2 citations · 2 across the 4 of their papers we have counts for
4 papers
Learning to Execute Graph Algorithms Exactly with Graph Neural Networks
Muhammad Fetrat Qharabagh, Artur Back de Luca, George Giapitzakis +1
Understanding what graph neural networks can learn, especially their ability to learn to execute algorithms, remains a central theoretical challenge. In this work, we prove exact l…
Applying Graph Explanation to Operator Fusion
Keith G. Mills, Muhammad Fetrat Qharabagh, Weichen Qiu +5
Layer fusion techniques are critical to improving the inference efficiency of deep neural networks (DNN) for deployment. Fusion aims to lower inference costs by reducing data trans…
LVLM-COUNT: Enhancing the Counting Ability of Large Vision-Language Models
Muhammad Fetrat Qharabagh, Mohammadreza Ghofrani, Kimon Fountoulakis
Counting is a fundamental operation for various real-world visual tasks, requiring both object recognition and robust counting capabilities. Despite their advanced visual perceptio…
Reparameterization through Spatial Gradient Scaling
Alexander Detkov, Mohammad Salameh, Muhammad Fetrat Qharabagh +4
Reparameterization aims to improve the generalization of deep neural networks by transforming convolutional layers into equivalent multi-branched structures during training. Howeve…