Overview of Tensor Networks in Machine Learning
- 2021年7月28日(水)13:30 - 14:50 (JST)
- チビン・チョウ (理化学研究所 革新知能統合研究センター (AIP) テンソル学習チーム チームリーダー)
- via Zoom
Tensor Networks (TNs) are factorizations of high dimensional tensors into networks of many low-dimensional tensors, which have been studied in quantum physics, high-performance computing, and applied mathematics. In recent years, TNs have been increasingly investigated and applied to machine learning and signal processing, due to its significant advances in handling large-scale and high-dimensional problems, model compression in deep neural networks, and efficient computations for learning algorithms. This talk aims to present a broad overview of recent progress of TNs technology applied to machine learning from perspectives of basic principle and algorithms, novel approaches in unsupervised learning, tensor completion, multi-task, multi-model learning and various applications in DNN, CNN, RNN and etc. We also discuss the future research directions and new trend in this area.