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Found 7 papers, 2 papers with code
Machine Learning Techniques with Fairness for Prediction of Completion of Drug and Alcohol Rehabilitation
Demographic data is highly categorical which led to binary encoding being used and various fairness measures being utilized to mitigate bias of nine demographic variables.
Exploring Machine Learning Models for Federated Learning: A Review of Approaches, Performance, and Limitations
In the growing world of artificial intelligence, federated learning is a distributed learning framework enhanced to preserve the privacy of individuals' data.
The Paradox of Noise: An Empirical Study of Noise-Infusion Mechanisms to Improve Generalization, Stability, and Privacy in Federated Learning
In a data-centric era, concerns regarding privacy and ethical data handling grow as machine learning relies more on personal information.
A Distributed Approach to Meteorological Predictions: Addressing Data Imbalance in Precipitation Prediction Models through Federated Learning and GANs
Moreover, with advancements in federated learning, machine learning models can be trained across decentralized databases, ensuring privacy and data integrity while mitigating the need for centralized data storage and processing.
A Review of Machine Learning Techniques in Imbalanced Data and Future Trends
For over two decades, detecting rare events has been a challenging task among researchers in the data mining and machine learning domain.
Optimizing Data Augmentation Policy Through Random Unidimensional Search
It is no secret amongst deep learning researchers that finding the optimal data augmentation strategy during training can mean the difference between state-of-the-art performance and a run-of-the-mill result.
To Raise or Not To Raise: The Autonomous Learning Rate Question
There is a parameter ubiquitous throughout the deep learning world: learning rate.
