A two-stage algorithm that fits non-negative tensor trains to high-dimensional discrete distributions using Newton-based alternating minimization with a log barrier converges much faster than the previous multiplicative update method.
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Variational inference and density estimation with non-negative tensor train
A two-stage algorithm that fits non-negative tensor trains to high-dimensional discrete distributions using Newton-based alternating minimization with a log barrier converges much faster than the previous multiplicative update method.