no code implementations • 14 Feb 2024 • Marianna Pensky
The paper introduces a Signed Generalized Random Dot Product Graph (SGRDPG) model, which is a variant of the Generalized Random Dot Product Graph (GRDPG), where, in addition, edges can be positive or negative.
no code implementations • 15 Jun 2022 • Majid Noroozi, Marianna Pensky
The DIMPLE model generalizes a multitude of papers that study multilayer networks with the same community structures in all layers, as well as the Mixture Multilayer Stochastic Block Model (MMLSBM), where the layers in the same group have identical matrices of block connection probabilities.
no code implementations • 20 Feb 2021 • Xing Fan, Marianna Pensky, Feng Yu, Teng Zhang
The paper considers a Mixture Multilayer Stochastic Block Model (MMLSBM), where layers can be partitioned into groups of similar networks, and networks in each group are equipped with a distinct Stochastic Block Model.
no code implementations • 7 Feb 2020 • Majid Noroozi, Marianna Pensky
There exist various types of network block models such as the Stochastic Block Model (SBM), the Degree Corrected Block Model (DCBM), and the Popularity Adjusted Block Model (PABM).
no code implementations • 3 Oct 2019 • Majid Noroozi, Marianna Pensky, Ramchandra Rimal
In the present paper we study a sparse stochastic network enabled with a block structure.
no code implementations • 1 Feb 2019 • Majid Noroozi, Ramchandra Rimal, Marianna Pensky
The paper considers the Popularity Adjusted Block model (PABM) introduced by Sengupta and Chen (2018).
Statistics Theory Statistics Theory 62F12, 62H30
no code implementations • 21 Dec 2018 • Maryam Jaberi, Marianna Pensky, Hassan Foroosh
One of the main approaches that is explored in the literature to tackle the problems of size and dimensionality is sampling subsets of the data in order to estimate the characteristics of the whole population, e. g. estimating the underlying clusters or structures in the data.
no code implementations • 28 Aug 2018 • Maryam Jaberi, Marianna Pensky, Hassan Foroosh
(ii) We demonstrate that delayed association is better suited for clustering subspaces that have ambiguities, i. e. when subspaces intersect or data are contaminated with outliers/noise.
no code implementations • 2 May 2017 • Marianna Pensky, Teng Zhang
We estimate the edge probability tensor by a kernel-type procedure and extract the group memberships of the nodes by spectral clustering.
no code implementations • 25 May 2016 • Pawan Gupta, Marianna Pensky
In the present paper we consider application of overcomplete dictionaries to solution of general ill-posed linear inverse problems.
no code implementations • 4 Jun 2015 • Felix Abramovich, Marianna Pensky
The objective of the paper is to study accuracy of multi-class classification in high-dimensional setting, where the number of classes is also large ("large $L$, large $p$, small $n$" model).
no code implementations • CVPR 2015 • Maryam Jaberi, Marianna Pensky, Hassan Foroosh
We study the simultaneous detection of multiple structures in the presence of overwhelming number of outliers in a large population of points.
no code implementations • CVPR 2015 • Baoyuan Liu, Min Wang, Hassan Foroosh, Marshall Tappen, Marianna Pensky
Deep neural networks have achieved remarkable performance in both image classification and object detection problems, at the cost of a large number of parameters and computational complexity.