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High-quality 3D object recognition is an important component of many vision and robotics systems.
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Imagenet classification with deep convolutional neural networks
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Decaf: A deep convolutional activation feature for generic visual recognition
J. Donahue, Y. Jia, O. Vinyals, J. Hoffman, N. Zhang, E. Tzeng, and T. Darrell · 2013
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C. Farabet, C. Couprie, L. Najman, and Y. LeCun · 2013
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R. B. Girshick, J. Donahue, T. Darrell, and J. Malik · 2013
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How transferable are features in deep neural networks?
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Voxnet: A 3d convolutional neural network for real-time object recognition
D. Maturana and S. Scherer · 2015
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You only look once: Unified, real-time object detection
J. Redmon, S. K. Divvala, R. B. Girshick, and A. Farhadi · 2015
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Deeppano: Deep panoramic representation for 3-d shape recognition
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M. Lin, Q. Chen, and S. Yan · 2013
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Dimension independent matrix square using mapreduce
R. B. Zadeh and G. Carlsson · 2013
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Caffe: Convolutional architecture for fast feature embedding
Y. Jia, E. Shelhamer, J. Donahue, S. Karayev, J. Long, R. Girshick, S. Guadarrama, and T. Darrell · 2014
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Very deep convolutional networks for large-scale image recognition
K. Simonyan and A. Zisserman · 2014
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Dropout: A simple way to prevent neural networks from overfitting
N. Srivastava, G. Hinton, A. Krizhevsky, I. Sutskever, and R. Salakhutdinov · 2014
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Multi-view convolutional neural networks for 3d shape recognition
H. Su, S. Maji, E. Kalogerakis, and E. Learned-Miller · 2015
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3d shapenets: A deep representation for volumetric shapes
Z. Wu, S. Song, A. Khosla, F. Yu, L. Zhang, X. Tang, and J. Xiao · 2015
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Pairwise decomposition of image sequences for active multi-view recognition
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Volumetric and multi-view cnns for object classification on 3d data
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Matrix computations and optimization in apache spark
R. B. Zadeh, X. Meng, B. Yavuz, A. Staple, L. Pu, S. Venkataraman, E. Sparks, A. Ulanov, and M. Zaharia · 2016
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