Fetching the paper…
Reading the bibliography…
Multi-output learning aims to simultaneously predict multiple outputs given an input.
A. Bendale and T. E. Boult, “Towards open world recognition,” in CVPR , 2015, pp. 1893–1902
1902
Earlier work this paper cites.
D. Park, J. Neeman, J. Zhang, S. Sanghavi, and I. S. Dhillon, “Preference completion: Large-scale collaborative ranking from pairwise comparisons,” in ICML , 2015, pp. 1907–1916
1916
Earlier work this paper cites.
Y. Li, J. Yang, Y. Song, L. Cao, J. Luo, and L. Li, “Learning from noisy labels with distillation,” in ICCV , 2017, pp. 1928–1936
1936
Earlier work this paper cites.
A. Joulin, F. R. Bach, and J. Ponce, “Discriminative clustering for image co-segmentation,” in CVPR , 2010, pp. 1943–1950
1950
Earlier work this paper cites.
S. C. Deerwester, S. T. Dumais, G. W. Furnas, R. A. Harshman, T. K. Landauer, K. E. Lochbaum, and L. A. Streeter, “Computer information retrieval using latent semantic structure,” 1989
1989
Earlier work this paper cites.
G. A. Miller, “Wordnet: A lexical database for english,” Communications of the ACM , vol. 38, no. 11, pp. 39–41, 1995
1995
Earlier work this paper cites.
G. Widmer and M. Kubat, “Learning in the presence of concept drift and hidden contexts,” Machine learning , vol. 23, no. 1, pp. 69–101, 1996
1996
Earlier work this paper cites.
R. Caruana, “Multitask learning,” Machine learning , vol. 28, no. 1, pp. 41–75, 1997
1997
Earlier work this paper cites.
S. Thrun and J. O’Sullivan, “Clustering learning tasks and the selective cross-task transfer of knowledge,” in Learning to learn . Springer, 1998, pp. 235–257
1998
Earlier work this paper cites.
C. E. Brodley and M. A. Friedl, “Identifying mislabeled training data,” JAIR , vol. 11, pp. 131–167, 1999
1999
Earlier work this paper cites.
S. Dzeroski, D. Demsar, and J. Grbovic, “Predicting chemical parameters of river water quality from bioindicator data,” Appl. Intell. , vol. 13, no. 1, pp. 7–17, 2000
2000
Earlier work this paper cites.
A. W. M. Smeulders, M. Worring, S. Santini, A. Gupta, and R. C. Jain, “Content-based image retrieval at the end of the early years,” TPAMI , vol. 22, no. 12, pp. 1349–1380, 2000
2000
Earlier work this paper cites.
N. Maria and M. J. Silva, “Theme-based retrieval of web news,” in SIGIR , 2000, pp. 354–356
2000
Earlier work this paper cites.
E. F. T. K. Sang and S. Buchholz, “Introduction to the conll-2000 shared task chunking,” in Proceedings of the 2nd workshop on Learning language in logic and the 4th conference on Computational natural language learning , 2000, pp. 127–132
2000
Earlier work this paper cites.
R. Barandela and E. Gasca, “Decontamination of training samples for supervised pattern recognition methods,” in Joint IAPR International Workshops on SPR and SSPR , 2000, pp. 621–630
2000
Earlier work this paper cites.
J. D. Lafferty, A. McCallum, and F. C. N. Pereira, “Conditional random fields: Probabilistic models for segmenting and labeling sequence data,” in ICML , 2001, pp. 282–289
2001
Earlier work this paper cites.
H. Brighton and C. Mellish, “Advances in instance selection for instance-based learning algorithms,” Data mining and knowledge discovery , vol. 6, no. 2, pp. 153–172, 2002
2002
Earlier work this paper cites.
M. Collins, “Discriminative training methods for hidden markov models: Theory and experiments with perceptron algorithms,” in Empirical Methods in Natural Language Processing , 2002
2002
Earlier work this paper cites.
N. V. Chawla, K. W. Bowyer, L. O. Hall, and W. P. Kegelmeyer, “SMOTE: synthetic minority over-sampling technique,” JAIR , vol. 16, pp. 321–357, 2002
2002
Earlier work this paper cites.
K. Crammer and Y. Singer, “A family of additive online algorithms for category ranking,” JMLR , vol. 3, pp. 1025–1058, 2003
2003
Earlier work this paper cites.
S. C. Park, M. K. Park, and M. G. Kang, “Super-resolution image reconstruction: a technical overview,” IEEE signal processing magazine , vol. 20, no. 3, pp. 21–36, 2003
2003
Earlier work this paper cites.
F. J. Och, “Minimum error rate training in statistical machine translation,” in Association for Computational Linguistics , 2003, pp. 160–167
2003
Earlier work this paper cites.
B. Taskar, C. Guestrin, and D. Koller, “Max-margin markov networks,” in NIPS , 2003, pp. 25–32
2003
Earlier work this paper cites.
I. Mani and I. Zhang, “knn approach to unbalanced data distributions: a case study involving information extraction,” in Proceedings of workshop on learning from imbalanced datasets , vol. 126, 2003
2003
Earlier work this paper cites.
L. Cai and T. Hofmann, “Hierarchical document categorization with support vector machines,” in CIKM , 2004, pp. 78–87
2004
Earlier work this paper cites.
H. Aras and N. Aras, “Forecasting residential natural gas demand,” Energy Sources , vol. 26, no. 5, pp. 463–472, 2004
2004
Earlier work this paper cites.
B. Taskar, D. Klein, M. Collins, D. Koller, and C. Manning, “Max-margin parsing,” in EMNLP , 2004
2004
Earlier work this paper cites.
