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Thorsteinn S. Rögnvaldsson
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2020 – today
- 2024
- [j22]Abdallah Alabdallah
, Mattias Ohlsson
The Concordance Index decomposition: A measure for a deeper understanding of survival prediction models. Artif. Intell. Medicine 148: 102781 (2024) - [j21]Anna Vettoruzzo
Meta-learning for efficient unsupervised domain adaptation. Neurocomputing 574: 127264 (2024) - [j20]Emmanuella Budu
Evaluation of synthetic electronic health records: A systematic review and experimental assessment. Neurocomputing 603: 128253 (2024) - [j19]Anna Vettoruzzo
Multimodal meta-learning through meta-learned task representations. Neural Comput. Appl. 36(15): 8519-8529 (2024) - [j18]Anna Vettoruzzo
Advances and Challenges in Meta-Learning: A Technical Review. IEEE Trans. Pattern Anal. Mach. Intell. 46(7): 4763-4779 (2024) - [c25]Mohammed Ghaith Altarabichi
Improving Concordance Index in Regression-based Survival Analysis: Evolutionary Discovery of Loss Function for Neural Networks. GECCO Companion 2024: 1863-1869 - [c24]Abdallah Alabdallah, Jakub Jakubowski, Sepideh Pashami, Szymon Bobek, Mattias Ohlsson, Thorsteinn S. Rögnvaldsson, Grzegorz J. Nalepa:
Understanding Survival Models Through Counterfactual Explanations. ICCS (4) 2024: 310-324 - [c23]Anna Vettoruzzo, Mohamed-Rafik Bouguelia, Thorsteinn S. Rögnvaldsson:
Personalized Federated Learning with Contextual Modulation and Meta-Learning. SDM 2024: 842-850 - [i9]Abdallah Alabdallah, Omar Hamed, Mattias Ohlsson, Thorsteinn S. Rögnvaldsson, Sepideh Pashami:
CoxSE: Exploring the Potential of Self-Explaining Neural Networks with Cox Proportional Hazards Model for Survival Analysis. CoRR abs/2407.13849 (2024) - [i8]Anna Vettoruzzo, Joaquin Vanschoren, Mohamed-Rafik Bouguelia, Thorsteinn S. Rögnvaldsson:
Learning to Learn without Forgetting using Attention. CoRR abs/2408.03219 (2024) - [i7]Mahmoud Rahat, Peyman Sheikholharam Mashhadi, Slawomir Nowaczyk, Shamik Choudhury, Leo Petrin, Thorsteinn S. Rögnvaldsson, Andreas Voskou, Carlo Metta, Claudio Savelli:
Volvo Discovery Challenge at ECML-PKDD 2024. CoRR abs/2409.11446 (2024) - 2023
- [j17]Felix Nilsson
Practical joint human-machine exploration of industrial time series using the matrix profile. Data Min. Knowl. Discov. 37(1): 1-38 (2023) - [j16]Kunru Chen
Material handling machine activity recognition by context ensemble with gated recurrent units. Eng. Appl. Artif. Intell. 126: 106992 (2023) - [j15]Reza Khoshkangini
Predicting Vehicle Behavior Using Multi-task Ensemble Learning. Expert Syst. Appl. 212: 118716 (2023) - [c22]Hamid Sarmadi, Thorsteinn S. Rögnvaldsson, Nils Roger Carlsson, Mattias Ohlsson, Ibrahim Wahab, Ola Hall:
Towards Explaining Satellite Based Poverty Predictions with Convolutional Neural Networks. DSAA 2023: 1-10 - [c21]Kunru Chen, Thorsteinn S. Rögnvaldsson, Slawomir Nowaczyk
Toward Solving Domain Adaptation with Limited Source Labeled Data. ICDM (Workshops) 2023: 1240-1246 - [c20]Reza Khoshkangini, Mohsen Tajgardan, Peyman Sheikholharam Mashhadi, Thorsteinn S. Rögnvaldsson, Daniel Tegnered:
Optimal Task Grouping Approach in Multitask Learning. ICONIP (6) 2023: 206-225 - [c19]Anna Vettoruzzo
Meta-Learning from Multimodal Task Distributions Using Multiple Sets of Meta-Parameters. IJCNN 2023: 1-8 - [c18]Emmanuella Budu
