37 citations · 61 across the 9 of their papers we have counts for
9 papers
Fractal Structure and Generalization Properties of Stochastic Optimization Algorithms
Alexander Camuto, George Deligiannidis, Murat A. Erdogdu +3
Understanding generalization in deep learning has been one of the major challenges in statistical learning theory over the last decade. While recent work has illustrated that the d…
Variational Autoencoders: A Harmonic Perspective
Alexander Camuto, Matthew Willetts
In this work we study Variational Autoencoders (VAEs) from the perspective of harmonic analysis. By viewing a VAE's latent space as a Gaussian Space, a variety of measure space, we…
Certifiably Robust Variational Autoencoders
Ben Barrett, Alexander Camuto, Matthew Willetts +1
We introduce an approach for training Variational Autoencoders (VAEs) that are certifiably robust to adversarial attack. Specifically, we first derive actionable bounds on the mini…
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…
Learning Bijective Feature Maps for Linear ICA
Alexander Camuto, Matthew Willetts, Brooks Paige +2
Separating high-dimensional data like images into independent latent factors, i.e independent component analysis (ICA), remains an open research problem. As we show, existing proba…
Regularising Deep Networks with Deep Generative Models
Matthew Willetts, Alexander Camuto, Stephen Roberts +1
We develop a new method for regularising neural networks. We learn a probability distribution over the activations of all layers of the model and then insert imputed values into th…
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…