You need to log in to edit.

You can create a new account if you don't have one.

Or, discuss a change on Slack.

You can create a new account if you don't have one.

Or, discuss a change on Slack.

no code implementations • 25 May 2022 • Antônio H. Ribeiro, Dave Zachariah, Thomas B. Schön

We prove that adversarial training with small disturbances gives the solution with the minimum-norm that interpolates the training data.

1 code implementation • 12 May 2022 • Zheng Zhao, Simo Särkkä, Jens Sjölund, Thomas B. Schön

We present a probabilistic approach for estimating chirp signal and its instantaneous frequency function when the true forms of the chirp and instantaneous frequency are unknown.

no code implementations • 13 Apr 2022 • Antônio H. Ribeiro, Thomas B. Schön

This is then used as a tool to study how adversarial training and other regularization methods might affect the robustness of the estimated models.

1 code implementation • 3 Feb 2022 • Philipp Pilar, Carl Jidling, Thomas B. Schön, Niklas Wahlström

Machine learning models can be improved by adapting them to respect existing background knowledge.

no code implementations • 2 Nov 2021 • Conor Rosato, Vincent Beraud, Paul Horridge, Thomas B. Schön, Simon Maskell

It has been widely documented that the sampling and resampling steps in particle filters cannot be differentiated.

1 code implementation • 22 Oct 2021 • Fredrik K. Gustafsson, Martin Danelljan, Thomas B. Schön

Energy-based models (EBMs) have experienced a resurgence within machine learning in recent years, including as a promising alternative for probabilistic regression.

no code implementations • 28 Apr 2021 • 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.

2 code implementations • 1 Mar 2021 • Niklas Gunnarsson, Peter Kimstrand, Jens Sjölund, Thomas B. Schön

For this, we use a conditional variational auto-encoder (CVAE) to nonlinearly map the higher-dimensional image to a lower-dimensional space, wherein we model the dynamics with a linear Gaussian state-space model (LG-SSM).

1 code implementation • 15 Feb 2021 • Antônio H. Ribeiro, Thomas B. Schön

The convolutional neural network (CNN) remains an essential tool in solving computer vision problems.

1 code implementation • 11 Dec 2020 • Antônio H. Ribeiro, Johannes N. Hendriks, Adrian G. Wills, Thomas B. Schön

It is typically observed that the model validation performance follows a U-shaped curve as the model complexity increases.

1 code implementation • 8 Dec 2020 • Fredrik K. Gustafsson, Martin Danelljan, Thomas B. Schön

On the KITTI dataset, our proposed approach consistently outperforms the SA-SSD baseline across all 3DOD metrics, demonstrating the potential of EBM-based regression for highly accurate 3DOD.

Ranked #1 on 3D Object Detection on KITTI Cars Easy val

1 code implementation • 8 Dec 2020 • Johannes N. Hendriks, Fredrik K. Gustafsson, Antônio H. Ribeiro, Adrian G. Wills, Thomas B. Schön

This paper is directed towards the problem of learning nonlinear ARX models based on system input--output data.

1 code implementation • 4 May 2020 • Fredrik K. Gustafsson, Martin Danelljan, Radu Timofte, Thomas B. Schön

While they are commonly employed for generative image modeling, recent work has applied EBMs also for regression tasks, achieving state-of-the-art performance on object detection and visual tracking.

Ranked #1 on Visual Object Tracking on OTB-100

1 code implementation • 31 Mar 2020 • Daniel Gedon, Niklas Wahlström, Thomas B. Schön, Lennart Ljung

Deep state space models (SSMs) are an actively researched model class for temporal models developed in the deep learning community which have a close connection to classic SSMs.

no code implementations • 24 Mar 2020 • Niklas Gunnarsson, Jens Sjölund, Thomas B. Schön

Together with a sparse-to-dense interpolation scheme we can then estimate of the displacement field.

no code implementations • 7 Feb 2020 • Jarrad Courts, Adrian Wills, Thomas B. Schön

In this paper, the problem of state estimation, in the context of both filtering and smoothing, for nonlinear state-space models is considered.

no code implementations • 21 Oct 2019 • Jan Kudlicka, Lawrence M. Murray, Thomas B. Schön, Fredrik Lindsten

While the variance reducing properties of rejection control are known, there has not been (to the best of our knowledge) any work on unbiased estimation of the marginal likelihood (also known as the model evidence or the normalizing constant) in this type of particle filter.

