Variational Dynamic Mixtures
- URL: http://arxiv.org/abs/2010.10403v2
- Date: Fri, 4 Dec 2020 11:18:26 GMT
- Title: Variational Dynamic Mixtures
- Authors: Chen Qiu, Stephan Mandt, Maja Rudolph
- Abstract summary: We develop variational dynamic mixtures (VDM) to infer sequential latent variables.
In an empirical study, we show that VDM outperforms competing approaches on highly multi-modal datasets.
- Score: 18.730501689781214
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Deep probabilistic time series forecasting models have become an integral
part of machine learning. While several powerful generative models have been
proposed, we provide evidence that their associated inference models are
oftentimes too limited and cause the generative model to predict mode-averaged
dynamics. Modeaveraging is problematic since many real-world sequences are
highly multi-modal, and their averaged dynamics are unphysical (e.g., predicted
taxi trajectories might run through buildings on the street map). To better
capture multi-modality, we develop variational dynamic mixtures (VDM): a new
variational family to infer sequential latent variables. The VDM approximate
posterior at each time step is a mixture density network, whose parameters come
from propagating multiple samples through a recurrent architecture. This
results in an expressive multi-modal posterior approximation. In an empirical
study, we show that VDM outperforms competing approaches on highly multi-modal
datasets from different domains.
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