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Efficient Inference And Learning For Structured Models

Abstract: Sensors acquire an increasing amount of diverse information posing two challenges. Firstly, how can we efficiently deal with such a big amount of data and secondly, how can we benefit from this diversity? In this talk I will first present an approach to deal with large graphical models. The presented method distributes and parallelizes the computation and memory requirements while preserving convergence and optimality guarantees of existing algorithms. I will demonstrate the effectiveness of the approach on stereo reconstruction from high-resolution imagery. In the second part I will present a unified framework for structured prediction with latent variables which includes hidden conditional random fields and latent structured support vector machines as special cases. This framework allows to linearly combine different sources of information and I will demonstrate its efficacy on the problem of estimating the 3D room layout given a single image. For the latter problem, I will introduce a globally optimal yet efficient inference algorithm based on branch-and-bound.

Bio: Alexander Schwing studied electrical engineering and information technology at Technical University Munich (TUM). Currently he is a PhD student at ETH Zurich, supervised by Tamir Hazan (TTI-C), Marc Pollefeys (ETHZ) and Raquel Urtasun (TTI-C). His research focuses on optimization algorithms for inference and learning tasks and his work is motivated among others by applications arising from indoor 3D scene understanding topics.

Website: http://alexander-schwing.de