Associate Professor
School of Engineering, University of Guelph and
Vector Institute
CIFAR Azrieli Global Scholar
Learning in Machines & Brains program
Academic Director, NextAI
Richards Building, Room 3515
I lead the Machine Learning Research Group at the University of Guelph. I am interested in statistical machine learning and biologically-inspired computer vision, with an emphasis on deep learning and time series analysis.
I received received my PhD in Computer Science from the University of Toronto in 2009, where I was advised by Geoffrey Hinton and Sam Roweis. I spent two years as a postdoc at the Courant Institute of Mathematical Sciences, New York University working with Chris Bregler, Rob Fergus, and Yann LeCun. In 2012, I joined the School of Engineering at the University of Guelph as an Assistant Professor.
A complete list of my publications is available on Google Scholar.
Angus Galloway, Graham Taylor, and Medhat Moussa. Predicting adversarial examples with high confidence. arXiv preprint arXiv:1802.04457, 2018. [ bib | http ]
Terrance Devries and Graham Taylor. Learning confidence for out-of-distribution detection in neural networks. arXiv preprint arXiv:1802.04865, 2018. [ bib | http ]
Daniel Jiwoong Im, He Ma, Graham Taylor, and Kristen Branson. Quantitatively evaluating GANs with divergences proposed for training. In International Conference on Learning Representations (ICLR), 2018. To appear. [ bib ]
Angus Galloway, Graham Taylor, and Medhat Moussa. Attacking binarized neural networks. In International Conference on Learning Representations (ICLR), 2018. To appear. [ bib | http ]
Dhanesh Ramachandram and Graham Taylor. Deep multimodal learning: A survey on recent advances and trends. IEEE Signal Processing Magazine, 34:96--108, 2017. [ bib | http ]
Natalia Neverova, Christian Wolf, Florian Nebout, and Graham Taylor. Hand pose estimation through semi-supervised and weakly-supervised learning. Computer Vision and Image Understanding, 164:56--67, 2017. [ bib | http ]
Terrance Devries and Graham Taylor. Improved regularization of convolutional neural networks with Cutout. arXiv preprint arXiv:1708.04552, 2017. [ bib | http ]
Devinder Kumar, Alexander Wong, and Graham Taylor. Explaining the unexplained: A class-enhanced attentive response (CLEAR) approach to understanding deep neural networks. arXiv preprint arXiv:1704.04133, 2017. [ bib | http ]
Terrance Devries and Graham Taylor. Dataset augmentation in feature space. In International Conference on Learning Representations (ICLR) Workshop Track, 2017. [ bib | http ]
Dhanesh Ramachandram, Michal Lisicki, Timothy Shields, Mohamed Amer, and Graham Taylor. Structure optimization for deep multimodal fusion networks using graph-induced kernels. In European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN), 2017. [ bib | .pdf ]
Fan Li, Natalia Neverova, Christian Wolf, and Graham Taylor. Modout: Learning multi-modal architectures by stochastic regularization. In 2017 IEEE Conference on Automatic Face and Gesture Recognition (FG), 2017. [ bib | .pdf ]
Matthew Veres, Medhat Moussa, and Graham Taylor. Modeling grasp motor imagery through deep conditional generative models. IEEE Robotics and Automation Letters, 2(2), 2017. [ bib | http ]
Roberto DiCecco, Griffin Lacey, Jasmina Vasiljevic, Paul Chow, Graham Taylor, and Shawki Areibi. Caffeinated FPGAs: FPGA framework for convolutional neural networks. In Field-Programmable Technology (FPT), 2016. [ bib | http ]
He Ma, Fei Mao, and Graham Taylor. Theano-MPI: a Theano-based distributed training framework. arXiv preprint arXiv:1605.08325, 2016. [ bib | http ]
Natalia Neverova, Christian Wolf, Griffin Lacey, Lex Fridman, Deepak Chandra, Brandon Barbello, and Graham Taylor. Learning human identity from motion patterns. IEEE Access, 4:1810--1820, 2016. [ bib ]
Griffin Lacey, Graham Taylor, and Shawki Areibi. Deep learning on FPGAs: Past, present, and future. arXiv preprint arXiv:1602.04283, 2016. [ bib | http ]
Weiguang Ding and Graham Taylor. Automatic moth detection from trap images for pest management. Computers and Electronics in Agriculture, 123:17--28, 2016. [ bib | DOI | http ]