Eugene Belilovsky
Eugene Belilovsky
Postdoctoral Fellow at University of Montreal
Verified email at - Homepage
TitleCited byYear
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
E Belilovsky, W Bounliphone, M Blaschko, I Antonoglou, A Gretton
International Conference on Learning Representations (ICLR), 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
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
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
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
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, 2018
Learning to Discover Sparse Graphical Models
E Belilovsky, K Kastner, G Varoquaux, MB Blaschko
International Conference on Machine Learning (ICML), 2017
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
Decoupled Greedy Learning of CNNs
E Belilovsky, M Eickenberg, E Oyallon
arXiv preprint arXiv:1901.08164, 2019
Greedy Layerwise Learning Can Scale to ImageNet
E Belilovsky, M Eickenberg, E Oyallon
Proceedings of the 36th International Conference on Machine Learning (ICML …, 2019
Kymatio: Scattering Transforms in Python
M Andreux, T Angles, G Exarchakis, R Leonarduzzi, G Rochette, L Thiry, ...
arXiv preprint arXiv:1812.11214, 2018
Greedy layerwise learning can scale to imagenet.
EO Eugene Belilovsky, Michael Eickenberg
arXiv:1812.11446v3, 2018
Convolutional Neural Networks for Speaker-Independent Speech Recognition
E Belilovsky
VideoNavQA: Bridging the Gap between Visual and Embodied Question Answering
C Cangea, E Belilovsky, P Liò, A Courville
BMVC arXiv preprint arXiv:1908.04950, 2019
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 (NIPS) arXiv preprint …, 2019
Apprentissage de graphes structuré et parcimonieux dans des données de haute dimension avec applications à l'imagerie cérébrale
E Belilovsky
Fast Non-Parametric Tests of Relative Dependency and Similarity
W Bounliphone, E Belilovsky, A Tenenhaus, I Antonoglou, A Gretton, ...
arXiv preprint arXiv:1611.05740, 2016
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