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Found 4 papers, 4 papers with code
M-ar-K-Fast Independent Component Analysis
This study presents the m-arcsinh Kernel ('m-ar-K') Fast Independent Component Analysis ('FastICA') method ('m-ar-K-FastICA') for feature extraction.
hyper-sinh: An Accurate and Reliable Function from Shallow to Deep Learning in TensorFlow and Keras
This paper presents the 'hyper-sinh', a variation of the m-arcsinh activation function suitable for Deep Learning (DL)-based algorithms for supervised learning, such as Convolutional Neural Networks (CNN).
QReLU and m-QReLU: Two novel quantum activation functions to aid medical diagnostics
The ReLU activation function (AF) has been extensively applied in deep neural networks, in particular Convolutional Neural Networks (CNN), for image classification despite its unresolved dying ReLU problem, which poses challenges to reliable applications.
m-arcsinh: An Efficient and Reliable Function for SVM and MLP in scikit-learn
This paper describes the 'm-arcsinh', a modified ('m-') version of the inverse hyperbolic sine function ('arcsinh').
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