To address this issue, we perform a comprehensive convergence rate analysis of stochastic gradient descent (SGD) with biased gradients for decentralized optimization. In non-convex settings, we show ...

Subsequently, by integrating the TRADE concept with the primal-dual stochastic gradient descent (SGD) algorithm, we propose a decentralized TRADE primal-dual SGD algorithm. We demonstrate that our ...

Researchers from King Abdullah University of Science and Technology (KAUST) and collaborators from SDAIA and the Swiss AI Lab IDSIA introduced semantic backpropagation and semantic gradient descent to ...

Lightweight implementations of popular concepts such as Stochastic Gradient Descent, various Loss functions and how they interact with automatic differentiation. How to speed up your neural networks ...

Gradient Able Pro is a free admin dashboard template built with different technologies. It is designed to offer an exceptional User Experience, with a wide range of customizable and feature-rich pages ...

while the stochastic gradient descent (SGD) algorithm reached 69.43% and 1.4246, respectively. The Adagrad algorithm, meanwhile, achieved 73.77% accuracy and 0.5202 loss in the training phase ...

Currently, the best methods for training and optimizing deep neural networks are variations of a technique called stochastic gradient descent (SGD). Training involves minimizing the errors the network ...