Browse Dept. of Computer Science by Title

  • 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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  • 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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  • Vardhan, Abhay; Viswanathan, Mahesh (2005-08)
    We present a new model checking algorithm for verifying computation tree logic (CTL) properties. Our technique is based on using language inference to learn the fixpoints necessary for checking a CTL formula instead of ...

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  • Vardhan, Abhay; Sen, Koushik; Viswanathan, Mahesh; Agha, Gul A. (2004-06)
    We present a novel approach for verifying safety properties of finite state machines communicating over unbounded FIFO channels that is based on applying machine learning techniques. We assume that we are given a model of ...

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  • Vardhan, Abhay (2006)
    We show how the learning-based verification paradigm can be applied to a number of systems and for different kinds of specifications. First, we use learning to verify safety properties of finite state machines communicating ...

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  • Vardhan, Abhay (2006-02)
    Making high quality and reliable software systems remains a difficult problem. One approach to address this problem is automated verification which attempts to demonstrate algorithmically that a software system meets its ...

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  • Klementiev, Alexandre A. (2010-01-06)
    Recent technological advances have facilitated the collection and distribution of a plethora of increasingly diverse and complex data. Supervised learning has been able to provide the toolbox of choice for exploiting it ...

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  • Cai, Deng; He, Xiaofei; Han, Jiawei (2006-04)
    Most of the existing learning algorithms take vectors as their input data. A function is then learned in such a vector space for classification, clustering, or dimensionality reduction. However, in some situations, there ...

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  • Garg, Pranav (2015-07-14)
    The problem of synthesizing adequate inductive invariants to prove a program correct lies at the heart of automated program verification. We investigate, herein, learning approaches to synthesize inductive invariants of ...

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  • Chen, Chun-cheng (2009)
    Wireless networks have raised great attention in the past decades because they provide tether-free connectivity. Although much of the effort in wireless network research has been spent on reducing the interference among ...

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  • Chen, Chun-cheng (2009-03)
    Wireless networks have raised great attention in the past decades because they provide tether-free connectivity. Although much of the e ort in wireless network research has been spent on reducing the interference among the ...

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  • Chen, Chun-cheng (2009-03)
    Wireless networks have raised great attention in the past decades because they provide tether-free connectivity. Although much of the e ort in wireless network research has been spent on reducing the interference among the ...

    application/pdf

    application/pdfPDF (7MB)