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Machine learning models based on the aggregated outputs of submodels, either at the activation or prediction levels, often exhibit strong performance compared to individual models.
Neural network ensembles
Lars Kai Hansen and Peter Salamon · 1990
Earlier work this paper cites.
Adaptive mixtures of local experts
R. A. Jacobs, M. I. Jordan, S. J. Nowlan, and G. E. Hinton · 1991
Earlier work this paper cites.
Neural networks and the bias/variance dilemma
Stuart Geman, Elie Bienenstock, and René Doursat · 1992
Earlier work this paper cites.
A practical bayesian framework for backpropagation networks
David JC MacKay · 1992
Earlier work this paper cites.
Keeping the neural networks simple by minimizing the description length of the weights
Geoffrey E Hinton and Drew Van Camp · 1993
Earlier work this paper cites.
Hierarchical mixtures of experts and the em algorithm
Michael I. Jordan and Robert A. Jacobs · 1994
Earlier work this paper cites.
Neural network ensembles, cross validation, and active learning
Anders Krogh and Jesper Vedelsby · 1995
Earlier work this paper cites.
Improved learning algorithms for mixture of experts in multiclass classification
Ke Chen, Lei Xu, and Huisheng Chi · 1999
Earlier work this paper cites.
Popular ensemble methods: An empirical study
David Opitz and Richard Maclin · 1999
Earlier work this paper cites.
Ensemble methods in machine learning
Thomas G Dietterich · 2000
Earlier work this paper cites.
Hydra: Preserving ensemble diversity for model distillation
Linh Tran, Bastiaan S. Veeling, Kevin Roth, Jakub Swiatkowski, Joshua V. Dillon, Jasper Snoek, Stephan Mandt, Tim Salimans, Sebastian Nowozin, and Rodolphe Jenatton · 2001
Earlier work this paper cites.
Learning with differentiable perturbed optimizers
Quentin Berthet, Mathieu Blondel, Olivier Teboul, Marco Cuturi, Jean-Philippe Vert, and Francis R. Bach · 2002
Earlier work this paper cites.
Automated flower classification over a large number of classes
Maria-Elena Nilsback and Andrew Zisserman · 2008
Earlier work this paper cites.
ImageNet: A large-scale hierarchical image database
Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei · 2009
Earlier work this paper cites.
Learning multiple layers of features from tiny images
Alex Krizhevsky · 2009
Earlier work this paper cites.
Bag-of-visual-words and spatial extensions for land-use classification
Yi Yang and Shawn Newsam · 2010
Earlier work this paper cites.
Practical variational inference for neural networks
Alex Graves · 2011
Earlier work this paper cites.
Towards fully autonomous driving: Systems and algorithms
Jesse Levinson, Jake Askeland, Jan Becker, Jennifer Dolson, David Held, Soeren Kammel, J Zico Kolter, Dirk Langer, Oliver Pink, Vaughan Pratt, et al · 2011
Earlier work this paper cites.
Reading digits in natural images with unsupervised feature learning
Yuval Netzer, Tao Wang, Adam Coates, Alessandro Bissacco, Bo Wu, and Andrew Y Ng · 2011
Earlier work this paper cites.
The caltech-ucsd birds-200-2011 dataset
Catherine Wah, Steve Branson, Peter Welinder, Pietro Perona, and Serge Belongie · 2011
Earlier work this paper cites.
Randomized smoothing for stochastic optimization
John C. Duchi, Peter L. Bartlett, and Martin J. Wainwright · 2012
Earlier work this paper cites.
Cats and dogs
Omkar M Parkhi, Andrea Vedaldi, Andrew Zisserman, and CV Jawahar · 2012
Earlier work this paper cites.
Twenty years of mixture of experts
Seniha Esen Yuksel, Joseph N Wilson, and Paul D Gader · 2012
Earlier work this paper cites.
Estimating or propagating gradients through stochastic neurons for conditional computation
Yoshua Bengio, Nicholas Léonard, and Aaron Courville · 2013
Earlier work this paper cites.
3d object representations for fine-grained categorization
Jonathan Krause, Michael Stark, Jia Deng, and Li Fei-Fei · 2013
Earlier work this paper cites.
Horizontal and vertical ensemble with deep representation for classification
Jingjing Xie, Bing Xu, and Chuang Zhang · 2013
Earlier work this paper cites.
Describing textures in the wild
M. Cimpoi, S. Maji, I. Kokkinos, S. Mohamed, , and A. Vedaldi · 2014
Earlier work this paper cites.
