3.3k citations · 9.7k across the 40 of their papers we have counts for
7 papers · 1 filter
Improved generator objectives for GANs
Ben Poole, Alexander A. Alemi, Jascha Sohl-Dickstein +1
We present a framework to understand GAN training as alternating density ratio estimation and approximate divergence minimization. This provides an interpretation for the mismatche…
Survey of Expressivity in Deep Neural Networks
Maithra Raghu, Ben Poole, Jon Kleinberg +2
We survey results on neural network expressivity described in "On the Expressive Power of Deep Neural Networks". The paper motivates and develops three natural measures of expressi…
Unrolled Generative Adversarial Networks
Luke Metz, Ben Poole, David Pfau +1
We introduce a method to stabilize Generative Adversarial Networks (GANs) by defining the generator objective with respect to an unrolled optimization of the discriminator. This al…
Categorical Reparameterization with Gumbel-Softmax
Eric Jang, Shixiang Gu, Ben Poole
Categorical variables are a natural choice for representing discrete structure in the world. However, stochastic neural networks rarely use categorical latent variables due to the…
Exponential expressivity in deep neural networks through transient chaos
Ben Poole, Subhaneil Lahiri, Maithra Raghu +2
We combine Riemannian geometry with the mean field theory of high dimensional chaos to study the nature of signal propagation in generic, deep neural networks with random weights.…
On the Expressive Power of Deep Neural Networks
Maithra Raghu, Ben Poole, Jon Kleinberg +2
We propose a new approach to the problem of neural network expressivity, which seeks to characterize how structural properties of a neural network family affect the functions it is…