Kevin Roy

• Roy, Kevin; Lopez Ramos, Luis Miguel & Beferull-Lozano, Baltasar (2022). Joint Signal Estimation and Nonlinear Topology Identification from Noisy Data with Missing Entries, 2022 56th Asilomar Conference on Signals, Systems, and Computers. IEEE conference proceedings. ISSN 978-1-6654-5906-8. doi: 10.1109/IEEECONF56349.2022.10051968.
• Lopez-Ramos, Luis M.; Roy, Kevin & Beferull-Lozano, Baltasar (2022). Explainable Nonlinear Modelling of Multiple Time Series with Invertible Neural Networks. In Sanfilippo, Filippo; Granmo, Ole-Christoffer; Yildirim Yayilgan, Sule & Bajwa, Imran Sarwar (Ed.), Intelligent Technologies and Applications: 4th International Conference, INTAP 2021, Grimstad, Norway, October 11–13, 2021, Revised Selected Papers. Springer. ISSN 978-3-031-10524-1. p. 17–30. doi: 10.1007/978-3-031-10525-8_2. Show summary

  A method for nonlinear topology identification is proposed, based on the assumption that a collection of time series are generated in two steps: i) a vector autoregressive process in a latent space, and ii) a nonlinear, component-wise, monotonically increasing observation mapping. The latter mappings are assumed invertible, and are modeled as shallow neural networks, so that their inverse can be numerically evaluated, and their parameters can be learned using a technique inspired in deep learning. Due to the function inversion, the backpropagation step is not straightforward, and this paper explains the steps needed to calculate the gradients applying implicit differentiation. Whereas the model explainability is the same as that for linear VAR processes, preliminary numerical tests show that the prediction error becomes smaller.

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