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Joshua T. Vogelstein

  1. Consistent adjacency-spectral partitioning for the stochastic block model when the model parameters are unknown.

    Thu, 05/03/2012 - 15:01 — SomNambul
    Authors: Joshua T. Vogelstein, Carey E. Priebe, Daniel L. Sussman, Minh Tang, Donniell E. Fishkind
    Subjects: Methodology
    Abstract

    A stochastic block model consists of a random partition of n vertices into
    blocks 1,2,...,K for which, conditioned on the partition, every pair of
    vertices has probability of adjacency entirely determined by the block
    membership of the two vertices.

  2. Fast Inexact Graph Matching with Applications in Statistical Connectomics.

    Mon, 12/26/2011 - 15:01 — SomNambul
    Authors: Joshua T. Vogelstein, R. Jacob Vogelstein, Carey E. Priebe, Donniell E. Fishkind, John M. Conroy, Louis J. Podrazik, Steven G. Kratzer
    Subjects: Optimization and Control
    Abstract

    It is becoming increasingly popular to represent myriad and diverse data sets
    as graphs. When the labels of vertices of these graphs are unavailable, graph
    matching (GM)---the process of determining which permutation assigns vertices
    of one graph to those of another---is a computationally daunting problem. This
    work presents an inexact strategy for GM. Specifically, we frame GM as a
    quadratic assignment problem, and then relax the feasible region to its convex
    hull.

  3. Graph Classification using Signal Subgraphs: Applications in Statistical Connectomics.

    Tue, 08/09/2011 - 15:01 — SomNambul
    Authors: Joshua T. Vogelstein, William R. Gray, R. Jacob Vogelstein, Carey E. Priebe
    Subjects: Applications
    Abstract

    This manuscript considers the following "graph classification" question:
    given a collection of graphs and associated classes, how can one predict the
    class of a newly observed graph? To address this question we propose a
    statistical model for graph/class pairs. This model naturally leads to a set of
    estimators to identify the class-conditional signal, or "signal subgraph,"
    defined as the collection of edges that are probabilistically different between
    the classes.

  4. A Bayesian approach for inferring neuronal connectivity from calcium fluorescent imaging data.

    Fri, 07/22/2011 - 15:01 — SomNambul
    Authors: Yuriy Mishchencko, Joshua T. Vogelstein, Liam Paninski
    Subjects: Applications
    Abstract

    Deducing the structure of neural circuits is one of the central problems of
    modern neuroscience. Recently-introduced calcium fluorescent imaging methods
    permit experimentalists to observe network activity in large populations of
    neurons, but these techniques provide only indirect observations of neural
    spike trains, with limited time resolution and signal quality. In this work we
    present a Bayesian approach for inferring neural circuitry given this type of
    imaging data.

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