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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/ZJCV/NetworkSlimming
Learning Efficient Convolutional Networks through Network Slimming
https://github.com/ZTao-z/ProxylessNAS
AMC: AutoML for Model Compression and Acceleration on Mobile Devices
https://github.com/ZachZhu7/banana-git
BANANAS: Bayesian Optimization with Neural Architectures for Neural Architecture Search
https://github.com/ZackPashkin/YOLOv3-EfficientNet-EffYolo
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/Zartris/TD3_continuous_control
https://github.com/ZilinGao/GM-SOP
A Downsampled Variant of ImageNet as an Alternative to the CIFAR datasets
https://github.com/aaditkapoor/PDFExtract
HAT: Hardware-Aware Transformers for Efficient Natural Language Processing
https://github.com/abcp4/DAPytorch
https://github.com/abcp4/MyDarts
https://github.com/abcp4/autokeras
https://github.com/abhoi/Keras-MnasNet
MnasNet: Platform-Aware Neural Architecture Search for Mobile
https://github.com/abhuse/pytorch-efficientnet
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/adammenges/DARTS-PyTorch
https://github.com/adamos581/ppo-keras-football
https://github.com/adik993/ppo-pytorch
https://github.com/ahundt/renas
https://github.com/ailab-pku/rl-framework
https://github.com/akrapukhin/MobileNetV3
https://github.com/alecfilios/Training-Intelligent-game-Agents-through-Competitive-Reinforcement-Learning
https://github.com/alex-petrenko/sample-factory
https://github.com/alexandonian/pretorched-x
https://github.com/alexbaumi/PPO-Algorithm
https://github.com/alexbaumi/PPO-Algorithms
https://github.com/alexmlamb/blocks_rl_gru_setup
https://github.com/alibaba/EasyCV
MnasNet: Platform-Aware Neural Architecture Search for Mobile
https://github.com/alibaba/EasyTransfer/tree/master/scripts/knowledge_distillation/adabert
AdaBERT: Task-Adaptive BERT Compression with Differentiable Neural Architecture Search
https://github.com/alibaba/esim-response-selection
Learning Efficient Convolutional Networks through Network Slimming
https://github.com/alililia/ascend_ISyNet
ISyNet: Convolutional Neural Networks design for AI accelerator
https://github.com/alililia/ms_extend/tree/main/gpu_efficientnet
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/alirezakazemipour/ppo-rnd
https://github.com/alphadl/darts.pytorch1.1
https://github.com/amanda-lambda/drl-experiments
https://github.com/amanda-lambda/hack-flappy-bird-drl
https://github.com/amartyamukherjee/ppo-packcooling
https://github.com/amaudruz/RL_openaigym
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.