167 citations
- Université de MontréalCA31 papers
- McGill UniversityCA16 papers
- Mila - Quebec Artificial Intelligence InstituteCA11 papers
- Concordia UniversityCA5 papers
- Flatiron Health (United States)US5 papers
- Flatiron Institute5 papers
- Google DeepMind (United Kingdom)GB5 papers
- Meta (Israel)IL5 papers
- Canadian Institute for Advanced ResearchCA4 papers
- Polytechnique MontréalCA4 papers
- University of TorontoCA4 papers
- Google (United States)US3 papers
56 papers
Bringing SAM to new heights: Leveraging elevation data for tree crown segmentation from drone imagery
Mélisande Teng, Arthur Ouaknine, Etienne Laliberté +3
Information on trees at the individual level is crucial for monitoring forest ecosystems and planning forest management. Current monitoring methods involve ground measurements, req…
ACCO: Accumulate While You Communicate for Communication-Overlapped Sharded LLM Training
Adel Nabli, Louis Fournier, Pierre Erbacher +3
Training LLMs relies on distributed implementations using multiple GPUs to compute gradients in parallel with sharded optimizers. However, synchronizing gradients in data parallel…
PETRA: Parallel End-to-end Training with Reversible Architectures
Stéphane Rivaud, Louis Fournier, Thomas Pumir +3
Reversible architectures have been shown to be capable of performing on par with their non-reversible architectures, being applied in deep learning for memory savings and generativ…
WASH: Train your Ensemble with Communication-Efficient Weight Shuffling, then Average
Louis Fournier, Adel Nabli, Masih Aminbeidokhti +3
The performance of deep neural networks is enhanced by ensemble methods, which average the output of several models. However, this comes at an increased cost at inference. Weight a…
Towards Detecting Contextual Real-Time Toxicity for In-Game Chat
Zachary Yang, Nicolas Grenan-Godbout, Reihaneh Rabbany
Real-time toxicity detection in online environments poses a significant challenge, due to the increasing prevalence of social media and gaming platforms. We introduce ToxBuster, a…
Can Forward Gradient Match Backpropagation?
Louis Fournier, Stéphane Rivaud, Eugene Belilovsky +2
Forward Gradients - the idea of using directional derivatives in forward differentiation mode - have recently been shown to be utilizable for neural network training while avoiding…