Sleep Stage Classification Using Unsupervised Feature Learning. - Martin Längkvist; Lars Karlsson; Amy Loutfi (Adv. Artif. Neural Syst., 2012)

Fast Classification of Meat Spoilage Markers Using Nanostructured ZnO Thin Films and Unsupervised Feature Learning. - Martin Längkvist; Silvia Coradeschi; Amy Loutfi; John Bosco Balaguru Rayappan (Sensors, 2013)

A review of unsupervised feature learning and deep learning for time-series modeling. - Martin Längkvist; Lars Karlsson; Amy Loutfi (Pattern Recognit. Lett., 2014)

Modeling time-series with deep networks. - Martin Längkvist (2014)

Learning Feature Representations with a Cost-Relevant Sparse Autoencoder. - Martin Längkvist; Amy Loutfi (Int. J. Neural Syst., 2015)

Open GeoSpatial Data as a Source of Ground Truth for Automated Labelling of Satellite Images. - Marjan Alirezaie; Martin Längkvist; Andrey Kiselev; Amy Loutfi (SDW@GIScience, 2016)

Learning Actions to Improve the Perceptual Anchoring of Objects. - Andreas Persson; Martin Längkvist; Amy Loutfi (Frontiers Robotics AI, 2016)

Classification and Segmentation of Satellite Orthoimagery Using Convolutional Neural Networks. - Martin Längkvist; Andrey Kiselev; Marjan Alirezaie; Amy Loutfi (Remote. Sens., 2016)

Exploiting Context and Semantics for UAV Path-finding in an Urban Setting. - Marjan Alirezaie; Andrey Kiselev; Franziska Klügl; Martin Längkvist; Amy Loutfi (AnSWeR@ESWC, 2017)

An Ontology-Based Reasoning Framework for Querying Satellite Images for Disaster Monitoring. - Marjan Alirezaie; Andrey Kiselev; Martin Längkvist; Franziska Klügl; Amy Loutfi (Sensors, 2017)

Computer aided detection of ureteral stones in thin slice computed tomography volumes using Convolutional Neural Networks. - Martin Längkvist; Johan Jendeberg; Per Thunberg; Amy Loutfi; Mats Lidén (Comput. Biol. Medicine, 2018)

A Deep Learning Approach with an Attention Mechanism for Automatic Sleep Stage Classification. - Martin Längkvist; Amy Loutfi (CoRR, 2018)

Interactive user interface based on Convolutional Auto-encoders for annotating CT-scans. - Martin Längkvist; Jonas Widell; Per Thunberg; Amy Loutfi; Mats Lidén (CoRR, 2019)

Semantic referee: A neural-symbolic framework for enhancing geospatial semantic segmentation. - Marjan Alirezaie; Martin Längkvist; Michael Sioutis; Amy Loutfi (Semantic Web, 2019)

Cluster-Based Parallel Testing Using Semantic Analysis. - Cristina Landing; Sahar Tahvili; Hugo Haggren; Martin Längkvist; Auwn Muhammad; Amy Loutfi (AITest, 2020)

An Analysis of Fast Learning Methods for Classifying Forest Cover Types. - Hugo Sjöqvist; Martin Längkvist; Farrukh Javed (Appl. Artif. Intell., 2020)

Knowledge Representation and Reasoning Methods to Explain Errors in Machine Learning. - Marjan Alirezaie; Martin Längkvist; Amy Loutfi (Knowledge Graphs for eXplainable Artificial Intelligence, 2020)

Deep Learning based Aerial Image Segmentation for Computing Green Area Factor. - G. M. Atiqur Rahaman; Martin Längkvist; Amy Loutfi (EUVIP, 2022)

Empirical analysis of the convergence of Double DQN in relation to reward sparsity. - Samuel Blad; Martin Längkvist; Franziska Klügl; Amy Loutfi (ICMLA, 2022)

An Intelligent Monitoring Algorithm to Detect Dependencies between Test Cases in the Manual Integration Process. - Cristina Landing; Xinrong Zhao; Martin Längkvist; Amy Loutfi (ICSTW, 2023)

Affordance-Based Goal Imagination for Embodied AI Agents. - Victor Aregbede; Savitha Sam Abraham; Andreas Persson; Martin Längkvist; Amy Loutfi (ICDL, 2024)

Machine Learning for Lithology Analysis using a Multi-Modal Approach of Integrating XRF and XCT data. - Suraj Neelakantan; Alexander Hansson; Jesper Norell; Johan Schött; Martin Längkvist; Amy Loutfi (SCAI, 2024)