Variational Autoencoders and Probabilistic Latent Representations (VAE) This implementation presents a Variational Autoencoder (VAE) using PyTorch, applied to the MNIST handwritten digit dataset. VAEs ...

The proposed model attacks classification models by utilizing a distilled model to imitate the output of the multivariate time series classification model. In addition, the adversarial generator ...

Deep probabilistic generative models have achieved incredible success in many fields of application. Among such models, variational autoencoders (VAEs) have proved their ability in modeling a ...

Defining success for early-stage, small biotech trials can be crucial for the success of Phase 1 and Phase 2 clinical trials. Learn more about the key metrics and considerations in this process.

Biologically plausible synaptic plasticity rules enable recurrent neural networks to spontaneously replay sensory experiences with appropriate probabilistic structure.