Alex Tseng, Avanti Shrikumar, Anshul Kundaje. Fourier-transform-based attribution priors improve the interpretability and stability of deep learning models for genomics. In Hugo Larochelle, Marc'Aurelio Ranzato, Raia Hadsell, Maria-Florina Balcan, Hsuan-Tien Lin, editors, Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, NeurIPS 2020, December 6-12, 2020, virtual. 2020. [doi]
@inproceedings{TsengSK20, title = {Fourier-transform-based attribution priors improve the interpretability and stability of deep learning models for genomics}, author = {Alex Tseng and Avanti Shrikumar and Anshul Kundaje}, year = {2020}, url = {https://proceedings.neurips.cc/paper/2020/hash/1487987e862c44b91a0296cf3866387e-Abstract.html}, researchr = {https://researchr.org/publication/TsengSK20}, cites = {0}, citedby = {0}, booktitle = {Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, NeurIPS 2020, December 6-12, 2020, virtual}, editor = {Hugo Larochelle and Marc'Aurelio Ranzato and Raia Hadsell and Maria-Florina Balcan and Hsuan-Tien Lin}, }