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Martin Längkvist

Tjänstetitel: Forskare Organisation: Institutionen för naturvetenskap och teknik

E-post:

Telefon: 019 303749

Rum: T2235

Forskningsämne

Datavetenskap

Forskningsmiljöer

Centrum för tillämpade autonoma sensorsystem (AASS)

Om 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

Forskningsprojekt

Forskargrupper

Maskin Perception och Interaktion

Publikationer

Artiklar i tidskrifter

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.

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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.

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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.

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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).

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Persson, A. , Längkvist, M. & Loutfi, A. (2017). Learning Actions to Improve the Perceptual Anchoring of Object. Frontiers in Robotics and AI, 3 (76).

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Längkvist, M. , Kiselev, A. , Alirezaie, M. & Loutfi, A. (2016). Classification and Segmentation of Satellite Orthoimagery Using Convolutional Neural Networks. Remote Sensing, 8 (4).

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Längkvist, M. & Loutfi, A. (2015). Learning feature representations with a cost-relevant sparse autoencoder. International Journal of Neural Systems, 25 (1), 1450034.

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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.

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Längkvist, M. , Karlsson, L. & Loutfi, A. (2012). Sleep stage classification using unsupervised feature learning. Advances in Artificial Neural Systems, 107046.

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Artiklar, forskningsöversikter

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.

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Doktorsavhandlingar, sammanläggningar

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

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Konferensbidrag

Alirezaie, M. , Längkvist, M. , Sioutis, M. & Loutfi, A. (2018). A Symbolic Approach for Explaining Errors in Image Classification Tasks. Konferensbidrag vid 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. Konferensbidrag vid 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. I: 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. Konferensbidrag vid 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 (ss. 11-20). Technical University Aachen.

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Längkvist, M. , Alirezaie, M. , Kiselev, A. & Loutfi, A. (2016). Interactive Learning with Convolutional Neural Networks for Image Labeling. I: International Joint Conference on Artificial Intelligence (IJCAI). Konferensbidrag vid International Joint Conference on Artificial Intelligence (IJCAI), New York, USA, 9-15th July, 2016.

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Alirezaie, M. , Längkvist, M. , Kiselev, A. & Loutfi, A. (2016). Open GeoSpatial Data as a Source of Ground Truth for Automated Labelling of Satellite Images. I: Krzysztof Janowicz et al., SDW 2016 Spatial Data on the Web, Proceedings. Konferensbidrag vid The 9th International Conference on Geographic Information Science (GIScience 2016), Montreal, Canada, September 27-30, 2016 (ss. 5-8). CEUR Workshop Proceedings.

Mer information på annan webbplats

Längkvist, M. & Loutfi, A. (2012). Learning Representations with a Dynamic Objective Sparse Autoencoder. Konferensbidrag vid Neural Information Processing Systems.

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Längkvist, M. & Loutfi, A. (2012). Not all signals are created equal: Dynamic objective auto-encoder for multivariate data. Konferensbidrag vid NIPS Workshop on Deep Learning and Unsupervised Feature Learning, 2012.

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Längkvist, M. & Loutfi, A. (2011). Unsupervised feature learning for electronic nose data applied to Bacteria Identification in Blood. Konferensbidrag vid NIPS 2011 Workshop on Deep Learning and Unsupervised Feature Learning.

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Manuskript

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