6 papers · 1 filter
Bayesian Tensor Decomposition with Diffusion Model Prior
Zerui Tao, Qibin Zhao
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…
On the Approximation Complexity of Matrix Product Operator Born Machines
Chao Li, Zerui Tao, Yuchen Cong +2
Matrix product operator Born machines (MPO-BMs) are tractable tensor-network models for probabilistic modeling, but their efficient approximation capability remains unclear. We cha…
Model-Free Adversarial Purification via Coarse-To-Fine Tensor Network Representation
Guang Lin, Duc Thien Nguyen, Zerui Tao +3
Deep neural networks are known to be vulnerable to well-designed adversarial attacks. Although numerous defense strategies have been proposed, many are tailored to the specific att…
Transformed Low-rank Adaptation via Tensor Decomposition and Its Applications to Text-to-image Models
Zerui Tao, Yuhta Takida, Naoki Murata +2
Parameter-Efficient Fine-Tuning (PEFT) of text-to-image models has become an increasingly popular technique with many applications. Among the various PEFT methods, Low-Rank Adaptat…
Scalable Bayesian Tensor Ring Factorization for Multiway Data Analysis
Zerui Tao, Toshihisa Tanaka, Qibin Zhao
Tensor decompositions play a crucial role in numerous applications related to multi-way data analysis. By employing a Bayesian framework with sparsity-inducing priors, Bayesian Ten…
Efficient Nonparametric Tensor Decomposition for Binary and Count Data
Zerui Tao, Toshihisa Tanaka, Qibin Zhao
In numerous applications, binary reactions or event counts are observed and stored within high-order tensors. Tensor decompositions (TDs) serve as a powerful tool to handle such hi…