Pattern Discrimination Using Feedforward Networks: A Benchmark Study of Scaling Behavior. - Thorsteinn S. Rögnvaldsson (Neural Comput., 1993)

On Langevin Updating in Multilayer Perceptrons. - Thorsteinn S. Rögnvaldsson (Neural Comput., 1994)

Smoothing Regularizers for Projective Basis Function Networks. - John E. Moody; Thorsteinn S. Rögnvaldsson (NIPS, 1996)

A Simple Trick for Estimating the Weight Decay Parameter. - Thorsteinn S. Rögnvaldsson (Neural Networks: Tricks of the Trade, 1996)

Why neural networks should not be used for HIV-1 protease cleavage site prediction. - Thorsteinn S. Rögnvaldsson; Liwen You (Bioinform., 2004)

Modular, scriptable and automated analysis tools for high-throughput peptide mass fingerprinting. - Jim Samuelsson; Daniel Dalevi; Fredrik Levander; Thorsteinn S. Rögnvaldsson (Bioinform., 2004)

Biometric Identification of Mice. - Kenneth Nilsson; Thorsteinn S. Rögnvaldsson; Jens Cameron; Christina Jacobson (ICPR (4), 2006)

Modular Neural Network and Classical Reinforcement Learning for Autonomous Robot Navigation: Inhibiting Undesirable Behaviors. - Eric A. Antonelo; Albert-Jan Baerveldt; Thorsteinn S. Rögnvaldsson; Maurício F. Figueiredo (IJCNN, 2006)

Almost Linear Biobasis Function Neural Networks. - Liwen You; Thorsteinn S. Rögnvaldsson (IJCNN, 2007)

A self-organized approach for unsupervised fault detection in multiple systems. - Thorsteinn S. Rögnvaldsson; Georg Panholzer; Stefan Byttner; Magnus Svensson (ICPR, 2008)

An Autonomous Robotic System for Load Transportation. - Abdelbaki Bouguerra; Henrik Andreasson; Achim J. Lilienthal; Björn Åstrand; Thorsteinn S. Rögnvaldsson (ETFA, 2009)

Networked Vehicles for Automated Fault Detection. - Stefan Byttner; Thorsteinn S. Rögnvaldsson; Magnus Svensson; George Bitar; Wesley Chominsky (ISCAS, 2009)

How to find simple and accurate rules for viral protease cleavage specificities. - Thorsteinn S. Rögnvaldsson; Terence A. Etchells; Liwen You; Daniel Garwicz; Ian H. Jarman; Paulo J. G. Lisboa (BMC Bioinform., 2009)

Finding the Odd-One-Out in Fleets of Mechatronic Systems using Embedded Intelligent Agents. - Stefan Byttner; Magnus Svensson; Thorsteinn S. Rögnvaldsson (AAAI Spring Symposium: Embedded Reasoning, 2010)

Consensus self-organized models for fault detection (COSMO). - Stefan Byttner; Thorsteinn S. Rögnvaldsson; Magnus Svensson (Eng. Appl. Artif. Intell., 2011)

Gold-Fish SLAM: An Application of SLAM to Localize AGVs. - Henrik Andreasson; Abdelbaki Bouguerra; Björn Åstrand; Thorsteinn S. Rögnvaldsson (FSR, 2012)

A Simple Trick for Estimating the Weight Decay Parameter. - Thorsteinn S. Rögnvaldsson (Neural Networks: Tricks of the Trade (2nd ed.), 2012)

Towards a Machine Learning Algorithm for Predicting Truck Compressor Failures Using Logged Vehicle Data. - Slawomir Nowaczyk; Rune Prytz; Thorsteinn S. Rögnvaldsson; Stefan Byttner (SCAI, 2013)

Estimating p-Values for Deviation Detection. - Thorsteinn S. Rögnvaldsson; Henrik Norrman; Stefan Byttner; Eric Järpe (SASO, 2014)

Evaluation of Self-Organized Approach for Predicting Compressor Faults in a City Bus Fleet. - Yuantao Fan; Slawomir Nowaczyk; Thorsteinn S. Rögnvaldsson (INNS Conference on Big Data, 2015)

Incorporating Expert Knowledge into a Self-Organized Approach for Predicting Compressor Faults in a City Bus Fleet. - Yuantao Fan; Slawomir Nowaczyk; Thorsteinn S. Rögnvaldsson (SCAI, 2015)

