An architecture for deep, hierarchical generative models

Advances in Neural Information Processing Systems

Volumn , Issue , 2016, Pages 4833-4841

  Bachman, Philip ^a  

^a Maluuba Research   (Canada)

Author keywords

[No Author keywords available]

Indexed keywords

AUTO REGRESSIVE MODELS; EFFECTIVE COMMUNICATION; GENERATIVE MODEL; IMPROVE PERFORMANCE; LABEL INFORMATION; LATENT VARIABLE; STANDARD IMAGES; STATE-OF-THE-ART PERFORMANCE;

BENCHMARKING;

EID: 85018886752     PISSN: 10495258     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (26)

1. P. Bachman and D. Precup. Data generation as sequential decision making. In Advances in Neural Information Processing Systems (NIPS), 2015.
2. Y. Burda, R. Grosse, and R. Salakhutdinov. Importance weighted auto-encoders. arXiv:1509.00519v1 [cs.LG], 2015.
3. E. L. Denton, S. Chintala, A. Szlam, and R. Fergus. Deep generative models using a laplacian pyramid of adversarial networks. arXiv:1506.05751 [cs.CV], 2015.
4. S. M. A. Eslami, N. Heess, T. Weber, Y. Tassa, K. Kavucuoglu, and G. E. Hinton. Attend, infer, repeat: Fast scene understanding with generative models. arXiv:1603.08575 [cs.CV], 2016.
5. K. Gregor, I. Danihelka, A. Graves, and D. Wierstra. Draw: A recurrent neural network for image generation. In International Conference on Machine Learning (ICML), 2015.
6. K. Gregor, F. Besse, D. J. Rezende, I. Danihelka, and D. Wierstra. Towards conceptual compression. In arXiv:1604.08772v1 [stat.ML], 2016.
7. K. He, X. Zhang, S. Ren, and J. Sun. Deep residual learning for image recognition. arXiv:1512.03385v1 [cs.CV], 2015.
8. S. Honari, J. Yosinski, P. Vincent, and C. Pal. Recombinator networks: Learning coarse-to-fine feature aggregation. In Computer Vision and Pattern Recognition (CVPR), 2016.
9. L. Kaiser and I. Sutskever. Neural gpus learn algorithms. In International Conference on Learning Representations (ICLR), 2016.
10. D. P. Kingma and M. Welling. Auto-encoding variational bayes. In International Conference on Learning Representations (ICLR), 2014.
11. D. P. Kingma, T. Salimans, and M. Welling. Improving variational inference with inverse autoregressive flow. arXiv:1606.04934 [cs.LG], 2016.
12. A. Krizhevsky and G. E. Hinton. Learning multiple layers of features from tiny images. Master's thesis, University of Toronto, 2009.
13. B. M. Lake, R. Salakhutdinov, and J. B. Tenebaum. Human-level concept learning through probabilistic program induction. Science, 350(6266):1332-1338, 2015.
14. Z. Liu, P. Luo, X. Wang, and X. Tang. Deep learning face attributes in the wild. In International Conference on Computer Vision (ICCV), 2015.
15. L. Maaløe, C. K. Sønderby, S. K. Sønderby, and O. Winther. Auxiliary deep generative models. In International Conference on Machine Learning (ICML), 2016.
16. R. Ranganath, D. Tran, and D. M. Blei. Hierarchical variational models. In International Conference on Machine Learning (ICML), 2016.
17. A. Rasmus, H. Valpola, M. Honkala, M. Berglund, and T. Raiko. Semi-supervised learning with ladder networks. In Advances in Neural Information Processing Systems (NIPS), 2015.
18. D. J. Rezende, S. Mohamed, and D. Wierstra. Stochastic backpropagation and approximate inference in deep generative models. In International Conference on Machine Learning (ICML), 2014.
19. D. J. Rezende, S. Mohamed, I. Danihelka, K. Gregor, and D. Wierstra. One-shot generalization in deep generative models. In International Conference on Machine Learning (ICML), 2016.
20. R. Salakhutdinov and I. Murray. On the quantitative analysis of deep belief networks. In International Conference on Machine Learning (ICML), 2008.
21. J. Sohl-Dickstein, E. A. Weiss, N. Maheswaranathan, and S. Ganguli. Deep unsupervised learning using nonequilibrium thermodynamics. In International Conference on Machine Learning (ICML), 2015.
22. K. Sohn, H. Lee, and X. Yan. Learning structured output representation using deep conditional generative models. In Advances in Neural Information Processing Systems (NIPS), 2015.
23. C. K. Sønderby, T. Raiko, L. Maaløe, S. K. Sønderby, and O. Winther. How to train deep variational autoencoders and probabilistic ladder networks. International Conference on Machine Learning (ICML), 2016.
24. L. Theis and M. Bethge. Generative image modeling using spatial lstms. In Advances in Neural Information Processing Systems (NIPS), 2015.
25. A. van den Oord, N. Kalchbrenner, and K. Kavucuoglu. Pixel recurrent neural networks. International Conference on Machine Learning (ICML), 2016.
26. A. van den Oord, N. Kalchbrenner, O. Vinyals, L. Espeholt, A. Graves, and K. Kavucuoglu. Conditional image generation with pixelcnn decoders. arXiv:1606.05328 [cs.CV], 2016.