Human-level concept learning through probabilistic program induction
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Abstract
People learning new concepts can often generalize successfully from just a single example, yet machine learning algorithms typically require tens or hundreds of examples to perform with similar accuracy. People can also use learned concepts in richer ways than conventional algorithms-for action, imagination, and explanation. We present a computational model that captures these human learning abilities for a large class of simple visual concepts: handwritten characters from the world's alphabets. The model represents concepts as simple programs that best explain observed examples under a Bayesian criterion. On a challenging one-shot classification task, the model achieves human-level performance while outperforming recent deep learning approaches. We also present several "visual Turing tests" probing the model's creative generalization abilities, which in many cases are indistinguishable from human behavior.
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The record
- Venue
- Science
- Topic
- Machine Learning and Algorithms
- Field
- Computer Science
- Canadian institutions
- Canada Research ChairsUniversity of Toronto
- Funders
- Army Research OfficeOffice of Naval ResearchNatural Sciences and Engineering Research Council of CanadaCanadian Institute for Advanced Research
- Keywords
- Computer scienceAlphabetArtificial intelligenceProbabilistic logicTuringNatural language processingMachine learningProgramming languageLinguistics
- Has abstract in OpenAlex
- yes