default search action
Google
Google Scholar
Semantic Scholar
Internet Archive Scholar
CiteSeerX
ORCIDThomas B. Schön
Person information
- affiliation: Uppsala University, Sweden
- affiliation (former): Linköping University, Department of Electrical Engineering
Refine list
refinements active!
zoomed in on ?? of ?? records
view refined list in
export refined list as
showing all ?? records
Books and Theses
- 2006
[b1]Thomas B. Schön:
Estimation of Nonlinear Dynamic Systems: Theory and Applications. Linköping University, Sweden, 2006
Journal Articles
- 2025
- [j60]Gianluigi Pillonetto, Aleksandr Y. Aravkin, Daniel Gedon, Lennart Ljung, Antônio H. Ribeiro, Thomas B. Schön:
Deep networks for system identification: A survey. Autom. 171: 111907 (2025) - [j59]Dominik Baumann, Erfaun Noorani, James Price, Ole Peters, Colm Connaughton, Thomas B. Schön:
Reinforcement learning with non-ergodic reward increments: robustness via ergodicity transformations. Trans. Mach. Learn. Res. 2025 (2025) - 2024
- [j58]Per Mattsson
, Fabio Bonassi, Valentina Breschi, Thomas B. Schön:
On the Equivalence of Direct and Indirect Data-Driven Predictive Control Approaches. IEEE Control. Syst. Lett. 8: 796-801 (2024) - [j57]Dominik Baumann, Thomas B. Schön:
Safe Reinforcement Learning in Uncertain Contexts. IEEE Trans. Robotics 40: 1828-1841 (2024) - 2023
- [j56]Tim Martin, Thomas B. Schön, Frank Allgöwer:
Guarantees for data-driven control of nonlinear systems using semidefinite programming: A survey. Annu. Rev. Control. 56: 100911 (2023) - [j55]Adrian G. Wills, Thomas B. Schön:
Sequential Monte Carlo: A Unified Review. Annu. Rev. Control. Robotics Auton. Syst. 6: 159-182 (2023) - [j54]Jarrad Courts, Adrian G. Wills, Thomas B. Schön, Brett Ninness:
Variational system identification for nonlinear state-space models. Autom. 147: 110687 (2023) - [j53]Daniel Gedon, Antônio H. Ribeiro, Niklas Wahlström, Thomas B. Schön:
Invertible Kernel PCA With Random Fourier Features. IEEE Signal Process. Lett. 30: 563-567 (2023) - [j52]Li-Hui Geng, Adrian G. Wills, Brett Ninness, Thomas B. Schön:
Smoothed State Estimation via Efficient Solution of Linear Equations. IEEE Trans. Autom. Control. 68(10): 5877-5889 (2023) - [j51]Maria Bånkestad, Jens Sjölund, Jalil Taghia, Thomas B. Schön:
Variational Elliptical Processes. Trans. Mach. Learn. Res. 2023 (2023) - [j50]Fredrik K. Gustafsson, Martin Danelljan, Thomas B. Schön:
How Reliable is Your Regression Model's Uncertainty Under Real-World Distribution Shifts? Trans. Mach. Learn. Res. 2023 (2023) - [j49]Muhammad Osama, Dave Zachariah, Peter Stoica, Thomas B. Schön:
Online Learning for Prediction via Covariance Fitting: Computation, Performance and Robustness. Trans. Mach. Learn. Res. 2023 (2023) - [j48]Zheng Zhao, Simo Särkkä, Jens Sjölund, Thomas B. Schön:
Probabilistic Estimation of Instantaneous Frequencies of Chirp Signals. IEEE Trans. Signal Process. 71: 461-476 (2023) - [j47]Antônio H. Ribeiro, Thomas B. Schön:
Overparameterized Linear Regression Under Adversarial Attacks. IEEE Trans. Signal Process. 71: 601-614 (2023) - 2022
- [j46]Johannes N. Hendriks, James R. Z. Holdsworth, Adrian G. Wills, Thomas B. Schön, Brett Ninness:
Data to Controller for Nonlinear Systems: An Approximate Solution. IEEE Control. Syst. Lett. 6: 1196-1201 (2022) - [j45]Philipp Pilar, Carl Jidling, Thomas B. Schön, Niklas Wahlström:
Incorporating Sum Constraints into Multitask Gaussian Processes. Trans. Mach. Learn. Res. 2022 (2022) - [j44]Conor Rosato, Lee Devlin, Vincent Béraud, Paul R. Horridge, Thomas B. Schön, Simon Maskell:
Efficient Learning of the Parameters of Non-Linear Models Using Differentiable Resampling in Particle Filters. IEEE Trans. Signal Process. 70: 3676-3692 (2022) - [j43]Daniel Jönsson, Joel Kronander, Jonas Unger, Thomas B. Schön, Magnus Wrenninge:
Direct Transmittance Estimation in Heterogeneous Participating Media Using Approximated Taylor Expansions. IEEE Trans. Vis. Comput. Graph. 28(7): 2602-2614 (2022) - 2021
- [j42]Adrian G. Wills, Thomas B. Schön:
Stochastic quasi-Newton with line-search regularisation. Autom. 127: 109503 (2021) - [j41]Jarrad Courts, Adrian G. Wills, Thomas B. Schön:
Gaussian Variational State Estimation for Nonlinear State-Space Models. IEEE Trans. Signal Process. 69: 5979-5993 (2021) - 2020
- [j40]Antônio H. Ribeiro, Koen Tiels, Jack Umenberger, Thomas B. Schön, Luis Antonio Aguirre:
On the smoothness of nonlinear system identification. Autom. 121: 109158 (2020) - [j39]Mina Ferizbegovic, Jack Umenberger, Håkan Hjalmarsson, Thomas B. Schön:
Learning Robust LQ-Controllers Using Application Oriented Exploration. IEEE Control. Syst. Lett. 4(1): 19-24 (2020) - [j38]Jack Umenberger, Thomas B. Schön:
Nonlinear Input Design as Optimal Control of a Hamiltonian System. IEEE Control. Syst. Lett. 4(1): 85-90 (2020) - [j37]Kristian Soltesz, Fredrik Gustafsson, Toomas Timpka, Joakim Jaldén, Carl Jidling, Albin Heimerson, Thomas B. Schön, Armin Spreco, Joakim Ekberg, Örjan Dahlström, Fredrik Bagge Carlson, Anna Jöud, Bo Bernhardsson:
The effect of interventions on COVID-19. Nat. 588(7839): E26-E28 (2020) - 2019
- [j36]Andreas Lindholm, Dave Zachariah, Petre Stoica, Thomas B. Schön:
Data Consistency Approach to Model Validation. IEEE Access 7: 59788-59796 (2019) - [j35]Hildo Bijl, Thomas B. Schön:
Optimal controller/observer gains of discounted-cost LQG systems. Autom. 101: 471-474 (2019) - [j34]Christian A. Naesseth, Fredrik Lindsten, Thomas B. Schön:
Elements of Sequential Monte Carlo. Found. Trends Mach. Learn. 12(3): 307-392 (2019) - [j33]Patricio E. Valenzuela, Thomas B. Schön, Cristian R. Rojas:
On model order priors for Bayesian identification of SISO linear systems. Int. J. Control 92(7): 1645-1661 (2019) - [j32]Manon Kok, Thomas B. Schön:
A Fast and Robust Algorithm for Orientation Estimation Using Inertial Sensors. IEEE Signal Process. Lett. 26(11): 1673-1677 (2019) - [j31]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) - 2018
- [j30]Lawrence M. Murray, Thomas B. Schön:
Automated learning with a probabilistic programming language: Birch. Annu. Rev. Control. 46: 29-43 (2018) - [j29]Jack Umenberger, Johan Wågberg, Ian R. Manchester, Thomas B. Schön:
Maximum likelihood identification of stable linear dynamical systems. Autom. 96: 280-292 (2018) - [j28]Arno Solin, Manon Kok, Niklas Wahlstrom, Thomas B. Schön, Simo Särkkä:
Modeling and Interpolation of the Ambient Magnetic Field by Gaussian Processes. IEEE Trans. Robotics 34(4): 1112-1127 (2018) - 2017
- [j27]Patricio E. Valenzuela, Johan Dahlin, Cristian R. Rojas, Thomas B. Schön:
On robust input design for nonlinear dynamical models. Autom. 77: 268-278 (2017) - [j26]Andreas Svensson, Thomas B. Schön:
