| Tensorflow distributions JV Dillon, I Langmore, D Tran, E Brevdo, S Vasudevan, D Moore, B Patton, ... arXiv preprint arXiv:1711.10604, 2017 | 621 | 2017 |
| 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 | 83 | 2018 |
| 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 | 37 | 2020 |
| Meta-learning MCMC proposals T Wang, Y Wu, D Moore, SJ Russell Advances in neural information processing systems 31, 2018 | 37 | 2018 |
| Automatic reparameterisation of probabilistic programs M Gorinova, D Moore, M Hoffman International Conference on Machine Learning, 3648-3657, 2020 | 33 | 2020 |
| Tensorflow distributions. arXiv 2017 JV Dillon, I Langmore, D Tran, E Brevdo, S Vasudevan, D Moore, B Patton, ... arXiv preprint arXiv:1711.10604, 0 | 32 | |
| 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 | 31 | 2021 |
| Gaussian process random fields D Moore, SJ Russell Advances in Neural Information Processing Systems 28, 2015 | 24 | 2015 |
| Joint distributions for tensorflow probability D Piponi, D Moore, JV Dillon arXiv preprint arXiv:2001.11819, 2020 | 18 | 2020 |
| Effect handling for composable program transformations in edward2 D Moore, MI Gorinova arXiv preprint arXiv:1811.06150, 2018 | 17 | 2018 |
| 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 | 9 | 2017 |
| Symmetrized variational inference DA Moore NIPS Workshop on Advances in Approximate Bayesian Inference 4, 31, 2016 | 9 | 2016 |
| & Saurous, RA (2017). Tensorflow distributions JV Dillon, I Langmore, D Tran, E Brevdo, S Vasudevan, D Moore arXiv preprint arXiv 1711, 0 | 9 | |
| Fast Gaussian Process Posteriors with Product Trees. DA Moore, S Russell UAI, 613-622, 2014 | 6 | 2014 |
| 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 | 5 | 2021 |
| Signal-based Bayesian seismic monitoring D Moore, S Russell Artificial Intelligence and Statistics, 1293-1301, 2017 | 5 | 2017 |
| 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 | 5 | 2012 |
| Automatic reparameterisation in probabilistic programming MI Gorinova, D Moore, MD Hoffman 1st Symposium on Advances in Approximate Bayesian Inference, 1-8, 2018 | 2 | 2018 |
| Deep transfer as structure learning in Markov logic networks DA Moore, AP Danyluk Workshops at the Twenty-Fourth AAAI Conference on Artificial Intelligence, 2010 | 2 | 2010 |
| Parallel Chromatic MCMC with Spatial Partitioning J Song, D Moore Workshops at the Thirty-First AAAI Conference on Artificial Intelligence, 2017 | 1 | 2017 |
