When training data are scarce, it is challenging to train a deep neural network without causing the overfitting problem. For overcoming this challenge, this article proposes a new data augmentation ...

In the cause of working out the challenge of remaining life prediction (RUL) of proton exchange membrane fuel cell (PEMFC) under dynamic operating conditions, this article proposes a PEMFC RUL ...

Each thin blue arrow represents a neural weight, which is just a number, typically between about -2 and +2. Weights are sometimes called trainable parameters. The small red arrows are special weights ...

The Data Science Lab Data Anomaly Detection Using a Neural Autoencoder with C# Dr. James McCaffrey of Microsoft Research tackles the process of examining a set of source data to find data items that ...

MNIST is a large database containing 70 000 handwritten digits images. 60 000 of them are in the built-in training set and 10 000 are in the built-in test set. Each image is stored as a 28*28 matrix ...

IBM Research AI, Yorktown Heights, NY, United States Deep neural networks (DNNs) are typically trained using the conventional stochastic gradient descent (SGD) algorithm. However, SGD performs poorly ...

3 School of Life Sciences, Yunnan Normal University, Kunming, China Graph neural networks (GNNs), as a branch of deep learning in non-Euclidean space, perform particularly well in various tasks that ...