Fredrik Lindsten

According to our database, Fredrik Lindsten authored at least 66 papers between 2010 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2024

On Partial Prototype Collapse in the DINO Family of Self-Supervised Methods.

Hariprasath Govindarajan, Per Sidén, Jacob Roll, Fredrik Lindsten

CoRR, 2024

Continuous Ensemble Weather Forecasting with Diffusion models.

Martin Andrae, Tomas Landelius, Joel Oskarsson, Fredrik Lindsten

CoRR, 2024

Prior Learning in Introspective VAEs.

Ioannis Athanasiadis, Shashi Nagarajan, Fredrik Lindsten, Michael Felsberg

CoRR, 2024

Towards understanding epoch-wise double descent in two-layer linear neural networks.

Amanda Olmin, Fredrik Lindsten

CoRR, 2024

cryoSPHERE: Single-particle heterogeneous reconstruction from cryo EM.

Gabriel Ducrocq, Lukas Grunewald, Sebastian Westenhoff, Fredrik Lindsten

CoRR, 2024

Probabilistic Weather Forecasting with Hierarchical Graph Neural Networks.

Joel Oskarsson, Tomas Landelius, Marc Peter Deisenroth, Fredrik Lindsten

CoRR, 2024

On the connection between Noise-Contrastive Estimation and Contrastive Divergence.

Amanda Olmin, Jakob Lindqvist, Lennart Svensson, Fredrik Lindsten

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2024

Discriminator Guidance for Autoregressive Diffusion Models.

Filip Ekström Kelvinius, Fredrik Lindsten

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2024

Unsupervised Novelty Detection in Pretrained Representation Space with Locally Adapted Likelihood Ratio.

Amirhossein Ahmadian, Yifan Ding, Gabriel Eilertsen, Fredrik Lindsten

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2024

2023

A Variational Perspective on Generative Flow Networks.

Heiko Zimmermann, Fredrik Lindsten, Jan-Willem van de Meent, Christian A. Naesseth

Trans. Mach. Learn. Res., 2023

Graph-based Neural Weather Prediction for Limited Area Modeling.

Joel Oskarsson, Tomas Landelius, Fredrik Lindsten

CoRR, 2023

Fast and scalable score-based kernel calibration tests.

Pierre Glaser, David Widmann, Fredrik Lindsten, Arthur Gretton

Proceedings of the Uncertainty in Artificial Intelligence, 2023

Speeding Up Logic-Based Benders Decomposition by Strengthening Cuts with Graph Neural Networks.

Johannes Varga, Emil Karlsson, Günther R. Raidl, Elina Rönnberg, Fredrik Lindsten, Tobias Rodemann

Proceedings of the Machine Learning, Optimization, and Data Science, 2023

Generalised Active Learning With Annotation Quality Selection.

Jakob Lindqvist, Amanda Olmin, Lennart Svensson, Fredrik Lindsten

Proceedings of the 33rd IEEE International Workshop on Machine Learning for Signal Processing, 2023

DINO as a von Mises-Fisher mixture model.

Hariprasath Govindarajan, Per Sidén, Jacob Roll, Fredrik Lindsten

Proceedings of the Eleventh International Conference on Learning Representations, 2023

Enhancing Representation Learning with Deep Classifiers in Presence of Shortcut.

Amirhossein Ahmadian, Fredrik Lindsten

Proceedings of the IEEE International Conference on Acoustics, 2023

Temporal Graph Neural Networks for Irregular Data.

Joel Oskarsson, Per Sidén, Fredrik Lindsten

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2023

2022

Active Learning with Weak Labels for Gaussian Processes.

Amanda Olmin, Jakob Lindqvist, Lennart Svensson, Fredrik Lindsten

CoRR, 2022

Active Learning with Weak Supervision for Gaussian Processes.

Amanda Olmin, Jakob Lindqvist, Lennart Svensson, Fredrik Lindsten

Proceedings of the Neural Information Processing - 29th International Conference, 2022

Scalable Deep Gaussian Markov Random Fields for General Graphs.

Joel Oskarsson, Per Sidén, Fredrik Lindsten

Proceedings of the International Conference on Machine Learning, 2022

Robustness and Reliability When Training With Noisy Labels.

Amanda Olmin, Fredrik Lindsten

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2022

2021

Pseudo-Marginal Hamiltonian Monte Carlo.

Johan Alenlöv, Arnoud Doucet, Fredrik Lindsten

J. Mach. Learn. Res., 2021

Self-Supervised Representation Learning for Content Based Image Retrieval of Complex Scenes.

