Thorsteinn S. Rögnvaldsson

发表

Smoothing Regularizers for Projective Basis Function Networks

John E. Moody, Thorsteinn S. Rögnvaldsson, J. Moody, 1996, NIPS.

Gold-Fish SLAM: An Application of SLAM to Localize AGVs

Henrik Andreasson, Thorsteinn S. Rögnvaldsson, Abdelbaki Bouguerra, 2012, FSR.

Evaluation of Self-Organized Approach for Predicting Compressor Faults in a City Bus Fleet

Slawomir Nowaczyk, Thorsteinn S. Rögnvaldsson, Yuantao Fan, 2015, INNS Conference on Big Data.

A Simple Trick for Estimating the Weight Decay Parameter

Thorsteinn S. Rögnvaldsson, 1996, Neural Networks: Tricks of the Trade.

Predicting the need for vehicle compressor repairs using maintenance records and logged vehicle data

Slawomir Nowaczyk, Stefan Byttner, Thorsteinn S. Rögnvaldsson, 2015, Eng. Appl. Artif. Intell..

Biometric Identification of Mice

Kenneth Nilsson, Thorsteinn S. Rögnvaldsson, Jens Cameron, 2006, 18th International Conference on Pattern Recognition (ICPR'06).

Modular, scriptable and automated analysis tools for high-throughput peptide mass fingerprinting

Fredrik Levander, Thorsteinn S. Rögnvaldsson, Daniel Dalevi, 2004, Bioinform..

Consensus self-organized models for fault detection (COSMO)

Stefan Byttner, Magnus Svensson, Thorsteinn S. Rögnvaldsson, 2011, Eng. Appl. Artif. Intell..

A self-organized approach for unsupervised fault detection in multiple systems

Stefan Byttner, Magnus Svensson, Thorsteinn S. Rögnvaldsson, 2008, 2008 19th International Conference on Pattern Recognition.

Towards a Machine Learning Algorithm for Predicting Truck Compressor Failures Using Logged Vehicle Data

Slawomir Nowaczyk, Stefan Byttner, Thorsteinn S. Rögnvaldsson, 2013, SCAI.

Why neural networks should not be used for HIV-1 protease cleavage site prediction

Thorsteinn S. Rögnvaldsson, Liwen You, Liwen You, 2004, Bioinform..

Finding the Odd-One-Out in Fleets of Mechatronic Systems using Embedded Intelligent Agents

Stefan Byttner, Magnus Svensson, Thorsteinn S. Rögnvaldsson, 2010, AAAI Spring Symposium: Embedded Reasoning.

How to find simple and accurate rules for viral protease cleavage specificities

Paulo J. G. Lisboa, Terence A. Etchells, Ian H. Jarman, 2009, BMC Bioinformatics.

State of the art prediction of HIV-1 protease cleavage sites

Thorsteinn S. Rögnvaldsson, Liwen You, Daniel Garwicz, 2015, Bioinform..

An autonomous robotic system for load transportation

Achim J. Lilienthal, Henrik Andreasson, Thorsteinn S. Rögnvaldsson, 2009, 2009 IEEE Conference on Emerging Technologies & Factory Automation.

Pattern Discrimination Using Feedforward Networks: A Benchmark Study of Scaling Behavior

Thorsteinn S. Rögnvaldsson, 1993, Neural Computation.

On Langevin Updating in Multilayer Perceptrons

Thorsteinn S. Rögnvaldsson, 1994, Neural Computation.

Estimating p-Values for Deviation Detection

Stefan Byttner, Thorsteinn S. Rögnvaldsson, Eric Järpe, 2014, 2014 IEEE Eighth International Conference on Self-Adaptive and Self-Organizing Systems.

Learning of Aggregate Features for Comparing Drivers Based on Naturalistic Data

Slawomir Nowaczyk, Thorsteinn S. Rögnvaldsson, Johan Lodin, 2016, 2016 15th IEEE International Conference on Machine Learning and Applications (ICMLA).

Almost Linear Biobasis Function Neural Networks

Thorsteinn S. Rögnvaldsson, Liwen You, Liwen You, 2007, 2007 International Joint Conference on Neural Networks.

Networked vehicles for automated fault detection

Stefan Byttner, Magnus Svensson, Thorsteinn S. Rögnvaldsson, 2009, 2009 IEEE International Symposium on Circuits and Systems.

