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classes | framework
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|---|---|---|---|---|---|---|---|---|---|
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/RuoyuGuo/Visualising-Image-Classification-Models-and-Saliency-Maps
| false | false | true |
tf
|
https://paperswithcode.com/paper/exploring-modern-gpu-memory-system-design
|
Exploring Modern GPU Memory System Design Challenges through Accurate Modeling
|
1810.07269
|
http://arxiv.org/abs/1810.07269v1
|
http://arxiv.org/pdf/1810.07269v1.pdf
|
https://github.com/prdalmia/gpgpu-sim-tlb
| false | false | true |
pytorch
|
https://paperswithcode.com/paper/a-simple-exponential-family-framework-for
|
A Simple Exponential Family Framework for Zero-Shot Learning
|
1707.08040
|
http://arxiv.org/abs/1707.08040v3
|
http://arxiv.org/pdf/1707.08040v3.pdf
|
https://github.com/vkverma01/Zero-Shot
| true | true | true |
none
|
https://paperswithcode.com/paper/from-word-embeddings-to-item-recommendation
|
From Word Embeddings to Item Recommendation
|
1601.01356
|
http://arxiv.org/abs/1601.01356v3
|
http://arxiv.org/pdf/1601.01356v3.pdf
|
https://github.com/mgulcin/DL_Rec
| false | false | true |
none
|
https://paperswithcode.com/paper/190807906
|
PCRNet: Point Cloud Registration Network using PointNet Encoding
|
1908.07906
|
https://arxiv.org/abs/1908.07906v2
|
https://arxiv.org/pdf/1908.07906v2.pdf
|
https://github.com/vinits5/pcrnet_pytorch
| true | false | true |
pytorch
|
https://paperswithcode.com/paper/flavio-a-python-package-for-flavour-and
|
flavio: a Python package for flavour and precision phenomenology in the Standard Model and beyond
|
1810.08132
|
http://arxiv.org/abs/1810.08132v1
|
http://arxiv.org/pdf/1810.08132v1.pdf
|
https://github.com/smelli/smelli
| false | false | true |
none
|
https://paperswithcode.com/paper/nvisii-a-scriptable-tool-for-photorealistic
|
NViSII: A Scriptable Tool for Photorealistic Image Generation
|
2105.13962
|
https://arxiv.org/abs/2105.13962v1
|
https://arxiv.org/pdf/2105.13962v1.pdf
|
https://github.com/owl-project/ViSII
| false | false | true |
none
|
https://paperswithcode.com/paper/very-deep-convolutional-networks-for-text
|
Very Deep Convolutional Networks for Text Classification
|
1606.01781
|
http://arxiv.org/abs/1606.01781v2
|
http://arxiv.org/pdf/1606.01781v2.pdf
|
https://github.com/nithishkaviyan/Sentiment-Analysis-of-Yelp-Reviews
| false | false | true |
pytorch
|
https://paperswithcode.com/paper/estimating-seal-pup-production-in-the
|
Estimating seal pup production in the Greenland Sea using Bayesian hierarchical modeling
|
1808.09254
|
https://arxiv.org/abs/1808.09254v2
|
https://arxiv.org/pdf/1808.09254v2.pdf
|
https://github.com/PointProcess/SealCoxProcess-JRSSC-code
| false | false | true |
none
|
https://paperswithcode.com/paper/democratizing-contrastive-language-image-pre
|
Democratizing Contrastive Language-Image Pre-training: A CLIP Benchmark of Data, Model, and Supervision
|
2203.05796
|
https://arxiv.org/abs/2203.05796v1
|
https://arxiv.org/pdf/2203.05796v1.pdf
|
https://github.com/sense-gvt/declip
| true | true | true |
pytorch
|
https://paperswithcode.com/paper/pose-normalized-image-generation-for-person
|
Pose-Normalized Image Generation for Person Re-identification
|
1712.02225
|
http://arxiv.org/abs/1712.02225v6
|
http://arxiv.org/pdf/1712.02225v6.pdf
|
https://github.com/NVlabs/DG-Net
| false | false | false |
pytorch
|
https://paperswithcode.com/paper/simulating-content-consistent-vehicle
|
Simulating Content Consistent Vehicle Datasets with Attribute Descent
|
1912.08855
|
https://arxiv.org/abs/1912.08855v2
|
https://arxiv.org/pdf/1912.08855v2.pdf
|
https://github.com/yorkeyao/VehicleX
| true | true | true |
pytorch
|
https://paperswithcode.com/paper/robust-group-synchronization-via-cycle-edge
|
Robust Group Synchronization via Cycle-Edge Message Passing
