In this talk, Ted and Warren went over Kolmogorov-Arnold Networks (KANs). KAN is a new network architecture that could end up replacing the
MultiLayer Preceptrons (MLPs) that are at the heart of the leading high-performance models today such as Chat-GPT, Gemini, and others. In fact, KANs have been described by some as a new technique that could change the very foundation on which the AI industry is based and replace MLPs as the default Network Architecture.

The talk is based on Kolmogorov and Arnold's work described in the paper, KAN: Kologorov-Arnold Networks. This paper describes how with a parameterized univariate function, such as a spline, we can create networks that outperforms MLPs in accuracy and interpretability. And that KANs are easier for us to visualize and interact with. These are bold claims and we discussed them throughout the meetup.

Ted and Warren did a great job of going through the paper and contrasting how the KANs and MLPs vary in fundamental ways. During the discussion opinions were offered as to whether KANs are being over, or under, hyped.

Links
Our Meetup: https://www.meetup.com/East-Bay-Tri-V...
KAN - Kolmogorov-Arnold Networks: https://arxiv.org/abs/2404.19756
Code - pykan: https://github.com/KindXiaoming/pykan

Content
00:00 Introduction
10:04 K-A representation
18:57 Motivation
22:10 Architecture
27:58 Terminology
30:30 K-A example
35:05 MLP-KAN comparison
37:54 KAN details
59:10 Interpretability
1:00:05 Examples
1:08:21 Discussion

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