| 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 | 407 | 2023 |
| Measuring calibration in deep learning. J Nixon, MW Dusenberry, L Zhang, G Jerfel, D Tran CVPR workshops 2 (7), 2019 | 405 | 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 | 245* | 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 | 243 | 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 | 212 | 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 | 126 | 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 | 103 | 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 | 97 | 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 | 80 | 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 | 51 | 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 | 36 | 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 | 19 | 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 | 17 | 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 | 12 | 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 | 6 | 2020 |
| 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 | 2 | 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 | 2 | 2019 |
| Morse Neural Networks for Uncertainty Quantification B Dherin, H Hu, J Ren, MW Dusenberry, B Lakshminarayanan arXiv preprint arXiv:2307.00667, 2023 | 1 | 2023 |
| 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 | 1 | 2022 |
| Deep Learning with Apache SystemML N Pansare, M Dusenberry, N Jindal, M Boehm, B Reinwald, P Sen arXiv preprint arXiv:1802.04647, 2018 | 1 | 2018 |
Dustin TranResearch Scientist, GoogleVerified email at google.com
