Jin, W., Barzilay, R. and Jaakkola, T. (2018) Junction Tree Variational Autoencoder for Molecular Graph Generation. International Conference on Machine Learning, Stockholm, 10-15 July 2018, 2323-2332.

Variational autoencoders (VAEs) are a powerful class of generative models that can learn to produce realistic and diverse samples of data, such as images, text, or audio. In this tutorial, you ...

A technical paper titled “Improving Semiconductor Device Modeling for Electronic Design Automation by Machine Learning Techniques” was published by researchers at Commonwealth Scientific and ...

Training a Variational Autoencoder Training a VAE involves two measures of similarity (or equivalently measures of loss). First, you must measure how closely the reconstructed output matches the ...

The overall structure of the PyTorch autoencoder anomaly detection demo program, with a few minor edits to save space, is shown in Listing 3. I prefer to indent my Python programs using two spaces ...

In this work, we provide a self-contained tutorial on a conditional variational autoencoder (CVAE) approach to human behavior prediction which, at its core, can produce a multimodal probability ...