Learning Infinite-Layer Networks: Without the Kernel Trick

Understand

Infinite--Layer Networks (ILN) have recently been proposed as an architecture that mimics neural networks while enjoying some of the advantages of kernel methods.

• ILN are networks that integrate over infinitely many nodes within a single hidden layer.
• It has been demonstrated by several authors that the problem of learning ILN can be reduced to the kernel trick, implying that whenever a certain integral can be computed analytically they are efficiently learnable.
• In this work we give an online algorithm for ILN, which avoids the kernel trick assumption.