Obserwuj
Yarin Gal
Tytuł
Cytowane przez
Cytowane przez
Rok
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
107642015
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
55802017
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
35862018
Uncertainty in Deep Learning
Y Gal
University of Cambridge, 2016
20842016
A theoretically grounded application of dropout in recurrent neural networks
Y Gal, Z Ghahramani
Advances in neural information processing systems 29, 1019-1027, 2016
20602016
Deep Bayesian Active Learning with Image Data
Y Gal, R Islam, Z Ghahramani
International Conference on Machine Learning (ICML), 1183-1192, 2017
19532017
Inferring the effectiveness of government interventions against COVID-19
JM Brauner, S Mindermann, M Sharma, D Johnston, J Salvatier, ...
Science 371 (6531), eabd9338, 2021
10242021
Bayesian Convolutional Neural Networks with Bernoulli Approximate Variational Inference
Y Gal, Z Ghahramani
4th International Conference on Learning Representations (ICLR) workshop track, 2015
9472015
Concrete dropout
Y Gal, J Hron, A Kendall
Advances in Neural Information Processing Systems, 3581-3590, 2017
7332017
Real time image saliency for black box classifiers
P Dabkowski, Y Gal
Advances in Neural Information Processing Systems, 6967-6976, 2017
7132017
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
6402019
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
5042020
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
4832021
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
4802020
Understanding Measures of Uncertainty for Adversarial Example Detection
L Smith, Y Gal
Uncertainty in Artificial Intelligence (UAI), 2018
4032018
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
3192018
Improving PILCO with Bayesian neural network dynamics models
Y Gal, R McAllister, CE Rasmussen
Data-Efficient Machine Learning workshop, ICML, 2016
3152016
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
3072018
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
2742020
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