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 ...
Amsterdam’s struggles with its welfare fraud algorithm show us the stakes of deploying AI in situations that directly affect human lives. What Amsterdam’s welfare fraud algorithm taught me about fair ...
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: 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 ...
Tesla is facing a new scandal that once again sees the electric automaker accused of misleading customers. In the past, it has been caught making “misleading statements” about the safety of its ...
This is Part 3 of Embedded Bias, a series revealing how race-based clinical algorithms pervade medicine and why it's so difficult to change them. There it was on James Cannon’s lab report, two tiny ...
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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