https://github.com/ray-project/ray/tree/master/python/ray/tune
Tune: A Research Platform for Distributed Model Selection and Training
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
Tune: A Research Platform for Distributed Model Selection and Training
MnasNet: Platform-Aware Neural Architecture Search for Mobile
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
Optuna: A Next-generation Hyperparameter Optimization Framework
LightSpeech: Lightweight and Fast Text to Speech with Neural Architecture Search
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
Speedy Performance Estimation for Neural Architecture Search
Searching for A Robust Neural Architecture in Four GPU Hours
MnasNet: Platform-Aware Neural Architecture Search for Mobile
MnasNet: Platform-Aware Neural Architecture Search for Mobile
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
LightAutoML: AutoML Solution for a Large Financial Services Ecosystem
ProxylessNAS: Direct Neural Architecture Search on Target Task and Hardware
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.