Each small 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 ...

Autoregressive models are among the most successful neural network methods for estimating a distribution from a set of samples. However, these models, such as other neural methods, need large data ...

This demo highlights how one can use a semi-supervised machine learning technique based on autoencoder to detect an anomaly in sensor data (output pressure of a triplex pump). The demo also shows how ...

A deep count autoencoder network to denoise scRNA-seq data and remove the dropout effect by taking the count structure, overdispersed nature and sparsity of the data into account using a deep ...

Various dimensionality reduction methods have been developed, but they are not potent with the small-sample-sized high-dimensional datasets and suffer from ... High-dimensional limited-sample ...

In the complex industrial processes, the process data have the characteristics of imbalance and are incomplete due to the difficult-to-measure key variables, leading to the performance degradation of ...

The application of deep learning to generative molecule design has shown early promise for accelerating lead series development. However, questions remain concerning how factors like training, data ...

Data anomaly detection is the process of examining a set of source data to find data items that are different in some way from the majority of the source items. There are many different types of ...