IEEE6d
On the Convergence of Decentralized Stochastic Gradient Descent With Biased Gradients
Abstract: Stochastic optimization algorithms are widely used to solve large-scale ... To address this issue, we perform a comprehensive convergence rate analysis of stochastic gradient descent (SGD) ...
IEEE23d
Differentially Private Federated Stochastic Primal-Dual Learning for Internet of Vehicles
The FedSPD-DP algorithm leverages multiple steps of local stochastic gradient descent (SGD) and partial client participation (PCP) to improve communication efficiency while incorporating differential ...
Scientific Research Publishing26d
Ruder, S. (2017) An Overview of Gradient Descent Optimization Algorithms. arXiv: 1609.04747.
ABSTRACT: As drivers age, roadway conditions may become more challenging, particularly when normal aging is coupled with cognitive decline. Driving during lower visibility conditions, such as ...
Semiconductor Engineering26d
Analog Accelerator For AI/ML Training Workloads Using Stochastic Gradient Descent (Imperial College London)
A new technical paper titled “Learning in Log-Domain: Subthreshold Analog AI Accelerator Based on Stochastic Gradient Descent” was published by researchers at Imperial College London. “The rapid ...
Quanta Magazine26d
New Book-Sorting Algorithm Almost Reaches Perfection
Computer scientists often deal with abstract problems that are hard to comprehend, but an exciting new algorithm matters to anyone who owns books and at least one shelf. The algorithm addresses ...