Anita Faul
Anita Faul
Data Scientist, British Antarctic Survey
Verified email at bas.ac.uk - Homepage
Title
Cited by
Cited by
Year
Fast marginal likelihood maximisation for sparse Bayesian models.
ME Tipping, AC Faul
AISTATS, 2003
10602003
Analysis of sparse Bayesian learning
AC Faul, ME Tipping
Advances in neural information processing systems, 383-389, 2002
337*2002
Proof of convergence of an iterative technique for thin plate spline interpolation in two dimensions
AC Faul, MJD Powell
Advances in Computational Mathematics 11 (2-3), 183-192, 1999
631999
A Krylov subspace algorithm for multiquadric interpolation in many dimensions
AC Faul, G Goodsell, MJD Powell
IMA Journal of Numerical Analysis 25 (1), 1-24, 2005
532005
A variational approach to robust regression
AC Faul, ME Tipping
International Conference on Artificial Neural Networks, 95-102, 2001
412001
Krylov subspace methods for radial basis function interpolation
AC Faul, MJD Powell
CHAPMAN AND HALL CRC RESEARCH NOTES IN MATHEMATICS, 115-142, 2000
412000
A Concise Introduction to Machine Learning
AC Faul
CRC Press, 2019
132019
Bayesian feature learning for seismic compressive sensing and denoising
G Pilikos, AC Faul
Geophysics 82 (6), O91-O104, 2017
132017
A Concise introduction to numerical analysis
AC Faul
CRC Press, 2016
122016
Relevance vector machines with uncertainty measure for seismic Bayesian compressive sensing and survey design
G Pilikos, AC Faul
2016 15th IEEE International Conference on Machine Learning and Applications …, 2016
92016
Deep learning applied to seismic data interpolation
A Mikhailiuk, A Faul
80th EAGE Conference and Exhibition 2018 2018 (1), 1-5, 2018
82018
Iterative techniques for radial basis function interpolation
AC Faul
University of Cambridge, 2001
82001
The model is simple, until proven otherwise: How to cope in an ever-changing world
AC Faul, G Pilikos
Data for Policy 2016; Frontiers of Data Sciencefor Government: Ideas …, 2016
52016
Semi-supervised Learning with Graphs: Covariance Based Superpixels For Hyperspectral Image Classification
P Sellars, AI Aviles-Rivero, N Papadakis, D Coomes, A Faul, ...
IGARSS 2019-2019 IEEE International Geoscience and Remote Sensing Symposium …, 2019
42019
Seismic compressive sensing beyond aliasing using Bayesian feature learning
G Pilikos, AC Faul, N Philip
SEG Technical Program Expanded Abstracts 2017, 4328-4332, 2017
32017
Bayesian modeling for uncertainty quantification in seismic compressive sensing
G Pilikos, AC Faul
Geophysics 84 (2), P15-P25, 2019
12019
A Fast and Greedy Subset-of-Data (SoD) Scheme for Sparsification in Gaussian processes
V Lalchand, AC Faul
arXiv preprint arXiv:1811.07199, 2018
2018
A Greedy approximation scheme for Sparse Gaussian process regression.
V Lalchand, AC Faul
CoRR, 2018
2018
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Articles 1–18