3 papers
cs.LG2020
The Variational InfoMax Learning Objective
Vincenzo Crescimanna, Bruce Graham
Bayesian Inference and Information Bottleneck are the two most popular objectives for neural networks, but they can be optimised only via a variational lower bound: the Variational…
cs.LG2019
The Variational InfoMax AutoEncoder
Vincenzo Crescimanna, Bruce Graham
The Variational AutoEncoder (VAE) learns simultaneously an inference and a generative model, but only one of these models can be learned at optimum, this behaviour is associated to…
cs.LG2019
An information theoretic approach to the autoencoder
Vincenzo Crescimanna, Bruce Graham
We present a variation of the Autoencoder (AE) that explicitly maximizes the mutual information between the input data and the hidden representation. The proposed model, the InfoMa…