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Balaji Lakshminarayanan
Balaji Lakshminarayanan
Senior Staff Research Scientist at Google DeepMind
Verified email at google.com - Homepage
Title
Cited by
Cited by
Year
Simple and scalable predictive uncertainty estimation using deep ensembles
B Lakshminarayanan, A Pritzel, C Blundell
Advances in Neural Information Processing Systems, 6393-6395, 2017
59272017
Clinically applicable deep learning for diagnosis and referral in retinal disease
J De Fauw, JR Ledsam, B Romera-Paredes, S Nikolov, N Tomasev, ...
Nature medicine 24 (9), 1342-1350, 2018
23392018
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, ...
NeurIPS 2019, 2019
17352019
Normalizing Flows for Probabilistic Modeling and Inference
G Papamakarios, E Nalisnick, DJ Rezende, S Mohamed, ...
Journal of Machine Learning Research 22 (57), 1-64, 2021
15672021
AugMix: A Simple Data Processing Method to Improve Robustness and Uncertainty
D Hendrycks, N Mu, ED Cubuk, B Zoph, J Gilmer, B Lakshminarayanan
ICLR 2020, 2020
13032020
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
10132023
Do Deep Generative Models Know What They Don't Know?
E Nalisnick, A Matsukawa, YW Teh, D Gorur, B Lakshminarayanan
ICLR 2019, 2019
7702019
Likelihood ratios for out-of-distribution detection
J Ren, PJ Liu, E Fertig, J Snoek, R Poplin, M Depristo, J Dillon, ...
NeurIPS 2019, 14707-14718, 2019
7242019
Deep Ensembles: A Loss Landscape Perspective
S Fort, H Hu, B Lakshminarayanan
arXiv preprint arXiv:1912.02757, 2019
6192019
Learning in Implicit Generative Models
S Mohamed, B Lakshminarayanan
arXiv preprint arXiv:1610.03483, 2016
4772016
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, 2020
4282020
The Cramer Distance as a Solution to Biased Wasserstein Gradients
MG Bellemare, I Danihelka, W Dabney, S Mohamed, ...
arXiv preprint arXiv:1705.10743, 2017
4162017
Acoustic classification of multiple simultaneous bird species: A multi-instance multi-label approach
F Briggs, B Lakshminarayanan, L Neal, XZ Fern, R Raich, SJK Hadley, ...
The Journal of the Acoustical Society of America 131 (6), 4640-4650, 2012
3512012
Variational Approaches for Auto-Encoding Generative Adversarial Networks
M Rosca, B Lakshminarayanan, D Warde-Farley, S Mohamed
arXiv preprint arXiv:1706.04987, 2017
3252017
Exploring the limits of out-of-distribution detection
S Fort, J Ren, B Lakshminarayanan
Advances in Neural Information Processing Systems 34, 2021
3052021
Mondrian forests: Efficient online random forests
B Lakshminarayanan, DM Roy, YW Teh
Advances in neural information processing systems 27, 3140-3148, 2014
2802014
Many Paths to Equilibrium: GANs Do Not Need to Decrease a Divergence At Every Step
W Fedus, M Rosca, B Lakshminarayanan, AM Dai, S Mohamed, ...
ICLR 2018, 2018
2572018
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
2262020
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
2042020
Training independent subnetworks for robust prediction
M Havasi, R Jenatton, S Fort, JZ Liu, J Snoek, B Lakshminarayanan, ...
ICLR 2021, 2021
1992021
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