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In this article, we propose a novel conditional generative flow-induced variational autoencoder (CGlow-VAE) model to address the critical challenge of the small sample issue in plasma instances. This ...
Additionally, we conduct data-agnostic augmentation via learnable variational dropout, which removes redundant or irrelevant neurons in VAE to generate meaningful augmented views adequately for ...
Catalytic epoxidations are key chemical processes serving as essential steps in the synthesis of commercially valuable compounds. This study presents an innovative supervised machine learning (ML) ...
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 ...
A transfer-learned hierarchical variational autoencoder model for computational design of anticancer peptides.. If you have the appropriate software installed, you can download article citation data ...
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 ...
Deep learning methods for generating artificial data in health care include data augmentation by variational autoencoders (VAE) technology. Objective: We aimed to test the feasibility of generating ...
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