[ Survey Papers | Conference Papers | Journal Papers | Book Chapters | Others ]

BDL Survey

 

Conference Publication

 

('Underline' indicates students I (co-)advise/mentor. '*' indicates equal contribution.)

  • STRODE: Stochastic boundary ordinary differential equation.
    Hengguan Huang, Hongfu Liu, Hao Wang, Chang Xiao, Ye Wang.
    Thirty-Eighth International Conference on Machine Learning (ICML),
    2021.
    [pdf] [code and data] [talk] [slides]

  • Correcting exposure bias for link recommendation.
    Shantanu Gupta, Hao Wang, Zachary Lipton, Yuyang Wang.
    Thirty-Eighth International Conference on Machine Learning (ICML),
    2021.
    [pdf] [code and data] [talk] [slides]

  • Delving into deep imbalanced regression.
    Yuzhe Yang, Kaiwen Zha, Yingcong Chen, Hao Wang, Dina Katabi.
    Thirty-Eighth International Conference on Machine Learning (ICML),
    2021.
    [pdf] [code and data] [talk] [slides]

  • Generative interventions for causal learning.
    Chengzhi Mao, Augustine Cha*, Amogh Gupta*, Hao Wang, Junfeng Yang, Carl Vondrick.
    Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR),
    2021.
    [pdf] [code and data]

  • Continuously indexed domain adaptation.
    Hao Wang*, Hao He*, Dina Katabi.
    Thirty-Seventh International Conference on Machine Learning (ICML),
    2020.
    [pdf] [code and data] [project page] [blog] [talk] [slides]

  • Deep graph random process for relational-thinking-based speech recognition.
    Hengguan Huang, Fuzhao Xue, Hao Wang, Ye Wang.
    Thirty-Seventh International Conference on Machine Learning (ICML),
    2020.
    [pdf] [code and data]

  • BodyCompass: Monitoring sleep posture with wireless signals.
    Shichao Yue, Yuzhe Yang, Hao Wang, Hariharan Rahul, Dina Katabi.
    ACM International Joint Conference on Pervasive and Ubiquitous Computing (Ubicomp),
    2020.
    [pdf] [project page] [slides] [talk] [MIT News]

  • Learning guided electron microscopy with active acquisition.
    Lu Mi, Hao Wang, Yaron Meirovitch, Richard Schalek, Srinivas Turaga, Jeff Lichtman, Samuel Aravinthan, Nir Shavit.
    Medical Image Computing and Computer Assisted Interventions (MICCAI),
    2020.
    [pdf] [code and data]

  • Rethinking knowledge graph propagation for zero-shot learning.
    Michael C. Kampffmeyer*, Yinbo Chen*, Xiaodan Liang, Hao Wang, Yujia Zhang, Eric P. Xing.
    Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR),
    2019.
    [pdf] [code and data]

  • ProbGAN: Towards probabilistic GAN with theoretical guarantees.
    Hao He, Hao Wang, Guang-He Lee, Yonglong Tian.
    Seventh International Conference on Learning Representations (ICLR),
    2019.
    [pdf] [code and data]

  • Bidirectional inference networks: A class of deep Bayesian networks for health profiling.
    Hao Wang, Chengzhi Mao, Hao He, Mingmin Zhao, Tommi S. Jaakkola, Dina Katabi.
    Thirty-Third AAAI Conference on Artificial Intelligence (AAAI),
    2019.
    [pdf] [supplementary] [code and data] [slides] [MIT News]

  • Recurrent Poisson process unit for speech recognition.
    Hengguan Huang, Hao Wang, Brian Mak.
    Thirty-Third AAAI Conference on Artificial Intelligence (AAAI),
    2019.
    [pdf] [code and data] [slides]

  • Bayesian modelling and Monte Carlo inference for GAN.
    Hao He, Hao Wang, Guang-He Lee, Yonglong Tian.
    International Conference on Machine Learning (ICML) Workshop on Theoretical Foundations and Applications of Deep Generative Models,
    2018.
    [pdf]

  • Extracting multi-person respiration from entangled RF signals.
    Shichao Yue, Hao He, Hao Wang, Hariharan Rahul, Dina Katabi.
    ACM International Joint Conference on Pervasive and Ubiquitous Computing (Ubicomp),
    2018.
    [pdf] [project page]

  • Deep learning for precipitation nowcasting: A benchmark and a new model.
    Xingjian Shi, Zhihan Gao, Leonard Lausen, Hao Wang, Dit-Yan Yeung, Wai-kin Wong, and Wang-chun Woo.
    Thirty-First Annual Conference on Neural Information Processing Systems (NIPS),
    2017.
    [pdf] [code and data] [project page]

