Fetching the paper…
Reading the bibliography…
Recently, multi-view representation learning has become a rapidly growing direction in machine learning and data mining areas.
C. Wang, D. M. Blei, and F. Li, “Simultaneous image classification and annotation,” in
1910
Earlier work this paper cites.
H. Hotelling, “Relations between two sets of variates,”
1936
Earlier work this paper cites.
C. Feichtenhofer, A. Pinz, and A. Zisserman, “Convolutional two-stream network fusion for video action recognition,” in
1941
Earlier work this paper cites.
J. R. Kettenring, “Canonical analysis of several sets of variables,”
1971
Earlier work this paper cites.
H. Wold, “Soft modeling: the basic design and some extensions,”
1982
Earlier work this paper cites.
D. E. Rumelhart, J. L. McClelland, and C. PDP Research Group, Eds.,
1986
Earlier work this paper cites.
G. H. Golub and H. Zha, “The canonical correlations of matrix pairs and their numerical computation,” Stanford, CA, USA, Tech. Rep., 1992
1992
Earlier work this paper cites.
S. Becker and G. E. Hinton, “Self-organizing neural network that discovers surfaces in random-dot stereograms,”
1992
Earlier work this paper cites.
Y. Bengio, P. Simard, and P. Frasconi, “Learning long-term dependencies with gradient descent is difficult,”
1994
Earlier work this paper cites.
R. Tibshirani, “Regression shrinkage and selection via the lasso,”
1996
Earlier work this paper cites.
S. Becker, “Mutual information maximization: Models of cortical self-organization.”
1996
Earlier work this paper cites.
Y. Freund and R. E. Schapire, “A decision-theoretic generalization of on-line learning and an application to boosting,”
1997
Earlier work this paper cites.
S. Hochreiter and J. Schmidhuber, “Long short-term memory,”
1997
Earlier work this paper cites.
A. Blum and T. Mitchell, “Combining labeled and unlabeled data with co-training,” in
1998
Earlier work this paper cites.
P. L. Lai and C. Fyfe, “Canonical correlation analysis using artificial neural networks,” in
1998
Earlier work this paper cites.
——, “A neural implementation of canonical correlation analysis,”
1999
Earlier work this paper cites.
D. A. Cohn and T. Hofmann, “The missing link - A probabilistic model of document content and hypertext connectivity,” in
2000
Earlier work this paper cites.
P. L. Lai and C. Fyfe, “Kernel and nonlinear canonical correlation analysis,” in
2000
Earlier work this paper cites.
C. K. I. Williams and M. W. Seeger, “Using the nyström method to speed up kernel machines,” in
2000
Earlier work this paper cites.
W. W. Hsieh, “Nonlinear canonical correlation analysis by neural networks,”
2000
Earlier work this paper cites.
R. Herbrich, T. Graepel, and K. Obermayer, “Large margin rank boundaries for ordinal regression,” in
2000
Earlier work this paper cites.
S. Akaho, “A kernel method for canonical correlation analysis,” in
2001
Earlier work this paper cites.
B. Schölkopf and A. J. Smola,
2001
Earlier work this paper cites.
S. Wold, M. Sjöström, and L. Eriksson, “Pls-regression: a basic tool of chemometrics,”
2001
Earlier work this paper cites.
F. R. Bach and M. I. Jordan, “Kernel independent component analysis,”
2002
Earlier work this paper cites.
M. Brand, “Incremental singular value decomposition of uncertain data with missing values,” in
2002
Earlier work this paper cites.
M. Collins and N. Duffy, “New ranking algorithms for parsing and tagging: Kernels over discrete structures, and the voted perceptron,” in
2002
Earlier work this paper cites.
G. E. Hinton, “Training products of experts by minimizing contrastive divergence,”
2002
Earlier work this paper cites.
E. P. Xing, A. Y. Ng, M. I. Jordan, and S. J. Russell, “Distance metric learning with application to clustering with side-information,” in
2002
Earlier work this paper cites.
K. Barnard, P. Duygulu, D. Forsyth, N. de Freitas, D. M. Blei, and M. I. Jordan, “Matching words and pictures,”
2003
Earlier work this paper cites.
D. M. Blei and M. I. Jordan, “Modeling annotated data,” in
2003
Earlier work this paper cites.
D. Li, N. Dimitrova, M. Li, and I. K. Sethi, “Multimedia content processing through cross-modal association,” in
2003
Earlier work this paper cites.
Y. Yamanishi, J. Vert, A. Nakaya, and M. Kanehisa, “Extraction of correlated gene clusters from multiple genomic data by generalized kernel canonical correlation analysis,” in
2003
Earlier work this paper cites.
