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https://arxiv.org/abs/2404.19756v4 KAN: Kolmogorov-Arnold NetworksInspired 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 activatarxiv.org1. IntroductionKANs 네트워크 방식은 MLP를 대체하는 새로운 방식의 layer로 본 논문에서는 소개하고 ..

https://arxiv.org/abs/1503.02406 Deep Learning and the Information Bottleneck Principle Deep Neural Networks (DNNs) are analyzed via the theoretical framework of the information bottleneck (IB) principle. We first show that any DNN can be quantified by the mutual information between the layers and the input and output variables. Using this re arxiv.org 1. Introduction 논문에서는 현재 많이 사용하고 있는 딥러닝 분야에..