Li, Changbin Li, Li, Kangshui, Ou, Yuzhe, Kaplan, Lance M, Jøsang, Audun, Jeong, Dong H, Cho, Jin-Hee, and Chen, Feng. Hyper Evidential Deep Learning to Quantify Composite Classification Uncertainty. Retrieved from https://par.nsf.gov/biblio/10530172.

Li, Changbin Li, Li, Kangshui, Ou, Yuzhe, Kaplan, Lance M, Jøsang, Audun, Jeong, Dong H, Cho, Jin-Hee, & Chen, Feng. Hyper Evidential Deep Learning to Quantify Composite Classification Uncertainty. Retrieved from https://par.nsf.gov/biblio/10530172.

Li, Changbin Li, Li, Kangshui, Ou, Yuzhe, Kaplan, Lance M, Jøsang, Audun, Jeong, Dong H, Cho, Jin-Hee, and Chen, Feng. "Hyper Evidential Deep Learning to Quantify Composite Classification Uncertainty". Country unknown/Code not available: Proceeding of the International Conference on Learning Representations (ICLR). https://par.nsf.gov/biblio/10530172.

@article{osti_10530172,
place = {Country unknown/Code not available}, title = {Hyper Evidential Deep Learning to Quantify Composite Classification Uncertainty}, url = {https://par.nsf.gov/biblio/10530172}, abstractNote = {}, journal = {}, publisher = {Proceeding of the International Conference on Learning Representations (ICLR)}, author = {Li, Changbin Li and Li, Kangshui and Ou, Yuzhe and Kaplan, Lance M and Jøsang, Audun and Jeong, Dong H and Cho, Jin-Hee and Chen, Feng}, }