University of Birmingham > Talks@bham > Artificial Intelligence and Natural Computation seminars > Non-Markov Probabilistic Models for Sequence Data

Non-Markov Probabilistic Models for Sequence Data

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If you have a question about this talk, please contact Per Kristian Lehre.

In this talk I will present a new approach to modelling sequence data called the sequence memoizer. As opposed to most other sequence models, our model does not make any Markovian assumptions. Instead, we use a hierarchical Bayesian approach which enforces sharing of information across the different parts of the model to alleviate overfitting. To make computations with the model efficient, and to better model the power-law statistics often observed in sequence data, we use a Bayesian nonparametric prior called the Pitman-Yor process as building blocks in the hierarchical model. We show state-of-the-art results on language modelling and text compression.

Joint work with Frank Wood, Jan Gasthaus, Cedric Archambeau and Lancelot James.

This talk is part of the Artificial Intelligence and Natural Computation seminars series.

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