Frank Wood
Cited by
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A new approach to probabilistic programming inference
F Wood, JW Meent, V Mansinghka
Artificial Intelligence and Statistics, 1024-1032, 2014
On the variability of manual spike sorting
F Wood, MJ Black, C Vargas-Irwin, M Fellows, JP Donoghue
IEEE Transactions on Biomedical Engineering 51 (6), 912-918, 2004
Diagnosis code assignment: models and evaluation metrics
A Perotte, R Pivovarov, K Natarajan, N Weiskopf, F Wood, N Elhadad
Journal of the American Medical Informatics Association 21 (2), 231-237, 2014
Learning disentangled representations with semi-supervised deep generative models
S Narayanaswamy, T Paige, JW Van de Meent, A Desmaison, ...
A nonparametric Bayesian alternative to spike sorting
F Wood, MJ Black
Journal of neuroscience methods 173 (1), 1-12, 2008
Hierarchically supervised latent Dirichlet allocation
A Perotte, F Wood, N Elhadad, N Bartlett
Advances in neural information processing systems 24, 2609-2617, 2011
Tighter variational bounds are not necessarily better
T Rainforth, A Kosiorek, TA Le, C Maddison, M Igl, F Wood, YW Teh
International Conference on Machine Learning, 4277-4285, 2018
Deep variational reinforcement learning for POMDPs
M Igl, L Zintgraf, TA Le, F Wood, S Whiteson
International Conference on Machine Learning, 2117-2126, 2018
A stochastic memoizer for sequence data
F Wood, C Archambeau, J Gasthaus, L James, YW Teh
Proceedings of the 26th annual international conference on machine learning†…, 2009
Auto-encoding sequential monte carlo
TA Le, M Igl, T Rainforth, T Jin, F Wood
arXiv preprint arXiv:1705.10306, 2017
Online learning rate adaptation with hypergradient descent
AG Baydin, R Cornish, DM Rubio, M Schmidt, F Wood
arXiv preprint arXiv:1703.04782, 2017
Inference compilation and universal probabilistic programming
TA Le, AG Baydin, F Wood
Artificial Intelligence and Statistics, 1338-1348, 2017
Semantics for probabilistic programming: higher-order functions, continuous distributions, and soft constraints
S Staton, F Wood, H Yang, C Heunen, O Kammar
2016 31st annual acm/ieee symposium on logic in computer science (lics), 1-10, 2016
A non-parametric Bayesian method for inferring hidden causes
F Wood, T Griffiths, Z Ghahramani
arXiv preprint arXiv:1206.6865, 2012
Inference networks for sequential Monte Carlo in graphical models
B Paige, F Wood
International Conference on Machine Learning, 3040-3049, 2016
Learning disentangled representations with semi-supervised deep generative models
N Siddharth, B Paige, JW Van de Meent, A Desmaison, ND Goodman, ...
arXiv preprint arXiv:1706.00400, 2017
Using synthetic data to train neural networks is model-based reasoning
TA Le, AG Baydin, R Zinkov, F Wood
2017 International Joint Conference on Neural Networks (IJCNN), 3514-3521, 2017
The sequence memoizer
F Wood, J Gasthaus, C Archambeau, L James, YW Teh
Communications of the ACM 54 (2), 91-98, 2011
Design and implementation of probabilistic programming language anglican
D Tolpin, JW van de Meent, H Yang, F Wood
Proceedings of the 28th Symposium on the Implementation and Application of†…, 2016
An introduction to probabilistic programming
JW van de Meent, B Paige, H Yang, F Wood
arXiv preprint arXiv:1809.10756, 2018
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