode.com/paper/statmod-probability-calculations-for-the
|
statmod: Probability Calculations for the Inverse Gaussian Distribution
|
1603.06687
|
http://arxiv.org/abs/1603.06687v2
|
http://arxiv.org/pdf/1603.06687v2.pdf
|
https://cran.r-project.org/web/packages/statmod/index.html
| false | false | false |
none
|
https://paperswithcode.com/paper/clsgan-selective-attribute-editing-based-on
|
ClsGAN: Selective Attribute Editing Model Based On Classification Adversarial Network
|
1910.11764
|
https://arxiv.org/abs/1910.11764v2
|
https://arxiv.org/pdf/1910.11764v2.pdf
|
https://github.com/summar6/ClsGAN
| true | true | true |
pytorch
|
https://paperswithcode.com/paper/get-to-the-point-summarization-with-pointer
|
Get To The Point: Summarization with Pointer-Generator Networks
|
1704.04368
|
http://arxiv.org/abs/1704.04368v2
|
http://arxiv.org/pdf/1704.04368v2.pdf
|
https://github.com/AndreyKolomiets/News_Headline_Generation
| false | false | true |
tf
|
https://paperswithcode.com/paper/tackling-hybrid-heterogeneity-on-federated
|
On the Power of Adaptive Weighted Aggregation in Heterogeneous Federated Learning and Beyond
|
2310.02702
|
https://arxiv.org/abs/2310.02702v4
|
https://arxiv.org/pdf/2310.02702v4.pdf
|
https://github.com/dunzeng/more
| false | false | true |
pytorch
|
https://paperswithcode.com/paper/automated-discovery-of-local-rules-for
|
Automated Discovery of Local Rules for Desired Collective-Level Behavior Through Reinforcement Learning
| null |
https://www.frontiersin.org/articles/10.3389/fphy.2020.00200/full
|
https://pdfs.semanticscholar.org/de88/dc5bf1b9fd1383ff2da6709fadafd8d2cc31.pdf
|
https://gitlab.com/polavieja_lab/rl_collective_behaviour
| false | true | false |
none
|
https://paperswithcode.com/paper/the-cosmic-linear-anisotropy-solving-system-1
|
The Cosmic Linear Anisotropy Solving System (CLASS) II: Approximation schemes
|
1104.2933
|
http://arxiv.org/abs/1104.2933v3
|
http://arxiv.org/pdf/1104.2933v3.pdf
|
https://github.com/bufeo/class_v2.6_gcdm
| false | false | true |
none
|
https://paperswithcode.com/paper/visual-wake-words-dataset
|
Visual Wake Words Dataset
|
1906.05721
|
https://arxiv.org/abs/1906.05721v1
|
https://arxiv.org/pdf/1906.05721v1.pdf
|
https://github.com/arpit6232/visualwakeup_aesd
| false | false | true |
tf
|
https://paperswithcode.com/paper/bigfcm-fast-precise-and-scalable-fcm-on
|
BigFCM: Fast, Precise and Scalable FCM on Hadoop
|
1605.03047
|
http://arxiv.org/abs/1605.03047v1
|
http://arxiv.org/pdf/1605.03047v1.pdf
|
https://github.com/nghadiri/BigFCM
| true | false | true |
none
|
https://paperswithcode.com/paper/mask-wearing-status-estimation-with
|
Mask Wearing Status Estimation with Smartwatches
|
2205.06113
|
https://arxiv.org/abs/2205.06113v1
|
https://arxiv.org/pdf/2205.06113v1.pdf
|
https://github.com/aiotgroup/maskreminder
| true | true | false |
pytorch
|
https://paperswithcode.com/paper/uniformizing-techniques-to-process-ct-scans
|
Uniformizing Techniques to Process CT scans with 3D CNNs for Tuberculosis Prediction
|
2007.13224
|
https://arxiv.org/abs/2007.13224v1
|
https://arxiv.org/pdf/2007.13224v1.pdf
|
https://github.com/hasibzunair/uniformizing-3D
| true | true | true |
tf
|
https://paperswithcode.com/paper/symbolic-execution-and-debugging
|
Symbolic Execution and Debugging Synchronization
|
2006.16601
|
https://arxiv.org/abs/2006.16601v1
|
https://arxiv.org/pdf/2006.16601v1.pdf
|
https://github.com/andreafioraldi/angrgdb
| false | false | true |
none
|
https://paperswithcode.com/paper/prescriptive-business-process-monitoring-for
|
