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ORCIDFredrik Lindsten
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2020 – today
- 2024
[c42]Amirhossein Ahmadian, Yifan Ding, Gabriel Eilertsen, Fredrik Lindsten:
Unsupervised Novelty Detection in Pretrained Representation Space with Locally Adapted Likelihood Ratio. AISTATS 2024: 874-882- [c41]Amanda Olmin
, Jakob Lindqvist, Lennart Svensson, Fredrik Lindsten:
On the connection between Noise-Contrastive Estimation and Contrastive Divergence. AISTATS 2024: 3016-3024 - [c40]Filip Ekström Kelvinius, Fredrik Lindsten:
Discriminator Guidance for Autoregressive Diffusion Models. AISTATS 2024: 3403-3411 - [i29]Amanda Olmin, Jakob Lindqvist, Lennart Svensson, Fredrik Lindsten:
On the connection between Noise-Contrastive Estimation and Contrastive Divergence. CoRR abs/2402.16688 (2024) - [i28]Hariprasath Govindarajan, Per Sidén, Jacob Roll, Fredrik Lindsten:
DINO as a von Mises-Fisher mixture model. CoRR abs/2405.10939 (2024) - [i27]Joel Oskarsson, Tomas Landelius, Marc Peter Deisenroth, Fredrik Lindsten:
Probabilistic Weather Forecasting with Hierarchical Graph Neural Networks. CoRR abs/2406.04759 (2024) - [i26]Gabriel Ducrocq, Lukas Grunewald, Sebastian Westenhoff, Fredrik Lindsten:
cryoSPHERE: Single-particle heterogeneous reconstruction from cryo EM. CoRR abs/2407.01574 (2024) - [i25]Amanda Olmin, Fredrik Lindsten:
Towards understanding epoch-wise double descent in two-layer linear neural networks. CoRR abs/2407.09845 (2024) - [i24]Ioannis Athanasiadis, Shashi Nagarajan, Fredrik Lindsten, Michael Felsberg:
Prior Learning in Introspective VAEs. CoRR abs/2408.13805 (2024) - 2023
- [j11]Heiko Zimmermann, Fredrik Lindsten, Jan-Willem van de Meent, Christian A. Naesseth:
A Variational Perspective on Generative Flow Networks. Trans. Mach. Learn. Res. 2023 (2023) - [c39]Joel Oskarsson, Per Sidén, Fredrik Lindsten:
Temporal Graph Neural Networks for Irregular Data. AISTATS 2023: 4515-4531 - [c38]Amirhossein Ahmadian, Fredrik Lindsten:
Enhancing Representation Learning with Deep Classifiers in Presence of Shortcut. ICASSP 2023: 1-5 - [c37]Hariprasath Govindarajan, Per Sidén, Jacob Roll, Fredrik Lindsten:
DINO as a von Mises-Fisher mixture model. ICLR 2023 - [c36]Jakob Lindqvist, Amanda Olmin, Lennart Svensson, Fredrik Lindsten:
Generalised Active Learning With Annotation Quality Selection. MLSP 2023: 1-6 - [c35]Johannes Varga, Emil Karlsson, Günther R. Raidl, Elina Rönnberg, Fredrik Lindsten, Tobias Rodemann:
Speeding Up Logic-Based Benders Decomposition by Strengthening Cuts with Graph Neural Networks. LOD (1) 2023: 24-38 - [c34]Pierre Glaser, David Widmann, Fredrik Lindsten, Arthur Gretton:
Fast and scalable score-based kernel calibration tests. UAI 2023: 691-700 - [i23]Joel Oskarsson, Per Sidén, Fredrik Lindsten:
Temporal Graph Neural Networks for Irregular Data. CoRR abs/2302.08415 (2023) - [i22]Joel Oskarsson, Tomas Landelius, Fredrik Lindsten:
Graph-based Neural Weather Prediction for Limited Area Modeling. CoRR abs/2309.17370 (2023) - [i21]Filip Ekström Kelvinius, Fredrik Lindsten:
Discriminator Guidance for Autoregressive Diffusion Models. CoRR abs/2310.15817 (2023) - 2022
- [c33]Amanda Olmin, Fredrik Lindsten:
Robustness and Reliability When Training With Noisy Labels. AISTATS 2022: 922-942 - [c32]Joel Oskarsson, Per Sidén, Fredrik Lindsten:
