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Ensembles of neural networks achieve superior performance compared to stand-alone networks in terms of accuracy, uncertainty calibration and robustness to dataset shift.
Neural network ensembles
Lars K. Hansen and Peter Salamon · 1990
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Neural Network Ensembles, Cross Validation, and Active Learning
Anders Krogh and Jesper Vedelsby · 1995
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Ensemble learning via negative correlation
Y. Liu and X. Yao · 1999
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Ensemble Methods in Machine Learning
Thomas G. Dietterich · 2000
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Random forests
Leo Breiman · 2001
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Ensembling neural networks: Many could be better than all
Zhi-Hua Zhou, Jianxin Wu, and Wei Tang · 2002
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Ensemble Selection from Libraries of Models
Rich Caruana, Alexandru Niculescu-Mizil, Geoff Crew, and Alex Ksikes · 2004
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Visualizing data using t-SNE
Laurens Van der Maaten and Geoffrey Hinton · 2008
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Cifar-10 (canadian institute for advanced research), 2009
Alex Krizhevsky, Vinod Nair, and Geoffrey Hinton · 2009
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Bayesian learning via stochastic gradient langevin dynamics
Max Welling and Yee Whye Teh · 2011
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Ensemble Methods: Foundations and Algorithms
Zhi-Hua Zhou · 2012
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Weight uncertainty in neural network
Charles Blundell, Julien Cornebise, Koray Kavukcuoglu, and Daan Wierstra · 2015
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Efficient and robust automated machine learning
Matthias Feurer, Aaron Klein, Katharina Eggensperger, Jost Springenberg, Manuel Blum, and Frank Hutter · 2015
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Tiny imagenet visual recognition challenge, 2015
Y. Le and X. 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 F. Cooper, and Milos Hauskrecht · 2015
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Very Deep Convolutional Networks for Large-Scale Image Recognition
Karen Simonyan and Andrew Zisserman · 2015
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End to End Learning for Self-Driving Cars
Mariusz Bojarski, Davide Del Testa, Daniel Dworakowski, Bernhard Firner, Beat Flepp, Prasoon Goyal, Lawrence D. Jackel, Mathew Monfort, Urs Muller, Jiakai Zhang, Xin Zhang, Jake Zhao, and Karol Zieba · 2016
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Dropout as a bayesian approximation: Representing model uncertainty in deep learning
Yarin Gal and Zoubin Ghahramani · 2016
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Deep Learning
Ian Goodfellow, Yoshua Bengio, and Aaron Courville · 2016
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Deep Residual Learning for Image Recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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Bayesian hyperparameter optimization for ensemble learning
Julien-Charles Lévesque, Christian Gagné, and Robert Sabourin · 2016
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Towards Automatically-Tuned Neural Networks
Hector Mendoza, Aaron Klein, Matthias Feurer, Jost Tobias Springenberg, and Frank Hutter · 2016
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Tpot: A tree-based pipeline optimization tool for automating machine learning
Randal S. Olson and Jason H. Moore · 2016
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A downsampled variant of imagenet as an alternative to the cifar datasets
Patryk Chrabaszcz, Ilya Loshchilov, and Frank Hutter · 2017
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AdaNet: Adaptive Structural Learning of Artificial Neural Networks
Corinna Cortes, Xavier Gonzalvo, Vitaly Kuznetsov, Mehryar Mohri, and Scott Yang · 2017
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Dermatologist-level classification of skin cancer with deep neural networks
Andre Esteva, Brett Kuprel, Roberto A. Novoa, Justin Ko, Susan M. Swetter, Helen M. Blau, and Sebastian Thrun · 2017
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Random Search and Reproducibility for Neural Architecture Search
Liam Li and Ameet Talwalkar · 2019
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Best practices for scientific research on neural architecture search
Marius Lindauer and Frank Hutter · 2019
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DARTS: Differentiable Architecture Search
Hanxiao Liu, Karen Simonyan, and Yiming Yang · 2019
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Improving Neural Architecture Search Image Classifiers via Ensemble Learning
Vladimir Macko, Charles Weill, Hanna Mazzawi, and Javier Gonzalvo · 2019
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Can you trust your model's uncertainty? Evaluating predictive uncertainty under dataset shift
Yaniv Ovadia, Emily Fertig, Jie Ren, Zachary Nado, D. Sculley, Sebastian Nowozin, Joshua Dillon, Balaji Lakshminarayanan, and Jasper Snoek · 2019
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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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Snapshot Ensembles: Train 1, get m for free
Gao Huang, Yixuan Li, Geoff Pleiss, Zhuang Liu, John Hopcroft, and Kilian Weinberger · 2017
Cited alongside, same era.
Generalized Ambiguity Decompositions for Classification with Applications in Active Learning and Unsupervised Ensemble Pruning
Zhengshen Jiang, Hongzhi Liu, Bin Fu, and Zhonghai Wu · 2017
Cited alongside, same era.
Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles
Balaji Lakshminarayanan, Alexander Pritzel, and Charles Blundell · 2017
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SGDR: Stochastic Gradient Descent with Warm Restarts
Ilya Loshchilov and Frank Hutter · 2017
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Fashion-MNIST: A Novel Image Dataset for Benchmarking Machine Learning Algorithms
Han Xiao, Kashif Rasul, and Roland Vollgraf · 2017
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Neural Architecture Search with Reinforcement Learning
Barret Zoph and Quoc V Le · 2017
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Regularized Evolution for Image Classifier Architecture Search
Esteban Real, Alok Aggarwal, Yanping Huang, and Quoc V. Le · 2019
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Joint Training of Neural Network Ensembles
Andrew M. Webb, Charles Reynolds, Dan-Andrei Iliescu, Henry W. J. Reeve, Mikel Luján, and Gavin Brown · 2019
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Pitfalls of in-domain uncertainty estimation and ensembling in deep learning
Arsenii Ashukha, Alexander Lyzhov, Dmitry Molchanov, and Dmitry Vetrov · 2020
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Nas-bench-201: Extending the scope of reproducible neural architecture search
Xuanyi Dong and Yi Yang · 2020
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Immunecs: Neural committee search by an artificial immune system, 2020
Luc Frachon, Wei Pang, and George M. Coghill · 2020
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Evaluating Scalable Bayesian Deep Learning Methods for Robust Computer Vision
Fredrik K Gustafsson, Martin Danelljan, and Thomas B Schön · 2020
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Bayesian deep ensembles via the neural tangent kernel
Bobby He, Balaji Lakshminarayanan, and Yee Whye Teh · 2020
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Maximizing overall diversity for improved uncertainty estimates in deep ensembles
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Uncertainty in neural networks: Approximately bayesian ensembling
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Hyperparameter ensembles for robustness and uncertainty quantification
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Bayesian deep learning and a probabilistic perspective of generalization
Andrew G Wilson and Pavel Izmailov · 2020
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PC-DARTS: Partial Channel Connections for Memory-Efficient Architecture Search
Yuhui Xu, Lingxi Xie, Xiaopeng Zhang, Xin Chen, Guo-Jun Qi, Qi Tian, and Hongkai Xiong · 2020
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NAS evaluation is frustratingly hard
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Evaluating the Search Phase of Neural Architecture Search
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Nas-bench-1shot1: Benchmarking and dissecting one-shot neural architecture search
Arber Zela, Julien Siems, and Frank Hutter · 2020
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Dr{nas}: Dirichlet neural architecture search
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