A Framework for Evaluating Synthetic Electronic Health Records. MIE 2023: 378-379 - [i6]Anna Vettoruzzo, Mohamed-Rafik Bouguelia, Joaquin Vanschoren, Thorsteinn S. Rögnvaldsson, KC Santosh:
Advances and Challenges in Meta-Learning: A Technical Review. CoRR abs/2307.04722 (2023) - [i5]Hamid Sarmadi, Thorsteinn S. Rögnvaldsson, Nils Roger Carlsson, Mattias Ohlsson, Ibrahim Wahab, Ola Hall:
Towards Explaining Satellite Based Poverty Predictions with Convolutional Neural Networks. CoRR abs/2312.00416 (2023) - [i4]Anna Vettoruzzo, Mohamed-Rafik Bouguelia, Thorsteinn S. Rögnvaldsson:
Personalized Federated Learning with Contextual Modulation and Meta-Learning. CoRR abs/2312.15191 (2023) - 2022
- [j14]Jennifer David
Deterministic annealing with Potts neurons for multi-robot routing. Intell. Serv. Robotics 15(3): 321-334 (2022) - [j13]Ola Hall, Mattias Ohlsson, Thorsteinn S. Rögnvaldsson
A review of explainable AI in the satellite data, deep machine learning, and human poverty domain. Patterns 3(10): 100600 (2022) - [j12]Kunru Chen
Semi-Supervised Learning for Forklift Activity Recognition from Controller Area Network (CAN) Signals. Sensors 22(11): 4170 (2022) - [c17]Abdallah Alabdallah
SurvSHAP: A Proxy-Based Algorithm for Explaining Survival Models with SHAP. DSAA 2022: 1-10 - [i3]Abdallah Alabdallah
The Concordance Index decomposition: a measure for a deeper understanding of survival prediction models. CoRR abs/2203.00144 (2022) - [i2]Ola Hall, Mattias Ohlsson, Thorsteinn S. Rögnvaldsson:
Satellite Image and Machine Learning based Knowledge Extraction in the Poverty and Welfare Domain. CoRR abs/2203.01068 (2022) - 2021
- [j11]Jennifer David
Multi-Robot Routing Problem with Min-Max Objective. Robotics 10(4): 122 (2021) - 2020
- [j10]Yuantao Fan, Slawomir Nowaczyk
Transfer learning for remaining useful life prediction based on consensus self-organizing models. Reliab. Eng. Syst. Saf. 203: 107098 (2020) - [c16]Kunru Chen, Sepideh Pashami, Slawomir Nowaczyk
Forklift Truck Activity Recognition from CAN Data. IoT Streams/ITEM@PKDD/ECML 2020: 119-126
2010 – 2019
- 2019
- [i1]Yuantao Fan, Slawomir Nowaczyk
Transfer learning for Remaining Useful Life Prediction Based on Consensus Self-Organizing Models. CoRR abs/1909.07053 (2019) - 2018
- [j9]Thorsteinn S. Rögnvaldsson
Self-monitoring for maintenance of vehicle fleets. Data Min. Knowl. Discov. 32(2): 344-384 (2018) - 2016
- [c15]Iulian Carpatorea, Slawomir Nowaczyk
Learning of Aggregate Features for Comparing Drivers Based on Naturalistic Data. ICMLA 2016: 1067-1072 - [c14]Thorsteinn S. Rögnvaldsson, Antanas Verikas, Josef Bigün, Slawomir Nowaczyk, Anita Pinheiro Sant'Anna, Björn Åstrand, Jens Lundström, Stefan Byttner, Roland Thörner, Fernando Alonso-Fernandez, Martin Cooney, Rafael Valencia:
Center for Applied Intelligent Systems Research (Position paper). SAIS 2016: 129:005 - 2015
- [j8]Thorsteinn S. Rögnvaldsson
State of the art prediction of HIV-1 protease cleavage sites. Bioinform. 31(8): 1204-1210 (2015) - [j7]Rune Prytz, Slawomir Nowaczyk
Predicting the need for vehicle compressor repairs using maintenance records and logged vehicle data. Eng. Appl. Artif. Intell. 41: 139-150 (2015) - [c13]Yuantao Fan, Slawomir Nowaczyk
Evaluation of Self-Organized Approach for Predicting Compressor Faults in a City Bus Fleet. INNS Conference on Big Data 2015: 447-456 - [c12]Yuantao Fan, Slawomir Nowaczyk
