[Google Scholar] [dblp]
Online unsupervised learning of visual representations and categories. Mengye Ren, Tyler R. Scott, Michael L. Iuzzolino, Michael C. Mozer, Richard Zemel. arXiv preprint 2109.05675, 2021. [arxiv]
Probing few-shot generalization with attributes. 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]
Open-world machine learning with limited labeled data. Mengye Ren. Ph.D. Thesis, University of Toronto, 2021. [pdf]
Self-supervised representation learning from flow equivariance. Yuwen Xiong, Mengye Ren, Wenyuan Zeng, Raquel Urtasun. ICCV, 2021. [arxiv]
Adversarial attacks on multi-agent communication. James Tu*, Tsunhsuan Wang*, Jingkang Wang, Sivabalan Manivasagam, Mengye Ren, Raquel Urtasun. ICCV, 2021. [arxiv]
Just label what you need: Fine-grained active selection for perception and prediction through partially labeled scenes. Sean Segal, Nishanth Kumar, Sergio Casas, Wenyuan Zeng, Mengye Ren, Jingkang Wang, Raquel Urtasun. CoRL, 2021. [arxiv]
Exploring adversarial robustness of multi-sensor perception systems in self driving. James Tu, Huichen Li, Xinchen Yan, Mengye Ren, Yun Chen, Ming Liang, Eilyan Bitar, Ersin Yumer, Raquel Urtasun. CoRL, 2021. [arxiv]
SketchEmbedNet: Learning novel concepts by imitating drawings. Alexander Wang*, Mengye Ren*, Richard Zemel. ICML, 2021. [arxiv]
Wandering within a world: Online contextualized few-shot learning. Mengye Ren, Michael L. Iuzzolino, Michael C. Mozer, Richard Zemel. ICLR, 2021. [arxiv] [code] [video]
Theoretical bounds on estimation error for meta-learning. James Lucas, Mengye Ren, Irene Kameni, Toniann Pitassi, Richard Zemel. ICLR, 2021. [arxiv] [video]
Perceive, attend, and drive: Learning spatial attention for safe self-driving. Bob Wei*, Mengye Ren*, Wenyuan Zeng, Ming Liang, Bin Yang, Raquel Urtasun. ICRA, 2021. [arxiv] [video]
AdvSim: Generating safety-critical scenarios for self-driving vehicles. Jingkang Wang, Ava Pun, James Tu, Sivabalan Manivasagam, Abbas Sadat, Sergio Casas, Mengye Ren, Raquel Urtasun. CVPR, 2021. [arxiv]
SceneGen: Learning to generate realistic traffic scenes. Shuhan Tan*, Kelvin Wong*, Shenlong Wang, Sivabalan Manivasagam, Mengye Ren, Raquel Urtasun. CVPR, 2021. [arxiv]
LoCo: Local contrastive representation learning. Yuwen Xiong, Mengye Ren, Raquel Urtasun. NeurIPS, 2020. [arxiv] [video]
Multi-label incremental few-shot learning for medical image pathology classifiers. Laleh Seyyed-Kalantari, Karsten Roth, Mengye Ren, Parsa Torabian, Joseph P. Cohen, Marzyeh Ghassemi. Medical Imaging Meets NeurIPS Workshop, 2020. [video]
Learning to communicate and correct pose errors. Nicholas Vadivelu, Mengye Ren, James Tu, Jingkang Wang, Raquel Urtasun. CoRL, 2020. [arxiv] [video]
Multi-agent routing value iteration networks. Quinlan Sykora*, Mengye Ren*, Raquel Urtasun. ICML, 2020. [arxiv] [code] [video]
Cost-efficient online hyperparameter optimization. Jingkang Wang*, Mengye Ren*, Ilija Bogunovic, Yuwen Xiong, Raquel Urtasun. ICML RealML Workshop, 2020. [arxiv] [slide]
Perceive, predict, and plan: Safe motion planning through interpretable semantic representations. Abbas Sadat*, Sergio Casas Romero*, Mengye Ren, Xinyu Wu, Pranaab Dhawan, Raquel Urtasun. ECCV, 2020. [arxiv]
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. IROS, 2020. [arxiv]
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] [video]
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]