论文标题
Neucept：通过临界神经元识别局部发现神经网络的机制，并具有精度保证
NeuCEPT: Locally Discover Neural Networks' Mechanism via Critical Neurons Identification with Precision Guarantee
论文作者
论文摘要
尽管最近关于了解深神经网络（DNN）的研究，但关于DNN如何产生其预测的问题仍然存在许多问题。特别是，鉴于对不同输入样本的类似预测，基本机制是否会产生这些预测？在这项工作中，我们提出了neucept，这是一种局部发现关键神经元的方法，该神经元在模型的预测中起着主要作用，并确定模型在产生这些预测时的机制。我们首先提出一个关键的神经元鉴定问题，以最大程度地提高一系列相互信息目标，并提供一个理论框架，以有效地解决关键神经元，同时控制精度。 Neucept接下来以无监督的方式学习了不同模型的机制。我们的实验结果表明，Neucept鉴定的神经元不仅对模型的预测有很大影响，而且对模型的机制有有意义的信息。
Despite recent studies on understanding deep neural networks (DNNs), there exists numerous questions on how DNNs generate their predictions. Especially, given similar predictions on different input samples, are the underlying mechanisms generating those predictions the same? In this work, we propose NeuCEPT, a method to locally discover critical neurons that play a major role in the model's predictions and identify model's mechanisms in generating those predictions. We first formulate a critical neurons identification problem as maximizing a sequence of mutual-information objectives and provide a theoretical framework to efficiently solve for critical neurons while keeping the precision under control. NeuCEPT next heuristically learns different model's mechanisms in an unsupervised manner. Our experimental results show that neurons identified by NeuCEPT not only have strong influence on the model's predictions but also hold meaningful information about model's mechanisms.
