The article introduces a new approach, RQP-SGD, for privacy-preserving machine learning. The rise of IoT devices necessitates efficient and secure data processing. Traditional machine learning models can be impractical for…
Continue readingThe study aimed to identify machine learning models that could efficiently categorize tweets concerning eating disorders. Over a million tweets were collected over three months and classified using both traditional…
Continue readingThe article presents F AIRDEBUGGER, a system designed to identify and explain instances of fairness violations in the outcomes of random forest classifiers. It uses machine unlearning to estimate changes…
Continue readingThe research investigates the performance of autoencoders in the compression of structured data. Autoencoders are a prominent model in many branches of machine learning and data compression. The researchers prove…
Continue readingThe paper presents a new algorithm for Federated Learning (FL) that identifies beneficial collaborations among clients. The algorithm assigns adaptive aggregation weights to clients based on the conduciveness of their…
Continue readingThis comprehensive review focuses on the development and recent advancements in fault-tolerant control schemes for robotic manipulators. The authors trace the historical evolution of these schemes and discuss the significant…
Continue readingThe article discusses the importance of unlearning in machine learning models, particularly in the context of data regulations like GDPR. Unlearning involves removing the influence of certain data from a…
Continue readingThe paper argues that satellite data could create a significant shift in machine learning, suggesting a need to rethink existing practices designed for traditional data modalities. As machine learning for…
Continue readingThis article presents research on how to improve the accuracy of predictions in Machine Learning when there is a covariate shift – a change in the distribution of data between…
Continue readingIn response to concerns over reliability and robustness in machine learning, the authors propose a multiverse analysis approach with their PRESTO framework. This framework maps the ‘multiverse’ of machine learning…
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