IEEE Region 6 Central Area
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CASS-SCV Artificial Intelligence for Industry (AI4I) Forum – Fall 2019
September 6, 2019 @ 4:00 pm - 8:00 pm
Co-sponsored by: Solid State Circuits Society (SSCS), Computer Society (CS), Computational Intelligence Society
CASS-SCV Artificial Intelligence for Industry (AI4I) Forum – Fall 2019
Event sponsored and organized by:
IEEE Circuits and Systems Society (CASS)
Co-sponsors:
- Circuits and Systems Society – Santa Clara Valley Chapter (CASS-SCV)
- Computer Society (CS)
- Computational Intelligence Society (CIS)
- Solid-State Circuits Society(SSCS)
- Signal Processing Society(SPS)
- Communication Society (ComSoC)
DATE & TIME:
Friday, September 6th, 2019. 1 PM – 5 PM
PROGRAM:
1:00 – 1:30 PM Check-in / Networking & Refreshments
1:30 – 2:15 PM Prof. S.Y. Kung (Princeton)
2:15 – 3:00 PM Pete Warden (Google)
3:30 – 4:15 PM TBD
4:15 – 5:00 PM TBD
5:00 PM Adjourn
LOCATION:
Intel SC-9 Auditorium
2191 Laurelwood Rd, Santa Clara, CA 95054 (Northwest corner of 101 & Montague Expwy)
AGENDA:
1:30 – 2:15 PM Prof. S.Y. Kung (Princeton)
TITLE: From Deep Learning to X-Learning: An Internal and Explainable Learning for XAI
ABSTRACT: The success of deep Learning (or AI2.0) depends solely on Back-propagation (BP), a external learning paradigm, whose supervision is exclusively accessed via the external interfacing nodes (i.e. input/output neurons). As such, Deep Learning has been limited to the parameter training of the neural nets (NNs). The important task of designing optimal net structures has to resort to trial and error. Therefore, we shall design an Xnet which may be use to simultaneously train the structure and parameters of the net. In addition, it can facilitate Internal Neuron’s Explainablility so as to fully support DARPA’s Explainable AI (i.e. XAI or AI3.0). Our internal learning paradigm leads to an Explainable Neural Networks (Xnet) comprising (1) internal teacher labels (ITL) and (2) internal optimization metrics (IOM). X-learning allows us to effectively rank the internal neurons (hidden nodes) and thus sets the footing for the notion of structural gradient and structural learning.
Pursuant to our simulation studies, Xnet can simultaneously compress the structure and raise the accuracy. There is evidence supporting that it may outperform many popular pruning/compression methods. Most importantly, X-learning opens up promising research fronts on (1) explainable learning models for XAI and (2) machine-to-machine mutual learning which will become appealing in the 5G era
BIO: S.Y. Kung, Life Fellow of IEEE, is a Professor at Department of Electrical Engineering in Princeton University. His research areas include multimedia information processing, machine learning, systematic design of deep learning networks, VLSI array processors, and compressive privacy. He was a founding member of several Technical Committees (TC) of the IEEE Signal Processing Society. He was elected to Fellow in 1988 and served as a Member of the Board of Governors of the IEEE Signal Processing Society (1989-1991). He was a recipient of IEEE Signal Processing Society’s Technical Achievement Award for the contributions on “parallel processing and neural network algorithms for signal processing” (1992); a Distinguished Lecturer of IEEE Signal Processing Society (1994); a recipient of IEEE Signal Processing Society’s Best Paper Award; and a recipient of the IEEE Third Millennium Medal (2000). Since 1990, he has been the Editor-In-Chief of the Journal of VLSI Signal Processing Systems. He has authored and co-authored more than 500 technical publications and numerous textbooks including “VLSI Array Processors”, Prentice-Hall (1988); “Digital Neural Networks”, Prentice-Hall (1993) ; “Principal Component Neural Networks”, John-Wiley (1996); “Biometric Authentication: A Machine Learning Approach”, Prentice-Hall (2004); and “Kernel Methods and Machine Learning”, Cambridge University Press (2014).
