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1A comparative patient-level prediction study in OMOP CDM: applicative potential and insights from synthetic data
N. Ahmadi, {. Nguyen, M. Sedlmayr, and M. Wolfien. Scientific reports, (Jan 27, 2024)
5 days ago by @scadsfct
show all tags
Databases,
Electronic
FIS_scads
Factual,
Health
Humans,
Informatics,
Learning,
Machine
Medical
Records
topic_lifescience
Databases,ElectronicFIS_scadsFactual,HealthHumans,Informatics,Learning,MachineMedicalRecordstopic_lifescience
(0)
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1Towards Standardizing Ophthalmic Data for Seamless Interoperability in Eye Care
K. Hoffmann, Y. Peng, T. Schlosser, G. Stolze, H. Langner, M. Susky, T. Meyer, M. Ritter, D. Kowerko, V. Kakkassery and 2 other author(s). Studies in health technology and informatics, (Aug 30, 2024)Publisher Copyright: © 2024 The Authors..
5 days ago by @scadsfct
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topic_lifescience
Diseases/therapy,
Electronic
Eye
FIS_scads
Germany,
Health
Humans,
Information
Interoperability/standards,
Learning,
Level
Machine
Ophthalmology
Records/standards,
Seven/standards,
topic_lifescienceDiseases/therapy,ElectronicEyeFIS_scadsGermany,HealthHumans,InformationInteroperability/standards,Learning,LevelMachineOphthalmologyRecords/standards,Seven/standards,
(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 : the journal of the Pattern Recognition Society, (March 2024)Publisher Copyright: © 2023 Elsevier Ltd.
5 days ago by @scadsfct
show all tags
topic_lifescience
Convex
FIS_scads
GAN,
Imbalanced
LoRAS,
Tabular
data
data,
learning,
space
topic_lifescienceConvexFIS_scadsGAN,ImbalancedLoRAS,Tabulardatadata,learning,space
(0)
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1Leptin deficiency-caused behavioral change - A comparative analysis using EthoVision and DeepLabCut
D. Bühler, N. Power Guerra, L. Müller, O. Wolkenhauer, M. Düffer, B. Vollmar, A. Kuhla, and M. Wolfien. Frontiers in neuroscience, (Mar 24, 2023)Publisher Copyright: Copyright © 2023 Bühler, Power Guerra, Müller, Wolkenhauer, Düffer, Vollmar, Kuhla and Wolfien..
5 days ago by @scadsfct
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topic_lifescience
Behavioral
Deep
DeepLabCut,
EthoVision,
FIS_scads
Obesity
analysis,
learning,
topic_lifescienceBehavioralDeepDeepLabCut,EthoVision,FIS_scadsObesityanalysis,learning,
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1CCR2 macrophage response determines the functional outcome following cardiomyocyte transplantation
P. Vasudevan, M. Wolfien, H. Lemcke, {. Lang, A. Skorska, R. Gaebel, A. Galow, D. Koczan, T. Lindner, W. Bergmann and 6 other author(s). Genome medicine, (Aug 10, 2023)Publisher Copyright: © 2023, BioMed Central Ltd., part of Springer Nature..
5 days ago by @scadsfct
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topic_lifescience
Animals,
C57BL,
Cardiac/metabolism,
Cell
FIS_scads
Immunocompromised,
Inbred
Infarction,
Machine
Macrophages,
Macrophages/metabolism,
Mice,
Monocytes/metabolism
Myocardial
Myocytes,
Single-cell,
infarction,
learning,
therapy,
topic_lifescienceAnimals,C57BL,Cardiac/metabolism,CellFIS_scadsImmunocompromised,InbredInfarction,MachineMacrophages,Macrophages/metabolism,Mice,Monocytes/metabolismMyocardialMyocytes,Single-cell,infarction,learning,therapy,
(0)
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1Adaptive weighting of Bayesian physics informed neural networks for multitask and multiscale forward and inverse problems
S. Perez, S. Maddu, {. Sbalzarini, and P. Poncet. Journal of computational physics, (Oct 15, 2023)Publisher Copyright: © 2023 The Author(s).
5 days ago by @scadsfct
show all tags
topic_lifescience
Adaptive
Artificial
Bayesian
Carlo,
FIS_scads
Hamiltonian
Intelligence,
Monte
Multi-objective
Quantification
Uncertainty
learning,
networks,
neural
physics-informed
training,
weight
topic_lifescienceAdaptiveArtificialBayesianCarlo,FIS_scadsHamiltonianIntelligence,MonteMulti-objectiveQuantificationUncertaintylearning,networks,neuralphysics-informedtraining,weight
(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..
5 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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1Two-step dynamic obstacle avoidance
F. Hart, M. Waltz, and O. Okhrin. Knowledge-based systems, (Oct 25, 2024)Publisher Copyright: © 2024 The Author(s).
5 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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1Spatial–temporal recurrent reinforcement learning for autonomous ships
M. Waltz, and O. Okhrin. Neural Networks, 2023 (165): 634--653 (Jun 15, 2023)Publisher Copyright: © 2023 Elsevier Ltd.
5 days ago by @scadsfct
show all tags
topic_engineering
Algorithms,
Autonomous
COLREG,
Computer,
Deep
FIS_scads
Networks,
Neural
Psychology,
Recurrency,
Reinforcement,
Reward
Ships,
learning,
reinforcement
surface
vehicle,
topic_engineeringAlgorithms,AutonomousCOLREG,Computer,DeepFIS_scadsNetworks,NeuralPsychology,Recurrency,Reinforcement,RewardShips,learning,reinforcementsurfacevehicle,
(0)
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1Data-driven determination of plant growth stages for improved weather index insurance design
J. Zou, M. Odening, and O. Okhrin. Agricultural Finance Review, (2024)Publisher Copyright: © 2024, Jing Zou, Martin Odening and Ostap Okhrin..
5 days ago by @scadsfct
show all tags
topic_engineering
FIS_scads
Generalized
Machine
Plant
Temporal
Weather
additive
basis
growth
index
insurance
learning,
model,
risk,
stages,
topic_engineeringFIS_scadsGeneralizedMachinePlantTemporalWeatheradditivebasisgrowthindexinsurancelearning,model,risk,stages,
(0)
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1Addressing maximization bias in reinforcement learning with two-sample testing
M. Waltz, and O. Okhrin. Artificial intelligence, (November 2024)Publisher Copyright: © 2024 The Author(s).
5 days ago by @scadsfct
show all tags
topic_engineering
Estimation
FIS_scads
Maximum
Q-learning,
Reinforcement
Two-sample
bias,
expected
learning,
testing
value,
topic_engineeringEstimationFIS_scadsMaximumQ-learning,ReinforcementTwo-samplebias,expectedlearning,testingvalue,
(0)
