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Improving Convolutional Network Interpretability with Exponential Activations

Peter K. Koo, Matt Ploenzke
doi: https://doi.org/10.1101/650804
Peter K. Koo
1Howard Hughes Medical Institute, Har- vard University, Cambridge, MA
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  • For correspondence: peter_koo@harvard.edu ploen-zke@g.harvard.edu
Matt Ploenzke
2Department of Biostatistics, Harvard University, Boston, MA
3Department of Data Sciences, Dana-Farber Cancer Institute, Boston, MA
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  • For correspondence: peter_koo@harvard.edu ploen-zke@g.harvard.edu
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Abstract

Deep convolutional networks trained on regulatory genomic sequences tend to learn distributed representations of sequence motifs across many first layer filters. This makes it challenging to decipher which features are biologically meaningful. Here we introduce the exponential activation that – when applied to first layer filters – leads to more interpretable representations of motifs, both visually and quantitatively, compared to rectified linear units. We demonstrate this on synthetic DNA sequences which have ground truth with various convolutional networks, and then show that this phenomenon holds on in vivo DNA sequences.

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Posted May 27, 2019.
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Improving Convolutional Network Interpretability with Exponential Activations
Peter K. Koo, Matt Ploenzke
bioRxiv 650804; doi: https://doi.org/10.1101/650804
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Improving Convolutional Network Interpretability with Exponential Activations
Peter K. Koo, Matt Ploenzke
bioRxiv 650804; doi: https://doi.org/10.1101/650804

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