“Structure Search for Tensor Network Learning”
September 8, 2022 | Time 10:30am CET
Recent works put much effort into tensor network structure search (TN-SS), aiming to select good tensor network structures involving TN-ranks, formats, and so on, for improving the performance of TNs on machine learning tasks. In this presentation, we will focus on the TN-SS problem and talk about the following three questions: 1) what is TN-SS, and what motives the studies on this issue; 2) how to resolve it, and what we need to pay for the searching; 3) how much benefit can we achieve from TN-SS for machine learning? We will first show that TN-SS can be modeled as a combinatorial optimization problem. Then, two searching algorithms, TNGA and TNLS, will be introduced to solve the problem. Last, several applications will be discussed to demonstrate the potential benefit of TN-SS for machine learning. Related works were published in (Li et al., ICML’20, ICML’22).
Dr. Chao Li is currently an indefinite-term research scientist with the AIP center, RIKEN institute, since 2021. Before that, he was a post-doctoral researcher with RIKEN-AIP from 2018 to 2020. He obtained his bachelor’s and Ph. D. degrees at Harbin Engineering University (HEU) in China in 2006 and 2017, respectively. He regularly serves as a (senior) reviewer of ICML, NeurIPS, IJCAI, AAAI, and so on. His research interests include tensor network and machine learning.