Martin Längkvist
Martin Längkvist
PhD, Information Technology, AASS Machine Perception and Interaction Lab, Örebro University
Verified email at - Homepage
Cited by
Cited by
A review of unsupervised feature learning and deep learning for time-series modeling
M Längkvist, L Karlsson, A Loutfi
Pattern recognition letters 42, 11-24, 2014
Classification and segmentation of satellite orthoimagery using convolutional neural networks
M Längkvist, A Kiselev, M Alirezaie, A Loutfi
Remote Sensing 8 (4), 329, 2016
Sleep stage classification using unsupervised feature learning
M Längkvist, L Karlsson, A Loutfi
Advances in Artificial Neural Systems 2012, 5-5, 2012
Computer aided detection of ureteral stones in thin slice computed tomography volumes using Convolutional Neural Networks
M Längkvist, J Jendeberg, P Thunberg, A Loutfi, M Lidén
Computers in biology and medicine 97, 153-160, 2018
Unsupervised feature learning for electronic nose data applied to bacteria identification in blood
M Längkvist, A Loutfi
NIPS 2011 workshop on deep learning and unsupervised feature learning, 2011
Inception-v4, inception-ResNet and the impact of residual connections on learning
M Längkvist, L Karlsson, A Loutfi
Pattern Recognit. Lett 42 (1), 11-24, 2014
Fast classification of meat spoilage markers using nanostructured ZnO thin films and unsupervised feature learning
M Längkvist, S Coradeschi, A Loutfi, JBB Rayappan
Sensors 13 (2), 1578-1592, 2013
Semantic referee: A neural-symbolic framework for enhancing geospatial semantic segmentation
M Alirezaie, M Längkvist, M Sioutis, A Loutfi
Semantic Web 10 (5), 863-880, 2019
An ontology-based reasoning framework for querying satellite images for disaster monitoring
M Alirezaie, A Kiselev, M Längkvist, F Klügl, A Loutfi
Sensors 17 (11), 2545, 2017
Learning feature representations with a cost-relevant sparse autoencoder
M Längkvist, A Loutfi
International journal of neural systems 25 (01), 1450034, 2015
A symbolic approach for explaining errors in image classification tasks
M Alirezaie, M Längkvist, M Sioutis, A Loutfi
IJCAI Workshop on Learning and Reasoning. Stockholm, Sweden, 2018
Modeling time-series with deep networks
M Längkvist
Örebro university, 2014
A deep learning approach with an attention mechanism for automatic sleep stage classification
M Längkvist, A Loutfi
arXiv preprint arXiv:1805.05036, 2018
Interactive learning with convolutional neural networks for image labeling
M Längkvist, M Alirezaie, A Kiselev, A Loutfi
International Joint Conference on Artificial Intelligence (IJCAI), New York …, 2016
An analysis of fast learning methods for classifying forest cover types
H Sjöqvist, M Längkvist, F Javed
Applied Artificial Intelligence 34 (10), 691-709, 2020
Learning actions to improve the perceptual anchoring of objects
A Persson, M Längkvist, A Loutfi
Frontiers in Robotics and AI 3, 76, 2017
Exploiting context and semantics for UAV Path-finding in an urban setting
M Alirezaie, A Kiselev, F Klügl, M Längkvist, A Loutfi
International Workshop on Application of Semantic Web technologies in …, 2017
Open GeoSpatial Data as a Source of Ground Truth for Automated Labelling of Satellite Images.
M Alirezaie, M Längkvist, A Kiselev, A Loutfi
SDW@ GIScience, 5-8, 2016
Not all signals are created equal: Dynamic objective auto-encoder for multivariate data
M Längkvist, A Loutfi
NIPS Workshop on Deep Learning and Unsupervised Feature Learning, 2012, 2012
Interactive user interface based on Convolutional Auto-encoders for annotating CT-scans
M Längkvist, J Widell, P Thunberg, A Loutfi, M Lidén
arXiv preprint arXiv:1904.11701, 2019
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