Describe the tradeoff between maintaining a copy of the optimal parameters and the benefits of using early stopping to prevent overfitting. How can this cost be mitigated without compromising the effectiveness of early stopping?
Early stopping provides a balance between preventing overfitting and reducing training time, but it requires storing the optimal parameters in slower memory, such as host memory or disk drive. This tradeoff can be mitigated by using techniques like model pruning, knowledge distillation, or quantization to reduce the storage requirements of the optimal parameters.