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
80782015
Prototypical networks for few-shot learning
J Snell, K Swersky, RS Zemel
arXiv preprint arXiv:1703.05175, 2017
33762017
Siamese neural networks for one-shot image recognition
G Koch, R Zemel, R Salakhutdinov
ICML deep learning workshop 2, 2015
26382015
Skip-thought vectors
R Kiros, Y Zhu, RR Salakhutdinov, R Zemel, R Urtasun, A Torralba, ...
Advances in neural information processing systems, 3294-3302, 2015
23662015
Gated graph sequence neural networks
Y Li, D Tarlow, M Brockschmidt, R Zemel
arXiv preprint arXiv:1511.05493, 2015
19492015
Fairness through awareness
C Dwork, M Hardt, T Pitassi, O Reingold, R Zemel
Proceedings of the 3rd innovations in theoretical computer science …, 2012
18382012
The helmholtz machine
P Dayan, GE Hinton, RM Neal, RS Zemel
Neural computation 7 (5), 889-904, 1995
14011995
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
13462015
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
12172004
Autoencoders, minimum description length, and Helmholtz free energy
GE Hinton, RS Zemel
Advances in neural information processing systems 6, 3-10, 1994
11901994
Unifying visual-semantic embeddings with multimodal neural language models
R Kiros, R Salakhutdinov, RS Zemel
arXiv preprint arXiv:1411.2539, 2014
11732014
Learning fair representations
R Zemel, Y Wu, K Swersky, T Pitassi, C Dwork
International conference on machine learning, 325-333, 2013
11182013
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
8432016
Information processing with population codes
A Pouget, P Dayan, R Zemel
Nature Reviews Neuroscience 1 (2), 125-132, 2000
8062000
Generative moment matching networks
Y Li, K Swersky, R Zemel
International Conference on Machine Learning, 1718-1727, 2015
6992015
Multimodal neural language models
R Kiros, R Salakhutdinov, R Zemel
International conference on machine learning, 595-603, 2014
6862014
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
6582018
Exploring models and data for image question answering
M Ren, R Kiros, R Zemel
Advances in neural information processing systems 28, 2953-2961, 2015
6382015
Inference and computation with population codes
A Pouget, P Dayan, RS Zemel
Annual review of neuroscience 26 (1), 381-410, 2003
5452003
Probabilistic interpretation of population codes
RS Zemel, P Dayan, A Pouget
Neural computation 10 (2), 403-430, 1998
4891998
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