In this paper, we introduce the 3D Autoencoder Generative Adversarial Network (3DAE GAN) as a solution to generate high-resolution and multivariate synthetic time-series data capable ... Beyond the ...

In this paper, we propose a novel approach named Multi-scale Convolution Fusion and Memory-augmented Adversarial AutoEncoder (MCFMAAE) for multivariate time series anomaly detection ... to explore the ...

This paper innovatively proposes a temporal–spatial pyramid variational autoencoder (TS ... from multirate data. This structure not only selectively utilizes multirate data but also handles complex ...

The overall program structure is presented in Listing 1. All the control logic is in the Main() method. All of the neural autoencoder functionality ... Working with image data, working with time ...

The open-source program can predict a protein's 3D structure from its sequence of ... 1000s of times less energy "It took us quite a long time to go through this massive database of structures ...

This study proposed an autoencoder ... that the BNN structure with lower parameters and complexity can achieve the same performance as the dense and CNN models when transmitting symbols. The ...

Molecular dynamics (MD) simulations have been actively used in the study of protein structure and function. However, extensive sampling in the protein conformational space requires large computational ...

Here, by contrast, we apply the variational autoencoder (VAE), an unsupervised ... It limits the types of structure one can find in the data and is often difficult to relate to time series such as ...