Gemini: a family of highly capable multimodal models G Team, R Anil, S Borgeaud, JB Alayrac, J Yu, R Soricut, J Schalkwyk, ... arXiv preprint arXiv:2312.11805, 2023 | 2084 | 2023 |
Measuring Calibration in Deep Learning. J Nixon, MW Dusenberry, L Zhang, G Jerfel, D Tran CVPR workshops 2 (7), 2019 | 501 | 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 | 292* | 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 | 259 | 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 | 240 | 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 | 138 | 2019 |
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 | 113 | 2022 |
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 | 111 | 2021 |
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 | 111 | 2020 |
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 | 68 | 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 | 54 | 2022 |
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 | 31 | 2023 |
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 | 23 | 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 | 18 | 2017 |
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 | 7 | 2020 |
Measuring Calibration in Deep Learning. apr 2019 J Nixon, M Dusenberry, G Jerfel, T Nguyen, J Liu, L Zhang, D Tran URL http://arxiv. org/abs, 1904 | 6 | 1904 |
Morse Neural Networks for Uncertainty Quantification B Dherin, H Hu, J Ren, MW Dusenberry, B Lakshminarayanan arXiv preprint arXiv:2307.00667, 2023 | 4 | 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 | 3 | 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 | 2 | 2022 |
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 |