|
1912.11347
|
https://arxiv.org/abs/1912.11347v3
|
https://arxiv.org/pdf/1912.11347v3.pdf
|
https://github.com/yunpeng-shi/CEMP
| true | true | false |
none
|
https://paperswithcode.com/paper/partial-fc-training-10-million-identities-on
|
Partial FC: Training 10 Million Identities on a Single Machine
|
2010.05222
|
https://arxiv.org/abs/2010.05222v2
|
https://arxiv.org/pdf/2010.05222v2.pdf
|
https://github.com/JDAI-CV/fast-reid
| false | false | false |
pytorch
|
https://paperswithcode.com/paper/tab2know-building-a-knowledge-base-from
|
Tab2Know: Building a Knowledge Base from Tables in Scientific Papers
|
2107.13306
|
https://arxiv.org/abs/2107.13306v1
|
https://arxiv.org/pdf/2107.13306v1.pdf
|
https://github.com/karmaresearch/tab2know
| true | true | false |
none
|
https://paperswithcode.com/paper/inference-and-forecasting-for-continuous-time
|
Inference and forecasting for continuous-time integer-valued trawl processes
|
2107.03674
|
https://arxiv.org/abs/2107.03674v3
|
https://arxiv.org/pdf/2107.03674v3.pdf
|
https://github.com/mbennedsen/Likelihood-based-IVT
| true | true | false |
none
|
https://paperswithcode.com/paper/mintrec2-0-a-large-scale-benchmark-dataset
|
MIntRec2.0: A Large-scale Benchmark Dataset for Multimodal Intent Recognition and Out-of-scope Detection in Conversations
|
2403.10943
|
https://arxiv.org/abs/2403.10943v4
|
https://arxiv.org/pdf/2403.10943v4.pdf
|
https://github.com/thuiar/mintrec2.0
| true | true | false |
pytorch
|
https://paperswithcode.com/paper/faultnet-a-deep-convolutional-neural-network
|
FaultNet: A Deep Convolutional Neural Network for bearing fault classification
|
2010.02146
|
https://arxiv.org/abs/2010.02146v2
|
https://arxiv.org/pdf/2010.02146v2.pdf
|
https://github.com/BaratiLab/FaultNet
| true | true | true |
none
|
https://paperswithcode.com/paper/physics-informed-neural-networks-for-power
|
Physics-Informed Neural Networks for Power Systems
|
1911.03737
|
https://arxiv.org/abs/1911.03737v3
|
https://arxiv.org/pdf/1911.03737v3.pdf
|
https://github.com/gmisy/Phycics-informed-NN-for-Power-Systems
| false | false | true |
tf
|
https://paperswithcode.com/paper/proximal-policy-optimization-algorithms
|
Proximal Policy Optimization Algorithms
|
1707.06347
|
http://arxiv.org/abs/1707.06347v2
|
http://arxiv.org/pdf/1707.06347v2.pdf
|
https://github.com/jfpettit/flare
| false | false | true |
pytorch
|
https://paperswithcode.com/paper/susy-les-houches-accord-2
|
SUSY Les Houches Accord 2
|
0801.0045
|
http://arxiv.org/abs/0801.0045v3
|
http://arxiv.org/pdf/0801.0045v3.pdf
|
https://github.com/misho104/yaslha
| false | false | true |
none
|
https://paperswithcode.com/paper/a-guide-to-convolution-arithmetic-for-deep
|
A guide to convolution arithmetic for deep learning
|
1603.07285
|
http://arxiv.org/abs/1603.07285v2
|
http://arxiv.org/pdf/1603.07285v2.pdf
|
https://github.com/marbleton/FPGA_MNIST
| false | false | true |
pytorch
|
https://paperswithcode.com/paper/don-t-stop-pretraining-adapt-language-models
|
Don't Stop Pretraining: Adapt Language Models to Domains and Tasks
|
2004.10964
|
https://arxiv.org/abs/2004.10964v3
|
https://arxiv.org/pdf/2004.10964v3.pdf
|
https://github.com/allenai/dont-stop-pretraining
| true | true | false |
pytorch
|
https://paperswithcode.com/paper/hinglishnlp-fine-tuned-language-models-for
|
HinglishNLP: Fine-tuned Language Models for Hinglish Sentiment Detection
|
2008.09820
|
https://arxiv.org/abs/2008.09820v1
|
https://arxiv.org/pdf/2008.09820v1.pdf
|
https://github.com/NirantK/Hinglish
| true | true | false |
pytorch
|
https://paperswithcode.com/paper/a-low-cost-flexible-and-portable-volumetric
|
A Low-Cost, Flexible and Portable Volumetric Capturing System
|
1909.01207
|
https://arxiv.org/abs/1909.01207v1
|
https://arxiv.org/pdf/1909.01207v1.pdf
|
https://github.com/VCL3D/VolumetricCapture
| true | true | true |
none
|