M. Kuss and T. Graepel, “The geometry of kernel canonical correlation analysis,” Max Planck Institute for Biological Cybernetics, Tübingen, Germany, Tech. Rep. 108, may 2003
2003
Earlier work this paper cites.
M. Barker and W. Rayens, “Partial least squares for discrimination,”
2003
Earlier work this paper cites.
D. M. Blei, A. Y. Ng, and M. I. Jordan, “Latent dirichlet allocation,”
2003
Earlier work this paper cites.
D. R. Hardoon, S. R. Szedmak, and J. R. Shawe-taylor, “Canonical correlation analysis: An overview with application to learning methods,”
2004
Earlier work this paper cites.
B. Efron, T. Hastie, I. Johnstone, and R. Tibshirani, “Least angle regression,”
2004
Earlier work this paper cites.
R. Collobert and S. Bengio, “Links between perceptrons, mlps and svms,” in
2004
Earlier work this paper cites.
M. Welling, M. Rosen-Zvi, and G. E. Hinton, “Exponential family harmoniums with an application to information retrieval,” in
2004
Earlier work this paper cites.
E. P. Xing, R. Yan, and A. G. Hauptmann, “Mining associated text and images with dual-wing harmoniums,” in
2005
Earlier work this paper cites.
E. Kidron, Y. Y. Schechner, and M. Elad, “Pixels that sound.” in
2005
Earlier work this paper cites.
X. Z. Fern, C. E. Brodley, and M. A. Friedl, “Correlation clustering for learning mixtures of canonical correlation models,” in
2005
Earlier work this paper cites.
A. Gretton, R. Herbrich, A. J. Smola, O. Bousquet, and B. Schölkopf, “Kernel methods for measuring independence,”
2005
Earlier work this paper cites.
C. J. C. Burges, T. Shaked, E. Renshaw, A. Lazier, M. Deeds, N. Hamilton, and G. N. Hullender, “Learning to rank using gradient descent,” in
2005
Earlier work this paper cites.
G. Shakhnarovich, “Learning task-specific similarity,” Ph.D. dissertation, Cambridge, MA, USA, 2005
2005
Earlier work this paper cites.
M. Belkin, P. Niyogi, and V. Sindhwani, “Manifold regularization: A geometric framework for learning from labeled and unlabeled examples,”
2006
Earlier work this paper cites.
R. Rosipal and N. Krämer,
2006
Earlier work this paper cites.
C. M. Bishop,
2006
Earlier work this paper cites.
B. Long, Z. M. Zhang, and P. S. Yu, “A probabilistic framework for relational clustering,” in
2007
Earlier work this paper cites.
S. M. Kakade and D. P. Foster, “Multi-view regression via canonical correlation analysis,” in
2007
Earlier work this paper cites.
T. Kim, J. Kittler, and R. Cipolla, “Discriminative learning and recognition of image set classes using canonical correlations,”
2007
Earlier work this paper cites.
D. R. Hardoon and J. Shawe-Taylor, “Sparse canonical correlation analysis,” Department of Computer Science, University College London, Tech. Rep., 2007
2007
Earlier work this paper cites.
A. d’Aspremont, L. E. Ghaoui, M. I. Jordan, and G. R. G. Lanckriet, “A direct formulation for sparse PCA using semidefinite programming,”
2007
Earlier work this paper cites.
D. Torres, D. Turnbull, L. Barrington, B. Sriperumbudur, and G. Lanckriet, “Finding musically meaningful words by sparse cca,”
2007
Earlier work this paper cites.
A. d’Aspremont, F. R. Bach, and L. E. Ghaoui, “Full regularization path for sparse principal component analysis,” in
2007
Earlier work this paper cites.
K. Fukumizu, F. R. Bach, and A. Gretton, “Statistical consistency of kernel canonical correlation analysis,”
2007
Earlier work this paper cites.
L. Cao and F. Li, “Spatially coherent latent topic model for concurrent segmentation and classification of objects and scenes,” in
2007
Earlier work this paper cites.
P. Vincent, H. Larochelle, Y. Bengio, and P.-A. Manzagol, “Extracting and composing robust features with denoising autoencoders,” in
2008
Earlier work this paper cites.
A. P. Singh and G. J. Gordon, “Relational learning via collective matrix factorization,” in
2008
Earlier work this paper cites.
L. Sun, S. Ji, and J. Ye, “A least squares formulation for canonical correlation analysis,” in
2008
Earlier work this paper cites.
