Forward Gradient

Forward gradient methods aim to compute gradients for training neural networks without the backward pass of backpropagation, addressing its memory limitations and computational cost. Current research focuses on improving the accuracy and efficiency of forward gradient estimation, exploring techniques like variance reduction, local loss functions, and integration with specific architectures such as invertible networks and MLPMixer-inspired designs. These advancements offer potential for faster training and reduced memory consumption in various applications, including computational fluid dynamics, federated learning of large language models, and quantum chemistry simulations.