https://github.com/Thesys-lab/learned-cc
Learning a Code: Machine Learning for Approximate Non-Linear Coded Computation
Papers with Code 아카이브
BIG-bench Machine Learning
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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This branch include most common machine learning fundamental algorithms.
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/Thesys-lab/learned-coded-computation
Learning a Code: Machine Learning for Approximate Non-Linear Coded Computation
https://github.com/Thesys-lab/parity-models
Learning a Code: Machine Learning for Approximate Non-Linear Coded Computation
https://github.com/Tianxiaomo/pytorch-YOLOv4
https://github.com/TorchCraft/TorchCraft
TorchCraft: a Library for Machine Learning Research on Real-Time Strategy Games
https://github.com/TrustAI/DeepConcolic
Embedding and Extraction of Knowledge in Tree Ensemble Classifiers
https://github.com/Trustworthy-AI-Group/TransferAttack
https://github.com/TsingZ0/FL-IoT
https://github.com/TsingZ0/PFL-Non-IID
https://github.com/TusharMhaske28/Object-Detection
https://github.com/TusharMhaske28/Thermal-Object
https://github.com/TusharMhaske28/summer-project
https://github.com/UCI-CARL/CARLsim5
PyCARL: A PyNN Interface for Hardware-Software Co-Simulation of Spiking Neural Network
https://github.com/UMBC-DREAM-Lab/montemutacon
Evading Malware Classifiers via Monte Carlo Mutant Feature Discovery
https://github.com/UW-AMO/TimeSeriesES-Cell
https://github.com/UlionTse/mlgb
https://github.com/UnofficialJuliaMirror/Augmentor.jl-0612f1b9-51e2-5127-9fc2-313c368ba66d
Augmentor: An Image Augmentation Library for Machine Learning
https://github.com/UnofficialJuliaMirror/DeterminantalPointProcesses.jl-9d4a7304-c3b4-5347-99a8-9cc862165b3e
https://github.com/UnofficialJuliaMirror/DiffEqFlux.jl-aae7a2af-3d4f-5e19-a356-7da93b79d9d0
DiffEqFlux.jl - A Julia Library for Neural Differential Equations
https://github.com/UnofficialJuliaMirror/UMAP.jl-c4f8c510-2410-5be4-91d7-4fbaeb39457e
UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction
https://github.com/UnofficialJuliaMirror/Yao.jl-5872b779-8223-5990-8dd0-5abbb0748c8c
https://github.com/UnofficialJuliaMirrorSnapshots/Augmentor.jl-0612f1b9-51e2-5127-9fc2-313c368ba66d
Augmentor: An Image Augmentation Library for Machine Learning
https://github.com/UnofficialJuliaMirrorSnapshots/DeterminantalPointProcesses.jl-9d4a7304-c3b4-5347-99a8-9cc862165b3e
https://github.com/UnofficialJuliaMirrorSnapshots/DiffEqFlux.jl-aae7a2af-3d4f-5e19-a356-7da93b79d9d0
DiffEqFlux.jl - A Julia Library for Neural Differential Equations
https://github.com/UnofficialJuliaMirrorSnapshots/UMAP.jl-c4f8c510-2410-5be4-91d7-4fbaeb39457e
UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction
https://github.com/UnofficialJuliaMirrorSnapshots/Yao.jl-5872b779-8223-5990-8dd0-5abbb0748c8c
https://github.com/UoS-EEC/DynamicOFA
Optimising Resource Management for Embedded Machine Learning
https://github.com/UrosOgrizovic/SimpleGoogleQuickdraw
Gradient descent revisited via an adaptive online learning rate
https://github.com/UtkarshKharche29/Automatic-Number-Plate-Recognition
https://github.com/VCasecnikovs/Yet-Another-YOLOv4-Pytorch
https://github.com/VEK239/StructGNN-lipophilicity
https://github.com/VGalata/plsdb
UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction
https://github.com/VU-BEAM-Lab/ADMIRE
https://github.com/Vakihito/SentimentYoutube
https://github.com/Vatshayan/Final-Year-Machine-Learning-Stock-Price-Prediction-Project
Stock Price Prediction Using Machine Learning and LSTM-Based Deep Learning Models
https://github.com/Vatshayan/Final-Year-Project-Stock-Price-Prediction-by-Deep-Learning
Stock Price Prediction Using Machine Learning and LSTM-Based Deep Learning Models
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.