AICamp
Deep learning offers the promise of bypassing the process of manual feature engineering by learning representations in conjunction with statistical models in an end-to-end fashion. However, neural network architectures themselves are typically designed by experts in a painstaking, ad-hoc fashion. Neural architecture search (NAS) presents a promising path for alleviating this pain by automatically identifying architectures that are superior to hand-designed ones. In this talk we will present our recent GAEA framework, which provides principled and computationally efficient algorithms for NAS that yield SOTA performance on a wide range of leading NAS benchmarks in computer vision. We will also briefly discuss practical infrastructural hurdles associated with large-scale NAS workflows, and how we tackle these hurdles with Determined AI’s open-source training platform.
Speaker: Liam Li, Determined AI
Source
AutoML20: Demystifying NAS in Theory and Practice
