Today I’m speaking with Vasco Lopes, about the state of Neural Architecture Search, NAS, and about a new method that he published that takes a very creative look at how to do NAS.

We’ll be discussing the motivation behind NAS, the current state of its deployment, the biggest use-cases today, the three components that make up NAS, the drawbacks to the current NAS paradigm, search spaces and how to design them, search strategies and how to choose them, graph representations of neural architectures, evaluation strategies and zero-cost approximations, bias in search space design, risks, the future of hand-designed architectures, and other topics.

Vasco is a PhD student at NOVA School of Science and Technology in Portugal and a co-founder of a Computer Vision startup called DeepNeuronic.

Find his paper, Towards Less Constrained Macro-Neural Architecture Search, here: https://arxiv.org/pdf/2203.05508.pdf