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 | 1396 | 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 | 351 | 2016 |
Sleep stage classification using unsupervised feature learning M Längkvist, L Karlsson, A Loutfi Advances in Artificial Neural Systems 2012, 5-5, 2012 | 322 | 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 | 85 | 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 | 49 | 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 | 48 | 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 | 45 | 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 | 42 | 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 | 36 | 2017 |
Learning feature representations with a cost-relevant sparse autoencoder M Längkvist, A Loutfi International journal of neural systems 25 (01), 1450034, 2015 | 29 | 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 | 20 | 2018 |
A deep learning approach with an attention mechanism for automatic sleep stage classification M Längkvist, A Loutfi arXiv preprint arXiv:1805.05036, 2018 | 15 | 2018 |
Modeling time-series with deep networks M Längkvist Örebro university, 2014 | 15 | 2014 |
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 | 14 | 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 | 12 | 2020 |
Learning actions to improve the perceptual anchoring of objects A Persson, M Längkvist, A Loutfi Frontiers in Robotics and AI 3, 76, 2017 | 5 | 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 | 5 | 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 | 4 | 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 | 4 | 2012 |
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, ... The Fifteenth International Conference on Software Engineering Advances …, 2020 | 3 | 2020 |