About me

I’m a PhD student at the Machine Learning Research Group, University of Guelph (Ontario, Canada). My advisor is Graham Taylor. Recently, I interned at Mila working with Eugene Belilovsky and Aaron Courville. My research interests lie at the intersection of graph neural networks (GNNs) and computer vision. Previously, I did an internship at SRI International with Mohamed Amer, where I worked on training GNNs on image superpixels (presented at BMVC 2019). Before that I worked on unsupervised learning and pretraining of neural networks, face, emotion and facial attributes recognition, and video recognition.

bknyazev [at] uoguelph [dot] ca




See the full list at Google Scholar.

  • Graph Density-Aware Losses for Novel Compositions in Scene Graph Generation
    Boris Knyazev, Harm de Vries, Cătălina Cangea, Graham W. Taylor, Aaron Courville, Eugene Belilovsky, in submission html, pdf, code

  • Learning Temporal Attention in Dynamic Graphs with Bilinear Interactions
    Boris Knyazev*, Carolyn Augusta*, Graham Taylor (*equal contribution)
    in submission
    html, pdf, code

  • Understanding Attention and Generalization in Graph Neural Networks
    Boris Knyazev, Graham Taylor, Mohamed Amer
    Advances in Neural Information Processing Systems (NeurIPS), 2019
    html, pdf, ICLR workshop version, code, poster, slides

  • Image Classification with Hierarchical Multigraph Networks
    Boris Knyazev, Xiao Lin, Mohamed Amer, Graham Taylor
    British Machine Vision Conference (BMVC), 2019
    html, pdf, code, blog post

  • Spectral Multigraph Networks for Discovering and Fusing Relationships in Molecules
    Boris Knyazev, Xiao Lin, Mohamed Amer, Graham Taylor
    NeurIPS Workshop on Machine Learning for Molecules and Materials, 2018
    html, pdf, code

  • Leveraging Large Face Recognition Data for Emotion Classification
    Boris Knyazev, Roman Shvetsov, Natalia Efremova, Artem Kuharenko
    FG Workshop on Large-scale Emotion Recognition and Analysis (LERA), 2018
    html, pdf, code, Top-2 in EmotiW 2017 challenge

  • Recursive Autoconvolution for Unsupervised Learning of Convolutional Neural Networks
    Boris Knyazev, Erhardt Barth, Thomas Martinetz
    International Joint Conference on Neural Networks (IJCNN), 2017
    html, pdf, matlab code, python code, reddit

Blog posts

Open source contributions

  • - Geometric Deep Learning Extension Library for PyTorch