https://paperswithcode.com/paper/deep-soft-procrustes-for-markerless
|
Deep Soft Procrustes for Markerless Volumetric Sensor Alignment
|
2003.10176
|
https://arxiv.org/abs/2003.10176v1
|
https://arxiv.org/pdf/2003.10176v1.pdf
|
https://github.com/VCL3D/VolumetricCapture
| false | false | true |
none
|
https://paperswithcode.com/paper/conversations-with-search-engines
|
Conversations with Search Engines: SERP-based Conversational Response Generation
|
2004.14162
|
https://arxiv.org/abs/2004.14162v2
|
https://arxiv.org/pdf/2004.14162v2.pdf
|
https://github.com/PengjieRen/CaSE-1.0
| true | true | false |
pytorch
|
https://paperswithcode.com/paper/boilerplate-removal-using-a-neural-sequence
|
Boilerplate Removal using a Neural Sequence Labeling Model
|
2004.14294
|
https://arxiv.org/abs/2004.14294v1
|
https://arxiv.org/pdf/2004.14294v1.pdf
|
https://github.com/mrjleo/boilernet
| true | true | false |
tf
|
https://paperswithcode.com/paper/lifelong-learning-in-evolving-graphs-with
|
Lifelong Learning on Evolving Graphs Under the Constraints of Imbalanced Classes and New Classes
|
2112.10558
|
https://arxiv.org/abs/2112.10558v2
|
https://arxiv.org/pdf/2112.10558v2.pdf
|
https://github.com/lgalke/lifelong-learning
| true | true | false |
pytorch
|
https://paperswithcode.com/paper/towards-efficient-covid-19-ct-annotation-a
|
Towards Data-Efficient Learning: A Benchmark for COVID-19 CT Lung and Infection Segmentation
|
2004.12537
|
https://arxiv.org/abs/2004.12537v2
|
https://arxiv.org/pdf/2004.12537v2.pdf
|
https://github.com/JunMa11/COVID-19-CT-Seg-Benchmark
| false | false | true |
none
|
https://paperswithcode.com/paper/jack-the-reader-a-machine-reading-framework
|
Jack the Reader - A Machine Reading Framework
|
1806.08727
|
http://arxiv.org/abs/1806.08727v1
|
http://arxiv.org/pdf/1806.08727v1.pdf
|
https://github.com/uclnlp/jack
| false | false | true |
tf
|
https://paperswithcode.com/paper/making-neural-qa-as-simple-as-possible-but
|
Making Neural QA as Simple as Possible but not Simpler
|
1703.04816
|
http://arxiv.org/abs/1703.04816v3
|
http://arxiv.org/pdf/1703.04816v3.pdf
|
https://github.com/uclnlp/jack
| false | false | true |
tf
|
https://paperswithcode.com/paper/a-disentangling-invertible-interpretation
|
A Disentangling Invertible Interpretation Network for Explaining Latent Representations
|
2004.13166
|
https://arxiv.org/abs/2004.13166v1
|
https://arxiv.org/pdf/2004.13166v1.pdf
|
https://github.com/CompVis/iin
| false | false | true |
pytorch
|
https://paperswithcode.com/paper/weakly-supervised-open-retrieval
|
Weakly-Supervised Open-Retrieval Conversational Question Answering
|
2103.02537
|
https://arxiv.org/abs/2103.02537v1
|
https://arxiv.org/pdf/2103.02537v1.pdf
|
https://github.com/prdwb/ws-orconvqa
| true | true | false |
pytorch
|
https://paperswithcode.com/paper/aggregate-hardware-impairments-over-mixed-rf
|
Aggregate Hardware Impairments Over Mixed RF/FSO Relaying Systems With Outdated CSI
|
1902.03177
|
http://arxiv.org/abs/1902.03177v1
|
http://arxiv.org/pdf/1902.03177v1.pdf
|
https://github.com/ebalti/Malaga-Distribution
| true | false | false |
none
|
https://paperswithcode.com/paper/conditional-generative-adversarial-nets
|
Conditional Generative Adversarial Nets
|
1411.1784
|
https://arxiv.org/abs/1411.1784v1
|
https://arxiv.org/pdf/1411.1784v1.pdf
|
https://github.com/bhiziroglu/Conditional-Generative-Adversarial-Network
| false | false | true |
pytorch
|
https://paperswithcode.com/paper/inverse-kinematics-for-serial-kinematic
|
Inverse Kinematics for Serial Kinematic Chains via Sum of Squares Optimization
|
1909.09318
|
https://arxiv.org/abs/1909.09318v2
|
https://arxiv.org/pdf/1909.09318v2.pdf
|
https://github.com/utiasSTARS/sos-ik
| true | true | true |
none
|
https://paperswithcode.com/paper/how-low-is-too-low-a-computational
|
How Low is Too Low? A Computational Perspective on Extremely Low-Resource Languages
