Converting a single-ended signal to differential with an active circuit: a Class A/B output that operates from a single positive supply.

That said, applying a neural autoencoder anomaly detection system to tabular data is typically the best way to start. A limitation of the autoencoder architecture presented in this article is that it ...

In this letter, we propose ConvAE, a new channel autoencoder structure. ConvAE uses residual blocks with convolutional layers. This configuration increases performance while decreasing computational ...

Variational autoencoder for protein sequences - add metal binding sites and generate sequences for novel topologies - psipred/protein-vae ...

The output is expressed as the linear superposition of the input and a nonlinear transformation of input, thus solving the training problem of the deep network. Liu et al. (2018) takes the shortcut ...

Therefore, the autoencoder input and output both have 65 values -- 64 pixel grayscale values (0 to 16) plus a label (0 to 9). Notice that the demo program analyzes both the predictors (pixel values) ...

Swapping Autoencoder consists of autoencoding (top) and swapping (bottom) operation. Top: An encoder E embeds an input (Notre-Dame) into two codes. The structure code is a tensor with spatial ...