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Modeling Continuous Stochastic Processes with Dynamic Normalizing Flows
Ruizhi Deng · Bo Chang · Marcus Brubaker · Greg Mori · Andreas Lehrmann

Thu Dec 10 09:00 AM -- 11:00 AM (PST) @ Poster Session 5 #1415

Normalizing flows transform a simple base distribution into a complex target distribution and have proved to be powerful models for data generation and density estimation. In this work, we propose a novel type of normalizing flow driven by a differential deformation of the continuous-time Wiener process. As a result, we obtain a rich time series model whose observable process inherits many of the appealing properties of its base process, such as efficient computation of likelihoods and marginals. Furthermore, our continuous treatment provides a natural framework for irregular time series with an independent arrival process, including straightforward interpolation. We illustrate the desirable properties of the proposed model on popular stochastic processes and demonstrate its superior flexibility to variational RNN and latent ODE baselines in a series of experiments on synthetic and real-world data.

Author Information

Ruizhi Deng (Simon Fraser University)

Ruizhi Deng is a Master of Science student in computing science at Simon Fraser University. He works in VML lab and he is advised by [Dr. Greg Mori](http://www.cs.sfu.ca/~mori/). He's interested in studying fundamental problems in machine learning, especially deep learning. His recent research focus is adversarial machine learning. He also has past and on-going experience in designing network architectures and developing generative models in computer vision. Before coming to Simon Fraser University, he obtained his Bachelor of Science degree from the University of Michigan - Ann Arbor. His research advisor was [Dr. Honglak Lee](http://web.eecs.umich.edu/~honglak/).

Bo Chang (Borealis AI)
Marcus Brubaker (Borealis AI)
Greg Mori (Borealis AI)
Andreas Lehrmann (Borealis AI)

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