This is a paper breakdown video of the paper: Kolmogorov Arnold Networks, which brilliantly provides an alternative to standard Multi Layer Perceptrons. The video discusses the main contributions and core ideas of the paper, visually explaining the math, concepts, and challenges ahead.

#deeplearning #machinelearning #neuralnetworks

To access the animations, narration scripts, slides, notes, etc for the video, consider joining us on Patreon.

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Timestamps:
0:00 Intro
1:03 Kolmogorov Arnold Representation Theorem
5:05 KAN Layers
8:00 Comparisons
9:00 Bsplines
11:08 Grid Extension, Sparsification, Continual Learning
14:00 KANs get the best of MLPs and Splines
15:00 Advantages and Challenges for KANs

Check out the paper:
https://arxiv.org/abs/2404.19756

Check out code:
https://kindxiaoming.github.io/pykan/... and
https://github.com/KindXiaoming/pykan