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Deep Learning
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관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/FilippoAiraldi/global-optimization
PyTorch: An Imperative Style, High-Performance Deep Learning Library
https://github.com/FudanMPL/SecMML
https://github.com/GPUPhobia/vocal-mask
End-to-end music source separation: is it possible in the waveform domain?
https://github.com/GaetanBaert/OMR_deep
Optical Music Recognition with Convolutional Sequence-to-Sequence Models
https://github.com/GallupGovt/multivac
Recent Trends in Deep Learning Based Natural Language Processing
https://github.com/GeoVision-Lab/Geoseg
Geoseg: A Computer Vision Package for Automatic Building Segmentation and Outline Extraction
https://github.com/GeorgeWebber/WatchAuth-DL
Improving the Security of Smartwatch Payment with Deep Learning
https://github.com/Georgetown-IR-Lab/curricula-neural-ir
https://github.com/GiadaSimionato/Analysis_and_Comparison_of_Deep_Learning_Methods_for_Jazz_Music_Generation
https://github.com/GilbertoPucciarelli/uninet-pucciarelli-tatoli
https://github.com/GitHub-HongweiZhang/prediction-flow
https://github.com/Glutamat42/epistemic_ensemble_toy_dataset
What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision?
https://github.com/GokulKarthik/MagNet.pytorch
https://github.com/Graph-COM/xgdl
Interpretable Geometric Deep Learning via Learnable Randomness Injection
https://github.com/GuangfuWang/geometric-deep-learning-chinese-translation-scripts
Geometric Deep Learning: Grids, Groups, Graphs, Geodesics, and Gauges
https://github.com/HFrost0/TGC_torch
https://github.com/HKUST-KnowComp/R-Net
A Theoretically Grounded Application of Dropout in Recurrent Neural Networks
https://github.com/Hadisalman/robust-verify-benchmark
Towards Deep Learning Models Resistant to Adversarial Attacks
https://github.com/HaiDang9719/MatLabProj
Decision support from financial disclosures with deep neural networks and transfer learning
https://github.com/HaodiJiang/SolarUnet
Identifying and Tracking Solar Magnetic Flux Elements with Deep Learning
https://github.com/Haoyu1004/AGW_mindspore
Deep Learning for Person Re-identification: A Survey and Outlook
https://github.com/HeKrRuTe/OptStopRandNN
Pricing and hedging American-style options with deep learning
https://github.com/HeapHop30/graph-attention-nets
Geometric deep learning on graphs and manifolds using mixture model CNNs
https://github.com/HemanthTejaY/Deep-Learning-Image-Captioning---A-comparitive-study
A Comprehensive Survey of Deep Learning for Image Captioning
https://github.com/HemingwayLee/data-collection
https://github.com/HiLab-git/WSL4MIS
Mumford-Shah Loss Functional for Image Segmentation with Deep Learning
https://github.com/HowToMakeABomb101/Hot2MakeAB0mbSite
https://github.com/Huawei-MRC-OSI/optml-zoo
DLVM: A modern compiler infrastructure for deep learning systems
https://github.com/Hui-Li/multi-task-learning-example-PyTorch
Multi-Task Learning Using Uncertainty to Weigh Losses for Scene Geometry and Semantics
https://github.com/IAmSuyogJadhav/Brainy
Generalised Dice overlap as a deep learning loss function for highly unbalanced segmentations
https://github.com/IBM/GradSigns
https://github.com/IBM/LogicalFormulaEmbedder
Improving Graph Neural Network Representations of Logical Formulae with Subgraph Pooling
https://github.com/IMAC-projects/mesh-deformation
Kaolin: A PyTorch Library for Accelerating 3D Deep Learning Research
https://github.com/IMvision12/keras-vision-models
Xception: Deep Learning with Depthwise Separable Convolutions
https://github.com/INOS-soft/AI
A parallel Fortran framework for neural networks and deep learning
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.