The Recurrent Neural Tangent Kernel

• URL: http://arxiv.org/abs/2006.10246v4
• Date: Tue, 15 Jun 2021 00:43:41 GMT
• Title: The Recurrent Neural Tangent Kernel
• Authors: Sina Alemohammad, Zichao Wang, Randall Balestriero, Richard Baraniuk
• Abstract summary: We introduce and study the Recurrent Neural Tangent Kernel (RNTK), which provides new insights into the behavior of overparametrized RNNs. A synthetic and 56 real-world data experiments demonstrate that the RNTK offers significant performance gains over other kernels.
• Score: 11.591070761599328
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The study of deep neural networks (DNNs) in the infinite-width limit, via the so-called neural tangent kernel (NTK) approach, has provided new insights into the dynamics of learning, generalization, and the impact of initialization. One key DNN architecture remains to be kernelized, namely, the recurrent neural network (RNN). In this paper we introduce and study the Recurrent Neural Tangent Kernel (RNTK), which provides new insights into the behavior of overparametrized RNNs. A key property of the RNTK should greatly benefit practitioners is its ability to compare inputs of different length. To this end, we characterize how the RNTK weights different time steps to form its output under different initialization parameters and nonlinearity choices. A synthetic and 56 real-world data experiments demonstrate that the RNTK offers significant performance gains over other kernels, including standard NTKs, across a wide array of data sets.

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