Gradient Stability

Gradient stability in neural networks is a crucial area of research focusing on mitigating unstable gradients that hinder training and performance. Current efforts concentrate on developing techniques to stabilize gradients during training, including novel estimators, architectural modifications like low-curvature networks and orthogonal weight initialization, and improved training strategies such as temporal unrolling and contrastive loss functions. Addressing gradient instability is vital for improving the robustness, efficiency, and interpretability of neural networks across diverse applications, from image generation and physics simulation to binary neural network compression.