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강연자 정교민
소속 서울대 전기공학부
date 2014-10-16
이 강연에서는 최근 음악, 영화 추천 등 다양한 Recommendation System의 기본 아이디어인 Matrix Completion 문제와, 이를 해결하기 위해 Singular Value Decomposition을 통한 차원 축소 및 내재 공간 학습이 어떤 원리로 이루어 지는지 설명합니다. 그리고 PageRank 등 검색 알고리즘, 머신 러닝의 최적화 문제 등에서의 수학의 중요성을 설명하고자 합니다.

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첨부 '1'
  1. Spectral Analysis for the Anomalous Localized Resonance by Plasmonic Structures

  2. Space.Time.Noise

  3. Solver friendly finite element methods

  4. Sheaf quantization of Hamiltonian isotopies and non-displacability problems

  5. Seoul ICM 2014 유치과정 개요 및 준비전략

  6. Seifert fiberings

  7. Seeded Ising Model for Human Iris Templates and Secure Distributed Iris Recognition

  8. Satellite operators on knot concordance

  9. Root multiplicities of hyperbolic Kac-Moody algebras and Fourier coefficients of modular forms

  10. Role of Computational Mathematics and Image Processing in Magnetic Resonance Electrical Impedance Tomography (MREIT)

  11. Riemann-Hilbert correspondence for irregular holonomic D-modules

  12. Restriction theorems for real and complex curves

  13. Regularity theory for non-autonomous elliptic equations in divergence form

  14. Regularity for non-uniformly elliptic problems

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    Recommendation system and matrix completion: SVD and its applications (학부생을 위한 강연)

  16. Recent progress on the Brascamp-Lieb inequality and applications

  17. Randomness of prime numbers

  18. Random walks in spaces of negative curvature

  19. Random matrices and operator algebras

  20. Random conformal geometry of Coulomb gas formalism

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