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Classic results establish that encouraging predictive diversity improves performance in ensembles of low-capacity models, e.g.
Neural network ensembles
Lars Kai Hansen and Peter Salamon · 1990
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When networks disagree: Ensemble methods for hybrid neural networks, 1992
Michael P Perrone and Leon N Cooper · 1992
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Boosting a weak learning algorithm by majority
Yoav Freund · 1995
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Bagging predictors
Leo Breiman · 1996
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Covertype
Jock Blackard · 1998
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An experimental comparison of three methods for constructing ensembles of decision trees: Bagging, boosting and randomization
Thomas G Dietterich · 1998
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Gradient-based learning applied to document recognition
Yann LeCun, Léon Bottou, Yoshua Bengio, and Patrick Haffner · 1998
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Ensemble methods in machine learning
Thomas G Dietterich · 2000
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Algorithms for non-negative matrix factorization
Daniel Lee and H Sebastian Seung · 2000
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Random forests
Leo Breiman · 2001
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Pert-perfect random tree ensembles
Adele Cutler and Guohua Zhao · 2001
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Measures of diversity in classifier ensembles and their relationship with the ensemble accuracy
Ludmila I Kuncheva and Christopher J Whitaker · 2003
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ImageNet: A large-scale hierarchical image database
Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei · 2009
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Learning multiple layers of features from tiny images, 2009
Alex Krizhevsky, Geoffrey Hinton, et al · 2009
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Scikit-learn: Machine learning in Python
F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, P. Prettenhofer, R. Weiss, V. Dubourg, J. Vanderplas, A. Passos, D. Cournapeau, M. Brucher, M. Perrot, and E. Duchesnay · 2011
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Jensen-shannon divergence in ensembles of concurrently-trained neural networks
Aaron Mishtal and Itamar Arel · 2012
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Ensemble methods: foundations and algorithms
Zhi-Hua Zhou · 2012
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Very deep convolutional networks for large-scale image recognition
Karen Simonyan and Andrew Zisserman · 2014
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Tiny imagenet visual recognition challenge
Ya Le and Xuan Yang · 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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Obtaining well calibrated probabilities using Bayesian binning
Mahdi Pakdaman Naeini, Gregory Cooper, and Milos Hauskrecht · 2015
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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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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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Rethinking the inception architecture for computer vision
Christian Szegedy, Vincent Vanhoucke, Sergey Ioffe, Jon Shlens, and Zbigniew Wojna · 2016
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Sergey Zagoruyko and Nikos Komodakis · 2016
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Xavier Gastaldi · 2017
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On calibration of modern neural networks
Chuan Guo, Geoff Pleiss, Yu Sun, and Kilian Q Weinberger · 2017
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Deep pyramidal residual networks
Dongyoon Han, Jiwhan Kim, and Junmo Kim · 2017
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Densely connected convolutional networks
Gao Huang, Zhuang Liu, Laurens Van Der Maaten, and Kilian Q Weinberger · 2017
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Simple and scalable predictive uncertainty estimation using deep ensembles
Balaji Lakshminarayanan, Alexander Pritzel, and Charles Blundell · 2017
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Efficient model averaging for deep neural networks
Michael Opitz, Horst Possegger, and Horst Bischof · 2017
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Aggregated residual transformations for deep neural networks
Saining Xie, Ross Girshick, Piotr Dollár, Zhuowen Tu, and Kaiming He · 2017
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mixup: Beyond empirical risk minimization
Hongyi Zhang, Moustapha Cisse, Yann N Dauphin, and David Lopez-Paz · 2017
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CINIC-10 is not ImageNet or CIFAR-10
Luke N Darlow, Elliot J Crowley, Antreas Antoniou, and Amos J Storkey · 2018
Cited alongside, same era.
Squeeze-and-excitation networks
Jie Hu, Li Shen, and Gang Sun · 2018
Cited alongside, same era.
Knowledge distillation by on-the-fly native ensemble
Xu Lan, Xiatian Zhu, and Shaogang Gong · 2018
Cited alongside, same era.
