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An important goal of AutoML is to automate-away the design of neural networkson new tasks in under-explored domains. Motivated by this goal, we study theproblem of enabling users to discover the right neural operations given datafrom their specific domain. We introduce a search space of operations calledXD-Operations that mimic the inductive bias of standard multi-channelconvolutions while being much more expressive: we prove that it includes manynamed operations across multiple application areas. Starting with any standardbackbone such as ResNet, we show how to transform it into a search space overXD-operations and how to traverse the space using a simple weight-sharingscheme. On a diverse set of tasks -- solving PDEs, distance prediction forprotein folding, and music modeling -- our approach consistently yields modelswith lower error than baseline networks and often even lower error thanexpert-designed domain-specific approaches.

Rethinking Neural Operations for Diverse Tasks