https://www.selleckchem.com/pr....oducts/filgotinib.ht
In this article, a deep probability model, called the discriminative mixture variational autoencoder (DMVAE), is developed for the feature extraction in semisupervised learning. The DMVAE consists of three parts 1) the encoding; 2) decoding; and 3) classification modules. In the encoding module, the encoder projects the observation to the latent space, and then the latent representation is fed to the decoding part, which depicts the generative process from the hidden variable to data. In particular, the decoding module in our DMVAE p
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