At HTC Research, publication is highly encouraged. Writing high-quality technical papers helps one to organize ideas, and hence be more rigorous in both research and engineering.
Toward Personalized Treatment of Chronic Diseases – the CKD Case Study, Chih-Yang Chen, Chun-Nan Chou and I-Wen Wu, MM Health Workshop, 2017.
DeepQ Arrhythmia Database: A Large-Scale Dataset for Arrhythmia Detector Evaluation, Meng-Hsi Wu and Edward Chang, MM Health Workshop, 2017.
Artificial Intelligence in XPRIZE DeepQ Tricorder, Edward Chang, Meng-Hsi Wu, Kai-Fu Tang, Hao-Cheng Kao and Chun-Nan Chou, MM Health Workshop, 2017.
Aristo: An Augmented Reality Platform for Immersion and Interactivity, Zhongyang Zheng, Bo Wang, Yakun Wang, Shuang Yang, Zhongqian Dong Tianyang Yi, Cyrus Choi, Emily J. Chang, Edward Y. Chang, MM, 2017.
Distributed Training Large-Scale Deep Architectures, Mina Zou, et al., HTC Technical Report, 2017.
Representation Learning on Large and Small Data, J. Chou, et al., A book chapter in Big Data Analytics for Large-Scale Multimedia Search, Wiley & Sons (accepted), 2017.
CLKN: Cascaded Lucas-Kanade Networks for Image Alignment, Che-Han Chang, Chun-Nan Chou and Edward Y. Chang, Hawaii, CVPR, July 2017.
Inquire and Diagnose: Neural Symptom Checking Ensemble using Deep Reinforcement Learning, Kevin Tang, HaoCheng Kao, Jason Chou, and Edward Y. Chang, NIPS DeepRL Workshop, 2016.
Connecting Social Media to E-Commerce: Cold-Start Product Recommendation using Microblogging Information, Wayne Xin Zhao, Sui Li, Yulan He, Edward Chang, Ji-Rong Wen, and Xiaoming Li,IEEE Trans. Knowl. Data Eng. 28(5): 1147-1159 (2016).
A Probabilistic Lifestyle-Based Trajectory Model for Social Strength Inference from Human Trajectory Data, Wayne Xin Zhao, Ningnan Zhou, Wenhui Zhang, Ji-Rong Wen, Shan Wang, Edward Y. Chang:ACM Trans. Inf. Syst. 35(1): 8:1-8:28 (2016).
Tweet Timeline Generation with Determinantal Point Processes, Jin-ge Yao, Feifan Fan, Wayne Xin Zhao, Xiaojun Wan, Edward Chang, and Jianguo Xiao, Proceedings of AAAI, January 2016.
SpeeDO, Parallelizing Stochastic Gradient Descent for Deep Convolutional Neural Network, Zhongyang Zheng, Wenrui Jiang, Gang Wu, Cyrus Choi, and Edward Chang, NIPS Workshop on Learning Systems, December 2015.
Pan360: INS Assisted 360-Degree Panorama, Lun-Chen Chu, Andre Chen, Yuhsin Lin, Yu-Mei Chen, Scott Liao, and Edward Chang, ACM Multimedia Immersive Experience Workshop, October 2015.
Pan360: INS Assisted 360-Degree Panorama (demo Description) (best technical demo award), Yuhsin Lin, Yu-Mei Chen, Lun-Chen Chu, Andre Chen, Scott Liao, and Edward Chang, ACM Multimedia, October 2015.
Gaussian Processes for High-Dimensional Regression: A Method Based on Deep Neural Networks, Wenbing Huang, Deli Zhao, Fuchun Sun, Huaping Liu, and Edward Chang, IJCAI, July 2015.
Network Representation Learning with Rich Text Information, Cheng Yang, Zhiyuan Liu, Deli Zhao, Maosong Sun, and Edward Chang, IJCAI, July 2015.
A New Retraction for Accelerating the Riemannian Three-Factor Low-Rank Matrix Completion Algorithm, Zhizhong Li, Deli Zhao, Zhouchen Lin, and Edward Chang, CVPR, June 2015.
Transfer Representation Learning for Medical Image Analysis, Chuenkai Shie, Chung-Hisang Chuang, Chun-Nan Chou, Meng-Hsi Wu, and Edward Chang, HTC Technical Report, April 2015, IEEE EMBC, August 2015.
Exact Recoverability of Robust PCA via Outlier Pursuit with Tight Recovery Bounds, Hongyang Zhang, Zhouchen Lin, Chao Zhang, and Edward Chang, pp.3143-49, AAAI, January 2015.
Zeta Hull Pursuits: Learning Nonconvex Data Hulls, Yuanjun Xiong, Wei Liu, Deli Zhao, and Xiaoou Tang, Neural Information Processing Systems, NIPS, 2014.
Big Data, Small Footprint: The Design of a Low-Power Classifier for Detecting Transportation Modes, Meng-Chieh Yu, Tong Yu, Shao-Chen Wang, Chih-Jen Lin, and Edward Chang，
Very Large Data Bases (VLDB), August 2014.
Distant Supervision for Relation Extraction with Matrix Completion
Miao Fan, Deli Zhao, Qiang Zhou, Zhiyuan Liu, Thomas Fang Zheng and Edward Chang，Association for Computational Linguistics (ACL), 2014.
Context-Aware Computing: Opportunities and Open Issues, Edward Chang, Very Large Data Bases (VLDB), August 2013.
Public Invited Talks:
Artificial Intelligence Platform for Healthcare and VR/AR (keynote}, Edward Chang, ACM Multimedia, Edward Chang, Mountain View, October 2017.
DeepQ: Developing AlphaGo Of Healthcare (invited talk), Edward Chang, Tsinghua University, Beijing, March 2017.
Big Multimedia Data Analytics, Architectures, Algorithms, and Applications (keynote), Edward Chang, IEEE Big Multimedia Data Conference, Taipei, 2016.
Big Data Analytics for Healthcare (invited lecture), Edward Chang, BigDat 2016, Second International Winter School of Big Data, Bilbao Spain, February 2016.
Not-So-Big Clinical Data Predictive Analytics (keynote), Edward Chang, The 24th Wireless Optical and Communication Conference, Taipei, October 2015.
Predictive Analytics in Healthcare (panelist), Edward Chang, Stanford Data Science Initiative Annual Retreat, Stanford University, October 2015.
Big Data Analytics, Architectures, Algorithms, and Applications (keynote), Edward Chang, IEEE Big Data Congress, Taipei Satellite Session, Taipei 101, May 2015.
Signal Fusion and Big Data Analytics on Massive Sensor Data Sets (keynote), The IEEE 10th International Conference on Intelligent Sensors, Sensor Networks and Information Processing (ISSNIP), Singapore, April 2015.
Big Data Analytics, Architectures, Algorithms, and Applications (Invited Lecture),
BigDat 2015, First International Winter School of Big Data, Tarragona Spain January 2015.
Lecture #1, by Edward Chang
Lecture #2, by Edward Chang
Lecture #3, by Simon Wu
Big Data and Bio Informatics (keynote), Edward Chang, The 4th International Symposium on Dynamical Biomarkers for Translational Medicine, Taipei, April 2014.