Andrey Gritsenko
Andrey Gritsenko
Research Associate, Northeastern University
Geverifieerd e-mailadres voor ece.neu.edu
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Jaar
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
162020
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
152020
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
122015
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
102015
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
92019
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
72017
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
52019
Deformable Surface Registration with Extreme Learning Machines
A Gritsenko, Z Sun, S Baek, Y Miche, R Hu, A Lendasse
International Conference on Extreme Learning Machine, 304-316, 2017
52017
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
52016
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
52013
Prediction of DRMS workload by identification of patterns in job submission processes
A Gritsenko
Journal of International Scientific Publications: Materials, Methods and …, 2012
52012
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
42019
Topological brain network distances
MK Chung, H Lee, A Gritsenko, A DiChristofano, D Pluta, H Ombao, ...
arXiv preprint arXiv:1809.03878, 2018
42018
Advanced query strategies for Active Learning with Extreme Learning Machines.
A Akusok, E Eirola, Y Miche, A Gritsenko, A Lendasse
ESANN, 2017
42017
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
42016
A workflow-forecast approach to the task scheduling problem in distributed computing systems
A Gritsenko
arXiv preprint arXiv:1310.1553, 2013
32013
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
22020
Circular Pearson Correlation Using Cosine Series Expansion
SG Huang, A Gritsenko, MA Lindquist, MK Chung
2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019 …, 2019
22019
Extreme learning machines for visualization+ r: mastering visualization with target variables
A Gritsenko, A Akusok, S Baek, Y Miche, A Lendasse
Cognitive Computation 10 (3), 464-477, 2018
22018
Solve classification tasks with probabilities. statistically-modeled outputs
A Gritsenko, E Eirola, D Schupp, E Ratner, A Lendasse
International Conference on Hybrid Artificial Intelligence Systems, 293-305, 2017
22017
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Artikelen 1–20