Gated Recurrent Neural Networks with Weighted Time-Delay Feedback

• URL: http://arxiv.org/abs/2212.00228v1
• Date: Thu, 1 Dec 2022 02:26:34 GMT
• Title: Gated Recurrent Neural Networks with Weighted Time-Delay Feedback
• Authors: N. Benjamin Erichson and Soon Hoe Lim and Michael W. Mahoney
• Abstract summary: We introduce a novel gated recurrent unit (GRU) with a weighted time-delay feedback mechanism. We show that $tau$-GRU can converge faster and generalize better than state-of-the-art recurrent units and gated recurrent architectures.
• Score: 59.125047512495456
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We introduce a novel gated recurrent unit (GRU) with a weighted time-delay feedback mechanism in order to improve the modeling of long-term dependencies in sequential data. This model is a discretized version of a continuous-time formulation of a recurrent unit, where the dynamics are governed by delay differential equations (DDEs). By considering a suitable time-discretization scheme, we propose $\tau$-GRU, a discrete-time gated recurrent unit with delay. We prove the existence and uniqueness of solutions for the continuous-time model, and we demonstrate that the proposed feedback mechanism can help improve the modeling of long-term dependencies. Our empirical results show that $\tau$-GRU can converge faster and generalize better than state-of-the-art recurrent units and gated recurrent architectures on a range of tasks, including time-series classification, human activity recognition, and speech recognition.

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