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 |
|
