Deep

Results: 20988



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421

Averaged-DQN: Variance Reduction and Stabilization for Deep Reinforcement Learning Oron Anschel 1 Nir Baram 1 Nahum Shimkin 1 Abstract Instability and variability of Deep Reinforcement

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Source URL: proceedings.mlr.press

- Date: 2018-02-06 15:06:57
    422

    Advancing Geoscience Research through CIDER Barbara Romanowicz, Dept. of Earth and Planetary Science, Univ. of California Berkeley, 301 McCone Hall, Berkeley, California 94720, USA; Marc Hirschmann, Dept. of Earth Scienc

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    Source URL: www.deep-earth.org

    - Date: 2017-11-02 18:59:28
      423

      Learning to Track at 100 FPS with Deep Regression Networks - Supplementary Material David Held, Sebastian Thrun, Silvio Savarese Department of Computer Science Stanford University {davheld,thrun,ssilvio}@cs.stanford.edu

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      Source URL: davheld.github.io

      - Date: 2017-12-05 20:18:02
        424

        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
          425

          Deep Security Supported Features by Platform

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          Source URL: docs.trendmicro.com

          - Date: 2017-09-08 15:25:42
            426

            Frankenstein: Learning Deep Face Representations using Small Data Guosheng Hu, Xiaojiang Peng, Yongxin Yang, Timothy Hospedales, Jakob Verbeek To cite this version:

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            Source URL: hal.inria.fr

            - Date: 2018-03-29 11:04:00
              427

              Latte: A Language, Compiler, and Runtime for Elegant and Efficient Deep Neural Networks Leonard Truong Rajkishore Barik

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              Source URL: www.thev.net

              - Date: 2016-07-05 13:12:15
                428

                The LF Deep Learning Foundation Charter The Linux Foundation Effective March 26, Mission and Scope of the LF Deep Learning Foundation. a) The primary mission of the LF Deep Learning Foundation (the “Directed Fu

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                Source URL: www.linuxfoundation.jp

                - Date: 2018-03-23 20:34:23
                  429

                  World Wide Web: 585–622 DOIs11280Discovering Interesting Relationships among Deep Web Databases: A Source-Biased Approach James Caverlee & Ling Liu & Daniel Rocco

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                  Source URL: faculty.cse.tamu.edu

                  - Date: 2007-09-05 14:21:30
                    430

                    Making deep neural networks robust to label noise: ! a loss correction approach Giorgio Patrini 23 July 2017 CVPR, Honolulu

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                    Source URL: giorgiop.github.io

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