News

Scientific Research Publishing5mon
Jin, W., Barzilay, R. and Jaakkola, T. (2018) Junction Tree Variational Autoencoder for Molecular Graph Generation. International Conference on Machine Learning, Stockholm, 10 ...
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.
LinkedIn9mon
Variational Autoencoders Tutorial: Generate Images, Text, and Audio - LinkedIn
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 ...
Semiconductor Engineering1y
Improving ML-Based Device Modeling Using Variational Autoencoder Techniques - Semiconductor Engineering
A technical paper titled “Improving Semiconductor Device Modeling for Electronic Design Automation by Machine Learning Techniques” was published by researchers at Commonwealth Scientific and ...
Visual Studio Magazine4y
Generating Synthetic Data Using a Variational Autoencoder with PyTorch - Visual Studio Magazine
A variational autoencoder (VAE) is a deep neural system that can be used to generate synthetic data. VAEs share some architectural similarities with regular neural autoencoders (AEs) but an AE is not ...
Visual Studio Magazine4y
Autoencoder Anomaly Detection Using PyTorch - Visual Studio Magazine
The Data Science Lab Autoencoder Anomaly Detection Using PyTorch Dr. James McCaffrey of Microsoft Research provides full code and step-by-step examples of anomaly detection, used to find items in a ...
IEEE4y
Multimodal Deep Generative Models for Trajectory Prediction: A Conditional Variational Autoencoder Approach - IEEE Xplore
Human behavior prediction models enable robots to anticipate how humans may react to their actions, and hence are instrumental to devising safe and proactive robot planning algorithms. However, ...