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  • PhD Defense- Soravit (Beer) Changpinyo

    Tue, May 08, 2018 @ 12:00 PM - 02:00 PM

    Thomas Lord Department of Computer Science

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    PhD Candidate: Soravit (Beer) Changpinyo
    Committee: Fei Sha (chair), Kevin Knight, C.-C. Jay Kuo (outside member)

    Title: Modeling, Learning, and Leveraging Similarity
    Time & Place: Tuesday, May 8th, 12-2pm, SAL 213
    Measuring similarity between any two entities is an essential component in most machine learning tasks. In this defense, I will describe my research work that provides a set of techniques revolving around the notion of similarity.
    The first part involves "modeling and learning" similarity. We introduce Similarity Component Analysis (SCA), a Bayesian network for modeling instance-level similarity that does not observe the triangle inequality. Such a modeling choice avoids the transitivity bias in most existing similarity models, making SCA intuitively more aligned with the human perception of similarity.
    The second part involves "learning and leveraging" similarity for effective learning with limited data, with applications in computer vision and natural language processing. We first leverage incomplete and noisy similarity graphs in different modalities to aid the learning of object recognition models. In particular, we propose two novel zero-shot learning algorithms that utilize class-level semantic similarities as a building block, establishing state-of-the-art performance on the large-scale benchmark with more than 20,000 categories. As for natural language processing, we employ multi-task learning (MTL) to leverage unknown similarities between sequence tagging tasks. This study leads to insights regarding the benefit of going to beyond pairwise MTL, task selection strategies, as well as the nature of the relationships between those tasks.

    Location: Henry Salvatori Computer Science Center (SAL) - 213

    Audiences: Everyone Is Invited

    Contact: Lizsl De Leon

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