882 citations · 973 across the 3 of their papers we have counts for
6 papers
Structured state-space models are deep Wiener models
Fabio Bonassi, Carl Andersson, Per Mattsson +1
The goal of this paper is to provide a system identification-friendly introduction to the Structured State-space Models (SSMs). These models have become recently popular in the mac…
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…
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…
Automatic diagnosis of the 12-lead ECG using a deep neural network
Antônio H. Ribeiro, Manoel Horta Ribeiro, Gabriela M. M. Paixão +9
The role of automatic electrocardiogram (ECG) analysis in clinical practice is limited by the accuracy of existing models. Deep Neural Networks (DNNs) are models composed of stacke…
Evaluating model calibration in classification
Juozas Vaicenavicius, David Widmann, Carl Andersson +3
Probabilistic classifiers output a probability distribution on target classes rather than just a class prediction. Besides providing a clear separation of prediction and decision m…
Data-Driven Impulse Response Regularization via Deep Learning
Carl Andersson, Niklas Wahlström, Thomas B. Schön
We consider the problem of impulse response estimation of stable linear single-input single-output systems. It is a well-studied problem where flexible non-parametric models recent…