Issam H. Laradji
Issam H. Laradji
Senior Research Scientist at ServiceNow Research
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Software defect prediction using ensemble learning on selected features
IH Laradji, M Alshayeb, L Ghouti
Information and Software Technology 58, 388-402, 2015
Coordinate descent converges faster with the gauss-southwell rule than random selection
J Nutini, M Schmidt, I Laradji, M Friedlander, H Koepke
International Conference on Machine Learning (ICML), 1632-1641, 2015
Where are the blobs: Counting by localization with point supervision
IH Laradji, N Rostamzadeh, PO Pinheiro, D Vazquez, M Schmidt
Proceedings of the European Conference on Computer Vision (ECCV), 547-562, 2018
Painless stochastic gradient: Interpolation, line-search, and convergence rates
S Vaswani, A Mishkin, I Laradji, M Schmidt, G Gidel, S Lacoste-Julien
Advances in neural information processing systems 32, 2019
Embedding propagation: Smoother manifold for few-shot classification
P Rodríguez, I Laradji, A Drouin, A Lacoste
European Conference on Computer Vision, 121-138, 2020
Online fast adaptation and knowledge accumulation: a new approach to continual learning
M Caccia, P Rodriguez, O Ostapenko, F Normandin, M Lin, L Caccia, ...
arXiv preprint arXiv:2003.05856, 2020
Convergence rates for greedy Kaczmarz algorithms
J Nutini, B Sepehry, A Virani, I Laradji, M Schmidt, H Koepke
Conference on Uncertainty in Artificial Intelligence, 2016
Stochastic polyak step-size for sgd: An adaptive learning rate for fast convergence
N Loizou, S Vaswani, IH Laradji, S Lacoste-Julien
International Conference on Artificial Intelligence and Statistics, 1306-1314, 2021
Let's Make Block Coordinate Descent Go Fast: Faster Greedy Rules, Message-Passing, Active-Set Complexity, and Superlinear Convergence
J Nutini, I Laradji, M Schmidt
arXiv preprint arXiv:1712.08859, 2017
A weakly supervised consistency-based learning method for covid-19 segmentation in ct images
I Laradji, P Rodriguez, O Manas, K Lensink, M Law, L Kurzman, W Parker, ...
IEEE/CVF Winter Conference on Applications of Computer Vision, 2453-2462, 2021
Where are the masks: Instance segmentation with image-level supervision
IH Laradji, D Vazquez, M Schmidt
arXiv preprint arXiv:1907.01430, 2019
M-ADDA: Unsupervised domain adaptation with deep metric learning
IH Laradji, R Babanezhad
Domain adaptation for visual understanding, 17-31, 2020
Perceptual hashing of color images using hypercomplex representations
IH Laradji, L Ghouti, EH Khiari
2013 IEEE International Conference on Image Processing, 4402-4406, 2013
A realistic fish-habitat dataset to evaluate algorithms for underwater visual analysis
A Saleh, IH Laradji, DA Konovalov, M Bradley, D Vazquez, M Sheaves
Scientific Reports 10 (1), 1-10, 2020
A weakly supervised region-based active learning method for covid-19 segmentation in ct images
I Laradji, P Rodriguez, F Branchaud-Charron, K Lensink, P Atighehchian, ...
arXiv preprint arXiv:2007.07012, 2020
Fast and furious convergence: Stochastic second order methods under interpolation
SY Meng, S Vaswani, IH Laradji, M Schmidt, S Lacoste-Julien
International Conference on Artificial Intelligence and Statistics, 1375-1386, 2020
Instance segmentation with point supervision
IH Laradji, N Rostamzadeh, PO Pinheiro, D Vázquez, M Schmidt
arXiv preprint arXiv:1906.06392, 2019
CVPR 2020 continual learning in computer vision competition: Approaches, results, current challenges and future directions
V Lomonaco, L Pellegrini, P Rodriguez, M Caccia, Q She, Y Chen, ...
Artificial Intelligence 303, 103635, 2022
Class-based styling: Real-time localized style transfer with semantic segmentation
L Kurzman, D Vazquez, I Laradji
Proceedings of the IEEE/CVF International Conference on Computer Vision …, 2019
Learning data augmentation with online bilevel optimization for image classification
S Mounsaveng, I Laradji, I Ben Ayed, D Vazquez, M Pedersoli
Proceedings of the IEEE/CVF Winter Conference on Applications of Computer …, 2021
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