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Optimal Transport Kernels for Sequential and Parallel Neural Architecture Search
Vu Nguyen · Tam Le · Makoto Yamada · Michael A Osborne

Tue Jul 20 09:00 PM -- 11:00 PM (PDT) @

Neural architecture search (NAS) automates the design of deep neural networks. One of the main challenges in searching complex and non-continuous architectures is to compare the similarity of networks that the conventional Euclidean metric may fail to capture. Optimal transport (OT) is resilient to such complex structure by considering the minimal cost for transporting a network into another. However, the OT is generally not negative definite which may limit its ability to build the positive-definite kernels required in many kernel-dependent frameworks. Building upon tree-Wasserstein (TW), which is a negative definite variant of OT, we develop a novel discrepancy for neural architectures, and demonstrate it within a Gaussian process surrogate model for the sequential NAS settings. Furthermore, we derive a novel parallel NAS, using quality k-determinantal point process on the GP posterior, to select diverse and high-performing architectures from a discrete set of candidates. Empirically, we demonstrate that our TW-based approaches outperform other baselines in both sequential and parallel NAS.

Author Information

Vu Nguyen (Amazon Adelaide)

My name is Tam Le. I officially received my PhD degree from Kyoto University in 01/2016, under the supervision of Professor Marco Cuturi and Professor Akihiro Yamamoto. Currently, I have been working as a postdoc researcher at RIKEN AIP, Japan, mentored by Professor Makoto Yamada from September, 2017. (From 8/2021, I will work as Research Scientist at RIKEN AIP). Prior to this, I spent 1.5 year as a postdoc researcher at Nagoya Institute of Technology and National Institute of Materials Science, working with Professor Ichiro Takeuchi.

Makoto Yamada (RIKEN)
Michael A Osborne (U Oxford)

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