A recurrent Markov state-space generative model for sequences


Anand Ramachandran, Steve Lumetta, Eric Klee, Deming Chen ;
Proceedings of Machine Learning Research, PMLR 89:3070-3079, 2019.


While the Hidden Markov Model (HMM) is a versatile generative model of sequences capable of performing many exact inferences efficiently, it is not suited for capturing complex long-term structure in the data. Advanced state-space models based on Deep Neural Networks (DNN) overcome this limitation but cannot perform exact inferences. In this article, we present a new generative model for sequences that combines both aspects, the ability to perform exact inferences and the ability to model long-term structure, by augmenting the HMM with a deterministic, continuous state variable modeled through a Recurrent Neural Network. We empirically study the performance of the model on (i) synthetic data comparing it to the HMM, (ii) a supervised learning task in bioinformatics where it outperforms two DNN-based regressors and (iii) in the generative modeling of music where it outperforms many prominent DNN-based generative models.

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