While most research focuses on compressing input data, less attention has been given to reducing the size and complexity of the autoencoder model itself, which is crucial for deployment on ...

Area-selective atomic layer deposition (AS-ALD) has become an essential technique in precision patterning due to its ability to deposit thin films with high conformality and angstrom-level thickness ...

Generic Deep Autoencoder for Time-Series This toolbox enables the simple implementation of different deep autoencoder. The primary focus is on multi-channel time-series analysis. Each autoencoder ...

To address these issues, a novel SAE with a variant structure called deep layers-extended autoencoder (DLEAE) is proposed. In the DLEAE, an extended layer is introduced, and the original inputs and ...

An autoencoder is another method of simply “memorizing” the training data and reproducing them. The parameters of the intermediate hidden layer would completely fit the training set, and the content ...

For both cases, the variational autoencoder with 4 hidden layers reached the lowest values. This indicates that 4-layer VAE is capable of generating protein conformations that are closer to the ...

I am building an LSTM autoencoder in R keras with different timestep inputs. As ragged tensors are not implemented yet I opted for masking shorter length inputs. The problem I'm facing is in the ...