Papers with Code 아카이브
Neural Architecture Search
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Neural architecture search (NAS) is a technique for automating the design of artificial neural networks (ANN), a widely used model in the field of machine learning. NAS essentially takes the process of a human manually tweaking a neural network and learning what works well, and automates this task to discover more complex architectures.
Image Credit : NAS with Reinforcement Learning
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/dungscout96/DL-EEG
Small-Group Learning, with Application to Neural Architecture Search
https://github.com/dyabel/handson_rl
https://github.com/ekzhang/fastseg
https://github.com/eladsar/rbi
https://github.com/emerge-lab/nocturne_lab
https://github.com/emilianavt/OpenSeeFace
https://github.com/epfl-ml-reproducers/subspace-attack-reproduction
Searching for A Robust Neural Architecture in Four GPU Hours
https://github.com/epoc88/PFLD_68pts_Pytorch_2020
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/eric-erki/autokeras
https://github.com/eric-vader/HD-BO-Additive-Models
NAS-Bench-101: Towards Reproducible Neural Architecture Search
https://github.com/eric8607242/PONAS
PONAS: Progressive One-shot Neural Architecture Search for Very Efficient Deployment
https://github.com/ericyang789/Parallel-Compute-Project
Demystifying Parallel and Distributed Deep Learning: An In-Depth Concurrency Analysis
https://github.com/evieq01/oodil
https://github.com/f51980280/ENAS-Implement
https://github.com/facebookresearch/ClassyVision
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/facebookresearch/Horizon
https://github.com/facebookresearch/NASViT
https://github.com/facebookresearch/ReAgent
https://github.com/facebookresearch/fairscale
Training Large Neural Networks with Constant Memory using a New Execution Algorithm
https://github.com/facebookresearch/how-to-autorl
DEHB: Evolutionary Hyperband for Scalable, Robust and Efficient Hyperparameter Optimization
https://github.com/facebookresearch/la-mcts
Learning Search Space Partition for Black-box Optimization using Monte Carlo Tree Search
https://github.com/facebookresearch/nds
https://github.com/facebookresearch/ppuda
https://github.com/facebookresearch/pycls
Exploring Randomly Wired Neural Networks for Image Recognition
https://github.com/famishedrover/NAS
Simple And Efficient Architecture Search for Convolutional Neural Networks
https://github.com/farazahmeds/Classification-of-brain-tumor-using-Spatiotemporal-models
ConvNet Architecture Search for Spatiotemporal Feature Learning
https://github.com/fdcl-gwu/gym-rotor
https://github.com/federicopozzi33/MobileOne-PyTorch
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/feymanpriv/pymetric
https://github.com/filaPro/visda2019
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/filipmu/Kaggle-APTOS-2019-Blindness
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/finefun0/autokeras
https://github.com/flymin/darts
https://github.com/freedombenLiu/autokeras
https://github.com/gaetanserre/l2rpn-2022_ppo-baseline
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.