Technical Program

C-2-3: Machine Learning and Data Analysis 1

All times are in New Zealand Time (UTC +13)
Presentation Time: Wednesday, December 9, 17:15 - 19:15 Check your Time Zone
 
C-2-3.1: MERGING WELL-TRAINED DEEP CNN MODELS FOR EFFICIENT INFERENCE
         Cheng-En Wu; Academia Sinica
         Jia-Hong Lee; Academia Sinica
         Timmy S.T. Wan; Academia Sinica
         Yi-Ming Chan; Academia Sinica
         Chu-Song Chen; Academia Sinica
 
C-2-3.2: EFFICIENT DIVERSE RESPONSE GENERATION IN ATTENTION-BASED NEURAL CONVERSATIONAL MODEL WITH MAXIMUM MUTUAL INFORMATION
         Yuki Kishida; Doshisha University
         Tsuneo Kato; Doshisha University
         Yanan Wang; KDDI Research, Inc.
         Jianming Wu; KDDI Research, Inc.
         Gen Hattori; KDDI Research, Inc.
 
C-2-3.3: EXTENDING CONDITIONAL CONVOLUTION STRUCTURES FOR ENHANCING MULTITASKING CONTINUAL LEARNING
         Cheng-Hao Tu; Academia Sinica
         Cheng-En Wu; Academia Sinica
         Chu-Song Chen; Academia Sinica
 
C-2-3.4: MULTIPLE TARGET PREDICTION FOR DEEP REINFORCEMENT LEARNING
         Jen-Tzung Chien; National Chiao Tung University
         Po-Yen Hung; National Chiao Tung University
 
C-2-3.5: CAN-SIN: A CROSS-LAYER HETEROGENEOUS ACADEMIC NETWORK WITH SEMANTIC INFORMATION
         Yufei Tian; Tsinghua University
         Hong Hu; Tsinghua University
         Yuejiang Li; Tsinghua University
         H. Vicky Zhao; Tsinghua University
         Yan Chen; University of Science and Technology of China
 
C-2-3.6: NATURAL LANGUAGE PROCESSING METHODS FOR DETECTION OF INFLUENZA-LIKE ILLNESS FROM CHIEF COMPLAINTS
         Jia-Hao Hsu; National Cheng Kung University
         Ting-Chia Weng; National Cheng Kung University
         Chung-Hsien Wu; National Cheng Kung University
         Tzong-Shiann Ho; National Cheng Kung University
 
C-2-3.7: GENERALISATION TECHNIQUES USING A VARIATIONAL CEAE FOR CLASSIFYING MANUKA HONEY QUALITY
         Tessa Phillips; University of Auckland
         Waleed Abdulla; University of Auckland