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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)
8 days ago by @scadsfct
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Study
Learning
Computer
Data
Self-Supervised
Unlabelled
Vision.
Comparative
StudyLearningComputerDataSelf-SupervisedUnlabelledVision.Comparative
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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.
20 days ago by @scadsfct
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FIS_scads
Shape
learning
Machine
computing,
topic_federatedlearn
Runtime,
vision,
algorithms,
architecture,
Embedded
Computer
Scalability,
FIS_scadsShapelearningMachinecomputing,topic_federatedlearnRuntime,vision,algorithms,architecture,EmbeddedComputerScalability,
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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
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Disease
Cross-species
model,
learning
Deep
matching,
Single-cell
RNA-seq,
topic_mathfoundation
COVID-19,
analysis,
state
Hamster
DiseaseCross-speciesmodel,learningDeepmatching,Single-cellRNA-seq,topic_mathfoundationCOVID-19,analysis,stateHamster
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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
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principle,
near-duplicate-detection,
selection
novelty
bias
learning
to
rank,
principle,near-duplicate-detection,selectionnoveltybiaslearningtorank,
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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
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Convolutional
Artificial
intelligence;
Multiple-Instance
deep
neural
networks;
learning
Weakly-supervised
Learning;
transformers;
pathology;
topic_lifescience
Vision
Computational
ConvolutionalArtificialintelligence;Multiple-Instancedeepneuralnetworks;learningWeakly-supervisedLearning;transformers;pathology;topic_lifescienceVisionComputational
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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
Training
Dynamic
environment
Collision
Reinforcement
learning
metric
risk
avoidance
topic_engineering
obstacle
TrainingDynamicenvironmentCollisionReinforcementlearningmetricriskavoidancetopic_engineeringobstacle
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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).
20 days ago by @scadsfct
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FIS_scads
Dynamic
Local
learning
Deep
Supervised
planning,
path
learning,
avoidance,
reinforcement
topic_engineering
obstacle
FIS_scadsDynamicLocallearningDeepSupervisedplanning,pathlearning,avoidance,reinforcementtopic_engineeringobstacle
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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
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Cardiovascular
deep
Brain-age,
Structural
learning
Ageing,
a.i.,
mri,
Explainable
topic_lifescience
topic_neuroinspired
risk
factors,
CardiovasculardeepBrain-age,StructurallearningAgeing,a.i.,mri,Explainabletopic_lifesciencetopic_neuroinspiredriskfactors,
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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
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Neuroimaging
Investigating
Selection
Impact
Disease
Graph-Based
Prediction
topic_neuroinspired
learning
Feature
NeuroimagingInvestigatingSelectionImpactDiseaseGraph-BasedPredictiontopic_neuroinspiredlearningFeature
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1Common features in lncRNA annotation and classification: A survey
C. Klapproth, R. Sen, P. Stadler, S. Findeiß, and J. Fallmann. Noncoding RNA, 7 (4): 77 (December 2021)
3 months ago by @scadsfct
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problems;
coding
extraction;
feature
sequence;
machine
lncRNA;
learning
classification
problems;codingextraction;featuresequence;machinelncRNA;learningclassification
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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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Learning
Capacity
Parking
Estimation
Roadside
Based
Mobile
Machine
LearningCapacityParkingEstimationRoadsideBasedMobileMachine
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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
ep
FOS:
Machine
sciences
(cs.LG),
Security
Learning
(cs.CV),
and
Vision
Computer
(cs.CR),
Cryptography
information
Pattern
area_bigdata
area_responsibleai
Recognition
epFOS:Machinesciences(cs.LG),SecurityLearning(cs.CV),andVisionComputer(cs.CR),CryptographyinformationPatternarea_bigdataarea_responsibleaiRecognition
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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..
20 days ago by @scadsfct
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FIS_scads
PAR
statistical
proteins,
equations,
learning
selection,
sparse
differential
regression,
learning,
machine
topic_lifescience
stability
theory
FIS_scadsPARstatisticalproteins,equations,learningselection,sparsedifferentialregression,learning,machinetopic_lifesciencestabilitytheory
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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
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(physics.data-an),
Analysis,
