| 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 | 686* | 2017 |
| 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 | 382* | 2017 |
| Understanding probabilistic sparse Gaussian process approximations M Bauer, M Van der Wilk, CE Rasmussen Advances in neural information processing systems 29, 2016 | 301 | 2016 |
| 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 | 196 | 2014 |
| 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 | 195 | 2019 |
| Convolutional Gaussian Processes M van der Wilk, CE Rasmussen, J Hensman Advances in Neural Information Processing Systems, 2845-2854, 2017 | 159 | 2017 |
| Bayesian neural network priors revisited V Fortuin, A Garriga-Alonso, F Wenzel, G Rätsch, R Turner, ... International Conference on Learning Representations (ICLR), 2022 | 143 | 2022 |
| Bayesian layers: A module for neural network uncertainty D Tran, M Dusenberry, M Van Der Wilk, D Hafner Advances in neural information processing systems 32, 2019 | 134 | 2019 |
| 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 | 103 | 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 | 101 | 2020 |
| The promises and pitfalls of deep kernel learning SW Ober, CE Rasmussen, M van der Wilk Proceedings of the Thirty-Seventh Conference on Uncertainty in Artificial …, 2021 | 93 | 2021 |
| 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 | 88 | 2018 |
| 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 | 86* | 2020 |
| 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 | 76 | 2020 |
| Speedy performance estimation for neural architecture search R Ru, C Lyle, L Schut, M Fil, M van der Wilk, Y Gal Advances in Neural Information Processing Systems 34, 4079-4092, 2021 | 50 | 2021 |
| Understanding variational inference in function-space DR Burt, SW Ober, A Garriga-Alonso, M van der Wilk arXiv preprint arXiv:2011.09421, 2020 | 45 | 2020 |
| 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 | 43* | 2020 |
| 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 | 38* | 2019 |
| Invariance learning in deep neural networks with differentiable laplace approximations A Immer, T van der Ouderaa, G Rätsch, V Fortuin, M van der Wilk Advances in Neural Information Processing Systems 35, 12449-12463, 2022 | 35 | 2022 |
| Sparse Gaussian process approximations and applications M van der Wilk University of Cambridge, 2019 | 35 | 2019 |
James HensmanMicrosoft Research在 microsoft.com 的电子邮件经过验证
