Department of Computer Science
University of Toronto
Mengye Ren is a PhD student in the machine learning group of the Department of Computer Science at the University of Toronto, working with Prof. Richard Zemel. From 2017 to 2021, he was also a research scientist at Uber Advanced Technologies Group (ATG), working with Prof. Raquel Urtasun. He did undergraduate studies at the department of Engineering Science of the University of Toronto. His research focuses on making machine learning more natural and human-like, in order for AIs to continually learn, adapt, and reason in naturalistic environments.
Areas: machine learning, computer vision, meta-learning, representation learning, few-shot learning, brain & cognitively inspired learning, robot learning, self-driving vehicles
My key research question is: how do we enable human-like, agent-based machine intelligence to continually learn, adapt, and reason in naturalistic environments? Towards this goal of building a more general and flexible AI, my research has centered on developing meta-learning and representation learning algorithms.
Some recent research highlights include:
2021/05: One paper is accepted at ICML 2021.
2021/02: One paper is accepted at ICRA 2021.
2020/10: One paper is accepted at CoRL 2020.
2020/09: One paper is accepted at NeurIPS 2020.
2020/09: I will visit Stanford University and give a talk on Oct 12, 2020.
2020/09: I will visit Brown University and give a talk on Sept 25, 2020.
2020/08: I will visit MIT and give a talk on Sept 22, 2020.
2020/08: I will give a talk at Mila on Aug 28, 2020.
2020/07: One paper is accepted at ECCV 2020.
2020/07: One paper is accepted at IROS 2020.
2020/06: One paper is accepted at ICML 2020.
2020/02: One paper is accepted at CVPR 2020.
2019/09: One paper is accepted at CoRL 2019.
2019/09: One paper is accepted at NeurIPS 2019.
2019/09: I will visit Columbia University in NYC on Oct 9, 2019.
2019/06: One paper is accepted at IROS 2019.
2018/12: One paper is accepted at ICLR 2019.
2018/10: I will be teaching CSC 411 (Machine Learning and Data Mining) in the winter semester of 2019. [course website]
2018/06: I will visit INRIA Grenoble Rhône-Alpes and give a talk on July 19, 2018.
2018/06: I will visit TU Berlin on July 16, 2018.
2018/05: I will visit NEC lab in Princeton, NJ and give a talk on June 4, 2018.
2018/04: I will visit the University of Tübingen and MPI for Intelligent Systems from June 25 to July 20, 2018.
[Full List][Google Scholar] [dblp]
Self-supervised representation learning from flow equivariance. Yuwen Xiong, Mengye Ren, Wenyuan Zeng, Raquel Urtasun. ICCV, 2021. [arxiv]
SketchEmbedNet: Learning novel concepts by imitating drawings. Alexander Wang
*, Mengye Ren
*, Richard Zemel. ICML, 2021. [arxiv]
Flexible few-shot learning of contextual similarity. Mengye Ren
*, Eleni Triantafillou
*, Kuan-Chieh Wang
*, James Lucas
*, Jake Snell, Xaq Pitkow, Andreas S. Tolias, Richard Zemel. arXiv preprint 2012.05895, 2020. [arxiv][video]
Graph hypernetworks for neural architecture search. Chris Zhang, Mengye Ren, Raquel Urtasun. ICLR, 2019. [arxiv]
Meta-learning for semi-supervised few-shot classification. Mengye Ren, Eleni Triantafillou
*, Sachin Ravi
*, Jake Snell, Kevin Swersky, Joshua B. Tenenbaum, Hugo Larochelle, Richard S. Zemel. ICLR, 2018. [link][arxiv] [code]