3 papers
cs.LG2021
Learning deep autoregressive models for hierarchical data
Carl R. Andersson, Niklas Wahlström, Thomas B. Schön
We propose a model for hierarchical structured data as an extension to the stochastic temporal convolutional network. The proposed model combines an autoregressive model with a hie…
eess.SY2019
Deep Convolutional Networks in System Identification
Carl Andersson, Antônio H. Ribeiro, Koen Tiels +2
Recent developments within deep learning are relevant for nonlinear system identification problems. In this paper, we establish connections between the deep learning and the system…
cs.CV2018
Probabilistic approach to limited-data computed tomography reconstruction
Zenith Purisha, Carl Jidling, Niklas Wahlström +2
In this work, we consider the inverse problem of reconstructing the internal structure of an object from limited x-ray projections. We use a Gaussian process prior to model the tar…