Browse Dept. of Computer Science by Title

  • Yih, Wen-tau (2005-05)
    Information extraction is a process that extracts limited semantic concepts from text documents and presents them in an organized way. Unlike several other natural language tasks, information extraction has a direct impact ...

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  • Rizzolo, Nicholas (2012-02-06)
    Machine learning (ML) is the study of representations and algorithms used for building functions that improve their behavior with experience. Today, researchers in many domains are applying ML to solve their problems when ...

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  • Chan, Tak-Wai (1989)
    This thesis describes a new class of Intelligent Tutoring Systems (ITS) which I call the Learning Companion Systems (LCS). In the learning environment of such a system, there are three agents involved, namely, the human ...

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  • Perez, Eduardo (1997)
    When shortage of knowledge prevents human experts from choosing good attributes to represent empirical observations, learning is difficult. Expressing concepts by using only primitive (low-level) attributes is intricate ...

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  • Zhang, Ailing (2017-04-24)
    As an effective and provable primal method to estimate posterior distribution, Particle Mirror Descent is appealing for its simplicity and flexibility. In this thesis we explore the applications of Particle Mirror Descent ...

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  • Rajpal, Shreya (2018-04-25)
    In this work, we present a novel methodology to recommend items that are compatible with a given item of clothing. Compatibility is a hard notion to capture because of its diversity and subjectivity. We propose an embedding ...

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  • Pu, Wen; Choi, Jaesik; Amir, Eyal; Espelage, Dorothy L. (2013-06-25)

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  • Zhang, Jianping (1990)
    This thesis describes an exploration of methods involved in learning flexible concepts that is an important and less explored area in machine learning. The two approaches described in this thesis are based on the idea of ...

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  • Mishra, Nina (1997)
    The third learning setting combines the first and second in that we both give our computer preclassified examples and allow it to pose membership queries. The unknown function f may now classify examples in one of three ...

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  • Goldwasser, Dan (2013-02-03)
    In this work we take a first step towards Learning from Natural Instructions (LNI), a framework for communicating human knowledge to computer systems using natural language. In this framework the process of learning is ...

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  • Falkenhainer, Brian Carl (1989)
    To make programs that understand and interact with the world as well as people do, we must duplicate the kind of flexibility people exhibit when conjecturing plausible explanations of the diverse physical phenomena they ...

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  • Garg, Pranav; Neider, Daniel; Madhusudan, P.; Roth, Dan (2015)
    Inductive invariants can be robustly synthesized using a learning model where the teacher is a program verifier who instructs the learner through concrete program configurations, classified as positive, negative, and ...

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  • Wang, Liwei (2018-07-10)
    Computer vision is moving from predicting discrete, categorical labels to generating rich descriptions of visual data, in particular, in the form of natural language. Learning the joint latent representations for images ...

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  • Kushal, Akash M. (2008-10)
    This dissertation addresses the task of detecting instances of object categories in photographs. We propose modeling an object category as a collection of object parts linked together in a deformable configuration. We ...

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  • Kushal, Akash M. (2008)
    We also propose two different approaches for modeling the inter-part relations and algorithms for efficiently learning the model parameters. The first approach uses a generative model that models the joint probability ...

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  • Kushal, Akash M. (2008-10)
    This dissertation addresses the task of detecting instances of object categories in photographs. We propose modeling an object category as a collection of object parts linked together in a deformable configuration. We ...

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    application/pdfPDF (10MB)
  • Chang, Allan; Amir, Eyal (2005-11)
    We present new algorithms for learning a logical model of actions' effects and preconditions in partially observable domains. The algorithms maintain a logical representation of the set of possible action models after each ...

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  • Singh, Saurabh (2016-10-19)
    The world is full of tiny but useful objects such as the door handle of a car or the light switch in a room. Such objects are barely visible in an image and can be well approximated by a single point. We refer to these ...

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  • McHenry, Kenton Guadron (2008)
    This dissertation addresses the task of learning to segment images into meaningful material and object categories. With regards to materials we consider the difficult task of segmenting objects made of transparent materials ...

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  • McHenry, Kenton Guadron (2008-04)
    This dissertation addresses the task of learning to segment images into meaningful material and object categories. With regards to materials we consider the difficult task of segmenting objects made of transparent materials ...

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