Learning to communicate: Channel auto-encoders, domain specific regularizers, and attention TJ O'Shea, K Karra, TC Clancy 2016 IEEE International Symposium on Signal Processing and Information …, 2016 | 269 | 2016 |
Learning approximate neural estimators for wireless channel state information T O'Shea, K Karra, TC Clancy 2017 IEEE 27th international workshop on machine learning for signal …, 2017 | 65 | 2017 |
A data-driven nonparametric approach for probabilistic load-margin assessment considering wind power penetration Y Xu, L Mili, M Korkali, K Karra, Z Zheng, X Chen IEEE Transactions on Power Systems 35 (6), 4756-4768, 2020 | 30 | 2020 |
The trojai software framework: An opensource tool for embedding trojans into deep learning models K Karra, C Ashcraft, N Fendley arXiv preprint arXiv:2003.07233, 2020 | 27 | 2020 |
Poisoning deep reinforcement learning agents with in-distribution triggers C Ashcraft, K Karra arXiv preprint arXiv:2106.07798, 2021 | 22 | 2021 |
Hybrid copula Bayesian networks K Karra, L Mili Conference on probabilistic graphical models, 240-251, 2016 | 16 | 2016 |
Machine learning aided crop yield optimization C Ashcraft, K Karra arXiv preprint arXiv:2111.00963, 2021 | 13 | 2021 |
Learning approximate estimation networks for communication channel state information TJ O'shea, K Karra, TC Clancy US Patent 11,334,807, 2022 | 10 | 2022 |
Copula index for detecting dependence and monotonicity between stochastic signals K Karra, L Mili Information Sciences 485, 18-41, 2019 | 10 | 2019 |
An Empirical Assessment of the Complexity and Realism of Synthetic Social Contact Networks* K Karra, S Swarup, J Graham 2018 IEEE International Conference on Big Data (Big Data), 3959-3967, 2018 | 7 | 2018 |
Wireless Distributed Computing on the Android Platform K Karra Virginia Tech, 2012 | 7 | 2012 |
Probabilistic load-margin assessment using vine copula and gaussian process emulation Y Xu, K Karra, L Mili, M Korkali, X Chen, Z Hu 2020 IEEE Power & Energy Society General Meeting (PESGM), 1-5, 2020 | 4 | 2020 |
A copula statistic for measuring nonlinear dependence with application to feature selection in machine learning MB Hassine, L Mili, K Karra International Journal of Advanced Computer Science and Applications 8 (7), 2017 | 4 | 2017 |
Speaker diarization using two-pass leave-one-out gaussian PLDA clustering of DNN embeddings K Karra, A McCree arXiv preprint arXiv:2104.02469, 2021 | 2 | 2021 |
Smoothening block rewards: How much should miners pay for mining pools? A Cortes-Cubero, JP Madrigal-Cianci, K Karra, Z Zhang arXiv preprint arXiv:2309.02297, 2023 | 1 | 2023 |
An Agent-Based Model Framework for Utility-Based Cryptoeconomies K Karra, T Mellan, M Silva, JP Madrigal-Cianci, AC Cortes, Z Zhang arXiv preprint arXiv:2307.15200, 2023 | 1 | 2023 |
Adversarial Machine Learning and the Future Hybrid Battlespace C Ratto, M Pekala, N Fendley, N Drenkow, K Karra, C Ashcraft, C Costello, ... The Johns Hopkins University Applied Physics Laboratory, 0 | 1 | |
Quantifying Non-linear Dependencies in Blind Source Separation of Power System Signals using Copula Statistics P Algikar, L Mili, K Karra, A Algikar, MB Hassine 2024 IEEE Power & Energy Society Innovative Smart Grid Technologies …, 2024 | | 2024 |
Kullback-Leibler Divergence-Guided Copula Statistics-Based Blind Source Separation of Dependent Signals P Algikar, L Mili, K Karra, MB Hassine arXiv preprint arXiv:2309.07814, 2023 | | 2023 |
Learning approximate estimation networks for communication channel state information TJ O'shea, K Karra, TC Clancy US Patent 11,699,086, 2023 | | 2023 |