112 citations · 161 across the 5 of their papers we have counts for
5 papers
Adaptive Fourier Neural Operators: Efficient Token Mixers for Transformers
John Guibas, Morteza Mardani, Zongyi Li +3
Vision transformers have delivered tremendous success in representation learning. This is primarily due to effective token mixing through self attention. However, this scales quadr…
Accelerating Approximate Aggregation Queries with Expensive Predicates
Daniel Kang, John Guibas, Peter Bailis +3
Researchers and industry analysts are increasingly interested in computing aggregation queries over large, unstructured datasets with selective predicates that are computed using e…
Proof: Accelerating Approximate Aggregation Queries with Expensive Predicates
Daniel Kang, John Guibas, Peter Bailis +3
Given a dataset , we are interested in computing the mean of a subset of which matches a predicate. ABae leverages stratified sampling and proxy models t…
Semantic Indexes for Machine Learning-based Queries over Unstructured Data
Daniel Kang, John Guibas, Peter Bailis +2
Unstructured data (e.g., video or text) is now commonly queried by using computationally expensive deep neural networks or human labelers to produce structured information, e.g., o…
Synthetic Medical Images from Dual Generative Adversarial Networks
John T. Guibas, Tejpal S. Virdi, Peter S. Li
Currently there is strong interest in data-driven approaches to medical image classification. However, medical imaging data is scarce, expensive, and fraught with legal concerns re…