| Deep Learning for RF Fingerprinting: A Massive Experimental Study T Jian, BC Rendon, E Ojuba, N Soltani, Z Wang, K Sankhe, A Gritsenko, ... IEEE Internet of Things Magazine 3 (1), 50-57, 2020 | 107 | 2020 |
| Finding a ‘New’Needle in the Haystack: Unseen Radio Detection in Large Populations Using Deep Learning A Gritsenko, Z Wang, T Jian, J Dy, K Chowdhury, S Ioannidis 2019 IEEE International Symposium on Dynamic Spectrum Access Networks …, 2019 | 33 | 2019 |
| Embedded spectral descriptors: learning the point-wise correspondence metric via Siamese neural networks Z Sun, Y He, A Gritsenko, A Lendasse, S Baek Journal of Computational Design and Engineering 7 (1), 18-29, 2020 | 30 | 2020 |
| Decomposition Analysis and Machine Learning in a Workflow-forecast Approach to the Task Scheduling Problem for High-Loaded Distributed Systems AV Gritsenko, NG Demurchev, VV Kopytov, AO Shulgin Modern Applied Science 9 (5), 38-49, 2015 | 15 | 2015 |
| Statistical Inference on the Number of Cycles in Brain Networks MK Chung, SG Huang, A Gritsenko, L Shen, H Lee 2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019 …, 2019 | 14 | 2019 |
| Extreme Learning Machines for Multiclass Classification: Refining Predictions with Gaussian Mixture Models E Eirola, A Gritsenko, A Akusok, KM Björk, Y Miche, D Sovilj, R Nian, B He, ... International Work-Conference on Artificial Neural Networks, 153-164, 2015 | 13 | 2015 |
| Open-world class discovery with kernel networks Z Wang, B Salehi, A Gritsenko, K Chowdhury, S Ioannidis, J Dy 2020 IEEE International Conference on Data Mining (ICDM), 631-640, 2020 | 12 | 2020 |
| MAC ID Spoofing-Resistant Radio Fingerprinting T Jian, BC Rendon, A Gritsenko, J Dy, K Chowdhury, S Ioannidis 2019 IEEE Global Conference on Signal and Information Processing (GlobalSIP …, 2019 | 12 | 2019 |
| Deformable surface registration with extreme learning machines A Gritsenko, Z Sun, S Baek, Y Miche, R Hu, A Lendasse Proceedings of ELM-2017, 304-316, 2019 | 8 | 2019 |
| Adding Reliability to ELM Forecasts by Confidence Intervals A Akusok, A Gritsenko, Y Miche, KM Björk, R Nian, P Lauren, A Lendasse Neurocomputing 219, 232-241, 2017 | 7 | 2017 |
| Topological brain network distances MK Chung, H Lee, A Gritsenko, A DiChristofano, D Pluta, H Ombao, ... arXiv preprint arXiv:1809.03878, 2018 | 6 | 2018 |
| Probabilistic Methods for Multiclass Classification Problems A Gritsenko, E Eirola, D Schupp, E Ratner, A Lendasse Proceedings of ELM-2015 Volume 2, 385-397, 2016 | 5 | 2016 |
| A modern approach for constructing scheduling and resource providing algorithms based on the prediction of the workflow AV Gritsenko Applied and Fundamental Studies, 144-147, 2013 | 5 | 2013 |
| Prediction of DRMS workload by identification of patterns in job submission processes A Gritsenko Journal of International Scientific Publications: Materials, Methods and …, 2012 | 5 | 2012 |
| Advanced query strategies for Active Learning with Extreme Learning Machines. A Akusok, E Eirola, Y Miche, A Gritsenko, A Lendasse ESANN, 2017 | 4 | 2017 |
| Combined Nonlinear Visualization and Classification: ELMVIS++ C A Gritsenko, A Akusok, Y Miche, KM Björk, S Baek, A Lendasse Neural Networks (IJCNN), 2016 International Joint Conference on, 2617-2624, 2016 | 4 | 2016 |
| Graph transfer learning A Gritsenko, K Shayestehfard, Y Guo, A Moharrer, J Dy, S Ioannidis Knowledge and Information Systems, 1-30, 2022 | 3 | 2022 |
| Twin Classification in Resting-State Brain Connectivity A Gritsenko, M Lindquist, MK Chung 2020 IEEE 17th International Symposium on Biomedical Imaging (ISBI), 1391-1394, 2020 | 3 | 2020 |
| Extreme learning machines for visualization+ r: Mastering visualization with target variables A Gritsenko, A Akusok, S Baek, Y Miche, A Lendasse Cognitive Computation 10, 464-477, 2018 | 3 | 2018 |
| A workflow-forecast approach to the task scheduling problem in distributed computing systems A Gritsenko arXiv preprint arXiv:1310.1553, 2013 | 3 | 2013 |
Amaury LendasseUniversity of HoustonПотвърден имейл адрес: central.uh.edu
