Graphical Models Seminar |
DATE | TITLE | Speaker |
---|---|---|
11/1(¿ù) | A Tutorial on Learning with Bayesian Networks | K. Hwang |
11/3(¼ö) | Introduction to Inference for Bayesian Networks | S. Kim |
11/8(¿ù) | An Introduction to Variational Methods for Graphical Models | S. Park |
11/10(¼ö) | Improving the Mean Field Approximation Via the Use of Mixture Distributions | J. Lee |
11/10(¼ö) | Introduction to Monte Carlo Methods | J. Oh |
11/15(¿ù) | Suppressing Random Walks in Markov Chain Monte Carlo using Ordered Overrelaxation | J. Chang |
11/15(¿ù) | A View of the EM Algorithm that Justifies Incremental, Sparse, and Other Variations | K. Hwang |
11/17(¼ö) | Latent Variable Models | J. Oh |
11/17(¼ö) | Stochastic Algorithms for Exploratory Data Analysis: Data Clustering and Data Visualization | J. Lee |
11/22(¿ù) | A Hierarchical Community of Experts | J. Chang |
11/24(¼ö) | A Mean Field Learning Algorithm for Unsupervised Neural Networks | S. Kim |
11/24(¼ö) | Prediction with Gaussian Processes: From Linear Regression to Linear Prediction and Beyond | S. Park |