Casey Chu

Casey Chu

Hello! I’m a Ph.D. student in computational math at Stanford University, specializing in deep learning.

Previously, I majored in math at Harvey Mudd College, and I was an intern at Google and Facebook. Find me on Twitter, Stack Overflow, and GitHub, or check out my resume.

A Bayesian trains a classifier

November 2018

Perspectives on the variational autoencoder

November 2018

Flavors of Wasserstein GAN

March 2018

Why does algebra work?

January 2013

Min/max of two functions

March 2013

The Dirichlet function in closed form

March 2012

Probability Functional Descent: A Unifying Perspective on GANs, Variational Inference, and Reinforcement Learning

2019

This paper provides a unifying view of a wide range of problems of interest in machine learning by framing them as the minimization of functionals defined on the space of probability measures. In particular, we show that generative adversarial networks, variational inference, and actor-critic methods in reinforcement learning can all be seen through the lens of our framework. We then discuss a generic optimization algorithm for our formulation, called probability functional descent (PFD), and show how this algorithm recovers existing methods developed independently in the settings mentioned earlier.

CycleGAN, a Master of Steganography

2017

NIPS 2017 Workshop on Machine Deception

CycleGAN (Zhu et al. 2017) is one recent successful approach to learn a transformation between two image distributions. In a series of experiments, we demonstrate an intriguing property of the model: CycleGAN learns to "hide" information about a source image into the images it generates in a nearly imperceptible, high-frequency signal. This trick ensures that the generator can recover the original sample and thus satisfy the cyclic consistency requirement, while the generated image remains realistic. We connect this phenomenon with adversarial attacks by viewing CycleGAN's training procedure as training a generator of adversarial examples and demonstrate that the cyclic consistency loss causes CycleGAN to be especially vulnerable to adversarial attacks.