neural-dynamics

CAT-GS introduces a neural dynamics-based optimization controller that stabilizes multimodal learning by addressing modality imbalance and fusion interference, achieving improved accuracy on various benchmarks.

This paper proposes Lie group embedded dynamical neural networks (LieEDNN) with learning algorithms based on gradient descent and metric projection on smooth manifolds, enabling stable dynamics on Lie groups like SO(3) and SE(3) for robotics and control applications.