|
2105.14515
|
https://arxiv.org/abs/2105.14515v1
|
https://arxiv.org/pdf/2105.14515v1.pdf
|
https://github.com/cdli-gh/Semi-Supervised-NMT-for-Sumerian-English
| true | false | false |
pytorch
|
https://paperswithcode.com/paper/voxlingua107-a-dataset-for-spoken-language-1
|
VoxLingua107: a Dataset for Spoken Language Recognition
|
2011.12998
|
https://arxiv.org/abs/2011.12998v1
|
https://arxiv.org/pdf/2011.12998v1.pdf
|
https://github.com/rush-d/spoken-language-identification
| false | false | true |
none
|
https://paperswithcode.com/paper/warpgan-automatic-caricature-generation
|
WarpGAN: Automatic Caricature Generation
|
1811.10100
|
http://arxiv.org/abs/1811.10100v3
|
http://arxiv.org/pdf/1811.10100v3.pdf
|
https://github.com/ronny3050/AdvFaces
| false | false | true |
tf
|
https://paperswithcode.com/paper/advfaces-adversarial-face-synthesis
|
AdvFaces: Adversarial Face Synthesis
|
1908.05008
|
https://arxiv.org/abs/1908.05008v1
|
https://arxiv.org/pdf/1908.05008v1.pdf
|
https://github.com/ronny3050/AdvFaces
| false | false | true |
tf
|
https://paperswithcode.com/paper/segan-speech-enhancement-generative
|
SEGAN: Speech Enhancement Generative Adversarial Network
|
1703.09452
|
http://arxiv.org/abs/1703.09452v3
|
http://arxiv.org/pdf/1703.09452v3.pdf
|
https://github.com/usimarit/TiramisuASR
| false | false | true |
tf
|
https://paperswithcode.com/paper/simple-scalable-and-stable-variational-deep
|
Simple, Scalable, and Stable Variational Deep Clustering
|
2005.08047
|
https://arxiv.org/abs/2005.08047v2
|
https://arxiv.org/pdf/2005.08047v2.pdf
|
https://github.com/king/s3vdc
| true | true | true |
tf
|
https://paperswithcode.com/paper/a-neural-algorithm-of-artistic-style
|
A Neural Algorithm of Artistic Style
|
1508.06576
|
http://arxiv.org/abs/1508.06576v2
|
http://arxiv.org/pdf/1508.06576v2.pdf
|
https://github.com/kanishk16/Image-Style-Transfer
| false | false | true |
pytorch
|
https://paperswithcode.com/paper/a-mathematical-formalization-of-hierarchical
|
A Mathematical Formalization of Hierarchical Temporal Memory's Spatial Pooler
|
1601.06116
|
http://arxiv.org/abs/1601.06116v3
|
http://arxiv.org/pdf/1601.06116v3.pdf
|
https://github.com/mrkrynmdsco/htm-python
| false | false | true |
none
|
https://paperswithcode.com/paper/density-encoding-enables-resource-efficient
|
Density Encoding Enables Resource-Efficient Randomly Connected Neural Networks
|
1909.09153
|
https://arxiv.org/abs/1909.09153v2
|
https://arxiv.org/pdf/1909.09153v2.pdf
|
https://github.com/sweetwenwen/Stochastic-computing-based-neural-network-accelerator
| false | false | true |
none
|
https://paperswithcode.com/paper/a-distance-preserving-matrix-sketch
|
A Distance-preserving Matrix Sketch
|
2009.03979
|
https://arxiv.org/abs/2009.03979v3
|
https://arxiv.org/pdf/2009.03979v3.pdf
|
https://github.com/hrluo/DistancePreservingMatrixSketch
| true | true | true |
none
|
https://paperswithcode.com/paper/neural-collaborative-filtering
|
Neural Collaborative Filtering
|
1708.05031
|
http://arxiv.org/abs/1708.05031v2
|
http://arxiv.org/pdf/1708.05031v2.pdf
|
https://github.com/EdoardoPona/Neural-Collaborative-Filtering
| false | false | true |
pytorch
|
https://paperswithcode.com/paper/jobskape-a-framework-for-generating-synthetic
|
JOBSKAPE: A Framework for Generating Synthetic Job Postings to Enhance Skill Matching
|
2402.03242
|
https://arxiv.org/abs/2402.03242v1
|
https://arxiv.org/pdf/2402.03242v1.pdf
|
https://github.com/magantoine/jobskape
| true | true | true |
pytorch
|
https://paperswithcode.com/paper/practical-graph-isomorphism-ii
|
Practical graph isomorphism, II
|
1301.1493
|
http://arxiv.org/abs/1301.1493v1
|
http://arxiv.org/pdf/1301.1493v1.pdf
|
https://github.com/Mith13/Graphs-isomorphism
| false | false | true |
none
|
https://paperswithcode.com/paper/line-large-scale-information-network
|
