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pro vyhledávání: '"Krakovna, Viktoriya"'
As deep neural networks continue to revolutionize various application domains, there is increasing interest in making these powerful models more understandable and interpretable, and narrowing down the causes of good and bad predictions. We focus on
Externí odkaz:
http://arxiv.org/abs/1611.05934
Autor:
Krakovna, Viktoriya, Looks, Moshe
Sum-Product Networks (SPNs) are a class of expressive yet tractable hierarchical graphical models. LearnSPN is a structure learning algorithm for SPNs that uses hierarchical co-clustering to simultaneously identifying similar entities and similar fea
Externí odkaz:
http://arxiv.org/abs/1602.04259
It is becoming increasingly important for machine learning methods to make predictions that are interpretable as well as accurate. In many practical applications, it is of interest which features and feature interactions are relevant to the predictio
Externí odkaz:
http://arxiv.org/abs/1506.02371
Akademický článek
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