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06/14/2023
Unbiased Learning of Deep Generative Models with Structured Discrete Representations
By composing graphical models with deep learning architectures, we learn...
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12/12/2020
Learning Consistent Deep Generative Models from Sparse Data via Prediction Constraints
We develop a new framework for learning variational autoencoders and oth...
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06/11/2019
Clouds of Oriented Gradients for 3D Detection of Objects, Surfaces, and Indoor Scene Layouts
We develop new representations and algorithms for three-dimensional (3D)...
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02/18/2019
Multi-layer Depth and Epipolar Feature Transformers for 3D Scene Reconstruction
We tackle the problem of automatically reconstructing a complete 3D mode...
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10/23/2018
A Fusion Approach for Multi-Frame Optical Flow Estimation
To date, top-performing optical flow estimation methods only take pairs ...
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12/01/2017
Prediction-Constrained Topic Models for Antidepressant Recommendation
Supervisory signals can help topic models discover low-dimensional data ...
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11/10/2017
Bayesian Paragraph Vectors
Word2vec (Mikolov et al., 2013) has proven to be successful in natural l...
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07/26/2017
Cascaded Scene Flow Prediction using Semantic Segmentation
Given two consecutive frames from a pair of stereo cameras, 3D scene flo...
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07/23/2017
Prediction-Constrained Training for Semi-Supervised Mixture and Topic Models
Supervisory signals have the potential to make low-dimensional data repr...
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09/23/2016
Fast Learning of Clusters and Topics via Sparse Posteriors
Mixture models and topic models generate each observation from a single ...
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08/22/2013
Joint modeling of multiple time series via the beta process with application to motion capture segmentation
We propose a Bayesian nonparametric approach to the problem of jointly m...
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03/15/2012
Gibbs Sampling in Open-Universe Stochastic Languages
Languages for open-universe probabilistic models (OUPMs) can represent s...
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11/17/2011