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Efficient maximum a posterior (MAP) inference for computer vision and beyond

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If you have a question about this talk, please contact Carola-Bibiane Schoenlieb.

Many problems in computer vision and machine learning require inferring the most probable states of certain hidden or unobserved variables. This inference problem can be formulated in terms of minimizing a function of discrete variables. The scale and form of computer vision problems raise many challenges in this optimization task. For instance, functions encountered in vision may involve millions or sometimes even billions of variables. Furthermore, the functions may contain terms that encode very high-order interaction between variables. These properties ensure that the minimization of such functions using conventional algorithms is extremely computationally expensive.

In this talk, I will discuss how many of these challenges can be overcome by exploiting the sparse and heterogeneous nature of discrete optimization problems encountered in real world computer vision problems. Such problem-aware approaches to optimization can lead to substantial improvements in running time and allow us to produce good solutions to many important problems.

The slides of the talk are online at http://www.damtp.cam.ac.uk/user/cbs31/MI_Cambridge/MathAndInfo_Network_files/pushmeetkohlitalk.pdf

This talk is part of the Mathematics & Information in Cambridge series.

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