  • Relational deep learning: A deep latent variable model for link prediction.
    Hao Wang, Xingjian Shi, Dit-Yan Yeung.
    Thirty-First AAAI Conference on Artificial Intelligence (AAAI),
    2017.
    [pdf] [supplementary] [code and data] [slides]

  • Collaborative recurrent autoencoder: recommend while learning to fill in the blanks.
    Hao Wang, Xingjian Shi, Dit-Yan Yeung.
    Thirtieth Annual Conference on Neural Information Processing Systems (NIPS),
    2016.
    [pdf] [supplementary] [spotlight video] [code and data]

  • Natural parameter networks: a class of probabilistic neural networks.
    Hao Wang, Xingjian Shi, Dit-Yan Yeung.
    Thirtieth Annual Conference on Neural Information Processing Systems (NIPS),
    2016.
    [pdf] [project page] [supplementary] [spotlight video] [code] [PyTorch code]

  • Convolutional LSTM network: A machine learning approach for precipitation nowcasting.
    Xingjian Shi, Zhourong Chen, Hao Wang, Dit-Yan Yeung, Wai-kin Wong, Wang-chun Woo.
    Twenty-Ninth Annual Conference on Neural Information Processing Systems (NIPS),
    2015.
    [pdf] [code and data]

  • Collaborative deep learning for recommender systems.
    Hao Wang, Naiyan Wang, Dit-Yan Yeung.
    Twenty-First ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD),
    2015.
    Most cited paper among all papers at KDD 2015.
    [pdf] [project page] [code] [data] [MXNet code] [TensorFlow code] [third-party TensorFlow/Keras/Python code] [ipynb] [slides] [slides (long)]

  • Relational stacked denoising autoencoder for tag recommendation.
    Hao Wang, Xingjian Shi, Dit-Yan Yeung.
    Twenty-Ninth AAAI Conference on Artificial Intelligence (AAAI),
    2015.
    [pdf] [supplementary] [code] [data] [slides]

  • Online Egocentric models for citation networks.
    Hao Wang, Wu-Jun Li.
    Proceedings of the Twenty-Third International Joint Conference on Artificial Intelligence (IJCAI),
    2013.
    [pdf]

  • Collaborative topic regression with social regularization for tag recommendation.
    Hao Wang, Binyi Chen, Wu-Jun Li.
    Proceedings of the Twenty-Third International Joint Conference on Artificial Intelligence (IJCAI),
    2013.
    [pdf] [data] [citeulike-a] [citeulike-t]

Journal Publication

 

  • Assessment of medication self-administration using artificial intelligence.
    Mingmin Zhao*, Kreshnik Hoti*, Hao Wang, Aniruddh, Raghu, Dina Katabi.
    Nature Medicine,
    2021.
    [pdf] [nature medicine page] [MIT News] [model details]

  • A survey on Bayesian deep learning.
    Hao Wang, Dit-Yan Yeung.
    ACM Computing Surveys (CSUR),
    53(5), Article 108, 2020.
    [pdf] [blog] [github (updating)]

  • Towards Bayesian deep learning: a framework and some existing methods.
    Hao Wang, Dit-Yan Yeung.
    IEEE Transactions on Knowledge and Data Engineering (TKDE),
    28(12):3395-3408, 2016.
    [pdf] [blog] [github (updating)]

  • Relational collaborative topic regression for recommender systems.
    Hao Wang, Wu-Jun Li.
    IEEE Transactions on Knowledge and Data Engineering (TKDE),
    27(5): 1343-1355, 2015.
    [pdf] [data]

Book Chapters

 

  • Deep learning and the weather forecasting problem -- precipitation nowcasting.
    Zhihan Gao, Xingjian Shi, Hao Wang, Dit-Yan Yeung, Wang-chun Woo, and Wai-Kin Wong.
    Deep learning for the Earth Sciences: A Comprehensive Approach to Remote Sensing, Climate Science and Geosciences, G. Camps-Valls, D. Tuia, X.X. Zhu, and M. Reichstein (eds.), Wiley & Sons, 2021.
    [pdf]

PhD Thesis

 

  • Bayesian Deep Learning for Integrated Intelligence: Bridging the Gap between Perception and Inference.
    Hao Wang.
    Department of Computer Science and Engineering, Hong Kong University of Science and Technology,
    2017.
    [pdf]

 
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