45 citations · 126 across the 10 of their papers we have counts for
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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…
Stochastic Variational Optimization
Thomas Bird, Julius Kunze, David Barber
Variational Optimization forms a differentiable upper bound on an objective. We show that approaches such as Natural Evolution Strategies and Gaussian Perturbation, are special cas…
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…
Online Structured Laplace Approximations For Overcoming Catastrophic Forgetting
Hippolyt Ritter, Aleksandar Botev, David Barber
We introduce the Kronecker factored online Laplace approximation for overcoming catastrophic forgetting in neural networks. The method is grounded in a Bayesian online learning fra…