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 ...

We believe that knowing structural information about the data can improve their performance on small data sets. Masked autoencoder for distribution estimation (MADE) is a well-structured density ...

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 ...

Conceptual overview about Variational Autoencoder Modular Bayesian Network VAMBN ... situation can always occur by chance (specifically for small sample sizes) because synthetic data are randomly ...

We propose a local conformal autoencoder (LOCA) for standardized data coordinates. LOCA is a deep learning-based ... which is parameterized by a small number of latent variables. We assume that the ...

Further, machine learning algorithms are prone to adversarial attacks: small perturbations on attack ... which are used by the AAE (Adversarial Autoencoder) and WGAN (Wasserstein GAN) for generating ...

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 ...

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 ...