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[Big data analysis for complex biological systems, 1/3] A Bayesian Convolutional Neural Network-based Generalized Linear Model

August 22, 2022 @ 2:00 pm - 2:40 pm KST

B378 Seminar room, IBS, 55 Expo-ro Yuseong-gu
Daejeon, 34126 Korea, Republic of

Speaker

Jaewoo Park
Yonsei University

Abstract: Convolutional neural networks (CNNs) provide flexible function approximations for a wide variety of applications when the input variables are in the form of images or spatial data. Although CNNs often outperform traditional statistical models in prediction accuracy, statistical inference such as estimating the effects of covariates and quantifying the prediction uncertainty is not trivial due to the highly complicated model structure and overparameterization. To address this challenge, we propose a new Bayes approach by embedding CNNs within the generalized linear model (GLM) framework. We use extracted nodes from the last hidden layer of CNN with Monte Carlo dropout as informative covariates in GLM. This improves prediction accuracy and provides an interpretation of regression coefficients. By fitting ensemble GLMs across multiple realizations from Monte Carlo dropout, we can fully account for uncertainties in model estimation. We apply our methods to simulated and real data examples, including non-Gaussian spatial data, brain tumor image data, and fMRI data. The algorithm can be broadly applicable to image regressions or correlated data analysis by providing accurate Bayesian inference quickly.

Details

Date:
August 22, 2022
Time:
2:00 pm - 2:40 pm KST
Event Category:

Venue

B378 Seminar room, IBS
55 Expo-ro Yuseong-gu
Daejeon, 34126 Korea, Republic of

Organizer

Jae Kyoung Kim
Email
jaekkim@kaist.ac.kr
IBS 의생명수학그룹 Biomedical Mathematics Group
기초과학연구원 수리및계산과학연구단 의생명수학그룹
대전 유성구 엑스포로 55 (우) 34126
IBS Biomedical Mathematics Group (BIMAG)
Institute for Basic Science (IBS)
55 Expo-ro Yuseong-gu Daejeon 34126 South Korea
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