Zobrazeno 1 - 10
of 59
pro vyhledávání: '"Terilla, John"'
Autor:
Gastaldi, Juan Luis, Terilla, John, Malagutti, Luca, DuSell, Brian, Vieira, Tim, Cotterell, Ryan
Tokenization - the practice of converting strings of characters over an alphabet into sequences of tokens over a vocabulary - is a critical yet under-theorized step in the NLP pipeline. Notably, it remains the only major step not fully integrated int
Externí odkaz:
http://arxiv.org/abs/2407.11606
State of the art language models return a natural language text continuation from any piece of input text. This ability to generate coherent text extensions implies significant sophistication, including a knowledge of grammar and semantics. In this p
Externí odkaz:
http://arxiv.org/abs/2106.07890
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 mod
Externí odkaz:
http://arxiv.org/abs/2003.01039
Publikováno v:
2020 Mach. Learn.: Sci. Technol. 1 035008
Classical probability distributions on sets of sequences can be modeled using quantum states. Here, we do so with a quantum state that is pure and entangled. Because it is entangled, the reduced densities that describe subsystems also carry informati
Externí odkaz:
http://arxiv.org/abs/1910.07425
Autor:
Stokes, James, Terilla, John
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. The
Externí odkaz:
http://arxiv.org/abs/1902.06888
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. We interpret the probability measure as arising
Externí odkaz:
http://arxiv.org/abs/1711.01416
Homotopy probability theory is a version of probability theory in which the vector space of random variables is replaced with a chain complex. A natural example extends ordinary probability theory on a finite volume Riemannian manifold M. In this exa
Externí odkaz:
http://arxiv.org/abs/1608.00141
This note defines cones in homotopy probability theory and demonstrates that a cone over a space is a reasonable replacement for the space. The homotopy Gaussian distribution in one variable is revisited as a cone on the ordinary Gaussian.
Comme
Comme
Externí odkaz:
http://arxiv.org/abs/1410.5506
This is the second of two papers that introduce a deformation theoretic framework to explain and broaden a link between homotopy algebra and probability theory. This paper outlines how the framework can assist in the development of homotopy probabili
Externí odkaz:
http://arxiv.org/abs/1302.5325
Let TA denote the space underlying the tensor algebra of a vector space A. In this short note, we show that if A is a differential graded algebra, then TA is a differential Batalin-Vilkovisky algebra. Moreover, if A is an A-infinity algebra, then TA
Externí odkaz:
http://arxiv.org/abs/1106.1856