A flexible state-space model for learning nonlinear dynamical systems. Autom. 80: 189-199 (2017) - [j25]Manon Kok, Jeroen D. Hol, Thomas B. Schön:
Using Inertial Sensors for Position and Orientation Estimation. Found. Trends Signal Process. 11(1-2): 1-153 (2017) - [j24]Hildo Bijl, Thomas B. Schön, Jan-Willem van Wingerden, Michel Verhaegen:
System identification through online sparse Gaussian process regression with input noise. IFAC J. Syst. Control. 2: 1-11 (2017) - 2016
- [j23]Hildo Bijl, Jan-Willem van Wingerden, Thomas B. Schön, Michel Verhaegen:
Mean and variance of the LQG cost function. Autom. 67: 216-223 (2016) - [j22]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) - [j21]Liang Dai, Thomas B. Schön:
Using Convolution to Estimate the Score Function for Intractable State-Transition Models. IEEE Signal Process. Lett. 23(4): 498-501 (2016) - 2015
- [j20]Johan Dahlin, Fredrik Lindsten, Thomas B. Schön:
Particle Metropolis-Hastings using gradient and Hessian information. Stat. Comput. 25(1): 81-92 (2015) - [j19]Liang Dai, Thomas B. Schön:
On the Exponential Convergence of the Kaczmarz Algorithm. IEEE Signal Process. Lett. 22(10): 1571-1574 (2015) - [j18]Liang Dai, Thomas B. Schön:
A New Structure Exploiting Derivation of Recursive Direct Weight Optimization. IEEE Trans. Autom. Control. 60(6): 1683-1685 (2015) - [j17]Manon Kok, Jeroen D. Hol, Thomas B. Schön:
Indoor Positioning Using Ultrawideband and Inertial Measurements. IEEE Trans. Veh. Technol. 64(4): 1293-1303 (2015) - 2014
- [j16]Fredrik Lindsten, Michael I. Jordan, Thomas B. Schön:
Particle gibbs with ancestor sampling. J. Mach. Learn. Res. 15(1): 2145-2184 (2014) - 2013
- [j15]Adrian Wills, Thomas B. Schön, Lennart Ljung, Brett Ninness:
Identification of Hammerstein-Wiener models. Autom. 49(1): 70-81 (2013) - [j14]Fredrik Lindsten, Thomas B. Schön, Michael I. Jordan:
Bayesian semiparametric Wiener system identification. Autom. 49(7): 2053-2063 (2013) - [j13]Fredrik Lindsten, Thomas B. Schön:
Backward Simulation Methods for Monte Carlo Statistical Inference. Found. Trends Mach. Learn. 6(1): 1-143 (2013) - 2011
- [j12]Thomas B. Schön, Adrian Wills, Brett Ninness:
System identification of nonlinear state-space models. Autom. 47(1): 39-49 (2011) - [j11]Karl Granström, Thomas B. Schön, Juan I. Nieto, Fabio T. Ramos:
Learning to close loops from range data. Int. J. Robotics Res. 30(14): 1728-1754 (2011) - [j10]Christian Lundquist, Thomas B. Schön:
Joint ego-motion and road geometry estimation. Inf. Fusion 12(4): 253-263 (2011) - [j9]Tianshi Chen, Thomas B. Schön, Henrik Ohlsson, Lennart Ljung:
Decentralized Particle Filter With Arbitrary State Decomposition. IEEE Trans. Signal Process. 59(2): 465-478 (2011) - [j8]Xiao-Li Hu, Thomas B. Schön, Lennart Ljung:
A General Convergence Result for Particle Filtering. IEEE Trans. Signal Process. 59(7): 3424-3429 (2011) - 2010
- [j7]Jeroen D. Hol, Thomas B. Schön, Fredrik Gustafsson:
Modeling and Calibration of Inertial and Vision Sensors. Int. J. Robotics Res. 29(2-3): 231-244 (2010) - 2009
- [j6]David Törnqvist, Thomas B. Schön, Rickard Karlsson, Fredrik Gustafsson:
Particle Filter SLAM with High Dimensional Vehicle Model. J. Intell. Robotic Syst. 55(4-5): 249-266 (2009) - 2008
- [j5]Xiao-Li Hu, Thomas B. Schön, Lennart Ljung:
A Basic Convergence Result for Particle Filtering. IEEE Trans. Signal Process. 56(4): 1337-1348 (2008) - 2007
- [j4]Markus Gerdin, Thomas B. Schön, S. Torkel Glad, Fredrik Gustafsson, Lennart Ljung:
On parameter and state estimation for linear differential-algebraic equations. Autom. 43(3): 416-425 (2007) - [j3]Jeroen D. Hol, Thomas B. Schön, Henk Luinge, Per J. Slycke, Fredrik Gustafsson:
Robust real-time tracking by fusing measurements from inertial and vision sensors. J. Real Time Image Process. 2(2-3): 149-160 (2007) - 2005
- [j2]Thomas B. Schön, Fredrik Gustafsson, Per-Johan Nordlund:
Marginalized particle filters for mixed linear/nonlinear state-space models. IEEE Trans. Signal Process. 53(7): 2279-2289 (2005) - [j1]Rickard Karlsson, Thomas B. Schön, Fredrik Gustafsson:
Complexity analysis of the marginalized particle filter. IEEE Trans. Signal Process. 53(11): 4408-4411 (2005)
Conference and Workshop Papers
- 2025
- [c93]Abdullah Tokmak, Kiran G. Krishnan, Thomas B. Schön, Dominik Baumann:
Safe exploration in reproducing kernel Hilbert spaces. AISTATS 2025: 784-792 - [c92]Antônio H. Ribeiro, Thomas B. Schön, Dave Zachariah, Francis Bach:
Efficient Optimization Algorithms for Linear Adversarial Training. AISTATS 2025: 1207-1215 - [c91]Adrien Corenflos, Zheng Zhao, Thomas B. Schön, Simo Särkkä, Jens Sjölund:
Conditioning diffusion models by explicit forward-backward bridging. AISTATS 2025: 3709-3717 - [c90]Gabriel Y. Arteaga, Thomas B. Schön, Nicolas Pielawski:
Hallucination Detection in LLMs: Fast and Memory-Efficient Finetuned Models. NLDL 2025: 1-15 - 2024
- [c89]Zheng Zhao, Sebastian Mair, Thomas B. Schön, Jens Sjölund:
On Feynman-Kac training of partial Bayesian neural networks. AISTATS 2024: 3223-3231 - [c88]Fabio Bonassi, Carl R. Andersson, Per Mattsson, Thomas B. Schön:
Learning state observers for recurrent neural network models. CDC 2024: 7871-7877 - [c87]Jie Liang, Radu Timofte, Qiaosi Yi, Shuaizheng Liu, Lingchen Sun, Rongyuan Wu, Xindong Zhang, Hui Zeng, Lei Zhang, Yibin Huang, Shuai Liu, Yongqiang Li, Chaoyu Feng, Xiaotao Wang, Lei Lei, Yuxiang Chen, Xiangyu Chen, Qiubo Chen, Fengyu Sun, Mengying Cui, Jiaxu Chen, Zhenyu Hu, Jingyun Liu, Wenzhuo Ma, Ce Wang, Hanyou Zheng, Wanjie Sun, Zhenzhong Chen, Ziwei Luo, Fredrik K. Gustafsson, Zheng Zhao, Jens Sjölund, Thomas B. Schön, Xiong Dun, Pengzhou Ji, Yujie Xing, Xuquan Wang, Zhanshan Wang, Xinbin Cheng, Jun Xiao, Chenhang He, Xiuyuan Wang, Zhi-Song Liu, Zimeng Miao, Zhicun Yin, Ming Liu, Wangmeng Zuo, Shuai Li:
NTIRE 2024 Restore Any Image Model (RAIM) in the Wild Challenge. CVPR Workshops 2024: 6632-6640 - [c86]Ziwei Luo, Fredrik K. Gustafsson, Zheng Zhao, Jens Sjölund, Thomas B. Schön:
Photo-Realistic Image Restoration in the Wild with Controlled Vision-Language Models. CVPR Workshops 2024: 6641-6651 - [c85]Bernhard Wullt, Per Mattsson, Thomas B. Schön, Mikael Norrlöf:
A Model Predictive Control Approach to Motion Planning in Dynamic Environments. ECC 2024: 3247-3254 - [c84]Ziwei Luo, Fredrik K. Gustafsson, Zheng Zhao, Jens Sjölund, Thomas B. Schön:
Controlling Vision-Language Models for Multi-Task Image Restoration. ICLR 2024 - [c83]Daniel Gedon, Antônio H. Ribeiro, Thomas B. Schön:
No Double Descent in Principal Component Regression: A High-Dimensional Analysis. ICML 2024 - [c82]Niklas Gunnarsson, Jens Sjölund, Peter Kimstrand, Thomas B. Schön:
Online Learning in Motion Modeling for Intra-interventional Image Sequences. MICCAI (2) 2024: 706-716 - [c81]Ruoqi Zhang, Ziwei Luo, Jens Sjölund, Thomas B. Schön, Per Mattsson:
