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

[Mary Marre via Colloquium]

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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