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 ...
AZoOptics

Low-Dimensional Materials in Nonlinear Optics

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

An Optimized Port Operation Efficiency Prediction Model Based on ESN and LSTM ()

Liu, X. , Mei, R. , Zhao, Z. , Wang, S. and Duan, J. (2025) An Optimized Port Operation Efficiency Prediction Model Based on ...
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 ...
IEEE

Versatile Bipedal Locomotion and Walking-Running Transition: Coordinating Supervised Learning and Nonlinear Optimization

Abstract: Online gait planning plays a crucial role for the locomotion of humanoid robots. While simplified models often fail to capture critical dynamic features of the robot’s motion, making online ...
C&EN

Development and Optimization of a Scalable Enzymatic Cascade-Carbamate Formation Telescope Process for the Synthesis of CDK2 Selective Candidate Tegtociclib (PF-07104091)

Pfizer Chemical Research and Development, Pfizer Worldwide Research and Development, Groton, Connecticut 06340, United States ...
IEEE

A Predefined-Time Fuzzy Adaptive RNN for Time-Dependent Bound-Constrained Nonlinear Optimization With Applications

Abstract: Recently, recurrent neural network (RNN) models have been extensively investigated for addressing time-dependent nonlinear optimization problems. However, few existing RNN models take ...