Development of an autoencoder-based algorithm for fault detection in rotary machines is presented in this paper. As the cornerstone of any machine learning model, feature engineering is thoroughly ...

Autoencoders are data-specific, lossy algorithms and learned automatically from data examples. To build an autoencoder, one needs encoding, decoding and a loss function. Note that the size of encoder ...

Commonly used datasets in recommendation research suffer from unbalanced data distribution, sparsity, and different user rating preferences. All these problems affect the quality of recommendation.

The Data Science Lab. Autoencoder Anomaly Detection Using PyTorch. Dr. James McCaffrey of Microsoft Research provides full code and step-by-step examples of anomaly detection, used to find items in a ...

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In this respect, we have provided solutions to the challenge of both parametric and hyperparametric optimization of deep autoencoders. These solutions, characterized by a conditional pre-training ...