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 ...
ORLANDO, Fla. — Steering clinicians toward a cascade approach for thyroid function testing cut unnecessary orders by a monthly average of 15% and concurrent orders by 19% per month, according to ...
NEW YORK, July 9 (Reuters) - TikTok is preparing to launch a standalone app for U.S. users that is expected to operate on a separate algorithm and data system from its global app, laying the ...
Abstract: Nonconvexity is a usually overlooked factor in economic dispatch (ED). Enhancing the nonconvexity of the objective function leads traditional convex optimization algorithms easily to fall ...
ABSTRACT: The alternating direction method of multipliers (ADMM) and its symmetric version are efficient for minimizing two-block separable problems with linear constraints. However, both ADMM and ...
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 ...
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