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1Robust path following on rivers using bootstrapped reinforcement learning
N. Paulig, and O. Okhrin. Ocean engineering, (Apr 15, 2024)Publisher Copyright: © 2024 The Authors.
5 days ago by @scadsfct
show all tags
topic_engineering
Autonomous
Deep
FIS_scads
Path
Restricted
following,
learning,
reinforcement
surface
vessel,
waterways
topic_engineeringAutonomousDeepFIS_scadsPathRestrictedfollowing,learning,reinforcementsurfacevessel,waterways
(0)
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1A methodology for direct parameter identification for experimental results using machine learning — Real world application to the highly non-linear deformation behavior of FRP
J. Gerritzen, A. Hornig, P. Winkler, and M. Gude. Computational Materials Science, (September 2024)Publisher Copyright: © 2024 The Author(s).
5 days ago by @scadsfct
show all tags
topic_engineering
area_architectures
Constitutive
FIS_scads
Fiber
Machine
Neural
Parameter
identification
learning,
modeling,
networks,
plastics,
reinforced
topic_engineeringarea_architecturesConstitutiveFIS_scadsFiberMachineNeuralParameteridentificationlearning,modeling,networks,plastics,reinforced
(0)
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1Direct parameter identification for highly nonlinear strain rate dependent constitutive models using machine learning
J. Gerritzen, A. Hornig, P. Winkler, and M. Gude. ECCM21 - Proceedings of the 21st European Conference on Composite Materials, 3, page 1252--1259. European Society for Composite Materials (ESCM), (Jul 2, 2024)21st European Conference on Composite Materials, ECCM 21 ; Conference date: 02-07-2024 Through 05-07-2024.
5 days ago by @scadsfct
show all tags
topic_engineering
area_architectures
Convolutional
Direct
FIS_scads
Fiber
Machine
Strain
dependency,
identification,
learning,
networks,
neural
parameter
plastics
rate
reinforced
topic_engineeringarea_architecturesConvolutionalDirectFIS_scadsFiberMachineStraindependency,identification,learning,networks,neuralparameterplasticsratereinforced
(0)
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1Identifying Secondary School Students' Misconceptions about Machine Learning: An Interview Study
E. Marx, C. Witt, and T. Leonhardt. WiPSCE '24: Proceedings of the 19th WiPSCE Conference on Primary and Secondary Computing Education Research, page 1--10. Association for Computing Machinery, (Sep 16, 2024)Publisher Copyright: © 2024 Copyright held by the owner/author(s)..
5 days ago by @scadsfct
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area_responsibleai
FIS_scads
artificial
conceptions
intelligence,
interview
learning,
machine
mental
misconceptions,
models,
qualitative
research,
students
study,
area_responsibleaiFIS_scadsartificialconceptionsintelligence,interviewlearning,machinementalmisconceptions,models,qualitativeresearch,studentsstudy,
(0)
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1Brief Summary of Existing Research on Students’ Conceptions of AI
E. Marx, T. Leonhardt, and N. Bergner. (October 2022)Publisher Copyright: © 2022 Owner/Author.; WiPSCE 2022 ; Conference date: 31-10-2022 Through 02-11-2022.
5 days ago by @scadsfct
show all tags
area_responsibleai
FIS_scads
artificial
beliefs,
conceptions,
education,
ideas,
intelligence,
k-12
learning,
machine
mental
models,
preconceptions
area_responsibleaiFIS_scadsartificialbeliefs,conceptions,education,ideas,intelligence,k-12learning,machinementalmodels,preconceptions
(0)
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2How Interpretable Machine Learning Can Benefit Process Understanding in the Geosciences
S. Jiang, {. Sweet, G. Blougouras, A. Brenning, W. Li, M. Reichstein, J. Denzler, W. Shangguan, G. Yu, F. Huang and 1 other author(s). Earth's Future, (July 2024)Publisher Copyright: © 2024. The Author(s)..
5 days ago by @scadsfct
show all tags
FIS_scads
XAI
big
data,
discovery,
interpretability,
interpretable
knowledge
learning,
machine
FIS_scadsXAIbigdata,discovery,interpretability,interpretableknowledgelearning,machine
(0)
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1Few-Shot Learning for Argument Aspects of the Nuclear Energy Debate
L. Jurkschat, G. Wiedemann, M. Heinrich, M. Ruckdeschel, and S. Torge. 2022 Language Resources and Evaluation Conference, LREC 2022, page 663--672. European Language Resources Association (ELRA), (2022)Publisher Copyright: © European Language Resources Association (ELRA), licensed under CC-BY-NC-4.0.; 13th International Conference on Language Resources and Evaluation Conference, LREC 2022 ; Conference date: 20-06-2022 Through 25-06-2022.
5 days ago by @scadsfct
show all tags
FIS_scads
argument
aspect-based
aspects,
classification
discourse,
energy
few-shot
frames,
learning,
mining,
nuclear
text
FIS_scadsargumentaspect-basedaspects,classificationdiscourse,energyfew-shotframes,learning,mining,nucleartext
(0)
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1Federated Learning on Transcriptomic Data: Model Quality and Performance Trade-Offs
A. Hannemann, J. Ewald, L. Seeger, and E. Buchmann. Computational Science – ICCS 2024: 24th International Conference, Malaga, Spain, July 2–4, 2024, Proceedings, Part IV, page 279–293. Berlin, Heidelberg, Springer-Verlag, (2024)
21 days ago by @scadsfct
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topic_lifescience
area_responsibleai
Cell
Classification,
Disease
Federated
Learning,
Prognosis
Type
topic_lifesciencearea_responsibleaiCellClassification,DiseaseFederatedLearning,PrognosisType
(0)
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1Chapter 5 - Artificial intelligence (AI) enhanced nanomotors and active matter
F. Cichos, S. Landin, and R. Pradip. Intelligent Nanotechnology, Elsevier, (2023)
2 months ago by @scadsfct
show all tags
topic_physchemistry
Active
Feedback
Machine
Multi
Optical
Reinforcement
agent
control
control,
learning,
particles,
reinforcement
topic_physchemistryActiveFeedbackMachineMultiOpticalReinforcementagentcontrolcontrol,learning,particles,reinforcement
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publications (hide)22
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1A comparative patient-level prediction study in OMOP CDM: applicative potential and insights from synthetic data

