Reference implementation for a variational autoencoder in TensorFlow and PyTorch. I recommend the PyTorch version. It includes an example of a more expressive variational family, the inverse ...

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 ...

Variational Autoencoder with Arbitrary Conditioning (VAEAC) is a neural probabilistic model based on variational autoencoder that can be conditioned on an arbitrary subset of observed features and ...

Molecular dynamics (MD) simulations have been actively used in the study of protein structure and function. However, extensive sampling in the protein conformational space requires large computational ...

Generating synthetic data is useful when you have imbalanced training data for a particular class, for example, generating synthetic females in a dataset of employees that has many males but few ...

Dr. James McCaffrey of Microsoft Research provides full code and step-by-step examples of anomaly detection, used to find items in a dataset that are different from the majority for tasks like ...

An LSTM autoencoder combines encoder-decoder architecture to compress and reconstruct data effectively. Long Short Term Memory networks excel in learning dependencies in sequential data. The article ...