Incorporating Expert Knowledge into a Self-Organized Approach for Predicting Compressor Faults in a City Bus Fleet

Slawomir Nowaczyk, Thorsteinn S. Rögnvaldsson, Yuantao Fan, 2015, SCAI.

A Simple Trick for Estimating the Weight Decay Parameter

Thorsteinn S. Rögnvaldsson, 1996, Neural Networks: Tricks of the Trade.

Modular Neural Network and Classical Reinforcement Learning for Autonomous Robot Navigation: Inhibiting Undesirable Behaviors

Albert-Jan Baerveldt, Thorsteinn S. Rögnvaldsson, Eric A. Antonelo, 2006, The 2006 IEEE International Joint Conference on Neural Network Proceedings.

Center for Applied Intelligent Systems Research (Position paper)

Antanas Verikas, Fernando Alonso-Fernandez, Josef Bigün, 2016, SAIS.

Self-monitoring for maintenance of vehicle fleets

Slawomir Nowaczyk, Stefan Byttner, Magnus Svensson, 2017, Data Mining and Knowledge Discovery.

Mass reconstruction with a neural network

Thorsteinn S. Rögnvaldsson, C. Peterson, L. Lönnblad, 1992 .

JETNET 3.0—A versatile artificial neural network package

Carsten Peterson, Thorsteinn Rögnvaldsson, Thorsteinn S. Rögnvaldsson, 1994 .

Self-organizing networks for extracting jet features

Thorsteinn S. Rögnvaldsson, C. Peterson, H. Pi, 1991 .

Using neural networks to identify jets

Thorsteinn S. Rögnvaldsson, C. Peterson, L. Lönnblad, 1991 .

Center for Applied Intelligent Systems Research

Thorsteinn S. Rögnvaldsson, J. Bigun, A. Verikas, 2016 .

Automated methods for improved protein identification by peptide mass fingerprinting

Thorsteinn S. Rögnvaldsson, F. Levander, P. James, 2004, Proteomics.

Bioinformatic approaches for modeling the substrate specificity of HIV-1 protease: an overview

Thorsteinn Rögnvaldsson, Thorsteinn S. Rögnvaldsson, Liwen You, 2007, Expert review of molecular diagnostics.

Comprehensive Bioinformatic Analysis of the Specificity of Human Immunodeficiency Virus Type 1 Protease

Thorsteinn S. Rögnvaldsson, Liwen You, Daniel Garwicz, 2005, Journal of Virology.

A Simple Method for Estimating the Weight Decay Parameter

Thorsteinn S. Rögnvaldsson, 1996 .

Method Descriptions 2.1 Regression 2.1.1 the Regression Problem

Thorsteinn S. Rögnvaldsson, 2022 .

Estimation of combustion variability using in-cylinder ionization measurements

Thorsteinn S. Rögnvaldsson, Nicholas Wickström, S. Byttner, 2001 .

Improving automatic peptide mass fingerprint protein identification by combining many peak sets.

Thorsteinn S. Rögnvaldsson, J. Häkkinen, F. Potthast, 2004, Journal of chromatography. B, Analytical technologies in the biomedical and life sciences.

Evolution of gag and gp41 in Patients Receiving Ritonavir-Boosted Protease Inhibitors

Thorsteinn S. Rögnvaldsson, Philip L. Tzou, D. Katzenstein, 2017, Scientific Reports.

Semi-Supervised Learning for Forklift Activity Recognition from Controller Area Network (CAN) Signals

Thorsteinn S. Rögnvaldsson, Sepideh Pashami, Sławomir Nowaczyk, 2022, Sensors.

Predicting Vehicle Behavior Using Multi-task Ensemble Learning

Thorsteinn S. Rögnvaldsson, J. Lundström, D. Tegnered, 2022, Expert Syst. Appl..

Deterministic annealing with Potts neurons for multi-robot routing

Thorsteinn S. Rögnvaldsson, B. Söderberg, J. David, 2022, Intell. Serv. Robotics.

A review of explainable AI in the satellite data, deep machine learning, and human poverty domain

Thorsteinn S. Rögnvaldsson, M. Ohlsson, Ola Hall, 2022, Patterns.

Towards Autonomous Knowledge Creation from Big Data in Smart Cities

Thorsteinn S. Rögnvaldsson, Yuantao Fan, Ece Calikus, 2021, Handbook of Smart Cities.

