PPT-A Tensor-Based Algorithm for High-Order Graph Matching

Author : myesha-ticknor | Published Date : 2018-03-14

Olivier Duchenne Francis Bach Inso Kweon Jean Ponce École Normale Supérieure INRIA KAIST Team Willow Extension of Leordeanu amp Hebert to the case of

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A Tensor-Based Algorithm for High-Order Graph Matching: Transcript

Olivier Duchenne Francis Bach Inso Kweon Jean Ponce École Normale Supérieure INRIA KAIST Team Willow Extension of Leordeanu amp Hebert to the case of hypergraph. unoistitechacjp Abstract Forabipartitegraph VE 1perfect2maximumand3max imalmatchingsarematchings1suchthatallverticesareincidenttosomematching edges 2 whose cardinalities are maximum among all matchings 3 which are con tainedinnoothermatchingInthisp Two theorems to recall:. Theorem 3.1.10 (Berge).. . A matching . M . in a graph . G . is a maximum matching in . G . iff . G . has no . M-. augmenting path.. Theorem 3.1.16 (König,Egerváry). If . IndexTopicarraytensor,1%*t%(tensor),1%t*%(tensor),1%t*t%(tensor),1aperm,2matmult,2tensor,14 Based on. http://www.cs.engr.uky.edu/~. lewis/cs-heuristic/text/integer/linprog.html. The . bipartite graph matching problem. is to find a set of unconnected edges which cover as many of the vertices as possible. If we select the set of edges. He Zhang. 1. , He Huang. 2. , . Rui. Li. 1. , . Jie. Chen. 1. , Li-Shi Luo. 2. Jefferson Lab. Old Dominion University. FEIS-2, 05/15/2015. Outline. He Zhang. ---. 3. ---. Introduction of FMM. He Zhang. Arijit. Khan, Nan Li, . Xifeng. Yan, . Ziyu. Guan. Computer Science . UC Santa Barbara. {. arijitkhan. , . nanli. , . xyan. , . ziyuguan. }@. cs.ucsb.edu. . Supriyo. Chakraborty. UC Los Angeles. Overview. Theory. Basic . physics. Tensor. Diffusion . imaging . Practice. How . do you do DTI?. . Tractography. . DTI . in . FSL and other programs. Diffusion . Tensor Imaging. Brownian motion. Shenghan Jiang. Boston College. Benasque. February. , 09, 2017. Symmetric tensor-networks and topological phases. Collaborators:. Ying Ran (Boston College) . Panjin. Kim, . Hyungyong. Lee, Jung . Hoon. . & Huge Models. Alex Beutel. Based on work done with . Abhimanu. Kumar, . Vagelis. . Papalexakis. , . Partha. . Talukdar. , . Qirong. Ho, Christos . Faloutsos. , and Eric Xing. Outline. When to use SGD for distributed learning. Statement of the problem. Two sides of the market to be . matched.. Participants . on . both sides care about to whom they are matched.. M. oney can’t . be used to . determine . the assignment. .. Examples . near the ground states of nuclei. = Study by . 16. O(p,pd). 14. N reaction =. Isao Tanihata, H. J. Ong, s. Terashima and E443 collaboration at RCNP. IRCNPC and SPNEE, Beihang University, Beijing, China. Principle Component Analysis. (PCA. ). . Jiali. . zhang. , . X. iaohong. . Liu . MS Statistics Student. SAN JOSE STATE UNIVERSITY . 12/10/2015. T. he . D. efinition of Image . from . axion. -gauge couplings. Ippei. Obata (Kyoto University. , PhD. ). (in preparation). 29_Nov_. CosPA2016. Primordial GWs. . from the inflation. Energy scale of . early Universe. Red-tilted.. Parity-symmetric.. Gale-Shapley Algorithm. Christian Reyes. Enrique Treviño. Think About…. Imagine…. Finally…. Outline. Stable Matching and the Stable Marriage Problem. Richter Matching. What Next?. Stable Matching.

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