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1Time-aware Graph Neural Networks for Entity Alignment between Temporal Knowledge Graphs
C. Xu, F. Su, and J. Lehmann. (2022)
29 days ago by @scadsfct
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Alignment
Entity
Graph
Graphs
Knowledge
Networks
Neural
Temporal
Time-aware
Yaff
AlignmentEntityGraphGraphsKnowledgeNetworksNeuralTemporalTime-awareYaff
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2Learning algorithms for spiking neural networks: should one use learning algorithms from ANN/DL or neurological plausible learning? - A thought-provoking impulse
M. Bogdan. XLIII Jornadas de Automática: libro de actas: 7, 8 y 9 de septiembre de 2022, Logroño (La Rioja), Servizo de Publicacións da UDC, (September 2022)
29 days ago by @scadsfct
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Learning
algorithms
learning
networks
neural
neurological
plausible
spiking
Learningalgorithmslearningnetworksneuralneurologicalplausiblespiking
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2Predicting years with extremely low gross primary production from daily weather data using Convolutional Neural Networks
A. Marcolongo, M. Vladymyrov, S. Lienert, N. Peleg, S. Haug, and J. Zscheischler. Environmental Data Science, (2022)
2 months ago by @scadsfct
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Convolutional
Networks
Neural
Predicting
data
gross
low
primary
production
weather
ConvolutionalNetworksNeuralPredictingdatagrosslowprimaryproductionweather
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1Inadequacy of common stochastic neural networks for reliable clinical decision support
A. Lindenmeyer, M. Blattmann, S. Franke, T. Neumuth, and D. Schneider. (2024)
2 months ago by @scadsfct
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Inadequacy
clinical
decision
networks
neural
reliable
stochastic
support
Inadequacyclinicaldecisionnetworksneuralreliablestochasticsupport
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1OmniOpt – A tool for hyperparameter optimization on HPC
P. Winkler, N. Koch, A. Hornig, and J. Gerritzen. High Performance Computing - ISC High Performance Digital 2021 International Workshops, 2021, Revised Selected Papers, page 285--296. Germany, Springer, Berlin u. a., (Nov 13, 2021)Publisher Copyright: © 2021, Springer Nature Switzerland AG.; International Conference on High Performance Computing, ISC High Performance 2021 ; Conference date: 24-06-2021 Through 02-07-2021.
2 months ago by @scadsfct
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FIS_scads
High
Hyperparameter
Neural
computing,
networks
optimization,
performance
FIS_scadsHighHyperparameterNeuralcomputing,networksoptimization,performance
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1Robust Reconstruction of the Void Fraction from Noisy Magnetic Flux Density Using Invertible Neural Networks
N. Kumar, L. Krause, T. Wondrak, S. Eckert, K. Eckert, and S. Gumhold. Sensors, (2024)
3 months ago by @scadsfct
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topic_visualcomputing
Density
Flux
Fraction
Invertible
Magnetic
Networks
Neural
Noisy
Reconstruction
Void
topic_visualcomputingDensityFluxFractionInvertibleMagneticNetworksNeuralNoisyReconstructionVoid
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1TraceVis: Towards Visualization for Deep Statistical Model Checking
T. Gros, D. Groß, S. Gumhold, J. Hoffmann, M. Klauck, and M. Steinmetz. Leveraging Applications of Formal Methods, Verification and Validation: Tools and Trends: 9th International Symposium on Leveraging Applications of Formal Methods, ISoLA 2020, Rhodes, Greece, October 20–30, 2020, Proceedings, Part IV, page 27–46. Berlin, Heidelberg, Springer-Verlag, (2020)
3 months ago by @scadsfct
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Checking
Model
Networks
Neural
Statistical
Visualization
CheckingModelNetworksNeuralStatisticalVisualization
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1Time-aware Graph Neural Networks for Entity Alignment between Temporal Knowledge Graphs

2Learning algorithms for spiking neural networks: should one use learning algorithms from ANN/DL or neurological plausible learning? - A thought-provoking impulse

2Predicting years with extremely low gross primary production from daily weather data using Convolutional Neural Networks

1Inadequacy of common stochastic neural networks for reliable clinical decision support

1OmniOpt – A tool for hyperparameter optimization on HPC

1Robust Reconstruction of the Void Fraction from Noisy Magnetic Flux Density Using Invertible Neural Networks

1TraceVis: Towards Visualization for Deep Statistical Model Checking

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