D. D. Lewis, Y. Yang, T. G. Rose, and F. Li, “RCV1: A new benchmark collection for text categorization research,” JMLR , vol. 5, pp. 361–397, 2004
2004
Earlier work this paper cites.
M. R. Boutell, J. Luo, X. Shen, and C. M. Brown, “Learning multi-label scene classification,” Pattern Recognition , vol. 37, no. 9, pp. 1757–1771, 2004
2004
Earlier work this paper cites.
Y. Liu, E. P. Xing, and J. G. Carbonell, “Predicting protein folds with structural repeats using a chain graph model,” in ICML , 2005, pp. 513–520
2005
Earlier work this paper cites.
P. Koehn, “Europarl: A parallel corpus for statistical machine translation,” in MT summit , vol. 5, 2005, pp. 79–86
2005
Earlier work this paper cites.
I. Tsochantaridis, T. Joachims, T. Hofmann, and Y. Altun, “Large margin methods for structured and interdependent output variables,” Journal of machine learning research , vol. 6, no. Sep, pp. 1453–1484, 2005
2005
Earlier work this paper cites.
I. Tsochantaridis, T. Joachims, T. Hofmann, and Y. Altun, “Large margin methods for structured and interdependent output variables,” JMLR , vol. 6, pp. 1453–1484, 2005
2005
Earlier work this paper cites.
G. Qi, X. Hua, Y. Rui, J. Tang, T. Mei, and H. Zhang, “Correlative multi-label video annotation,” in ACM Multimedia , 2007, pp. 17–26
2007
Earlier work this paper cites.
J. Ko, E. Nyberg, and L. Si, “A probabilistic graphical model for joint answer ranking in question answering,” in SIGIR , 2007, pp. 343–350
2007
Earlier work this paper cites.
D. Liben-Nowell and J. M. Kleinberg, “The link-prediction problem for social networks,” JASIST , vol. 58, no. 7, pp. 1019–1031, 2007
2007
Earlier work this paper cites.
D. Shen, J. Sun, H. Li, Q. Yang, and Z. Chen, “Document summarization using conditional random fields,” in IJCAI , 2007, pp. 2862–2867
2007
Earlier work this paper cites.
G. Carneiro, A. B. Chan, P. J. Moreno, and N. Vasconcelos, “Supervised learning of semantic classes for image annotation and retrieval,” TPAMI , vol. 29, no. 3, pp. 394–410, 2007
2007
Earlier work this paper cites.
G. Tsoumakas and I. P. Vlahavas, “Random k -labelsets: An ensemble method for multilabel classification,” in ECML , 2007, pp. 406–417
2007
Earlier work this paper cites.
A. Tewari and P. L. Bartlett, “On the consistency of multiclass classification methods,” JMLR , vol. 8, pp. 1007–1025, 2007
2007
Earlier work this paper cites.
Y. Yue, T. Finley, F. Radlinski, and T. Joachims, “A support vector method for optimizing average precision,” in SIGIR , 2007, pp. 271–278
2007
Earlier work this paper cites.
S. Avidan, “Ensemble tracking,” TPAMI , vol. 29, no. 2, 2007
2007
Earlier work this paper cites.
B. C. Russell, A. Torralba, K. P. Murphy, and W. T. Freeman, “Labelme: A database and web-based tool for image annotation,” IJCV , vol. 77, no. 1-3, pp. 157–173, 2008
2008
Earlier work this paper cites.
A. Azadeh, S. Ghaderi, and S. Sohrabkhani, “Annual electricity consumption forecasting by neural network in high energy consuming industrial sectors,” Energy Conversion and management , vol. 49, no. 8, pp. 2272–2278, 2008
2008
Earlier work this paper cites.
R. Wetzker, C. Zimmermann, and C. Bauckhage, “Analyzing social bookmarking systems: A del.icio.us cookbook,” in Proceedings of the ECAI 2008 Mining Social Data Workshop , 2008, pp. 26–30
2008
Earlier work this paper cites.
D. Povey, D. Kanevsky, B. Kingsbury, B. Ramabhadran, G. Saon, and K. Visweswariah, “Boosted MMI for model and feature-space discriminative training,” in ICASSP , 2008, pp. 4057–4060
2008
Earlier work this paper cites.
G. Chen, Y. Song, F. Wang, and C. Zhang, “Semi-supervised multi-label learning by solving a sylvester equation,” in ICDM , 2008, pp. 410–419
2008
Earlier work this paper cites.
C. H. Lampert, H. Nickisch, and S. Harmeling, “Learning to detect unseen object classes by between-class attribute transfer,” in CVPR , 2009, pp. 951–958
2009
Earlier work this paper cites.
M. Palatucci, D. Pomerleau, G. E. Hinton, and T. M. Mitchell, “Zero-shot learning with semantic output codes,” in NIPS , 2009, pp. 1410–1418
2009
Earlier work this paper cites.
M. K. Choong, M. Charbit, and H. Yan, “Autoregressive-model-based missing value estimation for DNA microarray time series data,” IEEE Transactions on Information Technology in Biomedicine , vol. 13, no. 1, pp. 131–137, 2009
2009
Earlier work this paper cites.
X. Wang, X. Ma, and W. E. L. Grimson, “Unsupervised activity perception in crowded and complicated scenes using hierarchical bayesian models,” TPAMI , vol. 31, no. 3, pp. 539–555, 2009
2009
Earlier work this paper cites.
A. Farhadi, I. Endres, D. Hoiem, and D. A. Forsyth, “Describing objects by their attributes,” in CVPR , 2009, pp. 1778–1785
2009
Earlier work this paper cites.
M. Piorkowski, N. Sarafijanovic-Djukic, and M. Grossglauser, “Crawdad data set epfl/mobility (v. 2009-02-24),” Feb. 2009. [Online]. Available: Downloaded from http://crawdad.org/epfl/mobility/
2009
Earlier work this paper cites.