State of the art prediction of HIV-1 protease cleavage sites. - Thorsteinn S. Rögnvaldsson; Liwen You; Daniel Garwicz (Bioinform., 2015)

Predicting the need for vehicle compressor repairs using maintenance records and logged vehicle data. - Rune Prytz; Slawomir Nowaczyk; Thorsteinn S. Rögnvaldsson; Stefan Byttner (Eng. Appl. Artif. Intell., 2015)

HIV-1 protease cleavage. - Thorsteinn S. Rögnvaldsson (UCI Machine Learning Repository, 2015)

Learning of Aggregate Features for Comparing Drivers Based on Naturalistic Data. - Iulian Carpatorea; Slawomir Nowaczyk; Thorsteinn S. Rögnvaldsson; Marcus Elmer; Johan Lodin (ICMLA, 2016)

Center for Applied Intelligent Systems Research (Position paper). - 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 (SAIS, 2016)

Self-monitoring for maintenance of vehicle fleets. - Thorsteinn S. Rögnvaldsson; Slawomir Nowaczyk; Stefan Byttner; Rune Prytz; Magnus Svensson (Data Min. Knowl. Discov., 2018)

Forklift Truck Activity Recognition from CAN Data. - Kunru Chen; Sepideh Pashami; Slawomir Nowaczyk; Emilia Johansson; Gustav Sternelöv; Thorsteinn S. Rögnvaldsson (IoT Streams/ITEM@PKDD/ECML, 2020)

Transfer learning for remaining useful life prediction based on consensus self-organizing models. - Yuantao Fan; Slawomir Nowaczyk; Thorsteinn S. Rögnvaldsson (Reliab. Eng. Syst. Saf., 2020)

Multi-Robot Routing Problem with Min-Max Objective. - Jennifer David; Thorsteinn S. Rögnvaldsson (Robotics, 2021)

SurvSHAP: A Proxy-Based Algorithm for Explaining Survival Models with SHAP. - Abdallah Alabdallah; Sepideh Pashami; Thorsteinn S. Rögnvaldsson; Mattias Ohlsson (DSAA, 2022)

Satellite Image and Machine Learning based Knowledge Extraction in the Poverty and Welfare Domain. - Ola Hall; Mattias Ohlsson; Thorsteinn S. Rögnvaldsson (CoRR, 2022)

Deterministic annealing with Potts neurons for multi-robot routing. - Jennifer David; Thorsteinn S. Rögnvaldsson; Bo Söderberg; Mattias Ohlsson (Intell. Serv. Robotics, 2022)

A review of explainable AI in the satellite data, deep machine learning, and human poverty domain. - Ola Hall; Mattias Ohlsson; Thorsteinn S. Rögnvaldsson (Patterns, 2022)

Semi-Supervised Learning for Forklift Activity Recognition from Controller Area Network (CAN) Signals. - Kunru Chen; Thorsteinn S. Rögnvaldsson; Slawomir Nowaczyk; Sepideh Pashami; Emilia Johansson; Gustav Sternelöv (Sensors, 2022)

Towards Explaining Satellite Based Poverty Predictions with Convolutional Neural Networks. - Hamid Sarmadi; Thorsteinn S. Rögnvaldsson; Nils Roger Carlsson; Mattias Ohlsson; Ibrahim Wahab; Ola Hall (DSAA, 2023)

Toward Solving Domain Adaptation with Limited Source Labeled Data. - Kunru Chen; Thorsteinn S. Rögnvaldsson; Slawomir Nowaczyk; Sepideh Pashami; Jonas Klang; Gustav Sternelöv (ICDM (Workshops), 2023)

Optimal Task Grouping Approach in Multitask Learning. - Reza Khoshkangini; Mohsen Tajgardan; Peyman Sheikholharam Mashhadi; Thorsteinn S. Rögnvaldsson; Daniel Tegnered (ICONIP (6), 2023)

Meta-Learning from Multimodal Task Distributions Using Multiple Sets of Meta-Parameters. - Anna Vettoruzzo; Mohamed-Rafik Bouguelia; Thorsteinn S. Rögnvaldsson (IJCNN, 2023)

A Framework for Evaluating Synthetic Electronic Health Records. - Emmanuella Budu; Amira Soliman 002; Kobra Etminani; Thorsteinn S. Rögnvaldsson (MIE, 2023)

