Convolutional deep belief networks for scalable unsupervised learning of hierarchical representations H Lee, R Grosse, R Ranganath, AY Ng
Proceedings of the 26th annual international conference on machine learning …, 2009
3570 2009 Black box variational inference R Ranganath, S Gerrish, D Blei
Artificial intelligence and statistics, 814-822, 2014
1467 2014 Clinicalbert: Modeling clinical notes and predicting hospital readmission K Huang, J Altosaar, R Ranganath
arXiv preprint arXiv:1904.05342, 2019
1086 2019 Automatic differentiation variational inference A Kucukelbir, D Tran, R Ranganath, A Gelman, DM Blei
Journal of machine learning research 18 (14), 1-45, 2017
948 2017 Unsupervised learning of hierarchical representations with convolutional deep belief networks H Lee, R Grosse, R Ranganath, AY Ng
Communications of the ACM 54 (10), 95-103, 2011
534 2011 Hierarchical variational models R Ranganath, D Tran, D Blei
International conference on machine learning, 324-333, 2016
401 2016 A review of challenges and opportunities in machine learning for health M Ghassemi, T Naumann, P Schulam, AL Beam, IY Chen, R Ranganath
AMIA Summits on Translational Science Proceedings 2020, 191, 2020
397 2020 Backprop kf: Learning discriminative deterministic state estimators T Haarnoja, A Ajay, S Levine, P Abbeel
Advances in neural information processing systems 29, 2016
387 * 2016 Hierarchical implicit models and likelihood-free variational inference D Tran, R Ranganath, D Blei
Advances in Neural Information Processing Systems 30, 2017
374 * 2017 Automatic variational inference in Stan A Kucukelbir, R Ranganath, A Gelman, D Blei
Advances in neural information processing systems 28, 2015
311 2015 The role of machine learning in clinical research: transforming the future of evidence generation EH Weissler, T Naumann, T Andersson, R Ranganath, O Elemento, Y Luo, ...
Trials 22, 1-15, 2021
270 2021 Variational sequential monte carlo C Naesseth, S Linderman, R Ranganath, D Blei
International conference on artificial intelligence and statistics, 968-977, 2018
268 2018 Deep survival analysis R Ranganath, A Perotte, N Elhadad, D Blei
Machine Learning for Healthcare Conference, 101-114, 2016
253 2016 Reproducibility in machine learning for health research: Still a ways to go MBA McDermott, S Wang, N Marinsek, R Ranganath, L Foschini, ...
Science Translational Medicine 13 (586), eabb1655, 2021
228 2021 The variational Gaussian process D Tran, R Ranganath, DM Blei
arXiv preprint arXiv:1511.06499, 2015
219 2015 Support and invertibility in domain-invariant representations FD Johansson, D Sontag, R Ranganath
arXiv preprint arXiv:1903.03448, 2019
208 2019 Variational Inference via AB Dieng, D Tran, R Ranganath, J Paisley, D Blei
Advances in Neural Information Processing Systems 30, 2017
189 2017 Offline rl without off-policy evaluation D Brandfonbrener, W Whitney, R Ranganath, J Bruna
Advances in neural information processing systems 34, 4933-4946, 2021
184 2021 Deep exponential families R Ranganath, L Tang, L Charlin, D Blei
Artificial intelligence and statistics, 762-771, 2015
164 2015 Deep learning models for electrocardiograms are susceptible to adversarial attack X Han, Y Hu, L Foschini, L Chinitz, L Jankelson, R Ranganath
Nature medicine 26 (3), 360-363, 2020
157 2020 