18 citations · 27 across the 6 of their papers we have counts for
6 papers
Single-Frame Super-Resolution of Solar Magnetograms: Investigating Physics-Based Metrics & Losses
Anna Jungbluth, Xavier Gitiaux, Shane A. Maloney +7
Breakthroughs in our understanding of physical phenomena have traditionally followed improvements in instrumentation. Studies of the magnetic field of the Sun, and its influence on…
Probabilistic Super-Resolution of Solar Magnetograms: Generating Many Explanations and Measuring Uncertainties
Xavier Gitiaux, Shane A. Maloney, Anna Jungbluth +7
Machine learning techniques have been successfully applied to super-resolution tasks on natural images where visually pleasing results are sufficient. However in many scientific do…
A large-scale crowdsourced analysis of abuse against women journalists and politicians on Twitter
Laure Delisle, Alfredo Kalaitzis, Krzysztof Majewski +3
We report the first, to the best of our knowledge, hand-in-hand collaboration between human rights activists and machine learners, leveraging crowd-sourcing to study online abuse a…
Flexible sampling of discrete data correlations without the marginal distributions
Alfredo Kalaitzis, Ricardo Silva
Learning the joint dependence of discrete variables is a fundamental problem in machine learning, with many applications including prediction, clustering and dimensionality reducti…
Residual Component Analysis: Generalising PCA for more flexible inference in linear-Gaussian models
Alfredo Kalaitzis, Neil Lawrence
Probabilistic principal component analysis (PPCA) seeks a low dimensional representation of a data set in the presence of independent spherical Gaussian noise. The maximum likeliho…
Residual Component Analysis
Alfredo A. Kalaitzis, Neil D. Lawrence
Probabilistic principal component analysis (PPCA) seeks a low dimensional representation of a data set in the presence of independent spherical Gaussian noise, Sigma = (sigma^2)*I.…