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：我们探讨了将无序目标映射到固定排列的神经网络输出时出现的间断性问题，即所谓责任问题。已有研究通过识别单一间断点证明了该问题的存在。本文进一步证明，在此类模型下间断点具有不可数无限性，这为面向无序数据的神经网络研究提供了新的动力。