A. Krizhevsky and G. Hinton, “Learning multiple layers of features from tiny images,” Citeseer, Tech. Rep., 2009
2009
Earlier work this paper cites.
R. Zafarani and H. Liu, “Social computing data repository at ASU,” 2009. [Online]. Available: http://socialcomputing.asu.edu
2009
Earlier work this paper cites.
D. J. Hsu, S. Kakade, J. Langford, and T. Zhang, “Multi-label prediction via compressed sensing,” in NIPS , 2009, pp. 772–780
2009
Earlier work this paper cites.
J. Read, B. Pfahringer, G. Holmes, and E. Frank, “Classifier chains for multi-label classification,” in ECML PKDD , 2009, pp. 254–269
2009
Earlier work this paper cites.
T. Joachims, T. Finley, and C. J. Yu, “Cutting-plane training of structural svms,” Machine Learning , vol. 77, no. 1, pp. 27–59, 2009
2009
Earlier work this paper cites.
W. Qu, Y. Zhang, J. Zhu, and Q. Qiu, “Mining multi-label concept-drifting data streams using dynamic classifier ensemble,” in ACML , 2009, pp. 308–321
2009
Earlier work this paper cites.
A. Bifet, G. Holmes, B. Pfahringer, R. Kirkby, and R. Gavaldà, “New ensemble methods for evolving data streams,” in KDD , 2009, pp. 139–148
2009
Earlier work this paper cites.
S. Vembu and T. Gärtner, “Label ranking algorithms: A survey,” in Preference Learning. , 2010, pp. 45–64. [Online]. Available: https://doi.org/10.1007/978-3-642-14125-6_3
2010
Earlier work this paper cites.
J. Xiao, J. Hays, K. A. Ehinger, A. Oliva, and A. Torralba, “SUN database: Large-scale scene recognition from abbey to zoo,” in CVPR , 2010, pp. 3485–3492
2010
Earlier work this paper cites.
X. Geng, K. Smith-Miles, and Z. Zhou, “Facial age estimation by learning from label distributions,” in AAAI , 2010
2010
Earlier work this paper cites.
M. J. Choi, J. J. Lim, A. Torralba, and A. S. Willsky, “Exploiting hierarchical context on a large database of object categories,” in CVPR , 2010, pp. 129–136
2010
Earlier work this paper cites.
M. Everingham, L. J. V. Gool, C. K. I. Williams, J. M. Winn, and A. Zisserman, “The pascal visual object classes (VOC) challenge,” IJCV , vol. 88, no. 2, pp. 303–338, 2010
2010
Earlier work this paper cites.
O. Maimon and L. Rokach, Eds., Data Mining and Knowledge Discovery Handbook, 2nd ed . Springer, 2010
2010
Earlier work this paper cites.
K. Dembczynski, W. Cheng, and E. Hüllermeier, “Bayes optimal multilabel classification via probabilistic classifier chains,” in ICML , 2010, pp. 279–286
2010
Earlier work this paper cites.
K. Gimpel and N. A. Smith, “Softmax-margin crfs: Training log-linear models with cost functions,” in HLT-NAACL , 2010, pp. 733–736
2010
Earlier work this paper cites.
Y. Sun, Y. Zhang, and Z. Zhou, “Multi-label learning with weak label,” in AAAI , 2010
2010
Earlier work this paper cites.
C. Bielza, G. Li, and P. Larrañaga, “Multi-dimensional classification with bayesian networks,” Int. J. Approx. Reasoning , vol. 52, no. 6, pp. 705–727, 2011
2011
Earlier work this paper cites.
M. Rohrbach, M. Stark, and B. Schiele, “Evaluating knowledge transfer and zero-shot learning in a large-scale setting,” in CVPR , 2011, pp. 1641–1648
2011
Earlier work this paper cites.
C. H. Lau, Y. Li, and D. Tjondronegoro, “Microblog retrieval using topical features and query expansion,” in TREC , 2011
2011
Earlier work this paper cites.
G. Marques, M. A. Domingues, T. Langlois, and F. Gouyon, “Three current issues in music autotagging,” in International Society for Music Information Retrieval Conference , 2011, pp. 795–800
2011
Earlier work this paper cites.
C. Wah, S. Branson, P. Welinder, P. Perona, and S. Belongie, “The Caltech-UCSD Birds-200-2011 Dataset,” Tech. Rep., 2011
2011
Earlier work this paper cites.
S. Oh, A. Hoogs, A. G. A. Perera, N. P. Cuntoor, C. Chen, J. T. Lee, S. Mukherjee, J. K. Aggarwal, H. Lee, L. S. Davis, E. Swears, X. Wang, Q. Ji, K. K. Reddy, M. Shah, C. Vondrick, H. Pirsiavash, D. Ramanan, J. Yuen, A. Torralba, B. Song, A. Fong, A. K. Roy-Chowdhury, and M. Desai, “A large-scale benchmark dataset for event recognition in surveillance video,” in CVPR , 2011, pp. 3153–3160
2011
Earlier work this paper cites.
M. Mahoney, “Large text compression benchmark,” 2011. [Online]. Available: http://www.mattmahoney.net/text/text.html
2011
Earlier work this paper cites.
J. Read, B. Pfahringer, G. Holmes, and E. Frank, “Classifier chains for multi-label classification,” Machine Learning , vol. 85, no. 3, pp. 333–359, 2011
2011
Earlier work this paper cites.
W. Gao and Z.-H. Zhou, “On the consistency of multi-label learning,” in Proceedings of the 24th annual conference on learning theory , 2011, pp. 341–358
2011
Earlier work this paper cites.
D. A. McAllester and J. Keshet, “Generalization bounds and consistency for latent structural probit and ramp loss,” in NIPS , 2011, pp. 2205–2212
2011
Earlier work this paper cites.
