| GPflow: A Gaussian process library using TensorFlow AGG Matthews, M van der Wilk, T Nickson, K Fujii, A Boukouvalas, ... Journal of Machine Learning Research 18 (40), 1-6, 2017 | 265* | 2017 |
| Scalable Variational Gaussian Process Classification. J Hensman, A Matthews, Z Ghahramani The 18th International Conference on Artificial Intelligence and Statistics …, 2015 | 253 | 2015 |
| Gaussian Process Behaviour in Wide Deep Neural Networks AGG Matthews, J Hron, M Rowland, RE Turner, Z Ghahramani International Conference on Learning Representations (ICLR), 2018 | 148* | 2018 |
| On Sparse Variational Methods and the Kullback-Leibler Divergence between Stochastic Processes AGG Matthews, J Hensman, RE Turner, Z Ghahramani The 19th International Conference on Artificial Intelligence and Statistics …, 2016 | 96* | 2016 |
| MCMC for variationally sparse Gaussian processes J Hensman, AG Matthews, M Filippone, Z Ghahramani Advances in Neural Information Processing Systems, 1648-1656, 2015 | 80 | 2015 |
| Measurement and simulation of the effect of compaction on the pore structure and saturated hydraulic conductivity of grassland and arable soil GP Matthews, GM Laudone, AS Gregory, NRA Bird, AG de G Matthews, ... Water Resources Research 46 (5), 2010 | 57 | 2010 |
| Adversarial Examples, Uncertainty, and Transfer Testing Robustness in Gaussian Process Hybrid Deep Networks J Bradshaw, AGG Matthews, Z Ghahramani arXiv preprint arXiv:1707.02476, 2017 | 52 | 2017 |
| Ab-Initio Solution of the Many-Electron Schr\" odinger Equation with Deep Neural Networks D Pfau, JS Spencer, AGG Matthews, WMC Foulkes arXiv preprint arXiv:1909.02487, 2019 | 44* | 2019 |
| Scalable Gaussian process inference using variational methods AGG Matthews University of Cambridge, 2017 | 32 | 2017 |
| Variational Bayesian dropout: pitfalls and fixes J Hron, A Matthews, Z Ghahramani Proceedings of Machine Learning Research, 2018 | 28* | 2018 |
| Functional Regularisation for Continual Learning with Gaussian Processes MK Titsias, J Schwarz, AGG Matthews, R Pascanu, YW Teh International Conference on Learning Representations, 2019 | 21* | 2019 |
| Variational Gaussian Dropout is not Bayesian J Hron, AGG Matthews, Z Ghahramani arXiv preprint arXiv:1711.02989, 2017 | 17 | 2017 |
| A depth filtration model of straining within the void networks of stainless steel filters JC Price, GP Matthews, K Quinlan, J Sexton, AGG Matthews AIChE journal 55 (12), 3134-3144, 2009 | 14 | 2009 |
| Sample-then-optimize posterior sampling for Bayesian linear models AGG Matthews, J Hron, RE Turner, Z Ghahramani NIPS Workshop on Advances in Approximate Bayesian Inference, 2017 | 4 | 2017 |
| Scattering theory for quantum Hall anyons in a saddle point potential A Matthews, NR Cooper Physical Review B 80 (16), 165309, 2009 | 4 | 2009 |
| Estimation of structural element sizes in sand and compacted blocks of ground calcium carbonate using a void network model GM Laudone, GP Matthews, PAC Gane, AG Matthews, CJ Ridgway, ... Transport in porous media 66 (3), 403-419, 2007 | 3 | 2007 |
| Classification using log Gaussian Cox processes AG Matthews, Z Ghahramani arXiv preprint arXiv:1405.4141, 2014 | 1 | 2014 |
| The full natural gradient of a sparse variational Gaussian process AGG Matthews | | |
Zoubin GhahramaniProfessor, University of Cambridge, and Chief Scientist, UberVerified email at eng.cam.ac.uk
