Koo, P. K., Ploenzke, Matt (June 2019) Improving Convolutional Network Interpretability with Exponential Activations. In: ICML Workshop for Computational Biology, Long Beach, CA.
Abstract
Deep convolutional networks trained on regula- tory 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 meaning- ful. 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.
| Item Type: | Conference or Workshop Item (Paper) |
|---|---|
| Subjects: | bioinformatics > computational biology organs, tissues, organelles, cell types and functions > tissues types and functions > neural networks |
| CSHL Authors: | |
| Communities: | CSHL labs > Koo Lab |
| Depositing User: | Matthew Dunn |
| Date: | 14 June 2019 |
| Date Deposited: | 17 Sep 2019 19:50 |
| Last Modified: | 17 Sep 2019 19:50 |
| URI: | https://repository.cshl.edu/id/eprint/38418 |
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