along with bayesian search, it also employs tree-structured parzen. firstly, we define our objective function, which also has trial as an argument( to display the information after each trial). in our ...

We propose an algorithm, guided variational autoencoder (Guided-VAE), that is able to learn a controllable generative model by performing latent representation disentanglement learning. The learning ...

The function train_vae is responsible for building and training the VAE (Variational Autoencoder) model. It takes in the latent_dim parameter along with other hyperparameters of the model. The ...

However, a fundamental flaw exists in Variational Autoencoder (VAE) based approaches. Specifically, the objective function of VAE (reconstruction loss), deviates from its primary objective (i.e ...

Besides, the loss function of the variational autoencoder is revised and improved. The aim is to learn feature representations with fewer image features to obtain more accurate results. (2) In the ...

The variational autoencoder with 4 hidden layers performed the best with high Spearman and Pearson coefficients and low RMSD. In terms of the encoder ( Figures 4A,B ), a larger number of layers lead ...