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arXiv · 1904.03273

A Variational Auto-Encoder Model for Stochastic Point Processes

Nazanin Mehrasa·Akash Abdu Jyothi·Thibaut Durand·Jiawei He·Leonid Sigal·Greg Mori
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Abstract

We propose a novel probabilistic generative model for action sequences. The model is termed the Action Point Process VAE (APP-VAE), a variational auto-encoder that can capture the distribution over the times and categories of action sequences. Modeling the variety of possible action sequences is a challenge, which we show can be addressed via the APP-VAE's use of latent representations and non-linear functions to parameterize distributions over which event is likely to occur next in a sequence and at what time. We empirically validate the efficacy of APP-VAE for modeling action sequences on the MultiTHUMOS and Breakfast datasets.

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Nazanin Mehrasa, Akash Abdu Jyothi, Thibaut Durand, Jiawei He, Leonid Sigal, Greg Mori. 2019-04-05. A Variational Auto-Encoder Model for Stochastic Point Processes. https://arxiv.org/abs/1904.03273

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