Improving Sequential Latent Variable Models with Autoregressive FlowsDownload PDF

25 Sep 2019 (modified: 24 Dec 2019)ICLR 2020 Conference Blind SubmissionReaders: Everyone
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  • TL;DR: We show how autoregressive flows can be used to improve sequential latent variable models.
  • Abstract: We propose an approach for sequence modeling based on autoregressive normalizing flows. Each autoregressive transform, acting across time, serves as a moving reference frame for modeling higher-level dynamics. This technique provides a simple, general-purpose method for improving sequence modeling, with connections to existing and classical techniques. We demonstrate the proposed approach both with standalone models, as well as a part of larger sequential latent variable models. Results are presented on three benchmark video datasets, where flow-based dynamics improve log-likelihood performance over baseline models.
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  • Keywords: Autoregressive Flows, Sequence Modeling, Latent Variable Models, Video Modeling, Variational Inference
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