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 | 7824 | 2015 |

Prototypical networks for few-shot learning J Snell, K Swersky, RS Zemel arXiv preprint arXiv:1703.05175, 2017 | 3118 | 2017 |

Siamese neural networks for one-shot image recognition G Koch, R Zemel, R Salakhutdinov ICML deep learning workshop 2, 2015 | 2505 | 2015 |

Skip-thought vectors R Kiros, Y Zhu, RR Salakhutdinov, R Zemel, R Urtasun, A Torralba, ... Advances in neural information processing systems, 3294-3302, 2015 | 2326 | 2015 |

Gated graph sequence neural networks Y Li, D Tarlow, M Brockschmidt, R Zemel arXiv preprint arXiv:1511.05493, 2015 | 1851 | 2015 |

Fairness through awareness C Dwork, M Hardt, T Pitassi, O Reingold, R Zemel Proceedings of the 3rd innovations in theoretical computer science …, 2012 | 1749 | 2012 |

The helmholtz machine P Dayan, GE Hinton, RM Neal, RS Zemel Neural computation 7 (5), 889-904, 1995 | 1388 | 1995 |

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 | 1279 | 2015 |

Multiscale conditional random fields for image labeling X He, RS Zemel, MA Carreira-Perpinán Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision …, 2004 | 1214 | 2004 |

Autoencoders, minimum description length, and Helmholtz free energy GE Hinton, RS Zemel Advances in neural information processing systems 6, 3-10, 1994 | 1153 | 1994 |

Unifying visual-semantic embeddings with multimodal neural language models R Kiros, R Salakhutdinov, RS Zemel arXiv preprint arXiv:1411.2539, 2014 | 1152 | 2014 |

Learning fair representations R Zemel, Y Wu, K Swersky, T Pitassi, C Dwork International conference on machine learning, 325-333, 2013 | 1070 | 2013 |

Information processing with population codes A Pouget, P Dayan, R Zemel Nature Reviews Neuroscience 1 (2), 125-132, 2000 | 802 | 2000 |

Understanding the effective receptive field in deep convolutional neural networks W Luo, Y Li, R Urtasun, R Zemel Proceedings of the 30th International Conference on Neural Information …, 2016 | 799 | 2016 |

Generative moment matching networks Y Li, K Swersky, R Zemel International Conference on Machine Learning, 1718-1727, 2015 | 676 | 2015 |

Multimodal neural language models R Kiros, R Salakhutdinov, R Zemel International conference on machine learning, 595-603, 2014 | 671 | 2014 |

Exploring models and data for image question answering M Ren, R Kiros, R Zemel Advances in neural information processing systems 28, 2953-2961, 2015 | 622 | 2015 |

Meta-learning for semi-supervised few-shot classification M Ren, E Triantafillou, S Ravi, J Snell, K Swersky, JB Tenenbaum, ... arXiv preprint arXiv:1803.00676, 2018 | 609 | 2018 |

Inference and computation with population codes A Pouget, P Dayan, RS Zemel Annual review of neuroscience 26 (1), 381-410, 2003 | 541 | 2003 |

Probabilistic interpretation of population codes RS Zemel, P Dayan, A Pouget Neural computation 10 (2), 403-430, 1998 | 484 | 1998 |