Assistant Professor
Department of Computer Science
Courant Institute of Mathematical Sciences
Center for Data Science (joint)
New York University
Email: mengye@nyu.edu
Tel: +1 (212) 998-3369
Office: 60 5th Ave, Rm 508, New York, NY, 10011
Mengye Ren is an assistant professor of computer science and data science at New York University (NYU). Before joining NYU, he was a visiting faculty researcher at Google Brain Toronto working with Prof. Geoffrey Hinton. He received B.A.Sc. in Engineering Science (2015), and M.Sc. (2017) and Ph.D. (2022) in Computer Science from the University of Toronto, advised by Prof. Richard Zemel and Prof. Raquel Urtasun. From 2017 to 2021, he was also a senior research scientist at Uber Advanced Technologies Group (ATG) and Waabi, working on self-driving vehicles. His research focuses on making machine learning more natural and human-like, in order for AIs to continually learn, adapt, and reason in naturalistic environments.
Areas: machine learning, computer vision, representation learning, meta-learning, few-shot learning, brain & cognitively inspired learning, robot learning, self-driving vehicles
My key research question is: how do we enable human-like, agent-based machine intelligence to continually learn, adapt, and reason in naturalistic environments? I am interested in the emergence of intelligence by learning from a point-of-view experience. Current research topics in my group are:
Memorization and forgetting in sequentially changing environments
Visual representation learning in the wild using egocentric videos
Few-shot learning, reasoning, and abstraction in vision and language
Human-AI alignment in personalized AI
NYU DS-GA 1008 / CSCI-GA 2572: Deep Learning [2024 spring]
NYU CSCI-GA 2565: Machine Learning [2023 fall]
NYU DS-GA 1003: Machine Learning [2023 spring]
Vector Institute: Deep Learning II [2020 fall]
UofT CSC 411: Machine Learning and Data Mining [2019 winter]
2023/06: I am co-organizing Localized Learning Workshop at ICML 2023.
2023/04: One paper is accepted at CogSci 2023.
2023/02: One paper is accepted at CVPR 2023.
2023/01: Two papers are accepted at ICLR 2023.
2022/12: I gave an invited talk at NeurIPS 2022 Meta-Learn workshop.
2022/10: New preprint on biologically plausible learning with local activity perturbation.
2022/10: One paper accepted at MATH-AI workshop at NeurIPS.
2022/10: One paper accepted at MemARI workshop at NeurIPS.
2022/09: I have moved to New York and officially joined NYU.
CoLLEGe: Concept embedding generation for large language models. Ryan Teehan, Brenden M. Lake, Mengye Ren. arXiv preprint 2403.15362, 2024. [arxiv]
Reawakening knowledge: Anticipatory recovery from catastrophic interference via structured training. Yanlai Yang, Matt Jones, Michael C. Mozer, Mengye Ren. arXiv preprint 2403.09613, 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. arXiv preprint 2402.00300, 2024. [arxiv]
Learning and forgetting unsafe examples in large language models. Jiachen Zhao, Zhun Deng, David Madras, James Zou, Mengye Ren. arXiv preprint 2312.12736, 2023. [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]
[Full List] [Google Scholar] [dblp]
Scaling forward gradient with local losses. Mengye Ren, Simon Kornblith, Renjie Liao, Geoffrey Hinton. ICLR, 2023. [arxiv] [pdf] [code] [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, 2022. [arxiv] [pdf] [code] [html]
Self-supervised representation learning from flow equivariance. Yuwen Xiong, Mengye Ren, Wenyuan Zeng, Raquel Urtasun. ICCV, 2021. [arxiv] [pdf] [html]
SketchEmbedNet: Learning novel concepts by imitating drawings. Alexander Wang*, Mengye Ren*, Richard Zemel. ICML, 2021. [arxiv] [pdf] [code] [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]
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]
LoCo: Local contrastive representation learning. Yuwen Xiong, Mengye Ren, Raquel Urtasun. NeurIPS, 2020. [arxiv] [pdf] [video] [html]
Multi-agent routing value iteration network. Quinlan Sykora*, Mengye Ren*, Raquel Urtasun. ICML, 2020. [arxiv] [pdf] [code] [video] [html]
Incremental few-shot learning with attention attractor networks. Mengye Ren, Renjie Liao, Ethan Fetaya, Richard S. Zemel. NeurIPS, 2019. [arxiv] [code] [html]
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]
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]
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] [question generation]