NeurIPS 2015poster104 citations
Unsupervised Learning by Program Synthesis
Kevin Ellis, Armando Solar-Lezama, Josh Tenenbaum
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.
BibTeX
@inproceedings{NIPS2015_b73dfe25,
author = {Ellis, Kevin and Solar-Lezama, Armando and Tenenbaum, Josh},
booktitle = {Advances in Neural Information Processing Systems},
editor = {C. Cortes and N. Lawrence and D. Lee and M. Sugiyama and R. Garnett},
pages = {},
publisher = {Curran Associates, Inc.},
title = {Unsupervised Learning by Program Synthesis},
url = {https://proceedings.neurips.cc/paper_files/paper/2015/file/b73dfe25b4b8714c029b37a6ad3006fa-Paper.pdf},
volume = {28},
year = {2015}
}