Eugene Belilovsky
Eugene Belilovsky
Postdoctoral Fellow at University of Montreal
Verified email at - Homepage
Cited by
Cited by
Scaling the Scattering Transform: Deep Hybrid Networks
E Oyallon, E Belilovsky, S Zagoruyko
International Conference on Computer Vision (ICCV), 5618-5627, 2017
Testing for Differences in Gaussian Graphical Models: Applications to Brain Connectivity
E Belilovsky, G Varoquaux, MB Blaschko
Advances In neural Information Processing Systems (NIPS), 2016
A test of relative similarity for model selection in generative models
W Bounliphone, E Belilovsky, MB Blaschko, I Antonoglou, A Gretton
International Conference on Learning Representations (ICLR) arXiv preprint …, 2016
Blindfold Baselines for Embodied QA
A Anand, E Belilovsky, K Kastner, H Larochelle, A Courville
NIPS VIGIL Workshop arXiv preprint arXiv:1811.05013, 2018
Predictive sparse modeling of fMRI data for improved classification, regression, and visualization using the k-support norm
E Belilovsky, K Gkirtzou, M Misyrlis, AB Konova, J Honorio, N Alia-Klein, ...
Computerized Medical Imaging and Graphics 46, 40-46, 2015
Greedy Layerwise Learning Can Scale to ImageNet
E Belilovsky, M Eickenberg, E Oyallon
Proceedings of the 36th International Conference on Machine Learning (ICML …, 2019
Joint Embeddings of Scene Graphs and Images
E Belilovsky, M Blaschko, JR Kiros, R Urtasun, R Zemel
International Conference on Learning Representations (ICLR) Workshop track, 2017
Scattering networks for hybrid representation learning
E Oyallon, S Zagoruyko, G Huang, N Komodakis, S Lacoste-Julien, ...
IEEE transactions on pattern analysis and machine intelligence 41 (9), 2208-2221, 2018
Learning to Discover Sparse Graphical Models
E Belilovsky, K Kastner, G Varoquaux, MB Blaschko
International Conference on Machine Learning (ICML), 2017
Online Continual Learning with Maximally Interfered Retrieval
R Aljundi, L Caccia, E Belilovsky, M Caccia, L Charlin, T Tuytelaars
Advances In neural Information Processing Systems (NeurIPS), 2019
Kymatio: Scattering transforms in python
M Andreux, T Angles, G Exarchakis, R Leonarduzzi, G Rochette, L Thiry, ...
Journal of Machine Learning Research 21 (60), 1-6, 2020
Decoupled greedy learning of cnns
E Belilovsky, M Eickenberg, E Oyallon
arXiv preprint arXiv:1901.08164, 2019
Compressing the Input for CNNs with the First-Order Scattering Transform
E Oyallon, E Belilovsky, S Zagoruyko, M Valko
European Conference on Computer Vision (ECCV), 2018
Convex relaxations of penalties for sparse correlated variables with bounded total variation
E Belilovsky, A Argyriou, G Varoquaux, M Blaschko
Springer Machine Learning (ECML Journal Track) 100 (2-3), 533-553, 2015
Generalized cyclic transformations in speaker-independent speech recognition
F Müller, E Belilovsky, A Mertins
IEEE Workshop on Automatic Speech Recognition & Understanding (ASRU), 211-215, 2009
Convolutional Neural Networks for Speaker-Independent Speech Recognition
E Belilovsky
Graph Density-Aware Losses for Novel Compositions in Scene Graph Generation
B Knyazev, H de Vries, C Cangea, GW Taylor, A Courville, E Belilovsky
arXiv preprint arXiv:2005.08230, 2020
A Simple and Scalable Shape Representation for 3D Reconstruction
M Michalkiewicz, E Belilovsky, M Baktashmotlagh, A Eriksson
arXiv preprint arXiv:2005.04623, 2020
Few-Shot Single-View 3-D Object Reconstruction with Compositional Priors
M Michalkiewicz, S Parisot, S Tsogkas, M Baktashmotlagh, A Eriksson, ...
arXiv preprint arXiv:2004.06302, 2020
Online Learned Continual Compression with Adaptive Quantization Modules
L Caccia, E Belilovsky, M Caccia, J Pineau
arXiv, arXiv: 1911.08019, 2019
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