A key property of neural networks (both biological and artificial) is how
they learn to represent and manipulate input information in order to solve a
task. Different types of representations may be suited to different types of
tasks, making identifying and understanding learned representations a critical
part of understanding and designing useful networks. In this paper, we
introduce a new pseudo-kernel based tool for analyzing and predicting learned
representations, based only on the initial conditions of the network and the
training curriculum. We validate the method on a simple test case, before
demonstrating its use on a question about the effects of representational
learning on sequential single versus concurrent multitask performance. We show
that our method can be used to predict the effects of the scale of weight
initialization and training curriculum on representational learning and
downstream concurrent multitasking performance.

本文提出了一种基于伪核的新工具，用于分析和预测网络学习到的表示，通过验证其在简单测试案例上的使用，本文论证了该方法可预测权重初始化和训练计划对表示学习和下游并发多任务执行表现的影响。