Rahul G. Krishnan
Rahul G. Krishnan
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Cited by
Cited by
Variational autoencoders for collaborative filtering
D Liang, RG Krishnan, MD Hoffman, T Jebara
Proceedings of the 2018 World Wide Web Conference, 689-698, 2018
Structured Inference Networks for Nonlinear State Space Models
RG Krishnan, U Shalit, D Sontag
arXiv preprint arXiv:1609.09869, 2016
Deep Kalman Filters
RG Krishnan, U Shalit, D Sontag
arXiv preprint arXiv:1511.05121, 2015
On the challenges of learning with inference networks on sparse, high-dimensional data
RG Krishnan, D Liang, MD Hoffman
The 21st International Conference on Artificial Intelligence and Statistics, 2018
Barrier Frank-Wolfe for marginal inference
RG Krishnan, S Lacoste-Julien, D Sontag
Advances in Neural Information Processing Systems, 532-540, 2015
Representation Learning Approaches to Detect False Arrhythmia Alarms from ECG Dynamics
EP Lehman, RG Krishnan, X Zhao, RG Mark, HL Li-wei
Machine Learning for Healthcare Conference, 571-586, 2018
Early detection of diabetes from health claims
R Krishnan, N Razavian, Y Choi, S Nigam, S Blecker, A Schmidt, ...
Machine Learning in Healthcare Workshop, NIPS, 1-5, 2013
Neural pharmacodynamic state space modeling
ZM Hussain, RG Krishnan, D Sontag
International Conference on Machine Learning, 4500-4510, 2021
Max-margin learning with the Bayes factor.
RG Krishnan, A Khandelwal, R Ranganath, D Sontag
UAI, 896-905, 2018
Inference & Introspection in Deep Generative Models of Sparse Data
RG Krishnan, M Hoffman
Workshop for Advances in Approximate Bayesian Inference at NIPS 2016, 0
Self-Supervised Vision Transformers Learn Visual Concepts in Histopathology
RJ Chen, RG Krishnan
arXiv preprint arXiv:2203.00585, 2022
Using Time-Series Privileged Information for Provably Efficient Learning of Prediction Models
R Karlsson, M Willbo, Z Hussain, RG Krishnan, D Sontag, FD Johansson
arXiv preprint arXiv:2110.14993, 2021
Hierarchical Optimal Transport for Comparing Histopathology Datasets
A Yeaton, RG Krishnan, R Mieloszyk, D Alvarez-Melis, G Huynh
arXiv preprint arXiv:2204.08324, 2022
Mixture-of-experts VAEs can disregard variation in surjective multimodal data
J Wolff, T Klein, M Nabi, RG Krishnan, S Nakajima
arXiv preprint arXiv:2204.05229, 2022
Scaling Vision Transformers to Gigapixel Images via Hierarchical Self-Supervised Learning
RJ Chen, C Chen, Y Li, TY Chen, AD Trister, RG Krishnan, F Mahmood
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2022
Characterizing the Progression of Pulmonary Edema Severity: Can Pairwise Comparisons in Radiology Reports Help?
S Hu, S Horng, SJ Berkowitz, R Liao, RG Krishnan, HL Li-wei, RG Mark
medRxiv, 2022
Hierarchical Multimodal Variational Autoencoders
J Wolff, RG Krishnan, L Ruff, JN Morshuis, T Klein, S Nakajima, M Nabi
Clustering Left-Censored Multivariate Time-Series
IY Chen, RG Krishnan, D Sontag
arXiv preprint arXiv:2102.07005, 2021
Clustering Interval-Censored Time-Series for Disease Phenotyping
IY Chen, RG Krishnan, D Sontag
arXiv e-prints, arXiv: 2102.07005, 2021
Learning Deep Generative Models
RG Krishnan
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