LINE: Large-scale Information Network Embedding
|
1503.03578
|
http://arxiv.org/abs/1503.03578v1
|
http://arxiv.org/pdf/1503.03578v1.pdf
|
https://github.com/2myeonggyu/Graph-Embedding
| false | false | true |
none
|
https://paperswithcode.com/paper/imagenet-classification-with-deep
|
ImageNet Classification with Deep Convolutional Neural Networks
| null |
http://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks
|
http://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks.pdf
|
https://github.com/mindspore-courses/heads-on-mindspore/blob/main/1-best-practice/models/alexnet.py
| false | false | false |
mindspore
|
https://paperswithcode.com/paper/fully-convolutional-networks-for-semantic-1
|
Fully Convolutional Networks for Semantic Segmentation
|
1411.4038
|
http://arxiv.org/abs/1411.4038v2
|
http://arxiv.org/pdf/1411.4038v2.pdf
|
https://github.com/muramasa8191/DeepLearning
| false | false | true |
tf
|
https://paperswithcode.com/paper/instance-based-counterfactual-explanations
|
Instance-based Counterfactual Explanations for Time Series Classification
|
2009.13211
|
https://arxiv.org/abs/2009.13211v2
|
https://arxiv.org/pdf/2009.13211v2.pdf
|
https://github.com/e-delaney/Instance-Based_CFE_TSC
| true | true | false |
tf
|
https://paperswithcode.com/paper/bert-pre-training-of-deep-bidirectional
|
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
|
1810.04805
|
https://arxiv.org/abs/1810.04805v2
|
https://arxiv.org/pdf/1810.04805v2.pdf
|
https://github.com/Holldean/BERT-Pruning
| false | false | true |
tf
|
https://paperswithcode.com/paper/structured-pruning-of-large-language-models
|
Structured Pruning of Large Language Models
|
1910.04732
|
https://arxiv.org/abs/1910.04732v2
|
https://arxiv.org/pdf/1910.04732v2.pdf
|
https://github.com/Holldean/BERT-Pruning
| false | false | true |
tf
|
https://paperswithcode.com/paper/pretraining-based-natural-language-generation
|
Pretraining-Based Natural Language Generation for Text Summarization
|
1902.09243
|
http://arxiv.org/abs/1902.09243v2
|
http://arxiv.org/pdf/1902.09243v2.pdf
|
https://github.com/praveenjune17/BERT_text_summarisation
| false | false | true |
tf
|
https://paperswithcode.com/paper/bert-pre-training-of-deep-bidirectional
|
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
|
1810.04805
|
https://arxiv.org/abs/1810.04805v2
|
https://arxiv.org/pdf/1810.04805v2.pdf
|
https://github.com/Zehui127/SQUAD_BERT
| false | false | true |
tf
|
https://paperswithcode.com/paper/sdf-srn-learning-signed-distance-3d-object
|
SDF-SRN: Learning Signed Distance 3D Object Reconstruction from Static Images
|
2010.10505
|
https://arxiv.org/abs/2010.10505v1
|
https://arxiv.org/pdf/2010.10505v1.pdf
|
https://github.com/chenhsuanlin/signed-distance-SRN
| true | false | true |
pytorch
|
https://paperswithcode.com/paper/multifit-efficient-multi-lingual-language
|
MultiFiT: Efficient Multi-lingual Language Model Fine-tuning
|
1909.04761
|
https://arxiv.org/abs/1909.04761v2
|
https://arxiv.org/pdf/1909.04761v2.pdf
|
https://github.com/n-waves/multifit
| false | false | true |
none
|
https://paperswithcode.com/paper/rtfm-generalising-to-novel-environment
|
RTFM: Generalising to Novel Environment Dynamics via Reading
|
1910.08210
|
https://arxiv.org/abs/1910.08210v6
|
https://arxiv.org/pdf/1910.08210v6.pdf
|
https://github.com/facebookresearch/RTFM
| false | false | true |
none
|
https://paperswithcode.com/paper/what-are-people-asking-about-covid-19-a
|
What Are People Asking About COVID-19? A Question Classification Dataset
|
2005.12522
|
https://arxiv.org/abs/2005.12522v3
|
https://arxiv.org/pdf/2005.12522v3.pdf
|
https://github.com/JerryWei03/COVID-Q
| true | true | true |
none
|
https://paperswithcode.com/paper/gnn3dmot-graph-neural-network-for-3d-multi
|
GNN3DMOT: Graph Neural Network for 3D Multi-Object Tracking With 2D-3D Multi-Feature Learning
| null |
