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Deep neural networks have become the primary learning technique for object recognition.
S. Hochreiter and J. Schmidhuber, “Long short-term memory,” Neural Computing , 1997
1997
Earlier work this paper cites.
S. Hochreiter and J. Schmidhuber, “Long short-term memory,” Neural computation , vol. 9, no. 8, pp. 1735–1780, 1997
1997
Earlier work this paper cites.
M. Schuster and K. K. Paliwal, “Bidirectional recurrent neural networks,” Signal Processing, IEEE Transactions on , vol. 45, no. 11, pp. 2673–2681, 1997
1997
Earlier work this paper cites.
F. A. Gers, J. Schmidhuber, and F. Cummins, “Learning to forget: Continual prediction with lstm,” Neural computation , vol. 12, no. 10, pp. 2451–2471, 2000
2000
Earlier work this paper cites.
A. Graves and J. Schmidhuber, “Framewise phoneme classification with bidirectional lstm and other neural network architectures,” Neural Networks , vol. 18, no. 5, pp. 602–610, 2005
2005
Earlier work this paper cites.
A. Bosch, A. Zisserman, and X. Munoz, “Image classification using random forests and ferns,” in Computer Vision, 2007. ICCV 2007. IEEE 11th International Conference on . IEEE, 2007, pp. 1–8
2007
Earlier work this paper cites.
S. Gould, P. Baumstarck, M. Quigley, A. Y. Ng, and D. Koller, “Integrating visual and range data for robotic object detection,” in Workshop on Multi-camera and Multi-modal Sensor Fusion Algorithms and Applications-M2SFA2 2008 , 2008
2008
Earlier work this paper cites.
A. Graves, A.-R. Mohamed, and G. E. Hinton, “Offline handwriting recognition with multidimensional recurrent neural networks,” NIPS , 2008
2008
Earlier work this paper cites.
2008
Earlier work this paper cites.
A. Collet, D. Berenson, S. S. Srinivasa, and D. Ferguson, “Object recognition and full pose registration from a single image for robotic manipulation,” in Robotics and Automation, 2009. ICRA’09. IEEE International Conference on . IEEE, 2009, pp. 48–55
2009
Earlier work this paper cites.
A. Graves, M. Liwicki, S. Fernandez, H. B. R. Bertolami, and J. Schmidhuber, “A novel connectionist system for unconstrained handwriting recognition,” IEEE Trans. PAMI , 2009
2009
Cited alongside, same era.
J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, and L. Fei-Fei, “Imagenet: A large-scale hierarchical image database,” CVPR , 2009
2009
Cited alongside, same era.
K. Lai, L. Bo, X. Ren, and D. Fox, “A large-scale hierarchical multi-view rgb-d object dataset.” IEEE, 2011, pp. 1817–1824
2011
Cited alongside, same era.
A. Krizhevsky, I. Sutskever, and G. Hinton, “Imagenet classification with deep convolutional neural networks,” NIPS , 2012
2012
Cited alongside, same era.
R. Socher, B. Huval, B. Bath, C. D. Manning, and A. Y. Ng, “Convolutional-recursive deep learning for 3d object classification,” in Advances in Neural Information Processing Systems , 2012, pp. 665–673
2014
Later among the works it cites.
A. Karpathy, G. Toderici, S. Shetty, T. Leung, R. Sukthankar, and L. Fei-Fei, “Large-scale video classification with convolutional neural networks,” in Computer Vision and Pattern Recognition (CVPR), 2014 IEEE Conference on . IEEE, 2014, pp. 1725–1732
2014
Later among the works it cites.
2014
Later among the works it cites.
2015
Closest in time.
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2012
Cited alongside, same era.
L. Bo, X. Ren, and D. Fox, “Unsupervised feature learning for rgb-d based object recognition,” in Experimental Robotics . Springer, 2013, pp. 387–402
2013
Cited alongside, same era.
2013
Cited alongside, same era.
A. Graves and N. Jaitly, “Towards end-to-end speech recognition with recurrent neural networks,” ICML , 2014
2014
Cited alongside, same era.
I. Sutskever, O. Vinyals, and Q. V. Le, “Sequence to sequence learning with neural networks,” in Advances in neural information processing systems , 2014, pp. 3104–3112
2014
Cited alongside, same era.
M. Shwarz, H. Shulz, and S. Behnke, “Rgb-d object recognition and pose estimation based on pre-trained convolutional neural network features.” IEEE, 2015
2015
Closest in time.
I. Lenz, H. Lee, and A. Saxena, “Deep learning for detecting robotic grasps,” The International Journal of Robotics Research , vol. 34, no. 4-5, pp. 705–724, 2015
2015
Closest in time.
J. Ng, M. Hausknecht, S. Vijayanarasimhan, O. Vinyals, R. Monga, and G. Toderici, “Beyond short snippets: Deep networks for video classification,” CoRR 1503.08909 , 2015
2015
Closest in time.
2015
Closest in time.
Z. Jia, A. Saxena, and T. Chen, “Robotic object detection: Learning to improve the classifiers using sparse graphs for path planning,” in IJCAI Proceedings-International Joint Conference on Artificial Intelligence , vol. 22, no. 3, 2011, p. 2072
2072
Closest in time.