96 citations · 160 across the 7 of their papers we have counts for
7 papers
Exploring the potential of neural networks to predict statistics of solar wind turbulence
Daniel Wrench, Tulasi N. Parashar, Ritesh K. Singh +2
Time series datasets often have missing or corrupted entries, which need to be ignored in subsequent data analysis. For example, in the context of space physics, calibration issues…
The Shattered Gradients Problem: If resnets are the answer, then what is the question?
David Balduzzi, Marcus Frean, Lennox Leary +3
A long-standing obstacle to progress in deep learning is the problem of vanishing and exploding gradients. Although, the problem has largely been overcome via carefully constructed…
An Overview of the SKA Science Analysis Pipeline
C. Hollitt, M. Johnston-Hollitt, S. Dehghan +2
When completed the Square Kilometre Array (SKA) will feature an unprecedented rate of image generation. While previous generations of telescopes have relied on human expertise to e…
Detecting Diffuse Sources in Astronomical Images
T. Butler-Yeoman, M. Frean, C. P. Hollitt +2
We present an algorithm capable of detecting diffuse, dim sources of any size in an astronomical image. These sources often defeat traditional methods for source finding, which exp…
Dissecting magnetar variability with Bayesian hierarchical models
D. Huppenkothen, B. J. Brewer, D. W. Hogg +7
Neutron stars are a prime laboratory for testing physical processes under conditions of strong gravity, high density, and extreme magnetic fields. Among the zoo of neutron star phe…
Software graphs and programmer awareness
G. J. Baxter, M. R. Frean
Dependencies between types in object-oriented software can be viewed as directed graphs, with types as nodes and dependencies as edges. The in-degree and out-degree distributions o…