PhD Student, University of Toronto
Sr. Research Scientist, Uber ATG
Email: email@example.com, firstname.lastname@example.org
Mengye Ren is a PhD student in the machine learning group of the Department of Computer Science at the University of Toronto. His academic advisor is Prof. Richard Zemel. He also works as a research scientist at Uber Advanced Technologies Group (ATG) Toronto, directed by Prof. Raquel Urtasun, doing self-driving related research. During his undergrad, he studied Engineering Science with a focus on Electrical and Computer Engineering at the University of Toronto. He is originally from Shanghai, China.
Deep learning, machine learning, computer vision, autonomous driving
2019/09: One paper got accepted to CoRL 2019.
2019/09: One paper got accepted to NeurIPS 2019.
2019/09: I will visit Columbia University in NYC on Oct 9, 2019.
2019/06: One paper got accepted to IROS 2019.
2018/12: One paper got accepted to 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 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 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.
Learning to Remember from a Multi-Task Teacher. Yuwen Xiong
*, Mengye Ren
*, Raquel Urtasun. arXiv preprint 1910.04650, 2019. [arxiv]
Information-Theoretic Limitations on Novel Task Generalization. James Lucas, Mengye Ren, Richard S. Zemel. NeurIPS Workshop on Machine Learning with Guarantees, 2019.
Deformable Filter Convolution for Point Cloud Reasoning. Yuwen Xiong
*, Mengye Ren
*, Renjie Liao, Kelvin Wong, Raquel Urtasun. NeurIPS Workshop on Sets & Partitions, 2019. [arxiv]
Identifying Unknown Instances for Autonomous Driving. Kelvin Wong, Shenlong Wang, Mengye Ren, Ming Liang, Raquel Urtasun. CoRL, 2019. [arxiv]
Jointly Learnable Behavior and Trajectory Planning for Self-Driving Vehicles. Abbas Sadat
*, Mengye Ren
*, Andrei Pokrovsky, Yen-Chen Lin, Ersin Yumer, Raquel Urtasun. IROS, 2019. [arxiv]
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]
ECE 521: Inference Algorithms (2017 Winter)
CSC 401/2511: Natural Language Computing (2016 Winter)
CSC 190: Data Structure and Algorithm (Engineering Science) (2014 Winter)
TensorFlow Forward AD: Forward-mode automatic differentiation for TensorFlow. [github]
PySched: Python-based light weight pipeline scheduler for local and slurm jobs. [github]
Deep Dashboard: Visualize training process in real time. [github]
Meta-Learning for More Human-Like Learning Algorithms. Columbia University, Department of Statistics. New York, NY, USA. 2019/11. [slides]
Meta-Learning for Weakly Supervised Learning. INRIA Grenoble Rhône-Alpes. Grenoble, France. 2018/07. [slides]
Meta-Learning and Learning to Reweight Examples. Max Planck Institute for Intelligent Systems. Tübingen, Germany. 2018/06. [slides]
Meta-Learning for Weakly Supervised Learning. NEC Laboratories America. Princeton, NJ, USA. 2018/06. [slides]
SBNet: Sparse Blocks Network for Fast Inference. Borealis AI Lab. Toronto, ON, Canada. 2018/02. [slides]
Meta-Learning for Semi-Supervised Few-Shot Classification. Vector Institute. Toronto, ON, Canada. 2017/11. [slides]
End-to-End Instance Segmentation with Recurrent Attention. CVPR 2017. Honolulu, HI, USA. 2017/07. [video]
Sequence-to-Sequence Deep Learning with Recurrent Attention. Queen's University. Kingston, ON, Canada. 2017/05. [slides]
Recurrent Neural Networks. CSC 2541 Guest Lecture. University of Toronto. Toronto, ON, Canada. 2017/01. [slides]
Deep Dashboard Tutorial. University of Toronto. 2016/02. University of Guelph. Guelph, ON, Canada. 2016/03. [slides]
Exploring Data and Models for Image Question Answering. Lille, France. ICML 2015 Deep Learning Workshop. 2015/07. [slides]