Understand how Highway Networks work and why they matter for training deep neural networks. A clear, beginner-friendly guide ...

An international team led by Einstein Professor Cecilia Clementi in the Department of Physics at Freie Universität Berlin has introduced CGSchNet, a machine-learned coarse-grained (CG) model that can ...

We propose a novel regularization algorithm to train deep neural networks, in which data at training time is severely biased. Since a neural network efficiently learns data distribution, a network is ...

Signal processing has made extensive use of neural networks. This study examines neural network training using the PSO and BP algorithms. To train the neural network, the PSO algorithm alone, the BP ...

The performance of UHPC-CA was predicted in this paper based on five prediction models: multiple linear regression, multiple nonlinear regression, traditional neural network (T-BP), principal ...

Spiking neural networks (SNNs), which are the next generation of artificial neural networks (ANNs), offer a closer mimicry to natural neural networks and hold promise for significant improvements in ...