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Martin Längkvist

Title: Researcher School/office: School of Science and Technology


Phone: +46 19 303749

Room: T2235

Martin Längkvist

About Martin Längkvist

I am a post-doc 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 Personal Academic Website


Articles in journals |  Articles, reviews/surveys |  Chapters in books |  Conference papers |  Doctoral theses, comprehensive summaries |  Manuscripts | 

Articles in journals

Sjöqvist, H. , Längkvist, M. & Javed, F. (2020). An Analysis of Fast Learning Methods for Classifying Forest Cover Types. Applied Artificial Intelligence, 34 (10), 691-709.
Alirezaie, M. , Längkvist, M. , Sioutis, M. & Loutfi, A. (2019). Semantic Referee: A Neural-Symbolic Framework for Enhancing Geospatial Semantic Segmentation. Semantic Web, 10 (5), 863-880.
Längkvist, M. , Jendeberg, J. , Thunberg, P. , Loutfi, A. & Lidén, M. (2018). Computer aided detection of ureteral stones in thin slice computed tomography volumes using Convolutional Neural Networks. Computers in Biology and Medicine, 97, 153-160.
Alirezaie, M. , Kiselev, A. , Längkvist, M. , Klügl, F. & Loutfi, A. (2017). An Ontology-Based Reasoning Framework for Querying Satellite Images for Disaster Monitoring. Sensors, 17 (11).
Persson, A. , Längkvist, M. & Loutfi, A. (2017). Learning Actions to Improve the Perceptual Anchoring of Object. Frontiers in Robotics and AI, 3 (76).
Längkvist, M. & Loutfi, A. (2015). Learning feature representations with a cost-relevant sparse autoencoder. International Journal of Neural Systems, 25 (1), 1450034.
Längkvist, M. , Coradeschi, S. , Loutfi, A. & Rayappan, J. B. B. (2013). Fast Classification of Meat Spoilage Markers Using Nanostructured ZnO Thin Films and Unsupervised Feature Learning. Sensors, 13 (2), 1578-1592.
Längkvist, M. , Karlsson, L. & Loutfi, A. (2012). Sleep stage classification using unsupervised feature learning. Advances in Artificial Neural Systems, 107046.

Articles, reviews/surveys

Längkvist, M. , Karlsson, L. & Loutfi, A. (2014). A review of unsupervised feature learning and deep learning for time-series modeling. Pattern Recognition Letters, 42 (1), 11-24.

Chapters in books

Alirezaie, M. , Längkvist, M. & Loutfi, A. (2020). Knowledge Representation and Reasoning Methods to Explain Errors in Machine Learning. In: Ilaria Tiddi, Freddy Lécué, Pascal Hitzler, Knowledge Graphs for eXplainable Artificial Intelligence: Foundations, Applications and Challenges. . IOS Press.

Conference papers

Landin, C. , Tahvili, S. , Haggren, H. , Längkvist, M. , Muhammad, A. & Loutfi, A. (2020). Cluster-Based Parallel Testing Using Semantic Analysis. In: 2020 IEEE International Conference On Artificial Intelligence Testing (AITest). Paper presented at 2nd IEEE International Conference on Artificial Intelligence Testing (AITest 2020), Oxford, United Kingdom, August 3-6, 2020 (pp. 99-106). IEEE.
Längkvist, M. , Persson, A. & Loutfi, A. (2020). Learning Generative Image Manipulations from Language Instructions. Paper presented at Concepts in Action: Representation, Learning, and Application (CARLA 2020), Virtual workshop, September 22-23, 2020.
Landin, C. , Hatvani, L. , Tahvili, S. , Haggren, H. , Längkvist, M. , Loutfi, A. & Håkansson, A. (2020). Performance Comparison of Two Deep Learning Algorithms in Detecting Similarities Between Manual Integration Test Cases. In: The Fifteenth International Conference on Software Engineering Advances. Paper presented at The Fifteenth International Conference on Software Engineering Advances (ICSEA 2020), Porto, Portugal, October 18-22, 2020 (pp. 90-97). International Academy, Research and Industry Association (IARIA).
Alirezaie, M. , Längkvist, M. , Sioutis, M. & Loutfi, A. (2018). A Symbolic Approach for Explaining Errors in Image Classification Tasks. Paper presented at 27th International Joint Conference on Artificial Intelligence (IJCAI), Stockholm, Sweden, July 13-19, 2018.
Lidén, M. , Jendeberg, J. , Längkvist, M. , Loutfi, A. & Thunberg, P. (2018). Discrimination between distal ureteral stones and pelvic phleboliths in CT using a deep neural network: more than local features needed. Paper presented at European Congress of Radiology (ECR) 2018, Vienna, Austria, 28 Feb.-4 Mar., 2018.
Alirezaie, M. , Kiselev, A. , Klügl, F. , Längkvist, M. & Loutfi, A. (2017). Exploiting Context and Semantics for UAV Path-finding in an Urban Setting. In: Emanuele Bastianelli, Mathieu d'Aquin, Daniele Nardi, Proceedings of the 1st International Workshop on Application of Semantic Web technologies in Robotics (AnSWeR 2017), Portoroz, Slovenia, May 29th, 2017. Paper presented at International Workshop on Application of Semantic Web technologies in Robotics co-located with 14th Extended Semantic Web Conference (ESWC), Portoroz, Slovenia, 28th May-1st June, 2017 (pp. 11-20). Technical University Aachen.
Längkvist, M. , Alirezaie, M. , Kiselev, A. & Loutfi, A. (2016). Interactive Learning with Convolutional Neural Networks for Image Labeling. In: International Joint Conference on Artificial Intelligence (IJCAI). Paper presented at International Joint Conference on Artificial Intelligence (IJCAI), New York, USA, 9-15th July, 2016.
Alirezaie, M. , Längkvist, M. , Kiselev, A. & Loutfi, A. (2016). Open GeoSpatial Data as a Source of Ground Truth for Automated Labelling of Satellite Images. In: Krzysztof Janowicz et al., SDW 2016 Spatial Data on the Web, Proceedings. Paper presented at The 9th International Conference on Geographic Information Science (GIScience 2016), Montreal, Canada, September 27-30, 2016 (pp. 5-8). CEUR Workshop Proceedings.
Längkvist, M. & Loutfi, A. (2012). Learning Representations with a Dynamic Objective Sparse Autoencoder. Paper presented at Neural Information Processing Systems.
Längkvist, M. & Loutfi, A. (2012). Not all signals are created equal: Dynamic objective auto-encoder for multivariate data. Paper presented at NIPS Workshop on Deep Learning and Unsupervised Feature Learning, 2012.
Längkvist, M. & Loutfi, A. (2011). Unsupervised feature learning for electronic nose data applied to Bacteria Identification in Blood. Paper presented at NIPS 2011 Workshop on Deep Learning and Unsupervised Feature Learning.

Doctoral theses, comprehensive summaries

Längkvist, M. (2014). Modeling time-series with deep networks. (Doctoral dissertation). (Comprehensive summary) Örebro: Örebro university.


Längkvist, M. , Loutfi, A. & Karlsson, L. Selective attention auto-encoder for automatic sleep staging.