Adaptive Fourier Neural Operators: Efficient Token Mixers for Transformers

2 code implementations • 24 Nov 2021 • John Guibas, Morteza Mardani, Zongyi Li, Andrew Tao, Anima Anandkumar, Bryan Catanzaro

AFNO is based on a principled foundation of operator learning which allows us to frame token mixing as a continuous global convolution without any dependence on the input resolution.

Computational Efficiency Operator learning +1

Efficient Token Mixing for Transformers via Adaptive Fourier Neural Operators

no code implementations • ICLR 2022 • John Guibas, Morteza Mardani, Zongyi Li, Andrew Tao, Anima Anandkumar, Bryan Catanzaro

AFNO is based on a principled foundation of operator learning which allows us to frame token mixing as a continuous global convolution without any dependence on the input resolution.

Computational Efficiency Operator learning +1

Proof: Accelerating Approximate Aggregation Queries with Expensive Predicates

no code implementations • 27 Jul 2021 • Daniel Kang, John Guibas, Peter Bailis, Tatsunori Hashimoto, Yi Sun, Matei Zaharia

Given a dataset $\mathcal{D}$, we are interested in computing the mean of a subset of $\mathcal{D}$ which matches a predicate.