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

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Concept Learning by Induction

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Few Examples

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Transfer Learning Target (New) Source (Original)

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Define: Related Concept

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Transfer Learning Approaches Common Inductive Bias Common Instances Common Features

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Different Feature Space

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Example 023-3-2

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Example 023-3-2 0 49

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Example 023-3-2 0 49

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Common Inductive Bias 023-3-2 0 49

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Common Inductive Bias 023-3-2 0 49

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Common Instances 023-3-2 0 49

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Common Features 2 3 -3 -2 49

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New Approach to Transfer Learning

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Our Solution: Metaphors

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Metaphors Target (New) Source (Original)

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Concept Learner Metaphor Learner Source Target +/-

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Theorem

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The Metaphor Theorem

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Redefine Transfer Learning

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Metaphor Learning Framework

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Concept Learning Framework Search Algorithm Hypothesis Space Evaluation Function Data

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Source Target Metaphor Learning Framework Search Algorithm Metaphor Space Evaluation Function

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Metaphor Evaluation

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Metaphor Spaces

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General Few Degrees of Freedom Representation-Specific Bias

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Geometric Transformations ЯR

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Dictionary-Based Metaphors cheesequeso

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Linear Transformations

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Which metaphor space should I use?

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

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

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

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Automatic Selection of Metaphor Spaces

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Empirical Evaluation

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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

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Digits: Negative Image

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Digits: Higher Resolution

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Wine

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

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Discussion

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Recap Problem: Concept learning with few examples Solution: Metaphors

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Recap

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What if the concepts are not related?

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Metaphors are not a measure of relatedness

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

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Vision

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

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Concept Learning by Induction

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Few Examples

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Approaches Explanation-Based Learning Semi-Supervised Learning Transfer Learning

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Explanation-Based Learning Axioms Data Logical Deduction

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Semi-Supervised Learning

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Transfer Learning

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Target (New) Source (Original)

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Transfer Learning

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Target (New) Source (Original)

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Define: Related Concept

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Transfer Learning Approaches Common Inductive Bias Common Instances Common Features

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Common Inductive Bias

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Common Instances

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

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Which definition is better?

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Different Feature Space

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Example 023-3-2

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Example 023-3-2 0 49

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Example 023-3-2 0 49

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Common Inductive Bias 023-3-2 0 49

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Common Inductive Bias 023-3-2 0 49

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Common Instances 023-3-2 0 49

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Common Features 2 3 -3 -2 49

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Our Solution: Metaphors

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Performance with Automatic Selection of Metaphor Spaces Digits: Negative Image

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

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

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What if I have more than one source?

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Multiple Source Datasets B Я HRZ

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B Я H R Z

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Я R

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Performance with Multiple Source Datasets Latin & Cyrillic

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

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

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Performance with Multiple Source Datasets

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