1 code implementation • ECCV 2020 • Fredrik K. Gustafsson, Martin Danelljan, Goutam Bhat, Thomas B. Schön

In our proposed approach, we create an energy-based model of the conditional target density p(y|x), using a deep neural network to predict the un-normalized density from (x, y).

Ranked #1 on Object Detection on COCO test-dev (Hardware Burden metric)

1 code implementation • 4 Sep 2019 • Carl Andersson, Antônio H. Ribeiro, Koen Tiels, Niklas Wahlström, Thomas B. Schön

Recent developments within deep learning are relevant for nonlinear system identification problems.

no code implementations • 4 Sep 2019 • Carl Jidling, Johannes Hendriks, Thomas B. Schön, Adrian Wills

Deep kernel learning refers to a Gaussian process that incorporates neural networks to improve the modelling of complex functions.

1 code implementation • 10 Jul 2019 • Jan Kudlicka, Lawrence M. Murray, Fredrik Ronquist, Thomas B. Schön

Probabilistic programming languages (PPLs) give phylogeneticists a new and exciting tool: their models can be implemented as probabilistic programs with just a basic knowledge of programming.

1 code implementation • 20 Jun 2019 • Antônio H. Ribeiro, Koen Tiels, Luis A. Aguirre, Thomas B. Schön

The exploding and vanishing gradient problem has been the major conceptual principle behind most architecture and training improvements in recurrent neural networks (RNNs) during the last decade.

1 code implementation • 4 Jun 2019 • Fredrik K. Gustafsson, Martin Danelljan, Thomas B. Schön

We therefore accept this task and propose a comprehensive evaluation framework for scalable epistemic uncertainty estimation methods in deep learning.

1 code implementation • NeurIPS 2019 • Jack Umenberger, Mina Ferizbegovic, Thomas B. Schön, Håkan Hjalmarsson

This paper concerns the problem of learning control policies for an unknown linear dynamical system to minimize a quadratic cost function.

1 code implementation • 2 May 2019 • Antônio H. Ribeiro, Koen Tiels, Jack Umenberger, Thomas B. Schön, Luis A. Aguirre

We shed new light on the \textit{smoothness} of optimization problems arising in prediction error parameter estimation of linear and nonlinear systems.

1 code implementation • 2 Apr 2019 • Antônio H. Ribeiro, Manoel Horta Ribeiro, Gabriela M. M. Paixão, Derick M. Oliveira, Paulo R. Gomes, Jéssica A. Canazart, Milton P. S. Ferreira, Carl R. Andersson, Peter W. Macfarlane, Wagner Meira Jr., Thomas B. Schön, Antonio Luiz P. Ribeiro

The role of automatic electrocardiogram (ECG) analysis in clinical practice is limited by the accuracy of existing models.

no code implementations • 12 Mar 2019 • Christian A. Naesseth, Fredrik Lindsten, Thomas B. Schön

A core problem in statistics and probabilistic machine learning is to compute probability distributions and expectations.

no code implementations • 6 Mar 2019 • Jack Umenberger, Thomas B. Schön

We propose an input design method for a general class of parametric probabilistic models, including nonlinear dynamical systems with process noise.

1 code implementation • 19 Feb 2019 • Juozas Vaicenavicius, David Widmann, Carl Andersson, Fredrik Lindsten, Jacob Roll, Thomas B. Schön

Probabilistic classifiers output a probability distribution on target classes rather than just a class prediction.

no code implementations • 4 Feb 2019 • Jalil Taghia, Maria Bånkestad, Fredrik Lindsten, Thomas B. Schön

However, in certain scenarios we are interested in learning structured parameters (predictions) in the form of symmetric positive definite matrices.

