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Browsing MIT Theses by Title

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Browsing MIT Theses by Title

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  • Fang, Xiao, S.M. Massachusetts Institute of Technology (Massachusetts Institute of Technology, 2013)
    This thesis explores the use of Bayesian distance metric learning (Bayes_dml) for the task of speaker verification using the i-vector feature representation. We propose a framework that explores the distance constraints ...
  • Rabbat, Ralph R., 1978- (Massachusetts Institute of Technology, 2005)
    Online distance education provides students with a wealth of information. When students submit course-related search term queries, the search engine returns the search hits based on keyword and topic match. A student's ...
  • Tenenbaum, Joshua B. (Joshua Brett), 1972- (Massachusetts Institute of Technology, 1999)
  • Alterovitz, Gil, 1975- (Massachusetts Institute of Technology, 2006)
    Proteomics has been revolutionized in the last couple of years through integration of new mass spectrometry technologies such as -Enhanced Laser Desorption/Ionization (SELDI) mass spectrometry. As data is generated in an ...
  • Luong, Alda (Massachusetts Institute of Technology, 2004)
    This work explores the performance of Raw, a parallel hardware platform developed at MIT, running a Bayesian inference algorithm. Motivation for examining this parallel system is a growing interest in creating a self-learning ...
  • Lu, Peter Guang Yi (Massachusetts Institute of Technology, 2013)
    A new methodology for Bayesian inference of stochastic dynamical models is developed. The methodology leverages the dynamically orthogonal (DO) evolution equations for reduced-dimension uncertainty evolution and the Gaussian ...
  • Lowry, Nathan Christopher (Massachusetts Institute of Technology, 2013)
    Image segmentation and classification, the identification and demarcation of regions of interest within an image, is necessary prior to subsequent information extraction, analysis, and inference. Many available segmentation ...
  • Havasi, Catherine Andrea, 1981- (Massachusetts Institute of Technology, 2004)
    How people and computers can learn the meaning of words has long been a key question for both AI and cognitive science. It is hypothesized that a person acquires a bias to favor the characteristics of their native language, ...
  • Rahlin, Alexandra Sasha (Massachusetts Institute of Technology, 2008)
    In the past decade, advances in precision cosmology have pushed our understanding of the evolving Universe to new limits. Since the discovery of the cosmic microwave background (CMB) radiation in 1965 by Penzias and Wilson, ...
  • Vasconcelos, Nuno Miguel Borges de Pinho Cruz de (Massachusetts Institute of Technology, 2000)
  • Weiss, Yair (Massachusetts Institute of Technology, 1998)
    Estimating motion in scenes containing multiple moving objects remains a difficult problem in computer vision yet is solved effortlessly by humans. In this thesis we present a computational investigation of this astonishing ...
  • Sachs, Karen, Ph. D. Massachusetts Institute of Technology (Massachusetts Institute of Technology, 2006)
    Cells communicate with other cells, and process cues from their environment, via signaling pathways, in which extracellular cues trigger a cascade of information flow, causing signaling molecules to become chemically, ...
  • Roberts, Jennifer M. (Jennifer Marie) (Massachusetts Institute of Technology, 2006)
    In the Intensive Care Unit, physicians have access to many types of information when treating patients. Physicians attempt to consider as much of the relevant information as possible, but the astronomically large amounts ...
  • Doshi-Velez, Finale (Massachusetts Institute of Technology, 2012)
    Making intelligent decisions from incomplete information is critical in many applications: for example, medical decisions must often be made based on a few vital signs, without full knowledge of a patient's condition, and ...
  • Fox, Emily Beth (Massachusetts Institute of Technology, 2009)
    The complexity of many dynamical phenomena precludes the use of linear models for which exact analytic techniques are available. However, inference on standard nonlinear models quickly becomes intractable. In some cases, ...
  • Johnson, Matthew J., Ph. D. Massachusetts Institute of Technology (Matthew James) (Massachusetts Institute of Technology, 2010)
    There is much interest in the Hierarchical Dirichlet Process Hidden Markov Model (HDP-HMM) as a natural Bayesian nonparametric extension of the ubiquitous Hidden Markov Model for learning from sequential and time-series ...
  • Michini, Bernard (Bernard J.) (Massachusetts Institute of Technology, 2013)
    Learning from demonstration provides an attractive solution to the problem of teaching autonomous systems how to perform complex tasks. Demonstration opens autonomy development to non-experts and is an intuitive means of ...
  • Cosman, Eric Richard, 1977- (Massachusetts Institute of Technology, 2005)
    A hierarchical model based on the Multivariate Autoregessive (MAR) process is proposed to jointly model functional neuroimaging time series collected from multiple subjects, and to characterize the distribution of MAR ...
  • Baker, Chris L. (Chris Lawrence) (Massachusetts Institute of Technology, 2012)
    This thesis proposes a computational framework for understanding human Theory of Mind (ToM): our conception of others' mental states, how they relate to the world, and how they cause behavior. Humans use ToM to predict ...
  • Johnson, Matthew James, Ph. D. Massachusetts Institute of Technology (Massachusetts Institute of Technology, 2014)
    With large and growing datasets and complex models, there is an increasing need for scalable Bayesian inference. We describe two lines of work to address this need. In the first part, we develop new algorithms for inference ...
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