103 citations · 103 across the 8 of their papers we have counts for
4 papers · 1 filter
Adversarial Likelihood Estimation With One-Way Flows
Omri Ben-Dov, Pravir Singh Gupta, Victoria Abrevaya +2
Generative Adversarial Networks (GANs) can produce high-quality samples, but do not provide an estimate of the probability density around the samples. However, it has been noted th…
LED: Latent Variable-based Estimation of Density
Omri Ben-Dov, Pravir Singh Gupta, Victoria Fernandez Abrevaya +2
Modern generative models are roughly divided into two main categories: (1) models that can produce high-quality random samples, but cannot estimate the exact density of new data po…
From Variational to Deterministic Autoencoders
Partha Ghosh, Mehdi S. M. Sajjadi, Antonio Vergari +2
Variational Autoencoders (VAEs) provide a theoretically-backed and popular framework for deep generative models. However, learning a VAE from data poses still unanswered theoretica…
Resisting Adversarial Attacks using Gaussian Mixture Variational Autoencoders
Partha Ghosh, Arpan Losalka, Michael J Black
Susceptibility of deep neural networks to adversarial attacks poses a major theoretical and practical challenge. All efforts to harden classifiers against such attacks have seen li…