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Dustin Tran
Dustin Tran
Research Scientist, Google
Verified email at google.com - Homepage
Title
Cited by
Cited by
Year
Image transformer
N Parmar, A Vaswani, J Uszkoreit, L Kaiser, N Shazeer, A Ku, D Tran
International Conference on Machine Learning, 4055-4064, 2018
18732018
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
1105*2017
Gemini: a family of highly capable multimodal models
G Team, R Anil, S Borgeaud, Y Wu, JB Alayrac, J Yu, R Soricut, ...
arXiv preprint arXiv:2312.11805, 2023
8432023
Tensor2tensor for neural machine translation
A Vaswani, S Bengio, E Brevdo, F Chollet, AN Gomez, S Gouws, L Jones, ...
arXiv preprint arXiv:1803.07416, 2018
6262018
Tensorflow distributions
JV Dillon, I Langmore, D Tran, E Brevdo, S Vasudevan, D Moore, B Patton, ...
arXiv preprint arXiv:1711.10604, 2017
6122017
Measuring calibration in deep learning.
J Nixon, MW Dusenberry, L Zhang, G Jerfel, D Tran
CVPR workshops 2 (7), 2019
4322019
BatchEnsemble: An alternative approach to efficient ensemble and lifelong learning
Y Wen, D Tran, J Ba
International Conference on Learning Representations, 2020
4252020
Simple and principled uncertainty estimation with deterministic deep learning via distance awareness
J Liu, Z Lin, S Padhy, D Tran, T Bedrax Weiss, B Lakshminarayanan
Advances in neural information processing systems 33, 7498-7512, 2020
4152020
Hierarchical variational models
R Ranganath, D Tran, D Blei
International conference on machine learning, 324-333, 2016
3792016
Mesh-TensorFlow: Deep learning for supercomputers
N Shazeer, Y Cheng, N Parmar, D Tran, A Vaswani, P Koanantakool, ...
Neural Information Processing Systems, 2018
3782018
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
3682016
Operator variational inference
R Ranganath, D Tran, J Altosaar, D Blei
Advances in Neural Information Processing Systems 29, 2016
357*2016
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
3532018
Hierarchical implicit models and likelihood-free variational inference
D Tran, R Ranganath, DM Blei
Neural Information Processing Systems, 2017
351*2017
Scaling vision transformers to 22 billion parameters
M Dehghani, J Djolonga, B Mustafa, P Padlewski, J Heek, J Gilmer, ...
International Conference on Machine Learning, 7480-7512, 2023
3202023
Revisiting the calibration of modern neural networks
M Minderer, J Djolonga, R Romijnders, F Hubis, X Zhai, N Houlsby, ...
Advances in Neural Information Processing Systems 34, 15682-15694, 2021
2772021
Deep probabilistic programming
D Tran, MD Hoffman, RA Saurous, E Brevdo, K Murphy, DM Blei
International Conference on Learning Representations, 2017
2412017
Efficient and scalable Bayesian neural nets with rank-1 factors
M Dusenberry, G Jerfel, Y Wen, Y Ma, J Snoek, K Heller, ...
International Conference on Machine Learning, 2782-2792, 2020
2222020
Hyperparameter ensembles for robustness and uncertainty quantification
F Wenzel, J Snoek, D Tran, R Jenatton
Advances in Neural Information Processing Systems 33, 6514-6527, 2020
2152020
Variational Gaussian Process
D Tran, R Ranganath, DM Blei
International Conference on Learning Representations, Oral, 2016
2022016
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Articles 1–20