Chris Cremer

I'm a PhD student in Computer Science at the University of Toronto. I'm interested in reinforcement learning, deep learning, and Bayesian learning. I'm co-supervised by David Duvenaud and Quaid Morris.


Reinterpreting Importance-Weighted Autoencoders

The standard interpretation of importance-weighted autoencoders is that they maximize a tighter lower bound on the marginal likelihood than the standard evidence lower bound. We give an alternate interpretation of this procedure: that it optimizes the standard variational lower bound, but using a more complex distribution. We formally derive this result, present a tighter lower bound, and visualize the implicit importance-weighted distribution.
Chris Cremer, Quaid Morris, David Duvenaud
ICLR Workshop, April 2017

Presentation Slides

Uncertainty in Bayesian Neural Networks

Visualizations of decision boundaries
August 2017

Gradients of Deep Networks

A small look at skip-connection models
March 2017
[pdf] [blog]

Intro to Probability for ML

Review of probability theory
September 2015


Approximate Posterior Building Blocks

This is a short review of orthogonal methods for improving inference in latent variable models. I examine the lower bounds and complexities of these models as well as their combinations.
June 2017

MSc Dissertation: Gene Expression Deconvolution with Subpopulation Proportions

Personalized cancer strategies are currently being hindered by intratumor heterogeneity. One source of heterogeneity, clonal evolution, can lead to genetically distinct subpopulations within a sample. Through the use of subclonal reconstruction methods, we can obtain estimates of the subpopulation proportions within a single sample. Here, I leverage these proportion estimates by incorporating it into the deconvolution of tumour gene expression data in order to estimate the subclone specific gene expression profiles.
Advisor: Quaid Morris
University of Toronto, February 2016


As a side project, I started a deep learning company that helps businesses automate their workflow and learn from their data. We are currently serving a driving safety company where we automate the classification of their driving videos for their driver safety review process.