We proposed a convolutional autoencoder with sequential and channel attention (CAE-SCA) to address this issue. Sequential attention (SA) is based on long short-term memory (LSTM), which captures ...

In this study the applicability of autoencoder-based deep neural networks to solve fault classification problems using vibration signals. This work will aim to construct CNN and LSTM type ...

We propose a Crystal Diffusion Variational Autoencoder (CDVAE) that captures the physical inductive bias of material stability. By learning from the data distribution of stable materials, the decoder ...