Generating Synthetic Data Using a Variational Autoencoder with PyTorch. Generating synthetic data is useful when you have imbalanced training data for a particular ... Because the input values are ...

A transfer-learned hierarchical variational autoencoder model for computational design of anticancer peptides. Hossein Abbasi , Mahdi Malekpour , Shahin Yaghoobi , Sina Abdous , Mohammad Hossein ...

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

Next, Dear-DIA uses a variational autoencoder to extract the peak features of fragment ions and maps the features into Euclidean space, and then clusters the features, with different classes of ...

Merck to evaluate Variational AI’s Enki™ generative AI platform to design novel and selective small molecules January 25, 2024 07:00 AM Eastern Standard Time ...

The design pattern presented here will work for most variational autoencoder data generation scenarios. If your raw data contains a categorical variable, such as "color" with possible values "red," ...