This paper proposes Synonymous Variational Inference, a novel variational inference that first theoretically explains the core reason for the divergence term’s existence in the perceptual image ...

The Vector Quantized Variational AutoEncoder (VQ-VAE) has shown great potential in image generation, especially the methods with hierarchical features. However, the lack of decoupling of structural ...

Variational autoencoders (VAEs) are a powerful class of generative models that can learn to produce realistic and diverse samples of data, such as images, text, or audio.

In this paper, we propose a novel model, multi-view variational autoencoder with the product of expert (MVAE-PoE) for proximal femoral strength prediction, as shown in Figure 1. The proposed MVAE-PoE ...

This paper is a valuable step in multi-subject behavioral modeling using an extension of the Variational Autoencoder (VAE) framework. Using a novel partition of the latent space and in tandem with a ...

To obtain a nuanced understanding of fetal–neonatal brain development, including nonlinear growth, for the first time, we developed quantitative, systems-wide representations of brain activity in a ...