论文标题
神经网络中的形式概念观点
Formal Conceptual Views in Neural Networks
论文作者
论文摘要
解释神经网络模型是一项具有挑战性的任务，至今仍无法解决。对于高维和复杂数据尤其如此。通过目前的工作，我们介绍了两个概念，以了解神经网络的概念观点，尤其是一种多重值和象征性的观点。两者都提供了新颖的分析方法，以使人类AI分析师能够深入了解网络神经元所捕获的知识。我们通过对ImageNet和Fruit-360数据集的不同实验来测试新观点的概念表达。此外，我们表明观点在多大程度上允许量化不同学习体系结构的概念相似性。最后，我们证明了如何将概念观点应用于对神经元的人类可理解规则的学习。总而言之，通过我们的工作，我们为全球解释神经网络模型的最相关任务做出了贡献。
Explaining neural network models is a challenging task that remains unsolved in its entirety to this day. This is especially true for high dimensional and complex data. With the present work, we introduce two notions for conceptual views of a neural network, specifically a many-valued and a symbolic view. Both provide novel analysis methods to enable a human AI analyst to grasp deeper insights into the knowledge that is captured by the neurons of a network. We test the conceptual expressivity of our novel views through different experiments on the ImageNet and Fruit-360 data sets. Furthermore, we show to which extent the views allow to quantify the conceptual similarity of different learning architectures. Finally, we demonstrate how conceptual views can be applied for abductive learning of human comprehensible rules from neurons. In summary, with our work, we contribute to the most relevant task of globally explaining neural networks models.