Incorporating Expert Knowledge into a Self-Organized Approach for Predicting Compressor Faults in a City Bus Fleet. SCAI 2015: 58-67 - [d1]Thorsteinn S. Rögnvaldsson:
HIV-1 protease cleavage. UCI Machine Learning Repository, 2015 - 2014
- [c11]Thorsteinn S. Rögnvaldsson
Estimating p-Values for Deviation Detection. SASO 2014: 100-109 - 2013
- [c10]Slawomir Nowaczyk
Towards a Machine Learning Algorithm for Predicting Truck Compressor Failures Using Logged Vehicle Data. SCAI 2013: 205-214 - 2012
- [c9]Henrik Andreasson, Abdelbaki Bouguerra, Björn Åstrand, Thorsteinn S. Rögnvaldsson
Gold-Fish SLAM: An Application of SLAM to Localize AGVs. FSR 2012: 585-598 - [p2]Thorsteinn S. Rögnvaldsson:
A Simple Trick for Estimating the Weight Decay Parameter. Neural Networks: Tricks of the Trade (2nd ed.) 2012: 69-89 - 2011
- [j6]Stefan Byttner, Thorsteinn S. Rögnvaldsson
Consensus self-organized models for fault detection (COSMO). Eng. Appl. Artif. Intell. 24(5): 833-839 (2011) - 2010
- [c8]Stefan Byttner, Magnus Svensson, Thorsteinn S. Rögnvaldsson:
Finding the Odd-One-Out in Fleets of Mechatronic Systems using Embedded Intelligent Agents. AAAI Spring Symposium: Embedded Reasoning 2010
2000 – 2009
- 2009
- [j5]Thorsteinn S. Rögnvaldsson
How to find simple and accurate rules for viral protease cleavage specificities. BMC Bioinform. 10 (2009) - [c7]Abdelbaki Bouguerra, Henrik Andreasson, Achim J. Lilienthal
An Autonomous Robotic System for Load Transportation. ETFA 2009: 1-4 - [c6]Stefan Byttner, Thorsteinn S. Rögnvaldsson
Networked Vehicles for Automated Fault Detection. ISCAS 2009: 1213-1216 - 2008
- [c5]Thorsteinn S. Rögnvaldsson
A self-organized approach for unsupervised fault detection in multiple systems. ICPR 2008: 1-4 - 2007
- [c4]Liwen You, Thorsteinn S. Rögnvaldsson
Almost Linear Biobasis Function Neural Networks. IJCNN 2007: 1774-1778 - 2006
- [c3]Kenneth Nilsson, Thorsteinn S. Rögnvaldsson
Biometric Identification of Mice. ICPR (4) 2006: 465-468 - [c2]Eric A. Antonelo, Albert-Jan Baerveldt, Thorsteinn S. Rögnvaldsson, Maurício F. Figueiredo:
Modular Neural Network and Classical Reinforcement Learning for Autonomous Robot Navigation: Inhibiting Undesirable Behaviors. IJCNN 2006: 498-505 - 2004
- [j4]Thorsteinn S. Rögnvaldsson
Why neural networks should not be used for HIV-1 protease cleavage site prediction. Bioinform. 20(11): 1702-1709 (2004) - [j3]Jim Samuelsson, Daniel Dalevi, Fredrik Levander
Modular, scriptable and automated analysis tools for high-throughput peptide mass fingerprinting. Bioinform. 20(18): 3628-3635 (2004)
1990 – 1999
- 1996
- [c1]John E. Moody, Thorsteinn S. Rögnvaldsson:
Smoothing Regularizers for Projective Basis Function Networks. NIPS 1996: 585-591 - [p1]Thorsteinn S. Rögnvaldsson:
A Simple Trick for Estimating the Weight Decay Parameter. Neural Networks: Tricks of the Trade 1996: 71-92 - 1994
- [j2]Thorsteinn S. Rögnvaldsson
On Langevin Updating in Multilayer Perceptrons. Neural Comput. 6(5): 916-926 (1994) - 1993
- [j1]Thorsteinn S. Rögnvaldsson
Pattern Discrimination Using Feedforward Networks: A Benchmark Study of Scaling Behavior. Neural Comput. 5(3): 483-491 (1993)
Coauthor Index
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last updated on 2024-10-15 21:37 CEST by the dblp team
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