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David Moore
David Moore
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Cited by
Year
Tensorflow distributions
JV Dillon, I Langmore, D Tran, E Brevdo, S Vasudevan, D Moore, B Patton, ...
arXiv preprint arXiv:1711.10604, 2017
5752017
Simple, distributed, and accelerated probabilistic programming
D Tran, MW Hoffman, D Moore, C Suter, S Vasudevan, A Radul
Advances in Neural Information Processing Systems 31, 2018
802018
tfp. mcmc: Modern Markov chain Monte Carlo tools built for modern hardware
J Lao, C Suter, I Langmore, C Chimisov, A Saxena, P Sountsov, D Moore, ...
arXiv preprint arXiv:2002.01184, 2020
392020
Meta-learning MCMC proposals
T Wang, Y Wu, D Moore, SJ Russell
Advances in neural information processing systems 31, 2018
372018
Tensorflow distributions. arXiv 2017
JV Dillon, I Langmore, D Tran, E Brevdo, S Vasudevan, D Moore, B Patton, ...
arXiv preprint arXiv:1711.10604, 0
30
Automatic reparameterisation of probabilistic programs
M Gorinova, D Moore, M Hoffman
International Conference on Machine Learning, 3648-3657, 2020
292020
Automatic structured variational inference
L Ambrogioni, K Lin, E Fertig, S Vikram, M Hinne, D Moore, M van Gerven
International Conference on Artificial Intelligence and Statistics, 676-684, 2021
282021
Gaussian process random fields
D Moore, SJ Russell
Advances in Neural Information Processing Systems 28, 2015
222015
Joint distributions for tensorflow probability
D Piponi, D Moore, JV Dillon
arXiv preprint arXiv:2001.11819, 2020
182020
Effect handling for composable program transformations in edward2
D Moore, MI Gorinova
arXiv preprint arXiv:1811.06150, 2018
172018
Symmetrized variational inference
DA Moore
NIPS Workshop on Advances in Approximate Bayesian Inference 3, 2016
102016
Tensorflow distributions. arXiv e-prints
JV Dillon, I Langmore, D Tran, E Brevdo, S Vasudevan, D Moore, B Patton, ...
arXiv preprint arXiv:1711.10604, 2017
82017
& Saurous, RA (2017). Tensorflow distributions
JV Dillon, I Langmore, D Tran, E Brevdo, S Vasudevan, D Moore
arXiv preprint arXiv 1711, 0
8
Signal-based Bayesian seismic monitoring
D Moore, S Russell
Artificial Intelligence and Statistics, 1293-1301, 2017
62017
Fast Gaussian Process Posteriors with Product Trees.
DA Moore, S Russell
UAI, 613-622, 2014
62014
Embedded-model flows: Combining the inductive biases of model-free deep learning and explicit probabilistic modeling
G Silvestri, E Fertig, D Moore, L Ambrogioni
arXiv preprint arXiv:2110.06021, 2021
52021
Progress in signal-based Bayesian monitoring
DA Moore, KM Mayeda, SM Myers, MJ Seo, SJ Russell
Proceedings of the 2012 monitoring research review: ground-based nuclear …, 2012
52012
Automatic reparameterisation in probabilistic programming
MI Gorinova, D Moore, MD Hoffman
1st Symposium on Advances in Approximate Bayesian Inference, 1-8, 2018
22018
Deep transfer as structure learning in Markov logic networks
DA Moore, AP Danyluk
Workshops at the Twenty-Fourth AAAI Conference on Artificial Intelligence, 2010
22010
Parallel Chromatic MCMC with Spatial Partitioning
J Song, D Moore
Workshops at the Thirty-First AAAI Conference on Artificial Intelligence, 2017
12017
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