Practical joint human-machine exploration of industrial time series using the matrix profile. - Felix Nilsson; Mohamed-Rafik Bouguelia; Thorsteinn S. Rögnvaldsson (Data Min. Knowl. Discov., 2023)

Predicting Vehicle Behavior Using Multi-task Ensemble Learning. - Reza Khoshkangini; Peyman Sheikholharam Mashhadi; Daniel Tegnered; Jens Lundström; Thorsteinn S. Rögnvaldsson (Expert Syst. Appl., 2023)

Material handling machine activity recognition by context ensemble with gated recurrent units. - Kunru Chen; Thorsteinn S. Rögnvaldsson; Slawomir Nowaczyk; Sepideh Pashami; Jonas Klang; Gustav Sternelöv (Eng. Appl. Artif. Intell., 2023)

Learning to learn without forgetting using attention. - Anna Vettoruzzo; Joaquin Vanschoren; Mohamed-Rafik Bouguelia; Thorsteinn S. Rögnvaldsson (CoLLAs, 2024)

Improving Concordance Index in Regression-based Survival Analysis: Evolutionary Discovery of Loss Function for Neural Networks. - Mohammed Ghaith Altarabichi; Abdallah Alabdallah; Sepideh Pashami; Thorsteinn S. Rögnvaldsson; Slawomir Nowaczyk; Mattias Ohlsson (GECCO Companion, 2024)

Understanding Survival Models Through Counterfactual Explanations. - Abdallah Alabdallah; Jakub Jakubowski; Sepideh Pashami; Szymon Bobek; Mattias Ohlsson; Thorsteinn S. Rögnvaldsson; Grzegorz J. Nalepa (ICCS (4), 2024)

Personalized Federated Learning with Contextual Modulation and Meta-Learning. - Anna Vettoruzzo; Mohamed-Rafik Bouguelia; Thorsteinn S. Rögnvaldsson (SDM, 2024)

The Concordance Index decomposition: A measure for a deeper understanding of survival prediction models. - Abdallah Alabdallah; Mattias Ohlsson; Sepideh Pashami; Thorsteinn S. Rögnvaldsson (Artif. Intell. Medicine, 2024)

CoxSE: Exploring the Potential of Self-Explaining Neural Networks with Cox Proportional Hazards Model for Survival Analysis. - Abdallah Alabdallah; Omar Hamed; Mattias Ohlsson; Thorsteinn S. Rögnvaldsson; Sepideh Pashami (CoRR, 2024)

Volvo Discovery Challenge at ECML-PKDD 2024. - Mahmoud Rahat; Peyman Sheikholharam Mashhadi; Slawomir Nowaczyk; Shamik Choudhury; Leo Petrin; Thorsteinn S. Rögnvaldsson; Andreas Voskou; Carlo Metta; Claudio Savelli (CoRR, 2024)

Evaluation of synthetic electronic health records: A systematic review and experimental assessment. - Emmanuella Budu; Kobra Etminani; Amira Soliman 002; Thorsteinn S. Rögnvaldsson (Neurocomputing, 2024)

Advances and Challenges in Meta-Learning: A Technical Review. - Anna Vettoruzzo; Mohamed-Rafik Bouguelia; Joaquin Vanschoren; Thorsteinn S. Rögnvaldsson; KC Santosh (IEEE Trans. Pattern Anal. Mach. Intell., 2024)

Meta-learning for efficient unsupervised domain adaptation. - Anna Vettoruzzo; Mohamed-Rafik Bouguelia; Thorsteinn S. Rögnvaldsson (Neurocomputing, 2024)

Multimodal meta-learning through meta-learned task representations. - Anna Vettoruzzo; Mohamed-Rafik Bouguelia; Thorsteinn S. Rögnvaldsson (Neural Comput. Appl., 2024)

Leveraging ChatGPT's Multimodal Vision Capabilities to Rank Satellite Images by Poverty Level: Advancing Tools for Social Science Research. - Hamid Sarmadi; Ola Hall; Thorsteinn S. Rögnvaldsson; Mattias Ohlsson (CoRR, 2025)

Efficient few-shot human activity recognition via meta-learning and data augmentation. - Anna Vettoruzzo; Mohamed-Rafik Bouguelia; Thorsteinn S. Rögnvaldsson (Neural Comput. Appl., 2025)