Eugene Belilovsky bio photo

Eugene Belilovsky

Researcher in Machine Learning

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Publications

Also take a look at my Google Scholar profile

List of Publications

  • Louis Thiry, Michael Arbel, Eugene Belilovsky, Edouard oyallon: The Unreasonable Effectiveness of Patches in Deep Convolutional Kernels Methods. International Conference on Representation Learning 2021. PDF

  • Boris Knyazev, Harm de Vries, Cătălina Cangea, Graham W. Taylor, Aaron Courville, Eugene Belilovsky: Graph Density-Aware Losses for Novel Compositions in Scene Graph Generation. British Machine Vision Conference (BMVC) 2020. PDF

  • Mateusz Michalkiewicz, Eugene Belilovsky, Mahsa Baktashmotlagh, Anders P. Eriksson: A Simple and Scalable Shape Representation for 3D Reconstruction. British Machine Vision Conference (BMVC) 2020. PDF

  • Mateusz Michalkiewicz, Sarah Parisot, Stavros Tsogkas, Mahsa Baktashmotlagh, Anders P. Eriksson, Eugene Belilovsky: Few-Shot Single-View 3-D Object Reconstruction with Compositional Priors. ECCV 2020. PDF [Code]

  • Lucas Caccia, Eugene Belilovsky, Massimo Caccia, Joelle Pineau: Online Learned Continual Compression with Stacked Quantization Modules. ICML 2020.PDF [Code]

  • Rahaf Aljundi*, Lucas Caccia*, Eugene Belilovsky*, Massimo Caccia*, Min Lin, Laurent Charlin, Tinne Tuytelaars: Online Continual Learning with Maximally Inferred Retrieval. NeurIPS 2019. *Equal Contribution PDF [Code]

  • Eugene Belilovsky, Michael Eickenberg, Edouard Oyallon: Decoupled Greedy Learning of CNNs. ICML 2020. PDF [Code]

  • Cătălina Cangea, Eugene Belilovsky, Pietro Liò, Aaron Courville: VideoNavQA: Bridging the Gap between Visual and Embodied Question Answering. BMVC 2019. PDF [Code]

  • Mathieu Andreux, Tomás Angles, Georgios Exarchakis, Roberto Leonarduzzi, Gaspar Rochette, Louis Thiry, John Zarka, Stéphane Mallat, Joakim Andén, Eugene Belilovsky, Joan Bruna, Vincent Lostanlen, Matthew J. Hirn, Edouard Oyallon, Sixhin Zhang, Carmine Cella, Michael Eickenberg: Kymatio: Scattering Transforms in Python. Journal of Machine Learning (Software Track) 2020.PDF [Code]

  • Eugene Belilovsky, Michael Eickenberg, Edouard Oyallon: Greedy Layerwise Learning Can Scale to ImageNet. ICML 2019. PDF [Code]

  • Ankesh Anand, Eugene Belilovsky, Kyle Kastner, Hugo Larochelle, Aaron Courville: Blindfold Baselines for Embodied QA. NIPS VIGIL Workshop, December 2018. PDF [Code]

  • Edouard Oyallon, Sergey Zagoruyko, Gabriel Huang, Nikos Komodakis, Simon Lacoste-Julien, Matthew Blaschko, Eugene Belilovsky: Scattering Networks for Hybrid Representation Learning. IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), July 2018.PDF [Code]

  • Eugene Belilovsky: Structured Sparse Learning on Graphs in High-Dimensional Data with Applications to Neuroimaging. PhD Thesis. PDF

  • Edouard Oyallon, Eugene Belilovsky,Sergey Zagoruyko: Scaling the Scattering Transform: Deep Hybrid Networks. ICCV 2017. PDF [Code]

  • Eugene Belilovsky, Kyle Kastner, Gael Varoquaux, Matthew B. Blaschko: Learning to Discover Sparse Graphical Model Structures. ICML 2017. PDF [Code]

  • Eugene Belilovsky, Matthew B. Blaschko, Jamie R. Kiros, Raquel Urtasun, Richard Zemel: Joint Embeddings of Scene Graphs and Images. ICLR 2017 (Workshop). PDF

  • Eugene Belilovsky, Gael Varoquaux, Matthew B. Blaschko, : Testing for Differences in Gaussian Graphical Models: Applications to Brain Connectivity. NIPS 2016. PDF [Code]

  • Eugene Belilovsky*, Wacha Bounliphone*, Matthew Blaschko, Ioannis Antonoglou, Arthur Gretton : A Test of Relative Similarity For Model Selection in Generative Models . ICLR 2016. PDF [Code]

  • Eugene Belilovsky, Andreas Argyriou, Gael Varoquaux, and Matthew B. Blaschko: Convex Relaxations of Penalties for Sparse Correlated Variables With Bounded Total Variation . Machine Learning, Springer, 2015. ECML/KPDD 2015 Journal Track. PDF [Code]

  • Eugene Belilovsky, Katerina Gkirtzou, Michail Misyrlis, Anna Konova, Jean Honorio, Nelly Alia-Klein, Rita Goldstein, Dimitris Samaras, Matthew Blaschko Predictive sparse modeling of fMRI data for improved classification, regression, and visualization using the k-support norm . Computerized Medical Imaging and Graphics, Elsevier, 2015, pp.1. PDF

  • Eugene Belilovsky, Andreas Argyriou, Matthew Blaschko Approximating Combined Discrete Total Variation and Correlated Sparsity With Convex Relaxations. NIPS Workshop on Discrete and Combinatorial Problems in Machine Learning, Dec 2014, Montreal, Canada. pp.1. PDF

  • Eugene Belilovsky Convolutional Neural Networks for Speaker-Independent Speech Recognition Master’s Thesis, 2011 PDF

  • Müller, F., Belilovsky, E., and Mertins, A.: Generalized Cyclic Transformations in Speaker-Independent Speech Recognition. Proc. 2009 IEEE Automatic Speech Recognition and Understanding Workshop, Merano, Italy, Dec. 13-17 2009 PDF