D. P. Foster, R. Johnson, and T. Zhang, “Multi-view dimensionality reduction via canonical correlation analysis,” Tech. Rep., 2008
2008
Earlier work this paper cites.
M. B. Blaschko and C. H. Lampert, “Correlational spectral clustering,” in
2008
Earlier work this paper cites.
S. Waaijenborg, P. C. Verselewel de Witt Hamer, and A. H. Zwinderman, “Quantifying the association between gene expressions and DNA-markers by penalized canonical correlation analysis.”
2008
Earlier work this paper cites.
A. Wiesel, M. Kliger, and A. O. Hero, III, “A greedy approach to sparse canonical correlation analysis,”
2008
Earlier work this paper cites.
D. Grangier and S. Bengio, “A discriminative kernel-based approach to rank images from text queries,”
2008
Earlier work this paper cites.
Y. Weiss, A. Torralba, and R. Fergus, “Spectral hashing,” in
2008
Earlier work this paper cites.
R. Salakhutdinov and G. E. Hinton, “Deep boltzmann machines.” in
2009
Earlier work this paper cites.
J. Bleiholder and F. Naumann, “Data fusion,”
2009
Cited alongside, same era.
K. Chaudhuri, S. M. Kakade, K. Livescu, and K. Sridharan, “Multi-view clustering via canonical correlation analysis,” in
2009
Cited alongside, same era.
D. M. Witten, T. Hastie, and R. Tibshirani, “A penalized matrix decomposition, with applications to sparse principal components and canonical correlation analysis,”
2009
Cited alongside, same era.
D. R. Hardoon and J. Shawe-Taylor, “Convergence analysis of kernel canonical correlation analysis: theory and practice,”
2009
Cited alongside, same era.
W. R. Schwartz, A. Kembhavi, D. Harwood, and L. S. Davis, “Human detection using partial least squares analysis,” in
2009
Cited alongside, same era.
Y. Kim, “Convolutional neural networks for sentence classification,”
2014
Later among the works it cites.
O. Abdel-Hamid, A.-r. Mohamed, H. Jiang, L. Deng, G. Penn, and D. Yu, “Convolutional neural networks for speech recognition,”
2014
Later among the works it cites.
D. Kiela and L. Bottou, “Learning image embeddings using convolutional neural networks for improved multi-modal semantics,” in
2014
Later among the works it cites.
Y. Gong, Q. Ke, M. Isard, and S. Lazebnik, “A multi-view embedding space for modeling internet images, tags, and their semantics,”
2014
Later among the works it cites.
H. Avron, C. Boutsidis, S. Toledo, and A. Zouzias, “Efficient dimensionality reduction for canonical correlation analysis,”
2014
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
2009
Cited alongside, same era.
Y. Jia, M. Salzmann, and T. Darrell, “Factorized latent spaces with structured sparsity,” in
2010
Cited alongside, same era.
N. Chen, J. Zhu, and E. P. Xing, “Predictive subspace learning for multi-view data: a large margin approach,” in
2010
Cited alongside, same era.
P. K. Atrey, M. A. Hossain, A. El-Saddik, and M. S. Kankanhalli, “Multimodal fusion for multimedia analysis: a survey,”
2010
Cited alongside, same era.
Z. Li, J. Liu, X. Zhu, T. Liu, and H. Lu, “Image annotation using multi-correlation probabilistic matrix factorization,” in
2010
Cited alongside, same era.
N. Rasiwasia, J. Costa Pereira, E. Coviello, G. Doyle, G. R. Lanckriet, R. Levy, and N. Vasconcelos, “A new approach to cross-modal multimedia retrieval,” in
2010
Cited alongside, same era.
L. Sun, B. Ceran, and J. Ye, “A scalable two-stage approach for a class of dimensionality reduction techniques,” in
2010
Cited alongside, same era.
Y. Lu and D. P. Foster, “large scale canonical correlation analysis with iterative least squares,” in
2014
Later among the works it cites.
D. Lopez-Paz, S. Sra, A. J. Smola, Z. Ghahramani, and B. Schölkopf, “Randomized nonlinear component analysis,” in
2014
Later among the works it cites.
J. C. Pereira, E. Coviello, G. Doyle, N. Rasiwasia, G. R. G. Lanckriet, R. Levy, and N. Vasconcelos, “On the role of correlation and abstraction in cross-modal multimedia retrieval,”
2014
Later among the works it cites.
Z. Yu, F. Wu, Y. Yang, Q. Tian, J. Luo, and Y. Zhuang, “Discriminative coupled dictionary hashing for fast cross-media retrieval,” in
2014
Later among the works it cites.
