activity
20172025
most citedSpatial Evolutionary Generative Adversarial Networks

57 citations · 125 across the 18 of their papers we have counts for

collaborators
Showing cs.LGShow all

5 papers · 1 filter

cs.LG2021

Fostering Diversity in Spatial Evolutionary Generative Adversarial Networks

Jamal Toutouh, Erik Hemberg, Una-May O'Reilly

Generative adversary networks (GANs) suffer from training pathologies such as instability and mode collapse, which mainly arise from a lack of diversity in their adversarial intera…

cs.LG2020

Data Dieting in GAN Training

Jamal Toutouh, Una-May O'Reilly, Erik Hemberg

We investigate training Generative Adversarial Networks, GANs, with less data. Subsets of the training dataset can express empirical sample diversity while reducing training resour…

cs.LG2018

Transfer Learning using Representation Learning in Massive Open Online Courses

Mucong Ding, Yanbang Wang, Erik Hemberg +1

In a Massive Open Online Course (MOOC), predictive models of student behavior can support multiple aspects of learning, including instructor feedback and timely intervention. Ongoi…

cs.LG2018

On Visual Hallmarks of Robustness to Adversarial Malware

Alex Huang, Abdullah Al-Dujaili, Erik Hemberg +1

A central challenge of adversarial learning is to interpret the resulting hardened model. In this contribution, we ask how robust generalization can be visually discerned and wheth…

cs.LG20171 cited

Distributed Stratified Locality Sensitive Hashing for Critical Event Prediction in the Cloud

Alessandro De Palma, Erik Hemberg, Una-May O'Reilly

The availability of massive healthcare data repositories calls for efficient tools for data-driven medicine. We introduce a distributed system for Stratified Locality Sensitive Has…