1 code implementation • 28 Jan 2019 • Muhammad Osama, Dave Zachariah, Thomas B. Schön

We address the problem of inferring the causal effect of an exposure on an outcome across space, using observational data.

no code implementations • 28 Nov 2018 • Antônio H. Ribeiro, Manoel Horta Ribeiro, Gabriela Paixão, Derick Oliveira, Paulo R. Gomes, Jéssica A. Canazart, Milton Pifano, Wagner Meira Jr., Thomas B. Schön, Antonio Luiz Ribeiro

We present a model for predicting electrocardiogram (ECG) abnormalities in short-duration 12-lead ECG signals which outperformed medical doctors on the 4th year of their cardiology residency.

no code implementations • 27 Nov 2018 • Xiuming Liu, Dave Zachariah, Johan Wågberg, Thomas B. Schön

Semi-supervised learning methods are motivated by the availability of large datasets with unlabeled features in addition to labeled data.

no code implementations • 2 Oct 2018 • Lawrence M. Murray, Thomas B. Schön

We introduce a formal description of models expressed as programs, and discuss some of the ways in which probabilistic programming languages can reveal the structure and form of these, in order to tailor inference methods.

no code implementations • 11 Sep 2018 • Zenith Purisha, Carl Jidling, Niklas Wahlström, Simo Särkkä, Thomas B. Schön

The approach also allows for reformulation of come classical regularization methods as Laplacian and Tikhonov regularization as Gaussian process regression, and hence provides an efficient algorithm and principled means for their parameter tuning.

1 code implementation • 17 Aug 2018 • Andreas Svensson, Dave Zachariah, Petre Stoica, Thomas B. Schön

The contribution in this paper is a general criterion to evaluate the consistency of a set of statistical models with respect to observed data.

no code implementations • NeurIPS 2018 • Jack Umenberger, Thomas B. Schön

Learning to make decisions from observed data in dynamic environments remains a problem of fundamental importance in a number of fields, from artificial intelligence and robotics, to medicine and finance.

1 code implementation • 25 Feb 2018 • Jalil Taghia, Thomas B. Schön

This in turn results in models which are prone to overfitting in the sense of excessive sensitivity to the chosen resolution, and predictions which are non-smooth at the boundaries.

1 code implementation • ICML 2018 • Muhammad Osama, Dave Zachariah, Thomas B. Schön

We address the problem of predicting spatio-temporal processes with temporal patterns that vary across spatial regions, when data is obtained as a stream.

no code implementations • 25 Jan 2018 • Carl Andersson, Niklas Wahlström, Thomas B. Schön

We consider the problem of impulse response estimation of stable linear single-input single-output systems.

1 code implementation • 7 Dec 2017 • Andreas Svensson, Dave Zachariah, Thomas B. Schön

The choice of model class is fundamental in statistical learning and system identification, no matter whether the class is derived from physical principles or is a generic black-box.

1 code implementation • 25 Aug 2017 • Lawrence M. Murray, Daniel Lundén, Jan Kudlicka, David Broman, Thomas B. Schön

For inference with Sequential Monte Carlo, this automatically yields improvements such as locally-optimal proposals and Rao-Blackwellization.

1 code implementation • 20 Apr 2017 • Manon Kok, Jeroen D. Hol, Thomas B. Schön

In this tutorial we focus on the signal processing aspects of position and orientation estimation using inertial sensors.

Robotics Systems and Control

no code implementations • 5 Apr 2017 • Adrian G. Wills, Thomas B. Schön

It has recently been shown that many of the existing quasi-Newton algorithms can be formulated as learning algorithms, capable of learning local models of the cost functions.

