AutoML | Auto-PyTorch: Multi-Fidelity MetaLearning for Efficient and Robust AutoDL

Auto-PyTorch: Multi-Fidelity MetaLearning for Efficient and Robust AutoDL

By Auto-PyTorch is a framework for automated deep learning (AutoDL) that uses BOHB as a backend to optimize the full deep learning pipeline, including data preprocessing, network training techniques and regularization methods. Auto-PyTorch is the successor of AutoNet which was one of the first frameworks to perform this joint optimization.

NAS-Bench-301 and the Case for Surrogate NAS Benchmarks

The Need for Realistic NAS Benchmarks Neural Architecture Search (NAS) is a logical next step in representation learning as it removes human bias from architecture design, similar to deep learning removing human bias from feature engineering. As such, NAS has experienced rapid growth in recent years, leading to state-of-the-art performance on many tasks. However, empirical […]