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Exploring the robustness of TractOracle methods in RL-based tractography
Jeremi Levesque, Antoine Théberge, Maxime Descoteaux +1
Tractography algorithms leverage diffusion MRI to reconstruct the fibrous architecture of the brain's white matter. Among machine learning approaches, reinforcement learning (RL) h…
TractOracle: towards an anatomically-informed reward function for RL-based tractography
Antoine Théberge, Maxime Descoteaux, Pierre-Marc Jodoin
Reinforcement learning (RL)-based tractography is a competitive alternative to machine learning and classical tractography algorithms due to its high anatomical accuracy obtained w…
What Matters in Reinforcement Learning for Tractography
Antoine Théberge, Christian Desrosiers, Maxime Descoteaux +1
Recently, deep reinforcement learning (RL) has been proposed to learn the tractography procedure and train agents to reconstruct the structure of the white matter without manually…
Neural Teleportation
Marco Armenta, Thierry Judge, Nathan Painchaud +5
In this paper, we explore a process called neural teleportation, a mathematical consequence of applying quiver representation theory to neural networks. Neural teleportation "telep…
The Representation Theory of Neural Networks
Marco Antonio Armenta, Pierre-Marc Jodoin
In this work, we show that neural networks can be represented via the mathematical theory of quiver representations. More specifically, we prove that a neural network is a quiver r…
Spectral Metric for Dataset Complexity Assessment
Frederic Branchaud-Charron, Andrew Achkar, Pierre-Marc Jodoin
In this paper, we propose a new measure to gauge the complexity of image classification problems. Given an annotated image dataset, our method computes a complexity measure called…