This paper proposes a novel hybrid model that synergistically integrates Variational Autoencoders (VAEs) and Speeded-Up Robust Features (SURF) to address these challenges. The VAE component captures ...

Specifically, we demonstrate how exploring a variational autoencoder (VAE) latent space, trained on purely normal (valid) data, can effectively fuzz-test representational robustness by anomaly ...

As an important generation model, variational autoencoder plays an important role in image feature extraction, text generation, and text compression. In this paper, from the perspective of feature ...

In this letter, we propose a multilevel dual-direction modifying variational autoencoder (MD 2 MVAE) for hyperspectral feature extraction. Its architecture is inspired by the spectral–spatial ...

Keywords: protein system, conformational space, variational autoencoder, molecular dynamics, deep learning Citation: Tian H, Jiang X, Trozzi F, Xiao S, Larson EC and Tao P (2021) Explore Protein ...

• Longitudinal data with many missing values. Our novel proposed method [Variational Autoencoder Modular Bayesian Network (VAMBN)] is a combination of a Bayesian Network (Heckerman, 1997) with modular ...