Kernelized Synaptic Weight Matrices

Kernelized Synaptic Weight Matrices

In this paper we introduce a novel neural network architecture, in which weight matrices are re-parametrized in terms of low-dimensional vectors, interacting through kernel functions. A layer of our network can be interpreted as introducing a (potentially infinitely wide) linear layer between input and output.We describe the theory underpinning this model and validate it with concrete examples, exploring how it can be used to impose structure on neural networks in diverse applications ranging from data visualization to recommender systems. We achieve state-of-the-art performance in a collaborative filtering task (MovieLens).

核化突触权重矩阵

在本文中，我们介绍了一种新颖的神经网络体系结构，其中权重矩阵根据低维向量进行重新参数化，并通过内核函数进行交互。我们网络的一层可以解释为在输入和输出之间引入了一个（可能无限宽）线性层。.. 我们描述了该模型的基础理论，并通过具体示例进行了验证，探索了如何在从数据可视化到推荐系统的各种应用中将其强加于神经网络上。我们通过协作过滤任务（MovieLens）实现了最先进的性能。 （阅读更多）