A Workflow-Forecast Approach To The Task Scheduling Problem In Distributed Computing Systems. - Andrey Gritsenko (CoRR, 2013)

Extreme Learning Machines for Multiclass Classification: Refining Predictions with Gaussian Mixture Models. - Emil Eirola; Andrey Gritsenko; Anton Akusok; Kaj-Mikael Björk; Yoan Miche; Dusan Sovilj; Rui Nian; Bo He 002; Amaury Lendasse (IWANN (2), 2015)

Combined nonlinear visualization and classification: ELMVIS++C. - Andrey Gritsenko; Anton Akusok; Yoan Miché; Kaj-Mikael Björk; Stephen Baek; Amaury Lendasse (IJCNN, 2016)

Advanced query strategies for Active Learning with Extreme Learning Machines. - Anton Akusok; Emil Eirola; Yoan Miché; Andrey Gritsenko; Amaury Lendasse (ESANN, 2017)

Solve Classification Tasks with Probabilities. Statistically-Modeled Outputs. - Andrey Gritsenko; Emil Eirola; Daniel Schupp; Edward R. Ratner; Amaury Lendasse (HAIS, 2017)

Deep Spectral Descriptors: Learning the point-wise correspondence metric via Siamese deep neural networks. - Zhiyu Sun; Yusen He; Andrey Gritsenko; Amaury Lendasse; Stephen Baek (CoRR, 2017)

Adding reliability to ELM forecasts by confidence intervals. - Anton Akusok; Andrey Gritsenko; Yoan Miche; Kaj-Mikael Björk; Rui Nian; Paula Lauren; Amaury Lendasse (Neurocomputing, 2017)

Extreme Learning Machines for VISualization+R: Mastering Visualization with Target Variables. - Andrey Gritsenko; Anton Akusok; Stephen Baek; Yoan Miche; Amaury Lendasse (Cogn. Comput., 2018)

Hill Climbing Optimized Twin Classification Using Resting-State Functional MRI. - Andrey Gritsenko; Martin A. Lindquist; Gregory R. Kirk; Moo K. Chung (CoRR, 2018)

Finding a 'New' Needle in the Haystack: Unseen Radio Detection in Large Populations Using Deep Learning. - Andrey Gritsenko; Zifeng Wang 002; Tong Jian; Jennifer G. Dy; Kaushik R. Chowdhury; Stratis Ioannidis (DySPAN, 2019)

MAC ID Spoofing-Resistant Radio Fingerprinting. - Tong Jian; Bruno Costa Rendon; Andrey Gritsenko; Jennifer G. Dy; Kaushik R. Chowdhury; Stratis Ioannidis (GlobalSIP, 2019)

Statistical Inference on the Number of Cycles in Brain Networks. - Moo K. Chung; Shih-Gu Huang; Andrey Gritsenko; Li Shen 001; Hyekyoung Lee (ISBI, 2019)

Circular Pearson Correlation Using Cosine Series Expansion. - Shih-Gu Huang; Andrey Gritsenko; Martin A. Lindquist; Moo K. Chung (ISBI, 2019)

Incremental ELMVIS for unsupervised learning. - Anton Akusok; Emil Eirola; Yoan Miche; Ian Oliver; Kaj-Mikael Björk; Andrey Gritsenko; Stephen Baek; Amaury Lendasse (CoRR, 2019)

Open-World Class Discovery with Kernel Networks. - Zifeng Wang 002; Batool Salehi; Andrey Gritsenko; Kaushik R. Chowdhury; Stratis Ioannidis; Jennifer G. Dy (ICDM, 2020)

Twin Classification in Resting-State Brain Connectivity. - Andrey Gritsenko; Martin A. Lindquist; Moo K. Chung (ISBI, 2020)

Deep Learning for RF Fingerprinting: A Massive Experimental Study. - Tong Jian; Bruno Costa Rendon; Emmanuel Ojuba; Nasim Soltani; Zifeng Wang 002; Kunal Sankhe; Andrey Gritsenko; Jennifer G. Dy; Kaushik R. Chowdhury; Stratis Ioannidis (IEEE Internet Things Mag., 2020)

Embedded spectral descriptors: learning the point-wise correspondence metric via Siamese neural networks. - Zhiyu Sun; Yusen He; Andrey Gritsenko; Amaury Lendasse; Stephen Baek (J. Comput. Des. Eng., 2020)

Graph transfer learning. - Andrey Gritsenko; Kimia Shayestehfard; Yuan Guo; Armin Moharrer; Jennifer G. Dy; Stratis Ioannidis (Knowl. Inf. Syst., 2023)