1. Teaching Machines to Learn by Metaphors Omer Levy & Shaul Markovitch Technion – Israel Institute of Technology

3. Concept Learning by Induction

4. Few Examples

5. Transfer Learning Target (New) Source (Original)

6. Define: Related Concept

7. Transfer Learning Approaches Common Inductive Bias Common Instances Common Features

8. Different Feature Space

9. Example 023-3-2

10. Example 023-3-2 0 49

11. Example 023-3-2 0 49

12. Common Inductive Bias 023-3-2 0 49

13. Common Inductive Bias 023-3-2 0 49

14. Common Instances 023-3-2 0 49

15. Common Features 2 3 -3 -2 49

16. New Approach to Transfer Learning

17. Our Solution: Metaphors

18. Metaphors Target (New) Source (Original)

19. Concept Learner Metaphor Learner Source Target +/-

22. Theorem

23. The Metaphor Theorem

24. Redefine Transfer Learning

26. Metaphor Learning Framework

27. Concept Learning Framework Search Algorithm Hypothesis Space Evaluation Function Data

28. Source Target Metaphor Learning Framework Search Algorithm Metaphor Space Evaluation Function

29. Metaphor Evaluation

36. Metaphor Spaces

37. General Few Degrees of Freedom Representation-Specific Bias

38. Geometric Transformations ЯR

39. Dictionary-Based Metaphors cheesequeso

40. Linear Transformations

41. Which metaphor space should I use?

42. Automatic Selection of Metaphor Spaces Which metaphor space should I use?

43. Occam’s Razor Automatic Selection of Metaphor Spaces Which metaphor space should I use?

44. Structural Risk Minimization Occam’s Razor Automatic Selection of Metaphor Spaces Which metaphor space should I use?

45. Automatic Selection of Metaphor Spaces

48. Empirical Evaluation

49. Reference Methods Baseline Target Only Identity Metaphor Merge State-of-the-Art Frustratingly Easy Domain Adaptation – Daumé, 2007 MultiTask Learning – Caruana, 1997; Silver et al, 2010 TrAdaBoost – Dai et al, 2007

50. Digits: Negative Image

53. Digits: Higher Resolution

56. Wine

58. Qualitative Results Transfer Learning Task Target Instance Target Sample Size 12510 Digits: Negative Image Digits: Higher Resolution

59. Discussion

60. Recap Problem: Concept learning with few examples Solution: Metaphors

61. Recap

65. What if the concepts are not related?

67. Metaphors are not a measure of relatedness

68. Metaphors are not a measure of relatedness Metaphors explain how concepts are related

69. Vision

71. Explaining how concepts are related since 2012. M E T A P H O R S

72. Concept Learning by Induction

74. Few Examples

76. Approaches Explanation-Based Learning Semi-Supervised Learning Transfer Learning

77. Explanation-Based Learning Axioms Data Logical Deduction

78. Semi-Supervised Learning

79. Transfer Learning

80. Target (New) Source (Original)

81. Transfer Learning

82. Target (New) Source (Original)

83. Define: Related Concept

84. Transfer Learning Approaches Common Inductive Bias Common Instances Common Features

85. Common Inductive Bias

88. Common Instances

93. Common Features 1.Perform feature selection on source 2.Use that selection on target

94. Which definition is better?

95. Different Feature Space

96. Example 023-3-2

97. Example 023-3-2 0 49

98. Example 023-3-2 0 49

99. Common Inductive Bias 023-3-2 0 49

100. Common Inductive Bias 023-3-2 0 49

101. Common Instances 023-3-2 0 49

102. Common Features 2 3 -3 -2 49

103. Our Solution: Metaphors

104. Performance with Automatic Selection of Metaphor Spaces Digits: Negative Image

105. Performance with Automatic Selection of Metaphor Spaces Digits: Negative Image Geometric Transformations Feature Reordering Orthogonal Linear Transformations Orthogonal Quadratic Transformations

106. Performance with Automatic Selection of Metaphor Spaces Digits: Negative Image Geometric Transformations Feature Reordering Orthogonal Linear Transformations Orthogonal Quadratic Transformations

108. What if I have more than one source?

109. Multiple Source Datasets B Я HRZ

110. B Я H R Z

111. Я R

112. Performance with Multiple Source Datasets Latin & Cyrillic

113. Performance with Multiple Source Datasets Latin & Cyrillic ABCDEFG HIJKLMN OPQRSTU VWXYZ

114. Performance with Multiple Source Datasets Latin & Cyrillic ABCDEFG HIJKLMN OPQRSTU VWXYZ

115. Performance with Multiple Source Datasets