BiAtt-GVAE: Molecular design for specific target via graph variational autoencoder based on bi-channel interactive attention network ...

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 ...

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This also sidesteps the problem of scalar-based design representation and, unlike pixel-based representation, can be directly imported into finite element simulations for validation. To speed up the ...

Reference implementation for a variational autoencoder in TensorFlow and PyTorch. I recommend the PyTorch version. It includes an example of a more expressive variational family, the inverse ...

Variational Autoencoders (VAEs) are an artificial neural network architecture to generate new data which consist of an encoder and decoder.

In recent years, deep learning (DL) based methods, such as sparse convolutional denoising autoencoder (SCDA), have been developed for genotype imputation. However, it remains a challenging task to ...

To this end, we propose a multi-domain variational autoencoder framework consisting of multiple domain-specific branches and a latent space shared across all branches for cross-domain information ...