Issam Laradji is a PhD student at the University of British Columbia and a research intern at Element AI. His main research interests are in large-scale optimization and computer vision. His recent works include state-of-the-art counting methods, weakly supervised instance segmentation, and fast optimizers for deep learning.
- July 1st 2019: Our BMVC 2019 paper "Where are the Masks: Instance Segmentation with Image-level Supervision" is accepted as poster.
- June 14th 2019: Our paper "Instance Segmentation with Point Supervision" is in arXiv.
- May 24th 2019: Our paper "Painless Stochastic Gradient: Interpolation, Line-Search, and Convergence Rates" is in arXiv.
- July 1st 2019: Our IJCNN 2019 paper "Efficient Deep Gaussian Process Models for Variable-Sized Input" is accepted as poster.