TNT: An Interpretable Tree-Network-Tree Learning Framework using Knowledge Distillation

Deep Neural Networks (DNNs) usually work in an end-to-end manner. This makes the trained DNNs easy to use, but they remain an ambiguous decision process for every test case. Unfortunately, the interpretability of decisions is crucial in some scenarios, such as medical or financial data mining and de...

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Detalles Bibliográficos
Autores principales: Li, Jiawei, Li, Yiming, Xiang, Xingchun, Xia, Shu-Tao, Dong, Siyi, Cai, Yun
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2020
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7712003/
https://www.ncbi.nlm.nih.gov/pubmed/33286971
http://dx.doi.org/10.3390/e22111203

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7712003/
https://www.ncbi.nlm.nih.gov/pubmed/33286971
http://dx.doi.org/10.3390/e22111203

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