J. Deng, S. Satheesh, A. C. Berg, and F. Li, “Fast and balanced: Efficient label tree learning for large scale object recognition,” in NIPS , 2011, pp. 567–575
2011
Earlier work this paper cites.
J. Liu, B. Kuipers, and S. Savarese, “Recognizing human actions by attributes,” in CVPR , 2011, pp. 3337–3344
2011
Earlier work this paper cites.
S. S. Bucak, R. Jin, and A. K. Jain, “Multi-label learning with incomplete class assignments,” in CVPR , 2011, pp. 2801–2808
2011
Earlier work this paper cites.
R. S. Cabral, F. D. la Torre, J. P. Costeira, and A. Bernardino, “Matrix completion for multi-label image classification,” in NIPS , 2011, pp. 190–198
2011
Earlier work this paper cites.
X. Kong and P. S. Yu, “An ensemble-based approach to fast classification of multi-label data streams,” in International Conference on Collaborative Computing: Networking, Applications and Worksharing , 2011, pp. 95–104
2011
Earlier work this paper cites.
E. S. Xioufis, M. Spiliopoulou, G. Tsoumakas, and I. P. Vlahavas, “Dealing with concept drift and class imbalance in multi-label stream classification,” in IJCAI , 2011, pp. 1583–1588
2011
Earlier work this paper cites.
P. Stenetorp, S. Pyysalo, G. Topic, T. Ohta, S. Ananiadou, and J. Tsujii, “brat: a web-based tool for nlp-assisted text annotation,” in EACL , 2012
2012
Earlier work this paper cites.
T. R. Hoens, R. Polikar, and N. V. Chawla, “Learning from streaming data with concept drift and imbalance: an overview,” Progress in AI , vol. 1, no. 1, pp. 89–101, 2012
2012
Earlier work this paper cites.
S. Y. Bao, Y. Xiang, and S. Savarese, “Object co-detection,” in ECCV . Springer, 2012, pp. 86–101
2012
Earlier work this paper cites.
Q. Mao, I. W.-H. Tsang, and S. Gao, “Objective-guided image annotation,” IEEE Transactions on Image Processing , vol. 22, no. 4, pp. 1585–1597, 2012
2012
Earlier work this paper cites.
M. A. Tahir, J. Kittler, and F. Yan, “Inverse random under sampling for class imbalance problem and its application to multi-label classification,” Pattern Recognition , vol. 45, no. 10, pp. 3738–3750, 2012
2012
Earlier work this paper cites.
A. Gretton, K. M. Borgwardt, M. J. Rasch, B. Schölkopf, and A. Smola, “A kernel two-sample test,” Journal of Machine Learning Research , vol. 13, no. Mar, pp. 723–773, 2012
2012
Earlier work this paper cites.
A. Zubiaga, “Enhancing navigation on wikipedia with social tags,” CoRR , vol. abs/1202.5469, 2012
2012
Earlier work this paper cites.
V. Jouhet, G. Defossez, A. Burgun, P. Le Beux, P. Levillain, P. Ingrand, V. Claveau et al. , “Automated classification of free-text pathology reports for registration of incident cases of cancer,” Methods of information in medicine , vol. 51, no. 3, p. 242, 2012
2012
Earlier work this paper cites.
D. Zhou, J. C. Platt, S. Basu, and Y. Mao, “Learning from the wisdom of crowds by minimax entropy,” in Conference on Neural Information Processing Systems , 2012, pp. 2204–2212
2012
Earlier work this paper cites.
Y. Chen and H. Lin, “Feature-aware label space dimension reduction for multi-label classification,” in NIPS , 2012, pp. 1538–1546
2012
Earlier work this paper cites.
F. Tai and H. Lin, “Multilabel classification with principal label space transformation,” Neural Computation , vol. 24, no. 9, pp. 2508–2542, 2012
2012
Earlier work this paper cites.
A. Kapoor, R. Viswanathan, and P. Jain, “Multilabel classification using bayesian compressed sensing,” in NIPS , 2012, pp. 2654–2662
2012
Earlier work this paper cites.
V. Mnih and G. E. Hinton, “Learning to label aerial images from noisy data,” in ICML , 2012
2012
Earlier work this paper cites.
J. Read, A. Bifet, G. Holmes, and B. Pfahringer, “Scalable and efficient multi-label classification for evolving data streams,” Machine Learning , vol. 88, no. 1-2, pp. 243–272, 2012
2012
Earlier work this paper cites.
G. Eryigit, F. S. Çetin, M. Yanik, T. Temel, and I. Çiçekli, “TURKSENT: A sentiment annotation tool for social media,” in LAW@ACL , 2013, pp. 131–134
2013
Earlier work this paper cites.
T. Mikolov, I. Sutskever, K. Chen, G. S. Corrado, and J. Dean, “Distributed representations of words and phrases and their compositionality,” in NIPS , 2013, pp. 3111–3119
2013
Earlier work this paper cites.
W. J. Scheirer, A. de Rezende Rocha, A. Sapkota, and T. E. Boult, “Toward open set recognition,” TPAMI , vol. 35, no. 7, pp. 1757–1772, 2013
2013
Earlier work this paper cites.
A. Graves, A. Mohamed, and G. E. Hinton, “Speech recognition with deep recurrent neural networks,” in ICASSP , 2013, pp. 6645–6649
2013
Earlier work this paper cites.
J. J. McAuley and J. Leskovec, “Hidden factors and hidden topics: understanding rating dimensions with review text,” in Seventh ACM Conference on Recommender Systems , 2013, pp. 165–172
2013
Cited alongside, same era.
