Inference

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301

Noname Vol. 43 Monotonicity Inference for Higher-Order Formulas Jasmin Christian Blanchette · Alexander Krauss

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Source URL: people.mpi-inf.mpg.de

- Date: 2015-01-25 16:18:54
    302

    Reliable Communication over Insertion-Deletion Channels Abstract.......................................................................................................................... 1 Introduction ..................

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    Source URL: www.inference.phy.cam.ac.uk

    - Date: 2000-11-02 12:14:56
      303

      Designing for Different Levels of Social Inference Risk Sara Motahari, Sotirios Ziavras, Quentin Jones New Jersey Institute of Technology {sg262, quentin.jones, ziavras}@njit.edu 1. INTRODUCTION

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      Source URL: cups.cs.cmu.edu

      - Date: 2009-06-17 11:38:17
        304

        Misspecification, Sparsity, and Superpopulation Inference for Sparse Social Networks Alexander D’Amour∗ and Edoardo Airoldi Department of Statistics, Harvard University ∗

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        Source URL: www.alexdamour.com

        - Date: 2016-08-23 10:50:39
          305

          Inference and Checking of Object Ownership

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          Source URL: homes.cs.washington.edu

          - Date: 2013-10-15 01:25:06
            306

            Sidestepping Intractable Inference with Structured Ensemble Cascades David Weiss∗ Benjamin Sapp∗ Ben Taskar Computer and Information Science

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            Source URL: www.seas.upenn.edu

            - Date: 2010-11-04 10:32:10
              307

              PEIRCE, ABDUCCIÓN Y PRÁCTICA MÉDICA DOUGLAS NIÑO This paper presents an alternative view for understanding abduction as ‘inference to the best explanation’, than can account from the simplest perception to the

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              Source URL: www.unav.es

              - Date: 2001-11-30 12:25:15
                308

                Type Inference and Type Checking for Queries on Execution Traces Daniel Deutch Tova Milo Tel Aviv University {danielde,milo}@post.tau.ac.il

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                Source URL: www.cs.tau.ac.il

                - Date: 2008-03-14 06:30:05
                  309

                  In this talk, I will introduce hinge-loss Markov random fields (HLMRFs), a new kind of probabilistic graphical model that supports scalable collective inference from richly structured data. HL-MRFs unify three different

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                  Source URL: mmds-data.org

                  - Date: 2016-06-23 15:50:48
                    310

                    MLPolyR A (mini-)ML with type inference, polymorphic records, and functional record update by Matthias Blume for CMSC, Spring 2005 Department of Computer Science, University of Chicago

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                    Source URL: people.cs.uchicago.edu

                    - Date: 2009-07-14 15:46:45
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