technologynetworks

Single-Cell RNA-Seq Data Analysis With AI: Tools, Workflows, and Biological Interpretation

Single-cell RNA-seq AI analysis has become the default way to make sense of the millions of expression measurements a single experiment can now generate. Turning raw sequencing counts into ...
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

Descript Audio Codec - VAE Variant (.dac-vae): High-Fidelity Audio Compression with Variational Autoencoder

This repository contains training and inference scripts for the Descript Audio Codec VAE variant (.dac-vae), a modified version of the original DAC that replaces the RVQGAN architecture with 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 ...