Warming up recurrent neural networks to maximise reachable multistability greatly improves learning

TL;DR

This paper introduces a warmup initialization method for recurrent neural networks that maximizes their multistability, significantly enhancing their ability to learn long-term dependencies across various tasks.

Contribution

The paper proposes a novel warmup initialization procedure that increases network multistability, improving learning of long time dependencies in recurrent neural networks.

Findings

Warmup improves learning speed and performance on multiple benchmarks.

Double-layer architecture with partial warmup maintains high precision.

Other initialization methods also implicitly foster multistability.

Abstract

Training recurrent neural networks is known to be difficult when time dependencies become long. In this work, we show that most standard cells only have one stable equilibrium at initialisation, and that learning on tasks with long time dependencies generally occurs once the number of network stable equilibria increases; a property known as multistability. Multistability is often not easily attained by initially monostable networks, making learning of long time dependencies between inputs and outputs difficult. This insight leads to the design of a novel way to initialise any recurrent cell connectivity through a procedure called "warmup" to improve its capability to learn arbitrarily long time dependencies. This initialisation procedure is designed to maximise network reachable multistability, i.e., the number of equilibria within the network that can be reached through relevant input trajectories, in few gradient steps. We show on several information restitution, sequence classification, and reinforcement learning benchmarks that warming up greatly improves learning speed and performance, for multiple recurrent cells, but sometimes impedes precision. We therefore introduce a double-layer architecture initialised with a partial warmup that is shown to greatly improve learning of long time dependencies while maintaining high levels of precision. This approach provides a general framework for improving learning abilities of any recurrent cell when long time dependencies are present. We also show empirically that other initialisation and pretraining procedures from the literature implicitly foster reachable multistability of recurrent cells.