Can AlphaX framework be adapted to improve sample efficiency in neural architecture search by incorporating un

Abstract

Abstract: Neural Architecture Search (NAS) has shown great success in automating the design of neural networks, but the prohibitive amount of computations behind current NAS methods requires further investigations in improving the sample efficiency and the network evaluation cost to get better results in a shorter time. In this paper, we present a novel scalable Monte Carlo Tree Search (MCTS) based NAS agent, named AlphaX, to tackle these two aspects. AlphaX improves the search efficiency by adaptively balancing the exploration and exploitation at the state level, and by a Meta-Deep Neural Network (DNN)

Research Question

Can AlphaX framework be adapted to improve sample efficiency in neural architecture search by incorporating uncertainty quantification through Bayesian neural networks with fewer Monte Carlo samples?

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