P. Gong, J. Ye, and C. Zhang, “Multi-stage multi-task feature learning,” JMLR , vol. 14, no. 1, pp. 2979–3010, 2013
2013
Cited alongside, same era.
A. Talwalkar, S. Kumar, M. Mohri, and H. A. Rowley, “Large-scale SVD and manifold learning,” JMLR , vol. 14, no. 1, pp. 3129–3152, 2013
2013
Cited alongside, same era.
Y. Deng, Q. Dai, R. Liu, Z. Zhang, and S. Hu, “Low-rank structure learning via nonconvex heuristic recovery,” TNNLS , vol. 24, no. 3, pp. 383–396, 2013
2013
Cited alongside, same era.
H. Zhang, Q. M. J. Wu, and T. M. Nguyen, “Incorporating mean template into finite mixture model for image segmentation,” TNNLS , vol. 24, no. 2, pp. 328–335, 2013
C. Xu, D. Tao, and C. Xu, “Robust extreme multi-label learning,” in KDD , 2016, pp. 1275–1284
2016
Later among the works it cites.
N. Rosenfeld and A. Globerson, “Optimal tagging with markov chain optimization,” in NIPS , 2016, pp. 1307–1315
2016
Later among the works it cites.
H. Yu, N. Rao, and I. S. Dhillon, “Temporal regularized matrix factorization for high-dimensional time series prediction,” in NIPS , 2016, pp. 847–855
2016
Later among the works it cites.
C. Li, B. Wang, V. Pavlu, and J. A. Aslam, “Conditional bernoulli mixtures for multi-label classification,” in ICML , 2016, pp. 2482–2491
2016
Later among the works it cites.
I. E. Yen, X. Huang, P. Ravikumar, K. Zhong, and I. S. Dhillon, “Pd-sparse : A primal and dual sparse approach to extreme multiclass and multilabel classification,” in ICML , 2016, pp. 3069–3077
2016
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
2013
Cited alongside, same era.
Y. Pang, Z. Ji, P. Jing, and X. Li, “Ranking graph embedding for learning to rerank,” TNNLS , vol. 24, no. 8, pp. 1292–1303, 2013
2013
Cited alongside, same era.
Y. Luo, D. Tao, C. Xu, C. Xu, H. Liu, and Y. Wen, “Multiview vector-valued manifold regularization for multilabel image classification,” TNNLS , vol. 24, no. 5, pp. 709–722, 2013
2013
Cited alongside, same era.
P. Perakis, G. Passalis, T. Theoharis, and I. A. Kakadiaris, “3d facial landmark detection under large yaw and expression variations,” TPAMI , vol. 35, no. 7, pp. 1552–1564, 2013
2013
Cited alongside, same era.
Y. Gong, S. Lazebnik, A. Gordo, and F. Perronnin, “Iterative quantization: A procrustean approach to learning binary codes for large-scale image retrieval,” TPAMI , vol. 35, no. 12, pp. 2916–2929, 2013
2013
Cited alongside, same era.
X. Kong, B. Cao, and P. S. Yu, “Multi-label classification by mining label and instance correlations from heterogeneous information networks,” in KDD , 2013, pp. 614–622
2013
Cited alongside, same era.
X. Wang and G. Sukthankar, “Multi-label relational neighbor classification using social context features,” in KDD , 2013, pp. 464–472
2013
Cited alongside, same era.
M. Xu, R. Jin, and Z. Zhou, “Speedup matrix completion with side information: Application to multi-label learning,” in NIPS , 2013, pp. 2301–2309
2013
Cited alongside, same era.
M. Cissé, M. Al-Shedivat, and S. Bengio, “ADIOS: architectures deep in output space,” in ICML , 2016, pp. 2770–2779
2016
Later among the works it cites.
S. E. Reed, Z. Akata, S. Mohan, S. Tenka, B. Schiele, and H. Lee, “Learning what and where to draw,” in NIPS , 2016, pp. 217–225
2016
Later among the works it cites.
2016
Later among the works it cites.
C. Gan, T. Yang, and B. Gong, “Learning attributes equals multi-source domain generalization,” in CVPR , 2016, pp. 87–97
2016
Later among the works it cites.
A. Bendale and T. E. Boult, “Towards open set deep networks,” in CVPR , 2016, pp. 1563–1572
2016
Later among the works it cites.
H. Yang, J. T. Zhou, and J. Cai, “Improving multi-label learning with missing labels by structured semantic correlations,” in ECCV , 2016, pp. 835–851
2016
Later among the works it cites.
A. Joulin, L. van der Maaten, A. Jabri, and N. Vasilache, “Learning visual features from large weakly supervised data,” in ECCV , 2016, pp. 67–84
2016
Later among the works it cites.
I. Jindal, M. S. Nokleby, and X. Chen, “Learning deep networks from noisy labels with dropout regularization,” in ICDM , 2016, pp. 967–972
2016
Later among the works it cites.
Y. Mao, G. Cheung, C. Lin, and Y. Ji, “Joint learning of similarity graph and image classifier from partial labels,” in Asia-Pacific Signal and Information Processing Association Annual Summit and Conference , 2016, pp. 1–4
2016
Later among the works it cites.
H. Nam and B. Han, “Learning multi-domain convolutional neural networks for visual tracking,” in CVPR , 2016, pp. 4293–4302
2016
Later among the works it cites.
A. Mousavian, H. Pirsiavash, and J. Kosecka, “Joint semantic segmentation and depth estimation with deep convolutional networks,” in 3DV , 2016, pp. 611–619
2016
Later among the works it cites.
H. Li, W. Zhang, Y. Chen, Y. Guo, G.-Z. Li, and X. Zhu, “A novel multi-target regression framework for time-series prediction of drug efficacy,” Scientific reports , vol. 7, p. 40652, 2017
2017
Later among the works it cites.
