Tag: variational autoencoder

Researchers have built InterPet4D, the first large-scale multimodal 4D dataset of human–dog interactions, and used it to train an AI ...

An unsupervised machine learning analysis of ERA5 reanalysis data shows that Western European heatwaves are undergoing structural shifts in their ...

An explainable generative deep learning framework called SYNERGY-VAE has uncovered three hidden population subgroups with distinct health signatures and depression ...

A new deep-learning method called VIMA converts tissue patches into numerical fingerprints and uses rigorous statistics to uncover disease-associated spatial ...

Researchers have built a federated, explainable VAE-LSTM framework that detects previously unseen DDoS attacks in software-defined healthcare networks with over ...

Researchers have developed CytoVI, an open-source deep generative model that integrates, denoises and imputes antibody-based single-cell data from flow cytometry, ...

A new probabilistic deep learning model called ResolVI corrects misassigned RNA molecules and batch effects in spatial transcriptomics data, sharpening ...

Researchers have developed a hybrid deep learning architecture combining graph neural networks, Transformers, and variational autoencoders to detect and explain ...

Researchers have developed a spectrum-conditioned generative AI model, STFT-VAEGAN, that synthesizes realistic, nonstationary earthquake acceleration time histories matching prescribed target ...

Researchers have unveiled Hydra, an interpretable ensemble deep learning framework that jointly performs feature selection and cell type annotation across ...