17 citations · 36 across the 4 of their papers we have counts for
4 papers
Learning Consistent Deep Generative Models from Sparse Data via Prediction Constraints
Gabriel Hope, Madina Abdrakhmanova, Xiaoyin Chen +2
We develop a new framework for learning variational autoencoders and other deep generative models that balances generative and discriminative goals. Our framework optimizes model p…
Cascaded Scene Flow Prediction using Semantic Segmentation
Zhile Ren, Deqing Sun, Jan Kautz +1
Given two consecutive frames from a pair of stereo cameras, 3D scene flow methods simultaneously estimate the 3D geometry and motion of the observed scene. Many existing approaches…
The Nonparametric Metadata Dependent Relational Model
Dae Il Kim, Michael Hughes, Erik Sudderth
We introduce the nonparametric metadata dependent relational (NMDR) model, a Bayesian nonparametric stochastic block model for network data. The NMDR allows the entities associated…
Gibbs Sampling in Open-Universe Stochastic Languages
Nimar S. Arora, Rodrigo de Salvo Braz, Erik B. Sudderth +1
Languages for open-universe probabilistic models (OUPMs) can represent situations with an unknown number of objects and iden- tity uncertainty. While such cases arise in a wide ran…