A. Newell and J. Deng, “Pixels to graphs by associative embedding,” in NIPS , 2017, pp. 2168–2177
2017
Later among the works it cites.
L. Yang, S. Chou, and Y. Yang, “Midinet: A convolutional generative adversarial network for symbolic-domain music generation,” in ISMIR , 2017, pp. 324–331
2017
Later among the works it cites.
X. Liang, Z. Hu, H. Zhang, C. Gan, and E. P. Xing, “Recurrent topic-transition GAN for visual paragraph generation,” in ICCV , 2017, pp. 3382–3391
2017
Later among the works it cites.
R. Krishna, Y. Zhu, O. Groth, J. Johnson, K. Hata, J. Kravitz, S. Chen, Y. Kalantidis, L. Li, D. A. Shamma, M. S. Bernstein, and L. Fei-Fei, “Visual genome: Connecting language and vision using crowdsourced dense image annotations,” IJCV , vol. 123, no. 1, pp. 32–73, 2017
2017
Later among the works it cites.
I. O. Tolstikhin, S. Gelly, O. Bousquet, C.-J. Simon-Gabriel, and B. Scholkopf, “Adagan: Boosting generative models,” in Advances in Neural Information Processing Systems , 2017, pp. 5424–5433
2017
Later among the works it cites.
M. Heusel, H. Ramsauer, T. Unterthiner, B. Nessler, and S. Hochreiter, “Gans trained by a two time-scale update rule converge to a local nash equilibrium,” in Advances in Neural Information Processing Systems , 2017, pp. 6626–6637
2017
Later among the works it cites.
2017
Later among the works it cites.
W. Liu and I. W. Tsang, “Making decision trees feasible in ultrahigh feature and label dimensions,” JMLR , vol. 18, pp. 81:1–81:36, 2017
2017
Later among the works it cites.
C. Dupuy and F. Bach, “Online but accurate inference for latent variable models with local gibbs sampling,” JMLR , vol. 18, pp. 126:1–126:45, 2017
2017
Later among the works it cites.
W. Liu, I. W. Tsang, and K. Müller, “An easy-to-hard learning paradigm for multiple classes and multiple labels,” JMLR , vol. 18, pp. 94:1–94:38, 2017
2017
Later among the works it cites.
S. Fang, J. Li, Y. Tian, T. Huang, and X. Chen, “Learning discriminative subspaces on random contrasts for image saliency analysis,” TNNLS , vol. 28, no. 5, pp. 1095–1108, 2017
2017
Later among the works it cites.
K. Zhang, D. Tao, X. Gao, X. Li, and J. Li, “Coarse-to-fine learning for single-image super-resolution,” TNNLS , vol. 28, no. 5, pp. 1109–1122, 2017
2017
Later among the works it cites.
M. Kim, “Mixtures of conditional random fields for improved structured output prediction,” TNNLS , vol. 28, no. 5, pp. 1233–1240, 2017
2017
Later among the works it cites.
Y. Cheung, M. Li, Q. Peng, and C. L. P. Chen, “A cooperative learning-based clustering approach to lip segmentation without knowing segment number,” TNNLS , vol. 28, no. 1, pp. 80–93, 2017
2017
Later among the works it cites.
L. Wang, L. Liu, and L. Zhou, “A graph-embedding approach to hierarchical visual word mergence,” TNNLS , vol. 28, no. 2, pp. 308–320, 2017
2017
Later among the works it cites.
Z. Li, Z. Lai, Y. Xu, J. Yang, and D. Zhang, “A locality-constrained and label embedding dictionary learning algorithm for image classification,” TNNLS , vol. 28, no. 2, pp. 278–293, 2017
2017
Later among the works it cites.
M. Cordts, T. Rehfeld, M. Enzweiler, U. Franke, and S. Roth, “Tree-structured models for efficient multi-cue scene labeling,” TPAMI , vol. 39, no. 7, pp. 1444–1454, 2017
2017
Later among the works it cites.
Y. Xu, E. Carlinet, T. Géraud, and L. Najman, “Hierarchical segmentation using tree-based shape spaces,” TPAMI , vol. 39, no. 3, pp. 457–469, 2017
2017
Later among the works it cites.
K. Fu, J. Jin, R. Cui, F. Sha, and C. Zhang, “Aligning where to see and what to tell: Image captioning with region-based attention and scene-specific contexts,” TPAMI , vol. 39, no. 12, pp. 2321–2334, 2017
2017
Later among the works it cites.
I. E. Yen, X. Huang, W. Dai, P. Ravikumar, I. S. Dhillon, and E. P. Xing, “Ppdsparse: A parallel primal-dual sparse method for extreme classification,” in KDD , 2017, pp. 545–553
2017
Later among the works it cites.
Y. Tagami, “Annexml: Approximate nearest neighbor search for extreme multi-label classification,” in KDD , 2017, pp. 455–464
2017
Later among the works it cites.
E. Racah, C. Beckham, T. Maharaj, S. E. Kahou, Prabhat, and C. Pal, “Extremeweather: A large-scale climate dataset for semi-supervised detection, localization, and understanding of extreme weather events,” in NIPS , 2017, pp. 3405–3416
2017
Later among the works it cites.
Y. Hu, J. Huang, and A. G. Schwing, “Maskrnn: Instance level video object segmentation,” in NIPS , 2017, pp. 324–333
2017
Later among the works it cites.
B. Joshi, M. Amini, I. Partalas, F. Iutzeler, and Y. Maximov, “Aggressive sampling for multi-class to binary reduction with applications to text classification,” in NIPS , 2017, pp. 4162–4171
2017
Later among the works it cites.
J. Nam, E. Loza Mencía, H. J. Kim, and J. Fürnkranz, “Maximizing subset accuracy with recurrent neural networks in multi-label classification,” in NIPS , 2017, pp. 5419–5429
2017
Later among the works it cites.
