IEEE Computational Intelligence Society

Keynote: Federated Large Language Models
Keynote: Federated Large Language Models 1144 642 ieeeeduweek

Qiang YANG; Hong Kong University of Science and Technology. Abstract: Federated Learning is at the intersection of AI and privacy computing. How to make federated learning more trustworthy, effective and efficient is the focus of future industry and academia. In this talk, Prof Yang will review the progress and lay out challenges of trustworthy federated learning and federated large language models in the future.

IEEE-Affiliated Group Name: CIS

URL: https://resourcecenter.cis.ieee.org/conferences/cai-2024/ciscai2024conf0030

KEYNOTE: Trust in Optimization Algorithms – The End User Perspective
KEYNOTE: Trust in Optimization Algorithms – The End User Perspective 1138 640 ieeeeduweek

Tobias Rodemann, Honda Research Institute Europe, GERMANY. ABSTRACT: Evolutionary Algorithms have a potentially wide-spread usage. They can deal with various types of design parameters, constraints and objectives; non-linear, discontinuous, noisy fitness landscapes and many, even conflicting objectives can be handled. There are numerous open-source software packages for quickly applying EA methods on various problems. In practice, however, EAs are not used as frequently as we would hope.

IEEE-Affiliated Group Name: CIS

URL: https://resourcecenter.cis.ieee.org/conferences/wcci-2024/ciswcci2024conf0290

KEYNOTE: Fuzzy Systems to Support Safe and Trustworthy Artificial Intelligence
KEYNOTE: Fuzzy Systems to Support Safe and Trustworthy Artificial Intelligence 1137 643 ieeeeduweek

Francisco Herrera, University of Granada, Granada, SPAIN ABSTRACT: “Artificial Intelligence (AI) has matured as a technology, AI has quietly entered our lives, and it has taken a giant leap in the last year. Image generative AI models or the latest evolutions of large language models have meant that AI has gone, in just a few months, practically from science fiction to being an essential part of the daily lives of hundreds of millions of people around the world.

IEEE-Affiliated Group Name: CIS

URL: https://resourcecenter.cis.ieee.org/conferences/wcci-2024/ciswcci2024conf0160

Plenary – Physics-inspired learning on graphs
Plenary – Physics-inspired learning on graphs 1135 639 ieeeeduweek

Michael Bronstein, University of Oxford, UK; ABSTRACT: The message-passing paradigm has been the “battle horse” of deep learning on graphs for several years, making graph neural networks a big success in a wide range of applications, from particle physics to protein design. From a theoretical viewpoint, it established the link to the Weisfeiler-Lehman hierarchy, allowing to analyse the expressive power of GNNs.

IEEE-Affiliated Group Name: CIS

URL: https://resourcecenter.cis.ieee.org/conferences/wcci-2022/ciswcci2022con0250

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