论文标题
生物启发的神经路径发现
Biologically Inspired Neural Path Finding
论文作者
论文摘要
人脑可被认为是一种图形结构，包括数以千的通过突触连接的生物神经元。如果某些神经元损坏，它具有显着的能力，可以自动重新汇总信息流过备用路径。此外，大脑能够保留信息并将其应用于类似但完全看不见的情况。在本文中，我们从大脑的这些属性中汲取灵感，开发一个计算框架，以在广义图中找到源节点和目标节点之间的最佳低成本路径。我们表明，我们的框架能够在测试时处理看不见的图。此外，当在推理期间任意添加或删除节点时，可以找到替代的最佳路径，同时保持固定的预测时间。代码可在此处找到：https：//github.com/hangligit/pathfinding
The human brain can be considered to be a graphical structure comprising of tens of billions of biological neurons connected by synapses. It has the remarkable ability to automatically re-route information flow through alternate paths in case some neurons are damaged. Moreover, the brain is capable of retaining information and applying it to similar but completely unseen scenarios. In this paper, we take inspiration from these attributes of the brain, to develop a computational framework to find the optimal low cost path between a source node and a destination node in a generalized graph. We show that our framework is capable of handling unseen graphs at test time. Moreover, it can find alternate optimal paths, when nodes are arbitrarily added or removed during inference, while maintaining a fixed prediction time. Code is available here: https://github.com/hangligit/pathfinding
