KAN: Kolmogorov–Arnold Networks Paper Explained

#kan #mlp #deeplearning #machinelearning #ai In this video, I explained the recent research study that is Kolmogorov–Arnold representation theorem. The KAN is an approach in the field of machine learning that is based on the Kolmogorov-Arnold representation theorem from mathematical analysis. This method applies the theorem's insights to build predictive models for complex, high-dimensional datasets. KAN uses the idea that any multivariate function can be decomposed into sums and compositions of univariate functions. Full access of the paper: https://arxiv.org/html/2404.19756v1/ -------------------------------------------------------------------------------------------------------------------------------------------------------------- Generative AI Playlist: https://www.youtube.com/watch?v=ID04YmgzM38&list=PLzkBTicHqQFmdF62zHHramnBRZl6zUvmR -------------------------------------------------------------------------------------------------------------------------------------------------------------- Connect with me on social media platforms: Website: https://ai-researchstudies.com/ Google scholar: https://scholar.google.com/citations?user=kM4QN-8AAAAJ&hl=en LinkedIn: https://www.linkedin.com/in/manishasirsat GitHub:https://github.com/manishasirsat Quora: https://machinelearningresearch.quora.com/ Blogger: https://manisha-sirsat.blogspot.com/ Twitter: https://twitter.com/ManishaSirsat ⏱️ Timestamps 0:00 Intro 0:23 KAN Kolmogorov–Arnold Networks intro 1:04 Basics of MLP 1:56 Explained MLP approach presented in the paper 3:00 Explained KAN approach presented in the paper 4:18 KAN defined 6:13 symbolic regression with KAN 8:21 Accuracy 10:33 Interpretability 11:28 Should we use KAN or MLP?