Signature neural patterns may help predict recovery from traumatic brain injury

After traumatic brain injury (TBI), some patients may recover completely, while others retain severe disabilities. Accurately ...

Kolmogorov-Arnold networks bridge AI and scientific discovery by increasing interpretability

AI has successfully been applied in many areas of science, advancing technologies like weather prediction and protein folding ...
Scientific Research Publishing

Mostafanejad, M. (2023) Unification of Popular Artificial Neural Network Activation Functions. arXiv:2302.11007.

ABSTRACT: Artificial deep neural networks (ADNNs) have become a cornerstone of modern machine learning, but they are not immune to challenges. One of the most significant problems plaguing ADNNs is ...
Scientific Research Publishing

Vanishing Gradients in an Artificial Deep Neural Network: The Impact of Activation Functions and Optimization Techniques ()

Artificial deep neural networks (ADNNs) have become a cornerstone of modern machine learning, but they are not immune to challenges. One of the most significant problems plaguing ADNNs is the ...
Hosted on MSN

20 Activation Functions in Python for Deep Neural Networks – ELU, ReLU, Leaky-ReLU, Sigmoid, Cosine

Explore 20 different activation functions for deep neural networks, with Python examples including ELU, ReLU, Leaky-ReLU, Sigmoid, and more. #ActivationFunctions #DeepLearning #Python What the US ...
IEEE

An Optical Feedforward Neural Network with Nonlinear Activation Function Provided by a RSAM

Abstract: A feedforward optical neural network for image recognition is demonstrated through free-space optics in which the nonlinear activation function is provided by a resonant semiconductor ...
Frontiers

Deriving equivalent symbol-based decision models from feedforward neural networks

1 KNDS Deutschland GmbH & Co. KG, Munich, Germany 2 Institute for Software Technology, University of the Bundeswehr Munich, Neubiberg, Germany Artificial intelligence (AI) has emerged as a ...
Frontiers

Benchmarking of feed-forward neural network models for genomic prediction of quantitative traits in pigs

Artificial neural networks are machine learning models that have been applied to various genomic problems, with the ability to learn non-linear relationships and model high-dimensional data. These ...