Ludwig Schmidt

Ludwig Schmidt (18 July 1862 – 10 March 1944) was a German historian and librarian at the Saxon State and University Library Dresden. He is best known for his magnum opus, ''Die Geschichte der deutschen Stämme bis zum Ausgang der Völkerwanderung'' (1904-1918), which up to the present day remains the standard reference work on the history of the Germanic peoples in the Migration Period. Provided by Wikipedia
Showing 1 - 20 results of 21 for search 'Schmidt, Ludwig', query time: 0.29s Refine Results
  1. 1
    Monetary Policy Implications Through a VEC Model
    by Schmidt, Ludwig
    Published 2020
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  2. 2
    Volatility Forecasting Performance of GARCH Models : A Study on Nordic Indices During COVID-19
    by Schmidt, Ludwig
    Published 2021
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  3. 3
    Government Size and the Effect on Economic Growth in the EU?
    by Schmidt, Ludwig, Wigerstedt, Harald
    Published 2019
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  4. 4
    Fast recovery from a union of subspaces
    by Indyk, Piotr, Schmidt, Ludwig
    Published 2021
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  5. 5
    Better approximations for Tree Sparsity in Nearly-Linear Time
    Other Authors: “...Schmidt, Ludwig...”
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  6. 6
    A classification-based study of covariate shift in GAN distributions
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  7. 7
    A classification-based study of covariate shift in GAN distributions
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  8. 8
    On the fine-grained complexity of empirical risk minimization: Kernel methods and neural networks
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  9. 9
    Approximation Algorithms for Model-Based Compressive Sensing
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  10. 10
    On the fine-grained complexity of empirical risk minimization: Kernel methods and neural networks
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  11. 11
    Fast recovery from a union of subspaces
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  12. 12
    Approximation-tolerant model-based compressive sensing
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  13. 13
    Nearly Linear-Time Model-Based Compressive Sensing
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  14. 14
    Algorithms above the noise floor
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  15. 15
    Model-based compressive sensing with Earth Mover's Distance constraints
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  16. 16
    Recent Developments in the Sparse Fourier Transform: A compressed Fourier transform for big data
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  17. 17
    Practical and optimal LSH for angular distance
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  18. 18
    Adversarially Robust Generalization Requires More Data
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  19. 19
    Exploring the landscape of spatial robustness
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  20. 20
    Automatic fault localization using the generalized Earth Mover's distance
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