51 citations · 108 across the 15 of their papers we have counts for
18 papers · 1 filter
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…
A Maximum Entropy approach to Massive Graph Spectra
Diego Granziol, Robin Ru, Stefan Zohren +3
Graph spectral techniques for measuring graph similarity, or for learning the cluster number, require kernel smoothing. The choice of kernel function and bandwidth are typically ch…
Deep Curvature Suite
Diego Granziol, Xingchen Wan, Timur Garipov
We present MLRG Deep Curvature suite, a PyTorch-based, open-source package for analysis and visualisation of neural network curvature and loss landscape. Despite of providing rich…
MEMe: An Accurate Maximum Entropy Method for Efficient Approximations in Large-Scale Machine Learning
Diego Granziol, Binxin Ru, Stefan Zohren +3
Efficient approximation lies at the heart of large-scale machine learning problems. In this paper, we propose a novel, robust maximum entropy algorithm, which is capable of dealing…