178 citations · 537 across the 11 of their papers we have counts for
11 papers
Boosting Test Performance with Importance Sampling--a Subpopulation Perspective
Hongyu Shen, Zhizhen Zhao
Despite empirical risk minimization (ERM) is widely applied in the machine learning community, its performance is limited on data with spurious correlation or subpopulation that is…
DeepDRK: Deep Dependency Regularized Knockoff for Feature Selection
Hongyu Shen, Yici Yan, Zhizhen Zhao
Model-X knockoff has garnered significant attention among various feature selection methods due to its guarantees for controlling the false discovery rate (FDR). Since its introduc…
Learning Personalized Representations using Graph Convolutional Network
Hongyu Shen, Jinoh Oh, Shuai Zhao +3
Generating representations that precisely reflect customers' behavior is an important task for providing personalized skill routing experience in Alexa. Currently, Dynamic Routing…
Do You Live a Healthy Life? Analyzing Lifestyle by Visual Life Logging
Qing Gao, Mingtao Pei, Hongyu Shen
A healthy lifestyle is the key to better health and happiness and has a considerable effect on quality of life and disease prevention. Current lifelogging/egocentric datasets are n…
Enabling real-time multi-messenger astrophysics discoveries with deep learning
E. A. Huerta, Gabrielle Allen, Igor Andreoni +57
Multi-messenger astrophysics is a fast-growing, interdisciplinary field that combines data, which vary in volume and speed of data processing, from many different instruments that…
Denoising Gravitational Waves with Enhanced Deep Recurrent Denoising Auto-Encoders
Hongyu Shen, Daniel George, E. A. Huerta +1
Denoising of time domain data is a crucial task for many applications such as communication, translation, virtual assistants etc. For this task, a combination of a recurrent neural…
Statistically-informed deep learning for gravitational wave parameter estimation
Hongyu Shen, E. A. Huerta, Eamonn O'Shea +2
We introduce deep learning models to estimate the masses of the binary components of black hole mergers, , and three astrophysical properties of the post-merger compact…
Deep Learning for Multi-Messenger Astrophysics: A Gateway for Discovery in the Big Data Era
Gabrielle Allen, Igor Andreoni, Etienne Bachelet +45
This report provides an overview of recent work that harnesses the Big Data Revolution and Large Scale Computing to address grand computational challenges in Multi-Messenger Astrop…
Glitch Classification and Clustering for LIGO with Deep Transfer Learning
Daniel George, Hongyu Shen, E. A. Huerta
The detection of gravitational waves with LIGO and Virgo requires a detailed understanding of the response of these instruments in the presence of environmental and instrumental no…
Denoising Gravitational Waves using Deep Learning with Recurrent Denoising Autoencoders
Hongyu Shen, Daniel George, E. A. Huerta +1
Gravitational wave astronomy is a rapidly growing field of modern astrophysics, with observations being made frequently by the LIGO detectors. Gravitational wave signals are often…
Deep Transfer Learning: A new deep learning glitch classification method for advanced LIGO
Daniel George, Hongyu Shen, E. A. Huerta
The exquisite sensitivity of the advanced LIGO detectors has enabled the detection of multiple gravitational wave signals. The sophisticated design of these detectors mitigates the…