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Ensembles of machine learning models have been well established as a powerful method of improving performance over a single model.
Handbook of mathematical functions with formulas, graphs, and mathematical tables, 1988
Milton Abramowitz, Irene A Stegun, and Robert H Romer · 1988
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Anders Krogh and Jesper Vedelsby · 1994
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Extending and benchmarking Cascade-Correlation: extensions to the Cascade-Correlation architecture and benchmarking of feed-forward supervised artificial neural networks
Samuel George Waugh · 1995
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Bagging predictors
Leo Breiman · 1996
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Experiments with a new boosting algorithm
Yoav Freund, Robert E Schapire, et al · 1996
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Ensemble learning using decorrelated neural networks
Bruce E Rosen · 1996
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Selecting weighting factors in logarithmic opinion pools
Tom Heskes · 1997
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Ensemble learning via negative correlation
Yong Liu and Xin Yao · 1999
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Random forests
Leo Breiman · 2001
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Greedy function approximation: a gradient boosting machine
Jerome H Friedman · 2001
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Arthur Asuncion and David Newman · 2007
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Learning multiple layers of features from tiny images
Alex Krizhevsky, Geoffrey Hinton, et al · 2009
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“good” and “bad” diversity in majority vote ensembles
Gavin Brown and Ludmila I Kuncheva · 2010
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Reading digits in natural images with unsupervised feature learning
Yuval Netzer, Tao Wang, Adam Coates, Alessandro Bissacco, Bo Wu, and Andrew Y Ng · 2011
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Understanding dropout
Pierre Baldi and Peter J Sadowski · 2013
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Very deep convolutional networks for large-scale image recognition
Karen Simonyan and Andrew Zisserman · 2014
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Sequence to sequence learning with neural networks
Ilya Sutskever, Oriol Vinyals, and Quoc V Le · 2014
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Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba · 2014
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Going deeper with convolutions
Christian Szegedy, Wei Liu, Yangqing Jia, Pierre Sermanet, Scott Reed, Dragomir Anguelov, Dumitru Erhan, Vincent Vanhoucke, and Andrew Rabinovich · 2015
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A diversity-penalizing ensemble training method for deep learning
Xiaohui Zhang, Daniel Povey, and Sanjeev Khudanpur · 2015
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Why m heads are better than one: Training a diverse ensemble of deep networks
Stefan Lee, Senthil Purushwalkam, Michael Cogswell, David Crandall, and Dhruv Batra · 2015
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Imagenet large scale visual recognition challenge
Olga Russakovsky, Jia Deng, Hao Su, Jonathan Krause, Sanjeev Satheesh, Sean Ma, Zhiheng Huang, Andrej Karpathy, Aditya Khosla, Michael Bernstein, et al · 2015
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Qsar models for bioconcentration: is the increase in the complexity justified by more accurate predictions?
Francesca Grisoni, Viviana Consonni, Sara Villa, Marco Vighi, and Roberto Todeschini · 2015
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Pytorch: An imperative style, high-performance deep learning library
Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, et al · 2019
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Why are bootstrapped deep ensembles not better?
Jeremy Nixon, Balaji Lakshminarayanan, and Dustin Tran · 2020
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Generalized negative correlation learning for deep ensembling
Sebastian Buschjäger, Lukas Pfahler, and Katharina Morik · 2020
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Batchensemble: an alternative approach to efficient ensemble and lifelong learning
Yeming Wen, Dustin Tran, and Jimmy Ba · 2020
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Coupled ensembles of neural networks
Anuvabh Dutt, Denis Pellerin, and Georges Quénot · 2020
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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Squeezenet: Alexnet-level accuracy with 50x fewer parameters and¡ 0.5 mb model size
Forrest N Iandola, Song Han, Matthew W Moskewicz, Khalid Ashraf, William J Dally, and Kurt Keutzer · 2016
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Predicting social media performance metrics and evaluation of the impact on brand building: A data mining approach
Sérgio Moro, Paulo Rita, and Bernardo Vala · 2016
Cited alongside, same era.
Simple and scalable predictive uncertainty estimation using deep ensembles
Balaji Lakshminarayanan, Alexander Pritzel, and Charles Blundell · 2017
Cited alongside, same era.
Imagenet classification with deep convolutional neural networks
Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton · 2017
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Selective ensemble of svdds with renyi entropy based diversity measure
Hong-Jie Xing and Xi-Zhao Wang · 2017
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Densely connected convolutional networks
Gao Huang, Zhuang Liu, Laurens Van Der Maaten, and Kilian Q Weinberger · 2017
Cited alongside, same era.
To ensemble or not ensemble: When does end-to-end training fail?
Andrew Webb, Charles Reynolds, Wenlin Chen, Henry Reeve, Dan Iliescu, Mikel Lujan, and Gavin Brown · 2020
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Ensembles of spiking neural networks
Georgiana Neculae, Oliver Rhodes, and Gavin Brown · 2020
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Second order pac-bayesian bounds for the weighted majority vote
Andrés Masegosa, Stephan Lorenzen, Christian Igel, and Yevgeny Seldin · 2020
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Comparative assessment of various machine learning-based bias correction methods for numerical weather prediction model forecasts of extreme air temperatures in urban areas
Dongjin Cho, Cheolhee Yoo, Jungho Im, and Dong-Hyun Cha · 2020
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Revisiting deep learning models for tabular data
Yury Gorishniy, Ivan Rubachev, Valentin Khrulkov, and Artem Babenko · 2021
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Repulsive deep ensembles are bayesian
Francesco D’Angelo and Vincent Fortuin · 2021
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Mobilevit: light-weight, general-purpose, and mobile-friendly vision transformer
Sachin Mehta and Mohammad Rastegari · 2021
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Model soups: averaging weights of multiple fine-tuned models improves accuracy without increasing inference time
Mitchell Wortsman, Gabriel Ilharco, Samir Ya Gadre, Rebecca Roelofs, Raphael Gontijo-Lopes, Ari S Morcos, Hongseok Namkoong, Ali Farhadi, Yair Carmon, Simon Kornblith, et al · 2022
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Improving robustness and calibration in ensembles with diversity regularization
Hendrik Alexander Mehrtens, Camila González, and Anirban Mukhopadhyay · 2022
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Why do tree-based models still outperform deep learning on tabular data?
Léo Grinsztajn, Edouard Oyallon, and Gaël Varoquaux · 2022
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Diversity and generalization in neural network ensembles
Luis A Ortega, Rafael Cabañas, and Andres Masegosa · 2022
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Tangos: Regularizing tabular neural networks through gradient orthogonalization and specialization
Alan Jeffares, Tennison Liu, Jonathan Crabbé, Fergus Imrie, and Mihaela van der Schaar · 2023
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Pathologies of predictive diversity in deep ensembles
Taiga Abe, E Kelly Buchanan, Geoff Pleiss, and John P Cunningham · 2023
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