Richard E Turner
TitleCited byYear
The processing and perception of size information in speech sounds
DRR Smith, RD Patterson, R Turner, H Kawahara, T Irino
The Journal of the Acoustical Society of America 117 (1), 305-318, 2005
2282005
Two problems with variational expectation maximisation for time-series models
RE Turner, M Sahani
193*2011
Q-prop: Sample-efficient policy gradient with an off-policy critic
S Gu, T Lillicrap, Z Ghahramani, RE Turner, S Levine
arXiv preprint arXiv:1611.02247, 2016
1532016
Rényi divergence variational inference
Y Li, RE Turner
Advances in Neural Information Processing Systems, 1073-1081, 2016
99*2016
Variational continual learning
CV Nguyen, Y Li, TD Bui, RE Turner
arXiv preprint arXiv:1710.10628, 2017
962017
Deep Gaussian processes for regression using approximate expectation propagation
T Bui, D Hernández-Lobato, J Hernandez-Lobato, Y Li, R Turner
International Conference on Machine Learning, 1472-1481, 2016
912016
Black-box α-divergence minimization
JM Hernández-Lobato, Y Li, M Rowland, D Hernández-Lobato, T Bui, ...
International Machine Learning Society, 2016
882016
Neural adaptive sequential monte carlo
SS Gu, Z Ghahramani, RE Turner
Advances in Neural Information Processing Systems, 2629-2637, 2015
882015
Stochastic expectation propagation
Y Li, JM Hernández-Lobato, RE Turner
Advances in neural information processing systems, 2323-2331, 2015
652015
Interpolated policy gradient: Merging on-policy and off-policy gradient estimation for deep reinforcement learning
SS Gu, T Lillicrap, RE Turner, Z Ghahramani, B Schölkopf, S Levine
Advances in neural information processing systems, 3846-3855, 2017
612017
A maximum-likelihood interpretation for slow feature analysis
R Turner, M Sahani
Neural computation 19 (4), 1022-1038, 2007
582007
A statistical, formant-pattern model for segregating vowel type and vocal-tract length in developmental formant data
RE Turner, TC Walters, JJM Monaghan, RD Patterson
The Journal of the Acoustical Society of America 125 (4), 2374-2386, 2009
512009
Gaussian process behaviour in wide deep neural networks
AGG Matthews, M Rowland, J Hron, RE Turner, Z Ghahramani
arXiv preprint arXiv:1804.11271, 2018
472018
Sequence tutor: Conservative fine-tuning of sequence generation models with kl-control
N Jaques, S Gu, D Bahdanau, JM Hernández-Lobato, RE Turner, D Eck
Proceedings of the 34th International Conference on Machine Learning-Volume …, 2017
452017
Statistical models for natural sounds
RE Turner
UCL (University College London), 2010
422010
A structured model of video reproduces primary visual cortical organisation
P Berkes, RE Turner, M Sahani
PLoS computational biology 5 (9), e1000495, 2009
422009
On sparse variational methods and the Kullback-Leibler divergence between stochastic processes
AGG Matthews, J Hensman, R Turner, Z Ghahramani
Artificial Intelligence and Statistics, 231-239, 2016
402016
Demodulation as probabilistic inference
RE Turner, M Sahani
IEEE Transactions on Audio, Speech, and Language Processing 19 (8), 2398-2411, 2011
402011
A unifying framework for Gaussian process pseudo-point approximations using power expectation propagation
TD Bui, J Yan, RE Turner
The Journal of Machine Learning Research 18 (1), 3649-3720, 2017
39*2017
Improving the Gaussian process sparse spectrum approximation by representing uncertainty in frequency inputs
Y Gal, R Turner
Microtome Publishing, 2015
392015
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Articles 1–20