Heng-Tze Cheng

Heng-Tze Cheng is a technical lead manager and staff software engineer at Google Research. Heng-Tze founded the Wide & Deep Learning project in TensorFlow, and has worked on large-scale machine learning platforms that are widely used for retrieval, ranking, and recommender systems. Prior to joining Google, Heng-Tze received his Ph.D. from Carnegie Mellon University in 2013 and B.S. from National Taiwan University in 2008. His research interests range across machine learning, information retrieval, user behavior modeling, and human activity recognition.

Google Publications

Previous Publications

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    Nonparametric Discovery of Human Routine from Sensor Data

    Feng-Tso Sun, Yi-Ting Yeh, Heng-Tze Cheng, Cynthia Kuo, Martin Griss

    IEEE International Conference on Pervasive Computing and Communications (PerCom) (2014)

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    NuActiv: Recognizing Unseen New Activities Using Semantic Attribute-Based Learning

    Heng-Tze Cheng, Feng-Tso Sun, Martin Griss, Paul Davis, Jianguo Li, Di You

    Proceeding of the 11th Annual International Conference on Mobile Systems, Applications, and Services, ACM, New York, NY, USA (2013), pp. 361-374

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    Towards zero-shot learning for human activity recognition using semantic attribute sequence model

    Heng-Tze Cheng, Martin Griss, Paul Davis, Jianguo Li, Di You

    UbiComp '13 Proceedings of the 2013 ACM international joint conference on Pervasive and ubiquitous computing, ACM

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    SensOrchestra: Collaborative Sensing for Symbolic Location Recognition

    Heng-Tze Cheng, Feng-Tso Sun, Senaka Buthpitiya, Martin Griss

    International Conference on Mobile Computing, Applications, and Services, 2010

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    Automatic Chord Recognition for Music Classification and Retrieval

    Heng-Tze Cheng, Yi-Hsuan Yang, Yu-Ching Lin, I-Bin Liao, Homer H. Chen

    IEEE International Conference on Multimedia and Expo (ICME), 2008