AI RESEARCH
Bayesian Tensor Decomposition with Diffusion Model Prior
arXiv CS.LG
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ArXi:2606.03212v1 Announce Type: new Low-rank tensor decomposition (TD) is usually effective on clean, fully observed data, but it often degrades under severe missingness or noise. Low-rankness is itself a useful but limited structural prior, and additional handcrafted priors (e.g., sparsity or smoothness) still fall short of capturing the rich statistics of real-world data.