About

I am a graduate student at Montreal Institute for Learning Algorithms (MILA), under the supervision of Aaron Courville, and co-supervised by Laurent Charlin. My research is mostly about Deep Latent Variable models and efficient approximate inference. I am also working on meta learning and a bit of natural language processing.

Here’s my one-page CV and google scholar page.


Publications

Pre-prints

Workshops

  • Facilitating Multimodality in Normalizing Flows [BDL]
    • Chin-Wei Huang*, David Krueger*, Aaron Courville
    • presented in the NIPS (’17) workshop on Bayesian Deep Learning (BDL)
  • Sequentialized Sampling Importance Resampling and Scalable IWAE [BDL]
  • Learnable Explicit Density for Continuous Latent Space and Variational Inference [arXiv] [padl] [poster]
    • Chin-Wei Huang, Ahmed Touati, Laurent Dinh, Michal Drozdzal, Mohammad Havaei, Laurent Charlin, Aaron Courville
    • presented in the ICML (’17)  workshop on Principle Approaches to Deep Learning (padl)

Symposiums

  • Deconstructive Defense Against Adversarial Attacks [poster]
  • Data Imputation with Latent Variable Models
    • Michal Drozdzal, Mohammad Havaei, Chin-Wei Huang, Laurent Charlin, Nicolas Chapados, Aaron Courville
    • presented in the Montreal AI Symposium (17′)

 

Technical reports

  • Multilabel Topic Model and User-Item Representation for Personalized Display of Review [report]
    • Chin-Wei Huang, Pierre-André Brousseau
    • A final project report for IFT6266 (Probabilistic Graphical Models, 2016A)

 

 

 

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