Graham Taylor
Graham Taylor
University of Guelph and Vector Institute for Artificial Intelligence
Verified email at - Homepage
Cited by
Cited by
Deconvolutional networks
MD Zeiler, D Krishnan, GW Taylor, R Fergus
Computer Vision and Pattern Recognition (CVPR), 2010 IEEE Conference on …, 2010
Adaptive deconvolutional networks for mid and high level feature learning
MD Zeiler, GW Taylor, R Fergus
2011 International Conference on Computer Vision, 2018-2025, 2011
Modeling human motion using binary latent variables
GW Taylor, GE Hinton, ST Roweis
Advances in neural information processing systems, 1345-1352, 2007
Convolutional learning of spatio-temporal features
GW Taylor, R Fergus, Y LeCun, C Bregler
European conference on computer vision, 140-153, 2010
Improved regularization of convolutional neural networks with cutout
T DeVries, GW Taylor
arXiv preprint arXiv:1708.04552, 2017
The recurrent temporal restricted boltzmann machine
I Sutskever, GE Hinton, GW Taylor
Advances in neural information processing systems, 1601-1608, 2009
Factored conditional restricted Boltzmann machines for modeling motion style
GW Taylor, GE Hinton
Proceedings of the 26th annual international conference on machine learning …, 2009
Moddrop: adaptive multi-modal gesture recognition
N Neverova, C Wolf, G Taylor, F Nebout
IEEE Transactions on Pattern Analysis and Machine Intelligence 38 (8), 1692-1706, 2015
Multi-scale deep learning for gesture detection and localization
N Neverova, C Wolf, GW Taylor, F Nebout
European Conference on Computer Vision, 474-490, 2014
Learning human pose estimation features with convolutional networks
A Jain, J Tompson, M Andriluka, GW Taylor, C Bregler
arXiv preprint arXiv:1312.7302, 2013
Dynamical binary latent variable models for 3d human pose tracking
GW Taylor, L Sigal, DJ Fleet, GE Hinton
2010 IEEE Computer Society Conference on Computer Vision and Pattern …, 2010
Dataset augmentation in feature space
T DeVries, GW Taylor
arXiv preprint arXiv:1702.05538, 2017
Learning confidence for out-of-distribution detection in neural networks
T DeVries, GW Taylor
arXiv preprint arXiv:1802.04865, 2018
Automatic moth detection from trap images for pest management
W Ding, G Taylor
Computers and Electronics in Agriculture 123, 17-28, 2016
Deep learning on fpgas: Past, present, and future
G Lacey, GW Taylor, S Areibi
arXiv preprint arXiv:1602.04283, 2016
Deep multimodal learning: A survey on recent advances and trends
D Ramachandram, GW Taylor
IEEE Signal Processing Magazine 34 (6), 96-108, 2017
Learning human identity from motion patterns
N Neverova, C Wolf, G Lacey, L Fridman, D Chandra, B Barbello, G Taylor
IEEE Access 4, 1810-1820, 2016
Two Distributed-State Models For Generating High-Dimensional Time Series.
GW Taylor, GE Hinton, ST Roweis
Journal of Machine Learning Research 12 (3), 2011
Caffeinated FPGAs: FPGA framework for convolutional neural networks
R DiCecco, G Lacey, J Vasiljevic, P Chow, G Taylor, S Areibi
2016 International Conference on Field-Programmable Technology (FPT), 265-268, 2016
Prediction of flow duration curves for ungauged basins
M Atieh, G Taylor, AMA Sattar, B Gharabaghi
Journal of hydrology 545, 383-394, 2017
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