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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/bay3s/ppo-rl
https://github.com/ben-arnao/StoRM
Random Search and Reproducibility for Neural Architecture Search
https://github.com/ben-arnao/stochasticmutatortuner
Random Search and Reproducibility for Neural Architecture Search
https://github.com/benevolentAI/guacamol_baselines
https://github.com/benpetit/cs379c
https://github.com/bentrevett/pytorch-rl
https://github.com/bibliotecadebabel/EvAI
Traditional and accelerated gradient descent for neural architecture search
https://github.com/bonniesjli/PPO-Reacher_UnityML
https://github.com/bonniesjli/PPO_Reacher
https://github.com/bowenbaker/metaqnn
Accelerating Neural Architecture Search using Performance Prediction
https://github.com/buiquangmanhhp1999/age_gender_estimation
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/canturan10/satellighte
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/captaindario/dakanji-single-kanji-recognition
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/carolchenyx/MobilenetXT-tensorflow
Rethinking Bottleneck Structure for Efficient Mobile Network Design
https://github.com/carpedm20/ENAS-pytorch
https://github.com/cgebbe/kaggle_pku-autonomous-driving
MnasNet: Platform-Aware Neural Architecture Search for Mobile
https://github.com/chainer/chainerrl
https://github.com/chandlerbang/gcond
GC4NC: A Benchmark Framework for Graph Condensation on Node Classification with New Insights
https://github.com/chenfengxu714/image2point
Searching Efficient 3D Architectures with Sparse Point-Voxel Convolution
https://github.com/chenyaofo/ASE-NAS
Automated Dominative Subspace Mining for Efficient Neural Architecture Search
https://github.com/chris-boson/fashion_mnist
https://github.com/chrisqqq123/FA-Dist-EfficientNet
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/christiansafka/img2vec
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/chumingqian/Model_Compression_For_YOLOV3-V4
Learning Efficient Convolutional Networks through Network Slimming
https://github.com/cipher982/ppo-exploration
https://github.com/ckyrkou/EmergencyNet
https://github.com/clovaai/rexnet
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/clwainwright/proximal_policy_optimization
https://github.com/code-implementation1/Code1/tree/main/EF-ENAS
ES-ENAS: Efficient Evolutionary Optimization for Large Hybrid Search Spaces
https://github.com/code-implementation1/Code3/tree/main/efficientnet-b0
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/code-implementation1/Code4/tree/main/HourNAS
HourNAS: Extremely Fast Neural Architecture Search Through an Hourglass Lens
https://github.com/code-implementation1/Code4/tree/main/ISyNet
ISyNet: Convolutional Neural Networks design for AI accelerator
https://github.com/code-implementation1/Code5/tree/main/mnasnet
MnasNet: Platform-Aware Neural Architecture Search for Mobile
https://github.com/code-implementation1/Code6/tree/main/PDarts
Progressive DARTS: Bridging the Optimization Gap for NAS in the Wild
https://github.com/code-implementation1/Code6/tree/main/nas-fpn
NAS-FPN: Learning Scalable Feature Pyramid Architecture for Object Detection
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.