Hariprasath Govindarajan, Peter Lindskog, Dennis Lundström, Amanda Olmin, Jacob Roll, Fredrik Lindsten

Proceedings of the IEEE Intelligent Vehicles Symposium Workshops, 2021

Likelihood-free Out-of-Distribution Detection with Invertible Generative Models.

Amirhossein Ahmadian, Fredrik Lindsten

Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence, 2021

Calibration tests beyond classification.

David Widmann, Fredrik Lindsten, Dave Zachariah

Proceedings of the 9th International Conference on Learning Representations, 2021

2020

Markovian Score Climbing: Variational Inference with KL(p||q).

Christian A. Naesseth, Fredrik Lindsten, David M. Blei

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

A General Framework for Ensemble Distribution Distillation.

Jakob Lindqvist, Amanda Olmin, Fredrik Lindsten, Lennart Svensson

Proceedings of the 30th IEEE International Workshop on Machine Learning for Signal Processing, 2020

Deep Gaussian Markov Random Fields.

Per Sidén, Fredrik Lindsten

Proceedings of the 37th International Conference on Machine Learning, 2020

Particle Filter with Rejection Control and Unbiased Estimator of the Marginal Likelihood.

Jan Kudlicka, Lawrence M. Murray, Thomas B. Schön, Fredrik Lindsten

Proceedings of the 2020 IEEE International Conference on Acoustics, 2020

2019

High-Dimensional Filtering Using Nested Sequential Monte Carlo.

Christian A. Naesseth, Fredrik Lindsten, Thomas B. Schön

IEEE Trans. Signal Process., 2019

Elements of Sequential Monte Carlo.

Christian A. Naesseth, Fredrik Lindsten, Thomas B. Schön

Found. Trends Mach. Learn., 2019

Constructing the Matrix Multilayer Perceptron and its Application to the VAE.

Jalil Taghia, Maria Bånkestad, Fredrik Lindsten, Thomas B. Schön

CoRR, 2019

Bayesian nonparametric identification of Wiener systems.

Riccardo Sven Risuleo, Fredrik Lindsten, Håkan Hjalmarsson

Autom., 2019

Parameter elimination in particle Gibbs sampling.

Anna Wigren, Riccardo Sven Risuleo, Lawrence Murray, Fredrik Lindsten

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Calibration tests in multi-class classification: A unifying framework.

David Widmann, Fredrik Lindsten, Dave Zachariah

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Pseudo-Extended Markov chain Monte Carlo.

Christopher Nemeth, Fredrik Lindsten, Maurizio Filippone, James Hensman

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Bayesian identification of state-space models via adaptive thermostats.

Jack Umenberger, Thomas B. Schön, Fredrik Lindsten

Proceedings of the 58th IEEE Conference on Decision and Control, 2019

Evaluating model calibration in classification.

Juozas Vaicenavicius, David Widmann, Carl R. Andersson, Fredrik Lindsten, Jacob Roll, Thomas B. Schön

Proceedings of the 22nd International Conference on Artificial Intelligence and Statistics, 2019

2018

Learning dynamical systems with particle stochastic approximation EM.

Andreas Svensson, Fredrik Lindsten

CoRR, 2018

Graphical model inference: Sequential Monte Carlo meets deterministic approximations.

Fredrik Lindsten, Jouni Helske, Matti Vihola

Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

Semi-Parametric Kernel-Based Identification of Wiener Systems.

Riccardo Sven Risuleo, Fredrik Lindsten, Håkan Hjalmarsson

Proceedings of the 57th IEEE Conference on Decision and Control, 2018

2017

Learning nonlinear state-space models using smooth particle-filter-based likelihood approximations.

Andreas Svensson, Fredrik Lindsten, Thomas B. Schön

CoRR, 2017

Probabilistic learning of nonlinear dynamical systems using sequential Monte Carlo.

Thomas B. Schön, Andreas Svensson, Lawrence M. Murray, Fredrik Lindsten

CoRR, 2017

2016

Rao-Blackwellized Particle Smoothers for Conditionally Linear Gaussian Models.

Fredrik Lindsten, Pete Bunch, Simo Särkkä, Thomas B. Schön, Simon J. Godsill

IEEE J. Sel. Top. Signal Process., 2016

Interacting Particle Markov Chain Monte Carlo.

Tom Rainforth, Christian A. Naesseth, Fredrik Lindsten, Brooks Paige, Jan-Willem van de Meent, Arnaud Doucet, Frank D. Wood

Proceedings of the 33nd International Conference on Machine Learning, 2016

2015

Recursive Maximum Likelihood Identification of Jump Markov Nonlinear Systems.

