@article {1566,
	title = {Human-level concept learning through probabilistic program induction},
	journal = {Science},
	volume = {350},
	year = {2015},
	month = {12/11/2015},
	pages = {1332-1338 },
	abstract = {<p>People learning new concepts can often generalize successfully from just a single example, yet machine learning algorithms typically require tens or hundreds of examples to perform with similar accuracy. People can also use learned concepts in richer ways than conventional algorithms{\textemdash}for action, imagination, and explanation. We present a computational model that captures these human learning abilities for a large class of simple visual concepts: handwritten characters from the world{\textquoteright}s alphabets. The model represents concepts as simple programs that best explain observed examples under a Bayesian criterion. On a challenging one-shot classification task, the model achieves human-level performance while outperforming recent deep learning approaches. We also present several {\textquotedblleft}visual Turing tests{\textquotedblright} probing the model{\textquoteright}s creative generalization abilities, which in many cases are indistinguishable from human behavior.</p>
},
	keywords = {Machine Learning},
	doi = {10.1126/science.aab3050 },
	url = {http://www.sciencemag.org/content/350/6266/1332.short},
	author = {Brenden M Lake and Salakhutdinov, Ruslan and Joshua B. Tenenbaum}
}
