ProtoDash - Fast Interpretable Prototype Selection by Karthik Gurumoorthy

ProtoDash - Fast Interpretable Prototype Selection by Karthik Gurumoorthy

International Centre for Theoretical Sciences via YouTube Direct link

ProtoDash: Fast Interpretable Prototype Selection

1 of 34

1 of 34

ProtoDash: Fast Interpretable Prototype Selection

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ProtoDash - Fast Interpretable Prototype Selection by Karthik Gurumoorthy

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  1. 1 ProtoDash: Fast Interpretable Prototype Selection
  2. 2 Objective: Extract compact synapses of large data sets
  3. 3 Prototypes and Criticisms
  4. 4 Some applications:
  5. 5 Causal reasoning
  6. 6 Metric to quantify best for prototype selection
  7. 7 Reformulation as maximizing a set function
  8. 8 Properties of set function: Submodularity
  9. 9 Weak submodularity
  10. 10 ProtoDash algorithm: Greedily build the set based on gradients
  11. 11 ProtoGreedy algorithm: Greedily build the set based on maximum function increment
  12. 12 ProtoDash Vs ProtoGreedy
  13. 13 Classification accuracy Vs sparsity
  14. 14 Classification accuracy Vs skew
  15. 15 Prototype selection quality
  16. 16 Computation time
  17. 17 Visualizing selected prototypes
  18. 18 Visualizing selected prototypes from the same data set
  19. 19 Theoretical guarantees
  20. 20 Set function value Vs sparsity
  21. 21 Criticism selection
  22. 22 Greedy algorithm for choosing criticisms
  23. 23 Visualizing selected criticisms
  24. 24 Application 2: Finding relationships between data sets
  25. 25 Approach to identify relationships
  26. 26 Visualizing relationships
  27. 27 Human expert based evaluation
  28. 28 Application 3: Improving prediction accuracy by training data selection
  29. 29 Training data selection
  30. 30 Prediction accuracy
  31. 31 Interpreting selected prototype
  32. 32 Interpreting selected prototypes
  33. 33 Thank You
  34. 34 Q&A

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