Entropy-regularized Diffusion Policy with Q-Ensembles for Offline Reinforcement Learning. NeurIPS 2024 - [c80]Daniel Gedon, Amirhesam Abedsoltan, Thomas B. Schön, Mikhail Belkin:
Uncertainty Estimation with Recursive Feature Machines. UAI 2024: 1408-1437 - 2023
- [c79]Longguang Wang, Yulan Guo, Yingqian Wang, Juncheng Li, Shuhang Gu, Radu Timofte, Ming Cheng, Haoyu Ma, Qiufang Ma, Xiaopeng Sun, Shijie Zhao, Xuhan Sheng, Yukang Ding, Ming Sun, Xing Wen, Dafeng Zhang, Jia Li, Fan Wang, Zheng Xie, Zongyao He, Zidian Qiu, Zilin Pan, Zhihao Zhan, Xingyuan Xian, Zhi Jin, Yuanbo Zhou, Wei Deng, Ruofeng Nie, Jiajun Zhang, Qinquan Gao, Tong Tong, Kexin Zhang, Junpei Zhang, Rui Peng, Yanbiao Ma, Licheng Jiao, Haoran Bai, Lingshun Kong, Jinshan Pan, Jiangxin Dong, Jinhui Tang, Pu Cao, Tianrui Huang, Lu Yang, Qing Song, Bingxin Chen, Chunhua He, Meiyun Chen, Zijie Guo, Shaojuan Luo, Chengzhi Cao, Kunyu Wang, Fanrui Zhang, Qiang Zhang, Nancy Mehta, Subrahmanyam Murala, Akshay Dudhane, Yujin Wang, Lingen Li, Garas Gendy, Nabil Sabor, Jingchao Hou, Guanghui He, Junyang Chen, Hao Li, Yukai Shi, Zhijing Yang, Wenbin Zou, Yunchen Zhang, Mingchao Jiang, Zhongxin Yu, Ming Tan, Hongxia Gao, Ziwei Luo, Fredrik K. Gustafsson, Zheng Zhao, Jens Sjölund, Thomas B. Schön, Jingxiang Chen, Bo Yang, XiSheryl Zhang, Chenghua Li, Weijun Yuan, Zhan Li, Ruting Deng, Jintao Zeng, Pulkit Mahajan, Sahaj Mistry, Shreyas Chatterjee, Vinit Jakhetiya, Badri N. Subudhi, Sunil Prasad Jaiswal, Zhao Zhang, Huan Zheng, Suiyi Zhao, Yangcheng Gao, Yanyan Wei, Bo Wang, Gen Li, Aijin Li, Lei Sun, Ke Chen, Congling Tang, Yunzhe Li, Jun Chen, Yuan-Chun Chiang, Yi-Chung Chen, Zhi-Kai Huang, Hao-Hsiang Yang, I-Hsiang Chen, Sy-Yen Kuo, Yiheng Wang, Gang Zhu, Xingyi Yang, Songhua Liu, Yongcheng Jing, Xingyu Hu, Jianwen Song, Changming Sun, Arcot Sowmya, Seung Ho Park, Xiaoyan Lei, Jingchao Wang, Chenbo Zhai, Yufei Zhang, Weifeng Cao, Wenlong Zhang:
NTIRE 2023 Challenge on Stereo Image Super-Resolution: Methods and Results. CVPR Workshops 2023: 1346-1372 - [c78]Marcos V. Conde, Manuel Kolmet, Tim Seizinger, Tom E. Bishop, Radu Timofte, Xiangyu Kong, Dafeng Zhang, Jinlong Wu, Fan Wang, Juewen Peng, Zhiyu Pan, Chengxin Liu, Xianrui Luo, Huiqiang Sun, Liao Shen, Zhiguo Cao, Ke Xian, Chaowei Liu, Zigeng Chen, Xingyi Yang, Songhua Liu, Yongcheng Jing, Michael Bi Mi, Xinchao Wang, Zhihao Yang, Wenyi Lian, Siyuan Lai, Haichuan Zhang, Trung Hoang, Amirsaeed Yazdani, Vishal Monga, Ziwei Luo, Fredrik K. Gustafsson, Zheng Zhao, Jens Sjölund, Thomas B. Schön, Yuxuan Zhao, Baoliang Chen, Yiqing Xu, JiXiangNiu:
Lens-to-Lens Bokeh Effect Transformation. NTIRE 2023 Challenge Report. CVPR Workshops 2023: 1643-1659 - [c77]Ziwei Luo, Fredrik K. Gustafsson, Zheng Zhao, Jens Sjölund, Thomas B. Schön:
Refusion: Enabling Large-Size Realistic Image Restoration with Latent-Space Diffusion Models. CVPR Workshops 2023: 1680-1691 - [c76]Florin-Alexandru Vasluianu, Tim Seizinger, Radu Timofte, Shuhao Cui, Junshi Huang, Shuman Tian, Mingyuan Fan, Jiaqi Zhang, Li Zhu, Xiaoming Wei, Xiaolin Wei, Ziwei Luo, Fredrik K. Gustafsson, Zheng Zhao, Jens Sjölund, Thomas B. Schön, Xiaoyi Dong, Xi Sheryl Zhang, Chenghua Li, Cong Leng, Woon-Ha Yeo, Wang-Taek Oh, Yeoreum Lee, Han-Cheol Ryu, Jinting Luo, Chengzhi Jiang, Mingyan Han, Qi Wu, Wenjie Lin, Lei Yu, Xinpeng Li, Ting Jiang, Haoqiang Fan, Shuaicheng Liu, Shuning Xu, Binbin Song, Xiangyu Chen, Shile Zhang, Jiantao Zhou, Zhao Zhang, Suiyi Zhao, Huan Zheng, Yangcheng Gao, Yanyan Wei, Bo Wang, Jiahuan Ren, Yan Luo, Yuki Kondo, Riku Miyata, Fuma Yasue, Taito Naruki, Norimichi Ukita, Hua-En Chang, Hao-Hsiang Yang, Yi-Chung Chen, Yuan-Chun Chiang, Zhi-Kai Huang, Wei-Ting Chen, I-Hsiang Chen, Chia-Hsuan Hsieh, Sy-Yen Kuo, Xianwei Li, Huiyuan Fu, Chunlin Liu, Huadong Ma, Binglan Fu, Huiming He, Mengjia Wang, Wenxuan She, Yu Liu, Sabari Nathan, Priya Kansal, Zhongjian Zhang, Huabin Yang, Yan Wang, Yanru Zhang, Shruti S. Phutke, Ashutosh Kulkarni, Md Raqib Khan, Subrahmanyam Murala, Santosh Kumar Vipparthi, Heng Ye, Zixi Liu, Xingyi Yang, Songhua Liu, Yinwei Wu, Yongcheng Jing, Qianhao Yu, Naishan Zheng, Jie Huang, Yuhang Long, Mingde Yao, Feng Zhao, Bowen Zhao, Nan Ye, Ning Shen, Yanpeng Cao, Tong Xiong, Weiran Xia, Dingwen Li, Shuchen Xia:
NTIRE 2023 Image Shadow Removal Challenge Report. CVPR Workshops 2023: 1788-1807 - [c75]Codruta O. Ancuti, Cosmin Ancuti, Florin-Alexandru Vasluianu, Radu Timofte, Han Zhou, Wei Dong, Yangyi Liu, Jun Chen, Huan Liu, Liangyan Li, Zijun Wu, Yubo Dong, Yuyan Li, Tian Qiu, Yu He, Yonghong Lu, Yinwei Wu, Zhenxiang Jiang, Songhua Liu, Xingyi Yang, Yongcheng Jing, Bilel Benjdira, Anas M. Ali, Anis Koubaa, Hao-Hsiang Yang, I-Hsiang Chen, Wei-Ting Chen, Zhi-Kai Huang, Yi-Chung Chen, Chia-Hsuan Hsieh, Hua-En Chang, Yuan-Chun Chiang, Sy-Yen Kuo, Yu Guo, Yuan Gao, Ryan Wen Liu, Yuxu Lu, Jingxiang Qu, Shengfeng He, Wenqi Ren, Trung Hoang, Haichuan Zhang, Amirsaeed Yazdani, Vishal Monga, Lehan Yang, Alex Jiahao Wu, Tiancheng Mai, Xiaofeng Cong, Xuemeng Yin, Xuefei Yin, Hazim Emad, Ahmed Abdallah, Yahya Yasser, Dalia Elshahat, Esraa Elbaz, Zhan Li, Wenqing Kuang, Ziwei Luo, Fredrik K. Gustafsson, Zheng Zhao, Jens Sjölund, Thomas B. Schön, Zhao Zhang, Yanyan Wei, Junhu Wang, Suiyi Zhao, Huan Zheng, Jin Guo, Yangfan Sun, Tianli Liu, Dejun Hao, Kui Jiang, Anjali Sarvaiya, Kalpesh Prajapati, Ratnadeep Patra, Pragnesh Barik, Chaitanya Rathod, Kishor P. Upla, Kiran B. Raja, Raghavendra Ramachandra, Christoph Busch:
NTIRE 2023 HR NonHomogeneous Dehazing Challenge Report. CVPR Workshops 2023: 1808-1825 - [c74]Ziwei Luo, Fredrik K. Gustafsson, Zheng Zhao, Jens Sjölund, Thomas B. Schön:
Image Restoration with Mean-Reverting Stochastic Differential Equations. ICML 2023: 23045-23066 - [c73]Antônio H. Ribeiro, Dave Zachariah, Francis R. Bach, Thomas B. Schön:
Regularization properties of adversarially-trained linear regression. NeurIPS 2023 - 2022
- [c72]Fredrik K. Gustafsson, Martin Danelljan, Thomas B. Schön:
Learning Proposals for Practical Energy-Based Regression. AISTATS 2022: 4685-4704 - [c71]Niklas Gunnarsson, Jens Sjölund, Peter Kimstrand, Thomas B. Schön:
Unsupervised dynamic modeling of medical image transformations. FUSION 2022: 1-7 - 2021
- [c70]Mina Ferizbegovic, Håkan Hjalmarsson, Per Mattsson, Thomas B. Schön:
Willems' fundamental lemma based on second-order moments. CDC 2021: 396-401 - [c69]Daniel Gedon, Antônio H. Ribeiro, Niklas Wahlström, Thomas B. Schön:
First Steps Towards Self-Supervised Pretraining of the 12-Lead ECG. CinC 2021: 1-4 - [c68]Fredrik K. Gustafsson, Martin Danelljan, Thomas B. Schön:
