CoSy seminar: "Exploring the Connection Between Reservoir Computing and Approximation of Kernel Methods"

Everyone is welcome and the first 40 people to register will be treated to a free lunch sandwich. If you do not want lunch, you are still welcome to join.

Register for a lunch sandwich (deadline: Sunday 23 February).

Abstract: The talk will focus on reservoir computing that was originally proposed to address vanishing/exploding gradients problem in training recurrent neural networks. It builds on the idea that a randomly connected recurrent layer – the reservoir – can encode spatiotemporal input signals and enable efficient processing of time-series data. A canonical example where reservoir computing is considered particularly useful – prediction of chaotic dynamical systems – will be discussed. We will look into a novel technique for reservoir computing that uses a memory buffer of recent inputs and expands them into higher-order features. This technique can be interpreted as a polynomial kernel machine leading to a new approach that combines randomized representations from reservoir computing with the idea for approximating polynomial kernels. The approach offers competitive predictive performance and better scalability than the direct expansion of higher-order features. Additionally, the approach has an elegant realization based on a recurrent circuit of Sigma-Pi neurons that can iteratively compute randomized representations of higher-order features. This circuit is amenable for implementation on neuromorphic hardware.

This is a lecture in the seminar series held by CIM (Centre for Interdisciplinary Mathematics).