which may have resulted in the extraction of some features irrelevant to the key output variables. In this paper, a novel stacked maximal information coefficient-weighted autoencoder with attribute ...

In the research, they proposed to use a traditional autoencoder to reconstruct the representations of rating information and auxiliary information. The reconstructed output preserves auxiliary ...

To address these issues, this article first introduces a novel stacked dual-guided autoencoder (SDGAE ... to simultaneously extract features that are highly correlated with the output variable by ...

To normalize numeric variables, I recommend using the ... Behind the scenes, the autoencoder uses tanh() activation on the hidden nodes and tanh() activation on the output nodes. The result of the ...

They provide real-time prediction of quality variables and then guide production and improve ... This paper innovatively proposes a temporal–spatial pyramid variational autoencoder (TS-PVAE) model for ...

Dr. James McCaffrey of Microsoft Research tackles the process of examining a set of source data to find data items that are different in some way from the majority of the source items. Data anomaly ...

The multivariate landslide dataset was used as both the input and output to train the stacked autoencoder algorithm ... to interpret the boundaries with respect to all attributes or variables within ...

This paper is a valuable step in multi-subject behavioral modeling using an extension of the Variational Autoencoder (VAE ... disentanglement of behavior into interpretable latent variables with ...