Prescriptive Business Process Monitoring for Recommending Next Best Actions
|
2008.08693
|
https://arxiv.org/abs/2008.08693v1
|
https://arxiv.org/pdf/2008.08693v1.pdf
|
https://github.com/fau-is/next-best-action
| true | true | false |
tf
|
https://paperswithcode.com/paper/ssd-single-shot-multibox-detector
|
SSD: Single Shot MultiBox Detector
|
1512.02325
|
http://arxiv.org/abs/1512.02325v5
|
http://arxiv.org/pdf/1512.02325v5.pdf
|
https://github.com/leejang/two_stream_ssd_caffe
| false | false | true |
none
|
https://paperswithcode.com/paper/prioritized-experience-replay
|
Prioritized Experience Replay
|
1511.05952
|
http://arxiv.org/abs/1511.05952v4
|
http://arxiv.org/pdf/1511.05952v4.pdf
|
https://github.com/SayhoKim/tetrisRL
| false | false | true |
tf
|
https://paperswithcode.com/paper/objective-comparison-of-methods-to-decode
|
Objective comparison of methods to decode anomalous diffusion
|
2105.06766
|
https://arxiv.org/abs/2105.06766v1
|
https://arxiv.org/pdf/2105.06766v1.pdf
|
https://github.com/AnDiChallenge/ANDI_datasets
| true | true | false |
none
|
https://paperswithcode.com/paper/m-nca-texture-generation-with-ultra-compact
|
$μ$NCA: Texture Generation with Ultra-Compact Neural Cellular Automata
|
2111.13545
|
https://arxiv.org/abs/2111.13545v1
|
https://arxiv.org/pdf/2111.13545v1.pdf
|
https://github.com/google-research/self-organising-systems/blob/master/notebooks/%CE%BCNCA_pytorch.ipynb
| false | false | false |
jax
|
https://paperswithcode.com/paper/learning-transferable-architectures-for
|
Learning Transferable Architectures for Scalable Image Recognition
|
1707.07012
|
http://arxiv.org/abs/1707.07012v4
|
http://arxiv.org/pdf/1707.07012v4.pdf
|
https://github.com/DataCanvasIO/Hypernets
| false | false | false |
tf
|
https://paperswithcode.com/paper/mobiledets-searching-for-object-detection
|
MobileDets: Searching for Object Detection Architectures for Mobile Accelerators
|
2004.14525
|
https://arxiv.org/abs/2004.14525v3
|
https://arxiv.org/pdf/2004.14525v3.pdf
|
https://github.com/DataCanvasIO/Hypernets
| false | false | false |
tf
|
https://paperswithcode.com/paper/efficient-neural-architecture-search-via-1
|
Efficient Neural Architecture Search via Parameter Sharing
|
1802.03268
|
http://arxiv.org/abs/1802.03268v2
|
http://arxiv.org/pdf/1802.03268v2.pdf
|
https://github.com/DataCanvasIO/Hypernets
| false | false | false |
tf
|
https://paperswithcode.com/paper/face-super-resolution-guided-by-3d-facial-1
|
Face Super-Resolution Guided by 3D Facial Priors
|
2007.09454
|
https://arxiv.org/abs/2007.09454v1
|
https://arxiv.org/pdf/2007.09454v1.pdf
|
https://github.com/HUuxiaobin/Face-Super-Resolution-Guided-by-3D-Facial-Priors
| false | false | true |
pytorch
|
https://paperswithcode.com/paper/rank-reduction-matrix-balancing-and-mean
|
Fast Rank Reduction for Non-negative Matrices via Mean Field Theory
|
2006.05321
|
https://arxiv.org/abs/2006.05321v2
|
https://arxiv.org/pdf/2006.05321v2.pdf
|
https://github.com/gkazunii/Legendre-tucker-rank-reduction
| false | false | false |
none
|
https://paperswithcode.com/paper/regularized-evolution-for-image-classifier
|
Regularized Evolution for Image Classifier Architecture Search
|
1802.01548
|
http://arxiv.org/abs/1802.01548v7
|
http://arxiv.org/pdf/1802.01548v7.pdf
|
https://github.com/DataCanvasIO/Hypernets
| false | false | false |
tf
|
https://paperswithcode.com/paper/neural-architecture-search-with-reinforcement
|
Neural Architecture Search with Reinforcement Learning
|
1611.01578
|
http://arxiv.org/abs/1611.01578v2
|
http://arxiv.org/pdf/1611.01578v2.pdf
|
