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Ensemble methods are arguably the most trustworthy techniques for boosting the performance of machine learning models.
Cubic convolution interpolation for digital image processing
Keys, Robert · 1981
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Adaptive mixtures of local experts
Jacobs, Robert A, Jordan, Michael I, Nowlan, Steven J, and Hinton, Geoffrey E · 1991
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Bagging predictors
Breiman, Leo · 1996
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A short introduction to boosting
Freund, Yoav, Schapire, Robert, and Abe, N · 1999
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Bayesian averaging of classifiers and the overfitting problem
Domingos, Pedro · 2000
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Discriminative reranking for natural language parsing
Collins, Michael and Koo, Terry · 2005
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Better k-best parsing
Huang, Liang and Chiang, David · 2005
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Learning multiple layers of features from tiny images
Krizhevsky, Alex and Hinton, Geoffrey · 2009
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icoseg: Interactive co-segmentation with intelligent scribble guidance
Batra, Dhruv, Kowdle, Adarsh, Parikh, Devi, Luo, Jiebo, and Chen, Tsuhan · 2010
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The pascal visual object classes (voc) challenge
Everingham, Mark, Van Gool, Luc, Williams, Christopher KI, Winn, John, and Zisserman, Andrew · 2010
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Reading digits in natural images with unsupervised feature learning
Netzer, Yuval, Wang, Tao, Coates, Adam, Bissacco, Alessandro, Wu, Bo, and Ng, Andrew Y · 2011
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N-best maximal decoders for part models
Park, Dennis and Ramanan, Deva · 2011
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Diverse m-best solutions in markov random fields
Batra, Dhruv, Yadollahpour, Payman, Guzman-Rivera, Abner, and Shakhnarovich, Gregory · 2012
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Multi-column deep neural networks for image classification
Ciregan, Dan, Meier, Ueli, and Schmidhuber, Jürgen · 2012
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Multiple choice learning: Learning to produce multiple structured outputs
Guzman-Rivera, Abner, Batra, Dhruv, and Kohli, Pushmeet · 2012
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Imagenet classification with deep convolutional neural networks
Krizhevsky, Alex, Sutskever, Ilya, and Hinton, Geoffrey E · 2012
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Maxout networks
Ian J. Goodfellow, David Warde-Farley, Mehdi Mirza Aaron Courville and Bengio, Yoshua · 2013
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Regularization of neural networks using dropconnect
Wan, Li, Zeiler, Matthew D., Zhang, Sixin, LeCun, Yann, and Fergus, Rob · 2013
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Fully convolutional networks for semantic segmentation
Long, Jonathan, Shelhamer, Evan, and Darrell, Trevor · 2015
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Deep neural networks are easily fooled: High confidence predictions for unrecognizable images
Nguyen, Anh, Yosinski, Jason, and Clune, Jeff · 2015
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Very deep convolutional networks for large-scale image recognition
Simonyan, Karen and Zisserman, Andrew · 2015
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Going deeper with convolutions
Szegedy, Christian, Liu, Wei, Jia, Yangqing, Sermanet, Pierre, Reed, Scott, Anguelov, Dragomir, Erhan, Dumitru, Vanhoucke, Vincent, and Rabinovich, Andrew · 2015
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Deep residual learning for image recognition
He, Kaiming, Zhang, Xiangyu, Ren, Shaoqing, and Sun, Jian · 2016
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Simple and scalable predictive uncertainty estimation using deep ensembles
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Efficiently enforcing diversity in multi-output structured prediction
Guzman-Rivera, Abner, Kohli, Pushmeet, Batra, Dhruv, and Rutenbar, Rob A · 2014
Cited alongside, same era.
Dropout: a simple way to prevent neural networks from overfitting
Srivastava, Nitish, Hinton, Geoffrey E, Krizhevsky, Alex, Sutskever, Ilya, and Salakhutdinov, Ruslan · 2014
Cited alongside, same era.
Predicting multiple structured visual interpretations
Dey, Debadeepta, Ramakrishna, Varun, Hebert, Martial, and Andrew Bagnell, J · 2015
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Batch normalization: Accelerating deep network training by reducing internal covariate shift
Ioffe, Sergey and Szegedy, Christian · 2015
Cited alongside, same era.
Adam: A method for stochastic optimization
Kingma, Diederik and Ba, Jimmy · 2015
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Why m heads are better than one: Training a diverse ensemble of deep networks
Lee, Stefan, Purushwalkam, Senthil, Cogswell, Michael, Crandall, David, and Batra, Dhruv · 2015
Cited alongside, same era.
Lakshminarayanan, Balaji, Pritzel, Alexander, and Blundell, Charles · 2016
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Stochastic multiple choice learning for training diverse deep ensembles
Lee, Stefan, Prakash, Senthil Purushwalkam Shiva, Cogswell, Michael, Ranjan, Viresh, Crandall, David, and Batra, Dhruv · 2016
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Cross-stitch networks for multi-task learning
Misra, Ishan, Shrivastava, Abhinav, Gupta, Abhinav, and Hebert, Martial · 2016
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Unsupervised representation learning with deep convolutional generative adversarial networks
Radford, Alec, Metz, Luke, and Chintala, Soumith · 2016
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Rusu, Andrei A, Rabinowitz, Neil C, Desjardins, Guillaume, Soyer, Hubert, Kirkpatrick, James, Kavukcuoglu, Koray, Pascanu, Razvan, and Hadsell, Raia · 2016
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Wide residual networks
Zagoruyko, Sergey and Komodakis, Nikos · 2016
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Regularizing neural networks by penalizing confident output distributions
Pereyra, Gabriel, Tucker, George, Chorowski, Jan, Kaiser, Łukasz, and Hinton, Geoffrey · 2017
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