| 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 | 3606 | 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 | 1812 | 2017 |
| A theoretically grounded application of dropout in recurrent neural networks Y Gal, Z Ghahramani Advances in neural information processing systems 29, 1019-1027, 2016 | 1350 | 2016 |
| 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 | 1020 | 2018 |
| Uncertainty in Deep Learning Y Gal University of Cambridge, 2016 | 970 | 2016 |
| Deep Bayesian Active Learning with Image Data Y Gal, R Islam, Z Ghahramani International Conference on Machine Learning (ICML), 1183-1192, 2017 | 618 | 2017 |
| Bayesian Convolutional Neural Networks with Bernoulli Approximate Variational Inference Y Gal, Z Ghahramani 4th International Conference on Learning Representations (ICLR) workshop track, 2015 | 453 | 2015 |
| Concrete dropout Y Gal, J Hron, A Kendall Advances in Neural Information Processing Systems, 3581-3590, 2017 | 307 | 2017 |
| Real time image saliency for black box classifiers P Dabkowski, Y Gal Advances in Neural Information Processing Systems, 6967-6976, 2017 | 271 | 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 (IJCAI), 2017 | 169* | 2017 |
| Improving PILCO with Bayesian neural network dynamics models Y Gal, R McAllister, CE Rasmussen Data-Efficient Machine Learning workshop, ICML, 2016 | 166 | 2016 |
| Distributed variational inference in sparse Gaussian process regression and latent variable models Y Gal, M van der Wilk, C Rasmussen Advances in Neural Information Processing Systems, 3257-3265, 2014 | 160 | 2014 |
| Inferring the effectiveness of government interventions against COVID-19 JM Brauner, S Mindermann, M Sharma, D Johnston, J Salvatier, ... Science 371 (6531), 2021 | 145 | 2021 |
| Dropout Inference in Bayesian Neural Networks with Alpha-divergences Y Li, Y Gal International Conference on Machine Learning (ICML), 2052-2061, 2017 | 143 | 2017 |
| Understanding Measures of Uncertainty for Adversarial Example Detection L Smith, Y Gal Uncertainty in Artificial Intelligence (UAI), 2018 | 140 | 2018 |
| Towards Robust Evaluations of Continual Learning S Farquhar, Y Gal Lifelong Learning: A Reinforcement Learning Approach workshop, ICML, 2018, 2018 | 125 | 2018 |
| 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 | 117 | 2018 |
| 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 | 103 | 2019 |
| Dropout as a Bayesian approximation: Insights and applications Y Gal, Z Ghahramani Deep Learning Workshop, ICML 1, 2, 2015 | 82 | 2015 |
| Learning Sparse Networks Using Targeted Dropout AN Gomez, I Zhang, K Swersky, Y Gal, GE Hinton Workshop on Compact Deep Neural Networks with industrial applications, NeurIPS, 2018 | 63* | 2018 |
Zoubin GhahramaniProfessor, University of Cambridge, and Distinguished Researcher, Googleeng.cam.ac.uk의 이메일 확인됨
