6 citations · 12 across the 4 of their papers we have counts for
5 papers · 1 filter
Learning Deep-Latent Hierarchies by Stacking Wasserstein Autoencoders
Benoit Gaujac, Ilya Feige, David Barber
Probabilistic models with hierarchical-latent-variable structures provide state-of-the-art results amongst non-autoregressive, unsupervised density-based models. However, the most…
Learning disentangled representations with the Wasserstein Autoencoder
Benoit Gaujac, Ilya Feige, David Barber
Disentangled representation learning has undoubtedly benefited from objective function surgery. However, a delicate balancing act of tuning is still required in order to trade off…
Invariant-equivariant representation learning for multi-class data
Ilya Feige
Representations learnt through deep neural networks tend to be highly informative, but opaque in terms of what information they learn to encode. We introduce an approach to probabi…
Improving latent variable descriptiveness with AutoGen
Alex Mansbridge, Roberto Fierimonte, Ilya Feige +1
Powerful generative models, particularly in Natural Language Modelling, are commonly trained by maximizing a variational lower bound on the data log likelihood. These models often…
Gaussian mixture models with Wasserstein distance
Benoit Gaujac, Ilya Feige, David Barber
Generative models with both discrete and continuous latent variables are highly motivated by the structure of many real-world data sets. They present, however, subtleties in traini…