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Ensemble averaging (machine learning)

Known as: Ensemble, Ensemble average (machine learning), Ensemble averaging 
In machine learning, particularly in the creation of artificial neural networks, ensemble averaging is the process of creating multiple models and… 
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Related topics

Related topics

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Broader (1)

Artificial intelligence
Committee machineEnsemble learningGlossary of artificial intelligenceMachine learning

Papers overview

Semantic Scholar uses AI to extract papers important to this topic.
2017
2017
Cell Detection with Deep Convolutional Neural Network and Compressed Sensing
The ability to automatically detect certain types of cells or cellular subunits in microscopy images is of significant interest… 
Highly Cited
2010
Highly Cited
2010
The crossover from strong to weak chaos for nonlinear waves in disordered systems
We observe a crossover from strong to weak chaos in the spatiotemporal evolution of multiple-site excitations within disordered… 
2010
2010
Application of particle image velocimetry measurement techniques to study turbulence characteristics of oscillatory flows around parallel-plate structures in thermoacoustic devices
This paper describes the development of the experimental setup and measurement methodologies to study the physics of oscillatory… 
2002
2002
Macrodispersivity for transport in arbitrary nonuniform flow fields: Asymptotic and preasymptotic results
We use homogenization theory to investigate the asymptotic macrodispersion in arbitrary nonuniform velocity fields, which show… 
2002
2002
DPIV study of near-stall wake-rotor interactions in a transonic compressor
Abstract. Wake-rotor interactions at near-stall conditions are studied using digital particle image velocimetry (DPIV) in a… 
2000
2000
A Dynamic Light Scattering Study of Hydrogels Based on Telechelic Poly(vinyl alcohol)
Chemical hydrogels based on poly(vinyl alcohol), PVA, were obtained by reacting telechelic PVA bearing aldehydic groups at both… 
2000
2000
Predicting chaotic time series by ensemble self-generating neural networks
We introduce ensemble self-generating neural networks (ESGNNs) for chaotic time series prediction. ESGNNs combine the ensemble… 
Highly Cited
1992
Highly Cited
1992
I2− photofragmentation/recombination dynamics in size‐selected I2−(CO2)n cluster ions: Observation of coherent I...I− vibrational motion
We have employed picosecond pump–probe techniques in conjunction with a tandem time‐of‐flight mass spectrometer to investigate… 
1990
1990
Interpretation of cyclic flow variations in motored internal combustion engines
Cyclic variations and turbulence characterisation are considered in the context of in-cylinder flows. Three methods of data… 
Review
1981
Review
1981
A posteriori time-varying filtering of averaged evoked potentials
This paper forms a preface and introduction to a new method for the estimation of evoked potentials: a posteriori time-varying… 
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