[Theory] NOW: [Talks at TTIC] 10/23 Young Researcher Seminar Series: Kaylene Stocking, UC Berkeley

[Brandie Jones via Theory]

*When:    *Wednesday, October 23rd* at **11AM 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=1daf7bc2-ba7f-482c-90de-b20300ef032b>
)

*Who:      *Kaylene Stocking, UC Berkeley

*Title:*       Comparing deep learning-based autonomous driving algorithms
with the brain activity of human drivers

*Abstract: *Understanding how cognition and learned representations give
rise to intelligent behavior is a fundamental goal in both machine learning
and neuroscience. However, in both domains, the most well-understood
behaviors are passive and open-loop, such as image recognition or speech
processing. In this work, we compare human brain activity measured via
functional magnetic resonance imaging with deep neural network (DNN)
activations for an active taxi-driving task in a naturalistic simulated
environment. To do so, we used DNN activations to build voxelwise encoding
models for brain activity. Results show that encoding models for DNN
activations explain significant amounts of variance in brain activity
across many regions of the brain. Furthermore, each functional module in
the DNN explains brain activity in a distinct network of functional regions
in the brain. The functions of each DNN module correspond well to the known
functional properties of its corresponding brain regions, suggesting that
both the DNN and the human brain may partition the task in a similar
manner. These results represent a first step towards understanding how
humans and current deep learning methods agree or differ in active
closed-loop tasks such as driving.

*Bio:  *Kaylene is a PhD candidate in electrical engineering and computer
sciences at UC Berkeley, advised by Claire Tomlin and working at the
intersection of robotics, machine learning, and cognitive science. She is
especially interested in general principles that make intelligent behavior
possible across both machine and biological systems. Her research has been
supported by the Berkeley Fellowship and the Kavli Ethics, Science, and the
Public Graduate Fellowship.

*Host: Matt Walter <mwalter at ttic.edu>*


-- 
*Brandie Jones *
*Executive **Administrative Assistant*
Toyota Technological Institute
6045 S. Kenwood Avenue
Chicago, IL  60637
www.ttic.edu
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