A Convolutional Variational Autoencoder (CVAE) was developed for this purpose. We demonstrate the efficacy of our approach using the transient data generated from the simulations. The simulation data ...

Reference implementation for a variational autoencoder in TensorFlow and PyTorch. I recommend the PyTorch version. It includes an example of a more expressive variational family, the inverse ...

Overview of the Constrained Subspace Variational Autoencoder (CS-VAE ... Our computational experiments were carried out using TensorFlow and Keras. The image decoder we use is symmetric to the encoder ...

Abstract: With the emergence of AI(artificial intelligence), it is becoming more and more critical for organizations to utilize it to their advantage. However, organizations that possess a decent ...

The Variational Autoencoder model is a neural network that provides collaborative ... Data-parallel multi-GPU training with Horovod Mixed precision support with TensorFlow Automatic Mixed Precision ...

Autoencoder architecture for ADK protein ... In our implementation, the autoencoders and variational autoencoders were developed in Python 3.7 using the Keras package with Tensorflow Abadi et al.