Explore a detailed video breakdown of groundbreaking research that introduces Kolmogorov Arnold Networks (KAN) as an alternative to traditional Multi Layer Perceptrons in deep learning. Delve into the mathematical foundations through the Kolmogorov Arnold Representation Theorem, understand the architecture of KAN layers, and examine their performance comparisons with existing models. Learn about the integration of B-splines, innovative approaches to grid extension, sparsification, and continual learning capabilities. Discover how KANs combine the advantages of both MLPs and splines while exploring their potential benefits and implementation challenges. Access comprehensive visual explanations of complex mathematical concepts and architectural designs, supported by practical examples and detailed timestamps for easy navigation through specific topics. Follow along with available code implementations through PyKAN and explore further through provided research paper references.
Kolmogorov Arnold Networks: An Alternative Paradigm for Deep Learning
Neural Breakdown with AVB via YouTube
Overview
Syllabus
- Intro
- Kolmogorov Arnold Representation Theorem
- KAN Layers
- Comparisons
- B-splines
- Grid Extension, Sparsification, Continual Learning
- KANs get the best of MLPs and Splines
- Advantages and Challenges for KANs
Taught by
Neural Breakdown with AVB
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