http://openaccess.thecvf.com/content_CVPR_2020/html/Weng_GNN3DMOT_Graph_Neural_Network_for_3D_Multi-Object_Tracking_With_2D-3D_CVPR_2020_paper.html
|
http://openaccess.thecvf.com/content_CVPR_2020/papers/Weng_GNN3DMOT_Graph_Neural_Network_for_3D_Multi-Object_Tracking_With_2D-3D_CVPR_2020_paper.pdf
|
https://github.com/xinshuoweng/GNN3DMOT
| true | true | false |
pytorch
|
https://paperswithcode.com/paper/mpnet-masked-and-permuted-pre-training-for
|
MPNet: Masked and Permuted Pre-training for Language Understanding
|
2004.09297
|
https://arxiv.org/abs/2004.09297v2
|
https://arxiv.org/pdf/2004.09297v2.pdf
|
https://github.com/JunnYu/paddle-mpnet
| false | false | false |
paddle
|
https://paperswithcode.com/paper/logical-inference-for-counting-on-semi
|
Logical Inference for Counting on Semi-structured Tables
|
2204.07803
|
https://arxiv.org/abs/2204.07803v2
|
https://arxiv.org/pdf/2204.07803v2.pdf
|
https://github.com/ynklab/sst_count
| true | true | false |
none
|
https://paperswithcode.com/paper/contrastive-learning-with-hard-negative
|
Contrastive Learning with Hard Negative Entities for Entity Set Expansion
|
2204.07789
|
https://arxiv.org/abs/2204.07789v2
|
https://arxiv.org/pdf/2204.07789v2.pdf
|
https://github.com/geekjuruo/probexpan
| true | true | false |
pytorch
|
https://paperswithcode.com/paper/reordering-examples-helps-during-priming
|
Reordering Examples Helps during Priming-based Few-Shot Learning
|
2106.01751
|
https://arxiv.org/abs/2106.01751v1
|
https://arxiv.org/pdf/2106.01751v1.pdf
|
https://github.com/SawanKumar28/pero
| true | true | false |
pytorch
|
https://paperswithcode.com/paper/conversational-neuro-symbolic-commonsense
|
Conversational Neuro-Symbolic Commonsense Reasoning
|
2006.10022
|
https://arxiv.org/abs/2006.10022v3
|
https://arxiv.org/pdf/2006.10022v3.pdf
|
https://github.com/ForoughA/CORGI
| true | true | true |
pytorch
|
https://paperswithcode.com/paper/depth-aware-video-frame-interpolation
|
Depth-Aware Video Frame Interpolation
|
1904.00830
|
http://arxiv.org/abs/1904.00830v1
|
http://arxiv.org/pdf/1904.00830v1.pdf
|
https://github.com/BurguerJohn/Dain-App
| false | false | false |
pytorch
|
https://paperswithcode.com/paper/planning-to-explore-via-self-supervised-world
|
Planning to Explore via Self-Supervised World Models
|
2005.05960
|
https://arxiv.org/abs/2005.05960v2
|
https://arxiv.org/pdf/2005.05960v2.pdf
|
https://github.com/ramanans1/plan2explore
| true | false | false |
tf
|
https://paperswithcode.com/paper/quota-based-debiasing-can-decrease
|
Quota-based debiasing can decrease representation of already underrepresented groups
|
2006.07647
|
https://arxiv.org/abs/2006.07647v1
|
https://arxiv.org/pdf/2006.07647v1.pdf
|
https://github.com/ibsmirnov/debiasing
| true | true | true |
none
|
https://paperswithcode.com/paper/efficientdet-scalable-and-efficient-object
|
EfficientDet: Scalable and Efficient Object Detection
|
1911.09070
|
https://arxiv.org/abs/1911.09070v7
|
https://arxiv.org/pdf/1911.09070v7.pdf
|
https://github.com/JensSettelmeier/EfficientDet-DeepSORT-Tracker
| false | false | true |
pytorch
|
https://paperswithcode.com/paper/selection-bias-tracking-and-detailed-subset
|
Selection Bias Tracking and Detailed Subset Comparison for High-Dimensional Data
|
1906.07625
|
https://arxiv.org/abs/1906.07625v2
|
https://arxiv.org/pdf/1906.07625v2.pdf
|
https://github.com/VACLab/CadenceEVA
| false | false | true |
none
|
https://paperswithcode.com/paper/simple-online-and-realtime-tracking-with-a
|
Simple Online and Realtime Tracking with a Deep Association Metric
|
1703.07402
|
http://arxiv.org/abs/1703.07402v1
|
http://arxiv.org/pdf/1703.07402v1.pdf
|
https://github.com/JensSettelmeier/EfficientDet-DeepSORT-Tracker
| false | false | true |
pytorch
|
https://paperswithcode.com/paper/pseudo-labeling-and-confirmation-bias-in-deep
|
Pseudo-Labeling and Confirmation Bias in Deep Semi-Supervised Learning
