Fourier-transform-based attribution priors improve the interpretability and stability of deep learning models for genomics

Meta Review

This paper proposes a novel Fourier-based attribution prior, which can facilitate the application of deep learning to sequence data by improving the interpretation of the learned model. There is significant technical novelty, the authors presented strong experimental results, and the proposed approach is likely of high impact in the field of genomics. Reviewers are largely satisfied with the author feedback. Therefore, the submission is clearly above the bar for the acceptance to NeurIPS.