https://github.com/mindspore-ai/models/tree/master/research/cv/single_path_nas
Single-Path NAS: Designing Hardware-Efficient ConvNets in less than 4 Hours
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.
Single-Path NAS: Designing Hardware-Efficient ConvNets in less than 4 Hours
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
MnasNet: Platform-Aware Neural Architecture Search for Mobile
Learning Transferable Architectures for Scalable Image Recognition
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
MnasNet: Platform-Aware Neural Architecture Search for Mobile
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
AMC: AutoML for Model Compression and Acceleration on Mobile Devices
AMC: AutoML for Model Compression and Acceleration on Mobile Devices
AMC: AutoML for Model Compression and Acceleration on Mobile Devices
Searching Efficient 3D Architectures with Sparse Point-Voxel Convolution
HAT: Hardware-Aware Transformers for Efficient Natural Language Processing
Cardea: An Open Automated Machine Learning Framework for Electronic Health Records
Hyperband: A Novel Bandit-Based Approach to Hyperparameter Optimization
Non-stochastic Best Arm Identification and Hyperparameter Optimization
Optimizing Millions of Hyperparameters by Implicit Differentiation
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
Optimizing Millions of Hyperparameters by Implicit Differentiation
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.