Unsupervised Neural Hidden Markov Models with a Continuous latent state space

Firas Jarboui; Vianney Perchet

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arXiv · 2106.06536

Unsupervised Neural Hidden Markov Models with a Continuous latent state space

Firas Jarboui·Vianney Perchet

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Abstract

We introduce a new procedure to neuralize unsupervised Hidden Markov Models in the continuous case. This provides higher flexibility to solve problems with underlying latent variables. This approach is evaluated on both synthetic and real data. On top of generating likely model parameters with comparable performances to off-the-shelf neural architecture (LSTMs, GRUs,..), the obtained results are easily interpretable.

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Firas Jarboui, Vianney Perchet. 2021-06-10. Unsupervised Neural Hidden Markov Models with a Continuous latent state space. https://arxiv.org/abs/2106.06536

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