Emre Özkan, Fredrik Lindsten, Carsten Fritsche, Fredrik Gustafsson

IEEE Trans. Signal Process., 2015

Particle Metropolis-Hastings using gradient and Hessian information.

Johan Dahlin, Fredrik Lindsten, Thomas B. Schön

Stat. Comput., 2015

Nested Sequential Monte Carlo Methods.

Christian A. Naesseth, Fredrik Lindsten, Thomas B. Schön

Proceedings of the 32nd International Conference on Machine Learning, 2015

Particle Gibbs with refreshed backward simulation.

Pete Bunch, Fredrik Lindsten, Sumeetpal S. Singh

Proceedings of the 2015 IEEE International Conference on Acoustics, 2015

Sequential Kernel Herding: Frank-Wolfe Optimization for Particle Filtering.

Simon Lacoste-Julien, Fredrik Lindsten, Francis R. Bach

Proceedings of the Eighteenth International Conference on Artificial Intelligence and Statistics, 2015

2014

Particle gibbs with ancestor sampling.

Fredrik Lindsten, Michael I. Jordan, Thomas B. Schön

J. Mach. Learn. Res., 2014

Sequential Monte Carlo for Graphical Models.

Christian A. Naesseth, Fredrik Lindsten, Thomas B. Schön

Proceedings of the Advances in Neural Information Processing Systems 27: Annual Conference on Neural Information Processing Systems 2014, 2014

Capacity estimation of two-dimensional channels using Sequential Monte Carlo.

Christian A. Naesseth, Fredrik Lindsten, Thomas B. Schön

Proceedings of the 2014 IEEE Information Theory Workshop, 2014

Identification of jump Markov linear models using particle filters.

Andreas Svensson, Thomas B. Schön, Fredrik Lindsten

Proceedings of the 53rd IEEE Conference on Decision and Control, 2014

2013

Backward Simulation Methods for Monte Carlo Statistical Inference.

Fredrik Lindsten, Thomas B. Schön

Found. Trends Mach. Learn., 2013

Identification of Gaussian Process State-Space Models with Particle Stochastic Approximation EM.

Roger Frigola, Fredrik Lindsten, Thomas B. Schön, Carl E. Rasmussen

CoRR, 2013

Bayesian semiparametric Wiener system identification.

Fredrik Lindsten, Thomas B. Schön, Michael I. Jordan

Autom., 2013

Bayesian Inference and Learning in Gaussian Process State-Space Models with Particle MCMC.

Roger Frigola, Fredrik Lindsten, Thomas B. Schön, Carl E. Rasmussen

Proceedings of the Advances in Neural Information Processing Systems 26: 27th Annual Conference on Neural Information Processing Systems 2013. Proceedings of a meeting held December 5-8, 2013

Adaptive stopping for fast particle smoothing.

Ehsan Taghavi, Fredrik Lindsten, Lennart Svensson, Thomas B. Schön

Proceedings of the IEEE International Conference on Acoustics, 2013

Rao-Blackwellized particle smoothers for mixed linear/nonlinear state-space models.

Fredrik Lindsten, Pete Bunch, Simon J. Godsill, Thomas B. Schön

Proceedings of the IEEE International Conference on Acoustics, 2013

An efficient stochastic approximation EM algorithm using conditional particle filters.

Fredrik Lindsten

Proceedings of the IEEE International Conference on Acoustics, 2013

Particle metropolis hastings using Langevin dynamics.

Johan Dahlin, Fredrik Lindsten, Thomas B. Schön

Proceedings of the IEEE International Conference on Acoustics, 2013

2012

Ancestor Sampling for Particle Gibbs.

Fredrik Lindsten, Michael I. Jordan, Thomas B. Schön

Proceedings of the Advances in Neural Information Processing Systems 25: 26th Annual Conference on Neural Information Processing Systems 2012. Proceedings of a meeting held December 3-6, 2012

On the use of backward simulation in the particle Gibbs sampler.

Fredrik Lindsten, Thomas B. Schön

Proceedings of the 2012 IEEE International Conference on Acoustics, 2012

2010

Geo-referencing for UAV navigation using environmental classification.

Fredrik Lindsten, Jonas Callmer, Henrik Ohlsson, David Törnqvist, Thomas B. Schön, Fredrik Gustafsson

Proceedings of the IEEE International Conference on Robotics and Automation, 2010

Identification of mixed linear/nonlinear state-space models.

Fredrik Lindsten, Thomas B. Schön

Proceedings of the 49th IEEE Conference on Decision and Control, 2010