| Fast marginal likelihood maximisation for sparse Bayesian models. ME Tipping, AC Faul AISTATS, 2003 | 1281 | 2003 |
| Analysis of sparse Bayesian learning AC Faul, ME Tipping Advances in neural information processing systems, 383-389, 2002 | 393 | 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 | 63 | 1999 |
| 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 | 60 | 2005 |
| A variational approach to robust regression AC Faul, ME Tipping International Conference on Artificial Neural Networks, 95-102, 2001 | 52 | 2001 |
| A Concise Introduction to Machine Learning AC Faul CRC Press, 2019 | 49 | 2019 |
| Krylov subspace methods for radial basis function interpolation AC Faul, MJD Powell CHAPMAN AND HALL CRC RESEARCH NOTES IN MATHEMATICS, 115-142, 2000 | 44 | 2000 |
| Bayesian feature learning for seismic compressive sensing and denoising G Pilikos, AC Faul Geophysics 82 (6), O91-O104, 2017 | 29 | 2017 |
| Deep learning applied to seismic data interpolation A Mikhailiuk, A Faul 80th EAGE Conference and Exhibition 2018 2018 (1), 1-5, 2018 | 26 | 2018 |
| A Concise introduction to numerical analysis AC Faul CRC Press, 2016 | 23 | 2016 |
| Defining Southern Ocean fronts using unsupervised classification SDA Thomas, DC Jones, A Faul, E Mackie, E Pauthenet Ocean Science 17 (6), 1545-1562, 2021 | 15 | 2021 |
| 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 | 9 | 2016 |
| Iterative techniques for radial basis function interpolation AC Faul University of Cambridge, 2001 | 8 | 2001 |
| 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 | 6 | 2019 |
| A Fast and Greedy Subset-of-Data (SoD) Scheme for Sparsification in Gaussian processes V Lalchand, AC Faul arXiv preprint arXiv:1811.07199, 2018 | 5 | 2018 |
| 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 | 5 | 2016 |
| Unsupervised machine learning detection of iceberg populations within sea ice from dual-polarisation SAR imagery B Evans, A Faul, A Fleming, DG Vaughan, JS Hosking Remote Sensing of Environment 297, 113780, 2023 | 4 | 2023 |
| Bayesian modeling for uncertainty quantification in seismic compressive sensing G Pilikos, AC Faul Geophysics 84 (2), P15-P25, 2019 | 3 | 2019 |
| Seismic compressive sensing beyond aliasing using Bayesian feature learning G Pilikos, AC Faul, N Philip SEG Technical Program Expanded Abstracts 2017, 4328-4332, 2017 | 3 | 2017 |
| A Greedy approximation scheme for Sparse Gaussian process regression. V Lalchand, AC Faul CoRR, 2018 | | 2018 |
Mike TippingProfessor of Machine Learning, University of Bath, U.K. :: Advisor at HomeLink & PropfloVerified email at miketipping.com
