Random features for Kernel Deep Convex Network

ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings

Volumn , Issue , 2013, Pages 3143-3147

  Huang, Po Sen ^a   Deng, Li ^b   Hasegawa Johnson, Mark ^a   He, Xiaodong ^b  

^a University of Illinois at Urbana Champaign   (United States)

^b MICROSOFT RESEARCH   (United States)

Author keywords

deep learning; kernel regression; random features; spoken language understanding

Indexed keywords

COMPUTATIONAL SAVINGS; DEEP LEARNING; EFFICIENT COMPUTATION; EVALUATION EXPERIMENTS; KERNEL REGRESSION; RANDOM FEATURES; SCALABILITY PROBLEMS; SPOKEN LANGUAGE UNDERSTANDING;

GAUSSIAN DISTRIBUTION; SIGNAL PROCESSING; SPEECH RECOGNITION;

NETWORK ARCHITECTURE;

EID: 84890480288     PISSN: 15206149     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICASSP.2013.6638237     Document Type: Conference Paper

References (20)

1. G. Hinton, L. Deng, D. Yu, G. Dahl, A. Mohamed, N. Jaitly, A. Senior, V. Vanhoucke, P. Nguyen, T. Sainath, and B. Kingsbury, "Deep neural networks for acoustic modeling in speech recognition," IEEE Signal Processing Magazine, vol. 29, pp. 82-97, Nov. 2012.
2. G.E. Dahl, D. Yu, L. Deng, and A. Acero, "Contextdependent pre-trained deep neural networks for largevocabulary speech recognition," IEEE Transactions on Audio, Speech, and Language Processing, vol. 20, no. 1, pp. 30-42, Jan. 2012.
3. L. Deng and D. Yu, "Deep convex network: A scalable architecture for speech pattern classification," in Interspeech, 2011.
4. L. Deng, D. Yu, and J. Platt, "Scalable stacking and learning for building deep architectures," in Intenational Conference on Acoustics, Speech, and Signal Processing, 2012.
5. L. Deng, G. Tur, X. He, and D. Hakkani-Tur, "Use of kernel deep convex networks and end-to-end learning for spoken language understanding," in IEEE Workshop on Spoken Language Technology, 2012.
6. C. M. Bishop, Pattern Recognition and Machine Learning (Information Science and Statistics), Springer-Verlag New York, Inc., 2006.
7. C. J.C. Burges and B. Schölkopf, "Improving the accuracy and speed of support vector machines," in Advances in Neural Information Processing Systems (NIPS). 1997, pp. 375-381, MIT Press.
8. G. Baudat and F. Anouar, "Feature vector selection and projection using kernels," Neurocomputing, vol. 55, pp. 21-38, 2003.
9. D. Achlioptas, F. McSherry, and B. Schlkopf, "Sampling techniques for kernel methods," in Advances in Neural Infomration Processing Systems (NIPS), 2001, pp. 335-342.
10. P. Drineas and M. W. Mahoney, "On the Nyström method for approximating a gram matrix for improved kernel-based learning," Journal of Machine Learning Research, vol. 6, pp. 2153-2175, Dec. 2005.
11. S. Kumar, M. Mohri, and A. Talwalkar, "Ensemble Nyström method," in Advances in Neural Infomration Processing Systems (NIPS), 2009.
12. A. Rahimi and B. Recht, "Random features for largescale kernel machines," in Advances in Neural Infomration Processing Systems (NIPS), 2007.
13. C. Cortes, M. Mohri, and A. Talwalkar, "On the impact of kernel approximation on learning accuracy," in International Conference on Artificial Intelligence and Statistics (AISTATS), 2010.
14. W. Rudin, Fourier Analysis on Groups, Wiley-Interscience, 1994.
15. A. Singh, N. Ahuja, and P. Moulin, "Online learning with kernels: Overcoming the growing sum problem," in IEEE Workshop on Machine Learning for Signal Processing (MLSP), 2012.
16. G. Tür, D. Hakkani-Tür, and L. Heck, "What's left to be understood in atis," in Proc. of IEEE SLT Workshop, 2010.
17. R. Collobert, J. Weston, L. Bottou, M. Karlen, K. Kavukcuoglu, and P. Kuksa, "Natural language processing (almost) from scratch," Journal of Machine Learning Research, 2011.
18. C. Raymond and G. Riccardi, "Generative and discriminative algorithms for spoken language understanding," in Proc. of Interspeech, 2007.
19. O. Vinyals and L. Deng, "Are sparse representations rich enough for acoustic modeling?," in Proceedings of INTERSPEECH, 2012.
20. Y. Cho and L. K. Saul, "Kernel methods for deep learning," in Advances in Neural Infomration Processing Systems (NIPS), 2009.