As global banking institutions accelerate their digital transformation, leaders in artificial intelligence and machine ...

Instead of gathering data in the cloud from users to train data sets, federated learning trains AI models on mobile devices in large batches, then transfers those learnings back to a global model ...

Embedded and federated machine learning offer several advantages. These include: • Improved Performance: Embedded machine learning can improve performance because the model doesn't have to be ...

Dr. James McCaffrey of Microsoft Research tackles the process of examining a set of source data to find data items that are different in some way from the majority of the source items. Data anomaly ...

The main disadvantage of using a neural autoencoder is that you must fine-tune the training parameters (max epochs, learning rate, batch size) and the number of nodes in the hidden layer. [Click on ...

Intel Labs and the Perelman School of Medicine at the University of Pennsylvania today released a joint research study that used federated learning, a distributed machine learning and artificial ...