1 citations · 1 across the 1 of their papers we have counts for
3 papers
cs.LG2020
Assisting the Adversary to Improve GAN Training
Andreas Munk, William Harvey, Frank Wood
Some of the most popular methods for improving the stability and performance of GANs involve constraining or regularizing the discriminator. In this paper we consider a largely ove…
cs.LG2019★ 1 cited
Attention for Inference Compilation
William Harvey, Andreas Munk, Atılım Güneş Baydin +2
We present a new approach to automatic amortized inference in universal probabilistic programs which improves performance compared to current methods. Our approach is a variation o…
cs.LG2019
Near-Optimal Glimpse Sequences for Improved Hard Attention Neural Network Training
William Harvey, Michael Teng, Frank Wood
Hard visual attention is a promising approach to reduce the computational burden of modern computer vision methodologies. Hard attention mechanisms are typically non-differentiable…