Yarin Gal
Yarin Gal
Associate Professor, University of Oxford
Підтверджена електронна адреса в cs.ox.ac.uk - Домашня сторінка
Назва
Посилання
Посилання
Рік
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
42592015
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
21562017
A theoretically grounded application of dropout in recurrent neural networks
Y Gal, Z Ghahramani
Advances in neural information processing systems 29, 1019-1027, 2016
14392016
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
12432018
Uncertainty in Deep Learning
Y Gal
University of Cambridge, 2016
11212016
Deep Bayesian Active Learning with Image Data
Y Gal, R Islam, Z Ghahramani
International Conference on Machine Learning (ICML), 1183-1192, 2017
7422017
Bayesian Convolutional Neural Networks with Bernoulli Approximate Variational Inference
Y Gal, Z Ghahramani
4th International Conference on Learning Representations (ICLR) workshop track, 2015
5172015
Concrete dropout
Y Gal, J Hron, A Kendall
Advances in Neural Information Processing Systems, 3581-3590, 2017
3442017
Real time image saliency for black box classifiers
P Dabkowski, Y Gal
Advances in Neural Information Processing Systems, 6967-6976, 2017
3112017
Inferring the effectiveness of government interventions against COVID-19
JM Brauner, S Mindermann, M Sharma, D Johnston, J Salvatier, ...
Science 371 (6531), 2021
2902021
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
197*2017
Improving PILCO with Bayesian neural network dynamics models
Y Gal, R McAllister, CE Rasmussen
Data-Efficient Machine Learning workshop, ICML, 2016
1942016
Understanding Measures of Uncertainty for Adversarial Example Detection
L Smith, Y Gal
Uncertainty in Artificial Intelligence (UAI), 2018
1662018
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
1612014
Dropout Inference in Bayesian Neural Networks with Alpha-divergences
Y Li, Y Gal
International Conference on Machine Learning (ICML), 2052-2061, 2017
1592017
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
1512019
Towards Robust Evaluations of Continual Learning
S Farquhar, Y Gal
Lifelong Learning: A Reinforcement Learning Approach workshop, ICML, 2018, 2018
1422018
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
1312018
Dropout as a Bayesian approximation: Insights and applications
Y Gal, Z Ghahramani
Deep Learning Workshop, ICML 1, 2, 2015
892015
Uncertainty estimation using a single deep deterministic neural network
J van Amersfoort, L Smith, YW Teh, Y Gal
International Conference on Machine Learning, 2020
79*2020
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