[UPDATE][CMU AI Seminar] Mar 24 (Zoom) -- Le Song (Georgia Tech/MBZUAI) -- Understanding Deep Architectures with Reasoning Layer -- AI Seminar sponsored by Fortive

[Shaojie Bai]

Sorry for spamming everyone, it was pointed out to me that the Zoom link in
the last email is incorrect.

Here is the correct link:
https://cmu.zoom.us/j/94092736800?pwd=OWlCRlV6RERtRHByWWpkZEl0YkNOQT09.

*See you tomorrow (Wednesday) at 1pm ET for the talk with Le Song!*

On Tue, Mar 23, 2021 at 12:06 PM Shaojie Bai <shaojieb at andrew.cmu.edu>
wrote:

> Hi all,
>
> *NOTE*: This seminar is tomorrow at *1pm*, not the usual 12pm (due to a
> faculty candidate talk at 12pm).
>
> Just a reminder that the CMU AI Seminar
> <http://www.cs.cmu.edu/~aiseminar/> is tomorrow *1pm-2pm*:
> https://cmu.zoom.us/j/93418102649?pwd=TTd4dElxWnBOZHJ5QndUNVBWUjZCZz09.
>
> Le Song (GeorgiaTech/MBZUAI) will be talking about some cool insights
> related to deep reasoning layers (see below).
>
> Thanks,
> Shaojie
>
>
> On Wed, Mar 17, 2021 at 9:40 AM Shaojie Bai <shaojieb at andrew.cmu.edu>
> wrote:
>
>> *UPDATE*: As the planned time (3/23 12pm) overlaps with a SCS faculty
>> candidate talk, this seminar will be moved to next Wednesday (3/24) at 12pm
>> instead.
>> --------------------------------------------
>>
>> Dear all,
>>
>> We look forward to seeing you *next Wednesday (3/24)* 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 3/24, *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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