Accurate 3D Object Detection Using Energy-Based Models. CVPR Workshops 2021: 2855-2864 - [c67]Antônio H. Ribeiro, Thomas B. Schön:
How Convolutional Neural Networks Deal with Aliasing. ICASSP 2021: 2755-2759 - 2020
- [c66]Antônio H. Ribeiro, Koen Tiels, Luis Antonio Aguirre, Thomas B. Schön:
Beyond exploding and vanishing gradients: analysing RNN training using attractors and smoothness. AISTATS 2020: 2370-2380 - [c65]Fredrik Gustafsson, Martin Danelljan, Radu Timofte, Thomas B. Schön:
How to Train Your Energy-Based Model for Regression. BMVC 2020 - [c64]Antônio H. Ribeiro, Daniel Gedon, Daniel Martins Teixeira, Manoel Horta Ribeiro, Antônio Luiz Pinho Ribeiro, Thomas B. Schön, Wagner Meira Jr.:
Automatic 12-lead ECG Classification Using a Convolutional Network Ensemble. CinC 2020: 1-4 - [c63]Fredrik K. Gustafsson, Martin Danelljan, Thomas B. Schön:
Evaluating Scalable Bayesian Deep Learning Methods for Robust Computer Vision. CVPR Workshops 2020: 1289-1298 - [c62]Fredrik K. Gustafsson, Martin Danelljan, Goutam Bhat, Thomas B. Schön:
Energy-Based Models for Deep Probabilistic Regression. ECCV (20) 2020: 325-343 - [c61]Matej Kristan, Ales Leonardis, Jiri Matas, Michael Felsberg, Roman P. Pflugfelder, Joni-Kristian Kämäräinen, Martin Danelljan, Luka Cehovin Zajc, Alan Lukezic, Ondrej Drbohlav, Linbo He, Yushan Zhang, Song Yan, Jinyu Yang, Gustavo Fernández, Alexander G. Hauptmann, Alireza Memarmoghadam, Álvaro García-Martín, Andreas Robinson, Anton Varfolomieiev, Awet Haileslassie Gebrehiwot, Bedirhan Uzun, Bin Yan, Bing Li, Chen Qian, Chi-Yi Tsai, Christian Micheloni, Dong Wang, Fei Wang, Fei Xie, Felix Järemo Lawin, Fredrik Gustafsson, Gian Luca Foresti, Goutam Bhat, Guangqi Chen, Haibin Ling, Haitao Zhang, Hakan Cevikalp, Haojie Zhao, Haoran Bai, Hari Chandana Kuchibhotla, Hasan Saribas, Heng Fan, Hossein Ghanei-Yakhdan, Houqiang Li, Houwen Peng, Huchuan Lu, Hui Li, Javad Khaghani, Jesús Bescós, Jianhua Li, Jianlong Fu, Jiaqian Yu, Jingtao Xu, Josef Kittler, Jun Yin, Junhyun Lee, Kaicheng Yu, Kaiwen Liu, Kang Yang, Kenan Dai, Li Cheng, Li Zhang, Lijun Wang, Linyuan Wang, Luc Van Gool, Luca Bertinetto, Matteo Dunnhofer, Miao Cheng, Mohana Murali Dasari, Ning Wang, Pengyu Zhang, Philip H. S. Torr, Qiang Wang, Radu Timofte, Rama Krishna Sai Subrahmanyam Gorthi, Seokeon Choi, Seyed Mojtaba Marvasti-Zadeh, Shao-Chuan Zhao, Shohreh Kasaei, Shoumeng Qiu, Shuhao Chen, Thomas B. Schön, Tianyang Xu, Wei Lu, Weiming Hu, Wengang Zhou, Xi Qiu, Xiao Ke, Xiao-Jun Wu, Xiaolin Zhang, Xiaoyun Yang, Xuefeng Zhu, Yingjie Jiang, Yingming Wang, Yiwei Chen, Yu Ye, Yuezhou Li, Yuncon Yao, Yunsung Lee, Yuzhang Gu, Zezhou Wang, Zhangyong Tang, Zhenhua Feng, Zhijun Mai, Zhipeng Zhang, Zhirong Wu, Ziang Ma:
The Eighth Visual Object Tracking VOT2020 Challenge Results. ECCV Workshops (5) 2020: 547-601 - [c60]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 - [c59]Jack Umenberger, Thomas B. Schön:
Optimistic robust linear quadratic dual control. L4DC 2020: 550-560 - [c58]Niklas Gunnarsson, Jens Sjölund, Thomas B. Schön:
Learning a Deformable Registration Pyramid. MICCAI (Challenges) 2020: 80-86 - 2019
- [c57]Jalil Taghia, Thomas B. Schön:
Conditionally Independent Multiresolution Gaussian Processes. AISTATS 2019: 964-973 - [c56]Juozas Vaicenavicius, David Widmann, Carl R. Andersson, Fredrik Lindsten, Jacob Roll, Thomas B. Schön:
Evaluating model calibration in classification. AISTATS 2019: 3459-3467 - [c55]Carl R. Andersson, Antônio H. Ribeiro, Koen Tiels, Niklas Wahlström, Thomas B. Schön:
Deep Convolutional Networks in System Identification. CDC 2019: 3670-3676 - [c54]Jack Umenberger, Thomas B. Schön, Fredrik Lindsten:
Bayesian identification of state-space models via adaptive thermostats. CDC 2019: 7382-7388 - [c53]Muhammad Osama, Dave Zachariah, Thomas B. Schön:
Inferring Heterogeneous Causal Effects in Presence of Spatial Confounding. ICML 2019: 4942-4950 - [c52]Jack Umenberger, Mina Ferizbegovic, Thomas B. Schön, Håkan Hjalmarsson:
Robust exploration in linear quadratic reinforcement learning. NeurIPS 2019: 15310-15320 - [c51]Jan Kudlicka, Lawrence M. Murray, Fredrik Ronquist, Thomas B. Schön:
Probabilistic Programming for Birth-Death Models of Evolution Using an Alive Particle Filter with Delayed Sampling. UAI 2019: 679-689 - 2018
- [c50]Lawrence M. Murray, Daniel Lundén, Jan Kudlicka, David Broman, Thomas B. Schön:
Delayed Sampling and Automatic Rao-Blackwellization of Probabilistic Programs. AISTATS 2018: 1037-1046 - [c49]Johan Wågberg, Dave Zachariah, Thomas B. Schön:
Regularized parametric system identification: a decision-theoretic formulation. ACC 2018: 1895-1900 - [c48]Roland Hostettler, Thomas B. Schön:
Auxiliary-Particle-Filter-Based Two-Filter Smoothing for Wiener State-Space Models. FUSION 2018: 1-5 - [c47]Muhammad Osama, Dave Zachariah, Thomas B. Schön:
Learning Localized Spatio-Temporal Models From Streaming Data. ICML 2018: 3924-3932 - [c46]Jack Umenberger, Thomas B. Schön:
Learning convex bounds for linear quadratic control policy synthesis. NeurIPS 2018: 9584-9595 - 2017
- [c45]Johan Wågberg, Dave Zachariah, Thomas B. Schön, Petre Stoica:
Prediction Performance After Learning in Gaussian Process Regression. AISTATS 2017: 1264-1272 - [c44]Adrian G. Wills, Thomas B. Schön:
On the construction of probabilistic Newton-type algorithms. CDC 2017: 6499-6504 - [c43]Carl Jidling, Niklas Wahlström, Adrian Wills, Thomas B. Schön:
Linearly constrained Gaussian processes. NIPS 2017: 1215-1224 - 2016
- [c42]Andreas Svensson, Arno Solin, Simo Särkkä, Thomas B. Schön:
Computationally Efficient Bayesian Learning of Gaussian Process State Space Models. AISTATS 2016: 213-221 - [c41]Patricio E. Valenzuela, Johan Dahlin, Cristian R. Rojas, Thomas B. Schön:
Particle-based Gaussian process optimization for input design in nonlinear dynamical models. CDC 2016: 2085-2090 - [c40]Fredrik Olsson, Manon Kok, Kjartan Halvorsen, Thomas B. Schön:
Accelerometer calibration using sensor fusion with a gyroscope. SSP 2016: 1-5 - 2015
- [c39]Andreas Svensson, Johan Dahlin, Thomas B. Schön:
Marginalizing Gaussian process hyperparameters using sequential Monte Carlo. CAMSAP 2015: 477-480 - [c38]Andreas Svensson, Thomas B. Schön, Arno Solin, Simo Särkkä:
Nonlinear state space model identification using a regularized basis function expansion. CAMSAP 2015: 481-484 - [c37]Christian A. Naesseth, Fredrik Lindsten, Thomas B. Schön:
Nested Sequential Monte Carlo Methods. ICML 2015: 1292-1301 - [c36]Joel Kronander, Thomas B. Schön, Jonas Unger:
Pseudo-marginal metropolis light transport. SIGGRAPH Asia Technical Briefs 2015: 13:1-13:4 - 2014
- [c35]Andreas Svensson, Thomas B. Schön, Fredrik Lindsten:
Identification of jump Markov linear models using particle filters. CDC 2014: 6504-6509 - [c34]Joel Kronander, Johan Dahlin, Daniel Jönsson, Manon Kok, Thomas B. Schön, Jonas Unger:
