We discuss the discontinuities that arise when mapping unordered objects to neural network outputs of fixed permutation, referred to as the responsibility problem. Prior work has proved the existence of the issue by identifying a single discontinuity. Here, we show that discontinuities under such models are uncountably infinite, motivating further research into neural networks for unordered data.
翻译:我们讨论了将无序对象映射到具有固定置换的神经网络输出时出现的不连续性,称为责任问题。之前的研究通过确定单个不连续点证明了该问题的存在。在这里,我们表明在这种模型下,不连续点是无限不可数的,这进一步推动了神经网络在无序数据方面的研究。