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2Content-Aware Depth-Adaptive Image Restoration
T. Vargis, und S. Ghiasvand. Proceedings of the 29th International Conference on Automation and Computing, Sunderland, UK, (Januar 2024)arXiv:2401.05049 cs.
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Learning,
myOwn
from:ghiasvan
and
Computer
Vision
Pattern
Machine
Recognition,
Science
Learning,myOwnfrom:ghiasvanandComputerVisionPatternMachineRecognition,Science
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2Content-Aware Depth-Adaptive Image Restoration
T. Vargis, und S. Ghiasvand. Proceedings of the 29th International Conference on Automation and Computing, Sunderland, UK, (Januar 2024)arXiv:2401.05049 cs.
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Computer
Learning,
Machine
Pattern
Recognition,
Science
Vision
and
myOwn
ComputerLearning,MachinePatternRecognition,ScienceVisionandmyOwn
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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, und M. Gude. Computational Materials Science, (September 2024)Publisher Copyright: © 2024 The Author(s).
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area_architectures
topic_engineering
Constitutive
FIS_scads
Fiber
Machine
Neural
Parameter
identification
learning,
modeling,
networks,
plastics,
reinforced
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1Direct parameter identification for highly nonlinear strain rate dependent constitutive models using machine learning
J. Gerritzen, A. Hornig, P. Winkler, und M. Gude. ECCM21 - Proceedings of the 21st European Conference on Composite Materials, 3, Seite 1252--1259. European Society for Composite Materials (ESCM), (02.07.2024)21st European Conference on Composite Materials, ECCM 21 ; Conference date: 02-07-2024 Through 05-07-2024.
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area_architectures
topic_engineering
Convolutional
Direct
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Fiber
Machine
Strain
dependency,
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learning,
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parameter
plastics
rate
reinforced
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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, und M. Wolfien. Scientific reports, (27.01.2024)
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Databases,
Electronic
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Factual,
Health
Humans,
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Learning,
Machine
Medical
Records
topic_lifescience
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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 und 2 andere Autor(en). Studies in health technology and informatics, (30.08.2024)Publisher Copyright: © 2024 The Authors..
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topic_lifescience
Diseases/therapy,
Electronic
Eye
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Germany,
Health
Humans,
Information
Interoperability/standards,
Learning,
Level
Machine
Ophthalmology
Records/standards,
Seven/standards,
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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, und M. Wolfien. Frontiers in neuroscience, (24.03.2023)Publisher Copyright: Copyright © 2023 Bühler, Power Guerra, Müller, Wolkenhauer, Düffer, Vollmar, Kuhla and Wolfien..
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topic_lifescience
Behavioral
Deep
DeepLabCut,
EthoVision,
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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 und 6 andere Autor(en). Genome medicine, (10.08.2023)Publisher Copyright: © 2023, BioMed Central Ltd., part of Springer Nature..
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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,
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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, und P. Poncet. Journal of computational physics, (15.10.2023)Publisher Copyright: © 2023 The Author(s).
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topic_lifescience
Adaptive
Artificial
Bayesian
Carlo,
FIS_scads
Hamiltonian
Intelligence,
Monte
Multi-objective
Quantification
Uncertainty
learning,
networks,
neural
physics-informed
training,
weight
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1Stability selection enables robust learning of differential equations from limited noisy data
S. Maddu, {. Cheeseman, {. Sbalzarini, und {. Müller. Proceedings of the Royal Society of London : Series A, Mathematical, physical and engineering sciences, (Juni 2022)Publisher Copyright: © 2022 Royal Society Publishing. All rights reserved..
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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
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1Two-step dynamic obstacle avoidance
F. Hart, M. Waltz, und O. Okhrin. Knowledge-based systems, (25.10.2024)Publisher Copyright: © 2024 The Author(s).
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topic_engineering
Deep
Dynamic
FIS_scads
Local
Supervised
avoidance,
learning
learning,
obstacle
path
planning,
reinforcement
