| Spatial distance dependent Chinese restaurant processes for image segmentation S Ghosh, A Ungureanu, E Sudderth, D Blei Advances in Neural Information Processing Systems 24, 1476-1484, 2011 | 84 | 2011 |
| 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 | 68 | 2019 |
| Model selection in Bayesian neural networks via horseshoe priors S Ghosh, F Doshi-Velez arXiv preprint arXiv:1705.10388, 2017 | 36 | 2017 |
| 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 | 35 | 2018 |
| Quality of uncertainty quantification for Bayesian neural network inference J Yao, W Pan, S Ghosh, F Doshi-Velez arXiv preprint arXiv:1906.09686, 2019 | 32 | 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 | 29 | 2006 |
| 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 | 26 | 2009 |
| 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 | 24 | 2017 |
| From deformations to parts: Motion-based segmentation of 3D objects S Ghosh, M Loper, E Sudderth, M Black Advances in Neural Information Processing Systems 25, 1997-2005, 2012 | 24 | 2012 |
| 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 | 23 | 2016 |
| Machine learning for automatic mapping of planetary surfaces TF Stepinski, S Ghosh, R Vilalta AAAI, 1807-1812, 2007 | 22 | 2007 |
| 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 | 21 | 2018 |
| A general framework for reconciling multiple weak segmentations of an image S Ghosh, JJ Pfeiffer, J Mulligan 2009 Workshop on Applications of Computer Vision (WACV), 1-8, 2009 | 20 | 2009 |
| Nonparametric learning for layered segmentation of natural images S Ghosh, EB Sudderth 2012 IEEE Conference on Computer Vision and Pattern Recognition, 2272-2279, 2012 | 17 | 2012 |
| 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 | 12 | 2019 |
| Model Selection in Bayesian Neural Networks via Horseshoe Priors. S Ghosh, J Yao, F Doshi-Velez Journal of Machine Learning Research 20 (182), 1-46, 2019 | 10 | 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 | 9 | 2019 |
| Latent projection bnns: Avoiding weight-space pathologies by learning latent representations of neural network weights MF Pradier, W Pan, J Yao, S Ghosh, F Doshi-Velez Workshop on Bayesian Deep Learning, NIPS, 2018 | 8 | 2018 |
| Nonparametric Clustering with Distance Dependent Hierarchies. S Ghosh, M Raptis, L Sigal, EB Sudderth UAI, 260-269, 2014 | 8 | 2014 |
| A segmentation guided label propagation scheme for autonomous navigation S Ghosh, J Mulligan 2010 IEEE International Conference on Robotics and Automation, 895-902, 2010 | 8 | 2010 |
Finale DoshiAssistant Professor, HarvardAdresse e-mail validée de seas.harvard.edu
