Tag: Variational Autoencoder

A novel variational autoencoder framework named MyeVAE integrates clinical traits and multi-modal genomic data to enhance survival risk prediction in ...

A machine learning framework combining a small experimental dataset with 300 VAE-generated synthetic mixes predicts the compressive strength of recycled-concrete-powder ...

A leakage-controlled machine learning pipeline converts routine online gambling telemetry into an auditable three-level risk flag that outperformed incumbent queues ...

A new convolutional variational deep embedding model learns a generative latent manifold of EEG microstate topographies, offering soft cluster assignments ...

A hybrid deep learning framework that combines ant colony optimization with a spatio-temporal variational autoencoder recognized nine opera teaching postures ...

A new framework called EvoLatent-FS uses evolutionary feature optimization before variational encoding to learn more compact and discriminative latent representations ...

A new machine learning framework predicts the mechanical properties of lignin-based polyurethane foams from microscope images within 2.3 percent on ...

A new contrastive learning framework called VMCL improves knowledge graph embedding for unseen entities and shows smooth, predictable gains as ...

Researchers at the University of Oklahoma combined density functional theory calculations with graph neural networks to predict lithium-ion migration across ...

Researchers have developed an unsupervised deep learning model called GSA-VAE that detects faults in electric vehicle batteries by fusing causal ...

A new hybrid framework combining a Variational Autoencoder with Proximal Policy Optimization reinforcement learning generates melodies with measurably improved tonal ...

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

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 ...

In an era where the intricate complexities of biology are continually being unraveled, a novel approach to spatial transcriptomics has ...