The discovered optimizers, decay functions, and learning rate schedules can be further analyzed to gain insights into their strengths and weaknesses for different types of deep learning tasks and architectures by conducting the following analyses:
Performance on Various Datasets: Evaluate the optimizers, decay functions, and learning rate schedules on a diverse set of datasets beyond the ones mentioned in the study. This will help understand their generalization capabilities and robustness across different data distributions.
Architectural Compatibility: Analyze how the discovered components interact with different neural network architectures. Assess their performance on various architectures such as CNNs, RNNs, Transformers, etc., to determine if they exhibit consistent improvements across different model types.
Hyperparameter Sensitivity: Investigate how sensitive the discovered components are to hyperparameter settings such as batch size, initialization methods, and regularization techniques. Understanding their sensitivity can provide insights into their adaptability to different training scenarios.
Transfer Learning Performance: Evaluate the discovered components in transfer learning scenarios across a wide range of pre-trained models and downstream tasks. This analysis can reveal their effectiveness in leveraging pre-trained representations for new tasks.
Robustness Analysis: Conduct robustness tests to assess how the optimizers, decay functions, and learning rate schedules perform under noisy or adversarial conditions. This analysis can shed light on their stability and reliability in challenging environments.
Scalability Testing: Test the scalability of the discovered components by analyzing their performance on larger datasets and more complex models. Understanding how well they scale can provide insights into their applicability to real-world, large-scale deep learning tasks.