심리학이 알려주는 연구의 기술: 논리적 사고부터 논문 작성까지 – 서수연
본 세미나에서는 성신여자대학교 서수연 교수님께서 "심리학이 알려주는 연구의 기술: 논리적 사고부터 논문 작성까지"라는 내용으로 강연을 해주실 예정입니다.
6 events found.
Biomedical Mathematics Seminar
Dimensionality Reduction and Summary-Statistical Modeling in Genetic Studies – Fatemeh Yavartanoo
B232 Seminar Room, IBS 55 Expo-ro Yuseong-gu, Daejeon, Daejeon, Korea, Republic ofAbstract: This presentation introduces DRLPC and a refined summary-statistics method to improve genetic association analysis. Applications to cognition, neurodegenerative diseases, and high cholesterol are discussed, with future directions in single-cell …
FoodSeq: Using Genomics to Track and Study Diet – Lawrence David
Conference room, (B109) Daejeon, Daejeon, Korea, Republic ofAbstract Dietary assessment is crucial for understanding the relationship between diet and health. Yet traditional recall-based methods for tracking diet often face challenges like participant compliance and accurate recall. To …
U Jin Choi – Simulation-Free Schrodinger Bridges Via Score and Flow Matching (by Tong et al, AISTATS 2024).
B232 Seminar Room, IBS 55 Expo-ro Yuseong-gu, Daejeon, Daejeon, Korea, Republic ofAbstract: 임의로 정한 Initial Distribution Q1 와 Terminal Distribution Q2가 주어 졌을 때 시점과 종점 사이의 contiinious time상에 정의 되는 의미 있는 최적의 Probability Path Measure P 를 찾는 Schrodinger Bridges Problem 은 자연과학,공학, 의료 및 생명공학,경제학 및 금융공학 등의 여러 분야에 나타나는 모델들을 푸는 Unified AI Model 사용 되고 있습니다. Schrodinger Bridges Problem은 유일한 해가 존재 하는 정리는( Follmer,1988) 증명 되었으므로 데이터를 이용하여 Neural Network Models에 대한 효율적으로 학습방법, 빠른 알고리즘 연구에 집중 되고 있습니다. Tong et al 연구팀은 2023년 부터 ODE에 기반한 획기적인 생성모델인 Flow Matching for Generative Modeling 기법을 SDE 기반 Diffusion Generative Models에 접목하여 Schrodinger Bridges Problem의 해법을 제시하였습니다.
Jihun Han – Bridging PDEs and machine learning
B232 Seminar Room, IBS 55 Expo-ro Yuseong-gu, Daejeon, Daejeon, Korea, Republic ofAbstract: This talk consists of two main parts. In the first part, I will discuss a numerical method for solving PDEs based on a stochastic representation of the solution. This …
Jae-Kwang Kim – Weight calibration for causal inference and transfer learning
B232 Seminar Room, IBS 55 Expo-ro Yuseong-gu, Daejeon, Daejeon, Korea, Republic ofAbstract: Weight calibration is a popular technique in handling covariate-shift problem in causal inference. It can be viewed as a dual optimization problem for incorporating the implicit regression model. We introduce the generalized entropy calibration as a general tool for weight calibration. Several interesting applications will be introduced in the context of causal inference. Furthermore, weight calibration can be used to transfer learning, which combines information from two different samples, one for source data and the other for target data.