Martin Längkvist
Martin Längkvist
PhD, Information Technology, AASS Machine Perception and Interaction Lab, Örebro University
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Zitiert von
Zitiert von
Jahr
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
8372014
Sleep stage classification using unsupervised feature learning
M Längkvist, L Karlsson, A Loutfi
Advances in Artificial Neural Systems 2012, 2012
2422012
Classification and segmentation of satellite orthoimagery using convolutional neural networks
M Längkvist, A Kiselev, M Alirezaie, A Loutfi
Remote Sensing 8 (4), 329, 2016
2192016
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
342013
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
342011
Learning feature representations with a cost-relevant sparse autoencoder
M Längkvist, A Loutfi
International journal of neural systems 25 (01), 1450034, 2015
222015
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
212018
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
192017
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
132019
Modeling time-series with deep networks
M Längkvist
Örebro university, 2014
102014
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
62016
A deep learning approach with an attention mechanism for automatic sleep stage classification
M Längkvist, A Loutfi
arXiv preprint arXiv:1805.05036, 2018
52018
Learning actions to improve the perceptual anchoring of objects
A Persson, M Längkvist, A Loutfi
Frontiers in Robotics and AI 3, 76, 2017
52017
A symbolic approach for explaining errors in image classification tasks
M Alirezaie, M Längkvist, M Sioutis, A Loutfi
Working Papers and Documents of the IJCAI-ECAI-2018 Workshop on, 2018
42018
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
42012
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
22017
Open GeoSpatial Data as a Source of Ground Truth for Automated Labelling of Satellite Images
M Alirezaie, M Längkvist, A Kiselev, A Loutfi
The 9th International Conference on Geographic Information Science …, 2016
22016
Learning representations with a dynamic objective sparse autoencoder
M Längkvist, A Loutfi
Neural information processing systems, 2012
22012
Online Identification of Friction Coefficients in an Industrial Robot
M Längkvist
22009
Performance Comparison of Two Deep Learning Algorithms in Detecting Similarities Between Manual Integration Test Cases
C Landin, L Hatvani, S Tahvili, H Haggren, M Längkvist, A Loutfi, ...
ICSEA 2020, 100, 2020
12020
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