| dc.contributor.author | Liao, Qianli | |
| dc.date.accessioned | 2020-06-18T18:15:26Z | |
| dc.date.available | 2020-06-18T18:15:26Z | |
| dc.date.issued | 2020-06-18 | |
| dc.identifier.uri | https://hdl.handle.net/1721.1/125866 | |
| dc.description.abstract | We discuss the problem of flexibility in intelligence, a relatively little-studied topic in machine learning and AI. Flexibility can be understood as out-of-distribution generalization, and it can be achieved by converting novel distribution into known distributions. Such conversions may play the role of knowledge and is accumulated in the intelligent system, leading to human-like learning and generalizations. | en_US |
| dc.description.sponsorship | This work was supported by the Center for Brains, Minds and Machines (CBMM), funded by NSF STC award CCF - 1231216. | en_US |
| dc.title | Flexible Intelligence | en_US |
| dc.type | Article | en_US |
| dc.type | Technical Report | en_US |
| dc.type | Working Paper | en_US |
| dc.contributor.department | Center for Brains, Minds, and Machines | |
