[Theory] 2/29 Talks at TTIC: Zhuang Liu, Meta AI Research

[Mary Marre]

*When:*        Thursday, February 29, 2024 at* 11:00** a**m CT   *


*Where:       *Talk will be given *live, in-person* at

                   TTIC, 6045 S. Kenwood Avenue

                   5th Floor, Room 530


*Virtually:*   *via *Panopto (*livestream
<https://uchicago.hosted.panopto.com/Panopto/Pages/Viewer.aspx?id=d964b941-9fd5-409d-a531-b11e0189c3fc>*
)

*                         *limited access: see info below*


*Who: *         Zhuang Liu, Meta AI Research
------------------------------
*Title:          *Scaling Deep Learning Up and Down

*Abstract: *Deep learning with neural networks has emerged as a key
approach for discovering patterns and modeling relationships in complex
data. AI systems powered by deep learning are used widely in applications
across a broad spectrum of scales. There are strong needs for scaling deep
learning both upward and downward. Scaling up highlights the pursuit of
scalability - the ability to utilize increasingly abundant computing and
data resources to achieve superior capabilities, overcoming diminishing
returns. Scaling down represents the demand for efficiency - there is
limited data for many application domains, and deployment is often in
compute-limited settings.

In this talk, we present studies in both directions. For scaling up, we
first explore the design of scalable neural network architectures that are
widely adopted in various fields. We then discuss an intriguing observation
on modern vision datasets and its implication on scaling training data. For
scaling down, we introduce simple, effective, and popularly used approaches
for compressing convolutional networks and large language models, alongside
interesting empirical findings. Notably, a recurring theme in this talk is
the careful examination of implicit assumptions in the literature, which
often leads to surprising revelations that reshape community understanding.
Finally, we discuss exciting avenues for future deep learning and vision
research, such as developing next-gen architectures and modeling datasets.

*Bio: *Zhuang Liu is currently a Research Scientist at Meta AI Research
(FAIR) in New York City. He received his Ph.D. from UC Berkeley EECS in
2022, advised by Trevor Darrell. His research areas include deep learning
and computer vision. His work focuses on scaling neural networks both up
and down, to build capable models and understand their behaviors in
different computational and data environments. His work is broadly applied
in different areas of computing and other disciplines. He is a recipient of
the CVPR 2017 Best Paper Award.

*Host: **David McAllester* <mcallester at ttic.edu>

*Access to this livestream is limited to TTIC / UChicago (press panopto
link and sign in to your UChicago account with CNetID).




Mary C. Marre
Faculty Administrative Support
*Toyota Technological Institute*
*6045 S. Kenwood Avenue, Rm 517*
*Chicago, IL  60637*
*773-834-1757*
*mmarre at ttic.edu <mmarre at ttic.edu>*
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