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Improving sequential latent variable models with autoregressive flows

Marino, Joseph and Chen, Lei and He, Jiawei and Mandt, Stephan (2022) Improving sequential latent variable models with autoregressive flows. Machine Learning, 111 (4). pp. 1597-1620. ISSN 0885-6125. doi:10.1007/s10994-021-06092-6. https://resolver.caltech.edu/CaltechAUTHORS:20220104-767137600

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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 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.


Item Type:Article
Related URLs:
URLURL TypeDescription
https://doi.org/10.1007/s10994-021-06092-6DOIArticle
https://rdcu.be/cEkVFPublisherFree ReadCube access
https://arxiv.org/abs/2010.03172arXivDiscussion Paper
https://anonymous.4open.science/r/f02199f7-86d2-45ee-ad23-3f13f769ee10/Related ItemCode
ORCID:
AuthorORCID
Marino, Joseph0000-0001-6387-8062
He, Jiawei0000-0003-1996-3264
Mandt, Stephan0000-0001-7836-7839
Additional Information:© The Author(s), under exclusive licence to Springer Science+Business Media LLC, part of Springer Nature 2021. Received 15 November 2020; Revised 02 August 2021; Accepted 30 September 2021; Published 18 November 2021. Author Contributions: Joseph Marino: forming idea, running simulation and writing paper; Lei Chen: running simulation; Jiawei He: writing paper, and Stephan Mandt: forming idea, writing paper and providing feedback. All authors worked toegther for most of the ta sks involved. Conflict of interest: California Institute of Technology (caltech.edu); Simon Fraser University (sfu.ca); University of California Irvine (uci.edu); Disney Research (disneyresearch.com); Borealis AI (borealisai.com); DeepMind (deepmind.com, google.com). Code availability: Source code is available at https://anonymous.4open.science/r/f02199f7-86d2-45ee-ad23-3f13f769ee10/.
Subject Keywords:Autoregressive flows; Latent variable models; Sequence modeling
Issue or Number:4
DOI:10.1007/s10994-021-06092-6
Record Number:CaltechAUTHORS:20220104-767137600
Persistent URL:https://resolver.caltech.edu/CaltechAUTHORS:20220104-767137600
Official Citation:Marino, J., Chen, L., He, J. et al. Improving sequential latent variable models with autoregressive flows. Mach Learn 111, 1597–1620 (2022). https://doi.org/10.1007/s10994-021-06092-6
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:112663
Collection:CaltechAUTHORS
Deposited By: Tony Diaz
Deposited On:04 Jan 2022 19:50
Last Modified:01 Jun 2022 17:51

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