1,105 research outputs found
A Consistent Histogram Estimator for Exchangeable Graph Models
Exchangeable graph models (ExGM) subsume a number of popular network models.
The mathematical object that characterizes an ExGM is termed a graphon. Finding
scalable estimators of graphons, provably consistent, remains an open issue. In
this paper, we propose a histogram estimator of a graphon that is provably
consistent and numerically efficient. The proposed estimator is based on a
sorting-and-smoothing (SAS) algorithm, which first sorts the empirical degree
of a graph, then smooths the sorted graph using total variation minimization.
The consistency of the SAS algorithm is proved by leveraging sparsity concepts
from compressed sensing.Comment: 28 pages, 5 figure
Computational Image Formation
At the pinnacle of computational imaging is the co-optimization of camera and
algorithm. This, however, is not the only form of computational imaging. In
problems such as imaging through adverse weather, the bigger challenge is how
to accurately simulate the forward degradation process so that we can
synthesize data to train reconstruction models and/or integrating the forward
model as part of the reconstruction algorithm. This article introduces the
concept of computational image formation (CIF). Compared to the standard
inverse problems where the goal is to recover the latent image
from the observation , CIF shifts the
focus to designing an approximate mapping such that
while giving a better image
reconstruction result. The word ``computational'' highlights the fact that the
image formation is now replaced by a numerical simulator. While matching nature
remains an important goal, CIF pays even greater attention on strategically
choosing an so that the reconstruction performance is
maximized.
The goal of this article is to conceptualize the idea of CIF by elaborating
on its meaning and implications. The first part of the article is a discussion
on the four attributes of a CIF simulator: accurate enough to mimic
, fast enough to be integrated as part of the reconstruction,
providing a well-posed inverse problem when plugged into the reconstruction,
and differentiable in the backpropagation sense. The second part of the article
is a detailed case study based on imaging through atmospheric turbulence. The
third part of the article is a collection of other examples that fall into the
category of CIF. Finally, thoughts about the future direction and
recommendations to the community are shared
Stochastic blockmodel approximation of a graphon: Theory and consistent estimation
Non-parametric approaches for analyzing network data based on exchangeable
graph models (ExGM) have recently gained interest. The key object that defines
an ExGM is often referred to as a graphon. This non-parametric perspective on
network modeling poses challenging questions on how to make inference on the
graphon underlying observed network data. In this paper, we propose a
computationally efficient procedure to estimate a graphon from a set of
observed networks generated from it. This procedure is based on a stochastic
blockmodel approximation (SBA) of the graphon. We show that, by approximating
the graphon with a stochastic block model, the graphon can be consistently
estimated, that is, the estimation error vanishes as the size of the graph
approaches infinity.Comment: 20 pages, 4 figures, 2 algorithms. Neural Information Processing
Systems (NIPS), 201
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