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2Insights into the Drivers and Spatiotemporal Trends of Extreme Mediterranean Wildfires with Statistical Deep Learning
J. Richards, R. Huser, E. Bevacqua, and J. Zscheischler. Artificial Intelligence for the Earth Systems, (October 2023)
7 days ago by @scadsfct
show all tags
Deep
Extreme
Learning
Mediterranean
Spatiotemporal
Statistical
Trends
Wildfires
DeepExtremeLearningMediterraneanSpatiotemporalStatisticalTrendsWildfires
(0)
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1Machine Learning Based Mobile Capacity Estimation for Roadside Parking
A. Kramm, E. Peukert, A. Ludwig, and B. Franczyk. ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, (2024)
7 days ago by @scadsfct
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Based
Capacity
Estimation
Learning
Machine
Mobile
Parking
Roadside
BasedCapacityEstimationLearningMachineMobileParkingRoadside
(0)
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2Cloudy with a chance of precision: satellite’s autoconversion rates forecasting powered by machine learning
M. Novitasari, J. Quaas, and M. Rodrigues. Environmental Data Science, (2024)
7 days ago by @scadsfct
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autoconversion
forecasting
learning
machine
rates
satellite
autoconversionforecastinglearningmachineratessatellite
(0)
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2Can 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)
7 days ago by @scadsfct
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Comparative
Computer
Data
Learning
Self-Supervised
Study
Unlabelled
Vision.
ComparativeComputerDataLearningSelf-SupervisedStudyUnlabelledVision.
(0)
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1ConvGeN: A convex space learning approach for deep-generative oversampling and imbalanced classification of small tabular datasets
K. Schultz, S. Bej, W. Hahn, M. Wolfien, P. Srivastava, and O. Wolkenhauer. Pattern Recognition, (2024)
8 days ago by @scadsfct
show all tags
Convex
GAN
Imbalanced
LoRAS
Tabular
data
learning
space
ConvexGANImbalancedLoRASTabulardatalearningspace
(0)
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1Stability selection enables robust learning of differential equations from limited noisy data
S. Maddu, {. Cheeseman, {. Sbalzarini, and {. Müller. Proceedings of the Royal Society of London : Series A, Mathematical, physical and engineering sciences, (June 2022)Publisher Copyright: © 2022 Royal Society Publishing. All rights reserved..
19 days ago by @scadsfct
show all tags
topic_lifescience
FIS_scads
PAR
differential
equations,
learning
learning,
machine
proteins,
regression,
selection,
sparse
stability
statistical
theory
topic_lifescienceFIS_scadsPARdifferentialequations,learninglearning,machineproteins,regression,selection,sparsestabilitystatisticaltheory
(0)
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1RTASS: a RunTime Adaptable and Scalable System for Network-on-Chip-Based Architectures
N. Charaf, J. Haase, A. Kulisch, {. Elm, and D. Göhringer. 2023 26th Euromicro Conference on Digital System Design (DSD), page 585--592. IEEE, (Sep 8, 2023)Publisher Copyright: © 2023 IEEE.; 26th Euromicro Conference on Digital System Design, DSD 2023 ; Conference date: 06-09-2023 Through 08-09-2023.
19 days ago by @scadsfct
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topic_federatedlearn
Computer
Embedded
FIS_scads
Machine
Runtime,
Scalability,
Shape
algorithms,
architecture,
computing,
learning
vision,
topic_federatedlearnComputerEmbeddedFIS_scadsMachineRuntime,Scalability,Shapealgorithms,architecture,computing,learningvision,
(0)
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1Two-step dynamic obstacle avoidance
F. Hart, M. Waltz, and O. Okhrin. Knowledge-based systems, (Oct 25, 2024)Publisher Copyright: © 2024 The Author(s).
19 days ago by @scadsfct
show all tags
topic_engineering
Deep
Dynamic
FIS_scads
Local
Supervised
avoidance,
learning
learning,
obstacle
path
planning,
reinforcement
topic_engineeringDeepDynamicFIS_scadsLocalSupervisedavoidance,learninglearning,obstaclepathplanning,reinforcement
(0)
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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)
20 days ago by @scadsfct
show all tags
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
(0)
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1Deep reinforcement learning with artificial microswimmers
R. Pradip, and F. Cichos. Emerging Topics in Artificial Intelligence (ETAI) 2022, 12204, page 104--110. SPIE, (2022)
a month ago by @scadsfct
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topic_physchemistry
Deep
artificial
learning
microswimmers
reinforcement
topic_physchemistryDeepartificiallearningmicroswimmersreinforcement
(0)
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1Graph-Based Disease Prediction in Neuroimaging: Investigating the Impact of Feature Selection
D. Kiakou, A. Adamopoulos, and N. Scherf. Worldwide Congress on “Genetics, Geriatrics and Neurodegenerative Diseases Research", page 223--230. Springer, (2022)
a month ago by @scadsfct
show all tags
topic_neuroinspired
Disease
Feature
Graph-Based
Impact
Investigating
Neuroimaging
Prediction
Selection
learning
topic_neuroinspiredDiseaseFeatureGraph-BasedImpactInvestigatingNeuroimagingPredictionSelectionlearning
(0)
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1Steuerung von Compliant-Mechanismen durch Reinforcement Learning
