Richard Zemel
Richard Zemel
Professor of Computer Science, University of Toronto
Verified email at cs.toronto.edu - Homepage
Title
Cited by
Cited by
Year
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
79102015
Prototypical networks for few-shot learning
J Snell, K Swersky, RS Zemel
arXiv preprint arXiv:1703.05175, 2017
31802017
Siamese neural networks for one-shot image recognition
G Koch, R Zemel, R Salakhutdinov
ICML deep learning workshop 2, 2015
25282015
Skip-thought vectors
R Kiros, Y Zhu, RR Salakhutdinov, R Zemel, R Urtasun, A Torralba, ...
Advances in neural information processing systems, 3294-3302, 2015
23372015
Gated graph sequence neural networks
Y Li, D Tarlow, M Brockschmidt, R Zemel
arXiv preprint arXiv:1511.05493, 2015
18662015
Fairness through awareness
C Dwork, M Hardt, T Pitassi, O Reingold, R Zemel
Proceedings of the 3rd innovations in theoretical computer science …, 2012
17692012
The helmholtz machine
P Dayan, GE Hinton, RM Neal, RS Zemel
Neural computation 7 (5), 889-904, 1995
13881995
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
12942015
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
12162004
Autoencoders, minimum description length, and Helmholtz free energy
GE Hinton, RS Zemel
Advances in neural information processing systems 6, 3-10, 1994
11611994
Unifying visual-semantic embeddings with multimodal neural language models
R Kiros, R Salakhutdinov, RS Zemel
arXiv preprint arXiv:1411.2539, 2014
11582014
Learning fair representations
R Zemel, Y Wu, K Swersky, T Pitassi, C Dwork
International conference on machine learning, 325-333, 2013
10802013
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
8142016
Information processing with population codes
A Pouget, P Dayan, R Zemel
Nature Reviews Neuroscience 1 (2), 125-132, 2000
8022000
Generative moment matching networks
Y Li, K Swersky, R Zemel
International Conference on Machine Learning, 1718-1727, 2015
6782015
Multimodal neural language models
R Kiros, R Salakhutdinov, R Zemel
International conference on machine learning, 595-603, 2014
6712014
Exploring models and data for image question answering
M Ren, R Kiros, R Zemel
Advances in neural information processing systems 28, 2953-2961, 2015
6272015
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
6202018
Inference and computation with population codes
A Pouget, P Dayan, RS Zemel
Annual review of neuroscience 26 (1), 381-410, 2003
5422003
Probabilistic interpretation of population codes
RS Zemel, P Dayan, A Pouget
Neural computation 10 (2), 403-430, 1998
4841998
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