Real-time video based lighting using GPU raytracing. EUSIPCO 2014: 1627-1631 - [c33]Christian A. Naesseth, Fredrik Lindsten, Thomas B. Schön:
Capacity estimation of two-dimensional channels using Sequential Monte Carlo. ITW 2014: 431-435 - [c32]Daniel Hultqvist, Jacob Roll, Fredrik Svensson, Johan Dahlin, Thomas B. Schön:
Detecting and positioning overtaking vehicles using 1D optical flow. Intelligent Vehicles Symposium 2014: 861-866 - [c31]Christian A. Naesseth, Fredrik Lindsten, Thomas B. Schön:
Sequential Monte Carlo for Graphical Models. NIPS 2014: 1862-1870 - [c30]Joel Kronander, Thomas B. Schön:
Robust auxiliary particle filters using multiple importance sampling. SSP 2014: 268-271 - [c29]Joel Kronander, Thomas B. Schön, Johan Dahlin:
Backward sequential Monte Carlo for marginal smoothing. SSP 2014: 368-371 - 2013
- [c28]Niklas Wahlstrom, Manon Kok, Thomas B. Schön, Fredrik Gustafsson:
Modeling magnetic fields using Gaussian processes. ICASSP 2013: 3522-3526 - [c27]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 - [c26]Ehsan Taghavi, Fredrik Lindsten, Lennart Svensson, Thomas B. Schön:
Adaptive stopping for fast particle smoothing. ICASSP 2013: 6293-6297 - [c25]Johan Dahlin, Fredrik Lindsten, Thomas B. Schön:
Particle metropolis hastings using Langevin dynamics. ICASSP 2013: 6308-6312 - [c24]Manon Kok, Niklas Wahlstrom, Thomas B. Schön, Fredrik Gustafsson:
MEMS-based inertial navigation based on a magnetic field map. ICASSP 2013: 6466-6470 - [c23]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 - 2012
- [c22]Tohid Ardeshiri, Umut Orguner, Christian Lundquist, Thomas B. Schön:
On mixture reduction for multiple target tracking. FUSION 2012: 692-699 - [c21]Manon Kok, Jeroen D. Hol, Thomas B. Schön, Fredrik Gustafsson, Henk Luinge:
Calibration of a magnetometer in combination with inertial sensors. FUSION 2012: 787-793 - [c20]Fredrik Lindsten, Thomas B. Schön:
On the use of backward simulation in the particle Gibbs sampler. ICASSP 2012: 3845-3848 - [c19]Fredrik Lindsten, Michael I. Jordan, Thomas B. Schön:
Ancestor Sampling for Particle Gibbs. NIPS 2012: 2600-2608 - 2011
- [c18]Tohid Ardeshiri, Fredrik Larsson, Fredrik Gustafsson, Thomas B. Schön, Michael Felsberg:
Bicycle tracking using ellipse extraction. FUSION 2011: 1-8 - 2010
- [c17]Adrian Wills, Thomas B. Schön, Brett Ninness:
Estimating state-space models in innovations form using the expectation maximisation algorithm. CDC 2010: 5524-5529 - [c16]Brett Ninness, Adrian Wills, Thomas B. Schön:
Estimation of general nonlinear state-space systems. CDC 2010: 6371-6376 - [c15]Fredrik Lindsten, Thomas B. Schön:
Identification of mixed linear/nonlinear state-space models. CDC 2010: 6377-6382 - [c14]Tianshi Chen, Thomas B. Schön, Henrik Ohlsson, Lennart Ljung:
Decentralization of particle filters using arbitrary state decomposition. CDC 2010: 7383-7388 - [c13]Michael Felsberg, Fredrik Larsson, Han Wang, Anders Ynnerman, Thomas B. Schön:
Torchlight Navigation. ICPR 2010: 302-306 - [c12]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 - [c11]Karl Granström, Thomas B. Schön:
Learning to close the loop from 3D point clouds. IROS 2010: 2089-2095 - 2009
- [c10]Robert Henriksson, Mikael Norrlöf, Stig Moberg, Erik Wernholt, Thomas B. Schön:
Experimental comparison of observers for tool position estimation of industrial robots. CDC 2009: 8065-8070 - 2008
- [c9]David Törnqvist, Thomas B. Schön, Fredrik Gustafsson:
Detecting spurious features using parity space. ICARCV 2008: 353-358 - [c8]Jeroen D. Hol, Thomas B. Schön, Fredrik Gustafsson:
A new algorithm for calibrating a combined camera and IMU sensor unit. ICARCV 2008: 1857-1862 - [c7]Jeroen D. Hol, Thomas B. Schön, Fredrik Gustafsson:
Relative pose calibration of a spherical camera and an IMU. ISMAR 2008: 21-24 - 2007
- [c6]Xiao-Li Hu, Thomas B. Schön, Lennart Ljung:
A robust particle filter for state estimation - with convergence results. CDC 2007: 312-317 - [c5]Thomas B. Schön, David Törnqvist, Fredrik Gustafsson:
Fast particle filters for multi-rate sensors. EUSIPCO 2007: 876-880 - [c4]Thomas B. Schön, Rickard Karlsson, David Törnqvist, Fredrik Gustafsson:
A framework for simultaneous localization and mapping utilizing model structure. FUSION 2007: 1-8 - 2006
- [c3]Jeroen D. Hol, Thomas B. Schön, Fredrik Gustafsson, Per J. Slycke:
Sensor Fusion for Augmented Reality. FUSION 2006: 1-6 - 2003
- [c2]Thomas B. Schön, Markus Gerdin, S. Torkel Glad, Fredrik Gustafsson:
A modeling and filtering framework for linear differential-algebraic equations. CDC 2003: 892-897 - [c1]Thomas B. Schön, Fredrik Gustafsson, Anders Hansson:
A note on state estimation as a convex optimization problem. ICASSP (6) 2003: 61-64
Reference Works
- 2015
- [r1]Thomas B. Schön:
Nonlinear System Identification Using Particle Filters. Encyclopedia of Systems and Control 2015
Data and Artifacts
- 2021
- [d6]Antônio H. Ribeiro, Gabriela M. M. Paixão, Emilly M. Lima, Manoel Horta Ribeiro, Marcelo M. Pinto Filho, Paulo R. Gomes, Derick M. de Oliveira, Wagner Meira Jr., Thomas B. Schön, Antônio Luiz P. Ribeiro:
CODE-15%: a large scale annotated dataset of 12-lead ECGs. Zenodo, 2021
- [d5]Antônio Luiz P. Ribeiro, Antônio H. Ribeiro, Gabriela M. M. Paixão, Emilly M. Lima, Manoel Horta Ribeiro, Marcelo M. Pinto Filho, Paulo R. Gomes, Derick M. de Oliveira, Wagner Meira Jr., Thomas B. Schön, Ester C. Sabino:
Sami-Trop: 12-lead ECG traces with age and mortality annotations. Zenodo, 2021 - 2020
- [d4]Antônio H. Ribeiro, Manoel Horta Ribeiro, Gabriela M. M. Paixão, Derick M. de Oliveira, Paulo R. Gomes, Jéssica A. Canazart, Milton P. S. Ferreira, Carl R. Andersson, Peter W. Macfarlane, Wagner Meira Jr., Thomas B. Schön, Antônio Luiz P. Ribeiro:
Annotated 12 lead ECG dataset. Version v1.0. Zenodo, 2020 [all versions] - [d3]Antônio H. Ribeiro, Manoel Horta Ribeiro, Gabriela M. M. Paixão, Derick M. de Oliveira, Paulo R. Gomes, Jéssica A. Canazart, Milton P. S. Ferreira, Carl R. Andersson, Peter W. Macfarlane, Wagner Meira Jr., Thomas B. Schön, Antônio Luiz P. Ribeiro:
Annotated 12-lead ECG dataset. Version v1.0.1. Zenodo, 2020 [all versions] - [d2]Antônio H. Ribeiro, Manoel Horta Ribeiro, Gabriela M. M. Paixão, Derick M. de Oliveira, Paulo R. Gomes, Jéssica A. Canazart, Milton P. S. Ferreira, Carl R. Andersson, Peter W. Macfarlane, Wagner Meira Jr., Thomas B. Schön, Antônio Luiz P. Ribeiro:
Annotated 12-lead ECG dataset. Version v1.0.2. Zenodo, 2020 [all versions] - [d1]Antônio H. Ribeiro, Manoel Horta Ribeiro, Gabriela M. M. Paixão, Derick M. de Oliveira, Paulo R. Gomes, Jéssica A. Canazart, Milton P. S. Ferreira, Carl R. Andersson, Peter W. Macfarlane, Wagner Meira Jr., Thomas B. Schön, Antônio Luiz P. Ribeiro:
