Brian Baingana
Orcid: 0000-0002-8310-5793
According to our database1,
Brian Baingana
authored at least 21 papers
between 2013 and 2019.
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Bibliography
2019
Nonlinear Structural Vector Autoregressive Models With Application to Directed Brain Networks.
IEEE Trans. Signal Process., 2019
2017
Tensor Decompositions for Identifying Directed Graph Topologies and Tracking Dynamic Networks.
IEEE Trans. Signal Process., 2017
Kernel-Based Structural Equation Models for Topology Identification of Directed Networks.
IEEE Trans. Signal Process., 2017
IEEE Trans. Signal Process., 2017
Topology inference of directed graphs using nonlinear structural vector autoregressive models.
Proceedings of the 2017 IEEE International Conference on Acoustics, 2017
2016
IEEE Trans. Signal Process., 2016
Tracking dynamic piecewise-constant network topologies via adaptive tensor factorization.
Proceedings of the 2016 IEEE Global Conference on Signal and Information Processing, 2016
Proceedings of the 2016 IEEE Global Conference on Signal and Information Processing, 2016
Proceedings of the 2016 Annual Conference on Information Science and Systems, 2016
Proceedings of the 50th Asilomar Conference on Signals, Systems and Computers, 2016
2015
Proceedings of the 2015 IEEE International Conference on Acoustics, 2015
Proceedings of the 2015 IEEE Global Conference on Signal and Information Processing, 2015
Proceedings of the 6th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, 2015
Proceedings of the 49th Asilomar Conference on Signals, Systems and Computers, 2015
2014
IEEE J. Sel. Top. Signal Process., 2014
Proceedings of the IEEE International Conference on Acoustics, 2014
Proceedings of the 2014 IEEE Global Conference on Signal and Information Processing, 2014
2013
Proceedings of the IEEE International Conference on Acoustics, 2013
Identifiability of sparse structural equation models for directed and cyclic networks.
Proceedings of the IEEE Global Conference on Signal and Information Processing, 2013
Proceedings of the 5th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, 2013