Zobrazeno 1 - 10
of 434
pro vyhledávání: '"Lindsey, Michael A."'
Autor:
Kielstra, P. Michael, Lindsey, Michael
Gaussian Process Regression (GPR) is widely used for inferring functions from noisy data. GPR crucially relies on the choice of a kernel, which might be specified in terms of a collection of hyperparameters that must be chosen or learned. Fully Bayes
Externí odkaz:
http://arxiv.org/abs/2412.20884
Autor:
Lindsey, Michael, Sharma, Sandeep
In this article, we combine the periodic sinc basis set with a curvilinear coordinate system for electronic structure calculations. This extension allows for variable resolution across the computational domain, with higher resolution close to the nuc
Externí odkaz:
http://arxiv.org/abs/2407.06171
Autor:
Kielstra, P. Michael, Lindsey, Michael
We introduce a fast algorithm for Gaussian process regression in low dimensions, applicable to a widely-used family of non-stationary kernels. The non-stationarity of these kernels is induced by arbitrary spatially-varying vertical and horizontal sca
Externí odkaz:
http://arxiv.org/abs/2407.03608
Autor:
Fornace, Mark, Lindsey, Michael
Column selection is an essential tool for structure-preserving low-rank approximation, with wide-ranging applications across many fields, such as data science, machine learning, and theoretical chemistry. In this work, we develop unified methodologie
Externí odkaz:
http://arxiv.org/abs/2407.01698
The typical training of neural networks using large stepsize gradient descent (GD) under the logistic loss often involves two distinct phases, where the empirical risk oscillates in the first phase but decreases monotonically in the second phase. We
Externí odkaz:
http://arxiv.org/abs/2406.08654
Autor:
Chen, Jielun, Lindsey, Michael
The quantum Fourier transform (QFT), which can be viewed as a reindexing of the discrete Fourier transform (DFT), has been shown to be compressible as a low-rank matrix product operator (MPO) or quantized tensor train (QTT) operator. However, the ori
Externí odkaz:
http://arxiv.org/abs/2404.03182
Autor:
Shen, Yizhi, Klymko, Katherine, Rabani, Eran, Tubman, Norm M., Camps, Daan, Van Beeumen, Roel, Lindsey, Michael
Unitary designs are widely used in quantum computation, but in many practical settings it suffices to construct a diagonal state design generated with unitary gates diagonal in the computational basis. In this work, we introduce a simple and efficien
Externí odkaz:
http://arxiv.org/abs/2401.04176
Autor:
Lindsey, Michael
Quantized tensor trains (QTTs) have recently emerged as a framework for the numerical discretization of continuous functions, with the potential for widespread applications in numerical analysis. However, the theory of QTT approximation is not fully
Externí odkaz:
http://arxiv.org/abs/2311.12554
Given a set of $K$ probability densities, we consider the multimarginal generative modeling problem of learning a joint distribution that recovers these densities as marginals. The structure of this joint distribution should identify multi-way corres
Externí odkaz:
http://arxiv.org/abs/2310.03695
Autor:
White, Steven R., Lindsey, Michael J.
We introduce nested gausslet (NG) bases, an improvement on previous gausslet bases which can treat systems containing atoms with much larger atomic number. We also introduce pure Gaussian distorted gausslet bases, which allow the Hamiltonian integral
Externí odkaz:
http://arxiv.org/abs/2309.10704