Adaptive Sampling Techniques for Large-Scale Probabiligistic Graphical Models

(2002. 9)

Participants : B.-T. Zhang, J.-S. Yang, J.-W. Lee, S.-B. Park, J.-H. Chang, K.-B. Hwang, J.-M. Oh, D.-Y.Cho, S.-Y. Shin, S.-E. Lee, J.-S. Kim, S. Kim, J.-H. Eom, S.-J. Lee

Material: See the following
Time and date: Thu, 16:00-17:30
Location: 301-421
Object: 대규모 graphical model의 학습 및 추론에 있어서 bottleneck이 되는 핵심적인 computational issues를 identify하고 information theory, MCMC, EC, particle filtering, importance sampling, active leanring 등에 기초한 효율적인 adaptive sampling techniques을 도출한다.

Date

Title

Speaker

Sep 19

Introduction to this seminar

B.-T. Zhang

Sep 26

Construction of large-scale Bayesian networks with hidden variables added by clustering

K.-B. Hwang

Oct 10

How to formulate the effectiveness of active learning

S.-B. Park

Oct 31

Rao-Blackwellised Particle Filtering for Dynamic Bayesian Networks

J.-M. O

Oct 31

Being Bayesian about Network Structure: A Bayesian Approach to Structure Discovery in Bayesian Networks

D.-Y. Cho

Nov 7

J.-W. Lee

Nov 7

Particle Filters for Nonstationary ICA

S.-E. Lee

Nov 14

S.-Y. Shin

 


Paper List of Each Participants

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