Martin Längkvist

Martin Längkvist

Senior Lecturer

AASS Research Center
School of Science and Technology
Örebro University
70182 Örebro, Sweden
Room: T2235
Phone: No Phone Number Available
Email: No Email Address Available

Google Scholar

I am a researcher at the Machine Perception and Interaction Lab at AASS Research Center , Department of Science and Technology, Örebro University, Sweden. I received my Ph.D in Computer Science in Örebro in 2015.

My research interest is in machine learning, specifically learning good representations from raw sensory data. I believe finding good representations is the key to designing a system that can solve interesting challenging real-world problems, go beyond human-level intelligence, and ultimately explain complicated data for us that we don't understand. In order to achieve this, I envision a learning algorithm that can learn feature representations from both unlabeled and labeled data, be guided with and without human interaction, and that are on different levels of abstractions in order to bridge the gap between low-level sensory data and high-level abstract concepts.

You can find more about my research and publications at my Google Profile Page or Research Gate Page or MPI lab member website .


[1] S. Neelakantan, J. Norell, A. Hansson, M. Längkvist and A. Loutfi. Neural network approach for shape-based euhedral pyrite identification in X-ray CT data with adversarial unsupervised domain adaptation. Applied Computing and Geosciences, 21, 2024BibTeX | DiVA ]
[2] C. Landin, X. Zhao, M. Längkvist and A. Loutfi. An Intelligent Monitoring Algorithm to Detect Dependencies between Test Cases in the Manual Integration Process. In 2023 IEEE International Conference on Software Testing, Verification and Validation Workshops (ICSTW), pages 353-360, 2023BibTeX | DiVA ]
[3] B. Paylar, M. Längkvist, J. Jass and P. E. Olsson. Utilization of Computer Classification Methods for Exposure Prediction and Gene Selection in Daphnia magna Toxicogenomics. Biology, 12(5), 2023BibTeX | DiVA ]
[4] G. M. A. Rahaman, M. Längkvist and A. Loutfi. Deep Learning based Aerial Image Segmentation for Computing Green Area Factor. In 2022 10th European Workshop on Visual Information Processing (EUVIP) 2022BibTeX | DiVA ]
[5] S. Blad, M. Längkvist, F. Klügl and A. Loutfi. Empirical analysis of the convergence of Double DQN in relation to reward sparsity. In 21st IEEE International Conference on Machine Learning and Applications. ICMLA 2022 : Proceedings, pages 591-596, 2022BibTeX | DiVA ]
[6] H. Sjöqvist, M. Längkvist and F. Javed. An Analysis of Fast Learning Methods for Classifying Forest Cover Types. Applied Artificial Intelligence, 34(10):691-709, 2020BibTeX | DiVA ]
[7] C. Landin, S. Tahvili, H. Haggren, M. Längkvist, A. Muhammad and A. Loutfi. Cluster-Based Parallel Testing Using Semantic Analysis. In 2020 IEEE International Conference On Artificial Intelligence Testing (AITest), pages 99-106, 2020BibTeX | DiVA ]
[8] M. Alirezaie, M. Längkvist and A. Loutfi. Knowledge Representation and Reasoning Methods to Explain Errors in Machine Learning. In Knowledge Graphs for eXplainable Artificial Intelligence : Foundations, Applications and Challenges 2020BibTeX | DiVA ]
[9] M. Längkvist, A. Persson and A. Loutfi. Learning Generative Image Manipulations from Language Instructions. 2020BibTeX | DiVA | PDF ]
[10] C. Landin, L. Hatvani, S. Tahvili, H. Haggren, M. Längkvist, A. Loutfi and A. H\aakansson. Performance Comparison of Two Deep Learning Algorithms in Detecting Similarities Between Manual Integration Test Cases. In The Fifteenth International Conference on Software Engineering Advances, pages 90-97, 2020BibTeX | DiVA | PDF ]