https://github.com/DataCanvasIO/Hypernets
| false | false | false |
tf
|
https://paperswithcode.com/paper/tune-a-research-platform-for-distributed
|
Tune: A Research Platform for Distributed Model Selection and Training
|
1807.05118
|
http://arxiv.org/abs/1807.05118v1
|
http://arxiv.org/pdf/1807.05118v1.pdf
|
https://github.com/DataCanvasIO/Hypernets
| false | false | false |
tf
|
https://paperswithcode.com/paper/evaluation-of-a-tree-based-pipeline
|
Evaluation of a Tree-based Pipeline Optimization Tool for Automating Data Science
|
1603.06212
|
http://arxiv.org/abs/1603.06212v1
|
http://arxiv.org/pdf/1603.06212v1.pdf
|
https://github.com/DataCanvasIO/Hypernets
| false | false | false |
tf
|
https://paperswithcode.com/paper/efficient-and-robust-automated-machine
|
Efficient and Robust Automated Machine Learning
| null |
http://papers.nips.cc/paper/5872-efficient-and-robust-automated-machine-learning
|
http://papers.nips.cc/paper/5872-efficient-and-robust-automated-machine-learning.pdf
|
https://github.com/DataCanvasIO/Hypernets
| false | false | false |
tf
|
https://paperswithcode.com/paper/reconstructing-patchy-reionization-from-the
|
Reconstructing Patchy Reionization from the Cosmic Microwave Background
|
0812.1566
|
https://arxiv.org/abs/0812.1566v2
|
https://arxiv.org/pdf/0812.1566v2.pdf
|
https://github.com/abaleato/curved_sky_B_template
| false | false | true |
none
|
https://paperswithcode.com/paper/key-frame-proposal-network-for-efficient-pose
|
Key Frame Proposal Network for Efficient Pose Estimation in Videos
|
2007.15217
|
https://arxiv.org/abs/2007.15217v1
|
https://arxiv.org/pdf/2007.15217v1.pdf
|
https://github.com/Yuexiaoxi10/Key-Frame-Proposal-Network-for-Efficient-Pose-Estimation-in-Videos
| true | true | true |
pytorch
|
https://paperswithcode.com/paper/segnet-a-deep-convolutional-encoder-decoder
|
SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation
|
1511.00561
|
http://arxiv.org/abs/1511.00561v3
|
http://arxiv.org/pdf/1511.00561v3.pdf
|
https://github.com/ajoshi944/Segmentation-severstal-steel
| false | false | true |
none
|
https://paperswithcode.com/paper/evaluating-pronominal-anaphora-in-machine
|
Evaluating Pronominal Anaphora in Machine Translation: An Evaluation Measure and a Test Suite
|
1909.00131
|
https://arxiv.org/abs/1909.00131v1
|
https://arxiv.org/pdf/1909.00131v1.pdf
|
https://github.com/ntunlp/eval-anaphora
| false | false | true |
pytorch
|
https://paperswithcode.com/paper/shift-invert-diagonalization-of-large-many
|
Shift-invert diagonalization of large many-body localizing spin chains
|
1803.05395
|
http://arxiv.org/abs/1803.05395v3
|
http://arxiv.org/pdf/1803.05395v3.pdf
|
https://bitbucket.org/dluitz/sinvert_mbl
| true | true | false |
none
|
https://paperswithcode.com/paper/spin-glass-droplets-learning-and-approximate
|
Approximate optimization, sampling and spin-glass droplets discovery with tensor networks
|
1811.06518
|
https://arxiv.org/abs/1811.06518v5
|
https://arxiv.org/pdf/1811.06518v5.pdf
|
https://github.com/marekrams/tnac4o
| true | true | true |
none
|
https://paperswithcode.com/paper/gated-multimodal-units-for-information-fusion
|
Gated Multimodal Units for Information Fusion
|
1702.01992
|
http://arxiv.org/abs/1702.01992v1
|
http://arxiv.org/pdf/1702.01992v1.pdf
|
https://github.com/TashinAhmed/CNN_BERT
| false | false | true |
pytorch
|
https://paperswithcode.com/paper/how-to-0wn-the-nas-in-your-spare-time
|
How to 0wn the NAS in Your Spare Time
| null |
https://openreview.net/forum?id=S1erpeBFPB
|
https://openreview.net/pdf?id=S1erpeBFPB
|
