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International Journal of Neural Systems
|
December 18, 2014
Learning feature representations with a cost-relevant sparse autoencoder
Martin Längkvist, Amy Loutfi
Sensors (Basel, Switzerland)
|
November 9, 2017
An Ontology-Based Reasoning Framework for Querying Satellite Images for Disaster Monitoring
Marjan Alirezaie, Andrey Kiselev, Martin Längkvist, et al.
Biology
|
May 27, 2023
Utilization of Computer Classification Methods for Exposure Prediction and Gene Selection in <i>Daphnia magna</i> Toxicogenomics
Berkay Paylar, Martin Längkvist, Jana Jass, et al.
Computers in Biology and Medicine
|
May 7, 2018
Computer aided detection of ureteral stones in thin slice computed tomography volumes using Convolutional Neural Networks
Martin Längkvist, Johan Jendeberg, Per Thunberg, et al.
Sensors (Basel, Switzerland)
|
January 29, 2013
Fast classification of meat spoilage markers using nanostructured ZnO thin films and unsupervised feature learning
Martin Längkvist, Silvia Coradeschi, Amy Loutfi, et al.
Page
of 1
Search research articles
Search
Showing results (1-10 of 5) with videos related to
Sort By:
Page
of 1
International Journal of Neural Systems
|
December 18, 2014
Learning feature representations with a cost-relevant sparse autoencoder
Martin Längkvist, Amy Loutfi
Sensors (Basel, Switzerland)
|
November 9, 2017
An Ontology-Based Reasoning Framework for Querying Satellite Images for Disaster Monitoring
Marjan Alirezaie, Andrey Kiselev, Martin Längkvist, et al.
Biology
|
May 27, 2023
Utilization of Computer Classification Methods for Exposure Prediction and Gene Selection in <i>Daphnia magna</i> Toxicogenomics
Berkay Paylar, Martin Längkvist, Jana Jass, et al.
Computers in Biology and Medicine
|
May 7, 2018
Computer aided detection of ureteral stones in thin slice computed tomography volumes using Convolutional Neural Networks
Martin Längkvist, Johan Jendeberg, Per Thunberg, et al.
Sensors (Basel, Switzerland)
|
January 29, 2013
Fast classification of meat spoilage markers using nanostructured ZnO thin films and unsupervised feature learning
Martin Längkvist, Silvia Coradeschi, Amy Loutfi, et al.
Page
of 1