1Towards Standardizing Ophthalmic Data for Seamless Interoperability in Eye Care

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

1Leptin deficiency-caused behavioral change - A comparative analysis using EthoVision and DeepLabCut

1CCR2 macrophage response determines the functional outcome following cardiomyocyte transplantation

1Adaptive weighting of Bayesian physics informed neural networks for multitask and multiscale forward and inverse problems

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

1Two-step dynamic obstacle avoidance

1Spatial–temporal recurrent reinforcement learning for autonomous ships

1Data-driven determination of plant growth stages for improved weather index insurance design

1Addressing maximization bias in reinforcement learning with two-sample testing

1Robust path following on rivers using bootstrapped reinforcement learning

1A methodology for direct parameter identification for experimental results using machine learning — Real world application to the highly non-linear deformation behavior of FRP

1Direct parameter identification for highly nonlinear strain rate dependent constitutive models using machine learning

1Identifying Secondary School Students' Misconceptions about Machine Learning: An Interview Study

1Brief Summary of Existing Research on Students’ Conceptions of AI

2How Interpretable Machine Learning Can Benefit Process Understanding in the Geosciences

1Few-Shot Learning for Argument Aspects of the Nuclear Energy Debate

1Federated Learning on Transcriptomic Data: Model Quality and Performance Trade-Offs

1Chapter 5 - Artificial intelligence (AI) enhanced nanomotors and active matter

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