https://github.com/david-gimeno/lip-rtve
Visual Speech Recognition for Multiple Languages in the Wild
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.
Visual Speech Recognition for Multiple Languages in the Wild
TabPFN: A Transformer That Solves Small Tabular Classification Problems in a Second
AutoGluon-TimeSeries: AutoML for Probabilistic Time Series Forecasting
PC-DARTS: Partial Channel Connections for Memory-Efficient Architecture Search
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
Deep-n-Cheap: An Automated Search Framework for Low Complexity Deep Learning
Machine Learning in the Quantum Age: Quantum vs. Classical Support Vector Machines
Small-Group Learning, with Application to Neural Architecture Search
Hyperband: A Novel Bandit-Based Approach to Hyperparameter Optimization
Searching for A Robust Neural Architecture in Four GPU Hours
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.