Human-level concept learning through probabilistic program induction

Transcription

1 B.M Lake, R. Salakhutdinov, J.B. Tenenbaum Human-level concept learning through probabilistic program induction journal club at

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3 two aspects in which machine learning spectacularly lags behind human learning

4 two aspects in which machine learning spectacularly lags behind human learning one-shot learning of new concepts

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7 two aspects in which machine learning spectacularly lags behind human learning one-shot learning of new concepts

8 two aspects in which machine learning spectacularly lags behind human learning one-shot learning of new concepts rich representations

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19 one-shot learning

20 human or machine?

21 human or machine?

22 human or machine?

23 human or machine?

24 human or machine?

25 generate conditioned on alphabet

26 human or machine?

27 human or machine?

28 human or machine?

29 human or machine?

30 human or machine?

31 human or machine?

32 human or machine?

33 generate freely

34 human or machine?

35 human or machine?

36 human or machine?

37 human or machine?

38 human or machine?

39 human or machine?

40 human or machine?

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42 Bayesian Program Learner

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44 motivation reverse engineer human learning

45 motivation reverse engineer human learning method understand the computational problem of learning first

46 simulation

47 simulation model physical world

48 simulation model physical world

49 simulation model physical world

50 uncertainty

51 uncertainty underdetermination of inference problems by experience

52 uncertainty underdetermination of inference problems by experience computational intractability of environment

53 simulation uncertainty

54 simulation uncertainty rules of probability }(includes Bayes rule)

55 simulation uncertainty rules of probability }(includes Bayes rule) does the search for an ideal learner stop here?

56 simulation uncertainty rules of probability }(includes Bayes rule) does the search for an ideal learner stop here? model space?

57 simulation uncertainty rules of probability }(includes Bayes rule) does the search for an ideal learner stop here? model space? set of all models (luckily, this exists)

58 simulation uncertainty rules of probability }(includes Bayes rule) does the search for an ideal learner stop here? model space? set of all models (luckily, this exists) brute force search

59 simulation uncertainty rules of probability }(includes Bayes rule) does the search for an ideal learner stop here? model space? set of all models (luckily, this exists) brute force search how can this be made tractable?

60 simulation uncertainty rules of probability }(includes Bayes rule)

61 simulation uncertainty rules of probability }(includes Bayes rule) }principles for choice of representation

62 simulation uncertainty rules of probability }(includes Bayes rule) learning to learn }principles for choice of representation

63 simulation uncertainty rules of probability }(includes Bayes rule) learning to learn compositionality }principles for choice of representation

64 simulation uncertainty rules of probability }(includes Bayes rule) learning to learn compositionality causality }principles for choice of representation

65 learning to learn

66 learning to learn [Griffiths et al 2008]

67 learning to learn [Griffiths et al 2008]

68 compositionality

69 compositionality The green bear is eating chocolate.

70 compositionality The green bear is eating chocolate. [Grosse et al 2012]

71 logic cough sneeze flu TB possible? t t t t y t t f f n f f f f y

72 truth table

73 truth table operators

74 truth table operators propositional logic

75 truth table operators propositional logic predicates, quantifiers

76 truth table operators propositional logic predicates, quantifiers first-order logic

77 truth table operators propositional logic predicates, quantifiers first-order logic λ-calculus

78 truth table operators propositional logic predicates, quantifiers first-order logic turing-completeness λ-calculus

79 truth table operators propositional logic predicates, quantifiers first-order logic expressive power turing-completeness λ-calculus

80 logic cough sneeze flu TB possible? t t t t y t t f f n f f f f y

81 probability cough sneeze flu TB probability t t t t 0.1 t t f f f f f f 0.3

82 truth table propositional logic first-order logic turing-completeness λ-calculus

83 truth table joint probability table propositional logic first-order logic turing-completeness λ-calculus

84 truth table joint probability table propositional logic bayes nets first-order logic turing-completeness λ-calculus

85 truth table joint probability table propositional logic bayes nets first-order logic turing-completeness λ-calculus ψλ -calculus

86 truth table joint probability table propositional logic bayes nets first-order logic turing-completeness λ-calculus ψλ -calculus all programs all distributions

87 filename=wigner_jc_anglicandemo.clj

88 (define a (flip 0.3)) (define b (flip 0.3)) (define c (flip 0.3)) (+ a b c) ,5 0,375 0,25 0,125 0 P (n) = n n n

89 (define a (flip 0.3)) (define b (flip 0.3)) (define c (flip 0.3)) (+ a b c) ,5 0,375 0,25 0,125 0 P (n) = n n n sampling semantics distribution semantics

90 (define a (flip 0.3)) (define b (flip 0.3)) (define c (flip 0.3)) (+ a b c) ,5 0,375 0,25 0,125 0 P (n) = n n n sampling semantics distribution semantics theorem any computable distribution can be represented by a Church expression & any (a.s.) halting Church expression can be represented by a computable distribution

91 (define a (flip 0.3)) (define b (flip 0.3)) (define c (flip 0.3)) (+ a b c) ,5 0,375 0,25 0,125 0 P (n) = n n n

92 0,5 0,375 (define a (flip 0.3)) (define b (flip 0.3)) (define c (flip 0.3)) (+ a b c) composition doesn t require 0,25 0,125 0 P (n) = n n n integrals

93 0,5 0,375 (define a (flip 0.3)) (define b (flip 0.3)) (define c (flip 0.3)) (+ a b c) composition doesn t require 0,25 0,125 0 P (n) = n n n integrals generalisation of d.g.m.:

94 0,5 0,375 (define a (flip 0.3)) (define b (flip 0.3)) (define c (flip 0.3)) (+ a b c) composition doesn t require 0,25 0,125 0 P (n) = n n n integrals generalisation of d.g.m.: causality

95 0,5 0,375 (define a (flip 0.3)) (define b (flip 0.3)) (define c (flip 0.3)) (+ a b c) composition doesn t require 0,25 0,125 0 P (n) = n n n integrals generalisation of d.g.m.: causality hierarchical models

96 0,5 0,375 (define a (flip 0.3)) (define b (flip 0.3)) (define c (flip 0.3)) (+ a b c) composition doesn t require 0,25 0,125 0 P (n) = n n n integrals generalisation of d.g.m.: causality hierarchical models non-parametric models

97 models.html#example-causal-models-in-medical- diagnosis sequences.html#probabilistic-context-free- grammars

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99 generative model

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108 inference

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112 [Grosse et al 2012] Grosse, R. B., Salakhutdinov, R., Freeman, W. T., & Tenenbaum, J. B. (2012). Exploiting compositionality to explore a large space of model structures. Conference on Uncertainty in Artificial Intelligence. Retrieved from [Griffiths et al 2008] Griffiths, Thomas L., Charles Kemp, and Joshua B. Tenenbaum. "Bayesian models of cognition." (2008).