[Colloquium] REMINDER: 1/22 TTIC Colloquium: C.-C. Jay Kuo, University of Southern California

[Mary Marre via Colloquium]

When:     Monday, January 22nd at *10:30 a.m. *

Where:    TTIC, 6045 S. Kenwood Avenue, 5th Floor, Room 526

Who:       C.-C. Jay Kuo, University of Southern California


Title:        Why and Why Not Convolutional Neural Networks (CNNs)?

Abstract: The superior performance of Convolutional Neural Networks (CNNs)
has been demonstrated in many applications such as image classification,
detection and processing. Yet, the CNN solution has its own weaknesses such
as robustness against perturbation, scalability against the class number
and portability among different datasets. Furthermore, CNN’s working
principle remains mysterious. In this talk, I will first explain the
reasons behind the superior performance of CNNs. Then, I will present an
alternative solution, which is motivated by CNNs yet allows rigorous and
transparent mathematical treatment, based on a data-driven Saak (Subspace
approximation with augmented kernels) transform. The kernels of the Saak
transform are derived from the second-order statistics of inputs in a
one-pass feedforward way. Neither data labels nor backpropagation is needed
in kernel determination. The pros and cons of CNNs and multi-stage Saak
transforms are compared.

Bio: Dr. C.-C. Jay Kuo received his Ph.D. degree from the Massachusetts
Institute of Technology in 1987. He is now with the University of Southern
California (USC) as Director of the Media Communications Laboratory and
Dean’s Professor in Electrical Engineering-Systems. His research interests
are in the areas of digital media processing, compression, communication
and networking technologies. Dr. Kuo was the Editor-in-Chief for the IEEE
Trans. on Information Forensics and Security in 2012-2014. He was the
Editor-in-Chief for the Journal of Visual Communication and Image
Representation in 1997-2011, and served as Editor for 10 other
international journals. Dr. Kuo received the 1992 National Science
Foundation Young Investigator (NYI) Award, the 1993 National Science
Foundation Presidential Faculty Fellow (PFF) Award, the 2010 Electronic
Imaging Scientist of the Year Award, the 2010-11 Fulbright-Nokia
Distinguished Chair in Information and Communications Technologies, the
2011 Pan Wen-Yuan Outstanding Research Award, the 2014 USC Northrop Grumman
Excellence in Teaching Award, the 2016 USC Associates Award for Excellence
in Teaching, the 2016 IEEE Computer Society Taylor L. Booth Education
Award, the 2016 IEEE Circuits and Systems Society John Choma Education
Award, the 2016 IS&T Raymond C. Bowman Award, and the 2017 IEEE Leon K.
Kirchmayer Graduate Teaching Award. Dr. Kuo is a Fellow of AAAS, IEEE and
SPIE. He has guided 140 students to their Ph.D. degrees and supervised 25
postdoctoral research fellows. Dr. Kuo is a co-author of about 250 journal
papers, 900 conference papers and 14 books.


Host: Sadaoki Furui <furui at ttic.edu>


For more information on the colloquium series or to subscribe to the
mailing list, please see http://www.ttic.edu/colloquium.php


Mary C. Marre
Administrative Assistant
*Toyota Technological Institute*
*6045 S. Kenwood Avenue*
*Room 504*
*Chicago, IL  60637*
*p:(773) 834-1757*
*f: (773) 357-6970*
*mmarre at ttic.edu <mmarre at ttic.edu>*

On Tue, Jan 16, 2018 at 11:36 AM, Mary Marre <mmarre at ttic.edu> wrote:

> NOTE: this talk is *NOT CANCELLED!*
>
> When:     Monday, January 22nd at *10:30 a.m. *
>
> Where:    TTIC, 6045 S. Kenwood Avenue, 5th Floor, Room 526
>
> Who:       C.-C. Jay Kuo, University of Southern California
>
>
> Title:        Why and Why Not Convolutional Neural Networks (CNNs)?
>
> Abstract: The superior performance of Convolutional Neural Networks (CNNs)
> has been demonstrated in many applications such as image classification,
> detection and processing. Yet, the CNN solution has its own weaknesses such
> as robustness against perturbation, scalability against the class number
> and portability among different datasets. Furthermore, CNN’s working
> principle remains mysterious. In this talk, I will first explain the
> reasons behind the superior performance of CNNs. Then, I will present an
> alternative solution, which is motivated by CNNs yet allows rigorous and
> transparent mathematical treatment, based on a data-driven Saak (Subspace
> approximation with augmented kernels) transform. The kernels of the Saak
> transform are derived from the second-order statistics of inputs in a
> one-pass feedforward way. Neither data labels nor backpropagation is needed
> in kernel determination. The pros and cons of CNNs and multi-stage Saak
> transforms are compared.
>
>
> Host: Sadaoki Furui <furui at ttic.edu>
>
>
> For more information on the colloquium series or to subscribe to the
> mailing list, please see http://www.ttic.edu/colloquium.php
>
>
>
> Mary C. Marre
> Administrative Assistant
> *Toyota Technological Institute*
> *6045 S. Kenwood Avenue*
> *Room 504*
> *Chicago, IL  60637*
> *p:(773) 834-1757 <(773)%20834-1757>*
> *f: (773) 357-6970 <(773)%20357-6970>*
> *mmarre at ttic.edu <mmarre at ttic.edu>*
>
> On Mon, Jan 15, 2018 at 7:29 PM, Mary Marre <mmarre at ttic.edu> wrote:
>
>> When:     Monday, January 22nd at *10:30 a.m. *
>>
>> Where:    TTIC, 6045 S. Kenwood Avenue, 5th Floor, Room 526
>>
>> Who:       C.-C. Jay Kuo, University of Southern California
>>
>>
>> Title:        Why and Why Not Convolutional Neural Networks (CNNs)?
>>
>> Abstract: The superior performance of Convolutional Neural Networks
>> (CNNs) has been demonstrated in many applications such as image
>> classification, detection and processing. Yet, the CNN solution has its own
>> weaknesses such as robustness against perturbation, scalability against the
>> class number and portability among different datasets. Furthermore, CNN’s
>> working principle remains mysterious. In this talk, I will first explain
>> the reasons behind the superior performance of CNNs. Then, I will present
>> an alternative solution, which is motivated by CNNs yet allows rigorous and
>> transparent mathematical treatment, based on a data-driven Saak (Subspace
>> approximation with augmented kernels) transform. The kernels of the Saak
>> transform are derived from the second-order statistics of inputs in a
>> one-pass feedforward way. Neither data labels nor backpropagation is needed
>> in kernel determination. The pros and cons of CNNs and multi-stage Saak
>> transforms are compared.
>>
>>
>> Host: Sadaoki Furui <furui at ttic.edu>
>>
>>
>> For more information on the colloquium series or to subscribe to the
>> mailing list, please see http://www.ttic.edu/colloquium.php
>>
>>
>>
>>
>>
>> Mary C. Marre
>> Administrative Assistant
>> *Toyota Technological Institute*
>> *6045 S. Kenwood Avenue*
>> *Room 504*
>> *Chicago, IL  60637*
>> *p:(773) 834-1757 <(773)%20834-1757>*
>> *f: (773) 357-6970 <(773)%20357-6970>*
>> *mmarre at ttic.edu <mmarre at ttic.edu>*
>>
>
>
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