and Prediction

Results: 12348



#Item
121Future User Engagement Prediction and Its Application to Improve the Sensitivity of Online Experiments Alexey Drutsa Gleb Gusev

Future User Engagement Prediction and Its Application to Improve the Sensitivity of Online Experiments Alexey Drutsa Gleb Gusev

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Source URL: www.www2015.it

- Date: 2015-05-15 11:18:24
    122シンポジウム　第 2 日目　12 月 1 日（木） 第2日目12月1日（木） 2AS1 Room 1 (1F Main Hall) “Neo”-taxonomy of non-coding RNAs: classification and prediction of molecular functions

    シンポジウム　第 2 日目　12 月 1 日（木） 第2日目12月1日（木） 2AS1 Room 1 (1F Main Hall) “Neo”-taxonomy of non-coding RNAs: classification and prediction of molecular functions

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    Source URL: www.aeplan.co.jp

    - Date: 2016-11-01 05:00:35
      123Noisy Time Series Prediction using a Recurrent Neural Network and Grammatical Inference  C. Lee Giles , Steve Lawrence , Ah Chung Tsoi

      Noisy Time Series Prediction using a Recurrent Neural Network and Grammatical Inference  C. Lee Giles , Steve Lawrence , Ah Chung Tsoi

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      Source URL: clgiles.ist.psu.edu

      - Date: 2007-03-03 20:26:38
        124With blinkers on: robust prediction of eye movements across readers Franz Matties and Anders Søgaard University of Copenhagen Njalsgade 142 DK-2300 Copenhagen S Email:

        With blinkers on: robust prediction of eye movements across readers Franz Matties and Anders Søgaard University of Copenhagen Njalsgade 142 DK-2300 Copenhagen S Email:

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        Source URL: www.aclweb.org

        - Date: 2013-10-09 22:17:10
          125Greater Accuracy with Less Precision. A new paradigm for weather and climate prediction. Tim Palmer University of Oxford

          Greater Accuracy with Less Precision. A new paradigm for weather and climate prediction. Tim Palmer University of Oxford

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          Source URL: wapco.e-ce.uth.gr

          - Date: 2015-01-20 03:55:10
            126SALICON: Reducing the Semantic Gap in Saliency Prediction by Adapting Deep Neural Networks Xun Huang1,2† Chengyao Shen1 Xavier Boix1,3 Qi Zhao1∗ 1 Department of Electrical and Computer Engineering, National Universi

            SALICON: Reducing the Semantic Gap in Saliency Prediction by Adapting Deep Neural Networks Xun Huang1,2† Chengyao Shen1 Xavier Boix1,3 Qi Zhao1∗ 1 Department of Electrical and Computer Engineering, National Universi

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            Source URL: www.cv-foundation.org

            - Date: 2015-10-24 14:57:44
              127Multi-level Translation Quality Prediction with Q U E ST++ Lucia Specia, Gustavo Henrique Paetzold and Carolina Scarton Department of Computer Science University of Sheffield, UK {l.specia,ghpaetzold1,c.scarton}@sheffiel

              Multi-level Translation Quality Prediction with Q U E ST++ Lucia Specia, Gustavo Henrique Paetzold and Carolina Scarton Department of Computer Science University of Sheffield, UK {l.specia,ghpaetzold1,c.scarton}@sheffiel

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              Source URL: www.aclweb.org

              - Date: 2015-07-22 01:01:17
                128DropoutSeer: Visualizing Learning Patterns in Massive Open Online Courses for Dropout Reasoning and Prediction Yuanzhe Chen∗ 1 , Qing Chen† 1 , Mingqian Zhao‡ 1 , Sebastien Boyer§ 2 , Kalyan Veeramachaneni¶ 2 , a

                DropoutSeer: Visualizing Learning Patterns in Massive Open Online Courses for Dropout Reasoning and Prediction Yuanzhe Chen∗ 1 , Qing Chen† 1 , Mingqian Zhao‡ 1 , Sebastien Boyer§ 2 , Kalyan Veeramachaneni¶ 2 , a

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                Source URL: vis.cse.ust.hk

                - Date: 2016-08-28 09:53:20
                  129Journal of Artificial Intelligence Research546 Submitted 08/09; publishedRoxyBot-06: Stochastic Prediction and Optimization in TAC Travel

                  Journal of Artificial Intelligence Research546 Submitted 08/09; publishedRoxyBot-06: Stochastic Prediction and Optimization in TAC Travel

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                  Source URL: www.jair.org

                  - Date: 2009-12-29 15:12:44
                    130Session: Connected Vehicles Prediction of the connected vehicle toward the connected and automated vehicle November, 15, 2016

                    Session: Connected Vehicles Prediction of the connected vehicle toward the connected and automated vehicle November, 15, 2016

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                    Source URL: en.sip-adus.jp

                    - Date: 2016-12-09 01:30:22