A modern take on the bias-variance tradeoff in neural networks
Brady Neal, Sarthak Mittal, Aristide Baratin, Vinayak Tantia, Matthew Scicluna, Simon Lacoste-Julien, and Ioannis Mitliagkas · 2018
Cited alongside, same era.
BatchEnsemble: an alternative approach to efficient ensemble and lifelong learning
Yeming Wen, Dustin Tran, and Jimmy Ba · 2020
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Hyperparameter ensembles for robustness and uncertainty quantification
Florian Wenzel, Jasper Snoek, Dustin Tran, and Rodolphe Jenatton · 2020
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A review of uncertainty quantification in deep learning: Techniques, applications and challenges
Moloud Abdar, Farhad Pourpanah, Sadiq Hussain, Dana Rezazadegan, Li Liu, Mohammad Ghavamzadeh, Paul Fieguth, Xiaochun Cao, Abbas Khosravi, U Rajendra Acharya, et al · 2021
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Kernel interpolation as a bayes point machine
Jeremy Bernstein, Alex Farhang, and Yisong Yue · 2021
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Repulsive deep ensembles are bayesian
Francesco D’Angelo and Vincent Fortuin · 2021
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On robustness and transferability of convolutional neural networks
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Do cifar-10 classifiers generalize to cifar-10?
Benjamin Recht, Rebecca Roelofs, Ludwig Schmidt, and Vaishaal Shankar · 2018
Cited alongside, same era.
Reconciling modern machine-learning practice and the classical bias–variance trade-off
Mikhail Belkin, Daniel Hsu, Siyuan Ma, and Soumik Mandal · 2019
Cited alongside, same era.
Deep ensembles: A loss landscape perspective
Stanislav Fort, Huiyi Hu, and Balaji Lakshminarayanan · 2019
Cited alongside, same era.
Benchmarking neural network robustness to common corruptions and surface variations
Dan Hendrycks and Thomas G Dietterich · 2019
Cited alongside, same era.
Improving adversarial robustness of ensembles with diversity training
Sanjay Kariyappa and Moinuddin K Qureshi · 2019
Cited alongside, same era.
When does label smoothing help?
Rafael Müller, Simon Kornblith, and Geoffrey E Hinton · 2019
Cited alongside, same era.
Can you trust your model’s uncertainty? evaluating predictive uncertainty under dataset shift
Yaniv Ovadia, Emily Fertig, Jie Ren, Zachary Nado, David Sculley, Sebastian Nowozin, Joshua V Dillon, Balaji Lakshminarayanan, and Jasper Snoek · 2019
Cited alongside, same era.
Josip Djolonga, Jessica Yung, Michael Tschannen, Rob Romijnders, Lucas Beyer, Alexander Kolesnikov, Joan Puigcerver, Matthias Minderer, Alexander D’Amour, Dan Moldovan, et al · 2021
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Training independent subnetworks for robust prediction
Marton Havasi, Rodolphe Jenatton, Stanislav Fort, Jeremiah Zhe Liu, Jasper Snoek, Balaji Lakshminarayanan, Andrew Mingbo Dai, and Dustin Tran · 2021
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On the reversed bias-variance tradeoff in deep ensembles
Seijin Kobayashi, Johannes von Oswald, and Benjamin F Grewe · 2021
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Accuracy on the line: on the strong correlation between out-of-distribution and in-distribution generalization
John P Miller, Rohan Taori, Aditi Raghunathan, Shiori Sagawa, Pang Wei Koh, Vaishaal Shankar, Percy Liang, Yair Carmon, and Ludwig Schmidt · 2021
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Deep double descent: Where bigger models and more data hurt
Preetum Nakkiran, Gal Kaplun, Yamini Bansal, Tristan Yang, Boaz Barak, and Ilya Sutskever · 2021
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Dice: Diversity in deep ensembles via conditional redundancy adversarial estimation
Alexandre Rame and Matthieu Cord · 2021
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Dibs: Diversity inducing information bottleneck in model ensembles
Samarth Sinha, Homanga Bharadhwaj, Anirudh Goyal, Hugo Larochelle, Animesh Garg, and Florian Shkurti · 2021
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Boosting ensemble accuracy by revisiting ensemble diversity metrics