S. Si, H. Zhang, S. S. Keerthi, D. Mahajan, I. S. Dhillon, and C. Hsieh, “Gradient boosted decision trees for high dimensional sparse output,” in ICML , 2017, pp. 3182–3190
2017
Later among the works it cites.
V. Jain, N. Modhe, and P. Rai, “Scalable generative models for multi-label learning with missing labels,” in ICML , 2017, pp. 1636–1644
2017
Later among the works it cites.
T. Zhang and Z. Zhou, “Multi-class optimal margin distribution machine,” in ICML , 2017, pp. 4063–4071
2017
Later among the works it cites.
H. Zhang, T. Xu, and H. Li, “Stackgan: Text to photo-realistic image synthesis with stacked generative adversarial networks,” in ICCV , 2017, pp. 5908–5916
2017
Later among the works it cites.
Y. Jernite, A. Choromanska, and D. Sontag, “Simultaneous learning of trees and representations for extreme classification and density estimation,” in ICML , 2017, pp. 1665–1674
2017
Later among the works it cites.
J. Liu, W. Chang, Y. Wu, and Y. Yang, “Deep learning for extreme multi-label text classification,” in SIGIR , 2017, pp. 115–124
2017
Later among the works it cites.
S. Baker and A. Korhonen, “Initializing neural networks for hierarchical multi-label text classification,” in BioNLP , 2017, pp. 307–315
2017
Later among the works it cites.
S. Chen, C. Zhang, M. Dong, J. Le, and M. Rao, “Using ranking-cnn for age estimation,” in CVPR , 2017, pp. 742–751
2017
Later among the works it cites.
A. Gaure, A. Gupta, V. K. Verma, and P. Rai, “A probabilistic framework for zero-shot multi-label learning,” in The Conference on Uncertainty in Artificial Intelligence (UAI) , vol. 1, 2017, p. 3
2017
Later among the works it cites.
P. Mettes and C. G. M. Snoek, “Spatial-aware object embeddings for zero-shot localization and classification of actions,” in ICCV , 2017, pp. 4453–4462
2017
Later among the works it cites.
C. Gong, H. Zhang, J. Yang, and D. Tao, “Learning with inadequate and incorrect supervision,” in ICDM , 2017, pp. 889–894
2017
Later among the works it cites.
A. Veit, N. Alldrin, G. Chechik, I. Krasin, A. Gupta, and S. J. Belongie, “Learning from noisy large-scale datasets with minimal supervision,” in CVPR , 2017, pp. 6575–6583
2017
Later among the works it cites.
2017
Later among the works it cites.
F. Yu and M. Zhang, “Maximum margin partial label learning,” Machine Learning , vol. 106, no. 4, pp. 573–593, 2017
2017
Later among the works it cites.
M. Zhang, F. Yu, and C. Tang, “Disambiguation-free partial label learning,” TKDE , vol. 29, no. 10, pp. 2155–2167, 2017
2017
Later among the works it cites.
A. Milan, S. H. Rezatofighi, A. R. Dick, I. D. Reid, and K. Schindler, “Online multi-target tracking using recurrent neural networks,” in AAAI , 2017, pp. 4225–4232
2017
Later among the works it cites.
J. Johnson, A. Gupta, and L. Fei-Fei, “Image generation from scene graphs,” in CVPR , 2018, pp. 1219–1228
2018
Later among the works it cites.
H. Liu, J. Cai, and Y. Ong, “Remarks on multi-output gaussian process regression,” Knowledge-Based Systems , vol. 144, pp. 102–121, 2018
2018
Later among the works it cites.
A. S. Weigend, Time series prediction: forecasting the future and understanding the past . Routledge, 2018
2018
Later among the works it cites.
2018
Later among the works it cites.
2018
Later among the works it cites.
M. Lucic, K. Kurach, M. Michalski, S. Gelly, and O. Bousquet, “Are gans created equal? A large-scale study,” in NeualPS , 2018, pp. 698–707
2018
Later among the works it cites.
K. Lee, X. He, L. Zhang, and L. Yang, “Cleannet: Transfer learning for scalable image classifier training with label noise,” in CVPR , 2018, pp. 5447–5456
2018
Later among the works it cites.
C. Kümmerle and J. Sigl, “Harmonic mean iteratively reweighted least squares for low-rank matrix recovery,” JMLR , vol. 19, 2018
2018
Later among the works it cites.
K. Fu, J. Li, J. Jin, and C. Zhang, “Image-text surgery: Efficient concept learning in image captioning by generating pseudopairs,” TNNLS , vol. 29, no. 12, pp. 5910–5921, 2018
2018
Later among the works it cites.
E. Protas, J. D. Bratti, J. F. O. Gaya, P. Drews, and S. S. C. Botelho, “Visualization methods for image transformation convolutional neural networks,” TNNLS , 2018
2018
Later among the works it cites.
H. Zhang, S. Wang, X. Xu, T. W. S. Chow, and Q. M. J. Wu, “Tree2vector: Learning a vectorial representation for tree-structured data,” TNNLS , vol. 29, no. 11, pp. 5304–5318, 2018
2018
Later among the works it cites.
Z. Lin, G. Ding, J. Han, and L. Shao, “End-to-end feature-aware label space encoding for multilabel classification with many classes,” TNNLS , vol. 29, no. 6, pp. 2472–2487, 2018
2018
Later among the works it cites.
B. Zhang, D. Xiong, and J. Su, “Neural machine translation with deep attention,” TPAMI , 2018
2018
Later among the works it cites.
S. Jeong, J. Lee, B. Kim, Y. Kim, and J. Noh, “Object segmentation ensuring consistency across multi-viewpoint images,” TPAMI , vol. 40, no. 10, pp. 2455–2468, 2018
2018
Later among the works it cites.
