4 citations · 8 across the 4 of their papers we have counts for
8 papers · 1 filter
Deep Generative Pattern-Set Mixture Models for Nonignorable Missingness
Sahra Ghalebikesabi, Rob Cornish, Luke J. Kelly +1
We propose a variational autoencoder architecture to model both ignorable and nonignorable missing data using pattern-set mixtures as proposed by Little (1993). Our model explicitl…
Asymmetric Heavy Tails and Implicit Bias in Gaussian Noise Injections
Alexander Camuto, Xiaoyu Wang, Lingjiong Zhu +3
Gaussian noise injections (GNIs) are a family of simple and widely-used regularisation methods for training neural networks, where one injects additive or multiplicative Gaussian n…
Explicit Regularisation in Gaussian Noise Injections
Alexander Camuto, Matthew Willetts, Umut Şimşekli +2
We study the regularisation induced in neural networks by Gaussian noise injections (GNIs). Though such injections have been extensively studied when applied to data, there have be…
Towards a Theoretical Understanding of the Robustness of Variational Autoencoders
Alexander Camuto, Matthew Willetts, Stephen Roberts +2
We make inroads into understanding the robustness of Variational Autoencoders (VAEs) to adversarial attacks and other input perturbations. While previous work has developed algorit…
Relaxed-Responsibility Hierarchical Discrete VAEs
Matthew Willetts, Xenia Miscouridou, Stephen Roberts +1
Successfully training Variational Autoencoders (VAEs) with a hierarchy of discrete latent variables remains an area of active research. Vector-Quantised VAEs are a powerful approac…
Improving VAEs' Robustness to Adversarial Attack
Matthew Willetts, Alexander Camuto, Tom Rainforth +2
Variational autoencoders (VAEs) have recently been shown to be vulnerable to adversarial attacks, wherein they are fooled into reconstructing a chosen target image. However, how to…