메뉴 건너뛰기




Volumn 2, Issue , 2015, Pages 1083-1092

On deep multi-view representation learning

Author keywords

[No Author keywords available]

Indexed keywords

ARTIFICIAL INTELLIGENCE; FEEDFORWARD NEURAL NETWORKS; VISUAL LANGUAGES;

EID: 84969750897     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper
Times cited : (903)

References (55)
  • 2
    • 84897553529 scopus 로고    scopus 로고
    • Deep canonical correlation analysis
    • Andrew, Galen, Arora, Raman, Bilmes, Jeff, and Livescu, Karen. Deep canonical correlation analysis. In ICML, pp. 1247-1255, 2013.
    • (2013) ICML , pp. 1247-1255
    • Andrew, G.1    Arora, R.2    Bilmes, J.3    Livescu, K.4
  • 4
    • 84890540774 scopus 로고    scopus 로고
    • Multi-view CCA-based acoustic features for phonetic recognition across speakers and domains
    • Arora, Raman and Livescu, Karen. Multi-view CCA-based acoustic features for phonetic recognition across speakers and domains. In ICASSP, 2013.
    • (2013) ICASSP
    • Arora, R.1    Livescu, K.2
  • 6
    • 0026586030 scopus 로고
    • Self-organizing neural network that discovers surfaces in random-dot stereograms
    • Becker, Suzanna and Hinton, Geoffrey E. Self-organizing neural network that discovers surfaces in random-dot stereograms. Nature, 355:161-163, 1992.
    • (1992) Nature , vol.355 , pp. 161-163
    • Becker, S.1    Hinton, G.E.2
  • 8
    • 24644433110 scopus 로고    scopus 로고
    • On the regularization of canonical correlation analysis
    • Bie, Tijl De and Moor, Bart De. On the regularization of canonical correlation analysis. Int. Sympos. ICA and BSS, 2003.
    • (2003) Int. Sympos. ICA and BSS
    • De Bie, T.1    De Moor, B.2
  • 9
    • 84870673011 scopus 로고    scopus 로고
    • A comparison of vector-based representations for semantic composition
    • Blacoe, William and Lapata, Mirella. A comparison of vector-based representations for semantic composition. In EMNLP, pp. 546-556, 2012.
    • (2012) EMNLP , pp. 546-556
    • Blacoe, W.1    Lapata, M.2
  • 10
    • 51949086515 scopus 로고    scopus 로고
    • Correlational spectral clustering
    • Blaschko, Mathew B. and Lampert, Christoph H. Correlational spectral clustering. In CVPR, pp. 1-8, 2008.
    • (2008) CVPR , pp. 1-8
    • Blaschko, M.B.1    Lampert, C.H.2
  • 12
    • 30344483178 scopus 로고    scopus 로고
    • Document clustering using locality preserving indexing
    • Cai, Deng, He, Xiaofei, and Han, Jiawei. Document clustering using Locality Preserving Indexing. IEEE Trans. Knowledge and Data Engineering, 17(12): 1624-1637, 2005.
    • (2005) IEEE Trans. Knowledge and Data Engineering , vol.17 , Issue.12 , pp. 1624-1637
    • Cai, D.1    He, X.2    Han, J.3
  • 13
    • 84929385996 scopus 로고    scopus 로고
    • An autoencoder approach to learning bilingual word representations
    • Chandar, Sarath, Lauly, Stanislas, Larochelle, Hugo, Khapra, Mitesh M., Ravindran, Balaraman, Raykar, Vikas, and Saha, Amrita. An autoencoder approach to learning bilingual word representations. In NIPS, pp. 1853-1861,2014.
    • (2014) NIPS , pp. 1853-1861
    • Chandar, S.1    Lauly, S.2    Larochelle, H.3    Khapra, M.M.4    Ravindran, B.5    Raykar, V.6    Saha, A.7
  • 15
    • 71149099083 scopus 로고    scopus 로고
    • Multi-view clustering via canonical correlation analysis
    • Chaudhuri, Kamalika, Kakade, Sham M., Livescu, Karen, and Sridharan, Karthik. Multi-view clustering via canonical correlation analysis. In ICML, pp. 129-136, 2009.
    • (2009) ICML , pp. 129-136
    • Chaudhuri, K.1    Kakade, S.M.2    Livescu, K.3    Sridharan, K.4
