[Google Scholar] [dblp]
Learning to reason with relational abstractions. Andrew J. Nam*, Mengye Ren*, Chelsea Finn, James L. McClelland. arXiv preprint 2210.02615, 2022. [arxiv] [dataset]
Gaussian-Bernoulli RBMs without tears. Renjie Liao, Simon Kornblith, Mengye Ren, David J. Fleet, Geoffrey Hinton. arXiv preprint 2210.10318, 2022. [arxiv]
Towards unsupervised object detection from LiDAR point clouds. Lunjun Zhang, Anqi Joyce Yang, Yuwen Xiong, Sergio Casas, Bin Yang, Mengye Ren, Raquel Urtasun. CVPR, 2023.
Scaling forward gradient with local losses. Mengye Ren, Simon Kornblith, Renjie Liao, Geoffrey Hinton. ICLR, 2023. [arxiv] [code]
Learning in temporally structured environments. Matt Jones, Tyler R. Scott, Mengye Ren, Gamaleldin F. Elsayed, Katherine Hermann, David Mayo, Michael C. Mozer. ICLR, 2023.
Learning to reason with relational abstractions. Andrew J. Nam*, Mengye Ren*, Chelsea Finn, James L. McClelland. NeurIPS MATH-AI Worshop, 2022. [arxiv] [dataset]
Neural network online training with sensitivity to multiscale temporal structure. Matt Jones, Tyler R. Scott, Gamaleldin F. Elsayed, Mengye Ren, Katherine Hermann, David Mayo, Michael C. Mozer. NeurIPS MemARI Workshop, 2022.
Rethinking closed-loop training for autonomous driving. Chris Zhang*, Runsheng Guo*, Wenyuan Zeng*, Yuwen Xiong, Binbin Dai, Rui Hu, Mengye Ren, Raquel Urtasun. ECCV, 2022. [pdf]
Open-world machine learning with limited labeled data. Mengye Ren. Ph.D. Thesis, University of Toronto, 2022. [pdf]
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]
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] [code]
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]
Flexible Few-Shot Learning of Contextual Similarities. Mengye Ren*, Eleni Triantafillou*, Kuan-Chieh Wang*, James Lucas*, Jake Snell, Xaq Pitkow, Andreas S. Tolias, Richard Zemel. NeurIPS Meta-Learning Workshop, 2020. [pdf] [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]