5 papers
Variational (Energy-Based) Spectral Learning: A Machine Learning Framework for Solving Partial Differential Equations
M. M. Hammad
We introduce variational spectral learning (VSL), a machine learning framework for solving partial differential equations (PDEs) that operates directly in the coefficient space of…
Schrodinger Neural Network and Uncertainty Quantification: Quantum Machine
M. M. Hammad
We introduce the Schrodinger Neural Network (SNN), a principled architecture for conditional density estimation and uncertainty quantification inspired by quantum mechanics. The SN…
Artificial Neural Network and Deep Learning: Fundamentals and Theory
M. M. Hammad
"Artificial Neural Network and Deep Learning: Fundamentals and Theory" offers a comprehensive exploration of the foundational principles and advanced methodologies in neural networ…
Comprehensive Survey of Complex-Valued Neural Networks: Insights into Backpropagation and Activation Functions
M. M. Hammad
Artificial neural networks (ANNs), particularly those employing deep learning models, have found widespread application in fields such as computer vision, signal processing, and wi…
Deep Learning Activation Functions: Fixed-Shape, Parametric, Adaptive, Stochastic, Miscellaneous, Non-Standard, Ensemble
M. M. Hammad
In the architecture of deep learning models, inspired by biological neurons, activation functions (AFs) play a pivotal role. They significantly influence the performance of artific…