Please enable Javascript
Avançar para o conteúdo principal

Mengye Ren

Mengye is a Senior Research Scientist at Uber ATG R&D. He is also a PhD student in the Machine Learning Group of the Department of Computer Science at the University of Toronto. Mengye studied engineering science in his undergrad at the University of Toronto. His research interests are machine learning, neural networks, and computer vision. Mengye is originally from Shanghai, China.

Recent publications

Perceive, Predict, and Plan: Safe Motion Planning Through Interpretable Semantic Representations

Abbas Sadat*, Sergio Casas*, Mengye Ren, Xinyu Wu, Pranaab Dhawan, Raquel Urtasun (ECCV 2020)

End-to-end Contextual Perception and Prediction with Interaction Transformer

Lingyun Luke Li, Bin Yang, Ming Liang, Wenyuan Zeng, Mengye Ren, Sean Segal, Raquel Urtasun (ECCV 2020, oral)

Multi-Agent Routing Value Iteration Network

Quinlan Sykora, Mengye Ren, Raquel Urtasun (ICML 2020)

Physically Realizable Adversarial Examples for LiDAR Detection

James Tu, Mengye Ren, Siva Manivasagam, Bin Yang, Ming Liang, Richard Du, Frank Cheng, Raquel Urtasun (CVPR 2020)

Incremental Few-Shot Learning with Attention Attractor Networks

Mengye Ren, Renjie Liao, Ethan Fetaya, Richard S. Zemel (NeurIPS 2019)

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, oral)

Identifying Unknown Instances for Autonomous Driving

Kelvin Wong, Shenlong Wang, Mengye Ren, Ming Liang, Raquel Urtasun (CoRL 2019)

Graph HyperNetworks for Neural Architecture Search

Chris Zhang, Mengye Ren, Raquel Urtasun (ICLR 2019)

Incremental Few-Shot Learning with Attention Attractor Networks

Mengye Ren, Renjie Liao, Ethan Fetaya, Richard S. Zemel (NeurIPS 2018, Meta Learning workshop, spotlight)

SBNet: Sparse Blocks Network for Fast Inference

Mengye Ren*, Andrei Pokrovsky*, Bin Yang*, Raquel Urtasun (CVPR 2018, spotlight)

Learning to Reweight Examples for Robust Deep Learning

Mengye Ren, Wenyuan Zeng, Bin Yang, Raquel Urtasun (ICML 2018, spotlight)

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)

Understanding Short-Horizon Bias in Stochastic Meta-Optimization

Yuhuai Wu*, Mengye Ren*, Renjie Liao, Roger B. Grosse (ICLR 2018)

The Reversible Residual Network: Backpropagation Without Storing Activations

Aidan N. Gomez*, Mengye Ren*, Raquel Urtasun, Roger B. Grosse (NeurIPS 2017)

End-to-End Instance Segmentation with Recurrent Attention

Mengye Ren, Richard S. Zemel (CVPR 2017, spotlight)

Normalizing the Normalizers: Comparing and Extending Network Normalization Schemes

Mengye Ren*, Renjie Liao*, Raquel Urtasun, Fabian H. Sinz, Richard S. Zemel (ICLR 2017)