337 citations · 348 across the 4 of their papers we have counts for
5 papers · 1 filter
Discovery and Separation of Features for Invariant Representation Learning
Ayush Jaiswal, Rob Brekelmans, Daniel Moyer +3
Supervised machine learning models often associate irrelevant nuisance factors with the prediction target, which hurts generalization. We propose a framework for training robust ne…
Invariant Representations through Adversarial Forgetting
Ayush Jaiswal, Daniel Moyer, Greg Ver Steeg +2
We propose a novel approach to achieving invariance for deep neural networks in the form of inducing amnesia to unwanted factors of data through a new adversarial forgetting mechan…
Unified Adversarial Invariance
Ayush Jaiswal, Yue Wu, Wael AbdAlmageed +1
We present a unified invariance framework for supervised neural networks that can induce independence to nuisance factors of data without using any nuisance annotations, but can ad…
Unsupervised Adversarial Invariance
Ayush Jaiswal, Yue Wu, Wael AbdAlmageed +1
Data representations that contain all the information about target variables but are invariant to nuisance factors benefit supervised learning algorithms by preventing them from le…
Large-Scale Unsupervised Deep Representation Learning for Brain Structure
Ayush Jaiswal, Dong Guo, Cauligi S. Raghavendra +1
Machine Learning (ML) is increasingly being used for computer aided diagnosis of brain related disorders based on structural magnetic resonance imaging (MRI) data. Most of such wor…