Giorgio

Results: 1270



#Item
61

Fast Learning from Distributed Datasets without Entity Matching Giorgio Patrini1,2 , Richard Nock2,1 , Stephen Hardy2 , Tiberio Caetano3,1,4 Australian National University1 , NICTA2 , Ambiata3 , University of New South W

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Source URL: giorgiop.github.io

- Date: 2018-03-21 18:13:45
    62

    Combining local search techniques and path following for bimatrix games Nicola Gatti, Giorgio Patrini, Marco Rocco Dipartimento di Elettronica e Informazione Politecnico di Milano

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    Source URL: giorgiop.github.io

    - Date: 2018-03-21 18:13:45
      63

      (Almost) No Label No Cry Giorgio Patrini1,2 , Richard Nock1,2 , Paul Rivera1,2 , Tiberio Caetano1,3,4 Australian National University1 , NICTA2 , University of New South Wales3 , Ambiata4 Sydney, NSW, Australia {name.sur

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      Source URL: giorgiop.github.io

      - Date: 2018-03-21 18:13:45
        64

        Privacy-preserving entity resolution and logistic regression on encrypted data Mentari Djatmiko 1 Stephen Hardy 1 Wilko Henecka 1 Hamish Ivey-Law 1 Maximilian Ott 1 Giorgio Patrini 1 Guillaume Smith 1 Brian Thorne 1 Don

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        Source URL: giorgiop.github.io

        - Date: 2018-03-21 18:13:45
          65

          Privacy-preserving entity resolution and logistic regression on encrypted data Giorgio Patrini & Mentari Djatmiko, Stephen Hardy, Wilko Henecka, Hamish Ivey-Law, Maximilian Ott, Huy Pham, Guillaume Smith, Brian Thorne, D

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          Source URL: giorgiop.github.io

          - Date: 2018-03-21 18:13:46
            66

            C ONTRIBUTED RESEARCH ARTICLE 84 Discrete Time Markov Chains with R by Giorgio Alfredo Spedicato

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            Source URL: journal.r-project.org

            - Date: 2018-01-29 07:20:14
              67

              A Best-Effort Approach for Run-Time Channel Prioritization in Real-Time Robotic Application Ali Paikan, Ugo Pattacini, Daniele Domenichelli, Marco Randazzo, Giorgio Metta and Lorenzo Natale Abstract— Application domai

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              Source URL: lornat75.github.io

              - Date: 2018-04-03 06:23:22
                68

                Loss Factorization, Weakly Supervised Learning and Label Noise Robustness Giorgio Patrini1,2 GIORGIO . PATRINI @ ANU . EDU . AU Frank Nielsen3,4 NIELSEN @ LIX . POLYTECHNIQUE . FR

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                Source URL: giorgiop.github.io

                - Date: 2018-03-21 18:13:45
                  69

                  Making Deep Neural Networks Robust to Label Noise: a Loss Correction Approach Giorgio Patrini1,2 , Alessandro Rozza3 , Aditya Krishna Menon2,1 , Richard Nock2,1,4 , Lizhen Qu2,1 1 Australian National University, 2 Data61

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                  Source URL: giorgiop.github.io

                  - Date: 2018-03-21 18:13:45
                    70

                    Loss factorization, weakly supervised learning and label noise robustness Giorgio Patrini, Frank Nielsen, Richard Nock, Marcello Carioni Australian National University, Data61 (ex NICTA), Ecole Polytechnique, Sony CS Lab

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                    Source URL: giorgiop.github.io

                    - Date: 2018-03-21 18:13:46
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