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KAN: Kolmogorov-Arnold Networks | Ziming Liu

Portal is the home of the AI for drug discovery community. Join for more details on this talk and to connect with the speakers: https://portal.valencelabs.com/logg Abstract: Inspired by the Kolmogorov-Arnold representation theorem, we propose Kolmogorov-Arnold Networks (KANs) as promising alternatives to Multi-Layer Perceptrons (MLPs). While MLPs have fixed activation functions on nodes ("neurons"), KANs have learnable activation functions on edges ("weights"). KANs have no linear weights at all -- every weight parameter is replaced by a univariate function parametrized as a spline. We show that this seemingly simple change makes KANs outperform MLPs in terms of accuracy and interpretability. For accuracy, much smaller KANs can achieve comparable or better accuracy than much larger MLPs in data fitting and PDE solving. Theoretically and empirically, KANs possess faster neural scaling laws than MLPs. For interpretability, KANs can be intuitively visualized and can easily interact with human users. Through two examples in mathematics and physics, KANs are shown to be useful collaborators helping scientists (re)discover mathematical and physical laws. In summary, KANs are promising alternatives for MLPs, opening opportunities for further improving today's deep learning models which rely heavily on MLPs. Speakers: Ziming Liu Twitter Hannes:   / hannesstaerk   Twitter Dominique:   / dom_beaini   ~ Chapters 00:00 - Intro + Background 05:06 - From KART to KAN 07:56 - MLP vs KAN 16:05 - Accuracy: Scaling of KANs 26:35 - Interpretability: KAN for Science 38:04 - Q+A Break 57:15 - Strengths and Weaknesses 59:28 - Philosophy 1:08:45 - Anecdotes Behind the Scenes 1:11:49 - Final Thoughts 1:14:58 - Q+A