Bayesian nonparametric federated learning of neural networks M Yurochkin, M Agarwal, S Ghosh, K Greenewald, N Hoang, Y Khazaeni International conference on machine learning, 7252-7261, 2019 | 815 | 2019 |
Model Selection in Bayesian Neural Networks via Horseshoe Priors. S Ghosh, J Yao, F Doshi-Velez J. Mach. Learn. Res. 20 (182), 1-46, 2019 | 150 | 2019 |
Quality of uncertainty quantification for Bayesian neural network inference J Yao, W Pan, S Ghosh, F Doshi-Velez arXiv preprint arXiv:1906.09686, 2019 | 132 | 2019 |
Spatial distance dependent Chinese restaurant processes for image segmentation S Ghosh, A Ungureanu, E Sudderth, D Blei Advances in Neural Information Processing Systems 24, 2011 | 97 | 2011 |
Structured variational learning of Bayesian neural networks with horseshoe priors S Ghosh, J Yao, F Doshi-Velez International Conference on Machine Learning, 1744-1753, 2018 | 93 | 2018 |
DPVis: Visual analytics with hidden markov models for disease progression pathways BC Kwon, V Anand, KA Severson, S Ghosh, Z Sun, BI Frohnert, ... IEEE transactions on visualization and computer graphics 27 (9), 3685-3700, 2020 | 75 | 2020 |
Assumed density filtering methods for learning bayesian neural networks S Ghosh, F Delle Fave, J Yedidia Proceedings of the AAAI Conference on Artificial Intelligence 30 (1), 2016 | 63 | 2016 |
Discovery of Parkinson's disease states and disease progression modelling: a longitudinal data study using machine learning KA Severson, LM Chahine, LA Smolensky, M Dhuliawala, M Frasier, K Ng, ... The Lancet Digital Health 3 (9), e555-e564, 2021 | 60 | 2021 |
Unsupervised learning with contrastive latent variable models KA Severson, S Ghosh, K Ng Proceedings of the AAAI Conference on Artificial Intelligence 33 (01), 4862-4869, 2019 | 48 | 2019 |
A probabilistic disease progression modeling approach and its application to integrated Huntington’s disease observational data Z Sun, S Ghosh, Y Li, Y Cheng, A Mohan, C Sampaio, J Hu JAMIA open 2 (1), 123-130, 2019 | 46 | 2019 |
Automatic recognition of landforms on Mars using terrain segmentation and classification TF Stepinski, S Ghosh, R Vilalta International Conference on Discovery Science, 255-266, 2006 | 46 | 2006 |
Early prediction of diabetes complications from electronic health records: A multi-task survival analysis approach B Liu, Y Li, Z Sun, S Ghosh, K Ng Proceedings of the AAAI Conference on Artificial Intelligence 32 (1), 2018 | 45 | 2018 |
EVA: Generating longitudinal electronic health records using conditional variational autoencoders S Biswal, S Ghosh, J Duke, B Malin, W Stewart, C Xiao, J Sun Machine Learning for Healthcare Conference, 260-282, 2021 | 42 | 2021 |
Statistical model aggregation via parameter matching M Yurochkin, M Agarwal, S Ghosh, K Greenewald, N Hoang Advances in neural information processing systems 32, 2019 | 41 | 2019 |
Uncertainty quantification 360: A holistic toolkit for quantifying and communicating the uncertainty of ai S Ghosh, QV Liao, KN Ramamurthy, J Navratil, P Sattigeri, KR Varshney, ... arXiv preprint arXiv:2106.01410, 2021 | 39 | 2021 |
Model fusion with Kullback-Leibler divergence S Claici, M Yurochkin, S Ghosh, J Solomon International conference on machine learning, 2038-2047, 2020 | 38 | 2020 |
A machine‐learning derived Huntington's disease progression model: insights for clinical trial design A Mohan, Z Sun, S Ghosh, Y Li, S Sathe, J Hu, C Sampaio Movement Disorders 37 (3), 553-562, 2022 | 36 | 2022 |
Automatic annotation of planetary surfaces with geomorphic labels S Ghosh, TF Stepinski, R Vilalta IEEE Transactions on Geoscience and Remote Sensing 48 (1), 175-185, 2009 | 35 | 2009 |
Personalized input-output hidden markov models for disease progression modeling KA Severson, LM Chahine, L Smolensky, K Ng, J Hu, S Ghosh Machine learning for healthcare conference, 309-330, 2020 | 34 | 2020 |
Personalizing gesture recognition using hierarchical bayesian neural networks A Joshi, S Ghosh, M Betke, S Sclaroff, H Pfister Proceedings of the IEEE Conference on Computer Vision and Pattern …, 2017 | 34 | 2017 |