37 citations · 79 across the 15 of their papers we have counts for
15 papers
A Multi-Resolution Framework for U-Nets with Applications to Hierarchical VAEs
Fabian Falck, Christopher Williams, Dominic Danks +5
U-Net architectures are ubiquitous in state-of-the-art deep learning, however their regularisation properties and relationship to wavelets are understudied. In this paper, we formu…
Multi-Facet Clustering Variational Autoencoders
Fabian Falck, Haoting Zhang, Matthew Willetts +3
Work in deep clustering focuses on finding a single partition of data. However, high-dimensional data, such as images, typically feature multiple interesting characteristics one co…
I Don't Need u: Identifiable Non-Linear ICA Without Side Information
Matthew Willetts, Brooks Paige
In this paper, we investigate the algorithmic stability of unsupervised representation learning with deep generative models, as a function of repeated re-training on the same input…
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…
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…
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…
Non-Determinism in TensorFlow ResNets
Miguel Morin, Matthew Willetts
We show that the stochasticity in training ResNets for image classification on GPUs in TensorFlow is dominated by the non-determinism from GPUs, rather than by the initialisation o…
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…
Disentangling to Cluster: Gaussian Mixture Variational Ladder Autoencoders
Matthew Willetts, Stephen Roberts, Chris Holmes
In clustering we normally output one cluster variable for each datapoint. However it is not necessarily the case that there is only one way to partition a given dataset into cluste…
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…
Semi-Unsupervised Learning: Clustering and Classifying using Ultra-Sparse Labels
Matthew Willetts, Stephen J Roberts, Christopher C Holmes
In semi-supervised learning for classification, it is assumed that every ground truth class of data is present in the small labelled dataset. Many real-world sparsely-labelled data…
Semi-unsupervised Learning of Human Activity using Deep Generative Models
Matthew Willetts, Aiden Doherty, Stephen Roberts +1
We introduce 'semi-unsupervised learning', a problem regime related to transfer learning and zero-shot learning where, in the training data, some classes are sparsely labelled and…