Taxonomy of Dual Block-Coordinate Ascent Methods for Discrete Energy Minimization
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F20%3A00342652" target="_blank" >RIV/68407700:21230/20:00342652 - isvavai.cz</a>
Result on the web
<a href="http://proceedings.mlr.press/v108/tourani20a/tourani20a.pdf" target="_blank" >http://proceedings.mlr.press/v108/tourani20a/tourani20a.pdf</a>
DOI - Digital Object Identifier
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Alternative languages
Result language
angličtina
Original language name
Taxonomy of Dual Block-Coordinate Ascent Methods for Discrete Energy Minimization
Original language description
We consider the maximum-a-posteriori inference problem in discrete graphical models and study solvers based on the dual block-coordinate ascent rule. We map all existing solvers in a single framework, allowing for a better understanding of their design principles. We theoretically show that some block-optimizing updates are sub-optimal and how to strictly improve them. On a wide range of problem instances of varying graph connectivity, we study the performance of existing solvers as well as new variants that can be obtained within the framework. As a result of this exploration we build a new state-of-the art solver, performing uniformly better on the whole range of test instances.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
<a href="/en/project/EF18_070%2F0010457" target="_blank" >EF18_070/0010457: International Mobility of Researchers MSCA-IF II in CTU in Prague</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Others
Publication year
2020
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
Proceedings of Machine Learning Research
ISBN
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ISSN
2640-3498
e-ISSN
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Number of pages
10
Pages from-to
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Publisher name
Proceedings of Machine Learning Research
Place of publication
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Event location
Palermo
Event date
Jun 3, 2020
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
000559931303044