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Found 5 papers, 2 papers with code
An enriched category theory of language: from syntax to semantics
In this paper, we propose a mathematical framework for passing from probability distributions on extensions of given texts, such as the ones learned by today's large language models, to an enriched category containing semantic information.
Tensor Networks for Probabilistic Sequence Modeling
Tensor networks are a powerful modeling framework developed for computational many-body physics, which have only recently been applied within machine learning.
Modeling Sequences with Quantum States: A Look Under the Hood
Because it is entangled, the reduced densities that describe subsystems also carry information about the complementary subsystem.
Probabilistic Modeling with Matrix Product States
Inspired by the possibility that generative models based on quantum circuits can provide a useful inductive bias for sequence modeling tasks, we propose an efficient training algorithm for a subset of classically simulable quantum circuit models.
Language as a matrix product state
We propose a statistical model for natural language that begins by considering language as a monoid, then representing it in complex matrices with a compatible translation invariant probability measure.
