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Title: The Recurrent Neural Tangent Kernel
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.  more » « less
Award ID(s):
1911094 1838177 1730574
NSF-PAR ID:
10374184
Author(s) / Creator(s):
; ; ;
Date Published:
Journal Name:
The International Conference on Learning Representations
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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