Michael W. Dusenberry
Michael W. Dusenberry
Research Engineer, Google DeepMind
Verified email at - Homepage
Cited by
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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
Measuring calibration in deep learning.
J Nixon, MW Dusenberry, L Zhang, G Jerfel, D Tran
CVPR workshops 2 (7), 2019
Graph Convolutional Transformer: Learning the Graphical Structure of Electronic Health Records
E Choi, Z Xu, Y Li, MW Dusenberry, G Flores, Y Xue, AM Dai
AAAI Conference on Artificial Intelligence, 2020
Systemml: Declarative machine learning on spark
M Boehm, MW Dusenberry, D Eriksson, AV Evfimievski, FM Manshadi, ...
Proceedings of the VLDB Endowment 9 (13), 1425-1436, 2016
Efficient and Scalable Bayesian Neural Nets with Rank-1 Factors
MW Dusenberry, G Jerfel, Y Wen, Y Ma, J Snoek, K Heller, ...
International Conference on Machine Learning, 2020
Bayesian Layers: A Module for Neural Network Uncertainty
D Tran, MW Dusenberry, M van der Wilk, D Hafner
Advances in Neural Information Processing Systems, 14633-14645, 2019
Analyzing the Role of Model Uncertainty for Electronic Health Records
MW Dusenberry, D Tran, E Choi, J Kemp, J Nixon, G Jerfel, K Heller, ...
ACM Conference on Health, Inference, and Learning (ACM CHIL), 204-213, 2020
Uncertainty baselines: Benchmarks for uncertainty & robustness in deep learning
Z Nado, N Band, M Collier, J Djolonga, MW Dusenberry, S Farquhar, ...
arXiv preprint arXiv:2106.04015, 2021
Plex: Towards reliability using pretrained large model extensions
D Tran, J Liu, MW Dusenberry, D Phan, M Collier, J Ren, K Han, Z Wang, ...
arXiv preprint arXiv:2207.07411, 2022
Combining ensembles and data augmentation can harm your calibration
Y Wen, G Jerfel, R Muller, MW Dusenberry, J Snoek, ...
arXiv preprint arXiv:2010.09875, 2020
Benchmarking bayesian deep learning on diabetic retinopathy detection tasks
N Band, TGJ Rudner, Q Feng, A Filos, Z Nado, MW Dusenberry, G Jerfel, ...
arXiv preprint arXiv:2211.12717, 2022
A prospective evaluation of AI-augmented epidemiology to forecast COVID-19 in the USA and Japan
SÖ Arık, J Shor, R Sinha, J Yoon, JR Ledsam, LT Le, MW Dusenberry, ...
NPJ digital medicine 4 (1), 146, 2021
Artificial neural networks: Predicting head CT findings in elderly patients presenting with minor head injury after a fall
MW Dusenberry, CK Brown, KL Brewer
The American journal of emergency medicine 35 (2), 260-267, 2017
A simple zero-shot prompt weighting technique to improve prompt ensembling in text-image models
JU Allingham, J Ren, MW Dusenberry, X Gu, Y Cui, D Tran, JZ Liu, ...
International Conference on Machine Learning, 547-568, 2023
Improving calibration of batchensemble with data augmentation
Y Wen, G Jerfel, R Muller, MW Dusenberry, J Snoek, ...
TWorkshop on Uncertainty and Ro-Bustness in Deep Learning, 2020
Morse Neural Networks for Uncertainty Quantification
B Dherin, H Hu, J Ren, MW Dusenberry, B Lakshminarayanan
arXiv preprint arXiv:2307.00667, 2023
Neural spline search for quantile probabilistic modeling
R Sun, CL Li, SÖ Arik, MW Dusenberry, CY Lee, T Pfister
Proceedings of the AAAI Conference on Artificial Intelligence 37 (8), 9927-9934, 2023
Learning Graphical Structure of Electronic Health Records with Transformer for Predictive Healthcare
E Choi, MW Dusenberry, G Flores, Z Xu, Y Li, Y Xue, AM Dai
ICML Workshop on Learning and Reasoning with Graph-Structured Data, 2019
Reliability benchmarks for image segmentation
EK Buchanan, MW Dusenberry, J Ren, KP Murphy, B Lakshminarayanan, ...
NeurIPS 2022 Workshop on Distribution Shifts: Connecting Methods and …, 2022
Deep Learning with Apache SystemML
N Pansare, M Dusenberry, N Jindal, M Boehm, B Reinwald, P Sen
arXiv preprint arXiv:1802.04647, 2018
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