3 citations · 6 across the 4 of their papers we have counts for
5 papers
Resource-Efficient Invariant Networks: Exponential Gains by Unrolled Optimization
Sam Buchanan, Jingkai Yan, Ellie Haber +1
Achieving invariance to nuisance transformations is a fundamental challenge in the construction of robust and reliable vision systems. Existing approaches to invariance scale expon…
Principal Component Pursuit for Pattern Identification in Environmental Mixtures
Elizabeth A. Gibson, Junhui Zhang, Jingkai Yan +7
Environmental health researchers often aim to identify sources/behaviors that give rise to potentially harmful exposures. We adapted principal component pursuit (PCP)-a robust tech…
Deep Networks Provably Classify Data on Curves
Tingran Wang, Sam Buchanan, Dar Gilboa +1
Data with low-dimensional nonlinear structure are ubiquitous in engineering and scientific problems. We study a model problem with such structure -- a binary classification task th…
Square Root Principal Component Pursuit: Tuning-Free Noisy Robust Matrix Recovery
Junhui Zhang, Jingkai Yan, John Wright
We propose a new framework -- Square Root Principal Component Pursuit -- for low-rank matrix recovery from observations corrupted with noise and outliers. Inspired by the square ro…
Efficient Gravitational-wave Glitch Identification from Environmental Data Through Machine Learning
Robert E. Colgan, K. Rainer Corley, Yenson Lau +4
The LIGO observatories detect gravitational waves through monitoring changes in the detectors' length down to below \, variation---a small fraction of the si…