The paper proposes message-passing algorithms and a replica theory using cumulant expansion for tensor factorization inference in a dense limit on random graphs, avoiding Gaussian assumptions.
Yoshino, Spatially heterogeneous learning by a deep student machine, Physical Review Research5(3), 033068 (2023)
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Graphical model for factorization and completion of relatively high rank tensors by sparse sampling
The paper proposes message-passing algorithms and a replica theory using cumulant expansion for tensor factorization inference in a dense limit on random graphs, avoiding Gaussian assumptions.