Over the past years high-dimensional neural network potentials (HDNNPs), fitted to
accurately reproduce ab initio potential energy surfaces, have become a powerful tool in …

In this article, we introduce CURRENNT, an open-source parallel implementation of deep
recurrent neural networks (RNNs) supporting graphics processing units (GPUs) through …

In this paper we describe the implementation of a complete ANN training procedure using
the block mode back-propagation learning algorithm for sequential patterns–such as the …

Classification is an important topic in statistics and machine learning with great potential in
many real applications. In this paper, we investigate two popular large-margin classification …

Today the increasing complexity, performance requirements and cost of current (and future)
applications in society is transversal to a wide range of activities, from science to business …

The experimental research of the parallel batch pattern back propagation training algorithm
on the example of recirculation neural network on many-core high performance computing …

The training phases of Deep neural network~(DNN) consumes enormous processing time
and energy. Compression techniques utilizing the sparsity of DNNs can effectively …

The development of parallel algorithm for batch pattern training of a recurrent neural network
with the back propagation training algorithm and the research of its efficiency on general …

The development of parallel batch pattern training algorithm for deep multilayered neural
network architecture and its parallelization efficiency research on many-core system are …

The research of a parallelization efficiency of a batch pattern training algorithm of a
multilayer perceptron on computational clusters is presented in this paper. The multilayer …