Learn how to implement SGD with momentum from scratch in Python—boost your optimization skills for deep learning.

Abstract: In this article, we deal with stochastic optimization problems where the data distributions change in response to the decision variables. Traditionally, the study of optimization problems ...

A group of tech executives, app developers and Silicon Valley philosophers is seeking to streamline the messy matters of the ...

Mini Batch Gradient Descent is an algorithm that helps to speed up learning while dealing with a large dataset. Instead of ...

This study presents SynaptoGen, a differentiable extension of connectome models that links gene expression, protein-protein interaction probabilities, synaptic multiplicity, and synaptic weights, and ...

As renewable power rapidly reshapes global electricity systems, engineers face a growing challenge: how to operate increasingly complex grids with ...

ABSTRACT: Mathematical optimization is a fundamental aspect of machine learning (ML). An ML task can be conceptualized as optimizing a specific objective using the training dataset to discern patterns ...

With the increasing integration of a high proportion of renewable energy, the fluctuation characteristics of distributed power generation such as wind and photovoltaic energy affect the safe and ...

Abstract: This work develops and analyzes a class of adaptive biased stochastic optimization (ABSO) algorithms from the perspective of the GEneralized Adaptive gRadient (GEAR) method that contains ...

With the increasing penetration of electric vehicles (EVs) in road traffic, the spatial and temporal stochasticity of the travel pattern and charging demand of EVs as a mode of transportation and an ...