| Scaling the Scattering Transform: Deep Hybrid Networks E Oyallon, E Belilovsky, S Zagoruyko International Conference on Computer Vision (ICCV), 5618-5627, 2017 | 68 | 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 | 35 | 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 | 35 | 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 | 22 | 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 | 20* | 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 | 17* | 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 | 12 | 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 | 10 | 2018 |
| Learning to Discover Sparse Graphical Models E Belilovsky, K Kastner, G Varoquaux, MB Blaschko International Conference on Machine Learning (ICML), 2017 | 9* | 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 | 8 | 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 | 7 | 2020 |
| Decoupled greedy learning of cnns E Belilovsky, M Eickenberg, E Oyallon arXiv preprint arXiv:1901.08164, 2019 | 7 | 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 | 7 | 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 | 7 | 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 | 7 | 2009 |
| Convolutional Neural Networks for Speaker-Independent Speech Recognition E Belilovsky | 1 | 2011 |
| 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 | | 2020 |
| A Simple and Scalable Shape Representation for 3D Reconstruction M Michalkiewicz, E Belilovsky, M Baktashmotlagh, A Eriksson arXiv preprint arXiv:2005.04623, 2020 | | 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 | | 2020 |
| Online Learned Continual Compression with Adaptive Quantization Modules L Caccia, E Belilovsky, M Caccia, J Pineau arXiv, arXiv: 1911.08019, 2019 | | 2019 |
Matthew BlaschkoKU LeuvenVerified email at esat.kuleuven.be
