[Google Scholar] [dblp]
Are LLMs Prescient? A Continuous Evaluation using Daily News as the Oracle. Hui Dai, Ryan Teehan, Mengye Ren. arXiv preprint 2411.08324, 2024. [arxiv]
PooDLe: Pooled and dense self-supervised learning from naturalistic videos. Alex N. Wang, Christopher Hoang, Yuwen Xiong, Yann LeCun, Mengye Ren. arXiv preprint 2408.11208, 2024. [arxiv]
LifelongMemory: Leveraging LLMs for answering queries in egocentric videos. Ying Wang, Yanlai Yang, Mengye Ren. arXiv preprint 2312.05269, 2023. [webpage] [arxiv]
BIM: Block-wise self-supervised learning with masked image modeling. Yixuan Luo, Mengye Ren, Sai Qian Zhang. arXiv preprint 2311.17218, 2023. [arxiv]
Learning to reason with
relational abstractions. Andrew J. Nam*, Mengye
Ren*, Chelsea Finn, James L. McClelland. arXiv preprint
2210.02615, 2022. [arxiv] [pdf]
[dataset]
Gaussian-Bernoulli RBMs without tears. Renjie Liao, Simon Kornblith, Mengye Ren, David J. Fleet, Geoffrey Hinton. arXiv preprint 2210.10318, 2022. [arxiv]
Reawakening knowledge: Anticipatory recovery from catastrophic interference via structured training. Yanlai Yang, Matt Jones, Michael C. Mozer, Mengye Ren. NeurIPS, 2024. [arxiv]
CoLLEGe: Concept embedding generation for large language models. Ryan Teehan, Brenden M. Lake, Mengye Ren. COLM, 2024. [arxiv]
ProCreate, dont reproduce! Propulsive energy diffusion for creative generation. Jack Lu, Ryan Teehan, Mengye Ren. ECCV, 2024. [arxiv]
Integrating present and past in unsupervised continual learning. Yipeng Zhang, Laurent Charlin, Richard Zemel, Mengye Ren. CoLLAs, 2024. [arxiv]
Self-supervised learning of video representations from a child’s perspective. Emin Orhan, Wentao Wang, Alex N. Wang, Mengye Ren, Brenden M. Lake. CogSci, 2024. [arxiv]
Learning and forgetting unsafe examples in large language models. Jiachen Zhao, Zhun Deng, David Madras, James Zou, Mengye Ren. ICML, 2024. [arxiv]
Scaling forward gradient with local losses. Mengye Ren, Simon Kornblith, Renjie Liao, Geoffrey Hinton. ICLR, 2023. [arxiv] [pdf] [code] [html]
Learning in temporally structured environments. Matt Jones, Tyler R. Scott, Mengye Ren, Gamaleldin F. Elsayed, Katherine Hermann, David Mayo, Michael C. Mozer. ICLR, 2023. [pdf]
Multitask learning via interleaving: A neural network investigation. David Mayo, Tyler Scott, Mengye Ren, Gamaleldin Elsayed, Katherine Hermann, Matt Jones, Michael Mozer. CogSci, 2023. [pdf]
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. [arxiv] [pdf] [video] [website]
Egocentric video comprehension via large language model inner speech. Ying Wang, Dongdong Sun, Rui Chen, Yanlai Yang, Mengye Ren. 3rd International Ego4D Workshop at CVPR, 2023. [pdf]
Learning to reason with
relational abstractions. Andrew J. Nam*, Mengye
Ren*, Chelsea Finn, James L. McClelland. NeurIPS
MATH-AI Worshop, 2022. [arxiv] [pdf]
[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. [pdf]
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.
[arxiv] [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] [pdf]
[video]
[html]
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] [pdf] [code] [html]
Self-supervised representation learning from flow equivariance. Yuwen Xiong, Mengye Ren, Wenyuan Zeng, Raquel Urtasun. ICCV, 2021. [arxiv] [pdf] [html]
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] [pdf]
[html]
Wandering within a world: Online contextualized few-shot learning. Mengye Ren, Michael L. Iuzzolino, Michael C. Mozer, Richard Zemel. ICLR, 2021. [arxiv] [pdf] [code] [video] [html]
Theoretical bounds on estimation error for meta-learning. James Lucas, Mengye Ren, Irene Kameni, Toniann Pitassi, Richard Zemel. ICLR, 2021. [arxiv] [pdf] [video] [html]
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] [pdf]
[video] [html]
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] [pdf]
SceneGen: Learning to generate
realistic traffic scenes. Shuhan Tan*, Kelvin
Wong*, Shenlong Wang, Sivabalan Manivasagam, Mengye Ren,
Raquel Urtasun. CVPR, 2021. [arxiv] [pdf]
LoCo: Local contrastive representation learning. Yuwen Xiong, Mengye Ren, Raquel Urtasun. NeurIPS, 2020. [arxiv] [pdf] [video] [html]
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 network. Quinlan
Sykora*, Mengye Ren*, Raquel Urtasun.
ICML, 2020. [arxiv] [pdf]
[code] [video]
[html]
Cost-efficient online hyperparameter
optimization. Jingkang Wang*, Mengye
Ren*, Ilija Bogunovic, Yuwen Xiong, Raquel Urtasun.
ICML RealML Workshop, 2020. [arxiv] [pdf]
[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] [html]
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] [html]
Learning to reweight examples for robust deep learning. Mengye Ren, Wenyuan Zeng, Bin Yang, Raquel Urtasun. ICML, 2018. [arxiv] [code] [video] [html]
SBNet: Sparse
blocks network for fast inference. Mengye Ren*,
Andrei Pokrovsky*, Bin Yang*, Raquel Urtasun.
CVPR, 2018. [link] [arxiv] [blog] [code] [html]
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] [html]
Understanding
short-horizon bias in stochastic meta-optimization. Yuhuai
Wu*, Mengye Ren*, Renjie Liao, Roger B.
Grosse. ICLR, 2018. [link]
[arxiv] [code] [html]
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]