2 citations · 2 across the 2 of their papers we have counts for
2 papers
cs.LG2017
Summable Reparameterizations of Wasserstein Critics in the One-Dimensional Setting
Christopher Grimm, Yuhang Song, Michael L. Littman
Generative adversarial networks (GANs) are an exciting alternative to algorithms for solving density estimation problems---using data to assess how likely samples are to be drawn f…
cs.AI2017★ 2 cited
Modeling Latent Attention Within Neural Networks
Christopher Grimm, Dilip Arumugam, Siddharth Karamcheti +3
Deep neural networks are able to solve tasks across a variety of domains and modalities of data. Despite many empirical successes, we lack the ability to clearly understand and int…