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Andrey Gritsenko
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2020 – today
- 2023
- [j5]Andrey Gritsenko
, Kimia Shayestehfard
Graph transfer learning. Knowl. Inf. Syst. 65(4): 1627-1656 (2023) - 2021
- [c11]Andrey Gritsenko, Yuan Guo, Kimia Shayestehfard, Armin Moharrer, Jennifer G. Dy, Stratis Ioannidis
Graph Transfer Learning. ICDM 2021: 141-150 - 2020
- [j4]Tong Jian, Bruno Costa Rendon, Emmanuel Ojuba, Nasim Soltani, Zifeng Wang, Kunal Sankhe, Andrey Gritsenko, Jennifer G. Dy, Kaushik R. Chowdhury
Deep Learning for RF Fingerprinting: A Massive Experimental Study. IEEE Internet Things Mag. 3(1): 50-57 (2020) - [j3]Zhiyu Sun, Yusen He
Embedded spectral descriptors: learning the point-wise correspondence metric via Siamese neural networks. J. Comput. Des. Eng. 7(1): 18-29 (2020) - [c10]Zifeng Wang, Batool Salehi, Andrey Gritsenko, Kaushik R. Chowdhury
Open-World Class Discovery with Kernel Networks. ICDM 2020: 631-640 - [c9]Andrey Gritsenko, Martin A. Lindquist, Moo K. Chung:
Twin Classification in Resting-State Brain Connectivity. ISBI 2020: 1391-1394 - [i5]Zifeng Wang, Batool Salehi, Andrey Gritsenko, Kaushik R. Chowdhury, Stratis Ioannidis, Jennifer G. Dy:
Open-World Class Discovery with Kernel Networks. CoRR abs/2012.06957 (2020)
2010 – 2019
- 2019
- [c8]Andrey Gritsenko, Zifeng Wang, Tong Jian, Jennifer G. Dy, Kaushik R. Chowdhury
Finding a 'New' Needle in the Haystack: Unseen Radio Detection in Large Populations Using Deep Learning. DySPAN 2019: 1-10 - [c7]Tong Jian, Bruno Costa Rendon, Andrey Gritsenko, Jennifer G. Dy, Kaushik R. Chowdhury
MAC ID Spoofing-Resistant Radio Fingerprinting. GlobalSIP 2019: 1-5 - [c6]Moo K. Chung, Shih-Gu Huang, Andrey Gritsenko, Li Shen, Hyekyoung Lee:
Statistical Inference on the Number of Cycles in Brain Networks. ISBI 2019: 113-116 - [c5]Shih-Gu Huang, Andrey Gritsenko, Martin A. Lindquist, Moo K. Chung:
Circular Pearson Correlation Using Cosine Series Expansion. ISBI 2019: 1774-1777 - [i4]Anton Akusok, Emil Eirola, Yoan Miche, Ian Oliver, Kaj-Mikael Björk, Andrey Gritsenko, Stephen Baek, Amaury Lendasse:
Incremental ELMVIS for unsupervised learning. CoRR abs/1912.08638 (2019) - 2018
- [j2]Andrey Gritsenko
Extreme Learning Machines for VISualization+R: Mastering Visualization with Target Variables. Cogn. Comput. 10(3): 464-477 (2018) - [i3]Andrey Gritsenko, Martin A. Lindquist, Gregory R. Kirk, Moo K. Chung:
Hill Climbing Optimized Twin Classification Using Resting-State Functional MRI. CoRR abs/1807.00244 (2018) - 2017
- [j1]Anton Akusok, Andrey Gritsenko
Adding reliability to ELM forecasts by confidence intervals. Neurocomputing 219: 232-241 (2017) - [c4]Anton Akusok, Emil Eirola, Yoan Miché, Andrey Gritsenko, Amaury Lendasse:
Advanced query strategies for Active Learning with Extreme Learning Machines. ESANN 2017 - [c3]Andrey Gritsenko, Emil Eirola, Daniel Schupp, Edward R. Ratner
Solve Classification Tasks with Probabilities. Statistically-Modeled Outputs. HAIS 2017: 293-305 - [i2]Zhiyu Sun, Yusen He, Andrey Gritsenko, Amaury Lendasse, Stephen Baek:
Deep Spectral Descriptors: Learning the point-wise correspondence metric via Siamese deep neural networks. CoRR abs/1710.06368 (2017) - 2016
- [c2]Andrey Gritsenko, Anton Akusok, Yoan Miché, Kaj-Mikael Björk, Stephen Baek, Amaury Lendasse:
Combined nonlinear visualization and classification: ELMVIS++C. IJCNN 2016: 2617-2624 - 2015
- [c1]Emil Eirola
Extreme Learning Machines for Multiclass Classification: Refining Predictions with Gaussian Mixture Models. IWANN (2) 2015: 153-164 - 2013
- [i1]Andrey Gritsenko:
A Workflow-Forecast Approach To The Task Scheduling Problem In Distributed Computing Systems. CoRR abs/1310.1553 (2013)
Coauthor Index
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