RISE Seminar 9/18/20: Algorithmic foundations of neural architecture search, a talk by Ameet Talwalkar of CMU and Determined AI

September 18, 2020

Talk title: Algorithmic foundations of neural architecture search
Date & Time: Friday 9/18, 12-1 PM Pacific Time
Zoom Link: https://berkeley.zoom.us/j/94468083528
Abstract:
Neural architecture search (NAS)—the problem of selecting which neural model to use for your learning problem—is a promising direction for automating and democratizing machine learning. Early NAS methods achieved impressive results on canonical image classification and language modeling problems, yet these methods were massively expensive computationally. More recent heuristics relying on weight-sharing and gradient-based optimization are drastically more computationally efficient while also achieving state-of-the-art performance. However, these heuristics are also complex and are poorly understood. In this talk, we introduce the NAS problem and then present our work studying recent NAS heuristics from first principles. We first perform an extensive ablation study to identify the necessary components of leading NAS methods. We next introduce our geometry-aware framework called GAEA, which exploits the underlying structure of the weight-sharing NAS optimization problem to quickly find high-performance architectures. This leads to simple yet novel algorithms that enjoy faster convergence guarantees than existing gradient-based methods and achieve state-of-the-art accuracy on a wide range of leading NAS benchmarks. Finally, we will 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.
About the Speaker:
Ameet Talwalkar is an assistant professor in the Machine Learning Department at Carnegie Mellon University and is also co-founder and chief scientist at Determined AI. His interests are in the field of statistical machine learning. His current work is motivated by the goal of democratizing machine learning and focuses on topics related to scalability, automation, fairness, and interpretability of learning algorithms and systems.