The over-parameterized MLP approach used in GS2P, while beneficial for achieving high performance in the interpolating regime, does have potential limitations. One significant concern is the risk of overfitting, especially when the model is trained on limited labeled data. Over-parameterization can lead to the model memorizing the training data rather than learning generalizable patterns, which can negatively impact its performance on unseen data.
To mitigate this risk, techniques such as dropout, weight regularization, and early stopping can be employed during training to enhance the model's generalization capabilities. Additionally, incorporating ensemble methods, where multiple models are trained and their predictions are combined, can help reduce overfitting and improve robustness.
Another improvement could involve integrating the MLP with other architectures, such as recurrent neural networks (RNNs) or transformers, to capture sequential dependencies and contextual information more effectively. This hybrid approach could enhance the model's ability to process complex data structures and improve its performance on tasks that require understanding of temporal or contextual relationships.
Furthermore, leveraging transfer learning from pre-trained models on large datasets can provide a strong initialization for the MLP, allowing it to learn more effectively from smaller labeled datasets. This approach can help balance the benefits of over-parameterization with the need for robust generalization.