Probability
Data
sciences,
Numerical
Statistics
FOS:
Machine
Physical
sciences
Analysis
(cs.LG),
Learning
(math.NA),
Mathematics,
and
Computer
information
(physics.data-an),Analysis,ProbabilityDatasciences,NumericalStatisticsFOS:MachinePhysicalsciencesAnalysis(cs.LG),Learning(math.NA),Mathematics,andComputerinformation
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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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Learning
Compliant-Mechanismen
Steuerung
Reinforcement
topic_engineering
LearningCompliant-MechanismenSteuerungReinforcementtopic_engineering
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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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rates
forecasting
machine
autoconversion
learning
satellite
ratesforecastingmachineautoconversionlearningsatellite
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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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microswimmers
artificial
learning
topic_physchemistry
reinforcement
Deep
microswimmersartificiallearningtopic_physchemistryreinforcementDeep
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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
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Mediterranean
Extreme
Learning
Wildfires
Spatiotemporal
Trends
Statistical
Deep
MediterraneanExtremeLearningWildfiresSpatiotemporalTrendsStatisticalDeep
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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
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Human
Bewertung,
Empirical
Mediennutzung,
Lernprozess,
Untersuchung,
Use
Activities),
und
Qualitative
Hochschullehre,
institute,
intelligence,
being,
Project,
student,
Vergleich,
Bildungswesen,
Projekt,
process,
Judgment,
Medien,
research
Erziehung,
Mensch,
Judgement,
Utilisation
Student,
Forschung,
Nachteil,
Hochschule,
Empirische
Technologie,
Vorteil,
Education,
Utilization
Assessment,
Intelligenz,
University
media,
Higher
Schul-
Artificial
Medieneinsatz,
(Learning
Deployment
Male
Projects
370
education
K\"{u}nstliche
Technology,
teaching,
lecturing,
Learning
Digitale
study,
of
HumanBewertung,EmpiricalMediennutzung,Lernprozess,Untersuchung,UseActivities),undQualitativeHochschullehre,institute,intelligence,being,Project,student,Vergleich,Bildungswesen,Projekt,process,Judgment,Medien,researchErziehung,Mensch,Judgement,UtilisationStudent,Forschung,Nachteil,Hochschule,EmpirischeTechnologie,Vorteil,Education,UtilizationAssessment,Intelligenz,Universitymedia,HigherSchul-ArtificialMedieneinsatz,(LearningDeploymentMaleProjects370educationK\"{u}nstlicheTechnology,teaching,lecturing,LearningDigitalestudy,of
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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
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Tabular
Convex
LoRAS
GAN
Imbalanced
data
learning
space
TabularConvexLoRASGANImbalanceddatalearningspace
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1Individual modelling of haematotoxicity with NARX neural networks: A knowledge transfer approach
M. Steinacker, Y. Kheifetz, and M. Scholz. Heliyon, 9 (7): e17890 (July 2023)
3 months ago by @scadsfct
show all tags
neural
networks;
identification;
Recurrent
learning
Haematopoiesis;
unit_transfer
System
Precision
topic_mathfoundation
topic_neuroinspired
Transfer
medicine;
neuralnetworks;identification;RecurrentlearningHaematopoiesis;unit_transferSystemPrecisiontopic_mathfoundationtopic_neuroinspiredTransfermedicine;
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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
Urban
mobility
eVTOL
air
learning
reinforcement
Deep
topic_engineering
UrbanmobilityeVTOLairlearningreinforcementDeeptopic_engineering
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2Can Unlabelled Data Improve AI Applications? A Comparative Study on Self-Supervised Learning in Computer Vision.

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

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

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

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

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

1Two-step dynamic obstacle avoidance

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

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

1Common features in lncRNA annotation and classification: A survey

1Machine Learning Based Mobile Capacity Estimation for Roadside Parking

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

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

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

1Steuerung von Compliant-Mechanismen durch Reinforcement Learning

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

1Deep reinforcement learning with artificial microswimmers

2Insights into the Drivers and Spatiotemporal Trends of Extreme Mediterranean Wildfires with Statistical Deep Learning

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

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

1Individual modelling of haematotoxicity with NARX neural networks: A knowledge transfer approach

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

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