Several of our current PhD candidates and fellow researchers at the Data Science Institute have published, or in the proccess of publishing, papers to present their research.
@inproceedings{Karri:2019,
author = {Karri, SSK and Bach, F and Pock, T},
publisher = {Neural Information Processing Systems Foundation, Inc.},
title = {Fast decomposable submodular function minimization using constrained total variation},
url = {http://hdl.handle.net/10044/1/82863},
year = {2019}
}
TY - CPAPER
AB - We consider the problem of minimizing the sum of submodular set functions as-suming minimization oracles of each summand function. Most existing approachesreformulate the problem as the convex minimization of the sum of the correspond-ing Lovász extensions and the squared Euclidean norm, leading to algorithmsrequiring total variation oracles of the summand functions; without further assump-tions, these more complex oracles require many calls to the simpler minimizationoracles often available in practice. In this paper, we consider a modified convexproblem requiring a constrained version of the total variation oracles that can besolved with significantly fewer calls to the simple minimization oracles. We supportour claims by showing results on graph cuts for 2D and 3D graphs.
AU - Karri,SSK
AU - Bach,F
AU - Pock,T
PB - Neural Information Processing Systems Foundation, Inc.
PY - 2019///
TI - Fast decomposable submodular function minimization using constrained total variation
UR - http://hdl.handle.net/10044/1/82863
ER -