International Workshop on
Tensor Network Representations in Machine Learning

Title:

Low Rank Tensor Completion and its Applications

Abstract:

In this talk, we study low rank tensor completion problems, and discuss how to use framelet, dictionary coding, deep plug-and-play prior, multiple features, total-variation approaches to solving the problem. Both theoretical and numerical results are presented to illustrate the proposed methods.

Bio:

Michael K. Ng is the Director of Research Division for Mathematical and Statistical Science, Director of HKU-TCL for Joint Research Center in Artificial Intelligence, and Chair Professor of Department of Mathematics, the University of Hong Kong. His research areas are machine learning, data science, and scientific computing. https://hkumath.hku.hk/~mng/

Title:

Quantum Mechanics, Tensor Networks and Machine Learning

Bio:

Nicola Pancotti received his Master of Science degree from the University La Sapienza, Rome, in 2014, and is currently working towards the completion of his Ph.D. at the Technical University of Munich and the Max Planck Institute of Quantum Optics in Garching, Germany. His research interests cover the dynamics of quantum systems, tensor network methods and quantum machine learning. https://de.linkedin.com/in/nicola-pancotti-19052470