Witold Pedrycz (IEEE Life Fellow) is Professor in the Department of
Electrical and Computer Engineering, University of Alberta, Edmonton,
Canada. He is also with the Systems Research Institute of the Polish
Academy of Sciences, Warsaw, Poland. Dr. Pedrycz is a foreign member of
the Polish Academy of Sciences and a Fellow of the Royal Society of
Canada. He is a recipient of several awards including Norbert Wiener
award from the IEEE Systems, Man, and Cybernetics Society, IEEE Canada
Computer Engineering Medal, a Cajastur Prize for Soft Computing from the
European Centre for Soft Computing, a Killam Prize, a Fuzzy Pioneer
Award from the IEEE Computational Intelligence Society, and 2019
Meritorious Service Award from the IEEE Systems Man and Cybernetics
Society.
His main research directions involve Computational
Intelligence, Granular Computing, and Machine Learning, among others.
Professor Pedrycz serves as an Editor-in-Chief of Information
Sciences, Editor-in-Chief of WIREs Data Mining and Knowledge Discovery (Wiley), and Co-editor-in-Chief of Int. J. of Granular Computing (Springer) and J. of Data Information and Management (Springer).
Speech Title:
Credibility, Data Privacy, and Energy Awareness: Advances in Machine
Learning
Abstract: Over the recent years, we have been
witnessing spectacular achievements of Machine Learning with highly
visible accomplishments encountered, in particular, in natural language
processing and computer vision impacting numerous areas of human
endeavours. Driven inherently by the technologically advanced learning
and architectural developments, Machine Learning constructs are highly
impactful coming with far reaching consequences; just to mention
autonomous vehicles, control, health care imaging, decision-making in
critical areas, among others.
We advocate that the design and
analysis of ML constructs have to be carried out in a holistic manner by
identifying and addressing a series of central and unavoidable quests
coming from industrial environments and implied by a plethora of
requirements of interpretability, energy awareness (being also lucidly
identified on the agenda of green AI), efficient quantification of
quality of ML constructs, their brittleness and conceptual stability
coming hand in hand with the varying levels of abstraction. They are
highly intertwined and exhibit relationships with the technological end
of ML. As such, they deserve prudent attention, in particular when a
multicriterial facet of the problem is considered.
The talk
elaborates on the above challenges, offers definitions and identifies
the linkages among them. In the pursuit of coping with such quests, we
advocate that Granular Computing can play a pivotal role offering a
conceptual environment and realizing algorithmic development. We stress
and identify ways to effective assessments of credibility of ML
constructs. As a detailed study, we discuss the ideas of knowledge
transfer showing how a thoughtful and prudently arranged knowledge reuse
to support energy-aware ML computing. We discuss passive and active
modes of knowledge transfer. In both modes, the essential role of
information granularity is identified. In the passive approach,
information granularity serves as a vehicle to quantify the credibility
of the transferred knowledge. In the active approach, a new model is
constructed in the target domain whereas the design is guided by the
loss function, which involves granular regularization produced by the
granular model transferred from the source domain. A generalized
scenario of multi-source domains is discussed. Knowledge distillation
leading to model compression is also studied in the context of transfer
learning.