[CMU AI Seminar] April 12 at 12pm (Zoom) -- Tom Goldstein (University of Maryland) -- End-to-end algorithm synthesis with "thinking" networks -- AI Seminar sponsored by Morgan Stanley

[Asher Trockman]

Hi all,

Just a reminder that the talk today by Tom Goldstein on "End-to-end
algorithm synthesis with 'thinking' networks" is happening at 12pm!

Zoom Link:
https://cmu.zoom.us/j/99510233317?pwd=ZGx4aExNZ1FNaGY4SHI3Qlh0YjNWUT09

Best,
Asher

On Fri, Apr 8, 2022 at 1:41 PM Asher Trockman <ashert at cs.cmu.edu> wrote:

> Dear all,
>
> We look forward to seeing you *next Tuesday (4/12)* from *1**2:00-1:00 PM
> (U.S. Eastern time)* for the next talk of our *CMU AI seminar*, sponsored
> by Morgan Stanley <https://www.morganstanley.com/about-us/technology/>.
>
> To learn more about the seminar series or see the future schedule, please
> visit the seminar website <http://www.cs.cmu.edu/~aiseminar/>.
>
> On 4/12, *Tom Goldstein *(University of Maryland) will be giving a talk
> titled *"**End-to-end algorithm synthesis with 'thinking' networks**"* to survey
> adversarial machine learning and to explain his recent work on "thinking
> systems".
>
> *Title*: End-to-end algorithm synthesis with "thinking" networks
>
> *Talk Abstract*: This talk will have two parts.  In the first half of the
> talk, I'll survey the basics of adversarial machine learning, and discuss
> whether adversarial attacks and dataset poisoning can scale up to work on
> industrial systems.  I'll also present applications where adversarial
> methods provide benefits for domain shift robustness, dataset privacy, and
> data augmentation.  In the second half of the talk, I'll present my recent
> work on "thinking systems."  These systems use recurrent networks to
> emulate a human-like thinking process, in which problems are represented in
> memory and then iteratively manipulated and simplified over time until a
> solution to a problem is found.  When these models are trained only on
> "easy" problem instances, they can then solve "hard" problem instances
> without having ever seen one, provided the model is allowed the "think" for
> longer at test time.
>
> *Speaker Bio*: Tom Goldstein is the Perotto Associate Professor of
> Computer Science at the University of Maryland.  His research lies at the
> intersection of machine learning and optimization, and targets applications
> in computer vision and signal processing. Before joining the faculty at
> Maryland, Tom completed his PhD in Mathematics at UCLA, and was a research
> scientist at Rice University and Stanford University. Professor Goldstein
> has been the recipient of several awards, including SIAM’s DiPrima Prize, a
> DARPA Young Faculty Award, a JP Morgan Faculty award, and a Sloan
> Fellowship.
>
> *Zoom Link*:
> https://cmu.zoom.us/j/99510233317?pwd=ZGx4aExNZ1FNaGY4SHI3Qlh0YjNWUT09
>
> Thanks,
> Asher Trockman
>
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