1 code implementation • 15 Mar 2017 • Dave Zachariah, Petre Stoica, Thomas B. Schön

We develop an online learning method for prediction, which is important in problems with large and/or streaming data sets.

no code implementations • 7 Mar 2017 • Thomas B. Schön, Andreas Svensson, Lawrence Murray, Fredrik Lindsten

We are concerned with the problem of learning probabilistic models of dynamical systems from measured data.

no code implementations • NeurIPS 2017 • Carl Jidling, Niklas Wahlström, Adrian Wills, Thomas B. Schön

We consider a modification of the covariance function in Gaussian processes to correctly account for known linear constraints.

no code implementations • 6 Feb 2017 • Andreas Svensson, Thomas B. Schön, Fredrik Lindsten

In particular, for learning of unknown parameters in nonlinear state-space models, methods based on the particle filter (a Monte Carlo method) have proven very useful.

1 code implementation • 8 Jan 2017 • Pierre E. Jacob, Fredrik Lindsten, Thomas B. Schön

The method combines two recent breakthroughs: the first is a generic debiasing technique for Markov chains due to Rhee and Glynn, and the second is the introduction of a uniformly ergodic Markov chain for smoothing, the conditional particle filter of Andrieu, Doucet and Holenstein.

Methodology Computation

no code implementations • 29 Dec 2016 • Christian A. Naesseth, Fredrik Lindsten, Thomas B. Schön

Sequential Monte Carlo (SMC) methods comprise one of the most successful approaches to approximate Bayesian filtering.

no code implementations • 13 Jun 2016 • Johan Wågberg, Dave Zachariah, Thomas B. Schön, Petre Stoica

Starting from a generalization of the Cram\'er-Rao bound, we derive a more accurate MSE bound which provides a measure of uncertainty for prediction of Gaussian processes.

no code implementations • 1 Apr 2016 • Hildo Bijl, Thomas B. Schön, Jan-Willem van Wingerden, Michel Verhaegen

This recommendation is usually selected by optimizing a given acquisition function.

no code implementations • 17 Mar 2016 • Andreas Svensson, Thomas B. Schön

We consider a nonlinear state-space model with the state transition and observation functions expressed as basis function expansions.

1 code implementation • 29 Jan 2016 • Hildo Bijl, Thomas B. Schön, Jan-Willem van Wingerden, Michel Verhaegen

There has been a growing interest in using non-parametric regression methods like Gaussian Process (GP) regression for system identification.

no code implementations • 17 Nov 2015 • Johan Dahlin, Fredrik Lindsten, Joel Kronander, Thomas B. Schön

Pseudo-marginal Metropolis-Hastings (pmMH) is a powerful method for Bayesian inference in models where the posterior distribution is analytical intractable or computationally costly to evaluate directly.

1 code implementation • 5 Nov 2015 • Johan Dahlin, Thomas B. Schön

This tutorial provides a gentle introduction to the particle Metropolis-Hastings (PMH) algorithm for parameter inference in nonlinear state-space models together with a software implementation in the statistical programming language R. We employ a step-by-step approach to develop an implementation of the PMH algorithm (and the particle filter within) together with the reader.

no code implementations • 8 Oct 2015 • John-Alexander M. Assael, Niklas Wahlström, Thomas B. Schön, Marc Peter Deisenroth

We consider a particularly important instance of this challenge, the pixels-to-torques problem, where an RL agent learns a closed-loop control policy ("torques") from pixel information only.

no code implementations • 15 Sep 2015 • Arno Solin, Manon Kok, Niklas Wahlström, Thomas B. Schön, Simo Särkkä

Anomalies in the ambient magnetic field can be used as features in indoor positioning and navigation.

2 code implementations • 23 Jun 2015 • Johan Dahlin, Mattias Villani, Thomas B. Schön

We consider the problem of approximate Bayesian parameter inference in non-linear state-space models with intractable likelihoods.

no code implementations • 7 Jun 2015 • Andreas Svensson, Arno Solin, Simo Särkkä, Thomas B. Schön

We present a procedure for efficient Bayesian learning in Gaussian process state space models, where the representation is formed by projecting the problem onto a set of approximate eigenfunctions derived from the prior covariance structure.

no code implementations • 20 Mar 2015 • Thomas B. Schön, Fredrik Lindsten, Johan Dahlin, Johan Wågberg, Christian A. Naesseth, Andreas Svensson, Liang Dai

One of the key challenges in identifying nonlinear and possibly non-Gaussian state space models (SSMs) is the intractability of estimating the system state.

