Slides of paper presentation in the machine learning course

1. In the name of
God
1

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Institute for Advanced Studies in Basic Sciences
Human-level concept learning through probabilistic program induction
Mohammad Amid Abbasi
Teacher: Dr.P.Razzaghi
Winter 2018

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v2015
v378 citation
vScience journal
About paper
Joshua B. Tenenbaum
Brenden Lake
Ruslan Salakhutdinov

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vIntroduction
vHuman-level concept learning
Contents

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1) Learn from as much data as
possible
2) Learning simple models often
works
3) Aim for prediction not
understanding
Introduction
Principles of Big Data Principles of Human Learning
1) Learn from just one or a few
examples
2) Learning complex,
compositional models that
support many abilities
3) Aim for rich causal
understanding

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How do people learn such rich concepts from very little data?

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Human-level concept learning

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Human-level concept learning

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A testbed domain for one-shot learning

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Omniglot data set

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Human drawing

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Bayesian Program Learning (BPL)

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Principle 1: Compositionality

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Principle 2: Causality
Neural caption generation model:
“A group
of people
standing
on top of
a beach”

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Principle 3: Learning-to-learn

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Bayesian Program Learning (BPL)

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Bayesian Program Learning (BPL)

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Bayesian Program Learning (BPL)

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Bayesian Program Learning (BPL)

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Bayesian Program Learning (BPL)

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Turing test

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Turing test

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Visual Turing Tests

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Lesion analysis of key principles

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One-shot classification performance

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vhttps://cims.nyu.edu/~brenden/LakeEtAl2015Science.pdf
vhttps://github.com/brendenlake/omniglot
vhttps://www.youtube.com/watch?v=7tJsrILeti0&t=390s
vhttps://www.youtube.com/watch?v=wkWNfyocwQ8&t=9s
vhttps://www.youtube.com/watch?v=p1VpvOFJg6A&t=3s
vhttps://www.youtube.com/watch?v=orAylPDk6fQ&t=1s
Resources

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Thanks for your attention