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pro vyhledávání: '"Mitzenmacher, Michael M."'
Autor:
Reagen, Brandon, Gupta, Udit, Adolf, Robert, Mitzenmacher, Michael M., Rush, Alexander M., Wei, Gu-Yeon, Brooks, David
The large memory requirements of deep neural networks limit their deployment and adoption on many devices. Model compression methods effectively reduce the memory requirements of these models, usually through applying transformations such as weight p
Externí odkaz:
http://arxiv.org/abs/1711.04686
Autor:
Reshef, Yakir A., Reshef, David N., Finucane, Hilary K., Sabeti, Pardis C., Mitzenmacher, Michael M.
Publikováno v:
J.Mach.Learn.Res. 17 (2016), 1-63
Given a high-dimensional data set we often wish to find the strongest relationships within it. A common strategy is to evaluate a measure of dependence on every variable pair and retain the highest-scoring pairs for follow-up. This strategy works wel
Externí odkaz:
http://arxiv.org/abs/1505.02213
For analysis of a high-dimensional dataset, a common approach is to test a null hypothesis of statistical independence on all variable pairs using a non-parametric measure of dependence. However, because this approach attempts to identify any non-tri
Externí odkaz:
http://arxiv.org/abs/1505.02212
Publikováno v:
Ann.Appl.Stat. 12 (2018) 123-155
In exploratory data analysis, we are often interested in identifying promising pairwise associations for further analysis while filtering out weaker, less interesting ones. This can be accomplished by computing a measure of dependence on all variable
Externí odkaz:
http://arxiv.org/abs/1505.02214
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