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arXiv · 2103.15798

Rethinking Neural Operations for Diverse Tasks

Nicholas Roberts·Mikhail Khodak·Tri Dao·Liam Li·Christopher Ré·Ameet Talwalkar
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Abstract

An important goal of AutoML is to automate-away the design of neural networks on new tasks in under-explored domains. Motivated by this goal, we study the problem of enabling users to discover the right neural operations given data from their specific domain. We introduce a search space of operations called XD-Operations that mimic the inductive bias of standard multi-channel convolutions while being much more expressive: we prove that it includes many named operations across multiple application areas. Starting with any standard backbone such as ResNet, we show how to transform it into a search space over XD-operations and how to traverse the space using a simple weight-sharing scheme. On a diverse set of tasks -- solving PDEs, distance prediction for protein folding, and music modeling -- our approach consistently yields models with lower error than baseline networks and often even lower error than expert-designed domain-specific approaches.

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BibTeXRIS

Nicholas Roberts, Mikhail Khodak, Tri Dao, Liam Li, Christopher Ré, Ameet Talwalkar. 2021-03-29. Rethinking Neural Operations for Diverse Tasks. https://arxiv.org/abs/2103.15798

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