Mutual Information Decay Curves and Hyper-Parameter Grid Search Design for Recurrent Neural Architectures

• URL: http://arxiv.org/abs/2012.04632v1
• Date: Tue, 8 Dec 2020 18:52:01 GMT
• Title: Mutual Information Decay Curves and Hyper-Parameter Grid Search Design for Recurrent Neural Architectures
• Authors: Abhijit Mahalunkar and John D. Kelleher
• Abstract summary: We use mutual information to analyze long distance dependencies (LDDs) within a dataset. We obtain state-of-the-art results for DilatedRNNs across a range of benchmark datasets.
• Score: 1.2894104422808241
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: We present an approach to design the grid searches for hyper-parameter optimization for recurrent neural architectures. The basis for this approach is the use of mutual information to analyze long distance dependencies (LDDs) within a dataset. We also report a set of experiments that demonstrate how using this approach, we obtain state-of-the-art results for DilatedRNNs across a range of benchmark datasets.

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