CODE-test: An annotated 12-lead ECG dataset. Version v1.0.3. Zenodo, 2020 [all versions]
Informal and Other Publications
- 2025
- [i107]Bernhard Wullt, Mikael Norrlöf, Per Mattsson, Thomas B. Schön:
Probabilistic Bubble Roadmap. CoRR abs/2502.16205 (2025) - [i106]Abdullah Tokmak, Kiran G. Krishnan, Thomas B. Schön, Dominik Baumann:
Safe exploration in reproducing kernel Hilbert spaces. CoRR abs/2503.10352 (2025) - [i105]Oskar Allerbo, Thomas B. Schön:
Supervised Models Can Generalize Also When Trained on Random Labels. CoRR abs/2505.11006 (2025) - [i104]Ziwei Luo, Fredrik K. Gustafsson, Jens Sjölund, Thomas B. Schön:
Forward-only Diffusion Probabilistic Models. CoRR abs/2505.16733 (2025) - [i103]Abdullah Tokmak, Thomas B. Schön, Dominik Baumann:
Towards safe control parameter tuning in distributed multi-agent systems. CoRR abs/2508.13608 (2025) - [i102]Bernhard Wullt, Johannes Köhler, Per Mattsson, Mikael Norrlöf, Thomas B. Schön:
Robust Convex Model Predictive Control with collision avoidance guarantees for robot manipulators. CoRR abs/2508.21677 (2025) - [i101]Antônio H. Ribeiro, David Vävinggren, Dave Zachariah, Thomas B. Schön, Francis R. Bach:
Kernel Learning with Adversarial Features: Numerical Efficiency and Adaptive Regularization. CoRR abs/2510.20883 (2025) - 2024
- [i100]Dominik Baumann, Thomas B. Schön:
Safe reinforcement learning in uncertain contexts. CoRR abs/2401.05876 (2024) - [i99]Ruoqi Zhang, Ziwei Luo, Jens Sjölund, Thomas B. Schön, Per Mattsson:
Entropy-regularized Diffusion Policy with Q-Ensembles for Offline Reinforcement Learning. CoRR abs/2402.04080 (2024) - [i98]Per Mattsson, Fabio Bonassi, Valentina Breschi, Thomas B. Schön:
On the equivalence of direct and indirect data-driven predictive control approaches. CoRR abs/2403.05860 (2024) - [i97]Ziwei Luo, Fredrik K. Gustafsson, Zheng Zhao, Jens Sjölund, Thomas B. Schön:
Photo-Realistic Image Restoration in the Wild with Controlled Vision-Language Models. CoRR abs/2404.09732 (2024) - [i96]Adrien Corenflos, Zheng Zhao, Simo Särkkä, Jens Sjölund, Thomas B. Schön:
Conditioning diffusion models by explicit forward-backward bridging. CoRR abs/2405.13794 (2024) - [i95]Stijn van Esch, Fabio Bonassi, Thomas B. Schön:
Accounts of using the Tustin-Net architecture on a rotary inverted pendulum. CoRR abs/2408.12266 (2024) - [i94]Abdullah Tokmak, Thomas B. Schön, Dominik Baumann:
PACSBO: Probably approximately correct safe Bayesian optimization. CoRR abs/2409.01163 (2024) - [i93]Gabriel Y. Arteaga, Thomas B. Schön, Nicolas Pielawski:
Hallucination Detection in LLMs: Fast and Memory-Efficient Finetuned Models. CoRR abs/2409.02976 (2024) - [i92]Zheng Zhao, Ziwei Luo, Jens Sjölund, Thomas B. Schön:
Conditional sampling within generative diffusion models. CoRR abs/2409.09650 (2024) - [i91]Ziwei Luo, Fredrik K. Gustafsson, Zheng Zhao, Jens Sjölund, Thomas B. Schön:
Taming Diffusion Models for Image Restoration: A Review. CoRR abs/2409.10353 (2024) - [i90]Niklas Gunnarsson, Jens Sjölund, Peter Kimstrand, Thomas B. Schön:
Online learning in motion modeling for intra-interventional image sequences. CoRR abs/2410.11491 (2024) - [i89]Antônio H. Ribeiro, Thomas B. Schön, Dave Zahariah, Francis R. Bach:
Efficient Optimization Algorithms for Linear Adversarial Training. CoRR abs/2410.12677 (2024) - 2023
- [i88]Ziwei Luo, Fredrik K. Gustafsson, Zheng Zhao, Jens Sjölund, Thomas B. Schön:
Image Restoration with Mean-Reverting Stochastic Differential Equations. CoRR abs/2301.11699 (2023) - [i87]Gianluigi Pillonetto, Aleksandr Y. Aravkin, Daniel Gedon, Lennart Ljung, Antônio H. Ribeiro, Thomas B. Schön:
Deep networks for system identification: a Survey. CoRR abs/2301.12832 (2023) - [i86]Fredrik K. Gustafsson, Martin Danelljan, Thomas B. Schön:
How Reliable is Your Regression Model's Uncertainty Under Real-World Distribution Shifts? CoRR abs/2302.03679 (2023) - [i85]Daniel Gedon, Antônio H. Ribeiro, Niklas Wahlström, Thomas B. Schön:
Invertible Kernel PCA with Random Fourier Features. CoRR abs/2303.05043 (2023) - [i84]Dominik Baumann, Thomas B. Schön:
On the trade-off between event-based and periodic state estimation under bandwidth constraints. CoRR abs/2304.00559 (2023) - [i83]Ziwei Luo, Fredrik K. Gustafsson, Zheng Zhao, Jens Sjölund, Thomas B. Schön:
Refusion: Enabling Large-Size Realistic Image Restoration with Latent-Space Diffusion Models. CoRR abs/2304.08291 (2023) - [i82]Manon Kok, Arno Solin, Thomas B. Schön:
Rao-Blackwellized Particle Smoothing for Simultaneous Localization and Mapping. CoRR abs/2306.03953 (2023) - [i81]Tim Martin, Thomas B. Schön, Frank Allgöwer:
Guarantees for data-driven control of nonlinear systems using semidefinite programming: A survey. CoRR abs/2306.16042 (2023) - [i80]Theogene Habineza, Antônio H. Ribeiro, Daniel Gedon, Joachim A. Behar, Antônio Luiz Pinho Ribeiro, Thomas B. Schön:
End-to-end Risk Prediction of Atrial Fibrillation from the 12-Lead ECG by Deep Neural Networks. CoRR abs/2309.16335 (2023) - [i79]Ziwei Luo, Fredrik K. Gustafsson, Zheng Zhao, Jens Sjölund, Thomas B. Schön:
Controlling Vision-Language Models for Universal Image Restoration. CoRR abs/2310.01018 (2023) - [i78]Antônio H. Ribeiro, Dave Zachariah, Francis R. Bach, Thomas B. Schön:
Regularization properties of adversarially-trained linear regression. CoRR abs/2310.10807 (2023) - [i77]Dominik Baumann, Erfaun Noorani, James Price, Ole Peters, Colm Connaughton, Thomas B. Schön:
Non-ergodicity in reinforcement learning: robustness via ergodicity transformations. CoRR abs/2310.11335 (2023) - [i76]Zheng Zhao, Sebastian Mair, Thomas B. Schön, Jens Sjölund:
On Feynman-Kac training of partial Bayesian neural networks. CoRR abs/2310.19608 (2023) - [i75]Maria Bånkestad, Jens Sjölund, Jalil Taghia, Thomas B. Schön:
Variational Elliptical Processes. CoRR abs/2311.12566 (2023) - [i74]Fabio Bonassi, Carl R. Andersson, Per Mattsson, Thomas B. Schön:
Structured state-space models are deep Wiener models. CoRR abs/2312.06211 (2023) - 2022
- [i73]Philipp Pilar, Carl Jidling, Thomas B. Schön, Niklas Wahlström:
Incorporating Sum Constraints into Multitask Gaussian Processes. CoRR abs/2202.01793 (2022) - [i72]Antônio H. Ribeiro, Thomas B. Schön:
Overparameterized Linear Regression under Adversarial Attacks. CoRR abs/2204.06274 (2022) - [i71]Antônio H. Ribeiro, Dave Zachariah, Thomas B. Schön:
Surprises in adversarially-trained linear regression. CoRR abs/2205.12695 (2022) - [i70]Tim Martin, Thomas B. Schön, Frank Allgöwer:
Gaussian inference for data-driven state-feedback design of nonlinear systems. CoRR abs/2211.05639 (2022) - [i69]Philipp Von Bachmann, Daniel Gedon, Fredrik K. Gustafsson, Antônio H. Ribeiro, Erik Lampa, Stefan Gustafsson, Johan Sundström, Thomas B. Schön:
