Deep Neural Networks (DNNs) are becoming an important tool in modern computing
applications. Accelerating their training is a major challenge and techniques range from …

Deep Neural Networks (DNNs) have established themselves as a fundamental tool in
numerous computational modeling applications, overcoming the challenge of defining use …

The greatest concern facing renewable energy sources like wind is the uncertainty in
production volumes as their generation ability is inherently dependent on weather …

Full batch training of Graph Convolutional Network (GCN) models is not feasible on a single
GPU for large graphs containing tens of millions of vertices or more. Recent work has shown …

Credit risk modelling is an integral part of the global financial system. While there has been
great attention paid to neural network models for credit default prediction, such models often …

Оценка производительности добычи полезных ресурсов, в том числе определение
геометрических размеров объектов горной породы в открытом карьере, является …

With the rapid expansion of application range, Deep-Learning has increasingly become an
indispensable practical method to solve problems in various industries. In different …

Distributed deep learning is an important and indispensable direction in the field of deep
learning research. Earlier research has proposed many algorithms or techniques on …

Today, there is a lot of enthusiasm to fulfil global energy needs from alternative energy
resources. Due to the increasing demand for electricity, the traditional electricity market …

As one of the fundamental issues in the fields such as computer vision and deep learning,
accelerating the inference speed of the convolutional neural network (CNN) has attracted a …