Yarin Gal
TitleCited byYear
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
5592015
A theoretically grounded application of dropout in recurrent neural networks
Y Gal, Z Ghahramani
Advances in Neural Information Processing Systems, 1019-1027, 2016
4342016
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
2012017
Uncertainty in Deep Learning
Y Gal
University of Cambridge, 2016
1612016
Bayesian Convolutional Neural Networks with Bernoulli Approximate Variational Inference
Y Gal, Z Ghahramani
4th International Conference on Learning Representations (ICLR) workshop track, 2015
1452015
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
1012014
Deep Bayesian Active Learning with Image Data
Y Gal, R Islam, Z Ghahramani
International Conference on Machine Learning (ICML), 1183-1192, 2017
882017
Multi-task learning using uncertainty to weigh losses for scene geometry and semantics
A Kendall, Y Gal, R Cipolla
arXiv preprint arXiv:1705.07115 3, 2017
802017
Dropout as a Bayesian Approximation: Appendix
Y Gal, Z Ghahramani
arXiv preprint arXiv:1506.02157, 2015
712015
Concrete dropout
Y Gal, J Hron, A Kendall
Advances in Neural Information Processing Systems, 3581-3590, 2017
532017
Dropout Inference in Bayesian Neural Networks with Alpha-divergences
Y Li, Y Gal
International Conference on Machine Learning (ICML), 2052-2061, 2017
442017
Real time image saliency for black box classifiers
P Dabkowski, Y Gal
Advances in Neural Information Processing Systems, 6967-6976, 2017
412017
Dropout as a Bayesian approximation: Insights and applications
Y Gal, Z Ghahramani
Deep Learning Workshop, ICML 1, 2, 2015
362015
Improving PILCO with Bayesian neural network dynamics models
Y Gal, R McAllister, CE Rasmussen
Data-Efficient Machine Learning workshop, ICML, 2016
352016
Improving the Gaussian Process Sparse Spectrum Approximation by Representing Uncertainty in Frequency Inputs
Y Gal, R Turner
Proceedings of the 32nd International Conference on Machine Learning (ICML-15), 2015
252015
Pitfalls in the use of Parallel Inference for the Dirichlet Process
Y Gal, Z Ghahramani
Proceedings of the 31st International Conference on Machine Learning (ICML …, 2014
192014
Latent Gaussian Processes for Distribution Estimation of Multivariate Categorical Data
Y Gal, Y Chen, Z Ghahramani
Proceedings of the 32nd International Conference on Machine Learning (ICML-15), 2015
172015
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
132017
Fast and Scalable Bayesian Deep Learning by Weight-Perturbation in Adam
ME Khan, D Nielsen, V Tangkaratt, W Lin, Y Gal, A Srivastava
arXiv preprint arXiv:1806.04854, 2018
112018
Understanding Measures of Uncertainty for Adversarial Example Detection
L Smith, Y Gal
arXiv preprint arXiv:1803.08533, 2018
102018
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Articles 1–20