ECG-Based Electrolyte Prediction: Evaluating Regression and Probabilistic Methods. CoRR abs/2212.13890 (2022) - 2021
- [i68]Antônio H. Ribeiro, Thomas B. Schön:
How Convolutional Neural Networks Deal with Aliasing. CoRR abs/2102.07757 (2021) - [i67]Filip de Roos, Carl Jidling, Adrian Wills, Thomas B. Schön, Philipp Hennig:
A Probabilistically Motivated Learning Rate Adaptation for Stochastic Optimization. CoRR abs/2102.10880 (2021) - [i66]Niklas Gunnarsson, Jens Sjölund, Thomas B. Schön:
Latent linear dynamics in spatiotemporal medical data. CoRR abs/2103.00930 (2021) - [i65]Johannes N. Hendriks, James R. Z. Holdsworth, Adrian G. Wills, Thomas B. Schön, Brett Ninness:
Data to Controller for Nonlinear Systems: An Approximate Solution. CoRR abs/2103.08782 (2021) - [i64]Carl R. Andersson, Niklas Wahlström, Thomas B. Schön:
Learning deep autoregressive models for hierarchical data. CoRR abs/2104.13853 (2021) - [i63]Fredrik K. Gustafsson, Martin Danelljan, Thomas B. Schön:
Learning Proposals for Practical Energy-Based Regression. CoRR abs/2110.11948 (2021) - [i62]Conor Rosato, Paul R. Horridge, Thomas B. Schön, Simon Maskell:
Efficient Learning of the Parameters of Non-Linear Models using Differentiable Resampling in Particle Filters. CoRR abs/2111.01409 (2021) - 2020
- [i61]Johannes N. Hendriks, Carl Jidling, Adrian Wills, Thomas B. Schön:
Linearly Constrained Neural Networks. CoRR abs/2002.01600 (2020) - [i60]Jarrad Courts, Christopher Renton, Thomas B. Schön, Adrian Wills:
Constructing a variational family for nonlinear state-space models. CoRR abs/2002.02620 (2020) - [i59]Maria Bånkestad, Jens Sjölund, Jalil Taghia, Thomas B. Schön:
The Elliptical Processes: a New Family of Flexible Stochastic Processes. CoRR abs/2003.07201 (2020) - [i58]Niklas Gunnarsson, Jens Sjölund, Thomas B. Schön:
Registration by tracking for sequential 2D MRI. CoRR abs/2003.10819 (2020) - [i57]Daniel Gedon, Niklas Wahlström, Thomas B. Schön, Lennart Ljung:
Deep State Space Models for Nonlinear System Identification. CoRR abs/2003.14162 (2020) - [i56]Fredrik K. Gustafsson, Martin Danelljan, Radu Timofte, Thomas B. Schön:
How to Train Your Energy-Based Model for Regression. CoRR abs/2005.01698 (2020) - [i55]Johannes N. Hendriks, Fredrik K. Gustafsson, Antônio H. Ribeiro, Adrian G. Wills, Thomas B. Schön:
Deep Energy-Based NARX Models. CoRR abs/2012.04136 (2020) - [i54]Fredrik K. Gustafsson, Martin Danelljan, Thomas B. Schön:
Accurate 3D Object Detection using Energy-Based Models. CoRR abs/2012.04634 (2020) - [i53]Jarrad Courts, Adrian Wills, Thomas B. Schön, Brett Ninness:
Variational Nonlinear System Identification. CoRR abs/2012.05072 (2020) - [i52]Antônio H. Ribeiro, Johannes N. Hendriks, Adrian G. Wills, Thomas B. Schön:
Beyond Occam's Razor in System Identification: Double-Descent when Modeling Dynamics. CoRR abs/2012.06341 (2020) - [i51]Jarrad Courts, Johannes N. Hendriks, Adrian Wills, Thomas B. Schön, Brett Ninness:
Variational State and Parameter Estimation. CoRR abs/2012.07269 (2020) - 2019
- [i50]Muhammad Osama, Dave Zachariah, Thomas B. Schön:
Inferring Heterogeneous Causal Effects in Presence of Spatial Confounding. CoRR abs/1901.09919 (2019) - [i49]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) - [i48]Juozas Vaicenavicius, David Widmann, Carl R. Andersson, Fredrik Lindsten, Jacob Roll, Thomas B. Schön:
Evaluating model calibration in classification. CoRR abs/1902.06977 (2019) - [i47]Jack Umenberger, Thomas B. Schön:
Nonlinear input design as optimal control of a Hamiltonian system. CoRR abs/1903.02250 (2019) - [i46]Christian A. Naesseth, Fredrik Lindsten, Thomas B. Schön:
Elements of Sequential Monte Carlo. CoRR abs/1903.04797 (2019) - [i45]Antônio H. Ribeiro, Manoel Horta Ribeiro, Gabriela M. M. Paixão, Derick M. de Oliveira, Paulo R. Gomes, Jéssica A. Canazart, Milton P. S. Ferreira, Carl R. Andersson, Peter W. Macfarlane, Wagner Meira Jr., Thomas B. Schön, Antônio Luiz P. Ribeiro:
Automatic Diagnosis of the Short-Duration 12-Lead ECG using a Deep Neural Network: the CODE Study. CoRR abs/1904.01949 (2019) - [i44]Antônio H. Ribeiro, Koen Tiels, Jack Umenberger, Thomas B. Schön, Luis Antonio Aguirre:
On the Smoothness of Nonlinear System Identification. CoRR abs/1905.00820 (2019) - [i43]Jack Umenberger, Mina Ferizbegovic, Thomas B. Schön, Håkan Hjalmarsson:
Robust exploration in linear quadratic reinforcement learning. CoRR abs/1906.01584 (2019) - [i42]Fredrik K. Gustafsson, Martin Danelljan, Thomas B. Schön:
Evaluating Scalable Bayesian Deep Learning Methods for Robust Computer Vision. CoRR abs/1906.01620 (2019) - [i41]Antônio H. Ribeiro, Koen Tiels, Luis Antonio Aguirre, Thomas B. Schön:
The trade-off between long-term memory and smoothness for recurrent networks. CoRR abs/1906.08482 (2019) - [i40]Adrian Wills, Thomas B. Schön:
Stochastic quasi-Newton with line-search regularization. CoRR abs/1909.01238 (2019) - [i39]Carl R. Andersson, Antônio H. Ribeiro, Koen Tiels, Niklas Wahlström, Thomas B. Schön:
Deep Convolutional Networks in System Identification. CoRR abs/1909.01730 (2019) - [i38]Carl Jidling, Johannes N. Hendriks, Thomas B. Schön, Adrian Wills:
Deep kernel learning for integral measurements. CoRR abs/1909.01844 (2019) - [i37]Fredrik K. Gustafsson, Martin Danelljan, Goutam Bhat, Thomas B. Schön:
DCTD: Deep Conditional Target Densities for Accurate Regression. CoRR abs/1909.12297 (2019) - [i36]Manon Kok, Thomas B. Schön:
A Fast and Robust Algorithm for Orientation Estimation using Inertial Sensors. CoRR abs/1910.00463 (2019) - [i35]Jack Umenberger, Thomas B. Schön:
Optimistic robust linear quadratic dual control. CoRR abs/1912.13143 (2019) - 2018
- [i34]Carl R. Andersson, Niklas Wahlström, Thomas B. Schön:
Data-Driven Impulse Response Regularization via Deep Learning. CoRR abs/1801.08383 (2018) - [i33]Muhammad Osama, Dave Zachariah, Thomas B. Schön:
Learning Localized Spatio-Temporal Models From Streaming Data. CoRR abs/1802.03334 (2018) - [i32]Adrian Wills, Thomas B. Schön:
Stochastic quasi-Newton with adaptive step lengths for large-scale problems. CoRR abs/1802.04310 (2018) - [i31]Jack Umenberger, Thomas B. Schön:
Learning convex bounds for linear quadratic control policy synthesis. CoRR abs/1806.00319 (2018) - [i30]Zenith Purisha, Carl Jidling, Niklas Wahlström, Simo Särkkä, Thomas B. Schön:
Probabilistic approach to limited-data computed tomography reconstruction. CoRR abs/1809.03779 (2018) - [i29]Adrian Wills, Carl Jidling, Thomas B. Schön:
A fast quasi-Newton-type method for large-scale stochastic optimisation. CoRR abs/1810.01269 (2018) - [i28]Lawrence M. Murray, Thomas B. Schön:
