Weight Update

Weight update, a core process in machine learning, focuses on efficiently and effectively modifying model parameters to improve performance on a given task. Current research emphasizes improving the stability and efficiency of weight updates, exploring techniques like residual connections in neural networks, low-rank adaptations (LoRA) for parameter-efficient fine-tuning, and novel optimization algorithms such as ADMM for pruned models. These advancements are crucial for addressing challenges in various applications, including federated learning, partial differential equation solving, and large language model adaptation, ultimately leading to more robust, efficient, and explainable AI systems.