Kolmogorov-Arnold Networks

Tze-Chien Sun, Wayne State University

Lately, the paper "KAN: Kolmogorov-Arnold Networks" by Ziming Liu and seven other authors stirs a lot of interests, both praises and skepticisms. The Kolmogorov-Arnold Theorem roughly says that a continuous function of several variables can be written as compositions of continuous functions of one variable. They use a repeated version of this Theorem and make it into a deep layer network. They claim that it has better interpretability and easier interaction with users than the widely used Neural Networks. I will give you a brief introduction to this new method and let you decide whether it is really useful.

About Tze-Chien

Tze-Chien Sun is a professor emeritus in the Department of Mathematics at Wayne State University. His research interests include probability theory, time series analysis and, more recently, deep learning.

CAD Seminar Series: Advancing Knowledge, Innovation and Collaboration in Computation, AI and Data Science (CAD)

The CAD Seminar Series is a dedicated platform for advancing knowledge, fostering innovation and promoting collaboration across the fields of computation, artificial intelligence and data science. This series brings together leading experts, researchers and professionals to explore the latest developments, tackle emerging challenges and drive forward-thinking solutions at the convergence of these critical disciplines.

Objectives:

• Advance knowledge: Share cutting-edge research and insights that push the boundaries of what is known in CAD.
• Foster innovation: Encourage the development of novel ideas and solutions through interdisciplinary dialogue and creative thinking.
• Promote collaboration: Unite expertise across disciplines and build bridges between academia, industry and government to address complex problems and create opportunities for joint ventures.

The CAD Seminar Series is designed for a diverse audience, including faculty, researchers, students and professionals in Computation, AI, Data Science and related fields. It serves as a forum for exchanging ideas, networking and contributing to the growth of these rapidly evolving areas. We highly recommend in-person attendance to enhance engagement and networking opportunities with speakers and fellow participants.

Call for participation

We welcome contributions from researchers, practitioners and students. Whether presenting your work, participating in discussions, or attending as a learner, your involvement is crucial to the success of this collaborative initiative.