37 citations · 79 across the 15 of their papers we have counts for
5 papers · 1 filter
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…
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…