14 citations · 39 across the 6 of their papers we have counts for
6 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…
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…
Ranking architectures using meta-learning
Alina Dubatovka, Efi Kokiopoulou, Luciano Sbaiz +3
Neural architecture search has recently attracted lots of research efforts as it promises to automate the manual design of neural networks. However, it requires a large amount of c…
Flexible Multi-task Networks by Learning Parameter Allocation
Krzysztof Maziarz, Efi Kokiopoulou, Andrea Gesmundo +3
This paper proposes a novel learning method for multi-task applications. Multi-task neural networks can learn to transfer knowledge across different tasks by using parameter sharin…
Fast Task-Aware Architecture Inference
Efi Kokiopoulou, Anja Hauth, Luciano Sbaiz +3
Neural architecture search has been shown to hold great promise towards the automation of deep learning. However in spite of its potential, neural architecture search remains quite…