<p>The estimation error changes with the sparsity of directed random networks. There, the sparsity is defined as the ratio of the number of zero non-diagonal elements to , and is calculated using the -norm convex optimization strategy (with ). Furthermore, each black point represents the result of averaging over 30 random perturbations (with and being uniformly distributed in the range ) and the standard square error is given as well.</p
International audienceWe consider a deep structured linear network under sparsity constraints. We st...
Despite its generic title, this thesis is about a specific notion of sparsity, the one introduced by...
This paper addresses the problem of sparsity pattern detection for unknown k-sparse n-dimensional si...
<p>The estimation error changes with the node-pair connection possibility for two cases, i.e., und...
<p>(A) The estimation error surface of a directed network with and node-pair connection probability...
<p>The estimation error surfaces of a directed network (23) with and node-pair connection probabili...
<p>(A) The estimation error changes with the number of perturbations, where is calculated using t...
<p>The estimation error surfaces are calculated using two methods for a undirected network (23) with...
Sparsification is the process of decreasing the number of edges in a network while one or more topol...
Arrival Time Error Score averaged over nodes and over 1000 independent simulations for the (A) local...
<p>(A) The size of the largest connected component of structural networks as function of sparsity, (...
Most queries on probabilistic networks assume a possible world semantic, which causes an exponential...
We suggest a control based approach to topology estimation of networks with elements. This method fi...
The average fraction of nodes disconnected from the largest connected component of the network by sp...
<p> The topology estimation error surfaces are calculated using two methods for a undirected network...
International audienceWe consider a deep structured linear network under sparsity constraints. We st...
Despite its generic title, this thesis is about a specific notion of sparsity, the one introduced by...
This paper addresses the problem of sparsity pattern detection for unknown k-sparse n-dimensional si...
<p>The estimation error changes with the node-pair connection possibility for two cases, i.e., und...
<p>(A) The estimation error surface of a directed network with and node-pair connection probability...
<p>The estimation error surfaces of a directed network (23) with and node-pair connection probabili...
<p>(A) The estimation error changes with the number of perturbations, where is calculated using t...
<p>The estimation error surfaces are calculated using two methods for a undirected network (23) with...
Sparsification is the process of decreasing the number of edges in a network while one or more topol...
Arrival Time Error Score averaged over nodes and over 1000 independent simulations for the (A) local...
<p>(A) The size of the largest connected component of structural networks as function of sparsity, (...
Most queries on probabilistic networks assume a possible world semantic, which causes an exponential...
We suggest a control based approach to topology estimation of networks with elements. This method fi...
The average fraction of nodes disconnected from the largest connected component of the network by sp...
<p> The topology estimation error surfaces are calculated using two methods for a undirected network...
International audienceWe consider a deep structured linear network under sparsity constraints. We st...
Despite its generic title, this thesis is about a specific notion of sparsity, the one introduced by...
This paper addresses the problem of sparsity pattern detection for unknown k-sparse n-dimensional si...