Abstract: Federated Learning (FL) is an emerging computing paradigm to collaboratively train Machine Learning (ML) models across multi-source data while preserving privacy. The major challenge of ...

ABSTRACT: A new nano-based architectural design of multiple-stream convolutional homeomorphic error-control coding will be conducted, and a corresponding hierarchical implementation of important class ...

callback: A callback function passed information in each iteration step. The information is, in this order: the parameters, the function value, the number of function evaluations, the stepsize, ...

In the context of using DNSGA2 to solve dynamic multi-objective optimization problems (DMOPs), a critical issue arises regarding the timing of the callback function execution and its impact on ...

Abstract: In order to improve the convergence speed of the existing LMS algorithms, a new LMS algorithm with variable step size based on the Swish function is proposed, and the effects of the ...

As a very effective machine learning ML-born optimization setting, boosting requires one to efficiently learn arbitrarily good models using a weak learner oracle, which provides classifiers that ...

Tensor contradictions are used to solve problems related to different research fields, including model counting, quantum circuits, graph problems, and machine learning. But to minimize the ...