| Dropout as a Bayesian approximation: Representing model uncertainty in deep learning Y Gal, Z Ghahramani Proceedings of the 33rd International Conference on Machine Learning (ICML-16), 2015 | 10769 | 2015 |
| What uncertainties do we need in Bayesian deep learning for computer vision? A Kendall, Y Gal Advances in neural information processing systems, 5574-5584, 2017 | 5581 | 2017 |
| Multi-task learning using uncertainty to weigh losses for scene geometry and semantics A Kendall, Y Gal, R Cipolla Proceedings of the IEEE Conference on Computer Vision and Pattern …, 2018 | 3587 | 2018 |
| Uncertainty in Deep Learning Y Gal University of Cambridge, 2016 | 2083 | 2016 |
| A theoretically grounded application of dropout in recurrent neural networks Y Gal, Z Ghahramani Advances in neural information processing systems 29, 1019-1027, 2016 | 2060 | 2016 |
| Deep Bayesian Active Learning with Image Data Y Gal, R Islam, Z Ghahramani International Conference on Machine Learning (ICML), 1183-1192, 2017 | 1954 | 2017 |
| Inferring the effectiveness of government interventions against COVID-19 JM Brauner, S Mindermann, M Sharma, D Johnston, J Salvatier, ... Science 371 (6531), eabd9338, 2021 | 1024 | 2021 |
| Bayesian Convolutional Neural Networks with Bernoulli Approximate Variational Inference Y Gal, Z Ghahramani 4th International Conference on Learning Representations (ICLR) workshop track, 2015 | 948 | 2015 |
| Concrete dropout Y Gal, J Hron, A Kendall Advances in Neural Information Processing Systems, 3581-3590, 2017 | 733 | 2017 |
| Real time image saliency for black box classifiers P Dabkowski, Y Gal Advances in Neural Information Processing Systems, 6967-6976, 2017 | 713 | 2017 |
| BatchBALD: Efficient and Diverse Batch Acquisition for Deep Bayesian Active Learning A Kirsch, J van Amersfoort, Y Gal Advances in Neural Information Processing Systems, 2019, 2019 | 640 | 2019 |
| Uncertainty estimation using a single deep deterministic neural network J van Amersfoort, L Smith, YW Teh, Y Gal International Conference on Machine Learning (ICML), 2020 | 504 | 2020 |
| Disease variant prediction with deep generative models of evolutionary data J Frazer, P Notin, M Dias, A Gomez, JK Min, K Brock, Y Gal, DS Marks Nature 599 (7883), 91-95, 2021 | 484 | 2021 |
| Learning Invariant Representations for Reinforcement Learning without Reconstruction A Zhang, R McAllister, R Calandra, Y Gal, S Levine International Conference on Learning Representations (ICLR), 2020 | 480 | 2020 |
| Understanding Measures of Uncertainty for Adversarial Example Detection L Smith, Y Gal Uncertainty in Artificial Intelligence (UAI), 2018 | 403 | 2018 |
| 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 (IJCAI), 2017 | 399* | 2017 |
| Towards Robust Evaluations of Continual Learning S Farquhar, Y Gal Lifelong Learning: A Reinforcement Learning Approach workshop, ICML, 2018, 2018 | 319 | 2018 |
| Improving PILCO with Bayesian neural network dynamics models Y Gal, R McAllister, CE Rasmussen Data-Efficient Machine Learning workshop, ICML, 2016 | 315 | 2016 |
| Fast and Scalable Bayesian Deep Learning by Weight-Perturbation in Adam ME Khan, D Nielsen, V Tangkaratt, W Lin, Y Gal, A Srivastava ICML, 2018, 2018 | 307 | 2018 |
| VariBAD: a very good method for Bayes-adaptive deep RL via meta-learning L Zintgraf, K Shiarlis, M Igl, S Schulze, Y Gal, K Hofmann, S Whiteson International Conference on Learning Representations (ICLR), 2020 | 274 | 2020 |
Zoubin GhahramaniProfessor, University of Cambridge, and Distinguished Researcher, GoogleVerified email at eng.cam.ac.uk