https://github.com/Sanghyun-Hong/How-to-0wn-NAS-in-Your-Spare-Time
| true | false | false |
none
|
https://paperswithcode.com/paper/fast-guided-filter
|
Fast Guided Filter
|
1505.00996
|
http://arxiv.org/abs/1505.00996v1
|
http://arxiv.org/pdf/1505.00996v1.pdf
|
https://github.com/swehrwein/python-guided-filter
| false | false | true |
none
|
https://paperswithcode.com/paper/mobilenets-efficient-convolutional-neural
|
MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications
|
1704.04861
|
http://arxiv.org/abs/1704.04861v1
|
http://arxiv.org/pdf/1704.04861v1.pdf
|
https://github.com/christophmeyer/longboard-pothole-detection
| false | false | true |
tf
|
https://paperswithcode.com/paper/a-light-cnn-for-deep-face-representation-with
|
A Light CNN for Deep Face Representation with Noisy Labels
|
1511.02683
|
http://arxiv.org/abs/1511.02683v4
|
http://arxiv.org/pdf/1511.02683v4.pdf
|
https://github.com/ozora-ogino/LCNN
| false | false | true |
tf
|
https://paperswithcode.com/paper/monte-carlo-raytracing-method-for-calculating
|
Monte Carlo Raytracing Method for Calculating Secondary Electron Emission from Micro-Architected Surfaces
|
1806.00205
|
http://arxiv.org/abs/1806.00205v1
|
http://arxiv.org/pdf/1806.00205v1.pdf
|
https://github.com/irischang59/parallelSEE
| false | false | true |
none
|
https://paperswithcode.com/paper/ho-3d-a-multi-user-multi-object-dataset-for
|
HOnnotate: A method for 3D Annotation of Hand and Object Poses
|
1907.01481
|
https://arxiv.org/abs/1907.01481v6
|
https://arxiv.org/pdf/1907.01481v6.pdf
|
https://github.com/anilarmagan/HANDS19-Challenge-Toolbox
| false | false | true |
pytorch
|
https://paperswithcode.com/paper/end-to-end-recovery-of-human-shape-and-pose
|
End-to-end Recovery of Human Shape and Pose
|
1712.06584
|
http://arxiv.org/abs/1712.06584v2
|
http://arxiv.org/pdf/1712.06584v2.pdf
|
https://github.com/anilarmagan/HANDS19-Challenge-Toolbox
| false | false | true |
pytorch
|
https://paperswithcode.com/paper/turbocharging-treewidth-bounded-bayesian
|
Turbocharging Treewidth-Bounded Bayesian Network Structure Learning
|
2006.13843
|
https://arxiv.org/abs/2006.13843v2
|
https://arxiv.org/pdf/2006.13843v2.pdf
|
https://github.com/aditya95sriram/bn-slim
| true | false | false |
none
|
https://paperswithcode.com/paper/shape-from-polarization-for-complex-scenes-in
|
Shape from Polarization for Complex Scenes in the Wild
|
2112.11377
|
https://arxiv.org/abs/2112.11377v3
|
https://arxiv.org/pdf/2112.11377v3.pdf
|
https://github.com/chenyanglei/sfp-wild
| true | true | true |
pytorch
|
https://paperswithcode.com/paper/few-shot-text-classification-with-induction
|
Induction Networks for Few-Shot Text Classification
|
1902.10482
|
https://arxiv.org/abs/1902.10482v2
|
https://arxiv.org/pdf/1902.10482v2.pdf
|
https://github.com/hongshengxin/Induction_network
| false | false | true |
pytorch
|
https://paperswithcode.com/paper/using-deep-networks-for-scientific-discovery
|
Using Deep Networks for Scientific Discovery in Physiological Signals
|
2008.10936
|
https://arxiv.org/abs/2008.10936v1
|
https://arxiv.org/pdf/2008.10936v1.pdf
|
https://github.com/shalit-lab/deep-scientific-discovery
| true | true | false |
pytorch
|
https://paperswithcode.com/paper/self-supervised-gait-encoding-with-locality
|
Self-Supervised Gait Encoding with Locality-Aware Attention for Person Re-Identification
|
2008.09435
|
https://arxiv.org/abs/2008.09435v1
|
https://arxiv.org/pdf/2008.09435v1.pdf
|
https://github.com/Kali-Hac/SGE-LA
| true | true | false |
tf
|
https://paperswithcode.com/paper/ptt5-pretraining-and-validating-the-t5-model
