Gradient Vanishing

Gradient vanishing, the phenomenon where gradients used to update model parameters during training become extremely small, hindering effective learning, is a significant challenge across diverse machine learning models. Current research focuses on mitigating this issue in various architectures, including variational quantum circuits, spiking neural networks, and binary neural networks, often employing strategies like modified backpropagation methods, alternative loss functions, and selective data sampling. Overcoming gradient vanishing is crucial for improving the scalability and performance of these models, impacting fields ranging from quantum computing and neural network optimization to natural language processing and computer vision.