R. Kiros, R. Salakhutdinov, and R. S. Zemel, “Multimodal neural language models.” in
2014
Later among the works it cites.
J. Yu, Y. Rui, and D. Tao, “Click prediction for web image reranking using multimodal sparse coding,”
2014
Later among the works it cites.
F. Wu, Z. Yu, Y. Yang, S. Tang, Y. Zhang, and Y. Zhuang, “Sparse multi-modal hashing,”
2014
Later among the works it cites.
C. Silberer and M. Lapata, “Learning grounded meaning representations with autoencoders.” in
2014
Later among the works it cites.
K. Cho, B. van Merrienboer, Ç. Gülçehre, D. Bahdanau, F. Bougares, H. Schwenk, and Y. Bengio, “Learning phrase representations using RNN encoder-decoder for statistical machine translation,” in
2014
Later among the works it cites.
I. Sutskever, O. Vinyals, and Q. V. Le, “Sequence to sequence learning with neural networks,” in
2014
Later among the works it cites.
2014
Later among the works it cites.
2014
Later among the works it cites.
2014
Later among the works it cites.
2014
Later among the works it cites.
J. Ba, V. Mnih, and K. Kavukcuoglu, “Multiple object recognition with visual attention,”
2014
Later among the works it cites.
V. Mnih, N. Heess, A. Graves, and K. Kavukcuoglu, “Recurrent models of visual attention,”
2014
Later among the works it cites.
E. Bruni, N.-K. Tran, and M. Baroni, “Multimodal distributional semantics.”
2014
Later among the works it cites.
D. Kiela and L. Bottou, “Learning image embeddings using convolutional neural networks for improved multi-modal semantics.” in
2014
Later among the works it cites.
A. Graves and N. Jaitly, “Towards end-to-end speech recognition with recurrent neural networks,” in
2014
Later among the works it cites.
W. Wang, R. Arora, K. Livescu, and J. A. Bilmes, “On deep multi-view representation learning,” in
2015
Later among the works it cites.
J. Donahue, L. Anne Hendricks, S. Guadarrama, M. Rohrbach, S. Venugopalan, K. Saenko, and T. Darrell, “Long-term recurrent convolutional networks for visual recognition and description,” in
2015
Later among the works it cites.
D. Lahat, T. Adali, and C. Jutten, “Multimodal data fusion: An overview of methods, challenges, and prospects,”
2015
Later among the works it cites.
S. Gunasekar, M. Yamada, D. Yin, and Y. Chang, “Consistent collective matrix completion under joint low rank structure,” in
2015
Later among the works it cites.
W. Wang and K. Livescu, “Large-scale approximate kernel canonical correlation analysis,”
2015
Later among the works it cites.
W. Wang, R. Arora, K. Livescu, and J. A. Bilmes, “Unsupervised learning of acoustic features via deep canonical correlation analysis,” in
2015
Later among the works it cites.
A. Lu, W. Wang, M. Bansal, K. Gimpel, and K. Livescu, “Deep multilingual correlation for improved word embeddings,” in
2015
Later among the works it cites.
W. Wang, R. Arora, K. Livescu, and N. Srebro, “Stochastic optimization for deep CCA via nonlinear orthogonal iterations,” in
2015
Later among the works it cites.
F. Yan and K. Mikolajczyk, “Deep correlation for matching images and text,” in
2015
Later among the works it cites.
J. Wang, H. Wang, Y. Tu, K. Duan, Z. Zhan, and S. Chekuri, “Supervised cross-modal factor analysis,”
2015
Later among the works it cites.
A. M. Elkahky, Y. Song, and X. He, “A multi-view deep learning approach for cross domain user modeling in recommendation systems,” in
2015
Later among the works it cites.
2015
Later among the works it cites.
H. Fang, S. Gupta, F. Iandola, R. K. Srivastava, L. Deng, P. Dollár, J. Gao, X. He, M. Mitchell, J. C. Platt
2015
Later among the works it cites.
R. Xu, C. Xiong, W. Chen, and J. J. Corso, “Jointly modeling deep video and compositional text to bridge vision and language in a unified framework.” in
2015
Later among the works it cites.
L. Pang and C.-W. Ngo, “Mutlimodal learning with deep boltzmann machine for emotion prediction in user generated videos,” in
2015
Later among the works it cites.
H. Su, S. Maji, E. Kalogerakis, and E. Learned-Miller, “Multi-view convolutional neural networks for 3d shape recognition,” in
2015
Later among the works it cites.
