Alexander Lazovik

T3R: Deeper Test-Time Adaptation for Graph Neural Networks via Gradient Rotation

T3R is proposed, leveraging multiple Rotograd matrices to improve task affinity between the target and auxiliary tasks, essential for effective test-time training and introduces a rotation technique that reorients self-supervised signals using these matrices to create surrogate gradients for the target task, allowing deeper adaptation across nearly the entire architecture.