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Ensembles, where multiple neural networks are trained individually and their predictions are averaged, have been shown to be widely successful for improving both the accuracy and predictive uncertainty of single neural networks.
The well-calibrated Bayesian
A. Philip Dawid · 1982
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The comparison and evaluation of forecasters
Morris H. Degroot and Stephen E. Fienberg · 1983
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Catastrophic interference in connectionist networks: The sequential learning problem
M. W. McCloskey · 1989
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Neural network ensembles
Lars Kai Hansen and Péter Salamon · 1990
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When networks disagree: Ensemble methods for hybrid neural networks
Michael P. Perrone and Leon N. Cooper · 1992
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Bayesian learning for neural networks
Geoffrey E. Hinton and Radford M. Neal · 1995
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Neural network ensembles, cross validation, and active learning
Anders Krogh and Jesper Vedelsby · 1995
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Bagging predictors
Leo Breiman · 1996
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Lifelong learning algorithms
Sebastian Thrun · 1998
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Catastrophic forgetting in connectionist networks
Robert M. French · 1999
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Popular ensemble methods: An empirical study
Richard Maclin and David W. Opitz · 1999
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Ensemble methods in machine learning
Thomas G. Dietterich · 2000
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Stochastic learning
Léon Bottou · 2003
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Model compression
Cristian Bucila, Rich Caruana, and Alexandru Niculescu-Mizil · 2006
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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
Alex Krizhevsky · 2009
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Incremental self-improvement for life-time multi-agent reinforcement learning
Jian Hua Zhao and Jürgen Schmidhuber · 2009
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Pilco: A model-based and data-efficient approach to policy search
Marc Peter Deisenroth and Carl E. Rasmussen · 2011
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Horizontal and vertical ensemble with deep representation for classification
Jingjing Xie, Bing Xu, and Chuang Zhang · 2013
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Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba · 2014
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Dropout: a simple way to prevent neural networks from overfitting
Nitish Srivastava, Geoffrey E. Hinton, Alex Krizhevsky, Ilya Sutskever, and Ruslan R. Salakhutdinov · 2014
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An empirical analysis of dropout in piecewise linear networks
David Warde-Farley, Ian J. Goodfellow, Aaron C. Courville, and Yoshua Bengio · 2014
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Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning
Yarin Gal and Zoubin Ghahramani · 2015
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Escaping from saddle points—online stochastic gradient for tensor decomposition
Rong Ge, Furong Huang, Chi Jin, and Yang Yuan · 2015
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Deep learning
Ian J. Goodfellow, Yoshua Bengio, and Aaron C. Courville · 2015
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Distilling the knowledge in a neural network
Geoffrey E. Hinton, Oriol Vinyals, and Jeffrey Dean · 2015
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Batch Normalization: Accelerating deep network training by reducing internal covariate shift
Sergey Ioffe and Christian Szegedy · 2015
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Obtaining well calibrated probabilities using Bayesian binning
Simple and scalable predictive uncertainty estimation using deep ensembles
Balaji Lakshminarayanan, Alexander Pritzel, and Charles Blundell · 2017
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Gradient episodic memory for continuum learning
David Lopez-Paz and Marc’Aurelio Ranzato · 2017
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FiLM: Visual reasoning with a general conditioning layer
Ethan Perez, Florian Strub, Harm de Vries, Vincent Dumoulin, and Aaron C. Courville · 2017
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Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz Kaiser, and Illia Polosukhin · 2017
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Lifelong learning with dynamically expandable networks
Jaehong Yoon, Eunho Yang, Jeongtae Lee, and Sung Ju Hwang · 2017
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Mahdi Pakdaman Naeini, Gregory F. Cooper, and Milos Hauskrecht · 2015
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No more pesky learning rate guessing games
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Deep residual learning for image recognition
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Deep networks with stochastic depth
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Deep learning without poor local minima
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Arslan Chaudhry, Marc’Aurelio Ranzato, Marcus Rohrbach, and Mohamed Elhoseiny · 2018
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Loss Surfaces, Mode Connectivity, and Fast Ensembling of DNNs
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Model-ensemble trust-region policy optimization
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K for the price of 1: Parameter-efficient multi-task and transfer learning
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Learning to learn without forgetting by maximizing transfer and minimizing interference
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Deep Bayesian bandits showdown
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Experience replay for continual learning
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Flipout: Efficient pseudo-independent weight perturbations on mini-batches
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Reinforced continual learning
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Analyzing the role of model uncertainty for electronic health records
Michael W Dusenberry, Dustin Tran, Edward Choi, Jonas Kemp, Jeremy Nixon, Ghassen Jerfel, Katherine Heller, and Andrew M Dai · 2019
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Deep ensembles: A loss landscape perspective
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Evaluating scalable Bayesian deep learning methods for robust computer vision
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Benchmarking neural network robustness to common corruptions and perturbations
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Can you trust your model’s uncertainty? Evaluating predictive uncertainty under dataset shift
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Benchmarking model-based reinforcement learning
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