PhD Seminar • Artificial Intelligence — Neural Density Estimation using Sum-of-Squares Polynomial FlowsExport this event to calendar

Wednesday, June 5, 2019 — 4:00 PM EDT

Priyank Jaini, PhD candidate
David R. Cheriton School of Computer Science

Triangular map is a recent construct in probability theory that allows one to transform any source probability density function to any target density function. Based on triangular maps, we propose a general framework for high-dimensional density estimation, by specifying one-dimensional transformations (equivalently conditional densities) and appropriate conditioner networks. This framework (a) reveals the commonalities and differences of existing autoregressive and flow based methods, (b) allows a unified understanding of the limitations and representation power of these recent approaches and, (c) motivates us to uncover a new Sum-of-Squares (SOS) flow that is interpretable, universal, and easy to train. We perform several synthetic experiments on various density geometries to demonstrate the benefits (and short-comings) of such transformations. SOS flows achieve competitive results in simulations and several real-world datasets. 

Download the PDF of the paper.

This is a joint work with Kira Selby and Yaoliang Yu and has been accepted for a long oral presentation at the International Conference of Machine Learning (ICML), 2019.

Location 
DC - William G. Davis Computer Research Centre
2306 C
200 University Avenue West

Waterloo, ON N2L 3G1
Canada

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