ml session 02, part 1 deep graph network
TRANSCRIPT
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Javen Qinfeng Shi Associate Professor, The University of Adelaide (UoA)
Director and Founder, Probabilistic Graphical Model Group, UoA
Director of Advanced Reasoning and Learning, Australian Institute of Machine Learning (AIML), UoA
ML SESSION 02, PART 1 DEEP GRAPH NETWORK
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• Two types of deep neural networks • Deep belief networks are Markov random fields • CNN, RNN, … are Computational diagrams
• Two ways to combine graphical models and deep learning • Use a neural net to model the feature of a graphical model • Put a graphical model (or its approximation) into a neural net
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DEEP MIND’S GRAPH NETWORKS (OCT. 2018)
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• Why inference in Graph Net is this way? • How is Message Passing (Inference)
done in a graphical model?
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GRAPHICAL MODELS
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VARIABLE ELIMINATION (MARGINAL INFERENCE)
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SUM-PRODUCT
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MAP INFERENCE
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MAX-PRODUCT
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MESSAGE PASSING
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BACK TO DEEP GRAPH NET
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