Learning factored representations in a deep mixture of experts
David Eigen, Marc’Aurelio Ranzato, and Ilya Sutskever · 2014
Earlier work this paper cites.
Dropout: a simple way to prevent neural networks from overfitting
Nitish Srivastava, Geoffrey Hinton, Alex Krizhevsky, Ilya Sutskever, and Ruslan Salakhutdinov · 2014
Earlier work this paper cites.
Weight uncertainty in neural network
Charles Blundell, Julien Cornebise, Koray Kavukcuoglu, and Daan Wierstra · 2015
Earlier work this paper cites.
Distilling the knowledge in a neural network
Geoffrey Hinton, Oriol Vinyals, and Jeff Dean · 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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Perturbation techniques in online learning and optimization
Jacob D. Abernethy · 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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Multi-class texture analysis in colorectal cancer histology
Jakob Nikolas Kather, Cleo-Aron Weis, Francesco Bianconi, Susanne M Melchers, Lothar R Schad, Timo Gaiser, Alexander Marx, and Frank Gerrit Zöllner · 2016
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On calibration of modern neural networks
Evaluating scalable bayesian deep learning methods for robust computer vision
Fredrik K Gustafsson, Martin Danelljan, and Thomas B Schon · 2020
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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 · 2020
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Big transfer (bit): General visual representation learning
Alexander Kolesnikov, Lucas Beyer, Xiaohua Zhai, Joan Puigcerver, Jessica Yung, Sylvain Gelly, and Neil Houlsby · 2020
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Deep ensembles for low-data transfer learning
Basil Mustafa, Carlos Riquelme, Joan Puigcerver, André Susano Pinto, Daniel Keysers, and Neil Houlsby · 2020
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Aggregated learning: A vector-quantization approach to learning neural network classifiers
Masoumeh Soflaei, Hongyu Guo, Ali Al-Bashabsheh, Yongyi Mao, and Richong Zhang · 2020
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Chuan Guo, Geoff Pleiss, Yu Sun, and Kilian Q Weinberger · 2017
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The elements of statistical learning: data mining, inference, and prediction
Trevor Hastie, Robert Tibshirani, and Jerome Friedman · 2017
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Snapshot ensembles: Train 1, get m for free
Gao Huang, Yixuan Li, Geoff Pleiss, Zhuang Liu, John E Hopcroft, 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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Outrageously large neural networks: The sparsely-gated mixture-of-experts layer
Noam Shazeer, Azalia Mirhoseini, Krzysztof Maziarz, Andy Davis, Quoc Le, Geoffrey Hinton, and Jeff Dean · 2017
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Revisiting unreasonable effectiveness of data in deep learning era
Chen Sun, Abhinav Shrivastava, Saurabh Singh, and Abhinav Gupta · 2017
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Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin · 2017
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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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Transfer learning for image classification using vgg19: Caltech-101 image data set
Monika Bansal, Munish Kumar, Monika Sachdeva, and Ajay Mittal · 2021
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Repulsive deep ensembles are bayesian
Francesco D’Angelo and Vincent Fortuin · 2021
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Bayesian deep learning via subnetwork inference
Erik A. Daxberger, Eric T. Nalisnick, James Urquhart Allingham, Javier Antorán, and José Miguel Hernández-Lobato · 2021
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An image is worth 16x16 words: Transformers for image recognition at scale
Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, Jakob Uszkoreit, and Neil Houlsby · 2021
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Switch transformers: Scaling to trillion parameter models with simple and efficient sparsity
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Exploring the limits of out-of-distribution detection
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Bayesian neural network priors revisited
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GShard: Scaling giant models with conditional computation and automatic sharding
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No one representation to rule them all: Overlapping features of training methods
Raphael Gontijo Lopes, Yann Dauphin, and Ekin D. Cubuk · 2021
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Sparse-mlp: A fully-mlp architecture with conditional computation
Yuxuan Lou, Fuzhao Xue, Zangwei Zheng, and Yang You · 2021
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Xiaofeng Mao, Gege Qi, Yuefeng Chen, Xiaodan Li, Ranjie Duan, Shaokai Ye, Yuan He, and Hui Xue · 2021
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Revisiting the calibration of modern neural networks
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Diversity matters when learning from ensembles
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Carbon emissions and large neural network training
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Vision transformers are robust learners
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Learning transferable visual models from natural language supervision
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Scaling vision with sparse mixture of experts
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How to train your vit? data, augmentation, and regularization in vision transformers
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Go wider instead of deeper
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Wide mean-field bayesian neural networks ignore the data
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