AI Chat AI Image Generator AI Video Text to Speech

Experimental observation on a low-rank tensor model for eigenvalue problems

02/01/2023

∙

by Jun Hu, et al.

∙

∙

Here we utilize a low-rank tensor model (LTM) as a function approximator, combined with the gradient descent method, to solve eigenvalue problems including the Laplacian operator and the harmonic oscillator. Experimental results show the superiority of the polynomial-based low-rank tensor model (PLTM) compared to the tensor neural network (TNN). We also test such low-rank architectures for the classification problem on the MNIST dataset.

Jun Hu
49 publications
Pengzhan Jin
10 publications

research

∙ 10/08/2022

Almost-lossless compression of a low-rank random tensor

In this work, we establish an asymptotic limit of almost-lossless compre...

0 Minh Thành Vu, et al. ∙

research

∙ 10/22/2020

Beyond Lazy Training for Over-parameterized Tensor Decomposition

Over-parametrization is an important technique in training neural networ...

0 Xiang Wang, et al. ∙

research

∙ 08/22/2023

Low Tensor Rank Learning of Neural Dynamics

Learning relies on coordinated synaptic changes in recurrently connected...

0 Arthur Pellegrino, et al. ∙

research

∙ 03/06/2021

Low-Rank Isomap Algorithm

The Isomap is a well-known nonlinear dimensionality reduction method tha...

0 Eysan Mehrbani, et al. ∙

research

∙ 03/10/2016

Low-rank passthrough neural networks

Deep learning consists in training neural networks to perform computatio...

0 Antonio Valerio Miceli Barone, et al. ∙

research

∙ 03/21/2022

Solving for the low-rank tensor components of a scattering wave function

Atomic and molecular breakup reactions, such as multiple-ionisation, are...

0 Jacob Snoeijer, et al. ∙

research

∙ 04/13/2023

Solving Tensor Low Cycle Rank Approximation

Large language models have become ubiquitous in modern life, finding app...

0 Yichuan Deng, et al. ∙