Model Slicing

Model slicing involves partitioning data or models into smaller, more manageable subsets to improve analysis, training, or inference. Current research focuses on developing efficient slicing algorithms for diverse applications, including optimizing neural network training (e.g., using spiking neural networks or mixture-of-experts models), enhancing model interpretability and debugging (e.g., identifying bias or underperforming regions), and improving the efficiency of tasks like mesh reconstruction or network slicing. These advancements have significant implications for improving the performance, reliability, and explainability of machine learning models across various domains, from medical imaging to network optimization.