Gated Recurrent Neural Networks with Weighted Time-Delay Feedback

• URL: http://arxiv.org/abs/2212.00228v2
• Date: Mon, 19 May 2025 22:03:29 GMT
• Title: Gated Recurrent Neural Networks with Weighted Time-Delay Feedback
• Authors: N. Benjamin Erichson, Soon Hoe Lim, Michael W. Mahoney,
• Abstract summary: We present a novel approach to modeling long-term dependencies in sequential data by introducing a gated recurrent unit (GRU) with a weighted time-delay feedback mechanism.<n>Our proposed model, named $tau$-GRU, is a discretized version of a continuous-time formulation of a recurrent unit, where the dynamics are governed by delay differential equations (DDEs)
• Score: 55.596897987498174
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In this paper, we present a novel approach to modeling long-term dependencies in sequential data by introducing a gated recurrent unit (GRU) with a weighted time-delay feedback mechanism. Our proposed model, named $\tau$-GRU, is a discretized version of a continuous-time formulation of a recurrent unit, where the dynamics are governed by delay differential equations (DDEs). We prove the existence and uniqueness of solutions for the continuous-time model and show that the proposed feedback mechanism can significantly improve the modeling of long-term dependencies. Our empirical results indicate that $\tau$-GRU outperforms state-of-the-art recurrent units and gated recurrent architectures on a range of tasks, achieving faster convergence and better generalization.

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