EE512A – Advanced Inference in Graphical Models — Fall Quarter, Lecture 2 — http://j.ee.washington.edu/~bilmes/classes/ee512a_fall_2014/ Prof. Jeff Bilmes University of Washington, Seattle

Source URL: j.ee.washington.edu

On the concentration of expectation and approximate inference in layered networks Michael I. Jordan University of California Berkeley, CA 94720

Source URL: dept.stat.lsa.umich.edu

• Graphical models
• Probability distributions
• Bayesian network
• Networks
• Normal distribution
• Variational Bayesian methods
• Belief propagation
• Exponential family

Partition function loop series for a general graphical model: free energy corrections and message-passing equations Jing-Qing Xiao2,1 and Haijun Zhou1 1

Source URL: www.itp.ac.cn

EE512A – Advanced Inference in Graphical Models — Fall Quarter, Lecture 11 — http://j.ee.washington.edu/~bilmes/classes/ee512a_fall_2014/ Prof. Jeff Bilmes University of Washington, Seattle

Source URL: j.ee.washington.edu

Support Action Big Data Europe – Empowering Communities with Data Technologies Project Number: 644564

Source URL: www.big-data-europe.eu

• Software
• Digital media
• Graphical user interfaces
• Diaries
• New media
• Web 2.0
• Web services
• WordPress
• Blog
• Semantic Web
• Website
• Widget

EE512A – Advanced Inference in Graphical Models — Fall Quarter, Lecture 5 — http://j.ee.washington.edu/~bilmes/classes/ee512a_fall_2014/ Prof. Jeff Bilmes University of Washington, Seattle

Source URL: j.ee.washington.edu

EE512A – Advanced Inference in Graphical Models — Fall Quarter, Lecture 14 — http://j.ee.washington.edu/~bilmes/classes/ee512a_fall_2014/ Prof. Jeff Bilmes University of Washington, Seattle

Source URL: j.ee.washington.edu

Programming with “Big Code”: Lessons, Techniques and Applications Pavol Bielik1 , Veselin Raychev1 , and Martin Vechev1 1 Department of Computer Science, ETH Zurich, Switzerland

Source URL: www.srl.inf.ethz.ch

• Computing
• Artificial intelligence
• Machine learning
• Graphical models
• Structured prediction
• Computational statistics
• Statistical classification
• Conditional random field
• Probabilistic programming language
• Inference
• Artificial neural network
• Support vector machine

Graphical Models for Graph Matching Tib´erio S. Caetano†‡ ,Terry Caelli† and Dante A. C. Barone‡ †Department of Computing Science University of Alberta Edmonton, AB, Canada, T6G 2E8 ‡Instituto de Inform´ati

Source URL: tiberiocaetano.com

EE596A – Dynamic Graphical Models Winter Quarter 2013 Prof. Jeff Bilmes University of Washington, Seattle Department of Electrical Engineering Winter Quarter, 2013

Source URL: j.ee.washington.edu