Equivalent Policy Invariant Comparison

Equivalent Policy Invariant Comparison (EPIC) research focuses on identifying similarities and differences in model behavior across diverse datasets and architectures, aiming to understand the underlying principles driving model performance. Current work explores this through analyses of optimal subnetworks in transformer models and CNN architectures applied to various tasks like audio interaction recognition and question answering, often leveraging techniques like pruning and model fusion. These studies contribute to a deeper understanding of model robustness and generalizability, potentially leading to more efficient and reliable AI systems across different domains and data variations.