LIST OF PUBLICATIONS

TLDR

This work focuses on deep state space models for nonlinear system identification and how convolutional neural networks deal with aliasing in 3D object detection using energy-based models.

Abstract

Working manuscripts [WM8] Daniel Gedon, Niklas Wahlström, Thomas B. Schön and Lennart Ljung. Deep state space models for nonlinear system identification. November 2020. [WM7] Fredrik K. Gustafsson, Martin Danelljan, and Thomas B. Schön. Accurate 3D object detection using energy-based models. October, 2020. [WM6] Anna Wigren, Johan Wågberg, Fredrik Lindsten, Adrian Wills and Thomas B. Schön. Nonlinear system identification – Learning while respecting physical models using Sequential Monte Carlo. October, 2020. [WM5] Antônio H. Ribeiro and Thomas B. Schön. How convolutional neural networks deal with aliasing. October, 2020. [WM4] Jarrad Courts, Adrian Wills and Thomas B. Schön. Variational nonlinear state estimation. September, 2020. [WM3] Johannes Hendriks, Carl Jidling, Adrian Wills and Thomas B. Schön. Linearly constrained neural networks. arXiv:2002.01600, July, 2020. [WM2] Fredrik Ronquist, Jan Kudlicka, Viktor Senderov, Johannes Borgström, Nicolas Lartillot, Daniel Lundén, Lawrence Murray, Thomas B. Schön and David Broman. Probabilistic programming: a powerful new approach to statistical phylogenetics. bioRxiv preprint doi: 10.1101/2020.06.16.154443, July, 2020. [WM1] Adrian Wills and Thomas B. Schön Stochastic quasi-Newton with line-search regularization. September, 2019.