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AMC: AutoML for Model Compression and Acceleration on Mobile Devices
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.
AMC: AutoML for Model Compression and Acceleration on Mobile Devices
AMC: AutoML for Model Compression and Acceleration on Mobile Devices
Single Path One-Shot Neural Architecture Search with Uniform Sampling
Exploring Randomly Wired Neural Networks for Image Recognition
FBNet: Hardware-Aware Efficient ConvNet Design via Differentiable Neural Architecture Search
TabPFN: A Transformer That Solves Small Tabular Classification Problems in a Second
Hyperband: A Novel Bandit-Based Approach to Hyperparameter Optimization
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
Learning Transferable Architectures for Scalable Image Recognition
Regularized Evolution for Image Classifier Architecture Search
NAS-FPN: Learning Scalable Feature Pyramid Architecture for Object Detection
MnasNet: Platform-Aware Neural Architecture Search for Mobile
Hyperband: A Novel Bandit-Based Approach to Hyperparameter Optimization
MFES-HB: Efficient Hyperband with Multi-Fidelity Quality Measurements
MFES-HB: Efficient Hyperband with Multi-Fidelity Quality Measurements
MFES-HB: Efficient Hyperband with Multi-Fidelity Quality Measurements
MFES-HB: Efficient Hyperband with Multi-Fidelity Quality Measurements
Supervised Video Summarization via Multiple Feature Sets with Parallel Attention
Hyperband: A Novel Bandit-Based Approach to Hyperparameter Optimization
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.