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- W4383749308 abstract "In recent years, while Deep Neural Networks (DNNs) have revolutionized various fields, it is widely acknowledged that they are not always the optimal solution, and complementary Machine Learning (ML) tools are necessary. For instance, developing DNN models that can effectively handle tabular data with rows and columns remains a challenging open question. Additionally, the difficulty of interpreting DNN models poses a significant obstacle that hinders their use in many practical applications where the interpretability of the inference results and the ability to offer advice on how to modify input for desired output are required. In such cases, Decision Forests (DFs) have been widely considered a promising solution." @default.
- W4383749308 created "2023-07-11" @default.
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- W4383749308 date "2023-05-01" @default.
- W4383749308 modified "2023-09-27" @default.
- W4383749308 title "Decision Forest Training Accelerator Based on Binary Feature Decomposition" @default.
- W4383749308 doi "https://doi.org/10.1109/fccm57271.2023.00043" @default.
- W4383749308 hasPublicationYear "2023" @default.
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