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Martin Längkvist
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2020 – today
- 2024
- [c8]Victor Aregbede, Savitha Sam Abraham, Andreas Persson, Martin Längkvist, Amy Loutfi:
Affordance-Based Goal Imagination for Embodied AI Agents. ICDL 2024: 1-6 - [c7]Suraj Neelakantan, Alexander Hansson, Jesper Norell, Johan Schött, Martin Längkvist, Amy Loutfi:
Machine Learning for Lithology Analysis using a Multi-Modal Approach of Integrating XRF and XCT data. SCAI 2024: 188-194 - 2023
- [c6]Cristina Landing, Xinrong Zhao, Martin Längkvist, Amy Loutfi:
An Intelligent Monitoring Algorithm to Detect Dependencies between Test Cases in the Manual Integration Process. ICSTW 2023: 353-360 - 2022
- [c5]G. M. Atiqur Rahaman, Martin Längkvist, Amy Loutfi:
Deep Learning based Aerial Image Segmentation for Computing Green Area Factor. EUVIP 2022: 1-6 - [c4]Samuel Blad, Martin Längkvist, Franziska Klügl, Amy Loutfi:
Empirical analysis of the convergence of Double DQN in relation to reward sparsity. ICMLA 2022: 591-596 - 2020
- [j10]Hugo Sjöqvist, Martin Längkvist, Farrukh Javed:
An Analysis of Fast Learning Methods for Classifying Forest Cover Types. Appl. Artif. Intell. 34(10): 691-709 (2020) - [c3]Cristina Landing, Sahar Tahvili, Hugo Haggren, Martin Längkvist, Auwn Muhammad, Amy Loutfi:
Cluster-Based Parallel Testing Using Semantic Analysis. AITest 2020: 99-106 - [p1]Marjan Alirezaie, Martin Längkvist, Amy Loutfi:
Knowledge Representation and Reasoning Methods to Explain Errors in Machine Learning. Knowledge Graphs for eXplainable Artificial Intelligence 2020: 161-179
2010 – 2019
- 2019
- [j9]Marjan Alirezaie, Martin Längkvist, Michael Sioutis, Amy Loutfi:
Semantic referee: A neural-symbolic framework for enhancing geospatial semantic segmentation. Semantic Web 10(5): 863-880 (2019) - [i3]Martin Längkvist, Jonas Widell, Per Thunberg, Amy Loutfi, Mats Lidén:
Interactive user interface based on Convolutional Auto-encoders for annotating CT-scans. CoRR abs/1904.11701 (2019) - [i2]Marjan Alirezaie, Martin Längkvist, Michael Sioutis, Amy Loutfi:
Semantic Referee: A Neural-Symbolic Framework for Enhancing Geospatial Semantic Segmentation. CoRR abs/1904.13196 (2019) - 2018
- [j8]Martin Längkvist, Johan Jendeberg, Per Thunberg, Amy Loutfi, Mats Lidén:
Computer aided detection of ureteral stones in thin slice computed tomography volumes using Convolutional Neural Networks. Comput. Biol. Medicine 97: 153-160 (2018) - [i1]Martin Längkvist, Amy Loutfi:
A Deep Learning Approach with an Attention Mechanism for Automatic Sleep Stage Classification. CoRR abs/1805.05036 (2018) - 2017
- [j7]Marjan Alirezaie, Andrey Kiselev, Martin Längkvist, Franziska Klügl, Amy Loutfi:
An Ontology-Based Reasoning Framework for Querying Satellite Images for Disaster Monitoring. Sensors 17(11): 2545 (2017) - [c2]Marjan Alirezaie, Andrey Kiselev, Franziska Klügl, Martin Längkvist, Amy Loutfi:
Exploiting Context and Semantics for UAV Path-finding in an Urban Setting. AnSWeR@ESWC 2017: 11-20 - 2016
- [j6]Andreas Persson, Martin Längkvist, Amy Loutfi:
Learning Actions to Improve the Perceptual Anchoring of Objects. Frontiers Robotics AI 3: 76 (2016) - [j5]Martin Längkvist, Andrey Kiselev, Marjan Alirezaie, Amy Loutfi:
Classification and Segmentation of Satellite Orthoimagery Using Convolutional Neural Networks. Remote. Sens. 8(4): 329 (2016) - [c1]Marjan Alirezaie, Martin Längkvist, Andrey Kiselev, Amy Loutfi:
Open GeoSpatial Data as a Source of Ground Truth for Automated Labelling of Satellite Images. SDW@GIScience 2016: 5-8 - 2015
- [j4]Martin Längkvist, Amy Loutfi:
Learning Feature Representations with a Cost-Relevant Sparse Autoencoder. Int. J. Neural Syst. 25(1): 1450034:1-1450034:11 (2015) - 2014
- [b1]Martin Längkvist:
Modeling time-series with deep networks. Örebro University, Sweden, 2014 - [j3]Martin Längkvist, Lars Karlsson, Amy Loutfi:
A review of unsupervised feature learning and deep learning for time-series modeling. Pattern Recognit. Lett. 42: 11-24 (2014) - 2013
- [j2]Martin Längkvist, Silvia Coradeschi, Amy Loutfi, John Bosco Balaguru Rayappan:
Fast Classification of Meat Spoilage Markers Using Nanostructured ZnO Thin Films and Unsupervised Feature Learning. Sensors 13(2): 1578-1592 (2013) - 2012
- [j1]Martin Längkvist, Lars Karlsson, Amy Loutfi:
Sleep Stage Classification Using Unsupervised Feature Learning. Adv. Artif. Neural Syst. 2012: 107046:1-107046:9 (2012)
Coauthor Index
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last updated on 2024-09-26 23:40 CEST by the dblp team
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