Neural Network Ensembles

By Lars Kai Hansen, P. Salamon. 1990

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Citations in the corpus

Distributed deep learning networks among institutions for medical imaging (2018, background): “Ensembling of neural networks trained to perform the same task is a common approach to significantly improve the generalization performance.(22) In comparison, the concept of single weight transfer is very similar to that of transfer learning, which is derived from that idea that a model can solve n”
SPOT-Disorder2: Improved Protein Intrinsic Disorder Prediction by Ensembled Deep Learning (2019, methodology): “The use of an ensemble predictor minimizes the effect of generalization errors between models [34].”
BatchEnsemble: An Alternative Approach to Efficient Ensemble and Lifelong Learning (2020, background): “Moreover, ensembles achieve the best performance when each of their members makes independent errors (Goodfellow et al., 2015; Hansen & Salamon, 1990).”
An Investigation of How Neural Networks Learn from the Experiences of Peers Through Periodic Weight Averaging (2017, background): “Ensembles of neural networks started with Hansen and Salamon’s work [11].”
Combining the Predictions of Multiple Classifiers: Using Competitive Learning to Initialize Neural Networks (1995, result): “[Hansen and Salamon, 1990] L. Hansen and P. Salamon.”
A Survey on ensemble learning under the era of deep learning (2021, methodology): “The research history of ensembles of neural networks can be traced back to the early research stage of ensemble learning in 1990 (Hansen and Salamon 1990).”
Peer Collaborative Learning for Online Knowledge Distillation (2020, background): “isation capability of each peer and the ensemble, resulting in better performance. Neural Network Ensembling is a simple and effective solution for improving the generalisation performance of a model [4, 29, 15]. Although this can usually bring better performance, training multiple neural networks t”
Pitfalls of In-Domain Uncertainty Estimation and Ensembling in Deep Learning (2020, background): “Ensembles of deep neural networks have become a de-facto standard for uncertainty estimation and improving the quality of deep learning models (Hansen & Salamon, 1990; Krizhevsky et al., 2009; Lakshminarayanan et al., 2017).”
Ensemble deep learning in bioinformatics (2020, background): “The root of ensemble deep learning can be traced back two decades, when ensembles of neural networks were found to reduce generalization error 4 .”
Towards Holistic Scene Understanding: Feedback Enabled Cascaded Classification Models (2010, background): “The idea of cascading layers of classifiers to aid the final task was first introduced with neural networks as multi-level perceptrons where, the output of the first layer of perceptrons is passed on as input to the next hidden layer [16, 12, 6].”