[11] M. Alirezaie, M. Längkvist, M. Sioutis and A. Loutfi. Semantic Referee : A Neural-Symbolic Framework for Enhancing Geospatial Semantic Segmentation. Semantic Web, 10(5):863-880, 2019BibTeX | DiVA ]
[12] M. Alirezaie, M. Längkvist, M. Sioutis and A. Loutfi. A Symbolic Approach for Explaining Errors in Image Classification Tasks. 2018BibTeX | DiVA ]
[13] M. Längkvist, J. Jendeberg, P. Thunberg, A. Loutfi and M. Lidén. Computer aided detection of ureteral stones in thin slice computed tomography volumes using Convolutional Neural Networks. Computers in Biology and Medicine, 97:153-160, 2018BibTeX | DiVA ]
[14] M. Lidén, J. Jendeberg, M. Längkvist, A. Loutfi and P. Thunberg. Discrimination between distal ureteral stones and pelvic phleboliths in CT using a deep neural network : more than local features needed. 2018BibTeX | DiVA ]
[15] M. Alirezaie, A. Kiselev, M. Längkvist, F. Klügl and A. Loutfi. An Ontology-Based Reasoning Framework for Querying Satellite Images for Disaster Monitoring. Sensors, 17(11), 2017BibTeX | DiVA | PDF ]
[16] M. Alirezaie, A. Kiselev, F. Klügl, M. Längkvist and A. Loutfi. Exploiting Context and Semantics for UAV Path-finding in an Urban Setting. In Proceedings of the 1st International Workshop on Application of Semantic Web technologies in Robotics (AnSWeR 2017), Portoroz, Slovenia, May 29th, 2017, pages 11-20, 2017BibTeX | DiVA | PDF ]
[17] A. Persson, M. Längkvist and A. Loutfi. Learning Actions to Improve the Perceptual Anchoring of Object. Frontiers in Robotics and AI, 3(76), 2017BibTeX | DiVA ]
[18] M. Längkvist, A. Kiselev, M. Alirezaie and A. Loutfi. Classification and Segmentation of Satellite Orthoimagery Using Convolutional Neural Networks. Remote Sensing, 8(4), 2016BibTeX | DiVA ]
[19] M. Längkvist, M. Alirezaie, A. Kiselev and A. Loutfi. Interactive Learning with Convolutional Neural Networks for Image Labeling. In International Joint Conference on Artificial Intelligence (IJCAI) 2016BibTeX | DiVA | PDF ]
[20] M. Alirezaie, M. Längkvist, A. Kiselev and A. Loutfi. Open GeoSpatial Data as a Source of Ground Truth for Automated Labelling of Satellite Images. In SDW 2016 : Spatial Data on the Web, Proceedings, pages 5-8, 2016BibTeX | DiVA ]
[21] M. Längkvist and A. Loutfi. Learning feature representations with a cost-relevant sparse autoencoder. International Journal of Neural Systems, 25(1):1450034-, 2015BibTeX | DiVA ]
[22] M. Längkvist, L. Karlsson and A. Loutfi. A review of unsupervised feature learning and deep learning for time-series modeling. Pattern Recognition Letters, 42(1):11-24, 2014BibTeX | DiVA | PDF ]
[23] M. Längkvist. Modeling time-series with deep networks. Örebro University, School of Science and Technology, Ph.D. Thesis, 2014BibTeX | DiVA | PDF ]
[24] M. Längkvist, A. Loutfi and L. Karlsson. Selective attention auto-encoder for automatic sleep staging. 2014BibTeX | DiVA ]
[25] M. Längkvist, S. Coradeschi, A. Loutfi and J. B. B. Rayappan. Fast Classification of Meat Spoilage Markers Using Nanostructured ZnO Thin Films and Unsupervised Feature Learning. Sensors, 13(2):1578-1592, 2013BibTeX | DiVA | PDF ]
[26] M. Längkvist and A. Loutfi. Learning Representations with a Dynamic Objective Sparse Autoencoder. 2012BibTeX | DiVA | PDF ]
[27] M. Längkvist and A. Loutfi. Not all signals are created equal : Dynamic objective auto-encoder for multivariate data. 2012BibTeX | DiVA | PDF ]
[28] M. Längkvist, L. Karlsson and A. Loutfi. Sleep stage classification using unsupervised feature learning. Advances in Artificial Neural Systems, pages 107046-, 2012BibTeX | DiVA ]
[29] M. Längkvist and A. Loutfi. Unsupervised feature learning for electronic nose data applied to Bacteria Identification in Blood. 2011BibTeX | DiVA | PDF ]