More Like this

1. Robust Cough Detection With Out-of-Distribution Detection

   https://doi.org/10.1109/JBHI.2023.3264783  

   Chen, Yuhan; Attri, Pankaj; Barahona, Jeffrey; Hernandez, Michelle L.; Carpenter, Delesha; Bozkurt, Alper; Lobaton, Edgar (July 2023, IEEE Journal of Biomedical and Health Informatics)
2. Detection as Regression: Certified Object Detection with Median Smoothing

   Chiang, Ping-Yeh; Curry, Michael; Abdelkader, Ahmed; Kumar, Aounon; Dickerson, John; Goldstein, Tom (December 2020, Advances in neural information processing systems)

   null (Ed.)
3. Learning for Detection: MIMO-OFDM Symbol Detection Through Downlink Pilots

   https://doi.org/10.1109/TWC.2020.2976004  

   Zhou, Zhou; Liu, Lingjia; Chang, Hao-Hsuan (June 2020, IEEE Transactions on Wireless Communications)

   null (Ed.)
4. MANDA: On Adversarial Example Detection for Network Intrusion Detection System

   Wang, Ning; Chen, Yimin; Xiao, Yang; Hu, Yang; Lou, Wenjing; Hou, Thomas (January 2022, IEEE transactions on dependable and secure computing)
5. Semantic Pleonasm Detection

   https://doi.org/10.18653/v1/n18-2036  

   Kashefi, Omid; Lucas, Andrew T.; Hwa, Rebecca (June 2018, Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers))

   Pleonasms are words that are redundant. To aid the development of systems that detect pleonasms in text, we introduce an annotated corpus of semantic pleonasms. We validate the integrity of the corpus with inter-annotator agreement analyses. We also compare it against alternative resources in terms of their effects on several automatic redundancy detection methods. 

   more » « less