|
PTT5: Pretraining and validating the T5 model on Brazilian Portuguese data
|
2008.09144
|
https://arxiv.org/abs/2008.09144v2
|
https://arxiv.org/pdf/2008.09144v2.pdf
|
https://github.com/unicamp-dl/PTT5
| true | true | true |
tf
|
https://paperswithcode.com/paper/graudally-applying-weakly-supervised-and
|
Graudally Applying Weakly Supervised and Active Learning for Mass Detection in Breast Ultrasound Images
|
2008.08416
|
https://arxiv.org/abs/2008.08416v1
|
https://arxiv.org/pdf/2008.08416v1.pdf
|
https://github.com/YeolJ00/faster-rcnn-pytorch
| true | true | false |
pytorch
|
https://paperswithcode.com/paper/meantime-mixture-of-attention-mechanisms-with
|
MEANTIME: Mixture of Attention Mechanisms with Multi-temporal Embeddings for Sequential Recommendation
|
2008.08273
|
https://arxiv.org/abs/2008.08273v2
|
https://arxiv.org/pdf/2008.08273v2.pdf
|
https://github.com/SungMinCho/MEANTIME
| true | true | true |
pytorch
|
https://paperswithcode.com/paper/relational-reflection-entity-alignment
|
Relational Reflection Entity Alignment
|
2008.07962
|
https://arxiv.org/abs/2008.07962v1
|
https://arxiv.org/pdf/2008.07962v1.pdf
|
https://github.com/MaoXinn/RREA
| true | true | true |
tf
|
https://paperswithcode.com/paper/continuous-optimization-benchmarks-by
|
Continuous Optimization Benchmarks by Simulation
|
2008.06249
|
https://arxiv.org/abs/2008.06249v1
|
https://arxiv.org/pdf/2008.06249v1.pdf
|
https://github.com/martinzaefferer/zaef20b
| true | true | false |
none
|
https://paperswithcode.com/paper/a-generalised-approach-for-encoding-and
|
A Generalised Approach for Encoding and Reasoning with Qualitative Theories in Answer Set Programming
|
2008.01519
|
https://arxiv.org/abs/2008.01519v1
|
https://arxiv.org/pdf/2008.01519v1.pdf
|
https://github.com/gmparg/ICLP2020
| true | true | false |
none
|
https://paperswithcode.com/paper/approximated-bilinear-modules-for-temporal-1
|
Approximated Bilinear Modules for Temporal Modeling
|
2007.12887
|
https://arxiv.org/abs/2007.12887v1
|
https://arxiv.org/pdf/2007.12887v1.pdf
|
https://github.com/zhuxinqimac/abm-pytorch
| true | true | false |
pytorch
|
https://paperswithcode.com/paper/representative-discriminative-learning-for
|
Representative-Discriminative Learning for Open-set Land Cover Classification of Satellite Imagery
|
2007.10891
|
https://arxiv.org/abs/2007.10891v1
|
https://arxiv.org/pdf/2007.10891v1.pdf
|
https://github.com/raziehkaviani/rdosr
| true | true | true |
tf
|
https://paperswithcode.com/paper/multitask-learning-strengthens-adversarial
|
Multitask Learning Strengthens Adversarial Robustness
|
2007.07236
|
https://arxiv.org/abs/2007.07236v2
|
https://arxiv.org/pdf/2007.07236v2.pdf
|
https://github.com/columbia/MTRobust
| true | true | false |
pytorch
|
https://paperswithcode.com/paper/gradient-centralization-a-new-optimization
|
Gradient Centralization: A New Optimization Technique for Deep Neural Networks
|
2004.01461
|
https://arxiv.org/abs/2004.01461v2
|
https://arxiv.org/pdf/2004.01461v2.pdf
|
https://github.com/Yonghongwei/Gradient-Centralization
| true | true | true |
pytorch
|
https://paperswithcode.com/paper/topic-scene-graph-generation-by-attention-1
|
Topic Scene Graph Generation by Attention Distillation from Caption
|
2110.05731
|
https://arxiv.org/abs/2110.05731v1
|
https://arxiv.org/pdf/2110.05731v1.pdf
|
https://github.com/Kenneth-Wong/MMSceneGraph
| true | true | false |
pytorch
|
https://paperswithcode.com/paper/autofis-automatic-feature-interaction
|
AutoFIS: Automatic Feature Interaction Selection in Factorization Models for Click-Through Rate Prediction
