GitHub

vae-cnn

PyTorch-based pipeline that trains a convolutional variational autoencoder on cat images, optionally tunes hyperparameters with Ray Tune, and samples new images by fitting a Gaussian Mixture Model in ...
Business Wire

Variational AI Announces Oversubscribed $5.5 Million Financing to Launch Foundation Model for Small Molecule Drug Discovery

VANCOUVER, British Columbia--(BUSINESS WIRE)--Variational AI, the company behind Enki™, an advanced foundation model for small molecule drug discovery, today ...
Morningstar

Variational AI Enters Collaboration with Merck to Apply Generative AI to Drug Discovery

Variational AI, Inc., a generative AI drug discovery company, today announced a collaboration with Merck, known as MSD outside of the United States and Canada, to apply Variational AI’s Enki™ platform ...
Scientific Research Publishing

Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J. et al. (2019). PyTorch: An Imperative Style, High-Performance Deep Learning Library. Advances in Neural Information ...

ABSTRACT: Accurate measurement of time-varying systematic risk exposures is essential for robust financial risk management. Conventional asset pricing models, such as the Fama-French three-factor ...
GitHub

Dynamical Dual Latent Space Variational Autoencoder Model with Koopman Constraint for Semi-Supervised Soft Sensor Application

├── data_pre/ # Data preprocessing modules │ ├── data_pre.py # Data preprocessing script │ └── dataset.py # Dataset class definition ├── dataset/ # Data directory │ ├── SRU_data.npy # Process data in ...
IEEE

Prior Distribution Guided Gaussian Mixture Variational Autoencoder (PDGM-VAE) for Image Generation

Abstract: Variational Autoencoder(VAE) combines the ideas of autoencoders and variational inference, introducing the concept of latent space and variational inference to endow autoencoders to generate ...
IEEE

Multirate Dynamic Variational Autoencoder for Fault Detection in Nonlinear Industrial Processes

Abstract: The variational autoencoder (VAE) has proven highly effective in monitoring nonlinear stochastic processes, primarily under the assumption of complete and temporally independent data.