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cs.LG2016
Probabilistic Duality for Parallel Gibbs Sampling without Graph Coloring
Lars Mescheder, Sebastian Nowozin, Andreas Geiger
We present a new notion of probabilistic duality for random variables involving mixture distributions. Using this notion, we show how to implement a highly-parallelizable Gibbs sam…
cs.CV2016★ 34 cited
OctNet: Learning Deep 3D Representations at High Resolutions
Gernot Riegler, Ali Osman Ulusoy, Andreas Geiger
We present OctNet, a representation for deep learning with sparse 3D data. In contrast to existing models, our representation enables 3D convolutional networks which are both deep…