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ref: -2017 tags: human level concept learning through probabalistic program induction date: 01-20-2020 15:45 gmt revision:0 [head]

PMID-26659050 Human level concept learning through probabalistic program induction

  • Preface:
    • How do people learn new concepts from just one or a few examples?
    • And how do people learn such abstract, rich, and flexible representations?
    • How can learning succeed from such sparse dataset also produce such rich representations?
    • For any theory of learning, fitting a more complicated model requires more data, not less, to achieve some measure of good generalization, usually in the difference between new and old examples.
  • Learning proceeds bu constructing programs that best explain the observations under a Bayesian criterion, and the model 'learns to learn' by developing hierarchical priors that allow previous experience with related concepts to ease learning of new concepts.
  • These priors represent learned inductive bias that abstracts the key regularities and dimensions of variation holding actoss both types of concepts and across instances.
  • BPL can construct new programs by reusing pieced of existing ones, capturing the causal and compositional properties of real-world generative processes operating on multiple scales.
  • Posterior inference requires searching the large combinatorial space of programs that could have generated a raw image.
    • Our strategy uses fast bottom-up methods (31) to propose a range of candidate parses.
    • That is, they reduce the character to a set of lines (series of line segments), then simply the intersection of those lines, and run a series of parses to estimate the generation of those lines, with heuristic criteria to encourage continuity (e.g. no sharp angles, penalty for abruptly changing direction, etc).
    • The most promising candidates are refined by using continuous optimization and local search, forming a discrete approximation to the posterior distribution P(program, parameters | image).