  • 17
    • 85162328823 scopus 로고    scopus 로고
    • Multi-view learning of word embeddings via CCA
    • Dhillon, Paramveer, Foster, Dean, and Ungar, Lyle. Multi-view learning of word embeddings via CCA. In NIPS, pp. 199-207, 2011.
    • (2011) NIPS , pp. 199-207
    • Dhillon, P.1    Foster, D.2    Ungar, L.3
  • 20
    • 84857892556 scopus 로고    scopus 로고
    • Noise-contrastive estimation of unnormalized statistical models, with applications to natural image statistics
    • Gutmann, Michael and Hyvärinen, Aapo. Noise-contrastive estimation of unnormalized statistical models, with applications to natural image statistics. Journal of Machine Learning Research, 13:307-361, 2012.
    • (2012) Journal of Machine Learning Research , vol.13 , pp. 307-361
    • Gutmann, M.1    Hyvärinen, A.2
  • 22
    • 10044285992 scopus 로고    scopus 로고
    • Canonical correlation analysis: An overview with application to learning methods
    • Hardoon, David R., Szedmak, Sandor, and Shawe-Taylor, John. Canonical correlation analysis: An overview with application to learning methods. Neural Computation, 16(12):2639-2664, 2004.
    • (2004) Neural Computation , vol.16 , Issue.12 , pp. 2639-2664
    • Hardoon, D.R.1    Szedmak, S.2    Shawe-Taylor, J.3
  • 23
    • 0033709098 scopus 로고    scopus 로고
    • Tandem connectionist feature extraction for conventional HMM systems
    • Hermansky, Hynek, Ellis, Daniel P. W., and Sharma, Sangita. Tandem connectionist feature extraction for conventional HMM systems. In ICASSP, pp. 1635-1638, 2000.
    • (2000) ICASSP , pp. 1635-1638
    • Hermansky, H.1    Ellis, D.P.W.2    Sharma, S.3
  • 24
    • 33746600649 scopus 로고    scopus 로고
    • Reducing the dimensionality of data with neural networks
    • Hinton, G. E. and Salakhutdinov, R. R. Reducing the dimensionality of data with neural networks. Science, 313 (5786):504-507, 2006.
    • (2006) Science , vol.313 , Issue.5786 , pp. 504-507
    • Hinton, G.E.1    Salakhutdinov, R.R.2
  • 25
    • 84883394520 scopus 로고    scopus 로고
    • Framing image description as a ranking task: Data, models and evaluation metrics
    • Hodosh, Micah, Young, Peter, and Hockenmaier, Julia. Framing image description as a ranking task: Data, models and evaluation metrics. Journal of Artificial Intelligence Research, 47:853-899, 2013.
    • (2013) Journal of Artificial Intelligence Research , vol.47 , pp. 853-899
    • Hodosh, M.1    Young, P.2    Hockenmaier, J.3
  • 26
    • 0000107975 scopus 로고
    • Relations between two sets of variates
    • Hotelling, Harold. Relations between two sets of variates. Biometrika, 28(3/4):321-377, 1936.
    • (1936) Biometrika , vol.28 , Issue.3-4 , pp. 321-377
    • Hotelling, H.1
  • 27
    • 0034551784 scopus 로고    scopus 로고
    • Nonlinear canonical correlation analysis by neural networks
    • Hsieh, W. W. Nonlinear canonical correlation analysis by neural networks. Neural Networks, 13(10):1095-1105, 2000.
    • (2000) Neural Networks , vol.13 , Issue.10 , pp. 1095-1105
    • Hsieh, W.W.1
  • 28
    • 84905226732 scopus 로고    scopus 로고
    • Kernel methods match deep neural networks on TIMIT: Scalable learning in high-dimensional random Fourier spaces
    • Huang, Po-Sen, Avron, Haim, Sainath, Tara, Sindhwani, Vikas, and Ramabhadran, Bhuvana. Kernel methods match deep neural networks on TIMIT: Scalable learning in high-dimensional random Fourier spaces. In ICASSP, pp. 205-209, 2014.
    • (2014) ICASSP , pp. 205-209
    • Huang, P.-S.1    Avron, H.2    Sainath, T.3    Sindhwani, V.4    Ramabhadran, B.5
