日時
2021年7月28日13:30 - 14:50
講演者
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)
会場
via Zoom
言語
英語

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.

関連ニュース