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Intro
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Classroom Contents
Task-Driven Network Discovery via Deep Reinforcement Learning on Embedded Spaces
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- 1 Intro
- 2 complex networks are ubiquitous
- 3 Working with incomplete data can skew analyses
- 4 The network discovery question
- 5 A more accurate representation
- 6 Some issues that make the problem difficult
- 7 Selective harvesting via reinforcement learning
- 8 Policy function
- 9 Modeling future reward: Return function
- 10 Value function
- 11 What are current approaches missing?
- 12 State space representation
- 13 Map network states into canonical representations
- 14 Training set generation for offline learning
- 15 Episodic training
- 16 The learning objective
- 17 Our model: Network Actor Critic (NAC)
- 18 Experiments: Baselines & competitors
- 19 Experiments: Results on real data
- 20 Which graph embedding to choice?
- 21 Wrap-up: Network Actor-Critic (NAC)
- 22 Control of pandemics
- 23 Problem and high-level overview of our system: COANET
