Probabilistic Learning Research Group
at BI Lab
The Probabilistic Learning Research Group at Biointelligence Lab investigates machine learning algorithms for
probabilistic graphical models such as hierarchical Bayesian networks (HBN) and self-organizing latent lattice models (SOLL).
Our current research
focuses on structural learning of large-scale probabilistic graphical
models in a noisy and/or dynamic environment. For example, we are developing methods for learning large-scale Bayesian networks (having more than 10,000 nodes) from sparse datasets. Major application areas of these techniques include text mining and multi-modal associative information analysis.
ICML 2012, The 29th International Conference on Machine Learning, June 26 ~ July 1, 2012, Edinburgh, Scotland.
UAI 2012, The 28th Conference on Uncertainty in Artificial Intelligence, August 15 ~ 17, 2012, Catalina Island, California, USA.
ECML/PKDD 2012, The 23rd European Conference on Machine Learning / The 16th Practice of Knowledge Discovery in Databases, Sep. 24 ~ 28, 2012, Bristol, UK.
NIPS 2012, The 26th Conference on
Neural Information Processing Systems, 3 ~ 8, 2012, Lake Tahoe, Nevada, United States.
Bayesian Modelling, Graphical Models, and Semi-supervised Learning, 2012 Machine Learning Summer School. Hinton, G.,
Recent Developments in Deep Learning, Google Tech Talk, 2010. Hinton, G.,
Deep Belief Nets, NIPS 2007 Tutorial, 2007. Heckerman, D.,
Graphical Models for Data Mining, KDD 2004 Invited Talk, 2004. Zhang, B.-T.,
Machine Learning for Biological Data Mining, A tutorial presented at ETRI, Mar. 6, 2001. Friedman, N. and Koller, D., Learning Bayesian Networks From Data, NIPS
2001 Tutorial, 2001.
A Tutorial on Learning With Bayesian Networks, Technical Report MSR-TR-95-06, Microsoft Research, 1996. Dayan, P., Hinton, G.E., Neal, R.M., and Zemel, R.S., The Helmholtz Machine, Neural Computation, 7, 889-904, 1995.
Videome, Cognitive Machine Learning from Digital Videos
A Multimodal Associative Recommendation System
DNAChipBench (NRL Project) : Intelligent Design and Analysis Technology for DNA Chip BrainGene : DNA data mining for the analysis of expression patterns of vertebrate brain development-specific genes (rat)
LaText : Text mining based on latent variable models
MrHumor : A personalized Internet agent that recommends humors and jokes
This page is maintained by Byoung-Hee Kim
Last Update: October 15, 2012.