3 citations · 4 across the 5 of their papers we have counts for
5 papers
Autocorrelation Reintroduces Spectral Bias in KANs for Time Series Forecasting
Chen Zeng, Jiahui Wang, Qiao Wang
Existing theory suggests that Kolmogorov-Arnold Networks (KANs) can overcome the spectral bias commonly observed in neural networks under the assumption that inputs are statistical…
Audio Frequency-Time Dual Domain Evaluation on Depression Diagnosis
Yu Luo, Nan Huang, Sophie Yu +5
Depression, as a typical mental disorder, has become a prevalent issue significantly impacting public health. However, the prevention and treatment of depression still face multipl…
AR-KAN: Autoregressive-Weight-Enhanced Kolmogorov-Arnold Network for Time Series Forecasting
Chen Zeng, Tiehang Xu, Qiao Wang
Traditional neural networks struggle to capture the spectral structure of complex signals. Fourier neural networks (FNNs) attempt to address this by embedding Fourier series compon…
KAN versus MLP on Irregular or Noisy Functions
Chen Zeng, Jiahui Wang, Haoran Shen +1
In this paper, we compare the performance of Kolmogorov-Arnold Networks (KAN) and Multi-Layer Perceptron (MLP) networks on irregular or noisy functions. We control the number of pa…
Reduced Effectiveness of Kolmogorov-Arnold Networks on Functions with Noise
Haoran Shen, Chen Zeng, Jiahui Wang +1
It has been observed that even a small amount of noise introduced into the dataset can significantly degrade the performance of KAN. In this brief note, we aim to quantitatively ev…