Unsupervised learning by program synthesis
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Solar-Lezama_Unsupervised learning.pdf
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359.87 KB
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Author(s)
Ellis, Kevin M.
Solar Lezama, Armando
Tenenbaum, Joshua B
Date Issued
December 2015
Journal
Advances in Neural Information Processing Systems 28 (NIPS 2015)
Publisher
Neural Information Processing Systems Foundation
Citation
Ellis, Kevin, Armando Solar-Lezama, and Josh Tenenbaum. "Unsupervised Learning by Program Synthesis." Advances in Neural Information Processing Systems 28 (NIPS 2015), 7-12 December, 2015, Montreal, Canada, Neural Information Processing Systems Foundation, 2015. © 2015 Neural Information Processing Systems Foundation
Version
Final published version
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.
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Massachusetts Institute of Technology. Department of Brain and Cognitive Sciences
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Terms of Use
Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use.
Persistent DSpace Link
DOI of Published Version
http://papers.nips.cc/paper/5785-unsupervised-learning-by-program-synthesis
