Fuzzy clustering. Fig. 1 illustrates semi-supervised RNN and autoencoder neural network data-driven models for DIP. RNN produces time-based forecasts and the autoencoder reproduces drilling input ...

We introduce unFEAR, Unsupervised Feature Extraction Clustering, to identify economic crisis regimes. Given labeled crisis and non-crisis episodes and the corresponding features values, unFEAR uses ...

The network, SAUCIE — or sparse autoencoder for unsupervised clustering, imputation, and embedding — simultaneously analyzes and visualizes large datasets. Though the program could be used for other ...

Anomaly detection using a deep neural autoencoder is not a well-known technique. An advantage of using a neural technique compared to a standard clustering technique is that neural techniques can ...