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Learning Consistent Deep Generative Models from Sparse Data via Prediction Constraints



Gabriel Hope , Madina Abdrakhmanova , Xiaoyin Chen , Michael C. Hughes , Michael C. Hughes , Erik B. Sudderth


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Clouds of Oriented Gradients for 3D Detection of Objects, Surfaces, and Indoor Scene Layouts



Zhile Ren , Erik B. Sudderth

* Accepted in IEEE Transactions on Pattern Analysis and Machine Intelligence (T-PAMI) 

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Multi-layer Depth and Epipolar Feature Transformers for 3D Scene Reconstruction



Daeyun Shin , Zhile Ren , Erik B. Sudderth , Charless C. Fowlkes


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A Fusion Approach for Multi-Frame Optical Flow Estimation



Zhile Ren , Orazio Gallo , Deqing Sun , Ming-Hsuan Yang , Erik B. Sudderth , Jan Kautz

* Work currently under review 

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Bayesian Paragraph Vectors



Geng Ji , Robert Bamler , Erik B. Sudderth , Stephan Mandt

* Presented at the NIPS 2017 workshop "Advances in Approximate Bayesian Inference" 

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Prediction-Constrained Topic Models for Antidepressant Recommendation



Michael C. Hughes , Gabriel Hope , Leah Weiner , Thomas H. McCoy , Roy H. Perlis , Erik B. Sudderth , Finale Doshi-Velez

* Accepted poster at NIPS 2017 Workshop on Machine Learning for Health (https://ml4health.github.io/2017/

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Cascaded Scene Flow Prediction using Semantic Segmentation



Zhile Ren , Deqing Sun , Jan Kautz , Erik B. Sudderth

* International Conference on 3D Vision (3DV), 2017 (oral presentation) 

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Prediction-Constrained Training for Semi-Supervised Mixture and Topic Models



Michael C. Hughes , Leah Weiner , Gabriel Hope , Thomas H. McCoy Jr. , Roy H. Perlis , Erik B. Sudderth , Finale Doshi-Velez


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Fast Learning of Clusters and Topics via Sparse Posteriors



Michael C. Hughes , Erik B. Sudderth


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Joint modeling of multiple time series via the beta process with application to motion capture segmentation



Emily B. Fox , Michael C. Hughes , Erik B. Sudderth , Michael I. Jordan

* Annals of Applied Statistics 2014, Vol. 8, No. 3, 1281-1313 
* Published in at http://dx.doi.org/10.1214/14-AOAS742 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org). arXiv admin note: text overlap with arXiv:1111.4226 

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