Richard E Turner
Richard E Turner
University Reader, University of Cambridge
Verifierad e-postadress på cam.ac.uk - Startsida
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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
2432005
Two problems with variational expectation maximisation for time-series models
RE Turner, M Sahani
242*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
2322016
Variational continual learning
CV Nguyen, Y Li, TD Bui, RE Turner
arXiv preprint arXiv:1710.10628, 2017
2242017
Rényi divergence variational inference
Y Li, RE Turner
Advances in Neural Information Processing Systems, 1073-1081, 2016
1462016
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
1362016
Black-box alpha divergence minimization
J Hernandez-Lobato, Y Li, M Rowland, T Bui, D Hernández-Lobato, ...
International Conference on Machine Learning, 1511-1520, 2016
1272016
Gaussian process behaviour in wide deep neural networks
Matthews, J Hron, M Rowland, RE Turner, Z Ghahramani
International Conference on Learning Representations 4, 2018
1212018
Neural adaptive sequential monte carlo
SS Gu, Z Ghahramani, RE Turner
Advances in neural information processing systems, 2629-2637, 2015
1072015
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
1042017
Stochastic expectation propagation
Y Li, JM Hernández-Lobato, RE Turner
Advances in neural information processing systems, 2323-2331, 2015
842015
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
74*2017
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
International Conference on Machine Learning, 1645-1654, 2017
702017
On sparse variational methods and the Kullback-Leibler divergence between stochastic processes
AGG Matthews, J Hensman, R Turner, Z Ghahramani
Journal of Machine Learning Research 51, 231-239, 2016
66*2016
A maximum-likelihood interpretation for slow feature analysis
R Turner, M Sahani
Neural computation 19 (4), 1022-1038, 2007
662007
Meta-learning probabilistic inference for prediction
J Gordon, J Bronskill, M Bauer, S Nowozin, RE Turner
arXiv preprint arXiv:1805.09921, 2018
652018
Invariant models for causal transfer learning
M Rojas-Carulla, B Schölkopf, R Turner, J Peters
The Journal of Machine Learning Research 19 (1), 1309-1342, 2018
652018
The mirage of action-dependent baselines in reinforcement learning
G Tucker, S Bhupatiraju, S Gu, RE Turner, Z Ghahramani, S Levine
arXiv preprint arXiv:1802.10031, 2018
592018
Improving the Gaussian process sparse spectrum approximation by representing uncertainty in frequency inputs
Y Gal, R Turner
Microtome Publishing, 2015
572015
Deterministic variational inference for robust bayesian neural networks
A Wu, S Nowozin, E Meeds, RE Turner, JM Hernández-Lobato, AL Gaunt
arXiv preprint arXiv:1810.03958, 2018
56*2018
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Artiklar 1–20