Scalable Deep Gaussian Markov Random Fields for General Graphs. ICML 2022: 17117-17137 - [c31]Amanda Olmin, Jakob Lindqvist, Lennart Svensson, Fredrik Lindsten:
Active Learning with Weak Supervision for Gaussian Processes. ICONIP (5) 2022: 195-204 - [i20]Amanda Olmin, Jakob Lindqvist, Lennart Svensson, Fredrik Lindsten:
Active Learning with Weak Labels for Gaussian Processes. CoRR abs/2204.08335 (2022) - [i19]Joel Oskarsson, Per Sidén, Fredrik Lindsten:
Scalable Deep Gaussian Markov Random Fields for General Graphs. CoRR abs/2206.05032 (2022) - [i18]Heiko Zimmermann, Fredrik Lindsten, Jan-Willem van de Meent, Christian A. Naesseth:
A Variational Perspective on Generative Flow Networks. CoRR abs/2210.07992 (2022) - [i17]David Widmann, Fredrik Lindsten, Dave Zachariah:
Calibration tests beyond classification. CoRR abs/2210.13355 (2022) - 2021
- [j10]Johan Alenlöv, Arnoud Doucet, Fredrik Lindsten:
Pseudo-Marginal Hamiltonian Monte Carlo. J. Mach. Learn. Res. 22: 141:1-141:45 (2021) - [c30]David Widmann, Fredrik Lindsten, Dave Zachariah:
Calibration tests beyond classification. ICLR 2021 - [c29]Amirhossein Ahmadian, Fredrik Lindsten:
Likelihood-free Out-of-Distribution Detection with Invertible Generative Models. IJCAI 2021: 2119-2125 - [c28]Hariprasath Govindarajan, Peter Lindskog, Dennis Lundström, Amanda Olmin, Jacob Roll, Fredrik Lindsten:
Self-Supervised Representation Learning for Content Based Image Retrieval of Complex Scenes. IV Workshops 2021: 249-256 - [i16]Amanda Olmin, Fredrik Lindsten:
Robustness and reliability when training with noisy labels. CoRR abs/2110.03321 (2021) - 2020
- [c27]Jan Kudlicka, Lawrence M. Murray, Thomas B. Schön, Fredrik Lindsten:
Particle Filter with Rejection Control and Unbiased Estimator of the Marginal Likelihood. ICASSP 2020: 5860-5864 - [c26]Per Sidén, Fredrik Lindsten:
Deep Gaussian Markov Random Fields. ICML 2020: 8916-8926 - [c25]Jakob Lindqvist, Amanda Olmin, Fredrik Lindsten, Lennart Svensson:
A General Framework for Ensemble Distribution Distillation. MLSP 2020: 1-6 - [c24]Christian A. Naesseth, Fredrik Lindsten, David M. Blei:
Markovian Score Climbing: Variational Inference with KL(p||q). NeurIPS 2020 - [i15]Per Sidén, Fredrik Lindsten:
Deep Gaussian Markov random fields. CoRR abs/2002.07467 (2020) - [i14]Jakob Lindqvist, Amanda Olmin, Fredrik Lindsten, Lennart Svensson:
A general framework for ensemble distribution distillation. CoRR abs/2002.11531 (2020) - [i13]Christian A. Naesseth, Fredrik Lindsten, David M. Blei:
Markovian Score Climbing: Variational Inference with KL(p||q). CoRR abs/2003.10374 (2020)
2010 – 2019
- 2019
- [j9]Riccardo Sven Risuleo, Fredrik Lindsten, Håkan Hjalmarsson:
Bayesian nonparametric identification of Wiener systems. Autom. 108 (2019) - [j8]Christian A. Naesseth, Fredrik Lindsten, Thomas B. Schön:
Elements of Sequential Monte Carlo. Found. Trends Mach. Learn. 12(3): 307-392 (2019) - [j7]Christian A. Naesseth, Fredrik Lindsten, Thomas B. Schön:
High-Dimensional Filtering Using Nested Sequential Monte Carlo. IEEE Trans. Signal Process. 67(16): 4177-4188 (2019) - [c23]Juozas Vaicenavicius, David Widmann, Carl R. Andersson, Fredrik Lindsten, Jacob Roll, Thomas B. Schön:
Evaluating model calibration in classification. AISTATS 2019: 3459-3467 - [c22]Jack Umenberger, Thomas B. Schön, Fredrik Lindsten:
Bayesian identification of state-space models via adaptive thermostats. CDC 2019: 7382-7388 - [c21]Christopher Nemeth, Fredrik Lindsten, Maurizio Filippone, James Hensman:
Pseudo-Extended Markov chain Monte Carlo. NeurIPS 2019: 4314-4324 - [c20]Anna Wigren, Riccardo Sven Risuleo, Lawrence Murray, Fredrik Lindsten:
Parameter elimination in particle Gibbs sampling. NeurIPS 2019: 8916-8927 - [c19]David Widmann, Fredrik Lindsten, Dave Zachariah:
Calibration tests in multi-class classification: A unifying framework. NeurIPS 2019: 12236-12246 - [i12]Fredrik Lindsten, Jouni Helske, Matti Vihola:
Graphical model inference: Sequential Monte Carlo meets deterministic approximations. CoRR abs/1901.02374 (2019) - [i11]Jalil Taghia, Maria Bånkestad, Fredrik Lindsten, Thomas B. Schön:
Constructing the Matrix Multilayer Perceptron and its Application to the VAE. CoRR abs/1902.01182 (2019) - [i10]Juozas Vaicenavicius, David Widmann, Carl R. Andersson, Fredrik Lindsten, Jacob Roll, Thomas B. Schön:
Evaluating model calibration in classification. CoRR abs/1902.06977 (2019) - [i9]Christian A. Naesseth, Fredrik Lindsten, Thomas B. Schön:
Elements of Sequential Monte Carlo. CoRR abs/1903.04797 (2019) - [i8]David Widmann, Fredrik Lindsten, Dave Zachariah:
Calibration tests in multi-class classification: A unifying framework. CoRR abs/1910.11385 (2019) - 2018
- [c18]Riccardo Sven Risuleo, Fredrik Lindsten, Håkan Hjalmarsson:
Semi-Parametric Kernel-Based Identification of Wiener Systems. CDC 2018: 3874-3879 - [c17]Fredrik Lindsten, Jouni Helske, Matti Vihola:
Graphical model inference: Sequential Monte Carlo meets deterministic approximations. NeurIPS 2018: 8201-8211 - [i7]Andreas Svensson, Fredrik Lindsten:
Learning dynamical systems with particle stochastic approximation EM. CoRR abs/1806.09548 (2018) - 2017
- [i6]Thomas B. Schön, Andreas Svensson, Lawrence M. Murray, Fredrik Lindsten:
Probabilistic learning of nonlinear dynamical systems using sequential Monte Carlo. CoRR abs/1703.02419 (2017) - [i5]Andreas Svensson, Fredrik Lindsten, Thomas B. Schön:
Learning nonlinear state-space models using smooth particle-filter-based likelihood approximations. CoRR abs/1711.10765 (2017) - 2016
- [j6]Fredrik Lindsten, Pete Bunch, Simo Särkkä, Thomas B. Schön, Simon J. Godsill:
Rao-Blackwellized Particle Smoothers for Conditionally Linear Gaussian Models. IEEE J. Sel. Top. Signal Process. 10(2): 353-365 (2016) - [c16]Tom Rainforth, Christian A. Naesseth, Fredrik Lindsten, Brooks Paige, Jan-Willem van de Meent, Arnaud Doucet, Frank D. Wood:
Interacting Particle Markov Chain Monte Carlo. ICML 2016: 2616-2625 - 2015
- [j5]Johan Dahlin, Fredrik Lindsten, Thomas B. Schön:
Particle Metropolis-Hastings using gradient and Hessian information. Stat. Comput. 25(1): 81-92 (2015) - [j4]Emre Özkan, Fredrik Lindsten, Carsten Fritsche, Fredrik Gustafsson:
Recursive Maximum Likelihood Identification of Jump Markov Nonlinear Systems. IEEE Trans. Signal Process. 63(3): 754-765 (2015) - [c15]Simon Lacoste-Julien, Fredrik Lindsten, Francis R. Bach:
Sequential Kernel Herding: Frank-Wolfe Optimization for Particle Filtering. AISTATS 2015 - [c14]Pete Bunch, Fredrik Lindsten, Sumeetpal S. Singh:
Particle Gibbs with refreshed backward simulation. ICASSP 2015: 4115-4119 - [c13]Christian A. Naesseth, Fredrik Lindsten, Thomas B. Schön:
Nested Sequential Monte Carlo Methods. ICML 2015: 1292-1301 - [i4]Simon Lacoste-Julien, Fredrik Lindsten, Francis R. Bach:
Sequential Kernel Herding: Frank-Wolfe Optimization for Particle Filtering. CoRR abs/1501.02056 (2015) - 2014
- [j3]Fredrik Lindsten, Michael I. Jordan, Thomas B. Schön:
Particle gibbs with ancestor sampling. J. Mach. Learn. Res. 15(1): 2145-2184 (2014) - [c12]Andreas Svensson, Thomas B. Schön, Fredrik Lindsten:
Identification of jump Markov linear models using particle filters. CDC 2014: 6504-6509 - [c11]Christian A. Naesseth, Fredrik Lindsten, Thomas B. Schön:
Capacity estimation of two-dimensional channels using Sequential Monte Carlo. ITW 2014: 431-435 - [c10]Christian A. Naesseth, Fredrik Lindsten, Thomas B. Schön:
Sequential Monte Carlo for Graphical Models. NIPS 2014: 1862-1870 - [i3]Christian A. Naesseth, Fredrik Lindsten, Thomas B. Schön:
Capacity estimation of two-dimensional channels using Sequential Monte Carlo. CoRR abs/1405.0102 (2014) - 2013
- [j2]Fredrik Lindsten, Thomas B. Schön, Michael I. Jordan:
Bayesian semiparametric Wiener system identification. Autom. 49(7): 2053-2063 (2013) - [j1]Fredrik Lindsten, Thomas B. Schön:
Backward Simulation Methods for Monte Carlo Statistical Inference. Found. Trends Mach. Learn. 6(1): 1-143 (2013) - [c9]Fredrik Lindsten:
An efficient stochastic approximation EM algorithm using conditional particle filters. ICASSP 2013: 6274-6278 - [c8]Fredrik Lindsten, Pete Bunch, Simon J. Godsill, Thomas B. Schön:
Rao-Blackwellized particle smoothers for mixed linear/nonlinear state-space models. ICASSP 2013: 6288-6292 - [c7]Ehsan Taghavi, Fredrik Lindsten, Lennart Svensson, Thomas B. Schön:
Adaptive stopping for fast particle smoothing. ICASSP 2013: 6293-6297 - [c6]Johan Dahlin, Fredrik Lindsten, Thomas B. Schön:
Particle metropolis hastings using Langevin dynamics. ICASSP 2013: 6308-6312 - [c5]Roger Frigola, Fredrik Lindsten, Thomas B. Schön, Carl E. Rasmussen:
Bayesian Inference and Learning in Gaussian Process State-Space Models with Particle MCMC. NIPS 2013: 3156-3164 - [i2]Roger Frigola, Fredrik Lindsten, Thomas B. Schön, Carl E. Rasmussen:
Bayesian Inference and Learning in Gaussian Process State-Space Models with Particle MCMC. CoRR abs/1306.2861 (2013) - [i1]Roger Frigola, Fredrik Lindsten, Thomas B. Schön, Carl E. Rasmussen:
Identification of Gaussian Process State-Space Models with Particle Stochastic Approximation EM. CoRR abs/1312.4852 (2013) - 2012
- [c4]Fredrik Lindsten, Thomas B. Schön:
On the use of backward simulation in the particle Gibbs sampler. ICASSP 2012: 3845-3848 - [c3]Fredrik Lindsten, Michael I. Jordan, Thomas B. Schön:
Ancestor Sampling for Particle Gibbs. NIPS 2012: 2600-2608 - 2010
- [c2]Fredrik Lindsten, Thomas B. Schön:
Identification of mixed linear/nonlinear state-space models. CDC 2010: 6377-6382 - [c1]Fredrik Lindsten, Jonas Callmer, Henrik Ohlsson, David Törnqvist, Thomas B. Schön, Fredrik Gustafsson:
Geo-referencing for UAV navigation using environmental classification. ICRA 2010: 1420-1425
Coauthor Index
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last updated on 2024-09-30 00:07 CEST by the dblp team
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