Yubei Chen
Postdoctoral Associate (Incoming Assistant Professor, ECE @ UC Davis)
Center for Data Science, New York University
Postdoctoral Associate, Advisor Professor Yann LeCun
I am a postdoctoral associate at NYU Center for Data Science (CDS) and Meta Fundamental AI Research (FAIR), working with Prof. Yann LeCun. Prior to joining CDS and FAIR, I completed my Ph.D. at Redwood Center for Theoretical Neuroscience and Berkeley AI Research (BAIR), UC Berkeley, advised by Prof. Bruno Olshausen. Undergraduate studies from Tsinghua University, Beijing. Co-founded Aizip Inc. that builds robust, efficient, and scalable AI-IoT solutions.
My research is at the intersection of computational neuroscience and deep unsupervised (self-supervised) learning, enhancing our understanding of the computational principles governing unsupervised representation learning in both brains and machines, and reshaping our insights into natural signal statistics.
news
| Mar22, 2023 |
I will join the ECE department at UC Davis as an assistant professor in late 2023.
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| Mar21, 2023 | |
| Jan21, 2023 |
Three papers are accepted at ICLR 2023 with one Oral and one Spotlight!
 1) Oral: On the duality between contrastive and non-contrastive self-supervised learning arXiv. 2) Spotlight: Minimalistic unsupervised learning with the sparse manifold transform arXiv. 3) Simple Emergent Action Representations from Multi-task Policy Training arXiv. |
| Jan11, 2023 |
I gave talk at Center for Computational Neuroscience (CCN) Research Seminar, Flatiron Institute on the principles of unsupervised representation learning.
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| Nov18, 2022 |
I gave a talk at University of Washington NeuroAI Seminar on the principles of unsupervised representation learning.
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| Nov4, 2022 |
Simple Emergent Action Representations from Multi-Task Policy Training, is accepted to Deep RL Workshop@NeurIPS 2022!
arXiv, Website
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| Oct21, 2022 |
Disentangling images with Lie group transformations and sparse coding, is accepted to NeurReps@NeurIPS 2022 as a full PMLR paper!
arXiv, GitHub
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| Sep30, 2022 |
New Paper: Minimalistic Unsupervised Learning with the Sparse Manifold Transform. arXiv
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| Sep9, 2022 |
I gave a talk at Bay Area Vision Research Day (BAVRD) on the principles of unsupervised representation learning.
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| Aug16, 2022 |