The authors introduce a new algorithm for determining the minimal anisotropic Gaussian noise required to satisfy mutual ...

This notebook illustrates the nature of the Stochastic Gradient Descent (SGD) and walks through all the necessary steps to create SGD from scratch in Python. Gradient Descent is an essential part of ...

This notebook illustrates the nature of the Stochastic Gradient Descent (SGD) and walks through all the necessary steps to create SGD from scratch in Python. Gradient Descent is an essential part of ...

MULGONET combines deep learning and multi-omics analysis to predict tumor recurrence, addressing interpretability and ...

This study addresses the growing demand for news text classification driven by the rapid expansion of internet information by proposing a classification algorithm based on a Bidirectional Gated ...

Adaptive Optimization,Adaptive Scheme,Adaptive Step Size,Central Server,Convergence Rate,Data Distribution,Federated Learning,Gradient Descent,Gradient Descent ...

The formulation of the controller considers a magnetorheological damper represented through the Bouc-Wen model. A stochastic gradient descent algorithm with backward propagation is used to train the ...

This valuable study introduces a self-supervised machine learning method to classify C. elegans postures and behaviors directly from video data, offering an alternative to the skeleton-based ...