[CMU AI Seminar] Mar 23 (Zoom) -- Le Song (Georgia Tech/MBZUAI) -- Understanding Deep Architectures with Reasoning Layer -- AI Seminar sponsored by Fortive

[Shaojie Bai]

Dear all,

We look forward to seeing you *next Tuesday (3/23)* from 12:00-1:00 PM
(U.S. Eastern time) for the next talk of our *CMU AI seminar*, sponsored by
Fortive <https://careers.fortive.com/>.

To learn more about the seminar series or see the future schedule, please
visit the seminar website <http://www.cs.cmu.edu/~aiseminar/>.
<http://www.cs.cmu.edu/~aiseminar/>

On 3/23, *Le Song* (Georgia Tech / MBZUAI) will be giving a talk on
"*Understanding
Deep Architectures with Reasoning Layer*."

*Title*: Understanding Deep Architectures with Reasoning Layer

*Talk Abstract*: Recently, there has been a surge of interest in combining
deep learning models with reasoning in order to handle more sophisticated
learning tasks. In many cases, a reasoning task can be solved by an
iterative algorithm. This algorithm is often unrolled, and used as a
specialized layer in the deep architecture, which can be trained end-to-end
with other neural components. Although such hybrid deep architectures have
led to many empirical successes, the theoretical foundation of such
architectures, especially the interplay between algorithm layers and other
neural layers, remains largely unexplored. In this paper, we take an
initial step towards an understanding of such hybrid deep architectures by
showing that properties of the algorithm layers, such as convergence,
stability and sensitivity, are intimately related to the approximation and
generalization abilities of the end-to-end model. Furthermore, our analysis
matches closely our experimental observations under various conditions,
suggesting that our theory can provide useful guidelines for designing deep
architectures with reasoning layers.

*Speaker Bio*: Le Song is a Professor and the Deputy Chair of the Machine
Learning Department, Mohamed bin Sayed University of AI, UAE. He was an
Associate Professor in the Department of Computational Science and
Engineering, College of Computing, Georgia Institute of Technology, worked
as a research scientist at Google, and did his post-doc in Carnegie Mellon
University. His principal research area is machine learning, especially
kernel methods, deep learning, and probabilistic graphical models. He is
the recipient of many best paper awards at major machine learning
conferences, such as NeurIPS, ICML and AISTATS, and the NSF CAREER Award.
He has also served as the area chair or senior program committee for many
leading machine learning and AI conferences such as NeurIPS, ICML, ICLR,
AAAI and IJCAI, and the action editor for JMLR and IEEE TPAMI. He is also a
board member of the International Conference for Machine Learning.

*Zoom Link*:
https://cmu.zoom.us/j/94092736800?pwd=OWlCRlV6RERtRHByWWpkZEl0YkNOQT09


Thanks,
Shaojie Bai (MLD)
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