The PyTorch implementation of Generative Pre-trained Transformers (GPTs) using Kolmogorov-Arnold Networks (KANs) for language modeling
Kolmogorov–Arnold Networks (KAN) in PyTorch
Testing KAN-based text generation GPT models
Code for Kolmogorov-Arnold Network for Quantum Architecture Search i.e., KANQAS
NeuroBender, where the mystical powers of KAN network meet the robust stability of autoencoders!
Implementing KANs with TensorFlow
given beta-holder continuous function f:[0,1]^d -> R, this deep ReLU network approximates f up to approximation rate of 2^(-K beta) using 2^Kd parameters. Here, K is a set positive integer and d the dimension.
An implementation of the KAN architecture using learnable activation functions for knowledge distillation on the MNIST handwritten digits dataset. The project demonstrates distilling a three-layer teacher KAN model into a more compact two-layer student model, comparing the performance impacts of distillation versus non-distilled models.
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