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Intro
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Classroom Contents
Embedding as a Tool for Algorithm Design
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- 1 Intro
- 2 Embedding algorithms
- 3 Prediction for structured data
- 4 Big dataset, explosive feature space
- 5 Combinatorial optimizations over graphs
- 6 Key observation & fundamental question
- 7 Represent structure as latent variable model (LVM)
- 8 Posterior distribution as features
- 9 Mean field algorithm aggregates information
- 10 What's embedding?
- 11 Learning via embedding
- 12 Embedding mean field
- 13 Directly parameterize nonlinear mapping
- 14 Embed belief propagation
- 15 New tools for algorithm design
- 16 Motivation 2: Dynamic processes over networks
- 17 Unroll: time-varying dependency structure
- 18 Embedding algorithm for building generative model
- 19 Scenario 3: Combinatorial optimization over graph
- 20 Greedy algorithm as Markov decision process
