Yin, Fan, Li, Yao, Hsieh, Cho-Jui, and Chang, Kai-Wei. ADDMU: Detection of Far-Boundary Adversarial Examples with Data and Model Uncertainty Estimation. Retrieved from https://par.nsf.gov/biblio/10415498. Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing . Web. doi:10.18653/v1/2022.emnlp-main.440.

Yin, Fan, Li, Yao, Hsieh, Cho-Jui, & Chang, Kai-Wei. ADDMU: Detection of Far-Boundary Adversarial Examples with Data and Model Uncertainty Estimation. Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, (). Retrieved from https://par.nsf.gov/biblio/10415498. https://doi.org/10.18653/v1/2022.emnlp-main.440

Yin, Fan, Li, Yao, Hsieh, Cho-Jui, and Chang, Kai-Wei. "ADDMU: Detection of Far-Boundary Adversarial Examples with Data and Model Uncertainty Estimation". Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing (). Country unknown/Code not available: Association for Computational Linguistics. https://doi.org/10.18653/v1/2022.emnlp-main.440. https://par.nsf.gov/biblio/10415498.

@article{osti_10415498,
place = {Country unknown/Code not available}, title = {ADDMU: Detection of Far-Boundary Adversarial Examples with Data and Model Uncertainty Estimation}, url = {https://par.nsf.gov/biblio/10415498}, DOI = {10.18653/v1/2022.emnlp-main.440}, abstractNote = {}, journal = {Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing}, publisher = {Association for Computational Linguistics}, author = {Yin, Fan and Li, Yao and Hsieh, Cho-Jui and Chang, Kai-Wei}, }