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Uncovering motif interactions from convolutional-attention networks for genomics
A major goal of computational genomics is to understand how sequence patterns, called motifs, interact to regulate gene expression.
Towards trustworthy explanations with gradient-based attribution methods
In practice, gradient-based attribution methods, such as saliency maps, can yield noisy importance scores depending on model architecture and training procedure.
Single Layers of Attention Suffice to Predict Protein Contacts
On the one hand, factored attention is a direct simplification of multihead scaled dot-product attention in the Transformer.
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