Birs- 19w5061: Computational Harmonic Analysis and Data Science

  1. Title:
    Optimal transport

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  2. Title:
    Multi-Representation Manifold Learning on Fibre Bundles

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  3. Title:
    On the Continuum Between Models, Data-Driven Discovery and Machine Learning: Mapping the Continuum of Molecular Conformations Using Cryo-Electron Microscopy

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  4. Title:
    Group synchronization on complex networks: nonconvex optimization and Kuramoto model

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  5. Title:
    Sharpness, Restart, and Compressed Sensing Performance

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  6. Title:
    Ulam’s game and phase retrieval with random binary questions

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  7. Title:
    Sparse Multiscale with Phase to model Deep Neural Networks

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  8. Title:
    Dimension independent bounds for approximation of smooth functions on metric spaces

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  9. Title:
    Robust and efficient identification of neural networks

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  10. Title:
    Fundamental limits of deep neural network learning

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  11. Title:
    On the convex geometry of blind deconvolution

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  12. Title:
    Convergence of gradient flows for learning deep linear networks

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  13. Title:
    Graph neural networks for combinatorial optimization problems on graphs

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  14. Title:
    What is the role of curvature in complexity of optimization on manifolds?

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  15. Title:
    Geometry-based Data Exploration

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  16. Title:
    Estimating Fourier-sparse signals without a frequency gap

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  17. Title:
    Learning Interaction laws in particle- and agent-based systems

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