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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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  1. What is model theory?

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  2. Essential dimension of simple algebras

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  3. Restriction theorems for real and complex curves

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

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  5. Deformation spaces of Kleinian groups and beyond

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  6. Idempotents and topologies

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

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  8. Existence of positive solutions for φ-Laplacian systems

    SNU-LeeAbstract.pdf
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  9. Riemann-Hilbert correspondence for irregular holonomic D-modules

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  10. Normal form reduction for unconditional well-posedness of canonical dispersive equations

    Normal form method is a classical ODE technique begun by H. Poincare. Via a suitable transformation one reduce a differential equation to a simpler form, where most of nonresonant terms are cancelled. In this talk, I begin to explain the not...
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  11. Random conformal geometry of Coulomb gas formalism

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  12. Categorification of Donaldson-Thomas invariants

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  13. Noncommutative Surfaces

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  14. The Shape of Data

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  15. Topological Mapping of Point Cloud Data

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  16. Structures on Persistence Barcodes and Generalized Persistence

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