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This small project test the novel architecture Kolmogorov-Arnold Networks (KAN) in the reinforcement learning paradigm to the CartPole problem. KANs are promising alternatives of Multi-Layer Perceptrons (MLPs).
Python library : torch_kan. Simple implementation of a KAN Layer following the below paper. We use an optimized version of B-Splines computation. Only grid extension feature is implemented, where you are able to arbitrary increase the number of control points to get less smooth splines curves.
A quick and dirty modification of nanoGPT to support transformers with KAN (Kansformer). Install. Follow the [install] and [quick start] sections of nanoGPT below. Install kan dependencies. pip install matplotlib scikit-learn. Add use_kan=True in config file. Example: config/train_shakespeare_char_kan.py. nanoGPT.
Jun 5, 2024 · We investigate, modify and re-design the established U-Net pipeline by integrating the dedicated KAN layers on the tokenized intermediate representation, termed U-KAN. Rigorous medical image segmentation benchmarks verify the superiority of U-KAN by higher accuracy even with less computation cost.
May 8, 2024 · Kolmogorov-Arnold Networks, a.k.a KANs, is a type of neural network architecture inspired by the Kolmogorov-Arnold representation theorem. Unlike traditional neural networks that use fixed activation functions, KANs employ learnable activation functions on the edges of the network.
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Apr 30, 2024 · There, the authors propose an alternative to the MLP: the Kolmogorov-Arnold Network or KAN. Instead of using weights and fixed activation functions, the KAN uses learnable functions that are parametrized as splines.
