This work uses an elementary unsupervised convolutional autoencoder (CAE) to learn a compressed representation ... It is shown that a CAE that reduces the features output after each layer of the ...

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

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 ...

Therefore, the autoencoder input and output both have 65 values -- 64 pixel grayscale ... If your raw data contains a categorical variable, such as "color" with possible values "red", "blue" or "green ...

They are essential in handling sensors that output variable voltage or current signals (like 0-10V or 4-20mA), representing process values like temperature, pressure, or flow rate. Analog input ...

In the current study, we focus on an autoencoder (AE) as a DL algorithm that allows feature extraction without labels (Hinton, 2006). AE is supervised learning in a deep neural network having an ...

Each modality can have its own Bregman divergence as loss function, thereby assuming a particular output noise distribution. By default, the autoencoder network fusing multiple modalities consists of ...

Then, the characteristics of these intrinsic variables can be utilized for analyzing different emotional states. Specifically, in this work, three unsupervised autoencoder-like neural ... between the ...