Stabilizing and Enhancing Learning for Deep Complex and Real Neural Networks

The results obtained in the reliability prediction study of the benchmark data show that MLMVN out- performs other machine learning algorithms in terms of prediction precision and

We apply the random neural networks (RNN) [15], [16] developed for deep learning recently [17]–[19] to detecting network attacks using network metrics extract- ed from the

In both cases, the level of the simulations and the complexity of the models require the distribution of the networks over several process- ing units, with important constraints :

The proposed method for action recognition can be seen as composed by two parts: one as pose-based recognition, which uses a sequence of body joints coordinates to predict the

The prospects for large reserves of coal in the Cumberland basin depend on two main factors: (1) the continuance of the thick seams of the Springhill district

A series of experiments are designed to verify the anxiolytic effect of ethyl acetate extract and laetispicine in the light/dark test, elevated plus maze test and

Indeed, merely taking real data as input does not profit from using CVNN (M¨onning and Manandhar 2018). We propose in this paper to analyze the influence of the non-circular

• a novel training process is introduced, based on a single image (acquired at a reference pose), which includes the fast creation of a dataset using a simulator allowing for