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
One paper got accepted to IROS 2019!
One paper got accepted to ICLR 2019!
I will be teaching CSC 411 (Machine Learning and Data Mining) in the winter semester of 2019. [course website]
I will visit INRIA Grenoble Rhône-Alpes on July 19, 2018.
I will visit TU Berlin on July 16, 2018.
I will visit NEC lab in Princeton, NJ on June 4, 2018.
I will visit the University of Tübingen and MPI for Intelligent Systems from June 25 to July 20, 2018.
One paper got accepted to ICML 2018!
One paper got accepted to CVPR 2018!
Google Scholar [link]
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.
Graph HyperNetworks for Neural Architecture Search. Chris Zhang, Mengye Ren, Raquel Urtasun. ICLR, 2019. [arxiv]
Incremental Few-Shot Learning with Attention Attractor Networks. Mengye Ren, Renjie Liao, Ethan Fetaya, Richard S. Zemel. Technical Report, 2018. [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 Weakly Supervised Learning. INRIA Grenoble Rhône-Alpes. Grenoble, France. 2018/07. [slides]
Learning to Reweight Examples for Robust Deep Learning. Stockholm, Sweden. 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. 2018/02. [slides]
Meta-Learning for Semi-Supervised Few-Shot Classification. Vector Institute. 2017/11. [slides]
Sequence-to-Sequence Deep Learning with Recurrent Attention. Queen's University. 2017/05. [slides]
Recurrent Neural Networks. CSC 2541 Guest Lecture. University of Toronto. 2017/01. [slides]
Deep Dashboard Tutorial. University of Toronto. 2016/02. University of Guelph. 2016/03. [slides]
Exploring Data and Models for Image Question Answering. Lille, France. ICML 2015 Deep Learning Workshop. 2015/07. [slides]