JETNET 3.0—A versatile artificial neural network package

Thorsteinn S. Rögnvaldsson, C. Peterson, L. Lönnblad, 1994 .

An introduction to neural networks : CERN Yellow Report 92-02

Carsten Peterson, Thorsteinn Rögnvaldsson, Thorsteinn S. Rögnvaldsson, 1992 .

An introduction to artificial neural networks

Carsten Peterson, Thorsteinn Rögnvaldsson, Thorsteinn S. Rögnvaldsson, 1991 .

Brownian Motion Updating of Multi-layered Perceptrons

Thorsteinn Rögnvaldsson, Thorsteinn S. Rögnvaldsson, 1993 .

Self-monitoring for maintenance of vehicle fleets

Thorsteinn S. Rögnvaldsson, Sławomir Nowaczyk, S. Byttner, 2018, Data Mining and Knowledge Discovery.

Spark Advance Control Using the Ion Current and Neural Soft Sensors

Magnus Hellring, Thorsteinn Rögnvaldsson, Thorsteinn S. Rögnvaldsson, 1999 .

SurvSHAP: A Proxy-Based Algorithm for Explaining Survival Models with SHAP

Thorsteinn S. Rögnvaldsson, Sepideh Pashami, Mattias Ohlsson, 2022, 2022 IEEE 9th International Conference on Data Science and Advanced Analytics (DSAA).

Ion Current Based Pressure Peak Detection Under Different Air Humidity Conditions

Thorsteinn S. Rögnvaldsson, Nicholas Wickström, M. Larsson, 2000 .

Multi-Robot Routing Problem with Min-Max Objective

Thorsteinn Rögnvaldsson, Thorsteinn S. Rögnvaldsson, Jennifer David, 2021, Robotics.

Satellite Image and Machine Learning based Knowledge Extraction in the Poverty and Welfare Domain

Thorsteinn S. Rögnvaldsson, M. Ohlsson, Ola Hall, 2022, SSRN Electronic Journal.

Advances and Challenges in Meta-Learning: A Technical Review

Thorsteinn S. Rögnvaldsson, J. Vanschoren, Mohamed-Rafik Bouguelia, 2023, IEEE Transactions on Pattern Analysis and Machine Intelligence.

The Concordance Index decomposition: a measure for a deeper understanding of survival prediction models

Thorsteinn S. Rögnvaldsson, Sepideh Pashami, M. Ohlsson, 2022, Artificial Intelligence in Medicine.

Vehicles Behavioral Prediction Using Multi-Task Ensemble Learning

Thorsteinn S. Rögnvaldsson, D. Tegnered, P. Mashhadi, 2022, Social Science Research Network.

Robust AFR estimation using the ion current and neural networks

Magnus Hellring, Thorsteinn Rögnvaldsson, Thorsteinn S. Rögnvaldsson, 1999 .

Comprehensive Bioinformatic Analysis of the Specificity of Human Immunodeficiency Virus Type 1 Protease

Thorsteinn S. Rögnvaldsson, Liwen You, D. Garwicz, 2005, Journal of Virology.

Finding gluon jets with a neural trigger.

Thorsteinn S. Rögnvaldsson, L. Lönnblad, Carsten Peterson, 1990, Physical review letters.

Analysis of Truck Compressor Failures Based on Logged Vehicle Data

Slawomir Nowaczyk, Stefan Byttner, Thorsteinn Rögnvaldsson, 2013, ICDM 2013.

Towards Explaining Satellite Based Poverty Predictions with Convolutional Neural Networks

Thorsteinn S. Rögnvaldsson, Mattias Ohlsson, Hamid Sarmadi, 2023, 2023 IEEE 10th International Conference on Data Science and Advanced Analytics (DSAA).

Transfer learning for Remaining Useful Life Prediction Based on Consensus Self-Organizing Models pdf

Thorsteinn S. Rögnvaldsson, Yuantao Fan, Slawomir Nowaczyk, 2019, Reliab. Eng. Syst. Saf..

State of the art prediction of HIV-1 protease cleavage sites

Thorsteinn S. Rögnvaldsson, Liwen You, D. Garwicz, 2015, Bioinform..

Practical joint human-machine exploration of industrial time series using the matrix profile

Thorsteinn S. Rögnvaldsson, Mohamed-Rafik Bouguelia, Felix Nilsson, 2022, Data Mining and Knowledge Discovery.