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Enhancing Performance for Highly Imbalanced Medical Data via Data Regularization in a Federated Learning Setting
Specifically, the goal of the proposed method is to enhance model performance for cardiovascular disease prediction by tackling the class-imbalance that typically characterizes datasets used for this purpose, as well as by leveraging patient data available in different nodes of a federated ecosystem without compromising their privacy and enabling more resource sensitive allocation.
Benchmarking Advanced Text Anonymisation Methods: A Comparative Study on Novel and Traditional Approaches
In the realm of data privacy, the ability to effectively anonymise text is paramount.
Evaluating the Energy Efficiency of Few-Shot Learning for Object Detection in Industrial Settings
In the ever-evolving era of Artificial Intelligence (AI), model performance has constituted a key metric driving innovation, leading to an exponential growth in model size and complexity.
Toward Green and Human-Like Artificial Intelligence: A Complete Survey on Contemporary Few-Shot Learning Approaches
Despite deep learning's widespread success, its data-hungry and computationally expensive nature makes it impractical for many data-constrained real-world applications.