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1Spatial–temporal recurrent reinforcement learning for autonomous ships
M. Waltz, und O. Okhrin. Neural Networks, 2023 (165): 634--653 (15.06.2023)Publisher Copyright: © 2023 Elsevier Ltd.
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topic_engineering
Algorithms,
Autonomous
COLREG,
Computer,
Deep
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Networks,
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Recurrency,
Reinforcement,
Reward
Ships,
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1Data-driven determination of plant growth stages for improved weather index insurance design
J. Zou, M. Odening, und O. Okhrin. Agricultural Finance Review, (2024)Publisher Copyright: © 2024, Jing Zou, Martin Odening and Ostap Okhrin..
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FIS_scads
Generalized
Machine
Plant
Temporal
Weather
additive
basis
growth
index
insurance
learning,
model,
risk,
stages,
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1Addressing maximization bias in reinforcement learning with two-sample testing
M. Waltz, und O. Okhrin. Artificial intelligence, (November 2024)Publisher Copyright: © 2024 The Author(s).
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topic_engineering
Estimation
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Maximum
Q-learning,
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value,
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1Robust path following on rivers using bootstrapped reinforcement learning
N. Paulig, und O. Okhrin. Ocean engineering, (15.04.2024)Publisher Copyright: © 2024 The Authors.
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topic_engineering
Autonomous
Deep
FIS_scads
Path
Restricted
following,
learning,
reinforcement
surface
vessel,
waterways
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1Identifying Secondary School Students' Misconceptions about Machine Learning: An Interview Study
E. Marx, C. Witt, und T. Leonhardt. WiPSCE '24: Proceedings of the 19th WiPSCE Conference on Primary and Secondary Computing Education Research, Seite 1--10. Association for Computing Machinery, (16.09.2024)Publisher Copyright: © 2024 Copyright held by the owner/author(s)..
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FIS_scads
artificial
conceptions
intelligence,
interview
learning,
machine
mental
misconceptions,
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qualitative
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students
study,
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1Brief Summary of Existing Research on Students’ Conceptions of AI
E. Marx, T. Leonhardt, und N. Bergner. (Oktober 2022)Publisher Copyright: © 2022 Owner/Author.; WiPSCE 2022 ; Conference date: 31-10-2022 Through 02-11-2022.
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FIS_scads
artificial
beliefs,
conceptions,
education,
ideas,
intelligence,
k-12
learning,
machine
mental
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preconceptions
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1Few-Shot Learning for Argument Aspects of the Nuclear Energy Debate
L. Jurkschat, G. Wiedemann, M. Heinrich, M. Ruckdeschel, und S. Torge. 2022 Language Resources and Evaluation Conference, LREC 2022, Seite 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.
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argument
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discourse,
energy
few-shot
frames,
learning,
mining,
nuclear
text
FIS_scadsargumentaspect-basedaspects,classificationdiscourse,energyfew-shotframes,learning,mining,nucleartext
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1Chapter 5 - Artificial intelligence (AI) enhanced nanomotors and active matter
F. Cichos, S. Landin, und R. Pradip. Intelligent Nanotechnology, Elsevier, (2023)
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topic_physchemistry
Active
Feedback
Machine
Multi
Optical
Reinforcement
agent
control
control,
learning,
particles,
reinforcement
topic_physchemistryActiveFeedbackMachineMultiOpticalReinforcementagentcontrolcontrol,learning,particles,reinforcement
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1NDP-RANK: Prediction and ranking of NDP systems performance using machine learning
V. Iskandar, M. Abd El Ghany, und D. Goehringer. Microprocessors and Microsystems, (2023)
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topic_federatedlearn
Design
Machine
Modeling,
Near-data
Prediction,
exploration
learning,
processing,
space
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2Content-Aware Depth-Adaptive Image Restoration

2Content-Aware Depth-Adaptive Image Restoration

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

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

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

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

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

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

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

1NDP-RANK: Prediction and ranking of NDP systems performance using machine learning

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