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Filtering by: Creator Brandt, Tina L. Remove constraint Creator: Brandt, Tina L. Committee Member Wong, Weng-Keen Remove constraint Committee Member: Wong, Weng-Keen

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  • Modeling protein structure at atomic resolution

    Creator:
    Berkholz, Donald S.
    Abstract:
    This thesis includes three studies involving different aspects of modeling protein structure. The first study illustrates the levels of insight available from atomic-resolution protein structures. The second study derives general trends of protein geometry from atomic-resolution structures and shows their implications for modeling. The third study creates a model of...
    Resource Type:
    Dissertation
    Full Text:
    , B.K., Toth, M.V., Marshall, G.R., Clawson, L., Selk, L., Kent, S.B., and Wlodawer, A. (1989

  • Integrating learning and search for structured prediction

    Creator:
    Doppa, Janardhan Rao
    Abstract:
    We are witnessing the rise of the data-driven science paradigm, in which massive amounts of data - much of it collected as a side-effect of ordinary human activity - can be analyzed to make sense of the data and to make useful predictions. To fully realize the promise of this...
    Resource Type:
    Dissertation
    Full Text:
    number of training (#TR) and testing (#TS) examples; number of features (#F); number of labels (#L); and

  • Toward Disentangling the Activations of the Deep Networks via Low-dimensional Embedding and Non-negative Factorization

    Creator:
    Khorram, Saeed
    Abstract:
    In this thesis, we introduce a novel Explanation Neural Network (XNN) to explain the predictions made by a deep network. The XNN works by embedding a high-dimensional activation vector of a deep network layer non-linearly into a low-dimensional explanation space while retaining faithfulness i.e., the original deep learning predictions can...
    Resource Type:
    Masters Thesis
    Full Text:
    the augmented Lagrangian, L(x, z,y) = f(x) + g(z) + yT (Ax+Bz − c) + ρ 2 ‖Ax+Bz − c‖22 (2.4) where y

  • Active Learning from Examples, Queries and Explanations

    Creator:
    Hamidi-Haines, Mandana
    Abstract:
    Humans are remarkably efficient in learning by interacting with other people and observing their behavior. Children learn by watching their parents’ actions and mimic their behavior. When they are not sure about their parents demonstration, they communicate with them, ask questions, and learn from their feedback. On the other hand,...
    Resource Type:
    Dissertation
    Full Text:
    . . . . . . . . . . . . . . . . . . 26 3.1 The average results over 10 runs on six datasets with query size s and label set size L

  • Network Analysis and Change-point Detection

    Creator:
    Xu, Jiarui
    Abstract:
    Analysis of observations on sequential events over time is common in real life. Sequential measurements over time describing the behavior of systems are usually called time series data, which have been collected in a wide range of disciplines. Over the years there have been multiple research areas in studying stochastic...
    Resource Type:
    Dissertation

  • Generative Models Meet Explainable AI and Long-Tail Learning: from Attribution Maps to Image Synthesis

    Creator:
    Khorram, Saeed
    Abstract:
    The advancement of artificial intelligence (AI) has led to transformative developments across multiple sectors, fostering innovation and redefining our interactions with technology. As AI matures and becomes integrated into society, it offers numerous opportunities to address global challenges and revolutionize a wide array of human endeavors. These advances are driven...
    Resource Type:
    Dissertation
    Full Text:
    x̂l ∈ R3×L×L is passed only through Dl to calculate the unconditional objective Luc. The final