https://github.com/zhengjian2322/net2net
Simple And Efficient Architecture Search for Convolutional Neural Networks
Papers with Code 아카이브
AutoML
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
- 3
- 평가표
- 4
- 보존된 평가 기록
- 11
Automated Machine Learning (AutoML) is a general concept which covers diverse techniques for automated model learning including automatic data preprocessing, architecture search, and model selection. Source: Evaluating recommender systems for AI-driven data science (1905.09205)
Source: CHOPT : Automated Hyperparameter Optimization Framework for Cloud-Based Machine Learning Platforms
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/zhenyx19/medmnist
MedMNIST Classification Decathlon: A Lightweight AutoML Benchmark for Medical Image Analysis
https://github.com/zhoudaquan/AutoSpace
AutoSpace: Neural Architecture Search with Less Human Interference
https://github.com/zitonghuangcynthia/SWAT-NN
SWAT-NN: Simultaneous Weights and Architecture Training for Neural Networks in a Latent Space
https://github.com/zjlab-ammi/llm4rl
https://github.com/zsef123/EfficientNets-PyTorch
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/zsef123/MixNet-PyTorch
https://github.com/zsz-hst/RL_single_chase_point
https://github.com/zygmuntz/hyperband
Hyperband: A Novel Bandit-Based Approach to Hyperparameter Optimization
https://github.com/zylo117/Yet-Another-EfficientDet-Pytorch
https://github.com/zym1119/Partial-Order-Pruning-Demo
Partial Order Pruning: for Best Speed/Accuracy Trade-off in Neural Architecture Search
https://gitlab.com/birder/birder
MnasNet: Platform-Aware Neural Architecture Search for Mobile
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.