Exponential family embeddings

Advances in Neural Information Processing Systems

Volumn , Issue , 2016, Pages 478-486

  Rudolph, Maja ^a   Ruiz, Francisco J R ^a,b   Mandt, Stephan ^a   Blei, David M ^a  

^a Och Spine at New York Presbyterian Hospitals   (United States)

^b UNIVERSITY OF CAMBRIDGE   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

COMMERCE; NEURONS; SEMANTICS; STOCHASTIC SYSTEMS;

CONDITIONAL DISTRIBUTION; DIMENSION REDUCTION; EXPONENTIAL FAMILY; HIGH DIMENSIONAL DATA; MARKET BASKET ANALYSIS; SCALABLE ALGORITHMS; SEMANTIC SIMILARITY; STOCHASTIC GRADIENT DESCENT;

CLUSTERING ALGORITHMS;

EID: 85019220689     PISSN: 10495258     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (26)

1. Ahrens, M. B., Orger, M. B., Robson, D. N., Li, J. M., and Keller, P. J. (2013). Whole-brain functional imaging at cellular resolution using light-sheet microscopy. Nature Methods, 10(5):413-420.
2. Arnold, B. C., Castillo, E., Sarabia, J. M., et al. (2001). Conditionally specified distributions: an introduction (with comments and a rejoinder by the authors). Statistical Science, 16(3):249-274.
3. Bengio, Y., Schwenk, H., Senécal, J.-S., Morin, F., and Gauvain, J.-L. (2006). Neural probabilistic language models. In Innovations in Machine Learning, pages 137-186. Springer.
4. Bronnenberg, B. J., Kruger, M. W., and Mela, C. F. (2008). Database paper: The IRI marketing data set. Marketing Science, 27(4):745-748.
5. Brown, L. D. (1986). Fundamentals of statistical exponential families with applications in statistical decision theory. Lecture Notes-Monograph Series, 9:i-279.
6. Collins, M., Dasgupta, S., and Schapire, R. E. (2001). A generalization of principal components analysis to the exponential family. In Neural Information Processing Systems, pages 617-624.
7. Duchi, J., Hazan, E., and Singer, Y. (2011). Adaptive subgradient methods for online learning and stochastic optimization. Journal of Machine Learning Research, 12:2121-2159.
8. Friedrich, J., Soudry, D., Paninski, L., Mu, Y., Freeman, J., and Ahrens, M. (2015). Fast constrained non-negative matrix factorization for whole-brain calcium imaging data. In NIPS workshop on Neural Systems.
9. Gopalan, P., Hofman, J., and Blei, D. M. (2015). Scalable recommendation with hierarchical Poisson factorization. In Uncertainty in Artificial Intelligence.
10. Gutmann, M. and Hyvärinen, A. (2010). Noise-contrastive estimation: A new estimation principle for unnormal-ized statistical models. In Journal of Machine Learning Research.
11. Harper, F. M. and Konstan, J. A. (2015). The MovieLens datasets: History and context. ACM Transactions on Interactive Intelligent Systems (TiiS), 5(4):19.
12. Harris, Z. S. (1954). Distributional structure. Word, 10(2-3):146-162.
13. Hu, Y., Koren, Y., and Volinsky, C. (2008). Collaborative filtering for implicit feedback datasets. Data Mining.
14. Levy, O. and Goldberg, Y. (2014). Neural word embedding as implicit matrix factorization. In Neural Information Processing Systems, pages 2177-2185.
15. McCullagh, P. and Nelder, J. A. (1989). Generalized linear models, volume 37. CRC press.
16. Mikolov, T., Chen, K., Corrado, G., and Dean, J. (2013a). Efficient estimation of word representations in vector space. ICLR Workshop Proceedings. arXiv:1301.3781.
17. Mikolov, T., Sutskever, I., Chen, K., Corrado, G. S., and Dean, J. (2013b). Distributed representations of words and phrases and their compositionality. In Neural Information Processing Systems, pages 3111-3119.
18. Mikolov, T., Yih, W.-T. a., and Zweig, G. (2013c). Linguistic regularities in continuous space word representations. In HLT-NAACL, pages 746-751.
19. Mnih, A. and Kavukcuoglu, K. (2013). Learning word embeddings efficiently with noise-contrastive estimation. In Neural Information Processing Systems, pages 2265-2273.
20. Mnih, A. and Teh, Y. W. (2012). A fast and simple algorithm for training neural probabilistic language models. In International Conference on Machine Learning, pages 1751-1758.
21. Neal, R. M. (1990). Learning stochastic feedforward networks. Department of Computer Science, University of Toronto.
22. Pennington, J., Socher, R., and Manning, C. D. (2014). Glove: Global vectors for word representation. In Conference on Empirical Methods on Natural Language Processing, volume 14, pages 1532-1543.
23. Ranganath, R., Tang, L., Charlin, L., and Blei, D. M. (2015). Deep exponential families. Artificial Intelligence and Statistics.
24. Rumelhart, D. E., Hintont, G. E., and Williams, R. J. (1986). Learning representations by back-propagating errors. Nature, 323:9.
25. Taddy, M. et al. (2015). Distributed multinomial regression. The Annals of Applied Statistics, 9(3):1394-1414.
26. Vilnis, L. and McCallum, A. (2015). Word representations via Gaussian embedding. In International Conference on Learning Representations.