|
2003.11235
|
https://arxiv.org/abs/2003.11235v3
|
https://arxiv.org/pdf/2003.11235v3.pdf
|
https://github.com/zhuchenxv/AutoFIS
| true | true | false |
tf
|
https://paperswithcode.com/paper/modeling-and-solving-a-vehicle-sharing
|
Modeling and solving a vehicle-sharing problem considering multiple alternative modes of transport
|
2003.08207
|
https://arxiv.org/abs/2003.08207v2
|
https://arxiv.org/pdf/2003.08207v2.pdf
|
https://github.com/dts-ait/seamless
| true | true | false |
none
|
https://paperswithcode.com/paper/a-unified-2d3d-large-scale-software
|
A Unified 2D/3D Large Scale Software Environment for Nonlinear Inverse Problems
|
1703.09268
|
https://arxiv.org/abs/1703.09268v2
|
https://arxiv.org/pdf/1703.09268v2.pdf
|
https://github.com/slimgroup/WAVEFORM
| true | true | true |
none
|
https://paperswithcode.com/paper/a-flexible-framework-for-anomaly-detection
|
A Flexible Framework for Anomaly Detection via Dimensionality Reduction
|
1909.04060
|
https://arxiv.org/abs/1909.04060v1
|
https://arxiv.org/pdf/1909.04060v1.pdf
|
https://github.com/vafaei-ar/drama
| true | true | true |
tf
|
https://paperswithcode.com/paper/real-bogus-classification-for-the-zwicky
|
Real-bogus classification for the Zwicky Transient Facility using deep learning
|
1907.11259
|
https://arxiv.org/abs/1907.11259v1
|
https://arxiv.org/pdf/1907.11259v1.pdf
|
https://github.com/dmitryduev/braai
| true | true | true |
tf
|
https://paperswithcode.com/paper/collaborative-policy-learning-for-open
|
Collaborative Policy Learning for Open Knowledge Graph Reasoning
|
1909.00230
|
https://arxiv.org/abs/1909.00230v1
|
https://arxiv.org/pdf/1909.00230v1.pdf
|
https://github.com/shanzhenren/CPL
| true | true | true |
tf
|
https://paperswithcode.com/paper/nuclei-segmentation-via-a-deep-panoptic-model
|
Nuclei Segmentation via a Deep Panoptic Model with Semantic Feature Fusion
| null |
https://www.researchgate.net/publication/334843766_Nuclei_Segmentation_via_a_Deep_Panoptic_Model_with_Semantic_Feature_Fusion
|
https://www.ijcai.org/proceedings/2019/0121.pdf
|
https://github.com/dliu5812/PFFNet
| true | false | false |
pytorch
|
https://paperswithcode.com/paper/does-bert-agree-evaluating-knowledge-of
|
Does BERT agree? Evaluating knowledge of structure dependence through agreement relations
|
1908.09892
|
https://arxiv.org/abs/1908.09892v1
|
https://arxiv.org/pdf/1908.09892v1.pdf
|
https://github.com/geoffbacon/does-bert-agree
| true | true | true |
none
|
https://paperswithcode.com/paper/190807899
|
Evaluating Defensive Distillation For Defending Text Processing Neural Networks Against Adversarial Examples
|
1908.07899
|
https://arxiv.org/abs/1908.07899v1
|
https://arxiv.org/pdf/1908.07899v1.pdf
|
https://github.com/Top-Ranger/text_adversarial_attack
| true | true | false |
tf
|
https://paperswithcode.com/paper/learning-fixed-points-in-generative
|
Learning Fixed Points in Generative Adversarial Networks: From Image-to-Image Translation to Disease Detection and Localization
|
1908.06965
|
https://arxiv.org/abs/1908.06965v2
|
https://arxiv.org/pdf/1908.06965v2.pdf
|
https://github.com/jlianglab/Fixed-Point-GAN
| true | true | true |
pytorch
|
https://paperswithcode.com/paper/adn-artifact-disentanglement-network-for
|
ADN: Artifact Disentanglement Network for Unsupervised Metal Artifact Reduction
|
1908.01104
|
https://arxiv.org/abs/1908.01104v4
|
https://arxiv.org/pdf/1908.01104v4.pdf
|
https://github.com/liaohaofu/adn
| true | true | true |
pytorch
|
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.