4 papers
Online TT-ALS for Streaming Tensor Decomposition with Incremental Orthogonalization
Hiroki Takeda, Yuto Miyatake, Daisuke Furihata
Tensor Train (TT) decomposition is a powerful technique for analyzing high-dimensional data. Existing algorithms for computing TT decompositions can be categorized into two main ty…
Structure-Preserving Physics-Informed Neural Networks With Energy or Lyapunov Structure
Haoyu Chu, Yuto Miyatake, Wenjun Cui +2
Recently, there has been growing interest in using physics-informed neural networks (PINNs) to solve differential equations. However, the preservation of structure, such as energy…
A modified model for topic detection from a corpus and a new metric evaluating the understandability of topics
Tomoya Kitano, Yuto Miyatake, Daisuke Furihata
This paper presents a modified neural model for topic detection from a corpus and proposes a new metric to evaluate the detected topics. The new model builds upon the embedded topi…
Lyapunov-Stable Deep Equilibrium Models
Haoyu Chu, Shikui Wei, Ting Liu +2
Deep equilibrium (DEQ) models have emerged as a promising class of implicit layer models, which abandon traditional depth by solving for the fixed points of a single nonlinear laye…