2:15 – 3:00 PM Pete Warden (Google)
TITLE:
ABSTRACT:
BIO:
3:30 – 4:15 PM TBD
TITLE:
ABSTRACT:
BIO:
4:15 – 5:00 PM TBD
TITLE:
ABSTRACT:
BIO:
Agenda:
CASS-SCV Artificial Intelligence for Industry (AI4I) Forum – Fall 2019
Event sponsored and organized by:
IEEE Circuits and Systems Society (CASS)
Co-sponsors:
- Circuits and Systems Society – Santa Clara Valley Chapter (CASS-SCV)
- Computer Society (CS)
- Computational Intelligence Society (CIS)
- Solid-State Circuits Society(SSCS)
- Signal Processing Society(SPS)
- Communication Society (ComSoC)
DATE & TIME:
Friday, September 6th, 2019. 1 PM – 5 PM
PROGRAM:
1:00 – 1:30 PM Check-in / Networking & Refreshments
1:30 – 2:15 PM Prof. S.Y. Kung (Princeton)
2:15 – 3:00 PM Pete Warden (Google)
3:30 – 4:15 PM TBD
4:15 – 5:00 PM TBD
5:00 PM Adjourn
LOCATION:
Intel SC-9 Auditorium
2191 Laurelwood Rd, Santa Clara, CA 95054 (Northwest corner of 101 & Montague Expwy)
AGENDA:
1:30 – 2:15 PM Prof. S.Y. Kung (Princeton)
TITLE: From Deep Learning to X-Learning: An Internal and Explainable Learning for XAI
ABSTRACT: The success of deep Learning (or AI2.0) depends solely on Back-propagation (BP), a external learning paradigm, whose supervision is exclusively accessed via the external interfacing nodes (i.e. input/output neurons). As such, Deep Learning has been limited to the parameter training of the neural nets (NNs). The important task of designing optimal net structures has to resort to trial and error. Therefore, we shall design an Xnet which may be use to simultaneously train the structure and parameters of the net. In addition, it can facilitate Internal Neuron’s Explainablility so as to fully support DARPA’s Explainable AI (i.e. XAI or AI3.0). Our internal learning paradigm leads to an Explainable Neural Networks (Xnet) comprising (1) internal teacher labels (ITL) and (2) internal optimization metrics (IOM). X-learning allows us to effectively rank the internal neurons (hidden nodes) and thus sets the footing for the notion of structural gradient and structural learning.
Pursuant to our simulation studies, Xnet can simultaneously compress the structure and raise the accuracy. There is evidence supporting that it may outperform many popular pruning/compression methods. Most importantly, X-learning opens up promising research fronts on (1) explainable learning models for XAI and (2) machine-to-machine mutual learning which will become appealing in the 5G era
BIO: S.Y. Kung, Life Fellow of IEEE, is a Professor at Department of Electrical Engineering in Princeton University. His research areas include multimedia information processing, machine learning, systematic design of deep learning networks, VLSI array processors, and compressive privacy. He was a founding member of several Technical Committees (TC) of the IEEE Signal Processing Society. He was elected to Fellow in 1988 and served as a Member of the Board of Governors of the IEEE Signal Processing Society (1989-1991). He was a recipient of IEEE Signal Processing Society’s Technical Achievement Award for the contributions on “parallel processing and neural network algorithms for signal processing” (1992); a Distinguished Lecturer of IEEE Signal Processing Society (1994); a recipient of IEEE Signal Processing Society’s Best Paper Award; and a recipient of the IEEE Third Millennium Medal (2000). Since 1990, he has been the Editor-In-Chief of the Journal of VLSI Signal Processing Systems. He has authored and co-authored more than 500 technical publications and numerous textbooks including “VLSI Array Processors”, Prentice-Hall (1988); “Digital Neural Networks”, Prentice-Hall (1993) ; “Principal Component Neural Networks”, John-Wiley (1996); “Biometric Authentication: A Machine Learning Approach”, Prentice-Hall (2004); and “Kernel Methods and Machine Learning”, Cambridge University Press (2014).
2:15 – 3:00 PM Pete Warden (Google)
TITLE:
ABSTRACT:
BIO:
3:30 – 4:15 PM TBD
TITLE:
ABSTRACT:
BIO:
4:15 – 5:00 PM TBD
TITLE:
ABSTRACT:
BIO:
Location:
Bldg: Intel SC-9 Auditorium
Intel SC-9 Auditorium
2191 Laurelwood Rd
Santa Clara, California
95054