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.

News

Brief Biography

I 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. In 2017 I was promoted to Associate Professor and became a member of the Vector Institute for Artificial Intelligence.

Research Highlights

A complete list of my publications is available on Google Scholar.

Griffin Lacey, Graham Taylor, and Shawki Areibi. Stochastic layer-wise precision in deep neural networks. In Uncertainty in Artificial Intelligence (UAI), 2018. To appear. [ bib | http ]

Terrance Devries and Graham Taylor. Leveraging uncertainty estimates for predicting segmentation quality. arXiv preprint arXiv:1807.00502, 2018. [ bib | http ]

Jonathan Schneider, Nihal Murali, Graham Taylor, and Joel Levine. Can Drosophila melanogaster tell who's who? bioRxiv preprint bioRxiv:342857, 2018. [ bib | DOI | http ]

Angus Galloway, Thomas Tanay, and Graham Taylor. Adversarial training versus weight decay. arXiv preprint arXiv:1802.04457, 2018. [ bib | http ]

Fabien Baradel, Christian Wolf, Julien Mille, and Graham Taylor. Glimpse clouds: Human activity recognition from unstructured feature points. In Proc. of the 31st IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR), 2018. To appear. [ bib | http ]

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. [ bib | http ]

Angus Galloway, Graham Taylor, and Medhat Moussa. Attacking binarized neural networks. In International Conference on Learning Representations (ICLR), 2018. [ 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. Also presented at the IEEE International Conference on Robotics and Automation (ICRA). [ 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 ]

Group Members

Former Group Members