Abstract: As a commonly used model for anomaly detection, the autoencoder model for anomaly detection does not train the objective for extracted features, which is a downside of autoencoder model. In ...

The rapid growth of unlabeled time-series data in domains such as wireless communications, radar, biomedical engineering, and the Internet of Things (IoT) has driven advancements in unsupervised ...

This study aims to explore an autoencoder-based method for generating brain MRI images of patients with Autism Spectrum Disorder (ASD) and non-ASD individuals, and to discriminate ASD based on the ...

Dr. James McCaffrey of Microsoft Research presents a full-code, step-by-step tutorial on creating an approximation of a dataset that has fewer columns. Imagine that you have a dataset that has many ...

The sequence of amino acids within a protein dictates its structure and function. Protein engineering campaigns seek to discover protein sequences with desired functions. Data-driven models of the ...

Dr. James McCaffrey of Microsoft Research tackles 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. Data anomaly ...

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Abstract: Autoencoder is a widely used neural architecture for dimensionality reduction. It can be considered similar to the principal component analysis (PCA) methodology. However, the final ...