| Scalable Variational Gaussian Process Classification. J Hensman, A Matthews, Z Ghahramani The 18th International Conference on Artificial Intelligence and Statistics …, 2015 | 391 | 2015 |
| 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 | 374* | 2017 |
| 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 | 261* | 2018 |
| 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 | 147* | 2019 |
| 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 | 132* | 2016 |
| MCMC for variationally sparse Gaussian processes J Hensman, AG Matthews, M Filippone, Z Ghahramani Advances in Neural Information Processing Systems, 1648-1656, 2015 | 115 | 2015 |
| 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 | 79 | 2017 |
| 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 | 68 | 2010 |
| Functional Regularisation for Continual Learning with Gaussian Processes MK Titsias, J Schwarz, AGG Matthews, R Pascanu, YW Teh International Conference on Learning Representations, 2019 | 53* | 2019 |
| Scalable Gaussian process inference using variational methods AGG Matthews University of Cambridge, 2017 | 49 | 2017 |
| Variational Bayesian dropout: pitfalls and fixes J Hron, A Matthews, Z Ghahramani Proceedings of Machine Learning Research, 2018 | 45 | 2018 |
| Matthews, and Zoubin Ghahramani. Adversarial examples, uncertainty, and transfer testing robustness in Gaussian process hybrid deep networks J Bradshaw, GG Alexander arXiv preprint arXiv:1707.02476, 2017 | 38 | 2017 |
| Variational Gaussian Dropout is not Bayesian J Hron, AGG Matthews, Z Ghahramani arXiv preprint arXiv:1711.02989, 2017 | 26 | 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 | 17 | 2009 |
| Sample-then-optimize posterior sampling for bayesian linear models AGG Matthews, J Hron, RE Turner, Z Ghahramani NeurIPS Workshop on Advances in Approximate Bayesian Inference, 2017 | 8 | 2017 |
| Matthews, and Zoubin Ghahramani. 2017. Adversarial examples, uncertainty, and transfer testing robustness in gaussian process hybrid deep networks J Bradshaw, GG Alexander arXiv preprint arXiv:1707.02476, 2017 | 5 | 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 |
| Annealed Flow Transport Monte Carlo M Arbel, AGDG Matthews, A Doucet arXiv preprint arXiv:2102.07501, 2021 | 2 | 2021 |
| Classification using log Gaussian Cox processes AG Matthews, Z Ghahramani arXiv preprint arXiv:1405.4141, 2014 | 1 | 2014 |
Zoubin GhahramaniProfessor, University of Cambridge, and Distinguished Researcher, GoogleVerified email at eng.cam.ac.uk
