| Show, attend and tell: Neural image caption generation with visual attention K Xu, J Ba, R Kiros, K Cho, A Courville, R Salakhudinov, R Zemel, ... International conference on machine learning, 2048-2057, 2015 | 8310 | 2015 |
| Layer normalization JL Ba, JR Kiros, GE Hinton arXiv preprint arXiv:1607.06450, 2016 | 4446 | 2016 |
| Skip-thought vectors R Kiros, Y Zhu, RR Salakhutdinov, R Zemel, R Urtasun, A Torralba, ... Advances in neural information processing systems, 3294-3302, 2015 | 2412 | 2015 |
| Aligning books and movies: Towards story-like visual explanations by watching movies and reading books Y Zhu, R Kiros, R Zemel, R Salakhutdinov, R Urtasun, A Torralba, S Fidler Proceedings of the IEEE international conference on computer vision, 19-27, 2015 | 1407 | 2015 |
| Unifying visual-semantic embeddings with multimodal neural language models R Kiros, R Salakhutdinov, RS Zemel arXiv preprint arXiv:1411.2539, 2014 | 1199 | 2014 |
| Exploring models and data for image question answering M Ren, R Kiros, R Zemel Advances in neural information processing systems, 2953-2961, 2015 | 761* | 2015 |
| Multimodal neural language models R Kiros, R Salakhutdinov, R Zemel International Conference on Machine Learning, 595-603, 2014 | 696 | 2014 |
| Scalable bayesian optimization using deep neural networks J Snoek, O Rippel, K Swersky, R Kiros, N Satish, N Sundaram, M Patwary, ... International Conference on Machine Learning, 2171-2180, 2015 | 650 | 2015 |
| Action recognition using visual attention S Sharma, R Kiros, R Salakhutdinov arXiv preprint arXiv:1511.04119, 2015 | 640 | 2015 |
| Vse++: Improved visual-semantic embeddings F Faghri, DJ Fleet, JR Kiros, S Fidler arXiv preprint arXiv:1707.05612 2 (7), 8, 2017 | 601* | 2017 |
| Order-embeddings of images and language I Vendrov, R Kiros, S Fidler, R Urtasun arXiv preprint arXiv:1511.06361, 2015 | 457 | 2015 |
| Insertion Transformer: Flexible Sequence Generation via Insertion Operations M Stern, W Chan, J Kiros, J Uszkoreit arXiv preprint arXiv:1902.03249, 2019 | 146 | 2019 |
| Comparing algorithms for automated vessel segmentation in computed tomography scans of the lung: the VESSEL12 study RD Rudyanto, S Kerkstra, EM Van Rikxoort, C Fetita, PY Brillet, C Lefevre, ... Medical image analysis 18 (7), 1217-1232, 2014 | 127 | 2014 |
| A multiplicative model for learning distributed text-based attribute representations R Kiros, R Zemel, RR Salakhutdinov Advances in neural information processing systems, 2348-2356, 2014 | 77 | 2014 |
| Graph normalizing flows J Liu, A Kumar, J Ba, J Kiros, K Swersky Advances in Neural Information Processing Systems, 13556-13566, 2019 | 65 | 2019 |
| Training neural networks with stochastic hessian-free optimization R Kiros arXiv preprint arXiv:1301.3641, 2013 | 50 | 2013 |
| Deep representations and codes for image auto-annotation R Kiros, C Szepesvári Advances in Neural Information Processing Systems, 908-916, 2012 | 43 | 2012 |
| Illustrative Language Understanding: Large-Scale Visual Grounding with Image Search J Kiros, W Chan, G Hinton Proceedings of the 56th Annual Meeting of the Association for Computational …, 2018 | 31 | 2018 |
| Stacked multiscale feature learning for domain independent medical image segmentation R Kiros, K Popuri, D Cobzas, M Jagersand International Workshop on Machine Learning in Medical Imaging, 25-32, 2014 | 27 | 2014 |
| Towards generalizable sentence embeddings E Triantafillou, JR Kiros, R Urtasun, R Zemel Proceedings of the 1st Workshop on Representation Learning for NLP, 239-248, 2016 | 19 | 2016 |
Richard ZemelProfessor of Computer Science, University of Toronto在 cs.toronto.edu 的电子邮件经过验证
