Tech Xplore

Ferroelectric memory enables one chip to sample randomness and compute for generative AI

For the first time, a research team has demonstrated an artificial intelligence semiconductor technology that integrates the ...
IEEE

Low Latency Variational Autoencoder on FPGAs

Abstract: Variational Autoencoders (VAEs) are at the forefront of generative model research, combining probabilistic theory with neural networks to learn intricate data structures and synthesize ...
FierceBiotech

Merck inks $349M biobucks deal to apply AI to challenging targets

Merck & Co. has doubled down on its partnership with Variational AI, striking a deal worth up to $349 million to collaborate on small molecule candidates against two targets. Variational disclosed a ...
GitHub

graph-autoencoder

This project detects structural network anomalies using a GNN autoencoder. It contrasts this deep learning approach with the classic DBSCAN method. While DBSCAN only uses node features (CPU, RAM), the ...
IEEE

Leveraging Variational Autoencoders for Parameterized MMSE Estimation

Abstract: In this manuscript, we propose to use a variational autoencoder-based framework for parameterizing a conditional linear minimum mean squared error estimator ...
C&EN

Bayesian Optimization in the Latent Space of a Variational Autoencoder for the Generation of Selective FLT3 Inhibitors

Centre for Misfolding Diseases, Yusuf Hamied Department of Chemistry, University of Cambridge, Cambridge CB2 1EW, U.K.
GitHub

Spatially contrastive variational autoencoder for deciphering tissue heterogeneity from spatially resolved transcriptomics

SpaCAE (SPAtially Contrastive variational AutoEncoder) is a spatially contrastive variational autoencoder framework designed for spatial domains identification and highly sparse SRT data denoising.