|
1908.02983
|
https://arxiv.org/abs/1908.02983v5
|
https://arxiv.org/pdf/1908.02983v5.pdf
|
https://github.com/EricArazo/PseudoLabeling
| false | false | true |
pytorch
|
https://paperswithcode.com/paper/selective-kernel-networks
|
Selective Kernel Networks
|
1903.06586
|
http://arxiv.org/abs/1903.06586v2
|
http://arxiv.org/pdf/1903.06586v2.pdf
|
https://github.com/implus/PytorchInsight
| false | false | true |
pytorch
|
https://paperswithcode.com/paper/post-hoc-methods-for-debiasing-neural
|
Intra-Processing Methods for Debiasing Neural Networks
|
2006.08564
|
https://arxiv.org/abs/2006.08564v2
|
https://arxiv.org/pdf/2006.08564v2.pdf
|
https://github.com/realityengines/post_hoc_debiasing
| true | true | true |
pytorch
|
https://paperswithcode.com/paper/deep-packet-a-novel-approach-for-encrypted
|
Deep Packet: A Novel Approach For Encrypted Traffic Classification Using Deep Learning
|
1709.02656
|
http://arxiv.org/abs/1709.02656v3
|
http://arxiv.org/pdf/1709.02656v3.pdf
|
https://github.com/mhwong2007/Deep-Packet
| false | false | false |
pytorch
|
https://paperswithcode.com/paper/robust-differentially-private-training-of
|
On the effect of normalization layers on Differentially Private training of deep Neural networks
|
2006.10919
|
https://arxiv.org/abs/2006.10919v2
|
https://arxiv.org/pdf/2006.10919v2.pdf
|
https://github.com/uds-lsv/SIDP
| true | true | true |
pytorch
|
https://paperswithcode.com/paper/neural-combinatorial-optimization-with
|
Neural Combinatorial Optimization with Reinforcement Learning
|
1611.09940
|
http://arxiv.org/abs/1611.09940v3
|
http://arxiv.org/pdf/1611.09940v3.pdf
|
https://github.com/Rintarooo/TSP_DRL_PointerNet
| false | false | true |
pytorch
|
https://paperswithcode.com/paper/pointer-networks
|
Pointer Networks
|
1506.03134
|
http://arxiv.org/abs/1506.03134v2
|
http://arxiv.org/pdf/1506.03134v2.pdf
|
https://github.com/Rintarooo/TSP_DRL_PointerNet
| false | false | true |
pytorch
|
https://paperswithcode.com/paper/oops-predicting-unintentional-action-in-video
|
Oops! Predicting Unintentional Action in Video
|
1911.11206
|
https://arxiv.org/abs/1911.11206v1
|
https://arxiv.org/pdf/1911.11206v1.pdf
|
https://github.com/cvlab-columbia/oops
| false | false | false |
pytorch
|
https://paperswithcode.com/paper/mining-persistent-activity-in-continually
|
Mining Persistent Activity in Continually Evolving Networks
|
2006.15410
|
https://arxiv.org/abs/2006.15410v1
|
https://arxiv.org/pdf/2006.15410v1.pdf
|
https://github.com/GemsLab/PENminer
| true | true | false |
none
|
https://paperswithcode.com/paper/attention-is-all-you-need
|
Attention Is All You Need
|
1706.03762
|
https://arxiv.org/abs/1706.03762v7
|
https://arxiv.org/pdf/1706.03762v7.pdf
|
https://github.com/AssafSinger94/sigmorphon-2020-inflection
| false | false | true |
pytorch
|
https://paperswithcode.com/paper/modeling-relational-data-with-graph
|
Modeling Relational Data with Graph Convolutional Networks
|
1703.06103
|
http://arxiv.org/abs/1703.06103v4
|
http://arxiv.org/pdf/1703.06103v4.pdf
|
https://github.com/INK-USC/MHGRN
| false | false | true |
pytorch
|
https://paperswithcode.com/paper/kagnet-knowledge-aware-graph-networks-for
|
KagNet: Knowledge-Aware Graph Networks for Commonsense Reasoning
|
1909.02151
|
https://arxiv.org/abs/1909.02151v1
|
https://arxiv.org/pdf/1909.02151v1.pdf
|
https://github.com/INK-USC/MHGRN
| false | false | true |
pytorch
|
https://paperswithcode.com/paper/toward-the-first-quantum-simulation-with
|
Toward the first quantum simulation with quantum speedup
|
1711.10980
|
http://arxiv.org/abs/1711.10980v1
|
http://arxiv.org/pdf/1711.10980v1.pdf
|
https://github.com/njross/simcount
| true | true | true |
none
|
https://paperswithcode.com/paper/port-hamiltonian-approach-to-neural-network
|
Port-Hamiltonian Approach to Neural Network Training
|
1909.02702
|