Automated learning with a probabilistic programming language: Birch. CoRR abs/1810.01539 (2018) - [i27]Antônio H. Ribeiro, Manoel Horta Ribeiro, Gabriela Paixão, Derick M. de Oliveira, Paulo R. Gomes, Jéssica A. Canazart, Milton P. S. Ferreira, Wagner Meira Jr., Thomas B. Schön, Antônio Luiz P. Ribeiro:
Automatic Diagnosis of Short-Duration 12-Lead ECG using a Deep Convolutional Network. CoRR abs/1811.12194 (2018) - [i26]Johannes N. Hendriks, Carl Jidling, Adrian Wills, Thomas B. Schön:
Evaluating the squared-exponential covariance function in Gaussian processes with integral observations. CoRR abs/1812.07319 (2018) - 2017
- [i25]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) - [i24]Dave Zachariah, Petre Stoica, Thomas B. Schön:
Online Learning for Distribution-Free Prediction. CoRR abs/1703.05060 (2017) - [i23]Manon Kok, Jeroen D. Hol, Thomas B. Schön:
Using Inertial Sensors for Position and Orientation Estimation. CoRR abs/1704.06053 (2017) - [i22]Hildo Bijl, Thomas B. Schön:
Optimal controller/observer gains of discounted-cost LQG systems. CoRR abs/1706.01042 (2017) - [i21]Johan Wågberg, Dave Zachariah, Thomas B. Schön:
Regularized parametric system identification: a decision-theoretic formulation. CoRR abs/1710.04009 (2017) - [i20]Andreas Svensson, Fredrik Lindsten, Thomas B. Schön:
Learning nonlinear state-space models using smooth particle-filter-based likelihood approximations. CoRR abs/1711.10765 (2017) - [i19]Andreas Svensson, Dave Zachariah, Thomas B. Schön:
How consistent is my model with the data? Information-Theoretic Model Check. CoRR abs/1712.02675 (2017) - 2016
- [i18]Manon Kok, Thomas B. Schön:
Magnetometer calibration using inertial sensors. CoRR abs/1601.05257 (2016) - [i17]Hildo Bijl, Thomas B. Schön, Jan-Willem van Wingerden, Michel Verhaegen:
Online Sparse Gaussian Process Training with Input Noise. CoRR abs/1601.08068 (2016) - [i16]Hildo Bijl, Jan-Willem van Wingerden, Thomas B. Schön, Michel Verhaegen:
Mean and variance of the LQG cost function. CoRR abs/1602.02524 (2016) - [i15]Andreas Svensson, Thomas B. Schön:
A flexible state space model for learning nonlinear dynamical systems. CoRR abs/1603.05486 (2016) - [i14]Manon Kok, Sina Khoshfetrat Pakazad, Thomas B. Schön, Anders Hansson, Jeroen D. Hol:
A Scalable and Distributed Solution to the Inertial Motion Capture Problem. CoRR abs/1603.06443 (2016) - [i13]Jack Umenberger, Johan Wågberg, Ian R. Manchester, Thomas B. Schön:
Linear System Identification via EM with Latent Disturbances and Lagrangian Relaxation. CoRR abs/1603.09157 (2016) - [i12]Hildo Bijl, Thomas B. Schön, Jan-Willem van Wingerden, Michel Verhaegen:
Gaussian process optimization through sampling from the maximum distribution. CoRR abs/1604.00169 (2016) - 2015
- [i11]Niklas Wahlström, Thomas B. Schön, Marc Peter Deisenroth:
From Pixels to Torques: Policy Learning with Deep Dynamical Models. CoRR abs/1502.02251 (2015) - [i10]Andreas Svensson, Thomas B. Schön, Manon Kok:
Nonlinear state space smoothing using the conditional particle filter. CoRR abs/1502.03697 (2015) - [i9]Arno Solin, Manon Kok, Niklas Wahlström, Thomas B. Schön, Simo Särkkä:
Modeling and interpolation of the ambient magnetic field by Gaussian processes. CoRR abs/1509.04634 (2015) - [i8]Andreas Svensson, Thomas B. Schön, Arno Solin, Simo Särkkä:
Nonlinear State Space Model Identification Using a Regularized Basis Function Expansion. CoRR abs/1510.00563 (2015) - [i7]John-Alexander M. Assael, Niklas Wahlström, Thomas B. Schön, Marc Peter Deisenroth:
Data-Efficient Learning of Feedback Policies from Image Pixels using Deep Dynamical Models. CoRR abs/1510.02173 (2015) - 2014
- [i6]Christian A. Naesseth, Fredrik Lindsten, Thomas B. Schön:
Capacity estimation of two-dimensional channels using Sequential Monte Carlo. CoRR abs/1405.0102 (2014) - [i5]Niklas Wahlström, Thomas B. Schön, Marc Peter Deisenroth:
Learning deep dynamical models from image pixels. CoRR abs/1410.7550 (2014) - [i4]Liang Dai, Thomas B. Schön:
On the exponential convergence of the Kaczmarz algorithm. CoRR abs/1411.4017 (2014) - [i3]Liang Dai, Thomas B. Schön:
A new structure exploiting derivation of recursive direct weight optimization. CoRR abs/1411.4018 (2014) - 2013
- [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)
Coauthor Index
manage site settings
To protect your privacy, all features that rely on external API calls from your browser are turned off by default. You need to opt-in for them to become active. All settings here will be stored as cookies with your web browser. For more information see our F.A.Q.
Unpaywalled article links
Add open access links from 
to the list of external document links (if available).
Privacy notice: By enabling the option above, your browser will contact the API of unpaywall.org to load hyperlinks to open access articles. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the Unpaywall privacy policy.
Archived links via Wayback Machine
For web page which are no longer available, try to retrieve content from the 
of the Internet Archive (if available).
Privacy notice: By enabling the option above, your browser will contact the API of archive.org to check for archived content of web pages that are no longer available. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the Internet Archive privacy policy.
Reference lists
Add a list of references from 
, 
, and 
to record detail pages.
load references from crossref.org and opencitations.net
Privacy notice: By enabling the option above, your browser will contact the APIs of crossref.org, opencitations.net, and semanticscholar.org to load article reference information. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the Crossref privacy policy and the OpenCitations privacy policy, as well as the AI2 Privacy Policy covering Semantic Scholar.
Citation data
Add a list of citing articles from and to record detail pages.
load citations from opencitations.net
Privacy notice: By enabling the option above, your browser will contact the API of opencitations.net and semanticscholar.org to load citation information. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the OpenCitations privacy policy as well as the AI2 Privacy Policy covering Semantic Scholar.
OpenAlex data
Load additional information about publications from 
.
Privacy notice: By enabling the option above, your browser will contact the API of openalex.org to load additional information. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the information given by OpenAlex.
last updated on 2025-11-18 22:50 CET by the dblp team
all metadata released as open data under CC0 1.0 license
see also: Terms of Use | Privacy Policy | Imprint
dblp was originally created in 1993 at:
since 2018, dblp has been operated and maintained by:
the dblp computer science bibliography is funded and supported by:
