Stochastic Markov Recurrent Neural Networks
- 講者簡仁宗 教授 (國立交通大學電機工程學系)
邀請人:蘇克毅 - 時間2019-03-26 (Tue.) 10:00 ~ 12:00
- 地點資訊所新館106會議室
摘要
This talk will introduce a new stochastic Markov recurrent neural network for deep sequential learning. This work aims to strengthen the learning capability in sequential prediction where the trajectory of sequential patterns is driven by a stochastic Markov process with the state transitions are carried out by a multiple-state long short-term memory model. Such a latent state machine is capable of identifying the complicated latent structure in heterogeneous sequential data. Gumbel-softmax is developed to implement the stochastic backpropagation algorithm with discrete states. Some extensions to structural learning, self-attention and multi-stream fusion will be discussed.
BIO
Jen-Tzung Chien is now with the Department of Electrical and Computer Engineering, National Chiao Tung University, Taiwan. His interests include machine learning, deep learning, natural language processing and computer vision.