Fetching the paper…
Reading the bibliography…
Recently neural networks and multiple instance learning are both attractive topics in Artificial Intelligence related research fields.
R. J. Williams and D. Zipser, “A learning algorithm for continually running fully recurrent neural networks,” Neural computation , vol. 1, no. 2, pp. 270–280, 1989
1989
Earlier work this paper cites.
T. G. Dietterich, R. H. Lathrop, and T. Lozano-Pérez, “Solving the multiple instance problem with axis-parallel rectangles,” Artificial Intelligence , vol. 89, no. 1, pp. 31–71, 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.
Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner, “Gradient-based learning applied to document recognition,” Proceedings of the IEEE , vol. 86, no. 11, pp. 2278–2324, 1998
1998
Earlier work this paper cites.
J. Ramon and L. De Raedt, “Multi instance neural networks,” in Proceedings of the ICML-2000 workshop on attribute-value and relational learning , 2000, pp. 53–60
2000
Earlier work this paper cites.
Q. Zhang and S. A. Goldman, “EM-DD: An improved multiple-instance learning technique,” in NIPS , 2001, pp. 1073–1080
2001
Earlier work this paper cites.
Z.-H. Zhou and M.-L. Zhang, “Neural networks for multi-instance learning,” in Proceedings of the International Conference on Intelligent Information Technology, Beijing, China , 2002, pp. 455–459
2002
Earlier work this paper cites.
S. Andrews, I. Tsochantaridis, and T. Hofmann, “Support vector machines for multiple-instance learning,” in NIPS , 2002, pp. 561–568
2002
Earlier work this paper cites.
T. Gärtner, P. A. Flach, A. Kowalczyk, and A. J. Smola, “Multi-instance kernels,” in ICML , vol. 2, 2002, pp. 179–186
2002
Earlier work this paper cites.
M.-L. Zhang and Z.-H. Zhou, “Improve multi-instance neural networks through feature selection,” Neural Processing Letters , vol. 19, no. 1, pp. 1–10, 2004
2004
Earlier work this paper cites.
M. Zhang and Z. Zhou, “Ensembles of multi-instance neural networks,” in International Conference on Intelligent Information Processing . Springer, 2004, pp. 471–474
2004
Cited alongside, same era.
S. Boyd and L. Vandenberghe, Convex optimization . Cambridge university press, 2004
2004
Cited alongside, same era.
G. Hinton, S. Osindero, and Y. W. Teh, “A fast learning algorithm for deep belief nets,” Neural computation , vol. 18, no. 7, pp. 1527–1554, 2006
2006
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,” in CVPR , 2009, pp. 248–255
2009
Cited alongside, same era.
Z. H. Zhou, Y. Y. Sun, and Y. F. Li, “Multi-instance learning by treating instances as non-iid samples,” in ICML , 2009, pp. 1249–1256
2009
Cited alongside, same era.
J. Sánchez, F. Perronnin, T. Mensink, and J. J. Verbeek, “Image classification with the Fisher Vector: Theory and practice,” IJCV , vol. 105, no. 3, pp. 222–245, 2013
2013
Later among the works it cites.
N. Srivastava, G. Hinton, A. Krizhevsky, I. Sutskever, and R. Salakhutdinov, “Dropout: A simple way to prevent neural networks from overfitting,” JMLR , vol. 15, no. 1, pp. 1929–1958, 2014
2014
Later among the works it cites.
C. Y. Lee, S. Xie, P. Gallagher, Z. Zhang, and Z. Tu, “Deeply-Supervised Nets,” in AISTATS , 2015, pp. 562–570
2015
Later among the works it cites.
2015
Later among the works it cites.
J. Wu, Y. Yu, C. Huang, and K. Yu, “Deep multiple instance learning for image classification and auto-annotation,” in CVPR , 2015, pp. 3460–3469
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
V. Nair and G. Hinton, “Rectified linear units improve restricted boltzmann machines,” in ICML , 2010, pp. 807–814
2010
Cited alongside, same era.
X. Glorot and Y. Bengio, “Understanding the difficulty of training deep feedforward neural networks,” in AISTATS , 2010, pp. 249–256
2010
Cited alongside, same era.
X. Glorot, A. Bordes, and Y. Bengio, “Deep sparse rectifier neural networks.” in Aistats , vol. 15, no. 106, 2011, p. 275
2011
Cited alongside, same era.
A. Krizhevsky, I. Sutskever, and G. E. Hinton, “Imagenet classification with deep convolutional neural networks,” in NIPS , 2012, pp. 1097–1105
2012
Cited alongside, same era.
J. Amores, “Multiple instance classification: Review, taxonomy and comparative study,” Artificial Intelligence , vol. 201, pp. 81–105, 2013
2013
Cited alongside, same era.
2015
Later among the works it cites.
P. O. Pinheiro and R. Collobert, “From image-level to pixel-level labeling with convolutional networks,” in CVPR , 2015, pp. 1713–1721
2015
Later among the works it cites.
X. Wang, Z. Zhu, C. Yao, and X. Bai, “Relaxed multiple-instance SVM with application to object discovery,” in ICCV , 2015, pp. 1224–1232
2015
Later among the works it cites.
F. Chollet, “Keras,” https://github.com/fchollet/keras , 2015
2015
Later among the works it cites.
X. S. Wei, J. Wu, and Z. H. Zhou, “Scalable algorithms for multi-instance learning,” IEEE Transactions on Neural Networks and Learning Systems , vol. PP, no. 99, pp. 1–13, 2016
2016
Closest in time.