Yarin Gal
Title
Cited by
Cited by
Year
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
37912015
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
19182017
A theoretically grounded application of dropout in recurrent neural networks
Y Gal, Z Ghahramani
Advances in neural information processing systems 29, 1019-1027, 2016
13782016
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
10592018
Uncertainty in Deep Learning
Y Gal
University of Cambridge, 2016
10282016
Deep Bayesian Active Learning with Image Data
Y Gal, R Islam, Z Ghahramani
International Conference on Machine Learning (ICML), 1183-1192, 2017
6522017
Bayesian Convolutional Neural Networks with Bernoulli Approximate Variational Inference
Y Gal, Z Ghahramani
4th International Conference on Learning Representations (ICLR) workshop track, 2015
4792015
Concrete dropout
Y Gal, J Hron, A Kendall
Advances in Neural Information Processing Systems, 3581-3590, 2017
3182017
Real time image saliency for black box classifiers
P Dabkowski, Y Gal
Advances in Neural Information Processing Systems, 6967-6976, 2017
2782017
Inferring the effectiveness of government interventions against COVID-19
JM Brauner, S Mindermann, M Sharma, D Johnston, J Salvatier, ...
Science 371 (6531), 2021
2022021
Improving PILCO with Bayesian neural network dynamics models
Y Gal, R McAllister, CE Rasmussen
Data-Efficient Machine Learning workshop, ICML, 2016
1762016
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
172*2017
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
1592014
Understanding Measures of Uncertainty for Adversarial Example Detection
L Smith, Y Gal
Uncertainty in Artificial Intelligence (UAI), 2018
1472018
Dropout Inference in Bayesian Neural Networks with Alpha-divergences
Y Li, Y Gal
International Conference on Machine Learning (ICML), 2052-2061, 2017
1472017
Towards Robust Evaluations of Continual Learning
S Farquhar, Y Gal
Lifelong Learning: A Reinforcement Learning Approach workshop, ICML, 2018, 2018
1272018
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
1232018
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
1182019
Dropout as a Bayesian approximation: Insights and applications
Y Gal, Z Ghahramani
Deep Learning Workshop, ICML 1, 2, 2015
852015
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
68*2018
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Articles 1–20