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Lecturer 홍영준
Dept. 성균관대학교
date Apr 13, 2023

 

This lecture explores the topics and areas that have guided my research in computational mathematics and deep learning in recent years. Numerical methods in computational science are essential for comprehending real-world phenomena, and deep neural networks have achieved state-of-the-art results in a range of fields. The rapid expansion and outstanding success of deep learning and scientific computing have led to their applications across multiple disciplines. In this lecture, I will focus on connecting machine learning with applied mathematics, specifically discussing topics such as adversarial examples, generative models, and scientific machine learning.

 

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Attachment '1'
  1. 돈은 어떻게 우리 삶에 돈며들었는가? (불확실성 시대에 부는 선형적으로 증가하는가?)

  2. 극소곡면의 등주부등식

  3. 곡선의 정의란 무엇인가?

  4. Zeros of the derivatives of the Riemann zeta function

  5. Zeros of linear combinations of zeta functions

  6. What is model theory?

  7. What happens inside a black hole?

  8. WGAN with an Infinitely wide generator has no spurious stationary points

  9. Weyl character formula and Kac-Wakimoto conjecture

  10. Weak and strong well-posedness of critical and supercritical SDEs with singular coefficients

  11. W-algebras and related topics

  12. Volume entropy of hyperbolic buildings

  13. Vlasov-Maxwell equations and the Dynamics of Plasmas

  14. Variational Methods without Nondegeneracy

  15. Unprojection

  16. Universality of log-correlated fields

  17. Unique ergodicity for foliations

  18. Trends to equilibrium in collisional rarefied gas theory

  19. Towards Trustworthy Scientific Machine Learning: Theory, Algorithms, and Applications

  20. 14Apr
    by 김수현
    in Math Colloquia

    Toward bridging a connection between machine learning and applied mathematics

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