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arXiv · 2303.06726

Global Optimality of Elman-type RNN in the Mean-Field Regime

Andrea Agazzi·Jianfeng Lu·Sayan Mukherjee
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Abstract

We analyze Elman-type Recurrent Reural Networks (RNNs) and their training in the mean-field regime. Specifically, we show convergence of gradient descent training dynamics of the RNN to the corresponding mean-field formulation in the large width limit. We also show that the fixed points of the limiting infinite-width dynamics are globally optimal, under some assumptions on the initialization of the weights. Our results establish optimality for feature-learning with wide RNNs in the mean-field regime

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Andrea Agazzi, Jianfeng Lu, Sayan Mukherjee. 2023-03-12. Global Optimality of Elman-type RNN in the Mean-Field Regime. https://arxiv.org/abs/2303.06726

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