Deep Learning with Yacine on MSN

Muon Optimizer for Dense Linear Layers – Newton-Schulz Method with Momentum Explained

Dive deep into the Muon Optimizer and learn how it enhances dense linear layers using the Newton-Schulz method combined with ...
IEEE

DE-KAN: A Differential Evolution-Based Optimization Framework for Enhancing Kolmogorov-Arnold Networks in Complex Nonlinear Modeling

Abstract: Kolmogorov-Arnold Networks (KANs) offer superior fitting ability and interpretability compared to traditional Multi-Layer Perceptrons (MLPs). However, their complex structure and lengthy ...
AZoOptics

Low-Dimensional Materials in Nonlinear Optics

Advancements in nonlinear optics using 2D materials are transforming photonic devices, offering enhanced performance and ...
IEEE

Data-Based Modeling and Control of the Nonlinear Aircraft System Using Extended Implicit Sparse Identification

Abstract: Modeling and controlling aircraft can be particularly challenging when the system is highly nonlinear or only partially understood. While data-driven approaches can be promising in this ...
Frontiers

Advancing Computational Material Science and Mechanics through Integrated Deep Learning Models

The field of computational materials science has been profoundly transformed by integrating deep learning and other machine learning methodologies. These sophisticated data-driven approaches have ...

Researcher improves century-old equation to predict movement of dangerous air pollutants

A new method developed at the University of Warwick offers the first simple and predictive way to calculate how irregularly ...
GitHub

ut-beg-texnet/NonLinLoc

The NonLinLoc (Non-Linear Location) package is a set of programs for velocity model construction, travel-time calculation and probabilistic, non-linear, global-search earthquake location in 3D ...