The decoder has a symmetric architecture with respect to the encoder, designed to reconstruct the original ... EC2Vec embedding were combined into a 1324-dimensional feature vector for each ...

On the one hand, the molecular features of the training set are stored in latent space by encoder, and on the other hand, these molecular features are reconstituted into new molecules by decoder ...

What Is An Encoder ... Decoders can leverage the encoded information to produce creative and human-like outputs. Efficient processing: By encoding the entire input into a fixed-length vector ...

The loss function (Equation 1) looks like this: Where, x denotes the input vector and each value in ... it fuses the low-level features of the encoder with the high-level features of the decoder.

A typical encoder-decoder model, such as an autoencoder ... Further, it is possible to apply to unsupervised learning if the latent vector guarantees a sufficient distance between clusters in the ...