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20 та иш

Иш: What's in the box? Explaining the black-box model through an evaluation of its interpretable features

  1. ImageNet classification with deep convolutional neural networks

    Alex Krizhevsky, Ilya Sutskever, Geoffrey E. Hinton

    Мақола201723 иқтибос
    ABI

  2. Scikit-learn: Machine Learning in Python

    Fabián Pedregosa, Gaël Varoquaux, Alexandre Gramfort +16

    Препринт201214 иқтибос
    ABI

  3. Deep Neural Networks for Acoustic Modeling in Speech Recognition: The Shared Views of Four Research Groups

    Geoffrey E. Hinton, Li Deng, Dong Yu +8

    Мақола201211 иқтибос
    ABI

  4. Deep learning in neural networks: An overview

    Jürgen Schmidhuber

    Шарҳ мақола20148 иқтибос
    ABI

  5. Gradient-based learning applied to document recognition

    Yann LeCun, Léon Bottou, Yoshua Bengio +1

    Мақола19985 иқтибос
    ABI

  6. A unified architecture for natural language processing

    Ronan Collobert, Jason Weston

    Мақола20082 иқтибос
    ABI

  7. The segmental K-means algorithm for estimating parameters of hidden Markov models

    B.-H. Juang, L. R. Rabiner

    Мақола19902 иқтибос
    ABI

  8. Algorithmic Transparency via Quantitative Input Influence: Theory and Experiments with Learning Systems

    Anupam Datta, Shayak Sen, Yair Zick

    Мақола20162 иқтибос
    ABI

  9. The Tyranny of Data? The Bright and Dark Sides of Data-Driven Decision-Making for Social Good

    Bruno Lepri, Jacopo Staiano, David Sangokoya +2

    Боб20172 иқтибос
    ABI

  10. Interpretable explanations of black boxes by meaningful perturbation

    Vedaldi, A, Fong, RC

    Препринт20172 иқтибос
    ABI

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