Zachary Nado
Zachary Nado
Google Brain
Email verificata su google.com - Home page
Titolo
Citata da
Citata da
Anno
Can you trust your model's uncertainty? Evaluating predictive uncertainty under dataset shift
Y Ovadia, E Fertig, J Ren, Z Nado, D Sculley, S Nowozin, JV Dillon, ...
arXiv preprint arXiv:1906.02530, 2019
2622019
On empirical comparisons of optimizers for deep learning
D Choi, CJ Shallue, Z Nado, J Lee, CJ Maddison, GE Dahl
arXiv preprint arXiv:1910.05446, 2019
402019
Which algorithmic choices matter at which batch sizes? insights from a noisy quadratic model
G Zhang, L Li, Z Nado, J Martens, S Sachdeva, GE Dahl, CJ Shallue, ...
arXiv preprint arXiv:1907.04164, 2019
282019
Underspecification presents challenges for credibility in modern machine learning
A D'Amour, K Heller, D Moldovan, B Adlam, B Alipanahi, A Beutel, ...
arXiv preprint arXiv:2011.03395, 2020
192020
Autograph: Imperative-style coding with graph-based performance
D Moldovan, JM Decker, F Wang, AA Johnson, BK Lee, Z Nado, D Sculley, ...
arXiv preprint arXiv:1810.08061, 2018
172018
Stochastic gradient langevin dynamics that exploit neural network structure
Z Nado, J Snoek, R Grosse, D Duvenaud, B Xu, J Martens
72018
Evaluating prediction-time batch normalization for robustness under covariate shift
Z Nado, S Padhy, D Sculley, A D'Amour, B Lakshminarayanan, J Snoek
arXiv preprint arXiv:2006.10963, 2020
62020
Can you trust your model’s uncertainty
Y Ovadia, E Fertig, J Ren, Z Nado, D Sculley, S Nowozin, JV Dillon, ...
Evaluating Predictive Uncertainty Under Dataset Shift, 2019
62019
Tensorforest: scalable random forests on tensorflow
T Colthurst, D Sculley, G Hendry, Z Nado
Machine learning systems workshop at NIPS, 2016
52016
Revisiting one-vs-all classifiers for predictive uncertainty and out-of-distribution detection in neural networks
S Padhy, Z Nado, J Ren, J Liu, J Snoek, B Lakshminarayanan
arXiv preprint arXiv:2007.05134, 2020
32020
Deep recurrent and convolutional neural networks for automated behavior classification
Z Nado
Undergraduate Thesis, 2016
12016
A Large Batch Optimizer Reality Check: Traditional, Generic Optimizers Suffice Across Batch Sizes
Z Nado, JM Gilmer, CJ Shallue, R Anil, GE Dahl
arXiv preprint arXiv:2102.06356, 2021
2021
AG: Imperative-style Coding with Graph-based Performance
D Moldovan, J Decker, F Wang, A Johnson, B Lee, Z Nado, D Sculley, ...
Proceedings of Machine Learning and Systems 1, 389-405, 2019
2019
Method and system for automated behavior classification of test subjects
T Serre, Y Barhomi, Z Nado, K Bath, S Eberhardt
US Patent 10,181,082, 2019
2019
Il sistema al momento non pu eseguire l'operazione. Riprova pi tardi.
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