10 citations · 10 across the 2 of their papers we have counts for
9 papers · 1 filter
Correlated Input-Dependent Label Noise in Large-Scale Image Classification
Mark Collier, Basil Mustafa, Efi Kokiopoulou +2
Large scale image classification datasets often contain noisy labels. We take a principled probabilistic approach to modelling input-dependent, also known as heteroscedastic, label…
Routing Networks with Co-training for Continual Learning
Mark Collier, Efi Kokiopoulou, Andrea Gesmundo +1
The core challenge with continual learning is catastrophic forgetting, the phenomenon that when neural networks are trained on a sequence of tasks they rapidly forget previously le…
VAEs in the Presence of Missing Data
Mark Collier, Alfredo Nazabal, Christopher K. I. Williams
Real world datasets often contain entries with missing elements e.g. in a medical dataset, a patient is unlikely to have taken all possible diagnostic tests. Variational Autoencode…
A Simple Probabilistic Method for Deep Classification under Input-Dependent Label Noise
Mark Collier, Basil Mustafa, Efi Kokiopoulou +2
Datasets with noisy labels are a common occurrence in practical applications of classification methods. We propose a simple probabilistic method for training deep classifiers under…
Scalable Deep Unsupervised Clustering with Concrete GMVAEs
Mark Collier, Hector Urdiales
Discrete random variables are natural components of probabilistic clustering models. A number of VAE variants with discrete latent variables have been developed. Training such meth…
Memory-Augmented Neural Networks for Machine Translation
Mark Collier, Joeran Beel
Memory-augmented neural networks (MANNs) have been shown to outperform other recurrent neural network architectures on a series of artificial sequence learning tasks, yet they have…