https://github.com/Jmak12/Iris1
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
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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.
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
MetaPoison: Practical General-purpose Clean-label Data Poisoning
Bilevel Optimization: Convergence Analysis and Enhanced Design
NASE: Learning Knowledge Graph Embedding for Link Prediction via Neural Architecture Search
Learnable Embedding Space for Efficient Neural Architecture Compression
DiffMG: Differentiable Meta Graph Search for Heterogeneous Graph Neural Networks
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
Cream of the Crop: Distilling Prioritized Paths For One-Shot Neural Architecture Search
HAT: Hardware-Aware Transformers for Efficient Natural Language Processing
The Machine Learning Bazaar: Harnessing the ML Ecosystem for Effective System Development
The Machine Learning Bazaar: Harnessing the ML Ecosystem for Effective System Development
The Machine Learning Bazaar: Harnessing the ML Ecosystem for Effective System Development
MnasNet: Platform-Aware Neural Architecture Search for Mobile
PC-DARTS: Partial Channel Connections for Memory-Efficient Architecture Search
ProxylessNAS: Direct Neural Architecture Search on Target Task and Hardware
MnasNet: Platform-Aware Neural Architecture Search for Mobile
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.