274 citations · 423 across the 15 of their papers we have counts for
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An Empirical Study of Generative Models with Encoders
Paul K. Rubenstein, Yunpeng Li, Dominik Roblek
Generative adversarial networks (GANs) are capable of producing high quality image samples. However, unlike variational autoencoders (VAEs), GANs lack encoders that provide the inv…
Structural causal models for macro-variables in time-series
Dominik Janzing, Paul Rubenstein, Bernhard Schölkopf
We consider a bivariate time series that is given by a simple linear autoregressive model. Assuming that the equations describing each variable as a linear combination…
On the Latent Space of Wasserstein Auto-Encoders
Paul K. Rubenstein, Bernhard Schoelkopf, Ilya Tolstikhin
We study the role of latent space dimensionality in Wasserstein auto-encoders (WAEs). Through experimentation on synthetic and real datasets, we argue that random encoders should b…