  • 29
    • 38049026697 scopus 로고    scopus 로고
    • Multi-view regression via canonical correlation analysis
    • Kakade, Sham M. and Foster, Dean P. Multi-view regression via canonical correlation analysis. In COLT, pp. 82-96, 2007.
    • (2007) COLT , pp. 82-96
    • Kakade, S.M.1    Foster, D.P.2
  • 30
  • 31
    • 84925016449 scopus 로고    scopus 로고
    • Learning semantics with deep belief network for cross-language information retrieval
    • Kim, Jungi, Nam, Jinseok, and Gurevych, Iryna. Learning semantics with deep belief network for cross-language information retrieval. In COLING, pp. 579-588, 2012.
    • (2012) COLING , pp. 579-588
    • Kim, J.1    Nam, J.2    Gurevych, I.3
  • 32
    • 0033485911 scopus 로고    scopus 로고
    • A neural implementation of canonical correlation analysis
    • Lai, Pei Ling and Fyfe, Colin. A neural implementation of canonical correlation analysis. Neural Networks, 12(10): 1391-1397, 1999.
    • (1999) Neural Networks , vol.12 , Issue.10 , pp. 1391-1397
    • Lai, P.L.1    Fyfe, C.2
  • 33
    • 0034304404 scopus 로고    scopus 로고
    • Kernel and nonlinear canonical correlation analysis
    • Lai, Pei Ling and Fyfe, Colin. Kernel and nonlinear canonical correlation analysis. Int. J. Neural Syst., 10(5):365-377, 2000.
    • (2000) Int. J. Neural Syst. , vol.10 , Issue.5 , pp. 365-377
    • Lai, P.L.1    Fyfe, C.2
  • 34
    • 0032203257 scopus 로고    scopus 로고
    • Gradient-based learning applied to document recognition
    • Le Cun, Yann, Bottou, Léon, Bengio, Yoshua, and Haffner, Patrick. Gradient-based learning applied to document recognition. Proc. IEEE, 86(11):2278-2324, 1998.
    • (1998) Proc. IEEE , vol.86 , Issue.11 , pp. 2278-2324
    • Le Cun, Y.1    Bottou, L.2    Bengio, Y.3    Haffner, P.4
  • 36
    • 84960116111 scopus 로고    scopus 로고
    • Deep multilingual correlation for improved word embeddings
    • Lu, Ang, Wang, Weiran, Bansal, Mohit, Gimpel, Kevin, and Livescu, Karen. Deep multilingual correlation for improved word embeddings. In NAACL-HLT, 2015.
    • (2015) NAACL-HLT
    • Lu, A.1    Wang, W.2    Bansal, M.3    Gimpel, K.4    Livescu, K.5
  • 37
    • 84937951105 scopus 로고    scopus 로고
    • Large scale canonical correlation analysis with iterative least squares
    • Lu, Yichao and Foster, Dean P. Large scale canonical correlation analysis with iterative least squares. In NIPS, pp. 91-99, 2014.
    • (2014) NIPS , pp. 91-99
    • Lu, Y.1    Foster, D.P.2
  • 39
    • 80053288309 scopus 로고    scopus 로고
    • Composition in distributional models of semantics
    • Mitchell, Jeff and Lapata, Mirella. Composition in distributional models of semantics. Cognitive Science, 34(8): 1388-1429,2010.
    • (2010) Cognitive Science , vol.34 , Issue.8 , pp. 1388-1429
    • Mitchell, J.1    Lapata, M.2
  • 40
    • 84899013108 scopus 로고    scopus 로고
    • On spectral clustering: Analysis and an algorithm
    • Ng, Andrew Y., Jordan, Michael I., and Weiss, Yair. On spectral clustering: Analysis and an algorithm. In NIPS, pp. 849-856, 2002.
    • (2002) NIPS , pp. 849-856
    • Ng, A.Y.1    Jordan, M.I.2    Weiss, Y.3
  • 41
    • 80053437179 scopus 로고    scopus 로고
    • Multimodal deep learning
    • Ngiam, Jiquan, Khosla, Aditya, Kim, Mingyu, Nam, Juhan, Lee, Honglak, and Ng, Andrew. Multimodal deep learning. In ICML, pp. 689-696, 2011.
    • (2011) ICML , pp. 689-696
    • Ngiam, J.1    Khosla, A.2    Kim, M.3    Nam, J.4    Lee, H.5    Ng, A.6
  • 42
    • 84961289992 scopus 로고    scopus 로고
