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 ...

Cardiovascular Diseases (CVD) are the leading cause of death globally. Non-invasive, cost-effective imaging techniques play a crucial role in early detection and prevention of CVD. Optical Coherence ...

Same as traditional autoencoders, VAE architecture has two parts: an encoder and a decoder. Traditional AE models map inputs into a latent-space vector and reconstruct the output from this vector. VAE ...

This repository provides code to accompany the paper: Greener JG, Moffat L and Jones DT, Design of metalloproteins and novel protein folds using variational autoencoders, Scientific Reports 8:16189, ...

Generative AI has become the buzzword of 2023. Whether text-generating ChatGPT or image-generating Midjourney, generative AI tools have significantly impacted businesses and dominated the content ...

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 ...

Generative models are becoming a tool of choice for exploring the molecular space. These models learn on a large training dataset and produce novel molecular structures with similar properties.