E. Ahmed, M. Jones, and T. K. Marks, “An improved deep learning architecture for person re-identification,” in
2015
Later among the works it cites.
S. Venugopalan, M. Rohrbach, J. Donahue, R. Mooney, T. Darrell, and K. Saenko, “Sequence to sequence-video to text,” in
2015
Later among the works it cites.
S. Antol, A. Agrawal, J. Lu, M. Mitchell, D. Batra, C. Lawrence Zitnick, and D. Parikh, “Vqa: Visual question answering,” in
2015
Later among the works it cites.
2015
Later among the works it cites.
W. Yu, K. Yang, Y. Bai, H. Yao, and Y. Rui, “Learning cross space mapping via dnn using large scale click-through logs,”
2015
Later among the works it cites.
X. Jiang, F. Wu, X. Li, Z. Zhao, W. Lu, S. Tang, and Y. Zhuang, “Deep compositional cross-modal learning to rank via local-global alignment,” in
2015
Later among the works it cites.
2015
Later among the works it cites.
O. Vinyals and Q. Le, “A neural conversational model,”
2015
Later among the works it cites.
J. Yue-Hei Ng, M. Hausknecht, S. Vijayanarasimhan, O. Vinyals, R. Monga, and G. Toderici, “Beyond short snippets: Deep networks for video classification,” in
2015
Later among the works it cites.
D. Tran, L. Bourdev, R. Fergus, L. Torresani, and M. Paluri, “Learning spatiotemporal features with 3d convolutional networks,” in
2015
Later among the works it cites.
H. Wang, N. Wang, and D.-Y. Yeung, “Collaborative deep learning for recommender systems,” in
2015
Later among the works it cites.
N. McLaughlin, J. Martinez del Rincon, and P. Miller, “Recurrent convolutional network for video-based person re-identification,” in
2016
Closest in time.
Y. Zhen, Y. Gao, D. Yeung, H. Zha, and X. Li, “Spectral multimodal hashing and its application to multimedia retrieval,”
2016
Closest in time.
2016
Closest in time.
C. Cadena, A. R. Dick, and I. D. Reid, “Multi-modal auto-encoders as joint estimators for robotics scene understanding.” in
2016
Closest in time.
S. Rastegar, M. Soleymani, H. R. Rabiee, and S. Mohsen Shojaee, “Mdl-cw: A multimodal deep learning framework with cross weights,” in
2016
Closest in time.
F. Wang, W. Zuo, L. Lin, D. Zhang, and L. Zhang, “Joint learning of single-image and cross-image representations for person re-identification,” in
2016
Closest in time.
H. Palangi, L. Deng, Y. Shen, J. Gao, X. He, J. Chen, X. Song, and R. K. Ward, “Deep sentence embedding using long short-term memory networks: Analysis and application to information retrieval,”
2016
Closest in time.
Y. Wei, Y. Zhao, C. Lu, S. Wei, L. Liu, Z. Zhu, and S. Yan, “Cross-modal retrieval with cnn visual features: A new baseline,” 2016
2016
Closest in time.
F. Wu, X. Lu, J. Song, S. Yan, Z. M. Zhang, Y. Rui, and Y. Zhuang, “Learning of multimodal representations with random walks on the click graph,”
2016
Closest in time.
2016
Closest in time.
I. V. Serban, A. Sordoni, Y. Bengio, A. C. Courville, and J. Pineau, “Building end-to-end dialogue systems using generative hierarchical neural network models.” in
2016
Closest in time.
S. Yeung, O. Russakovsky, G. Mori, and L. Fei-Fei, “End-to-end learning of action detection from frame glimpses in videos,” in
2016
Closest in time.
J. Yuan, B. Ni, X. Yang, and A. A. Kassim, “Temporal action localization with pyramid of score distribution features,” in
2016
Closest in time.
V. Ramanishka, A. Das, D. H. Park, S. Venugopalan, L. A. Hendricks, M. Rohrbach, and K. Saenko, “Multimodal video description,” in
2016
Closest in time.
G. Collell, T. Zhang, and M. Moens, “Imagined visual representations as multimodal embeddings,” in
2017
Closest in time.
X. Dong, L. Yu, Z. Wu, Y. Sun, L. Yuan, and F. Zhang, “A hybrid collaborative filtering model with deep structure for recommender systems.” in
2017
Closest in time.
A. Kanezaki, Y. Matsushita, and Y. Nishida, “Rotationnet: Joint object categorization and pose estimation using multiviews from unsupervised viewpoints,” in
2018
Closest in time.