tag :: learning computer

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1On optimizing morphological neural networks for hyperspectral image classification
M. Kukushkin, M. Bogdan, and T. Schmid. Sixteenth International Conference on Machine Vision (ICMV 2023), 13072, page 1307202. International Society for Optics and Photonics, SPIE, (2024)
3 months ago by @scadsfct
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classification
computer
deep
hyperspectral
image
learning
mathematical
morphological
morphology
networks
neuronal
nopdf
remote
sensing
vision
classificationcomputerdeephyperspectralimagelearningmathematicalmorphologicalmorphologynetworksneuronalnopdfremotesensingvision
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2A Light-weight and Unsupervised Method for Near Real-time Behavioral Analysis using Operational Data Measurement
T. Vargis, and S. Ghiasvand. The International Conference for High Performance Computing, Networking, Storage, and Analysis, Dallas, Texas, USA, (January 2022)arXiv:2402.05114 cs.
3 months ago by @ghiasvan
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Cluster
Computer
Computing,
Distributed,
Learning
Machine
Parallel,
Science
and
myOwn
ClusterComputerComputing,Distributed,LearningMachineParallel,ScienceandmyOwn
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1Can Unlabelled Data Improve AI Applications? A Comparative Study on Self-Supervised Learning in Computer Vision.
M. Bauer, and C. Augenstein. Proceedings of the 18th Conference on Computer Science and Intelligence Systems, volume 35 of FedCSIS 2023, page 93–101. IEEE, (September 2023)
3 months ago by @scadsfct
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Comparative
Computer
Data
Learning
Self-Supervised
Study
Unlabelled
Vision
yaff
ComparativeComputerDataLearningSelf-SupervisedStudyUnlabelledVisionyaff
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2Explainable Earth Surface Forecasting under Extreme Events
O. Pellicer-Valero, M. Fernández-Torres, C. Ji, M. Mahecha, and G. Camps-Valls. (2024)
4 months ago by @joum576e
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(cs.LG),
Computer
FOS:
Learning
Machine
and
information
sciences
sciences,
topic_earthenvironment
(cs.LG),ComputerFOS:LearningMachineandinformationsciencessciences,topic_earthenvironment
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2Assessing the Impact of Image Dataset Features on Privacy-Preserving Machine Learning
L. Lange, M. Heykeroth, and E. Rahm. arXiv preprint arXiv:2409.01329, (September 2024)
4 months ago by @scadsfct
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area_responsibleai
area_bigdata
(cs.CR),
(cs.CV),
(cs.LG),
Computer
Cryptography
FOS:
Learning
Machine
Pattern
Recognition
Security
Vision
and
ep
information
sciences
area_responsibleaiarea_bigdata(cs.CR),(cs.CV),(cs.LG),ComputerCryptographyFOS:LearningMachinePatternRecognitionSecurityVisionandepinformationsciences
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1Stability selection enables robust learning of partial differential equations from limited noisy data
S. Maddu, B. Cheeseman, I. Sbalzarini, and C. Müller. (2019)
5 months ago by @scadsfct
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(cs.LG),
(math.NA),
(physics.data-an),
Analysis
Analysis,
Computer
Data
FOS:
Learning
Machine
Mathematics,
Numerical
Physical
Probability
Statistics
and
information
sciences
sciences,
(cs.LG),(math.NA),(physics.data-an),AnalysisAnalysis,ComputerDataFOS:LearningMachineMathematics,NumericalPhysicalProbabilityStatisticsandinformationsciencessciences,
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1On optimizing morphological neural networks for hyperspectral image classification

2A Light-weight and Unsupervised Method for Near Real-time Behavioral Analysis using Operational Data Measurement

1Can Unlabelled Data Improve AI Applications? A Comparative Study on Self-Supervised Learning in Computer Vision.

2Explainable Earth Surface Forecasting under Extreme Events

2Assessing the Impact of Image Dataset Features on Privacy-Preserving Machine Learning

1Stability selection enables robust learning of partial differential equations from limited noisy data

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PUMA

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publications (hide)6 displayallpublications onlypublications per page5102050100 sort byadded attitleauthorpublication dateentry typehelp for advanced sorting... RSSBibTeXRDFmore...

1On optimizing morphological neural networks for hyperspectral image classification

2A Light-weight and Unsupervised Method for Near Real-time Behavioral Analysis using Operational Data Measurement

1Can Unlabelled Data Improve AI Applications? A Comparative Study on Self-Supervised Learning in Computer Vision.

2Explainable Earth Surface Forecasting under Extreme Events

2Assessing the Impact of Image Dataset Features on Privacy-Preserving Machine Learning

1Stability selection enables robust learning of partial differential equations from limited noisy data

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