C. Raposo, M. Antunes, and J. P. Barreto, “Piecewise-planar stereoscan: Sequential structure and motion using plane primitives,” TPAMI , vol. 40, no. 8, pp. 1918–1931, 2018
2018
Later among the works it cites.
K. G. Dizaji, X. Wang, and H. Huang, “Semi-supervised generative adversarial network for gene expression inference,” in KDD , 2018, pp. 1435–1444
2018
Later among the works it cites.
M. Lee, B. Gao, and R. Zhang, “Rare query expansion through generative adversarial networks in search advertising,” in KDD , 2018, pp. 500–508
2018
Later among the works it cites.
S. Hong, X. Yan, T. S. Huang, and H. Lee, “Learning hierarchical semantic image manipulation through structured representations,” in NIPS , 2018, pp. 2713–2723
2018
Later among the works it cites.
M. Wydmuch, K. Jasinska, M. Kuznetsov, R. Busa-Fekete, and K. Dembczynski, “A no-regret generalization of hierarchical softmax to extreme multi-label classification,” in NIPS , 2018, pp. 6358–6368
2018
Later among the works it cites.
B. Pan, Y. Yang, H. Li, Z. Zhao, Y. Zhuang, D. Cai, and X. He, “Macnet: Transferring knowledge from machine comprehension to sequence-to-sequence models,” in NIPS , 2018, pp. 6095–6105
2018
Later among the works it cites.
W. Siblini, F. Meyer, and P. Kuntz, “Craftml, an efficient clustering-based random forest for extreme multi-label learning,” in ICML , 2018, pp. 4671–4680
2018
Later among the works it cites.
I. E. Yen, S. Kale, F. X. Yu, D. N. Holtmann-Rice, S. Kumar, and P. Ravikumar, “Loss decomposition for fast learning in large output spaces,” in ICML , 2018, pp. 5626–5635
2018
Later among the works it cites.
J. Wehrmann, R. Cerri, and R. C. Barros, “Hierarchical multi-label classification networks,” in ICML , 2018, pp. 5225–5234
2018
Later among the works it cites.
2018
Later among the works it cites.
2018
Later among the works it cites.
W. Liu, D. Xu, I. Tsang, and W. Zhang, “Metric learning for multi-output tasks,” TPAMI , 2018, doi
2018
Later among the works it cites.
X. Shen, W. Liu, I. W. Tsang, Q. Sun, and Y. Ong, “Compact multi-label learning,” in AAAI , 2018, pp. 4066–4073
2018
Later among the works it cites.
X. Shen, W. Liu, I. W. Tsang, Q. Sun, and Y. Ong, “Multilabel prediction via cross-view search,” TNNLS , vol. 29, no. 9, pp. 4324–4338, 2018
2018
Later among the works it cites.
X. Shen, W. Liu, Y. Luo, Y. Ong, and I. W. Tsang, “Deep discrete prototype multilabel learning,” in IJCAI , 2018, pp. 2675–2681
2018
Later among the works it cites.
2018
Later among the works it cites.
2018
Later among the works it cites.
2018
Later among the works it cites.
A. Rios and R. Kavuluru, “Few-shot and zero-shot multi-label learning for structured label spaces,” in Conference on Empirical Methods in Natural Language Processing , 2018, pp. 3132–3142
2018
Later among the works it cites.
C. Lee, W. Fang, C. Yeh, and Y. F. Wang, “Multi-label zero-shot learning with structured knowledge graphs,” in CVPR , 2018, pp. 1576–1585
2018
Later among the works it cites.
C. Gong, T. Liu, Y. Tang, J. Yang, J. Yang, and D. Tao, “A regularization approach for instance-based superset label learning,” TCYB , vol. 48, no. 3, pp. 967–978, 2018
2018
Later among the works it cites.
M. Xie and S. Huang, “Partial multi-label learning,” in AAAI , 2018, pp. 4302–4309
2018
Later among the works it cites.
A. Büyükçakir, H. R. Bonab, and F. Can, “A novel online stacked ensemble for multi-label stream classification,” in CIKM , 2018, pp. 1063–1072
2018
Later among the works it cites.
L. Huang, Q. Yang, and W. Zheng, “Online hashing,” TNNLS , vol. 29, no. 6, pp. 2309–2322, 2018
2018
Later among the works it cites.
D. Xu, I. W. Tsang, and Y. Zhang, “Online product quantization,” TKDE , vol. 30, no. 11, pp. 2185 – 2198, 2018
2018
Later among the works it cites.
Y. Wang, W. Liu, X. Ma, J. Bailey, H. Zha, L. Song, and S. Xia, “Iterative learning with open-set noisy labels,” in Computer Vision and Pattern Recognition , 2018, pp. 8688–8696
2018
Later among the works it cites.
Y. Shi, D. Xu, Y. Pan, I. W. Tsang, and S. Pan, “Label embedding with partial heterogeneous contexts,” in AAAI , 2019, pp. 4926–4933
2019
Closest in time.
K. Xu, J. Ba, R. Kiros, K. Cho, A. Courville, R. Salakhudinov, R. Zemel, and Y. Bengio, “Show, attend and tell: Neural image caption generation with visual attention,” in International conference on machine learning , 2015, pp. 2048–2057
2057
Closest in time.
T. Gao and D. Koller, “Discriminative learning of relaxed hierarchy for large-scale visual recognition,” in ICCV , 2011, pp. 2072–2079
2079
Closest in time.
S. S. Bucak, P. K. Mallapragada, R. Jin, and A. K. Jain, “Efficient multi-label ranking for multi-class learning: Application to object recognition,” in ICCV , 2009, pp. 2098–2105
2098
Closest in time.