Mark van der Wilk
Title
Cited by
Cited by
Year
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 (1), 1299-1304, 2017
321*2017
Understanding probabilistic sparse Gaussian process approximations
M Bauer, M van der Wilk, CE Rasmussen
arXiv preprint arXiv:1606.04820, 2016
1602016
Distributed variational inference in sparse Gaussian process regression and latent variable models
Y Gal*, M van der Wilk*, CE Rasmussen
Advances in Neural Information Processing Systems, 3257-3265, 2014
1562014
Concrete problems for autonomous vehicle safety: Advantages of Bayesian deep learning
R McAllister, Y Gal, A Kendall, M van der Wilk, A Shah, R Cipolla, ...
International Joint Conferences on Artificial Intelligence, Inc., 2017
153*2017
Convolutional Gaussian Processes
M van der Wilk, CE Rasmussen, J Hensman
Advances in Neural Information Processing Systems, 2845-2854, 2017
772017
Rates of Convergence for Sparse Variational Gaussian Process Regression
DR Burt, CE Rasmussen, M van der Wilk
Proceedings of the 36th International Conference on Machine Learning (ICML 2019), 2019
692019
Bayesian layers: A module for neural network uncertainty
D Tran, MW Dusenberry, M van der Wilk, D Hafner
arXiv preprint arXiv:1812.03973, 2018
412018
Learning invariances using the marginal likelihood
M van der Wilk, M Bauer, ST John, J Hensman
Advances in Neural Information Processing Systems 31, 9938-9948, 2018
212018
Bayesian Image Classification with Deep Convolutional Gaussian Processes
V Dutordoir, M van der Wilk, A Artemev, J Hensman
International Conference on Artificial Intelligence and Statistics (AISTATS …, 2020
17*2020
A framework for interdomain and multioutput Gaussian processes
M van der Wilk, V Dutordoir, ST John, A Artemev, V Adam, J Hensman
arXiv preprint arXiv:2003.01115, 2020
122020
Overcoming mean-field approximations in recurrent Gaussian process models
AD Ialongo, M Van Der Wilk, J Hensman, CE Rasmussen
Proceedings of the 36th International Conference on Machine Learning (ICML 2019), 2019
11*2019
On the benefits of invariance in neural networks
C Lyle, M van der Wilk, M Kwiatkowska, Y Gal, B Bloem-Reddy
arXiv preprint arXiv:2005.00178, 2020
82020
Closed-form Inference and Prediction in Gaussian Process State-Space Models
AD Ialongo, M van der Wilk, CE Rasmussen
NIPS 2017 Time-Series Workshop, 2017
82017
Sparse Gaussian process approximations and applications
M van der Wilk
University of Cambridge, 2019
62019
Convergence of Sparse Variational Inference in Gaussian Processes Regression
DR Burt, CE Rasmussen, M van der Wilk
Journal of Machine Learning Research 21, 1-63, 2020
52020
Stochastic segmentation networks: Modelling spatially correlated aleatoric uncertainty
M Monteiro, LL Folgoc, DC de Castro, N Pawlowski, B Marques, ...
Advances in Neural Information Processing Systems 33 (NeurIPS 2020), 2020
52020
Revisiting the train loss: an efficient performance estimator for neural architecture search
B Ru, C Lyle, L Schut, M van der Wilk, Y Gal
arXiv preprint arXiv:2006.04492, 2020
52020
A practical guide to Gaussian processes
MP Deisenroth, Y Luo, MVD Wilk
52019
Variational inference in sparse Gaussian process regression and latent variable models-a gentle tutorial
Y Gal, M van der Wilk
arXiv preprint arXiv:1402.1412, 2014
52014
Scalable Bayesian dynamic covariance modeling with variational Wishart and inverse Wishart processes
C Heaukulani, M van der Wilk
Advances in Neural Information Processing Systems 32 (NIPS 2019), 2019
32019
The system can't perform the operation now. Try again later.
Articles 1–20