2015
NIPS
NeurIPS 2015
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.
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Interdisciplinary Bridge
— Artificial Intelligence and Deep Learning and Machine Learning
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Trend Setter
— Unsupervised Learning
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Keyword Pioneer
— visual learning
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Hot Topic Early Bird
— probabilistic modeling
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Cross-Pollinator
— Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Robotics, Security & Privacy, Speech & Audio
Authors
Topics
Artificial Intelligence > Learning Paradigms > Transfer Learning
Machine Learning > Learning Types > Unsupervised Learning
Deep Learning > Models > Generative Models
Artificial Intelligence > Core AI > Reasoning
Machine Learning > Learning Paradigms > Unsupervised Learning
Deep Learning > Learning Types > Unsupervised Learning