论文标题
部分可观测时空混沌系统的无模型预测
Neural network layers as parametric spans
论文作者
论文摘要
诸如合并性和自动分化之类的属性使人工神经网络成为应用中普遍存在的工具。解决更具挑战性的问题导致神经网络逐渐变得更加复杂，因此很难从数学角度定义。我们提出了基于集成理论和参数跨度的概念的分类框架产生的线性层的一般定义。该定义概括并包含经典层（例如，密集，卷积），同时保证了层的衍生物对反向传播的存在和可计算性。
Properties such as composability and automatic differentiation made artificial neural networks a pervasive tool in applications. Tackling more challenging problems caused neural networks to progressively become more complex and thus difficult to define from a mathematical perspective. We present a general definition of linear layer arising from a categorical framework based on the notions of integration theory and parametric spans. This definition generalizes and encompasses classical layers (e.g., dense, convolutional), while guaranteeing existence and computability of the layer's derivatives for backpropagation.
