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Found 4 papers, 2 papers with code
Tensor Network-Constrained Kernel Machines as Gaussian Processes
We analyze the convergence of both CPD and TT-constrained models, and show how TT yields models exhibiting more GP behavior compared to CPD, for the same number of model parameters.
Quantized Fourier and Polynomial Features for more Expressive Tensor Network Models
Unless one considers the dual formulation of the learning problem, which renders exact large-scale learning unfeasible, the exponential increase of model parameters in the dimensionality of the data caused by their tensor-product structure prohibits to tackle high-dimensional problems.
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.
Large-Scale Learning with Fourier Features and Tensor Decompositions
Random Fourier features provide a way to tackle large-scale machine learning problems with kernel methods.
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