A neural autoregressive topic model

Advances in Neural Information Processing Systems

Volumn 4, Issue , 2012, Pages 2708-2716

  Larochelle, Hugo ^a   Lauly, Stanislas ^a  

^a UNIVERSITÉ DE SHERBROOKE   (Canada)

Author keywords

[No Author keywords available]

Indexed keywords

AUTO-REGRESSIVE; COLLECTION OF DOCUMENTS; DOCUMENT REPRESENTATION; GENERATIVE MODEL; GRAPHICAL MODEL; HIERARCHICAL DISTRIBUTION; RECURSIVE EQUATIONS; TRAINING COMPLEXITY;

BINARY TREES; INFORMATION RETRIEVAL SYSTEMS; LEARNING ALGORITHMS; NETWORK ARCHITECTURE;

MODELS;

EID: 84877761544     PISSN: 10495258     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (15)

1. David M. Blei, Andrew Y. Ng, and Michael I. Jordan. Latent Dirichlet Allocation. Journal of Machine Learning Research, 3(4-5):993-1022, 2003.
2. Ruslan Salakhutdinov and Geoffrey Hinton. Replicated Softmax: an Undirected Topic Model. In Advances in Neural Information Processing Systems 22 (NIPS 2009), pages 1607-1614, 2009.
3. Hugo Larochelle and Ian Murray. The Neural Autoregressive Distribution Estimator. In Proceedings of the 14th International Conference on Artificial Intelligence and Statistics (AISTATS 2011), volume 15, pages 29-37, Ft. Lauderdale, USA, 2011. JMLR W&CP.
4. Yoshua Bengio and Samy Bengio. Modeling High-Dimensional Discrete Data with Multi-Layer Neural Networks. In Advances in Neural Information Processing Systems 12 (NIPS 1999), pages 400-406. MIT Press, 2000.
5. Geoffrey E. Hinton. Training products of experts by minimizing contrastive divergence. Neural Computation, 14:1771-1800, 2002.
6. Frederic Morin and Yoshua Bengio. Hierarchical Probabilistic Neural Network Language Model. In Proceedings of the 10th International Workshop on Artificial Intelligence and Statistics (AISTATS 2005), pages 246-252. Society for Artificial Intelligence and Statistics, 2005.
7. Andriy Mnih and Geoffrey E Hinton. A Scalable Hierarchical Distributed Language Model. In Advances in Neural Information Processing Systems 21 (NIPS 2008), pages 1081-1088, 2009.
8. Jonathan Chang, Jordan Boyd-Graber, Sean Gerrish, Chong Wang, and David Blei. Reading Tea Leaves: How Humans Interpret Topic Models. In Advances in Neural Information Processing Systems 22 (NIPS 2009), pages 288-296, 2009.
9. Jacob Eisenstein, Amr Ahmed, and Eric P. Xing. Sparse Additive Generative Models of Text. In Proceedings of the 28th International Conference on Machine Learning (ICML 2011), pages 1041-1048. Omnipress, 2011.
10. George E. Dahl, Ryan P. Adams, and Hugo Larochelle. Training Restricted Boltzmann Machines onWord Observations. In Proceedings of the 29th International Conference on Machine Learning (ICML 2012), 2012.
11. Xavier Glorot, Antoine Bordes, and Yoshua Bengio. Domain Adaptation for Large-Scale Sentiment Classification: A Deep Learning Approach. In Proceedings of the 28th International Conference on Machine Learning (ICML 2011), pages 513-520. Omnipress, 2011.
12. Yann Dauphin, Xavier Glorot, and Yoshua Bengio. Large-Scale Learning of Embeddings with Reconstruction Sampling. In Proceedings of the 28th International Conference on Machine Learning (ICML 2011), pages 945-952. Omnipress, 2011.
13. Max Welling and Yee Whye Teh. Bayesian Learning via Stochastic Gradient Langevin Dynamics. In Proceedings of the 28th International Conference on Machine Learning (ICML 2011), pages 681-688. Omnipress, 2011.
14. Sungjin Ahn, Anoop Korattikara, and MaxWelling. Bayesian Posterior Sampling via Stochastic Gradient Fisher Scoring. In Proceedings of the 29th International Conference on Machine Learning (ICML 2012), 2012.
15. Laurens van der Maaten and Geoffrey E Hinton. Visualizing Data using t-SNE. Journal of Machine Learning Research, 9:2579-2605, 2008. URL http://www.jmlr.org/papers/volume9/vandermaaten08a/vandermaaten08a.pdf.