    • GloVe: Global vectors for word representation
    • Pennington, Jeffrey, Socher, Richard, and Manning, Christopher D. GloVe: Global vectors for word representation. In EMNLP, 2014.
    • (2014) EMNLP
    • Pennington, J.1    Socher, R.2    Manning, C.D.3
  • 43
    • 0034704222 scopus 로고    scopus 로고
    • Nonlinear dimensionality reduction by locally linear embedding
    • Roweis, Sam T. and Saul, Lawrence K. Nonlinear dimensionality reduction by locally linear embedding. Science, 290(5500):2323-2326, 2000.
    • (2000) Science , vol.290 , Issue.5500 , pp. 2323-2326
    • Roweis, S.T.1    Saul, L.K.2
  • 45
    • 77955998009 scopus 로고    scopus 로고
    • Connecting modalities: Semi-supervised segmentation and annotation of images using unaligned text corpora
    • Socher, Richard and Li, Fei-Fei. Connecting modalities: Semi-supervised segmentation and annotation of images using unaligned text corpora. In CVPR, pp. 966-973, 2010.
    • (2010) CVPR , pp. 966-973
    • Socher, R.1    Li, F.-F.2
  • 46
    • 84937873395 scopus 로고    scopus 로고
    • Improved multimodal deep learning with variation of information
    • Sohn, Kihyuk, Shang, Wenling, and Lee, Honglak. Improved multimodal deep learning with variation of information. In MPS, pp. 2141-2149, 2014.
    • (2014) MPS , pp. 2141-2149
    • Sohn, K.1    Shang, W.2    Lee, H.3
  • 50
    • 79551480483 scopus 로고    scopus 로고
    • Stacked de-noising autoencoders: Learning useful representations in a deep network with a local denoising criterion
    • Vincent, Pascal, Larochelle, Hugo, Lajoie, Isabelle, Bengio, Yoshua, and Manzagol, Pierre-Antoine. Stacked de-noising autoencoders: Learning useful representations in a deep network with a local denoising criterion. Journal of Machine Learning Research, 11:3371-3408, 2010.
    • (2010) Journal of Machine Learning Research , vol.11 , pp. 3371-3408
    • Vincent, P.1    Larochelle, H.2    Lajoie, I.3    Bengio, Y.4    Manzagol, P.-A.5
  • 51
    • 84873588359 scopus 로고    scopus 로고
    • Inferring a semantic representation of text via cross-language correlation analysis
    • Vinokourov, Alexei, Cristianini, Nello, and Shawe-Taylor, John. Inferring a semantic representation of text via cross-language correlation analysis. In NIPS, pp. 1497-1504, 2003.
    • (2003) NIPS , pp. 1497-1504
    • Vinokourov, A.1    Cristianini, N.2    Shawe-Taylor, J.3
  • 52
    • 84946062638 scopus 로고    scopus 로고
    • Unsupervised learning of acoustic features via deep canonical correlation analysis
    • Wang, Weiran, Arora, Raman, Livescu, Karen, and Bilmes, Jeff. Unsupervised learning of acoustic features via deep canonical correlation analysis. In ICASSP, 2015.
    • (2015) ICASSP
    • Wang, W.1    Arora, R.2    Livescu, K.3    Bilmes, J.4
  • 54
    • 84899010839 scopus 로고    scopus 로고
    • Using the Nyström method to speed up kernel machines
    • Williams, Christopher K. I. and Seeger, Matthias. Using the Nyström method to speed up kernel machines. In NIPS, pp. 682-688, 2001.
    • (2001) NIPS , pp. 682-688
    • Williams, C.K.I.1    Seeger, M.2
  • 55
    • 84877740547 scopus 로고    scopus 로고
    • Nyström method vs random Fourier features: A theoretical and empirical comparison
    • Yang, Tianbao, Li, Yu-Feng, Mahdavi, Mehrdad, Jin, Rong, and Zhou, Zhi-Hua. Nyström method vs random Fourier features: A theoretical and empirical comparison. In NIPS, pp. 476-484, 2012.
    • (2012) NIPS , pp. 476-484
    • Yang, T.1    Li, Y.-F.2    Mahdavi, M.3    Jin, R.4    Zhou, Z.-H.5


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.