Improving Sequential Latent Variable Models with Autoregressive Flows

• URL: http://arxiv.org/abs/2010.03172v2
• Date: Tue, 8 Mar 2022 05:32:41 GMT
• Title: Improving Sequential Latent Variable Models with Autoregressive Flows
• Authors: Joseph Marino, Lei Chen, Jiawei He, Stephan Mandt
• Abstract summary: We propose an approach for improving sequence modeling based on autoregressive normalizing flows. Results are presented on three benchmark video datasets, where autoregressive flow-based dynamics improve log-likelihood performance.
• Score: 30.053464816814348
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We propose an approach for improving sequence modeling based on autoregressive normalizing flows. Each autoregressive transform, acting across time, serves as a moving frame of reference, removing temporal correlations, and simplifying the modeling of 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 flow-based models and as a component within sequential latent variable models. Results are presented on three benchmark video datasets, where autoregressive flow-based dynamics improve log-likelihood performance over baseline models. Finally, we illustrate the decorrelation and improved generalization properties of using flow-based dynamics.

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