论文标题
人类可信神经模型的非媒体样参数激活函数
Uninorm-like parametric activation functions for human-understandable neural models
论文作者
论文摘要
我们提出了一个深度学习模型，用于在输入特征之间找到人类的可理解联系。我们的方法基于Nilpotent模糊逻辑和多准则决策（MCDM）的理论背景，使用参数化的，可区分的激活函数。可学习的参数具有语义含义，指示输入特征之间的补偿水平。神经网络使用梯度下降来确定参数，以在输入特征之间找到人为理解的关系。我们通过成功将其应用于UCI机器学习存储库中的分类问题来证明模型的实用性和有效性。
We present a deep learning model for finding human-understandable connections between input features. Our approach uses a parameterized, differentiable activation function, based on the theoretical background of nilpotent fuzzy logic and multi-criteria decision-making (MCDM). The learnable parameter has a semantic meaning indicating the level of compensation between input features. The neural network determines the parameters using gradient descent to find human-understandable relationships between input features. We demonstrate the utility and effectiveness of the model by successfully applying it to classification problems from the UCI Machine Learning Repository.