1 code implementation • 12 Feb 2015 • Johan Dahlin, Fredrik Lindsten, Thomas B. Schön

A possible application is parameter inference in the challenging class of SSMs with intractable likelihoods.

1 code implementation • 12 Feb 2015 • Manon Kok, Johan Dahlin, Thomas B. Schön, Adrian Wills

Maximum likelihood (ML) estimation using Newton's method in nonlinear state space models (SSMs) is a challenging problem due to the analytical intractability of the log-likelihood and its gradient and Hessian.

1 code implementation • 9 Feb 2015 • Christian A. Naesseth, Fredrik Lindsten, Thomas B. Schön

NSMC generalises the SMC framework by requiring only approximate, properly weighted, samples from the SMC proposal distribution, while still resulting in a correct SMC algorithm.

no code implementations • 8 Feb 2015 • Niklas Wahlström, Thomas B. Schön, Marc Peter Deisenroth

In this paper, we consider one instance of this challenge, the pixels to torques problem, where an agent must learn a closed-loop control policy from pixel information only.

no code implementations • 6 Feb 2015 • Andreas Svensson, Johan Dahlin, Thomas B. Schön

Gaussian process regression is a popular method for non-parametric probabilistic modeling of functions.

no code implementations • 28 Oct 2014 • Niklas Wahlström, Thomas B. Schön, Marc Peter Deisenroth

In particular, we jointly learn a low-dimensional embedding of the observation by means of deep auto-encoders and a predictive transition model in this low-dimensional space.

no code implementations • 25 Sep 2014 • Andreas Svensson, Thomas B. Schön, Fredrik Lindsten

Jump Markov linear models consists of a finite number of linear state space models and a discrete variable encoding the jumps (or switches) between the different linear models.

3 code implementations • 19 Jun 2014 • Fredrik Lindsten, Adam M. Johansen, Christian A. Naesseth, Bonnie Kirkpatrick, Thomas B. Schön, John Aston, Alexandre Bouchard-Côté

We propose a novel class of Sequential Monte Carlo (SMC) algorithms, appropriate for inference in probabilistic graphical models.

no code implementations • NeurIPS 2014 • Christian A. Naesseth, Fredrik Lindsten, Thomas B. Schön

We propose a new framework for how to use sequential Monte Carlo (SMC) algorithms for inference in probabilistic graphical models (PGM).

no code implementations • 3 Jan 2014 • Fredrik Lindsten, Michael. I. Jordan, Thomas B. Schön

Particle Markov chain Monte Carlo (PMCMC) is a systematic way of combining the two main tools used for Monte Carlo statistical inference: sequential Monte Carlo (SMC) and Markov chain Monte Carlo (MCMC).

no code implementations • 17 Dec 2013 • Roger Frigola, Fredrik Lindsten, Thomas B. Schön, Carl E. Rasmussen

Gaussian process state-space models (GP-SSMs) are a very flexible family of models of nonlinear dynamical systems.

no code implementations • 4 Nov 2013 • Johan Dahlin, Fredrik Lindsten, Thomas B. Schön

Particle Metropolis-Hastings (PMH) allows for Bayesian parameter inference in nonlinear state space models by combining Markov chain Monte Carlo (MCMC) and particle filtering.

no code implementations • NeurIPS 2013 • Roger Frigola, Fredrik Lindsten, Thomas B. Schön, Carl E. Rasmussen

State-space models are successfully used in many areas of science, engineering and economics to model time series and dynamical systems.

Cannot find the paper you are looking for? You can
Submit a new open access paper.

Contact us on:
hello@paperswithcode.com
.
Papers With Code is a free resource with all data licensed under CC-BY-SA.