Successfully deploying machine learning at the cluster scale requires a careful choice of software frameworks, as well as the use of computational strategies that ... the frameworks mentioned here are ...

Another pair of metrics that can help you measure the robustness of a machine learning model is sensitivity and specificity, which are especially useful for binary classification problems, where ...

Keywords: Machine Learning, Meta Learning, Robust Representation, Data-Efficient Learning, Open-World Important note: All contributions to this Research Topic must be within the scope of the section ...

On November 16, IDEAL hosted a workshop focused on new directions on robustness in machine learning as part of the fall 2021 special quarter. Machine learning systems are widely deployed to facilitate ...

Over the last couple of decades, those looking for a cluster management platform faced no shortage of choices. However, large-scale clusters are being asked to operate in different ways, namely by ...

If there are two clusters (the training images and the translated images) in a model, then AEGIS flags this model as clean. ... Towards Backdoor Attacks and Defense in Robust Machine Learning Models, ...

Cluster analysis, a commonly used machine-learning technique, uses these basic features to not only categorize materials and summarize similarities between them but also provide information ...