IEEE

Quaternion Vector Quantized Variational Autoencoder

Abstract: Vector quantized variational autoencoders, as variants of variational autoencoders, effectively capture discrete representations by quantizing continuous latent spaces and are widely used in ...
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 ...
Tech Xplore

AI model predicts robberies across US cities with 86.3% accuracy

Researchers have developed an artificial intelligence model that predicts crime more accurately than several existing ...
Medical Device and Diagnostic Industry (MD+DI)

The AI Era: How Deep Learning is Overcoming Data Scarcity in Medical Imaging

AI medical imaging market is projected to exceed $20B by 2035. Generative models address class imbalances in medical imaging ...
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

Its deal with Merck & Co. is the latest in a series of Variational AI collaborations. (iStock/Getty Images Plus) Merck & Co. has doubled down on its partnership with Variational AI, striking a deal ...
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 ...
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 ...
Nature

Reusability report: exploring the utility of variational graph encoders for predicting molecular toxicity in drug design

Variational graph encoders effectively combine graph convolutional networks with variational autoencoders, and have been widely employed for biomedical graph-structured data. Lam and colleagues ...
Nature

Biophysical modeling with variational autoencoders for bimodal, single-cell RNA sequencing data

Here we present biVI, which combines the variational autoencoder framework of scVI with biophysical models describing the transcription and splicing kinetics of RNA molecules. We demonstrate on ...
LinkedIn

Variational Autoencoder (VAE) — PyTorch Tutorial

In this article, we only focus on a simple VAE in PyTorch and visualize its latent representation after training on the MNIST dataset. Let’s begin by importing some libraries: import torch import ...
LinkedIn

Difference between AutoEncoder (AE) and Variational AutoEncoder (VAE) How can you compress data or even generate data from random values? That is what

Data compression is an essential phase in training a network. The idea is to compress the data so that the same amount of information can be represented by fewer bits. This also helps with the problem ...