59 citations · 102 across the 10 of their papers we have counts for
10 papers
TpopT: Efficient Trainable Template Optimization on Low-Dimensional Manifolds
Jingkai Yan, Shiyu Wang, Xinyu Rain Wei +4
In scientific and engineering scenarios, a recurring task is the detection of low-dimensional families of signals or patterns. A classic family of approaches, exemplified by templa…
Boosting the Efficiency of Parametric Detection with Hierarchical Neural Networks
Jingkai Yan, Robert Colgan, John Wright +3
Gravitational wave astronomy is a vibrant field that leverages both classic and modern data processing techniques for the understanding of the universe. Various approaches have bee…
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…
Detecting and Diagnosing Terrestrial Gravitational-Wave Mimics Through Feature Learning
Robert E. Colgan, Zsuzsa Márka, Jingkai Yan +3
As engineered systems grow in complexity, there is an increasing need for automatic methods that can detect, diagnose, and even correct transient anomalies that inevitably arise an…
Architectural Optimization and Feature Learning for High-Dimensional Time Series Datasets
Robert E. Colgan, Jingkai Yan, Zsuzsa Márka +3
As our ability to sense increases, we are experiencing a transition from data-poor problems, in which the central issue is a lack of relevant data, to data-rich problems, in which…
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…
Generalized Approach to Matched Filtering using Neural Networks
Jingkai Yan, Mariam Avagyan, Robert E. Colgan +5
Gravitational wave science is a pioneering field with rapidly evolving data analysis methodology currently assimilating and inventing deep learning techniques. The bulk of the soph…
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…