The increasing popularity and widespread use of Internet of Things (IoT) and Cyber-Physical
Systems (CPS) technologies have produced a significant need for the integration of cloud …

Evaluating web traffic on a web server is highly critical for web service providers since,
without a proper demand forecast, customers could have lengthy waiting times and abandon …

Reinforcement learning has recently been studied in various fields and also used to
optimally control IoT devices supporting the expansion of Internet connection beyond the …

High performance multi-GPU systems are widely used to accelerate training of deep neural
networks (DNNs) by exploiting the inherently massive parallel nature of the training process …

Distributed training frameworks, like TensorFlow, have been proposed as a means to reduce
the training time of deep learning models by using a cluster of GPU servers. While such …

Distributed optimization is widely used to solve large-scale optimization problems by
parallelizing gradient-based algorithms across multiple computing nodes. In asynchronous …

Asynchronous stochastic gradient descent (SGD) is attractive from a speed perspective
because workers do not wait for synchronization. However, the Transformer model …

Distributed optimization is widely used to solve large-scale optimization problems by
parallelizing gradient-based algorithms across multiple computing nodes. In asynchronous …

Neural machine translation (NMT) has been shown to outperform statistical machine
translation. However, NMT models typically require a large number of parameters and are …

Artificial Intelligent (AI) has become the most potent and forward-looking force in the
technologies over the past decade. It allowed breakthrough applications that have truly …