| Scaling the Scattering Transform: Deep Hybrid Networks E Oyallon, E Belilovsky, S Zagoruyko International Conference on Computer Vision (ICCV), 5618-5627, 2017 | 45 | 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 | 32 | 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 | 25* | 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 | 13 | 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 | 13 | 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 | 9 | 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 | 8 | 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 | 6 | 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 | 5 | 2018 |
| Learning to Discover Sparse Graphical Models E Belilovsky, K Kastner, G Varoquaux, MB Blaschko International Conference on Machine Learning (ICML), 2017 | 5* | 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 | 4 | 2018 |
| Decoupled Greedy Learning of CNNs E Belilovsky, M Eickenberg, E Oyallon arXiv preprint arXiv:1901.08164, 2019 | 3 | 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 | 3 | 2019 |
| Kymatio: Scattering Transforms in Python M Andreux, T Angles, G Exarchakis, R Leonarduzzi, G Rochette, L Thiry, ... arXiv preprint arXiv:1812.11214, 2018 | 3 | 2018 |
| Greedy layerwise learning can scale to imagenet. EO Eugene Belilovsky, Michael Eickenberg arXiv:1812.11446v3, 2018 | 3* | 2018 |
| Convolutional Neural Networks for Speaker-Independent Speech Recognition E Belilovsky | 1 | 2011 |
| 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 | | 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 | | 2019 |
| Apprentissage de graphes structuré et parcimonieux dans des données de haute dimension avec applications à l'imagerie cérébrale E Belilovsky | | 2018 |
| 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 | | 2016 |
Matthew BlaschkoKU LeuvenVerified email at esat.kuleuven.be