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Projecting basis functions with tensor networks for Gaussian process regression
The benefit of our approach comes from the projection to a smaller subspace: It modifies the shape of the basis functions in a way that it sees fit based on the given data, and it allows for efficient computations in the smaller subspace.
Large-scale magnetic field maps using structured kernel interpolation for Gaussian process regression
Because full GP regression has a complexity that grows cubically with the number of data points, approximations for GPs have been extensively studied.
Towards Green AI with tensor networks -- Sustainability and innovation enabled by efficient algorithms
Furthermore, we argue that better algorithms should be evaluated in terms of both accuracy and efficiency.
Alternating linear scheme in a Bayesian framework for low-rank tensor approximation
Multiway data often naturally occurs in a tensorial format which can be approximately represented by a low-rank tensor decomposition.
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