| 研究実績の概要 |
In this fiscal year, we further studied how to learn an optimal tensor network structure efficiently, and developed a fast algorithm for solving it. Then, we applied these algorithms to tensor completion task.
We have also studied nonlinear tensor decomposition methods for handling more complex data tensor. A scalable Bayesian tensor ring factorization, undirected probabilistic model for tensor decomposition, and efficient nonparametric tensor decomposition approaches are developed and evaluated on many datasets.
We also studied the adversarial robustness of neural networks by using tensorized parameterization. In addition, we studied how to perform adversarial purification more effectively by using adversarial loss.
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