Rich S. Zemel
Affiliations: | University of Toronto, Toronto, ON, Canada |
Area:
neural coding, visual perceptionWebsite:
http://www.cs.toronto.edu/~zemel/Google:
"Rich Zemel"Mean distance: 12.46 (cluster 17) | S | N | B | C | P |
Parents
Sign in to add mentor| Geoffrey E. Hinton | grad student | 1994 | University of Toronto |
| Terrence J. Sejnowski | post-doc | Salk Institute |
Children
Sign in to add trainee| Jonathan W. Pillow | research assistant | University of Arizona | |
| Maksims Volkovs | grad student | 2013 | University of Toronto |
| Ryan P. Adams | post-doc | 2009-2011 | University of Toronto |
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Publications
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| Zhu Y, Kiros R, Zemel R, et al. (2016) Aligning books and movies: Towards story-like visual explanations by watching movies and reading books Proceedings of the Ieee International Conference On Computer Vision. 11: 19-27 |
| Alahari K, Batra D, Ramalingam S, et al. (2015) Guest Editors' Introduction: Special Section on Higher Order Graphical Models in Computer Vision Ieee Transactions On Pattern Analysis and Machine Intelligence. 37: 1321-1322 |
| Zheng Y, Zemel RS, Zhang YJ, et al. (2015) A Neural Autoregressive Approach to Attention-based Recognition International Journal of Computer Vision. 113: 67-79 |
| Ren M, Kiros R, Zemel RS. (2015) Exploring models and data for image question answering Advances in Neural Information Processing Systems. 2015: 2953-2961 |
| Kiros R, Zhu Y, Salakhutdinov R, et al. (2015) Skip-thought vectors Advances in Neural Information Processing Systems. 2015: 3294-3302 |
| Xu K, Ba JL, Kiros R, et al. (2015) Show, attend and tell: Neural image caption generation with visual attention 32nd International Conference On Machine Learning, Icml 2015. 3: 2048-2057 |
| Charlin L, Zemel RS, Larochelle H. (2014) Leveraging user libraries to bootstrap collaborative filtering Proceedings of the Acm Sigkdd International Conference On Knowledge Discovery and Data Mining. 173-182 |
| Kiros R, Zemel RS, Salakhutdinov R. (2014) A multiplicative model for learning distributed text-based attribute representations Advances in Neural Information Processing Systems. 3: 2348-2356 |
| Volkovs MN, Zemel RS. (2014) New learning methods for supervised and unsupervised preference aggregation Journal of Machine Learning Research. 15: 1135-1176 |
| Snoek J, Swersky K, Zemel R, et al. (2014) Input warping for Bayesian optimization of non-stationary functions 31st International Conference On Machine Learning, Icml 2014. 5: 3654-3662 |