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Much recent machine learning research has been directed towards leveraging shared statistics among labels, instances and data views, commonly referred to as multi-label, multi-instance and multi-view learning.
Multitask learning
R. Caruana · 1997
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Solving the multiple instance problem with axis-parallel rectangles
T.G. Dietterich, R.H. Lathrop, and T. Lozano-Pérez · 1997
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A kernel method for multi-labelled classification
A. Elisseeff and J. Weston · 2001
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Collective multi-label classification
N. Ghamrawi and A. McCallum · 2005
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Uncovering shared structures in multiclass classification
Yonatan Amit, Michael Fink, Nathan Srebro, and Shimon Ullman · 2007
Earlier work this paper cites.
ML-KNN: A lazy learning approach to multi-label learning
Min-Ling Zhang and Zhi-Hua Zhou · 2007
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Learning from multiple sources
K. Crammer, M. Kearns, and J. Wortman · 2008
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NUS-WIDE: A real-world web image database from National University of Singapore
T.S. Chua, J. Tang, R. Hong, H. Li, Z. Luo, and Y. Zheng · 2009
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Label embedding trees for large multi-class tasks
Samy Bengio, Jason Weston, and David Grangier · 2010
Earlier work this paper cites.
Bayes optimal multilabel classification via probabilistic classifier chains
Weiwei Cheng, Eyke Hüllermeier, and Krzysztof J Dembczynski · 2010
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Factorized orthogonal latent spaces
Mathieu Salzmann, Carl Henrik Ek, Raquel Urtasun, and Trevor Darrell · 2010
Cited alongside, same era.
Natural language processing (almost) from scratch
Ronan Collobert, Jason Weston, Léon Bottou, Michael Karlen, Koray Kavukcuoglu, and Pavel Kuksa · 2011
Cited alongside, same era.
Multiple kernel learning algorithms
Mehmet Gönen and Ethem Alpaydın · 2011
Cited alongside, same era.
A co-training approach for multi-view spectral clustering
Abhishek Kumar and Hal Daumé · 2011
Cited alongside, same era.
On using nearly-independent feature families for high precision and confidence
Omid Madani, Manfred Georg, and David A Ross · 2013
Cited alongside, same era.
Distributed representations of sentences and documents
Quoc V Le and Tomas Mikolov · 2014
Cited alongside, same era.
Deep learning
Yann LeCun, Yoshua Bengio, and Geoffrey Hinton · 2015
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Training very deep networks
Rupesh K Srivastava, Klaus Greff, and Jürgen Schmidhuber · 2015
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Bridging the gaps between residual learning, recurrent neural networks and visual cortex
Qianli Liao and Tomaso Poggio · 2016
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Faster training of very deep networks via p-norm gates
Trang Pham, Truyen Tran, Dinh Phung, and Svetha Venkatesh · 2016
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Meka: a multi-label/multi-target extension to weka
Jesse Read, Peter Reutemann, Bernhard Pfahringer, and Geoff Holmes · 2016
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Order matters: Sequence to sequence for sets
Oriol Vinyals, Samy Bengio, and Manjunath Kudlur · 2016
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Multimodal learning with deep boltzmann machines
Nitish Srivastava and Ruslan Salakhutdinov · 2014
Cited alongside, same era.
Scalable multi-instance learning
Xiu-Shen Wei, Jianxin Wu, and Zhi-Hua Zhou · 2014
Cited alongside, same era.
A review on multi-label learning algorithms
Min-Ling Zhang and Zhi-Hua Zhou · 2014
Cited alongside, same era.
Learning label specific features for multi-label classification
Jun Huang, Guorong Li, Qingming Huang, and Xindong Wu · 2015
Cited alongside, same era.
Revisiting multiple instance neural networks
Xinggang Wang, Yongluan Yan, Peng Tang, Xiang Bai, and Wenyu Liu · 2016
Later among the works it cites.
Deep miml network
Ji Feng and Zhi-Hua Zhou · 2017
Closest in time.
Column networks for collective classification
Trang Pham, Truyen Tran, Dinh Phung, and Svetha Venkatesh · 2017
Closest in time.