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/gaussianer/fasterseg
FasterSeg: Searching for Faster Real-time Semantic Segmentation
https://github.com/georgkruse/cleanqrl
https://github.com/github-luffy/PFLD_68points_Pytorch
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/gmh14/IRLAS
IRLAS: Inverse Reinforcement Learning for Architecture Search
https://github.com/gmontamat/gentun
A Genetic Programming Approach to Designing Convolutional Neural Network Architectures
https://github.com/gmoss1/Kaggle-Halite-IV-RL
https://github.com/gomerudo/nas-dmrl
Learning to reinforcement learn for Neural Architecture Search
https://github.com/gomerudo/openai-baselines
Learning to reinforcement learn for Neural Architecture Search
https://github.com/gomezzz/MSMatch
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/gomezzz/distmsmatch
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/goncharom/PPOv1
https://github.com/google-research/google-research/tree/master/enas_lm
https://github.com/google-research/morph-net
MorphNet: Fast & Simple Resource-Constrained Structure Learning of Deep Networks
https://github.com/gouthamvgk/coreml_conversion_hub
https://github.com/grtzsohalf/pt.darts
https://github.com/gstoica27/cpg_ppo
https://github.com/guofeng201507/ISA-IPA-2020-05-05-IS1PT-GRP-High5-StockTrading
https://github.com/gwthomas/gtml
https://github.com/hamed-ahangari/Separation-Index-of-convolutional-layers-in-EfficientNet-B0
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/hamishs/JAX-RL
https://github.com/hankyul2/EfficientNetV2-pytorch
https://github.com/haozheliu-st/t-gate
Adaptive Guidance: Training-free Acceleration of Conditional Diffusion Models
https://github.com/harruff/Senior_Project_Repository
https://github.com/hdparks/AsteroidsDeepReinforcement
https://github.com/hebo1221/RandWireNN
Exploring Randomly Wired Neural Networks for Image Recognition
https://github.com/hieule88/geneNas
https://github.com/hill-a/stable-baselines
https://github.com/hmhuy0/SIM-RL
https://github.com/houstonsantos/CassavaLeafDisease
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/huggingface/pytorch-image-models
https://github.com/huggingface/transformers
https://github.com/huynhtuan17ti/AI4VN-Hackathon2020
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/huyz1117/BAM
https://github.com/huzpsb/EffTransfer
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/hvarfner/vanilla_bo_in_highdim
Increasing the Scope as You Learn: Adaptive Bayesian Optimization in Nested Subspaces
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.