728 citations · 1.7k across the 39 of their papers we have counts for
6 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…
Bidirectional Conditional Generative Adversarial Networks
Ayush Jaiswal, Wael AbdAlmageed, Yue Wu +1
Conditional Generative Adversarial Networks (cGANs) are generative models that can produce data samples () conditioned on both latent variables () and known auxiliary informa…
Temporal Learning and Sequence Modeling for a Job Recommender System
Kuan Liu, Xing Shi, Anoop Kumar +2
We present our solution to the job recommendation task for RecSys Challenge 2016. The main contribution of our work is to combine temporal learning with sequence modeling to captur…