학과 세미나 및 콜로퀴엄



ASARC 세미나 Discrete Math 학부생 콜로퀴엄 산업응용수학 세미나 BK21세미나 콜로퀴엄 Face the World with Mathematical Mind 금융수학 세미나 학과 세미나 기타 대학원생 세미나 위상수학 세미나 박사학위심사 PDE 세미나 미분기하 세미나 심플렉틱 기하 세미나 Basic Notions Seminar 금융공학특별강연 KMRS 세미나 계산수학 세미나 KAIST CMC 정오의 수학산책 KAIST CMC 세미나 NIMS 세미나 NIMS 산업문제 세미나 NIMS 콜로퀴엄 박사논문심사 KAIST-KSIAM 공동 콜로퀴엄 대수기하학 확률론 응용수학 세미나 정수론 해외 석학 특별 강연 시리즈 확률 * 통계 수리생물학 미분류 응용 및 계산수학 세미나 Mathematics & Beyond Seminar Topology, Geometry, and Data Analysis
수리생물학 대수기하학 이산수학

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In this talk, we discuss the Neural Tangent Kernel. The NTK is closely related to the dynamics of the neural network during training via the Gradient Flow(or Gradient Descent). But, since the NTK is random at initialization and varies during training, it is quite delicate to understand the dynamics of the neural network. In relation to this issue, we introduce an interesting result: in the infinite-width limit, the NTK converge to a deterministic kernel at initialization and remains constant during training. We provide a brief proof of the result for the simplest case.
9월 14일, 10월 4일, 5일 세 번에 걸친 발표.
Host: 홍영준     Contact: 이명수 ()     한국어     2023-09-25 15:35:17
In this talk, we discuss the Neural Tangent Kernel. The NTK is closely related to the dynamics of the neural network during training via the Gradient Flow(or Gradient Descent). But, since the NTK is random at initialization and varies during training, it is quite delicate to understand the dynamics of the neural network. In relation to this issue, we introduce an interesting result: in the infinite-width limit, the NTK converge to a deterministic kernel at initialization and remains constant during training. We provide a brief proof of the result for the simplest case.
9월 14일, 10월 4일, 5일 세 번에 걸친 발표로, 본 시간에는 주로 9월 14일 내용의 리뷰를 주로 다룸.
Host: 홍영준     Contact: 이명수 ()     한국어     2023-09-25 15:29:33
In this talk, we discuss the Neural Tangent Kernel. The NTK is closely related to the dynamics of the neural network during training via the Gradient Flow(or Gradient Descent). But, since the NTK is random at initialization and varies during training, it is quite delicate to understand the dynamics of the neural network. In relation to this issue, we introduce an interesting result: in the infinite-width limit, the NTK converge to a deterministic kernel at initialization and remains constant during training. We provide a brief proof of the result for the simplest case.
Host: 홍영준     Contact: 김규식 (T.2702)     미정     2023-09-11 16:14:54