Towards an Understanding of Stochastic Differentiable Neural Architecture Search (NAS@ICLR2020)
Channel: UVM Neurobotics Lab
Date: 2020-04-28

Abstract:
Shared-weight Neural Architecture Search (NAS) methods have led to an exponential speed up in the search process by allowing rapid evaluation of candidate models. However, frequently, only the performance of the final architecture discovered by these algorithms is reported, without evaluating the ability to approximate the trained-from-scratch performance. To address this, we evaluate the performance approximation directly of several stochastic differentiable NAS methods on the NASBench-201 benchmark, determining that SNAS significantly outperforms the other methods. We experimentally ablate components of these methods, ultimately revealing unnecessary efficiencies in the original conceptualization of each algorithm.

Meyer-Lee, G., Cheney, N.. (2020). Towards an Understanding of Stochastic Differentiable Neural Architecture Search.  

https://sites.google.com/view/nas2020/accepted-papers

http://www.uvm.edu/neurobotics