The trained neural autoencoder is subjected to a sanity check by computing the MSE for the 40-item validation dataset. The MSE is 0.0017 which is very close to the MSE of the dataset being reduced, ...

Graph Autoencoder Based on GATs Graph autoencoder is a very powerful neural network architecture for unsupervised representation learning on graph-structured data. Compared with regular autoencoder, ...

That's why I implemented a 5 step process to apply Shap values to an autoencoder output to explain which features were the most important for the recognized anomaly, and which on the other hand, were ...

Another strength of using the deep autoencoder for feature extraction is that it can extract features from non-quantizable questionnaire responses (e.g., dietary habit survey questionnaire), which ...

In the last decade, automatic writer identification using a convolutional neural network (CNN) has been well studied. For further performance improvement of the writer identification task, a ...

Hyperspectral unmixing is a technique in hyperspectral image processing that decomposes the spectra of mixed pixels into pure spectral components (endmembers) and their corresponding contributions ...

The initial implementation uses a convolutional autoencoder (CAE) model, as shown in Fig. 1, trained on electrical and electromagnetic time series data for anomaly detection in wind turbine ...