Search Results for author: John Guibas

Found 3 papers, 1 papers with code

Adaptive Fourier Neural Operators: Efficient Token Mixers for Transformers

1 code implementation24 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.

Operator learning Representation Learning

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.

Operator learning Representation Learning

Proof: Accelerating Approximate Aggregation Queries with Expensive Predicates

no code implementations27 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.

Cannot find the paper you are looking for? You can Submit a new open access paper.