11/30(三)論文研討演講資訊如下，歡迎聽講！

演講者：簡仁宗 講座教授 (交大電機系)

時間：11/30(三) 15:30-17:20

地點：電子資訊研究大樓第四會議室

主持人：林寶樹教授、童莉萍博士

主題： Deep Neural Network Learning

演講摘要(Abstract)：

In this talk, I will present a variety of learning strategies to deal with different issues in deep neural network model. In tensor factorized neural network, a tensor factorized error backpropagation algorithm is developed to preserve the structure of tensor inputs in layer-wise network during training a regression or classification network. We further present a semi-supervised learning for domain adaptation based on neural network model which jointly minimizes the divergence between the distributions from labeled and unlabeled data in source and target domains, the reconstruction errors due to an auto-encoder, and the classification errors due to the labeled data. Finally, a Bayesian unfolding inference is proposed to integrate the benefits from model-based method and neural network model. A Bayesian unfolded topic model is proposed to improve traditional topic model based on variational inference. A number of applications and future works will be addressed.

演講者簡歷(Profile)：

Jen-Tzung Chien received his Ph.D. degree in electrical engineering from National Tsing Hua University, Hsinchu, Taiwan, ROC, in 1997. During 1997-2012, he was with the National Cheng Kung University, Tainan, Taiwan. Since 2012, he has been with the Department of Electrical and Computer Engineering, National Chiao Tung University, Hsinchu. He held the Visiting Professor position at the IBM T. J. Watson Research Center, Yorktown Heights, NY, in 2010. His research interests include machine learning, deep learning, speech recognition, face recognition, information retrieval, and blind source separation.