Thomas Schön

schon2020

Our aim is to automate the extraction of knowledge and understanding from data. Allowing machines (and humans) to understand what is happening and to acquire new skills and learn new things. We achieve this by developing new probabilistic models and deriving algorithms capable of learnings these models from data. The systematic use of probability in representing and manipulating these models is key. It allows us to represent not only what we know, but to some extent also what we do not know. We take a particular interest in dynamical phenomena evolving over time.

Our research is multi-disciplinary and it sits somewhere on the intersection of the areas of Machine learning and statistics, signal processing, automatic control and computer vision. We pursue both basic and applied research, which explains our tight collaboration with various companies. A slightly more detailed overview of our research is available here.

Recent research results/news

April 16, 2021 [Paper for the CVPR Workshop on autonomous driving] Accurate 3D object detection (3DOD) is crucial for safe navigation of complex environments by autonomous robots. We address this challenging problem by exploring recent advances in conditional energy-based models (EBMs) for probabilistic regression. We design a differentiable pooling operator for 3D bounding boxes, serving as the core module of our EBM network. 

Fredrik K. Gustafsson, Martin Danelljan and Thomas B. Schön. Accurate 3D object detection using energy-based models. In the Workshop on Autonomous Driving (WAD) at the Conference on Computer Vision and Pattern  Recognition (CVPR), Online, 2021. [arXiv]

April 4, 2021 [6 Papers for SYSID] Quite a few new results to be presented at SYSID this summer, dominated by deep learning approaches for system identification problems, but there are also a few results on more classical topics in the area, like Hammerstein systems.

Antonio H. Ribeiro, Johannes Hendriks, Adrian Wills and Thomas B. Schön. Beyond Occam’s razor in system identification: double-descent when modeling dynamics. In Proceedings of the 19th IFAC Symposium on System Identification (SYSID), Online, July, 2021. [arXiv]

Carl Andersson, Niklas Wahlström and Thomas B. Schön. Learning deep autoregressive models for hierarchical data. In Proceedings of the 19th IFAC Symposium on System Identification (SYSID), Online, July, 2021.

Mina Ferizbegovic, Per Mattsson, Thomas B. Schön and Håkan Hjalmarsson. Bayes control of Hammerstein systems. In Proceedings of the 19th IFAC Symposium on System Identification (SYSID), Online, July, 2021.

Jarrad Courts, Johannes Hendriks, Adrian Wills, Thomas B. Schön and Brett Ninness. Variational state and parameter estimation. In Proceedings of the 19th IFAC Symposium on System Identification (SYSID), Online, July, 2021. [arXiv]

Johannes Hendriks, Fredrik K. Gustafsson, Antonio H. Ribeiro, Adrian Wills and Thomas B. Schön. Deep energy-based NARX models. In Proceedings of the 19th IFAC Symposium on System Identification (SYSID), Online, July, 2021. [arXiv]

Daniel Gedon, Niklas Wahlström, Thomas B. Schön and Lennart Ljung. Deep state space models for nonlinear system identification. In Proceedings of the 19th IFAC Symposium on System Identification (SYSID), Online, July, 2021. [arXiv]

March 4, 2021 [First paper in biology] This is the result of several years of work in a truly cross-disciplinaty team with researchers from biology, machine learning and programming languages performed within our SSF project ASSEMBLE. We consider the problem of statistical phylogenetic analysis. We show that universal probabilistic programming languages (PPLs) solve the expressivity problem, while still supporting automated generation of efficient inference algorithms. We have developed automated generation of sequential Monte Carlo (SMC) algorithms for PPL descriptions of arbitrary biological diversification (birth-death) models. SMC is a new inference strategy for these problems. Finally, applying these algorithms to 40 bird phylogenies, we show that models with slowing diversification, constant turnover and many small shifts generally explain the data best. Our work opens up several related problem domains to PPL approaches, and shows that few hurdles remain before these techniques can be effectively applied to the full range of phylogenetic models.

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. Communications Biology, 4, 244, 2021. [nature]


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 © Thomas Schön 2020