L. Muschalski, J. Wollmann, A. Hornig, and N. Modler. GETRIEBETAGUNG 2022, (2022)
a month ago by @scadsfct
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topic_engineering
Compliant-Mechanismen
Learning
Reinforcement
Steuerung
topic_engineeringCompliant-MechanismenLearningReinforcementSteuerung
(0)
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2Self-organized free-flight arrival for urban air mobility
M. Waltz, O. Okhrin, and M. Schultz. Transportation Research Part C: Emerging Technologies, (2024)
a month ago by @scadsfct
show all tags
topic_engineering
Deep
Urban
air
eVTOL
learning
mobility
reinforcement
topic_engineeringDeepUrbanaireVTOLlearningmobilityreinforcement
(0)
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1Enhanced method for reinforcement learning based dynamic obstacle avoidance by assessment of collision risk
F. Hart, and O. Okhrin. Neurocomputing, (2024)
a month ago by @scadsfct
show all tags
topic_engineering
Collision
Dynamic
Reinforcement
Training
avoidance
environment
learning
metric
obstacle
risk
topic_engineeringCollisionDynamicReinforcementTrainingavoidanceenvironmentlearningmetricobstaclerisk
(0)
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1Neural network-assisted humanisation of COVID-19 hamster transcriptomic data reveals matching severity states in human disease
V. Friedrich, P. Pennitz, E. Wyler, J. Adler, D. Postmus, K. Müller, L. Teixeira Alves, J. Prigann, F. Pott, D. Vladimirova and 10 other author(s). eBioMedicine, (2024)
a month ago by @scadsfct
show all tags
topic_mathfoundation
COVID-19,
Cross-species
Deep
Disease
Hamster
RNA-seq,
Single-cell
analysis,
learning
matching,
model,
state
topic_mathfoundationCOVID-19,Cross-speciesDeepDiseaseHamsterRNA-seq,Single-cellanalysis,learningmatching,model,state
(0)
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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)
2 months ago by @scadsfct
show all tags
(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,
(0)
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1Towards the interpretability of deep learning models for multi-modal neuroimaging: Finding structural changes of the ageing brain
S. Hofmann, F. Beyer, S. Lapuschkin, O. Goltermann, M. Loeffler, K. M√ºller, A. Villringer, W. Samek, and A. Witte. NeuroImage, (2022)
3 months ago by @scadsfct
show all tags
topic_neuroinspired
topic_lifescience
Ageing,
Brain-age,
Cardiovascular
Explainable
Structural
a.i.,
deep
factors,
learning
mri,
risk
topic_neuroinspiredtopic_lifescienceAgeing,Brain-age,CardiovascularExplainableStructurala.i.,deepfactors,learningmri,risk
(0)
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1Sampling Bias Due to Near-Duplicates in Learning to Rank
M. Fröbe, J. Bevendorff, J. Reimer, M. Potthast, and M. Hagen. Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval, page 1997–2000. New York, NY, USA, Association for Computing Machinery, (2020)
3 months ago by @scadsfct
show all tags
bias
learning
near-duplicate-detection,
novelty
principle,
rank,
selection
to
biaslearningnear-duplicate-detection,noveltyprinciple,rank,selectionto
(0)
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1Wie bereit sind Studierende für die Nutzung von KI-Technologien? Eine Annäherung an die KI-Readiness Studierender im Kontext des Projektes "tech4comp"
J. Riedel, and J. Kleppsch. (2021)
3 months ago by @scadsfct
show all tags
(Learning
370
Activities),
Artificial
Assessment,
Bewertung,
Bildungswesen,
Deployment
Digitale
Education,
Empirical
Empirische
Erziehung,
Forschung,
Higher
Hochschule,
Hochschullehre,
Human
Intelligenz,
Judgement,
Judgment,
K\"{u}nstliche
Learning
Lernprozess,
Male
Medien,
Medieneinsatz,
Mediennutzung,
Mensch,
Nachteil,
Project,
Projects
Projekt,
Qualitative
Schul-
Student,
Technologie,
Technology,
University
Untersuchung,
Use
Utilisation
Utilization
Vergleich,
Vorteil,
being,
education
institute,
intelligence,
lecturing,
media,
of
process,
research
student,
study,
teaching,
und
(Learning370Activities),ArtificialAssessment,Bewertung,Bildungswesen,DeploymentDigitaleEducation,EmpiricalEmpirischeErziehung,Forschung,HigherHochschule,Hochschullehre,HumanIntelligenz,Judgement,Judgment,K\"{u}nstlicheLearningLernprozess,MaleMedien,Medieneinsatz,Mediennutzung,Mensch,Nachteil,Project,ProjectsProjekt,QualitativeSchul-Student,Technologie,Technology,UniversityUntersuchung,UseUtilisationUtilizationVergleich,Vorteil,being,educationinstitute,intelligence,lecturing,media,ofprocess,researchstudent,study,teaching,und
(0)
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1Benchmarking weakly-supervised deep learning pipelines for whole slide classification in computational pathology
N. Ghaffari Laleh, H. Muti, C. Loeffler, A. Echle, O. Saldanha, F. Mahmood, M. Lu, C. Trautwein, R. Langer, B. Dislich and 13 other author(s). Med. Image Anal., 79 (102474): 102474 (July 2022)
3 months ago by @scadsfct
show all tags
topic_lifescience
Artificial
Computational
Convolutional
Learning;
Multiple-Instance
Vision
Weakly-supervised
deep
intelligence;
learning
networks;
neural
pathology;
transformers;
topic_lifescienceArtificialComputationalConvolutionalLearning;Multiple-InstanceVisionWeakly-superviseddeepintelligence;learningnetworks;neuralpathology;transformers;
(0)
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publications (hide)22
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2Insights into the Drivers and Spatiotemporal Trends of Extreme Mediterranean Wildfires with Statistical Deep Learning

