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.

Github Page
Curriculum Vitae


Reinterpreting Importance-Weighted Autoencoders

The standard interpretation of importance-weighted autoencoders is that they maximize a tighter lower bound on the marginal likelihood. 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, and visualize the implicit importance-weighted approximate posterior.
Chris Cremer, Quaid Morris, David Duvenaud
ICLR Workshop, 2017

MSc Research Paper

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, 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.


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