1![Modeling Exclusion with a Differentiable Factor Graph Constraint Jason Naradowsky 1 Sebastian Riedel 2 Abstract With the adoption of general neural network architectures, many researchers have opted to trade Modeling Exclusion with a Differentiable Factor Graph Constraint Jason Naradowsky 1 Sebastian Riedel 2 Abstract With the adoption of general neural network architectures, many researchers have opted to trade](https://www.pdfsearch.io/img/439de68b0100077d4a262ebbe42c1563.jpg) | Add to Reading ListSource URL: deepstruct.github.ioLanguage: English - Date: 2017-08-09 03:07:44
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2![Exact Solution of Graph Coloring Problems via Constraint Programming and Column Generation Stefano Gualandi, Federico Malucelli Dipartimento di Elettronica ed Informazione, Politecnico di Milano, Piazza L. da Vinci 32, M Exact Solution of Graph Coloring Problems via Constraint Programming and Column Generation Stefano Gualandi, Federico Malucelli Dipartimento di Elettronica ed Informazione, Politecnico di Milano, Piazza L. da Vinci 32, M](https://www.pdfsearch.io/img/58a6d922944dcf123e56a9cc42edef5b.jpg) | Add to Reading ListSource URL: www.optimization-online.org- Date: 2011-09-21 10:12:20
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3![Locally-biased graph algorithms are algorithms that attempt to find local or small-scale structure in a typically large data graph. In some cases, this can be accomplished by adding some sort of locality constraint and c Locally-biased graph algorithms are algorithms that attempt to find local or small-scale structure in a typically large data graph. In some cases, this can be accomplished by adding some sort of locality constraint and c](https://www.pdfsearch.io/img/705f81d411bd9e699e8610346e16be44.jpg) | Add to Reading ListSource URL: mmds-data.org- Date: 2016-06-23 15:50:48
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4![Generating tractable CSPs by means of adjoint functors Jan Foniok joint work with Claude Tardif Fête of Combinatorics and Computer Science Generating tractable CSPs by means of adjoint functors Jan Foniok joint work with Claude Tardif Fête of Combinatorics and Computer Science](https://www.pdfsearch.io/img/617d4c0a92ebdfe6e74a3b983095f7df.jpg) | Add to Reading ListSource URL: www.ifor.math.ethz.chLanguage: English - Date: 2008-11-18 08:48:31
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5![Rectangular Decomposition of Binary Images Tom´ aˇs Suk, Cyril H¨oschl IV, and Jan Flusser Institute of Information Theory and Automation of the ASCR, Pod vod´ arenskou vˇeˇz´ı 4, Praha 8, Czech Republic Rectangular Decomposition of Binary Images Tom´ aˇs Suk, Cyril H¨oschl IV, and Jan Flusser Institute of Information Theory and Automation of the ASCR, Pod vod´ arenskou vˇeˇz´ı 4, Praha 8, Czech Republic](https://www.pdfsearch.io/img/d8d661d8a495b2377d07798fae6b6c8f.jpg) | Add to Reading ListSource URL: library.utia.cas.czLanguage: English - Date: 2012-08-15 08:16:50
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6![Helmert’s and Bowie’s Geodetic Mapping Methods and Their Relation to Graph-Based SLAM Pratik Agarwal Wolfram Burgard Helmert’s and Bowie’s Geodetic Mapping Methods and Their Relation to Graph-Based SLAM Pratik Agarwal Wolfram Burgard](https://www.pdfsearch.io/img/7fb0fc0e46e55f9f41e01a6a59d317f7.jpg) | Add to Reading ListSource URL: www.lifelong-navigation.euLanguage: English - Date: 2014-02-18 08:09:23
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7![Locally-biased graph algorithms are algorithms that attempt to find local or small-scale structure in a typically large data graph. In some cases, this can be accomplished by adding some sort of locality constraint and c Locally-biased graph algorithms are algorithms that attempt to find local or small-scale structure in a typically large data graph. In some cases, this can be accomplished by adding some sort of locality constraint and c](https://www.pdfsearch.io/img/680efd47da363a4473c58842aafb960f.jpg) | Add to Reading ListSource URL: mmds-data.orgLanguage: English - Date: 2016-06-23 15:50:48
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8![sets-graph-msuc-opt.ipeps sets-graph-msuc-opt.ipeps](https://www.pdfsearch.io/img/dfa854be76fd102512d891868fce397d.jpg) | Add to Reading ListSource URL: tmancini.di.uniroma1.itLanguage: English - Date: 2008-12-16 11:04:43
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9![Exercise 12: Weed Weak models Task 1: Hyper, hyper! Recall that a hypergraph is a graph where edges may comprise more than 2 nodes. The degree of a hyperedge is the number of nodes in it. Consider a hypergraph of maximum Exercise 12: Weed Weak models Task 1: Hyper, hyper! Recall that a hypergraph is a graph where edges may comprise more than 2 nodes. The degree of a hyperedge is the number of nodes in it. Consider a hypergraph of maximum](https://www.pdfsearch.io/img/c2f228d0a40c0ea87c6361e996adb515.jpg) | Add to Reading ListSource URL: resources.mpi-inf.mpg.deLanguage: English - Date: 2015-01-20 10:45:26
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10![Popular Matching: A Constraint Programming Approach Danuta Sorina Chisca, Mohamed Siala, Gilles Simonin, Barry O’Sullivan Insight Centre for Data Analytics, University College Cork, Ireland ∗ {sorina.chisca, mohamed. Popular Matching: A Constraint Programming Approach Danuta Sorina Chisca, Mohamed Siala, Gilles Simonin, Barry O’Sullivan Insight Centre for Data Analytics, University College Cork, Ireland ∗ {sorina.chisca, mohamed.](https://www.pdfsearch.io/img/43389603df21808b1717279aa4bdb5cf.jpg) | Add to Reading ListSource URL: womencourage.acm.orgLanguage: English |
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