1Machine Learning Based Mobile Capacity Estimation for Roadside Parking

2Cloudy with a chance of precision: satellite’s autoconversion rates forecasting powered by machine learning

2Can Unlabelled Data Improve AI Applications? A Comparative Study on Self-Supervised Learning in Computer Vision.

1ConvGeN: A convex space learning approach for deep-generative oversampling and imbalanced classification of small tabular datasets

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

1RTASS: a RunTime Adaptable and Scalable System for Network-on-Chip-Based Architectures

1Two-step dynamic obstacle avoidance

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

1Deep reinforcement learning with artificial microswimmers

1Graph-Based Disease Prediction in Neuroimaging: Investigating the Impact of Feature Selection

1Steuerung von Compliant-Mechanismen durch Reinforcement Learning

2Self-organized free-flight arrival for urban air mobility

1Enhanced method for reinforcement learning based dynamic obstacle avoidance by assessment of collision risk

1Neural network-assisted humanisation of COVID-19 hamster transcriptomic data reveals matching severity states in human disease

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

1Towards the interpretability of deep learning models for multi-modal neuroimaging: Finding structural changes of the ageing brain

1Sampling Bias Due to Near-Duplicates in Learning to Rank

1Wie bereit sind Studierende für die Nutzung von KI-Technologien? Eine Annäherung an die KI-Readiness Studierender im Kontext des Projektes "tech4comp"

1Benchmarking weakly-supervised deep learning pipelines for whole slide classification in computational pathology

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