Two-shot learning of multiple strange attractors

TL;DR

This paper introduces a two-shot learning approach using a combined NGRC and ERT system to accurately process, store, and recall multiple strange attractors, enhancing stability and reducing hyperparameter tuning.

Contribution

It presents a novel two-shot learning method with NGRC+ERT for multi-attractor reconstruction, improving stability and efficiency over previous techniques.

Findings

Achieved high accuracy in reconstructing Lorenz and Halvorsen attractors.

Successfully trained a single system to recall 16 different attractors.

Identified defects in short-term memory processing can cause recall failures.

Abstract

The brain combines short- and long-term memory to process, store, and recall multiple different pieces of information. Inspired by this and recent results on multifunctional and parameter-aware learning, we extend a new machine learning technique that combines short- and long-term memory units, specifically, a system consisting of a next-generation reservoir computer (NGRC) and extremely randomized trees (ERT), to process, store, and recall multiple different strange attractors. We train the combined NGRC+ERT system using a two-shot learning approach which significantly improves performance by filtering out unnecessary features, thereby avoiding extensive hyperparameter optimization. We first show that an NGRC+ERT system achieves highly accurate reconstruction of the short- and long-term dynamics of both the Lorenz and Halvorsen chaotic attractors when using an exponential filtering scheme. We validate these finding by training the NGRC+ERT system to reconstruct different pairs of attractors and also a greater number of attractors. We focus on the task of training a single NGRC+ERT system to reconstruct 16 different attractors and show that sufficient index-based separation in feature space suppresses unwanted switching dynamics, thus stabilizing long-term memory recall. Finally, we identify that defects in short-term memory processing can provoke failure modes in long-term memory recall resulting in confabulation.