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.

Terrance DeVries, Adriana Romero, Luis Pineda, Graham Taylor, and Michal Drozdal. On the evaluation of conditional GANs. arXiv preprint arXiv:1907.08175, 2019. [ bib | http ]

Boris Knyazev, Graham Taylor, and Mohamed Amer. Understanding attention and generalization in graph neural networks. In Neural Information Processing Systems (NeurIPS), 2019. To appear. Early version appeared at the International Conference on Learning Representations (ICLR) Workshop on Representation Learning on Graphs and Manifolds. [ bib | http ]

Alaaeldin El-Nouby, Shikhar Sharma, Hannes Schulz, Devon Hjelm, Layla El Asri, Samira Ebrahimi Kahou, Yoshua Bengio, and Graham Taylor. Tell, draw, and repeat: Generating and modifying images based on continual linguistic instruction. In International Conference on Computer Vision (ICCV), 2019. Early version appeared at the Neural Information Processing Systems (NeurIPS) Workshop on Visually Grounded Interaction and Language (ViGIL). [ bib | http ]

Boris Knyazev, Xiao Lin, Mohamed Amer, and Graham Taylor. Image classification with hierarchical multigraph networks. In British Machine Vision Conference (BMVC), 2019. [ bib | http ]

Angus Galloway, Anna Golubeva, Thomas Tanay, Medhat Moussa, and Graham Taylor. Batch normalization is a cause of adversarial vulnerability. arXiv preprint arXiv:1905.02161, 2019. [ bib | http ]

Angus Galloway, Anna Golubeva, and Graham Taylor. Adversarial examples as an input-fault tolerance problem. In Neural Information Processing Systems (NeurIPS) Workshop on Security in Machine Learning, 2018. [ bib | http ]

Boris Knyazev, Xiao Lin, Mohamed R. Amer, and Graham Taylor. Spectral multigraph networks for discovering and fusing relationships in molecules. In Neural Information Processing Systems (NeurIPS) Workshop on Machine Learning for Molecules and Materials, 2018. [ bib | http ]

Nihal Murali, Jonathan Schneider, Joel Levine, and Graham Taylor. Classification and re-identification of fruit fly individuals across days with convolutional neural networks. In IEEE Winter Conference on Applications of Computer Vision (WACV), 2019. [ bib | http ]

Jonathan Schneider, Nihal Murali, Graham Taylor, and Joel Levine. Can Drosophila melanogaster tell who's who? PLOS One, 13(10), 2018. [ bib ]

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

Terrance DeVries and Graham Taylor. Leveraging uncertainty estimates for predicting segmentation quality. arXiv preprint arXiv:1807.00502, 2018. [ bib | 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. [ 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):757--764, 2017. Also presented at the IEEE International Conference on Robotics and Automation (ICRA). [ bib | http ]

Group Members

Former Group Members