Unsupervised Learning by Program Synthesis
Abstract
We introduce an unsupervised learning algorithmthat combines probabilistic modeling with solver-based techniques for program synthesis.We apply our techniques to both a visual learning domain and a language learning problem,showing that our algorithm can learn many visual concepts from only a few examplesand that it can recover some English inflectional morphology.Taken together, these results give both a new approach to unsupervised learning of symbolic compositional structures,and a technique for applying program synthesis tools to noisy data.
Cite
Text
Ellis et al. "Unsupervised Learning by Program Synthesis." Neural Information Processing Systems, 2015.Markdown
[Ellis et al. "Unsupervised Learning by Program Synthesis." Neural Information Processing Systems, 2015.](https://mlanthology.org/neurips/2015/ellis2015neurips-unsupervised/)BibTeX
@inproceedings{ellis2015neurips-unsupervised,
title = {{Unsupervised Learning by Program Synthesis}},
author = {Ellis, Kevin and Solar-Lezama, Armando and Tenenbaum, Josh},
booktitle = {Neural Information Processing Systems},
year = {2015},
pages = {973-981},
url = {https://mlanthology.org/neurips/2015/ellis2015neurips-unsupervised/}
}