71 citations · 604 across the 67 of their papers we have counts for
5 papers · 1 filter
On Signal-to-Noise Ratio Issues in Variational Inference for Deep Gaussian Processes
Tim G. J. Rudner, Oscar Key, Yarin Gal +1
We show that the gradient estimates used in training Deep Gaussian Processes (DGPs) with importance-weighted variational inference are susceptible to signal-to-noise ratio (SNR) is…
Improving Transformation Invariance in Contrastive Representation Learning
Adam Foster, Rattana Pukdee, Tom Rainforth
We propose methods to strengthen the invariance properties of representations obtained by contrastive learning. While existing approaches implicitly induce a degree of invariance a…
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…
Capturing Label Characteristics in VAEs
Tom Joy, Sebastian M. Schmon, Philip H. S. Torr +2
We present a principled approach to incorporating labels in VAEs that captures the rich characteristic information associated with those labels. While prior work has typically conf…
A note on blind contact tracing at scale with applications to the COVID-19 pandemic
Jack K. Fitzsimons, Atul Mantri, Robert Pisarczyk +2
The current COVID-19 pandemic highlights the utility of contact tracing, when combined with case isolation and social distancing, as an important tool for mitigating the spread of…