Zobrazeno 1 - 10
of 251
pro vyhledávání: '"Carey E, Priebe"'
Publikováno v:
Applied Network Science, Vol 9, Iss 1, Pp 1-26 (2024)
Abstract Random graphs are statistical models that have many applications, ranging from neuroscience to social network analysis. Of particular interest in some applications is the problem of testing two random graphs for equality of generating distri
Externí odkaz:
https://doaj.org/article/0a69544ee5414a8ba969e5128e32445f
Publikováno v:
Applied Network Science, Vol 8, Iss 1, Pp 1-26 (2023)
Abstract Random graphs are increasingly becoming objects of interest for modeling networks in a wide range of applications. Latent position random graph models posit that each node is associated with a latent position vector, and that these vectors f
Externí odkaz:
https://doaj.org/article/72280139bafe4a84b432ba74dfa411d5
Autor:
Tianyi Chen, Youngser Park, Ali Saad-Eldin, Zachary Lubberts, Avanti Athreya, Benjamin D. Pedigo, Joshua T. Vogelstein, Francesca Puppo, Gabriel A. Silva, Alysson R. Muotri, Weiwei Yang, Christopher M. White, Carey E. Priebe
Publikováno v:
Applied Network Science, Vol 8, Iss 1, Pp 1-13 (2023)
Abstract Recent advancements have been made in the development of cell-based in-vitro neuronal networks, or organoids. In order to better understand the network structure of these organoids, a super-selective algorithm has been proposed for inferring
Externí odkaz:
https://doaj.org/article/dd431d1a9d754a39a8e3c7919c7480ff
Publikováno v:
Network Neuroscience, Vol 7, Iss 2, Pp 522-538 (2023)
AbstractGraph matching algorithms attempt to find the best correspondence between the nodes of two networks. These techniques have been used to match individual neurons in nanoscale connectomes—in particular, to find pairings of neurons across hemi
Externí odkaz:
https://doaj.org/article/99b045b559b24fd58f00939d8e5499e9
Publikováno v:
Applied Network Science, Vol 8, Iss 1, Pp 1-18 (2023)
Abstract We propose a dynamic network sampling scheme to optimize block recovery for stochastic blockmodel in the case where it is prohibitively expensive to observe the entire graph. Theoretically, we provide justification of our proposed Chernoff-o
Externí odkaz:
https://doaj.org/article/bebd0c63a96d4e0da11aef12c57d278b
Publikováno v:
Mathematics, Vol 12, Iss 5, p 746 (2024)
We propose and study a data-driven method that can interpolate between a classical and a modern approach to classification for a class of linear models. The class is the convex combinations of an average of the source task classifiers and a classifie
Externí odkaz:
https://doaj.org/article/e87d663eaf48436f850d704983653221
Autor:
Konstantinos Pantazis, Daniel L. Sussman, Youngser Park, Zhirui Li, Carey E. Priebe, Vince Lyzinski
Publikováno v:
Applied Network Science, Vol 7, Iss 1, Pp 1-35 (2022)
Abstract We consider the problem of detecting a noisy induced multiplex template network in a larger multiplex background network. Our approach, which extends the graph matching matched filter framework of Sussman et al. (IEEE Trans Pattern Anal Mach
Externí odkaz:
https://doaj.org/article/4c617ab3e800468f8036b9d7ecd01668
Autor:
Benjamin D Pedigo, Mike Powell, Eric W Bridgeford, Michael Winding, Carey E Priebe, Joshua T Vogelstein
Publikováno v:
eLife, Vol 12 (2023)
Comparing connectomes can help explain how neural connectivity is related to genetics, disease, development, learning, and behavior. However, making statistical inferences about the significance and nature of differences between two networks is an op
Externí odkaz:
https://doaj.org/article/cbf2a17f659b482fb9dd712226e51cad
Publikováno v:
Frontiers in Human Neuroscience, Vol 16 (2022)
We consider the problem of extracting features from passive, multi-channel electroencephalogram (EEG) devices for downstream inference tasks related to high-level mental states such as stress and cognitive load. Our proposed feature extraction method
Externí odkaz:
https://doaj.org/article/cabec464a2b948de98c07c16a2b66770
Autor:
Ketan Mehta, Rebecca F. Goldin, David Marchette, Joshua T. Vogelstein, Carey E. Priebe, Giorgio A. Ascoli
Publikováno v:
Network Neuroscience, Vol 5, Iss 3, Pp 689-710 (2021)
AbstractThis work presents a novel strategy for classifying neurons, represented by nodes of a directed graph, based on their circuitry (edge connectivity). We assume a stochastic block model (SBM) in which neurons belong together if they connect to
Externí odkaz:
https://doaj.org/article/be9035929960411f89afef38f09a14f1