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BPL (Bayesian Program Learning) is a MATLAB implementation of the Bayesian Program Learning framework for one-shot concept learning (especially on handwritten characters). The approach treats each concept (e.g. a character) as being generated by a probabilistic program (motor primitives, strokes, spatial relationships), and inference proceeds by fitting those generative programs to a single example, generalizing to new examples, and generating new exemplars. The repository contains code for parsing stroke sequences, fitting motor programs, exemplar generation, classification, re-fitting, and demonstration scripts.
Features
- Motor program / stroke fitting to interpret character generation
- One-shot classification (given one example, classify novel ones)
- Exemplar generation (produce novel variants of a concept)
- Re-fitting of models (adjust generative programs for new data)
- Demo scripts illustrating parsing, classification, exemplar generation
- Support functions for generative priors, optimization, stroke utilities
Project Samples
Categories
Machine LearningLicense
MIT LicenseFollow BPL
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