The Pitfalls of Using ML-Based Optimization - IPAM at UCLA

The Pitfalls of Using ML-Based Optimization - IPAM at UCLA

Institute for Pure & Applied Mathematics (IPAM) via YouTube Direct link

Annotation and Balance

8 of 18

8 of 18

Annotation and Balance

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The Pitfalls of Using ML-Based Optimization - IPAM at UCLA

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  1. 1 Introduction
  2. 2 Instability of embeddings
  3. 3 Degenerate core
  4. 4 All empirical
  5. 5 Point of instability
  6. 6 Negative Sampling
  7. 7 Topological Shortcuts
  8. 8 Annotation and Balance
  9. 9 Man in the Middle Attacks
  10. 10 Force Path Cut
  11. 11 Weighted Graphs
  12. 12 MLbased Optimization
  13. 13 Application
  14. 14 Targeted Diffusion
  15. 15 Attack Vector
  16. 16 Budget
  17. 17 Diffusion
  18. 18 Cyber Security

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