Renjie Liao
About
Renjie Liao’s research focuses on geometric deep learning, deep generative models, and probabilistic inference. He has developed neural networks that can learn useful graph representations and can generate high-quality graph-structured data (e.g., molecules and proteins). His research contributions also include developing theoretical understandings of such models and applying them to solve practical problems in computer vision, self-driving, and reinforcement learning. Renjie’s long-term research goal is to build learning systems that can automatically discover structures from data and reason over structures.
Awards
- Top Reviewer, ICML (2020)
- Best Reviewer, NeurIPS (2019)
- Best Paper Award, ICML Workshop on Tractable Probabilistic Modeling (2019)
- RBC Graduate Fellowship, RBC (2019-2021)
- Connaught International Scholarship for Doctoral Students, University of Toronto (2015-2019)
Relevant Publications
- Liao, R., Kornblith, S., Ren, M., Fleet, D. J., & Hinton, G. (2022). Gaussian-Bernoulli RBMs Without Tears. arXiv preprint arXiv:2210.10318.
- Liao, R., Urtasun, R., & Zemel, R. (2021). A PAC-Bayesian Approach to Generalization Bounds for Graph Neural Networks. In International Conference on Learning Representations (ICLR).
- Liao, R., Li, Y., Song, Y., Wang, S., Hamilton, W., Duvenaud, D. K., ... & Zemel, R. (2019). Efficient graph generation with graph recurrent attention networks. Advances in neural information processing systems (NeurIPS).
- Liao, R., Zhao, Z., Urtasun, R., & Zemel, R. (2019). LanczosNet: Multi-Scale Deep Graph Convolutional Networks. In International Conference on Learning Representations (ICLR).
- Liao, R., Xiong, Y., Fetaya, E., Zhang, L., Yoon, K., Pitkow, X., ... & Zemel, R. (2018). Reviving and improving recurrent back-propagation. In International Conference on Machine Learning (ICML). Profile Link (two max) – e.g. your own webpage, Research Gate, Google Scholar: Homepage: https://lrjconan.github.io/
Support Us
CIFAR is a registered charitable organization supported by the governments of Canada, Alberta and Quebec, as well as foundations, individuals, corporations and Canadian and international partner organizations.