Dustin Tran
Dustin Tran
Research Scientist, Google
Verified email at google.com - Homepage
TitleCited byYear
Automatic differentiation variational inference
A Kucukelbir, D Tran, R Ranganath, A Gelman, DM Blei
The Journal of Machine Learning Research 18 (1), 430-474, 2017
2372017
Edward: A library for probabilistic modeling, inference, and criticism
D Tran, A Kucukelbir, AB Dieng, M Rudolph, D Liang, DM Blei
arXiv preprint arXiv:1610.09787, 2016
1722016
Hierarchical variational models
R Ranganath, D Tran, D Blei
International Conference on Machine Learning, 324-333, 2016
1432016
Operator variational inference
R Ranganath, D Tran, J Altosaar, D Blei
Advances in Neural Information Processing Systems, 496-504, 2016
132*2016
Hierarchical implicit models and likelihood-free variational inference
D Tran, R Ranganath, D Blei
Advances in Neural Information Processing Systems, 5523-5533, 2017
124*2017
Deep probabilistic programming
D Tran, MD Hoffman, RA Saurous, E Brevdo, K Murphy, DM Blei
arXiv preprint arXiv:1701.03757, 2017
1092017
Variational Gaussian Process
D Tran, R Ranganath, DM Blei
arXiv preprint arXiv:1511.06499, 2015
982015
Image transformer
N Parmar, A Vaswani, J Uszkoreit, Ł Kaiser, N Shazeer, A Ku, D Tran
arXiv preprint arXiv:1802.05751, 2018
812018
Expectation propagation as a way of life: A framework for Bayesian inference on partitioned data
A Gelman, A Vehtari, P Jylänki, T Sivula, D Tran, S Sahai, P Blomstedt, ...
arXiv preprint arXiv:1412.4869, 2017
572017
Tensorflow distributions
JV Dillon, I Langmore, D Tran, E Brevdo, S Vasudevan, D Moore, B Patton, ...
arXiv preprint arXiv:1711.10604, 2017
452017
Variational Inference via Upper Bound Minimization
AB Dieng, D Tran, R Ranganath, J Paisley, D Blei
Advances in Neural Information Processing Systems, 2732-2741, 2017
44*2017
Copula variational inference
D Tran, DM Blei, EM Airoldi
Advances in Neural Information Processing Systems, 3550-3558, 2015
442015
Towards stability and optimality in stochastic gradient descent
P Toulis, D Tran, EM Airoldi
Proceedings of the Nineteenth International Conference on Artificial …, 2015
30*2015
Flipout: Efficient pseudo-independent weight perturbations on mini-batches
Y Wen, P Vicol, J Ba, D Tran, R Grosse
arXiv preprint arXiv:1803.04386, 2018
242018
Reliable uncertainty estimates in deep neural networks using noise contrastive priors
D Hafner, D Tran, A Irpan, T Lillicrap, J Davidson
arXiv preprint arXiv:1807.09289, 2018
232018
Mesh-tensorflow: Deep learning for supercomputers
N Shazeer, Y Cheng, N Parmar, D Tran, A Vaswani, P Koanantakool, ...
Advances in Neural Information Processing Systems, 10414-10423, 2018
212018
Expectation propagation as a way of life: A framework for Bayesian inference on partitioned data
A Vehtari, A Gelman, T Sivula, P Jylänki, D Tran, S Sahai, P Blomstedt, ...
arXiv preprint arXiv:1412.4869, 2014
192014
Simple, distributed, and accelerated probabilistic programming
D Tran, MW Hoffman, D Moore, C Suter, S Vasudevan, A Radul
Advances in Neural Information Processing Systems, 7598-7609, 2018
162018
Implicit causal models for genome-wide association studies
D Tran, DM Blei
arXiv preprint arXiv:1710.10742, 2017
152017
Edward: A library for probabilistic modeling, inference, and criticism. 2016
D Tran, A Kucukelbir, AB Dieng, M Rudolph, D Liang, DM Blei
arXiv preprint arXiv:1610.09787, 0
11
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Articles 1–20