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[PDF] Top 20 Spectral inference methods on sparse graphs : theory and applications

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Spectral inference methods on sparse graphs : theory and applications

Spectral inference methods on sparse graphs : theory and applications

... of graphs, ...approximation, and therefore the non- backtracking operator and the Bethe Hessian, are expected to fail on graphs containing short ...one, and affects all spectral ... Voir le document complet

256

(Hyper)-Graphs Inference via Convex Relaxations and Move Making Algorithms: Contributions and Applications in artificial vision

(Hyper)-Graphs Inference via Convex Relaxations and Move Making Algorithms: Contributions and Applications in artificial vision

... in theory good guarantees as it concerns the optimality properties of the obtained ...meta-heuristic methods could lead to a good approximation of the optimal solution if temperature/radius are ... Voir le document complet

66

Spectral Detection on Sparse Hypergraphs

Spectral Detection on Sparse Hypergraphs

... [2], and spec- tral methods ...statistical inference methods based on belief propagation were predicted to be optimal in detecting planted hidden configurations [4], [5], ...[6]. ... Voir le document complet

9

Edge Label Inference in Generalized Stochastic Block Models: from Spectral Theory to Impossibility Results

Edge Label Inference in Generalized Stochastic Block Models: from Spectral Theory to Impossibility Results

... Blockmodel, Spectral Methods, Galton-Watson Tree ...attention and has found numer- ous applications across various disciplines including physics, sociology, biology, statistics, com- puter ... Voir le document complet

19

Spectral Gap of Random Hyperbolic Graphs and Related Parameters

Spectral Gap of Random Hyperbolic Graphs and Related Parameters

... hyperbolic graphs have been suggested as a promising model of social ...[GPP12] and essentially determine the spectral gap of their normalized ...parameter and D is the network diameter (which ... Voir le document complet

45

Statistical inference of Ornstein-Uhlenbeck processes : generation of stochastic graphs, sparsity, applications in finance

Statistical inference of Ornstein-Uhlenbeck processes : generation of stochastic graphs, sparsity, applications in finance

... 0 and take ∆ n = T /n , such that the observation is done on a fixed-length interval, but the observations become ...intrval, and we know from what we explained above that then Σ is known but the estimation ... Voir le document complet

185

Spectral redemption in clustering sparse networks

Spectral redemption in clustering sparse networks

... the sparse case where c is constant while n is large, this picture breaks down due to a number of ...degree, and the corresponding eigenvectors are localized around these vertices ...result, spectral ... Voir le document complet

12

Sparse Training Theory for Scalable and Efficient Agents

Sparse Training Theory for Scalable and Efficient Agents

... for sparse NNs in the medium-term ...for sparse NNs to be conceived, e.g. [8], for both edge and cloud, the problem can be addressed as a programming and software engineering ...for ... Voir le document complet

5

2-Distance Coloring of Sparse Graphs

2-Distance Coloring of Sparse Graphs

... Abstract A 2-distance coloring of a graph is a coloring of the vertices such that two vertices at distance at most 2 receive distinct colors. We prove that every graph with maximum degree ∆ at least 4 and maximum ... Voir le document complet

7

$(k,1)$-coloring of sparse graphs

$(k,1)$-coloring of sparse graphs

... G and its (k, ...[3] and is used, in particular, in [4, 5, 6, 7, 8, ...area” and ”soft component” are introduced in [7] and also used in our recent paper ... Voir le document complet

12

Symbolic possibilistic logic: completeness and inference methods

Symbolic possibilistic logic: completeness and inference methods

... syntactic inference methodologies that calculate the necessity degree N ⊢ (φ) of a possibilistic ...variables, and the use of prime implicates, which deals with the simplification of complex weights ... Voir le document complet

28

Symbolic Possibilistic Logic: Completeness and Inference Methods

Symbolic Possibilistic Logic: Completeness and Inference Methods

... symbolic and stand for variables that lie in a to- tally ordered scale, and only partial knowledge is available on the relative strength of these ...soundness and the completeness of this logic ac- ... Voir le document complet

12

Spectrum of Markov generators on sparse random graphs

Spectrum of Markov generators on sparse random graphs

... outlier, and therefore does not affect the limiting spectral distribution, see ...[2] and references therein for an introduction to the basic concepts of free ...algebra and τ is a normal, ... Voir le document complet

34

Policy Evaluation Using Causal Inference Methods

Policy Evaluation Using Causal Inference Methods

... Instrumental Variables. Let us suppose that we observe the wages of two groups of workers, the first group having recently benefited from an active labour market policy such as a training program, the other group having ... Voir le document complet

38

Theory and Inference for a Markov-Switching GARCH Model

Theory and Inference for a Markov-Switching GARCH Model

... CORE and Department of Economics, Université Catholique de Louvain Preminger : Department of Economics, University of Haifa, 31905 Israel Rombouts : Institute of Applied Economics at HEC Montréal, CIRANO, CIRPEE, ... Voir le document complet

27

A Bayesian inference theory of attention: neuroscience and algorithms

A Bayesian inference theory of attention: neuroscience and algorithms

... large and disparate body of literature on the role of attention [Itti et ...attention and is consistent with all known effects is still ...Bayesian inference process [Rao et al., 2002, Knill ... Voir le document complet

20

Convex optimization methods for graphs and statistical modeling

Convex optimization methods for graphs and statistical modeling

... In the setting of Gaussian graphical models where the random variables are jointly Gaussian, sparsity in the graph structure corresponds to sparsity in the inverse[r] ... Voir le document complet

220

Higher-Order Spectral Clustering for Geometric Graphs

Higher-Order Spectral Clustering for Geometric Graphs

... geometric graphs as the regions of space can contain a balanced number of ...render spectral clustering ...with spectral clustering in such a setting, one needs to select carefully the correct ... Voir le document complet

30

Mapping sparse signed graphs to (K_{2k} , M )

Mapping sparse signed graphs to (K_{2k} , M )

... main motivation of our work. On the other hand, for (K 3 , ∅), even with the extra con- dition of planarity, not only the necessary conditions of the no-homomorphism lemma are not sufficient but it is expected to be far ... Voir le document complet

35

Respondent-driven sampling on sparse Erdös-Rényi graphs

Respondent-driven sampling on sparse Erdös-Rényi graphs

... population and E ⊂ V 2 is the set of non-oriented edges ...chosen and interviewed. He or she names their contacts and then receives a maximum of c coupons, depending on the number of their contacts ... Voir le document complet

31

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