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 approaches by combining information about where crimes occur, when they happen and ...
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 ...
Scientific Research Publishing

Variational Auto-Encoder and Speeded-Up Robust Features Hybrid Model for Anomaly Detection and Localization in Video Sequence with Scale Variation ()

Jomo Kenyatta University of Agriculture and Technology, Juja, Kiambu County, Kenya. Where KL denotes the Kullback-Leibler divergence, and p(z) is a prior distribution over the latent space (typically ...