Improving sequential latent variable models with autoregressive flows

Joseph Marino

doi:10.1007/s10994-021-06092-6

Improving sequential latent variable models with autoregressive flows

2021-11-18

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DOI：10.1007/s10994-021-06092-6

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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 and three other time series 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.

Keywords:

Autoregressive flows

Latent variable models

Sequence modeling

Improving sequential latent variable models with autoregressive flows

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