Domain adaptation

Results: 105



#Item
1Domain Adaptation in Machine Translation Cristina España-Bonet, Georg Heigold Deutsches Forschungszentrum für Künstliche Intelligenz (DFKI) 15th March, 2017 This project has received funding from the

Domain Adaptation in Machine Translation Cristina España-Bonet, Georg Heigold Deutsches Forschungszentrum für Künstliche Intelligenz (DFKI) 15th March, 2017 This project has received funding from the

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Source URL: www.qt21.eu

Language: English - Date: 2018-02-15 09:27:19
    2Domain Adaptation for Neural Networks by Parameter Augmentation Yusuke Watanabe SONYKonan Minato-ku, Tokyo, Japan

    Domain Adaptation for Neural Networks by Parameter Augmentation Yusuke Watanabe SONYKonan Minato-ku, Tokyo, Japan

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    Source URL: aclweb.org

    Language: English - Date: 2016-08-01 10:43:09
      3Supplementary Material for Learning Cross-Domain Landmarks for Heterogeneous Domain Adaptation Yao-Hung Hubert Tsai1 , Yi-Ren Yeh2 , Yu-Chiang Frank Wang1 Research Center for IT Innovation,Academia Sinica,Taipei, Taiwan

      Supplementary Material for Learning Cross-Domain Landmarks for Heterogeneous Domain Adaptation Yao-Hung Hubert Tsai1 , Yi-Ren Yeh2 , Yu-Chiang Frank Wang1 Research Center for IT Innovation,Academia Sinica,Taipei, Taiwan

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      Source URL: yaohungt.github.io

      Language: English - Date: 2018-07-18 20:47:15
        4Parser Adaptation to the Biomedical Domain without Re-Training Mark Steedman School of Informatics University of Edinburgh Edinburgh, EH8 9AB, UK

        Parser Adaptation to the Biomedical Domain without Re-Training Mark Steedman School of Informatics University of Edinburgh Edinburgh, EH8 9AB, UK

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        Source URL: aclweb.org

        Language: English - Date: 2015-09-09 11:02:38
          5Supplementary Material for Connecting the Dots with Landmarks: Discriminatively Learning Domain-Invariant Features for Unsupervised Domain Adaptation Boqin Gong

          Supplementary Material for Connecting the Dots with Landmarks: Discriminatively Learning Domain-Invariant Features for Unsupervised Domain Adaptation Boqin Gong

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          Source URL: proceedings.mlr.press

          Language: English - Date: 2018-07-16 03:38:06
            6Latent Domain Phrase-based Models for Adaptation Hoang Cuong and Khalil Sima’an Institute for Logic, Language and Computation University of Amsterdam Science Park 107, 1098 XG Amsterdam, The Netherlands {c.hoang,k.sima

            Latent Domain Phrase-based Models for Adaptation Hoang Cuong and Khalil Sima’an Institute for Logic, Language and Computation University of Amsterdam Science Park 107, 1098 XG Amsterdam, The Netherlands {c.hoang,k.sima

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            Source URL: emnlp2014.org

            Language: English - Date: 2014-10-16 05:19:38
              7Supervised Distributional Hypernym Discovery via Domain Adaptation Luis Espinosa-Anke1 , Jose Camacho-Collados2 , Claudio Delli Bovi2 and Horacio Saggion1 1 Department of Information and Communication Technologies, Univ

              Supervised Distributional Hypernym Discovery via Domain Adaptation Luis Espinosa-Anke1 , Jose Camacho-Collados2 , Claudio Delli Bovi2 and Horacio Saggion1 1 Department of Information and Communication Technologies, Univ

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              Source URL: aclweb.org

              Language: English - Date: 2016-10-27 11:50:11
                8Domain Adaptation and Attention-Based Unknown Word Replacement in Chinese-to-Japanese Neural Machine Translation Kazuma Hashimoto, Akiko Eriguchi, and Yoshimasa Tsuruoka The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, T

                Domain Adaptation and Attention-Based Unknown Word Replacement in Chinese-to-Japanese Neural Machine Translation Kazuma Hashimoto, Akiko Eriguchi, and Yoshimasa Tsuruoka The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, T

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                Source URL: aclweb.org

                Language: English - Date: 2016-12-09 14:02:46
                  9Word Segmentation of Informal Arabic with Domain Adaptation Will Monroe, Spence Green, and Christopher D. Manning Computer Science Department, Stanford University {wmonroe4,spenceg,manning}@stanford.edu Abstract

                  Word Segmentation of Informal Arabic with Domain Adaptation Will Monroe, Spence Green, and Christopher D. Manning Computer Science Department, Stanford University {wmonroe4,spenceg,manning}@stanford.edu Abstract

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                  Source URL: www.spencegreen.com

                  Language: English - Date: 2014-08-25 16:16:51
                    10ICT Centre A NEW ROBUST FREQUENCY-DOMAIN ECHO CANCELLER WITH CLOSED-LOOP LEARNING RATE ADAPTATION Context: Acoustic echo cancellation in the presence of

                    ICT Centre A NEW ROBUST FREQUENCY-DOMAIN ECHO CANCELLER WITH CLOSED-LOOP LEARNING RATE ADAPTATION Context: Acoustic echo cancellation in the presence of

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                    Source URL: jmvalin.ca

                    - Date: 2016-10-08 15:08:45