Charles Blundell
Charles Blundell
Research Scientist at DeepMind
Verified email at
Cited by
Cited by
Matching networks for one shot learning
O Vinyals, C Blundell, T Lillicrap, D Wierstra
Advances in neural information processing systems, 3630-3638, 2016
Weight uncertainty in neural networks
C Blundell, J Cornebise, K Kavukcuoglu, D Wierstra
arXiv preprint arXiv:1505.05424, 2015
Simple and scalable predictive uncertainty estimation using deep ensembles
B Lakshminarayanan, A Pritzel, C Blundell
Advances in neural information processing systems, 6402-6413, 2017
Deep exploration via bootstrapped DQN
I Osband, C Blundell, A Pritzel, B Van Roy
Advances in neural information processing systems, 4026-4034, 2016
Learning to reinforcement learn
JX Wang, Z Kurth-Nelson, D Tirumala, H Soyer, JZ Leibo, R Munos, ...
arXiv preprint arXiv:1611.05763, 2016
Noisy networks for exploration
M Fortunato, MG Azar, B Piot, J Menick, I Osband, A Graves, V Mnih, ...
arXiv preprint arXiv:1706.10295, 2017
Pathnet: Evolution channels gradient descent in super neural networks
C Fernando, D Banarse, C Blundell, Y Zwols, D Ha, AA Rusu, A Pritzel, ...
arXiv preprint arXiv:1701.08734, 2017
Vector-based navigation using grid-like representations in artificial agents
A Banino, C Barry, B Uria, C Blundell, T Lillicrap, P Mirowski, A Pritzel, ...
Nature 557 (7705), 429-433, 2018
Deep AutoRegressive Networks
K Gregor, I Danihelka, A Mnih, C Blundell, D Wierstra
Proceedings of the 31st International Conference on Machine Learning (ICML …, 2014
Modelling reciprocating relationships with Hawkes processes
C Blundell, J Beck, KA Heller
Advances in Neural Information Processing Systems, 2600-2608, 2012
Darla: Improving zero-shot transfer in reinforcement learning
I Higgins, A Pal, AA Rusu, L Matthey, CP Burgess, A Pritzel, M Botvinick, ...
arXiv preprint arXiv:1707.08475, 2017
Model-free episodic control
C Blundell, B Uria, A Pritzel, Y Li, A Ruderman, JZ Leibo, J Rae, ...
arXiv preprint arXiv:1606.04460, 2016
Neural episodic control
A Pritzel, B Uria, S Srinivasan, A Puigdomenech, O Vinyals, D Hassabis, ...
arXiv preprint arXiv:1703.01988, 2017
Reinforcement learning, fast and slow
M Botvinick, S Ritter, JX Wang, Z Kurth-Nelson, C Blundell, D Hassabis
Trends in cognitive sciences 23 (5), 408-422, 2019
Early visual concept learning with unsupervised deep learning
I Higgins, L Matthey, X Glorot, A Pal, B Uria, C Blundell, S Mohamed, ...
arXiv preprint arXiv:1606.05579, 2016
Bayesian recurrent neural networks
M Fortunato, C Blundell, O Vinyals
arXiv preprint arXiv:1704.02798, 2017
Bayesian rose trees
C Blundell, YW Teh, KA Heller
Uncertainty in Artificial Intelligence, 2010
Computations underlying social hierarchy learning: distinct neural mechanisms for updating and representing self-relevant information
D Kumaran, A Banino, C Blundell, D Hassabis, P Dayan
Neuron 92 (5), 1135-1147, 2016
Distributed Bayesian learning with stochastic natural gradient expectation propagation and the posterior server
L Hasenclever, S Webb, T Lienart, S Vollmer, B Lakshminarayanan, ...
The Journal of Machine Learning Research 18 (1), 3744-3780, 2017
Memory-based parameter adaptation
P Sprechmann, SM Jayakumar, JW Rae, A Pritzel, AP Badia, B Uria, ...
arXiv preprint arXiv:1802.10542, 2018
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