3 papers
stat.ML2020
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…
stat.ML2020
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…
stat.ML2018
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…