论文标题
使用许多有价值的量子逻辑解决深度学习的可解释性问题
Addressing the interpretability problem for deep learning using many valued quantum logic
论文作者
论文摘要
深度学习模型被广泛用于各种工业和科学应用。尽管这些模型近年来取得了巨大的成功，但仍然对机器学习社区中这种决定的决策背后的理由缺乏理解。这种模型的复杂性日益严重，这种解释性问题进一步加剧了。本文利用机器学习，量子计算和量子场理论的概念来证明在特定类别的生成深度学习模型中，如何自然出现许多有价值的量子逻辑系统，称为卷积深度信念网络。它提供了一个强大的理论框架，用于构建具有许多有价值的量子逻辑系统的可解释性的深度学习模型，而不会损害其计算效率。
Deep learning models are widely used for various industrial and scientific applications. Even though these models have achieved considerable success in recent years, there exists a lack of understanding of the rationale behind decisions made by such systems in the machine learning community. This problem of interpretability is further aggravated by the increasing complexity of such models. This paper utilizes concepts from machine learning, quantum computation and quantum field theory to demonstrate how a many valued quantum logic system naturally arises in a specific class of generative deep learning models called Convolutional Deep Belief Networks. It provides a robust theoretical framework for constructing deep learning models equipped with the interpretability of many valued quantum logic systems without compromising their computing efficiency.
