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Creators/Authors contains: "Zhou, Kun"

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  1. ; ; ; ; ; (, The 62nd Annual Meeting of the Association for Computational Linguistics)
    Accepted Manuscript Full Text Available
  2. ; ; ; ; ; (, Proceedings of the AAAI Conference on Artificial Intelligence)
    We introduce a deep architecture named HoD-Net to enable high-order differentiability for deep learning. HoD-Net is based on and generalizes the complex-step finite difference (CSFD) method. While similar to classic finite difference, CSFD approaches the derivative of a function from a higher-dimension complex domain, leading to highly accurate and robust differentiation computation without numerical stability issues. This method can be coupled with backpropagation and adjoint perturbation methods for an efficient calculation of high-order derivatives. We show how this numerical scheme can be leveraged in challenging deep learning problems, such as high-order network training, deep learning-based physics simulation, and neural differential equations. 
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  3. ; ; ; ; (, Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2021)
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  4. ; ; ; ; ; ; ; ; (, Advanced Functional Materials)
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  5. ; ; ; ; ; ; (, ACM Transactions on Graphics)
    null (Ed.)
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  6. ; ; (, Journal of the American Statistical Association)
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  7. ; ; ; ; ; (, Angewandte Chemie International Edition)
    null (Ed.)
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  8. ; ; ; ; (, IEEE Transactions on Visualization and Computer Graphics)
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  9. ; ; ; ; (, Journal of the American Chemical Society)
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  10. ; ; ; ; ; ; (, IEEE Transactions on Visualization and Computer Graphics)
    Full Text Available