Artificial intelligence (AI) is infamous for its resource-heavy training, but a new study may have found a solution in a novel communications system, called ZEN, that markedly improves the way large ...

As the saying goes, we never stop learning. This is especially important in today’s job market, whether you are already enjoying gainful employment or you’re looking for your next gig. That's where ...

Shevitski, B., Watkins, Y., Man, N. and Girard, M. (2023) Digital Signal Processing Using Deep Neural Networks Evaluating the Effectiveness of Hybrid Autoencoder/ Transformer Models for RF Data. arXiv ...

Deep learning with ability to feature learning and nonlinear function approximation has shown its effectiveness for machine fault prediction. While, how to transfer a deep network trained by ...

Given the subjectivity of manually extracting arc fault features, and the problem of insufficient feature extraction when using current data as the input of deep learning algorithms, this paper ...

We trained a deep neural network with unsupervised learning (Autoencoder) to reconstruct vibratory patterns elicited by human haptic exploration of different materials. The learned compressed ...

Keywords: protein system, conformational space, variational autoencoder, molecular dynamics, deep learning Citation: Tian H, Jiang X, Trozzi F, Xiao S, Larson EC and Tao P (2021) Explore Protein ...

For an autoencoder anomaly detection system, model overfitting is characterized by a situation where all reconstructed inputs match the source inputs very closely, and therefore all reconstruction ...