Federated learning enables a cluster of decentralized mobile devices at the edge to
collaboratively train a shared machine learning model, while keeping all the raw training …

This review paper focuses on the use of ensemble neural networks (ENN) in the
development of storm surge flood models. Storm surges are a major concern in coastal …

Outlier detection (OD) literature exhibits numerous algorithms as it applies to diverse
domains. However, given a new detection task, it is unclear how to choose an algorithm to …

Traditional deep learning frameworks such as TensorFlow and PyTorch support training on
a single deep neural network (DNN) model, which involves computing the weights iteratively …

Deep learning (DL) has revolutionized unstructured data analytics. But in most cases, DL
needs massive labeled datasets and large compute clusters, which hinders its adoption …

We review previous approaches to nowcasting earthquakes and introduce new approaches
based on deep learning using three distinct models based on recurrent neural networks and …

As neural networks are increasingly employed in machine learning practice, how to
efficiently share limited training resources among a diverse set of model training tasks …

Compared to conventional neural networks, training a supernet for Neural Architecture
Search (NAS) is very time consuming. Although current works have demonstrated that …

Ensemble neural networks are widely applied in cloud-based inference services due to their
remarkable performance, while the growing demand for low-latency services leads …

For an extended period, graphics processing units (GPUs) have stood as the exclusive
choice for training deep neural network (DNN) models. Over time, to serve the growing …