Mengye Ren (任孟也)
PhD Student, University of Toronto
Sr. Research Scientist, Uber ATG
Email: mren@cs.toronto.edu, mren3@uber.com
PhD Student, University of Toronto
Sr. Research Scientist, Uber ATG
Email: mren@cs.toronto.edu, mren3@uber.com
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
2020/02: One paper got accepted to CVPR 2020.
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.
Physically Realizable Adversarial Examples for LiDAR Object Detection. James Tu, Mengye Ren, Sivabalan Manivasagam, Ming Liang, Bin Yang, Richard Du, Frank Cheng, Raquel Urtasun. CVPR, 2020. [arxiv]
Learning to Remember from a Multi-Task Teacher. Yuwen Xiong*, Mengye Ren*, Raquel Urtasun. arXiv preprint 1910.04650, 2019. [arxiv]
Incremental Few-Shot Learning with Attention Attractor Networks. Mengye Ren, Renjie Liao, Ethan Fetaya, Richard S. Zemel. NeurIPS, 2019. [arxiv][code]
Information-Theoretic Limitations on Novel Task Generalization. James Lucas, Mengye Ren, Richard S. Zemel. NeurIPS Workshop on Machine Learning with Guarantees, 2019. [pdf]
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]
Learning to Reweight Examples for Robust Deep Learning. Mengye Ren, Wenyuan Zeng, Bin Yang, Raquel Urtasun. ICML, 2018. [arxiv][code] [video]
SBNet: Sparse Blocks Network for Fast Inference. Mengye Ren*, Andrei Pokrovsky*, Bin Yang*, Raquel Urtasun. CVPR, 2018. [link][arxiv] [blog][code]
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]
Understanding Short-Horizon Bias in Stochastic Meta-Optimization. Yuhuai Wu*, Mengye Ren*, Renjie Liao, Roger B. Grosse. ICLR, 2018. [link][arxiv] [code]
The Reversible Residual Network: Backpropagation without Storing Actications. Aidan N. Gomez*, Mengye Ren*, Raquel Urtasun, Roger B. Grosse. NIPS, 2017. [link][arxiv] [code]
Normalizing the Normalizers: Comparing and Extending Network Normalization Schemes. Mengye Ren*, Renjie Liao*, Raquel Urtasun, Fabian H. Sinz, Richard S. Zemel. ICLR, 2017. [link][arxiv] [code]
End-to-End Instance Segmentation with Recurrent Attention. Mengye Ren, Richard S. Zemel. CVPR, 2017. [link][arxiv] [code][video]
Exploring Models and Data for Image Question Answering. Mengye Ren, Ryan Kiros, Richard S. Zemel. NIPS, 2015. [link][arxiv] [results][dataset] [code][code2]
Course instructor:
Teaching assistant:
ECE 521: Inference Algorithms (2017 Winter)
CSC 401/2511: Natural Language Computing (2016 Winter)
Naive Bayes and Gaussian Bayes Classifier Tutorial [slides][code]
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]
Learning to Reweight Examples for Robust Deep Learning. ICML 2018. Stockholm, Sweden. 2018/07. [slides][video]
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]
Journal reviewer: IEEE TPAMI IEEE TIP IEEE TNNLS Optim Method Softw
Conference reviewer: NIPS/NeurIPS 2016-2019, ICML 2017-2020, ICLR 2018-2020, CVPR 2018-2020, ICCV 2019, ECCV 2020, AAAI 2018, UAI 2018