GUYARD , Frederic

Deep neural networks (DNN) have been applied recently to different domains andperform better than classical state-of-the-art methods. However the high level of performances of DNNs is most often obtained with networks containing millions of parameters and for which training requires substantial computational power. To deal with this...

This paper deals with supervised discriminative and generative modeling. Classical methods are based on variational autoencoders or supervised variational autoencoders encourage the latent space to fit a prior distribution, like a Gaussian. However, they tend to make stronger assumptions on the data, often leading to higher asymptotic bias when...