Tensor Networks for Probabilistic Sequence Modeling

• URL: http://arxiv.org/abs/2003.01039v4
• Date: Fri, 23 Apr 2021 16:52:06 GMT
• Title: Tensor Networks for Probabilistic Sequence Modeling
• Authors: Jacob Miller, Guillaume Rabusseau, John Terilla
• Abstract summary: We use a uniform matrix product state (u-MPS) model for probabilistic modeling of sequence data. We then introduce a novel generative algorithm giving trained u-MPS the ability to efficiently sample from a wide variety of conditional distributions. Experiments on sequence modeling with synthetic and real text data show u-MPS outperforming a variety of baselines.
• Score: 7.846449972735859
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Tensor networks are a powerful modeling framework developed for computational many-body physics, which have only recently been applied within machine learning. In this work we utilize a uniform matrix product state (u-MPS) model for probabilistic modeling of sequence data. We first show that u-MPS enable sequence-level parallelism, with length-n sequences able to be evaluated in depth O(log n). We then introduce a novel generative algorithm giving trained u-MPS the ability to efficiently sample from a wide variety of conditional distributions, each one defined by a regular expression. Special cases of this algorithm correspond to autoregressive and fill-in-the-blank sampling, but more complex regular expressions permit the generation of richly structured data in a manner that has no direct analogue in neural generative models. Experiments on sequence modeling with synthetic and real text data show u-MPS outperforming a variety of baselines and effectively generalizing their predictions in the presence of limited data.

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