VALL-E is a neural codec language model using discrete codes derived from an off-the-shelf neural audio codec model, and regard TTS as a conditional language modeling task rather. VALL-E emerges ...

To address these challenges, we propose a Noise-Consistent hypeRgraph AutoEncoder framework with denoising strategies, termed NCRAE, aimed at achieving robust node embeddings in ceRNA regulatory ...

Methods: We developed a Deep Hierarchical Conditional Variational Autoencoder (CVAE) for de novo ACP design, using transfer learning by initializing the ESM-2 pre-trained encoder. A comprehensive ACP ...

Keywords: cardiac MRI, cardiovascular disease, cardiovascular risk prediction, ECG electrodes, ECG generation, variational autoencoder Citation: Sang Y, Banerjee A, Beetz M and Grau V (2025) Deep ...

The study introduces a novel hybrid Variational Autoencoder-SURF (VAE-SURF) model for anomaly detection in crowded environments, addressing critical challenges such as scale variance and temporal ...

A Convolutional Variational Autoencoder (CVAE) was developed for this purpose. We demonstrate the efficacy of our approach using the transient data generated from the simulations. The simulation data ...

This study examines the performance of Conditional Variational Autoencoder (CVAE) in handwritten digit recognition. Using the MNIST dataset, two variants of the CVAE models — convolutional and ...