http://arxiv.org/abs/1602.04938 I find this interesting less for its practical application and more for its algorithmic formulation of "explanation"
@BagelDaughter an explanation is a minimally complex model approximating a local portion of a highly complex model's input space
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@BagelDaughter can this technique be used to 1) automate more of the learning process? (i.e. training set selection) -
@BagelDaughter 2) teach humans things originally learned by black-box learners? (i.e. how to be even better at Go)
End of conversation
New conversation -
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