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Convolutional Neural Networks have achieved state-of-the-art performance on a wide range of tasks.
Neural network ensembles
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Bayesian model averaging is not model combination
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Pattern Recognition and Machine Learning
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The PASCAL Visual Object Classes Challenge 2007 (VOC2007) Results
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Contextual sequence prediction with application to control library optimization
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Multiple Choice Learning: Learning to Produce Multiple Structured Outputs
A. Guzman-Rivera, D. Batra, and P. Kohli · 2012
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Imagenet classification with deep convolutional neural networks
A. Krizhevsky, I. Sutskever, and G. Hinton · 2012
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A new selective neural network ensemble with negative correlation
H. Lee, E. Kim, and W. Pedrycz · 2012
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Ensemble learning
R. Polikar · 2012
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The ImageNet Large Scale Visual Recognition Challenge 2012 (ILSVRC2012)
O. Russakovsky, J. Deng, J. Krause, A. Berg, and L. Fei-Fei · 2012
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Multi-output learning for camera relocalization
A. Guzman-Rivera, P. Kohli, B. Glocker, J. Shotton, T. Sharp, A. Fitzgibbon, and S. Izadi · 2014
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Distilling the knowledge in a neural network
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Understanding image representations by measuring their equivariance and equivalence
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Imagenet large scale visual recognition challenge
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Very deep convolutional networks for large-scale image recognition
K. Simonyan and A. Zisserman · 2014
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Caffe: An open source convolutional architecture for fast feature embedding
Y. Jia · 2013
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M. Lin, Q. Chen, and S. Yan · 2013
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Fast decorrelated neural network ensembles with random weights
M. Alhamdoosh and D. Wang · 2014
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Learning with pseudo-ensembles
P. Bachman, O. Alsharif, and D. Precup · 2014
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Rich feature hierarchies for accurate object detection and semantic segmentation
R. Girshick, J. Donahue, T. Darrell, and J. Malik · 2014
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Efficiently enforcing diversity in multi-output structured prediction
A. Guzman-Rivera, P. Kohli, D. Batra, and R. Rutenbar · 2014
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Striving for simplicity: The all convolutional net
J. T. Springenberg, A. Dosovitskiy, T. Brox, and M. A. Riedmiller · 2014
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Sequence to sequence learning with neural networks
I. Sutskever, O. Vinyals, and Q. V. Le · 2014
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Going deeper with convolutions
C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. Reed, D. Anguelov, D. Erhan, V. Vanhoucke, and A. Rabinovich · 2014
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Visualizing and understanding convolutional networks
M. D. Zeiler and R. Fergus · 2014
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Retrieved from
MPI: A Message-Passing Interface Standard Version 3.1 · 2015
Closest in time.
R. Girshick · 2015
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Very deep multilingual convolutional neural networks for LVCSR
T. Sercu, C. Puhrsch, B. Kingsbury, and Y. LeCun · 2015
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