Unsupervised Neural Hidden Markov Models with a Continuous latent state space

• URL: http://arxiv.org/abs/2106.06536v1
• Date: Thu, 10 Jun 2021 11:53:38 GMT
• Title: Unsupervised Neural Hidden Markov Models with a Continuous latent state space
• Authors: Firas Jarboui, Vianney Perchet
• Abstract summary: 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.
• Score: 24.316047317028147
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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