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

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Presentation on theme: "Teaching Machines to Learn by Metaphors Omer Levy & Shaul Markovitch Technion – Israel Institute of Technology."— Presentation transcript:

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

2

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

10 Example

11 Example

12 Common Inductive Bias

13 Common Inductive Bias

14 Common Instances

15 Common Features

16 New Approach to Transfer Learning

17 Our Solution: Metaphors

18 Metaphors Target (New) Source (Original)

19 Concept Learner Metaphor Learner Source Target +/-

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22 Theorem

23 The Metaphor Theorem

24 Redefine Transfer Learning

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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

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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

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47

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

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53 Digits: Higher Resolution

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56 Wine

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

59 Discussion

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

61 Recap

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65 What if the concepts are not related?

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67 Metaphors are not a measure of relatedness

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

69 Vision

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

72 Concept Learning by Induction

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74 Few Examples

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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

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88 Common Instances

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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

97 Example

98 Example

99 Common Inductive Bias

100 Common Inductive Bias

101 Common Instances

102 Common Features

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

107

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

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