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Found 2 papers, 2 papers with code
Expressive power of tensor-network factorizations for probabilistic modeling
Inspired by these developments, and the natural correspondence between tensor networks and probabilistic graphical models, we provide a rigorous analysis of the expressive power of various tensor-network factorizations of discrete multivariate probability distributions.
Machine learning and the physical sciences
Machine learning encompasses a broad range of algorithms and modeling tools used for a vast array of data processing tasks, which has entered most scientific disciplines in recent years.
Computational Physics Cosmology and Nongalactic Astrophysics Disordered Systems and Neural Networks High Energy Physics - Theory Quantum Physics
