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Nov, 2023
一种基于NMF的可解释神经网络的构建模块与持续学习
An NMF-Based Building Block for Interpretable Neural Networks With Continual Learning
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Brian K. Vogel
TL;DR
我们的方法通过在NMF基础上结合监督神经网络训练方法,在保持NMF所具备的理想解释性的基础上,实现了高预测性能,并在小型数据集上验证了其具有与MLP相当的预测性能以及更好的解释性。
Abstract
Existing learning methods often struggle to balance
interpretability
and
predictive performance
. While models like nearest neighbors and non-negative matrix factorization (NMF) offer high
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