On the challenges of learning with inference networks on sparse, high-dimensional data

International Conference on Artificial Intelligence and Statistics, AISTATS 2018

Volumn , Issue , 2018, Pages 143-151

  Krishnan, Rahul G ^a   Liang, Dawen ^b   Hoffman, Matthew D ^c  

^a MASSACHUSETTS INSTITUTE OF TECHNOLOGY   (United States)

^b Netflix   (United States)

^c GOOGLE INC   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ARTIFICIAL INTELLIGENCE; DEEP NEURAL NETWORKS; ITERATIVE METHODS; LARGE DATASET; STOCHASTIC SYSTEMS;

DATA REPRESENTATIONS; HIGH DIMENSIONAL DATA; HIGH DIMENSIONAL DATASETS; INFERENCE NETWORK; ITERATIVE OPTIMIZATION; NONLINEAR FACTOR ANALYSIS; STATE OF THE ART; VARIATIONAL INFERENCE;

CLUSTERING ALGORITHMS;

EID: 85057273468     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (38)

1. R. Baeza-Yates, B. Ribeiro-Neto, et al. Modern information retrieval, volume 463. ACM press New York, 1999.
2. D. M. Blei, A. Y. Ng, and M. I. Jordan. Latent dirichlet allocation. JMLR, 2003.
3. S. R. Bowman, L. Vilnis, O. Vinyals, A. M. Dai, R. Jozefowicz, and S. Bengio. Generating sentences from a continuous space. In CoNLL, 2016.
4. Y. Burda, R. Grosse, and R. Salakhutdinov. Importance weighted autoencoders. In ICLR, 2015.
5. X. Chen, D. P. Kingma, T. Salimans, Y. Duan, P. Dhariwal, J. Schulman, I. Sutskever, and P. Abbeel. Variational lossy autoencoder. ICLR, 2017.
6. M. Collins, S. Dasgupta, and R. E. Schapire. A generalization of principal component analysis to the exponential family. In NIPS, 2001.
7. W. Gibson. Nonlinear factors in two dimensions. Psychometrika, 1960.
8. X. Glorot and Y. Bengio. Understanding the difficulty of training deep feedforward neural networks. In AISTATS, 2010.
9. F. M. Harper and J. A. Konstan. The MovieLens datasets: History and context. ACM Transactions on Interactive Intelligent Systems (TiiS), 2015.
10. G. E. Hinton and R. R. Salakhutdinov. Replicated softmax: an undirected topic model. In NIPS, 2009.
11. G. E. Hinton, P. Dayan, B. J. Frey, and R. M. Neal. The” wake-sleep” algorithm for unsupervised neural networks. Science, 1995.
12. R. D. Hjelm, K. Cho, J. Chung, R. Salakhutdinov, V. Calhoun, and N. Jojic. Iterative refinement of approximate posterior for training directed belief networks. In NIPS, 2016.
13. M. D. Hoffman, D. M. Blei, C. Wang, and J. W. Paisley. Stochastic variational inference. JMLR, 2013.
14. Y. Hu, Y. Koren, and C. Volinsky. Collaborative filtering for implicit feedback datasets. In ICDM, 2008.
15. E. H. Huang, R. Socher, C. D. Manning, and A. Y. Ng. Improving Word Representations via Global Context and Multiple Word Prototypes. In ACL, 2012.
16. K. Järvelin and J. Kekäläinen. Cumulated gain-based evaluation of IR techniques. ACM Transactions on Information Systems (TOIS), 2002.
17. C. S. Jones. A nonlinear factor analysis of s&p 500 index option returns. The Journal of Finance, 2006.
18. C. Jutten and J. Karhunen. Advances in nonlinear blind source separation. In ICA, 2003.
19. D. Kingma and J. Ba. Adam: A method for stochastic optimization. In ICLR, 2015.
20. D. P. Kingma and M. Welling. Auto-encoding variational bayes. In ICLR, 2014.
21. D. P. Kingma, S. Mohamed, D. J. Rezende, and M. Welling. Semi-supervised learning with deep generative models. In NIPS, 2014.
22. H. Larochelle, Y. Bengio, J. Louradour, and P. Lamblin. Exploring strategies for training deep neural networks. JMLR, 2009.
23. N. D. Lawrence. Gaussian process latent variable models for visualisation of high dimensional data. In NIPS, 2003.
24. D. D. Lewis, Y. Yang, T. G. Rose, and F. Li. RCV1: A new benchmark collection for text categorization research. JMLR, 2004.
25. B. M. Marlin and R. S. Zemel. Collaborative prediction and ranking with non-random missing data. In RecSys, 2009.
26. Y. Miao, L. Yu, and P. Blunsom. Neural variational inference for text processing. In ICML, 2016.
27. A. Mnih and K. Gregor. Neural variational inference and learning in belief networks. In ICML, 2014.
28. X. Ning and G. Karypis. Slim: Sparse linear methods for top-n recommender systems. In ICDM, 2011.
29. D. J. Rezende and S. Mohamed. Variational inference with normalizing flows. In ICML, 2015.
30. D. J. Rezende, S. Mohamed, and D. Wierstra. Stochastic backpropagation and approximate inference in deep generative models. In ICML, 2014.
31. R. Salakhutdinov and H. Larochelle. Efficient learning of deep boltzmann machines. In AISTATS, 2010.
32. T. Salimans, D. Kingma, and M. Welling. Markov chain monte carlo and variational inference: Bridging the gap. In ICML, 2015.
33. S. Sedhain, A. K. Menon, S. Sanner, and D. Braziunas. On the effectiveness of linear models for one-class collaborative filtering. In AAAI, 2016.
34. C. Spearman.”general intelligence,” objectively determined and measured. The American Journal of Psychology, 1904.
35. H. Valpola and J. Karhunen. An unsupervised ensemble learning method for nonlinear dynamic state-space models. Neural Computation, 2002.
36. P. Vincent, H. Larochelle, Y. Bengio, and P.-A. Manzagol. Extracting and composing robust features with denoising autoencoders. In ICML, 2008.
37. S. Wang, M. Plilipose, M. Richardson, K. Geras, G. Urban, and O. Aslan. Analysis of deep neural networks with the extended data jacobian matrix. In ICML, 2016.
38. Y. Wu, C. DuBois, A. X. Zheng, and M. Ester. Collaborative denoising auto-encoders for top-n recommender systems. In WSDM, 2016.