Adaptive Clipping

Adaptive clipping techniques in machine learning aim to improve model training and performance by dynamically adjusting the magnitude of gradients or activations. Current research focuses on optimizing clipping thresholds for various applications, including federated learning, differential privacy, and speech and vision models, often employing adaptive algorithms and exploring different clipping granularities (e.g., per-sample, per-layer, per-device). These advancements enhance model robustness, efficiency, and privacy, particularly in resource-constrained environments or when dealing with noisy data, impacting both theoretical understanding of optimization algorithms and practical deployment of machine learning systems.