Ranganath, Rajesh

Convolutional deep belief networks for scalable unsupervised learning of hierarchical representations

Lee, H., Grosse, R., Ranganath, R., & Ng, A. Y. (2009) · Proceedings of the 26th Annual International Conference on Machine Learning

Advanced Neural Network ApplicationsAdvanced Image and Video Retrieval Techniques

Cited by 2,412

ClinicalBERT: Modeling Clinical Notes and Predicting Hospital Readmission

Huang, K., Altosaar, J., & Ranganath, R. (2019) · arXiv

Machine Learning in HealthcareMental Health via Writing

Cited by 618

Automatic differentiation variational inference

Kucukelbir, A., Tran, D., Ranganath, R., Gelman, A., & Blei, D. M. (2016) · arXiv

Gaussian Processes and Bayesian InferenceMarkov Chains and Monte Carlo Methods

Cited by 515

Reproducibility in machine learning for health research: Still a ways to go

McDermott, M. B. A., Wang, S., Marinsek, N., Ranganath, R., Foschini, L., & Ghassemi, M. (2021) · Science Translational Medicine

Scientific Computing and Data ManagementResearch Data Management Practices

Cited by 273

Risk prediction for chronic kidney disease progression using heterogeneous electronic health record data and time series analysis

Perotte, A., Ranganath, R., Hirsch, J. S., Blei, D., & Elhadad, N. (2015) · Journal of the American Medical Informatics Association

Machine Learning in HealthcareChronic Kidney Disease and Diabetes

Cited by 128

Support and Invertibility in Domain-Invariant Representations

Johansson, F. D., Sontag, D., & Ranganath, R. (2019) · arXiv

Domain Adaptation and Few-Shot LearningMachine Learning and Algorithms

Cited by 70

Understanding Failures in Out-of-Distribution Detection with Deep Generative Models

Zhang, L. H., Goldstein, M., & Ranganath, R. (2021) · arXiv

Adversarial Robustness in Machine LearningAnomaly Detection Techniques and Applications

Cited by 42