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The first-layer filters employed in convolutional neural networks tend to learn, or extract, spatial features from the data. Within their application to genomic sequence data, these learned features are often visualized and interpreted by converting them to sequence logos; an information-based representation of the consensus nucleotide motif. The process to obtain such motifs, however, is done through post-training procedures which often discard the filter weights themselves and instead relydoi:10.1101/411934 fatcat:wilsrvvzvreurm3pddej2a5lqe