74 citations · 302 across the 26 of their papers we have counts for
6 papers · 1 filter
Is MC Dropout Bayesian?
Loic Le Folgoc, Vasileios Baltatzis, Sujal Desai +7
MC Dropout is a mainstream "free lunch" method in medical imaging for approximate Bayesian computations (ABC). Its appeal is to solve out-of-the-box the daunting task of ABC and un…
Bayesian analysis of the prevalence bias: learning and predicting from imbalanced data
Loic Le Folgoc, Vasileios Baltatzis, Amir Alansary +8
Datasets are rarely a realistic approximation of the target population. Say, prevalence is misrepresented, image quality is above clinical standards, etc. This mismatch is known as…
Overfitting of neural nets under class imbalance: Analysis and improvements for segmentation
Zeju Li, Konstantinos Kamnitsas, Ben Glocker
Overfitting in deep learning has been the focus of a number of recent works, yet its exact impact on the behavior of neural networks is not well understood. This study analyzes ove…
Graph Convolutional Gaussian Processes
Ian Walker, Ben Glocker
We propose a novel Bayesian nonparametric method to learn translation-invariant relationships on non-Euclidean domains. The resulting graph convolutional Gaussian processes can be…
Morpho-MNIST: Quantitative Assessment and Diagnostics for Representation Learning
Daniel C. Castro, Jeremy Tan, Bernhard Kainz +2
Revealing latent structure in data is an active field of research, having introduced exciting technologies such as variational autoencoders and adversarial networks, and is essenti…
Semi-Supervised Learning via Compact Latent Space Clustering
Konstantinos Kamnitsas, Daniel C. Castro, Loic Le Folgoc +6
We present a novel cost function for semi-supervised learning of neural networks that encourages compact clustering of the latent space to facilitate separation. The key idea is to…