https://arxiv.org/abs/1909.02702v1
|
https://arxiv.org/pdf/1909.02702v1.pdf
|
https://github.com/esclear/ph-nn
| false | false | true |
tf
|
https://paperswithcode.com/paper/the-shapley-value-of-coalition-of-variables
|
The Shapley Value of coalition of variables provides better explanations
|
2103.13342
|
https://arxiv.org/abs/2103.13342v3
|
https://arxiv.org/pdf/2103.13342v3.pdf
|
https://github.com/salimamoukou/acv00
| true | true | false |
none
|
https://paperswithcode.com/paper/gspn-generative-shape-proposal-network-for-3d
|
GSPN: Generative Shape Proposal Network for 3D Instance Segmentation in Point Cloud
|
1812.03320
|
http://arxiv.org/abs/1812.03320v1
|
http://arxiv.org/pdf/1812.03320v1.pdf
|
https://github.com/ericyi/GSPN
| true | false | true |
tf
|
https://paperswithcode.com/paper/yolact-better-real-time-instance-segmentation
|
YOLACT++: Better Real-time Instance Segmentation
|
1912.06218
|
https://arxiv.org/abs/1912.06218v2
|
https://arxiv.org/pdf/1912.06218v2.pdf
|
https://github.com/2023-MindSpore-1/ms-code-217/tree/main/Yolact%2B%2B
| false | false | false |
mindspore
|
https://paperswithcode.com/paper/feature-pyramid-networks-for-object-detection
|
Feature Pyramid Networks for Object Detection
|
1612.03144
|
http://arxiv.org/abs/1612.03144v2
|
http://arxiv.org/pdf/1612.03144v2.pdf
|
https://github.com/daxiapazi/faster-rcnn
| 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/TheClub4/car-detection-yolov2
| false | false | true |
tf
|
https://paperswithcode.com/paper/image-style-transfer-using-convolutional
|
Image Style Transfer Using Convolutional Neural Networks
| null |
http://openaccess.thecvf.com/content_cvpr_2016/html/Gatys_Image_Style_Transfer_CVPR_2016_paper.html
|
http://openaccess.thecvf.com/content_cvpr_2016/papers/Gatys_Image_Style_Transfer_CVPR_2016_paper.pdf
|
https://github.com/gsurma/style_transfer/blob/master/style-transfer.ipynb
| false | false | false |
none
|
https://paperswithcode.com/paper/pic-permutation-invariant-critic-for-multi
|
PIC: Permutation Invariant Critic for Multi-Agent Deep Reinforcement Learning
|
1911.00025
|
https://arxiv.org/abs/1911.00025v1
|
https://arxiv.org/pdf/1911.00025v1.pdf
|
https://github.com/IouJenLiu/PIC
| true | true | true |
pytorch
|
https://paperswithcode.com/paper/efficient-graph-similarity-computation-with
|
Efficient Graph Similarity Computation with Alignment Regularization
|
2406.14929
|
https://arxiv.org/abs/2406.14929v1
|
https://arxiv.org/pdf/2406.14929v1.pdf
|
https://github.com/jhuow/eric
| true | true | false |
pytorch
|
https://paperswithcode.com/paper/validations-and-corrections-of-the-sfd-and
|
Validations and Corrections of the SFD and Planck Reddening Maps Based on LAMOST and Gaia Data
|
2204.01521
|
https://arxiv.org/abs/2204.01521v3
|
https://arxiv.org/pdf/2204.01521v3.pdf
|
https://github.com/qy-sunyang/extinction-maps-correction
| true | true | true |
none
|
https://paperswithcode.com/paper/fake-review-detection-using-behavioral-and
|
Fake Review Detection Using Behavioral and Contextual Features
|
2003.00807
|
https://arxiv.org/abs/2003.00807v1
|
https://arxiv.org/pdf/2003.00807v1.pdf
|
https://github.com/JayKumarr/Fake-Review-Detection
| false | false | true |
none
|
https://paperswithcode.com/paper/assd-attentive-single-shot-multibox-detector
|
ASSD: Attentive Single Shot Multibox Detector
|
1909.12456
|
https://arxiv.org/abs/1909.12456v1
|
https://arxiv.org/pdf/1909.12456v1.pdf
|
https://github.com/yijingru/ASSD-Pytorch
| true | true | true |
pytorch
|
https://paperswithcode.com/paper/deep-inside-convolutional-networks
|
Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps
|
1312.6034
|
http://arxiv.org/abs/1312.6034v2
|
http://arxiv.org/pdf/1312.6034v2.pdf
|
https://github.com/RuoyuGuo/Visualising-Image-Classification-Models-and-Saliency-Maps
| false | false | true |
tf
|
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