Other deep learning components that could benefit from a similar joint optimization approach include:
Activation Functions: Optimizing activation functions along with optimizers, decay functions, and learning rate schedules can lead to improved convergence and generalization in deep learning models.
Regularization Techniques: Jointly optimizing regularization methods such as dropout, batch normalization, and weight decay with other components can enhance model performance and prevent overfitting.
Loss Functions: Incorporating the optimization of loss functions into the joint evolution process can help tailor the loss function to specific tasks, leading to better model performance.
Network Architectures: Jointly optimizing network architectures, including layer configurations, skip connections, and attention mechanisms, can result in more efficient and effective deep learning models.
Data Augmentation Strategies: Optimizing data augmentation techniques in conjunction with other components can improve model robustness and generalization capabilities.