Yanzhao Wu, Ling Liu, Zhongwei Xie, Ka-Ho Chow, and Wenqi Wei · 2021
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Transferability metrics for selecting source model ensembles
Andrea Agostinelli, Jasper Uijlings, Thomas Mensink, and Vittorio Ferrari · 2022
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Reliability benchmarks for image segmentation
E Kelly Buchanan, Michael W Dusenberry, Jie Ren, Kevin Patrick Murphy, Balaji Lakshminarayanan, and Dustin Tran · 2022
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How to fill the optimum set? population gradient descent with harmless diversity
Chengyue Gong, Lemeng Wu, and Qiang Liu · 2022
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No one representation to rule them all: Overlapping features of training methods
Raphael Gontijo-Lopes, Yann Dauphin, and Ekin Dogus Cubuk · 2022
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On embeddings for numerical features in tabular deep learning
Yury Gorishniy, Ivan Rubachev, and Artem Babenko · 2022
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Ensembles of classifiers: a bias-variance perspective
Neha Gupta, Jamie Smith, Ben Adlam, and Zelda E Mariet · 2022
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Diversify and disambiguate: Learning from underspecified data
Yoonho Lee, Huaxiu Yao, and Chelsea Finn · 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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Agree to disagree: Diversity through disagreement for better transferability
Matteo Pagliardini, Martin Jaggi, François Fleuret, and Sai Praneeth Karimireddy · 2022
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Regression as classification: Influence of task formulation on neural network features
Lawrence Stewart, Francis Bach, Quentin Berthet, and Jean-Philippe Vert · 2022
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Evading the simplicity bias: Training a diverse set of models discovers solutions with superior ood generalization
Damien Teney, Ehsan Abbasnejad, Simon Lucey, and Anton Van den Hengel · 2022
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Semi-supervised novelty detection using ensembles with regularized disagreement
Alexandru Tifrea, Eric Stavarache, and Fanny Yang · 2022
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Plex: Towards reliability using pretrained large model extensions
Dustin Tran, Jeremiah Liu, Du Phan Michael W Dusenberry, Mark Collier, Jie Ren, Kehang Han, Zi Wang, Zelda Mariet, Huiyi Hu, Neil Band, Tim GJ Rudner, Karan Singhal, Zachary Nado, Joost van Amersfoort, Andreas Kirsch, Rodolphe Jenatton, Nithum Thain, Honglin Yuan, Kelly Buchanan, Kevin Murphy, D Sculley, Yarin Gal, Zoubin Ghahramani, Jasper Snoek, and Balaji Lakshminarayanan · 2022
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Film-ensemble: Probabilistic deep learning via feature-wise linear modulation
Mehmet Ozgur Turkoglu, Alexander Becker, Hüseyin Anil Gündüz, Mina Rezaei, Bernd Bischl, Rodrigo Caye Daudt, Stefano D’Aronco, Jan Wegner, and Konrad Schindler · 2022
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Resnest: Split-attention networks
Hang Zhang, Chongruo Wu, Zhongyue Zhang, Yi Zhu, Haibin Lin, Zhi Zhang, Yue Sun, Tong He, Jonas Mueller, R Manmatha, et al · 2022
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Towards better accuracy-efficiency trade-offs: Divide and co-training
Shuai Zhao, Liguang Zhou, Wenxiao Wang, Deng Cai, Tin Lun Lam, and Yangsheng Xu · 2022
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Joint training of deep ensembles fails due to learner collusion
Alan Jeffares, Tennison Liu, Jonathan Crabbé, and Mihaela van der Schaar · 2023
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When are ensembles really effective?
Ryan Theisen, Hyunsuk Kim, Yaoqing Yang, Liam Hodgkinson, and Michael W Mahoney · 2023
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A unified theory of diversity in ensemble learning
Danny Wood, Tingting Mu, Andrew Webb, Henry Reeve, Mikel Lujan, and Gavin Brown · 2023
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