Custom ablation

Custom ablation is a process used in machine learning to identify which parts of a model or a dataset are most important for its performance. By selectively removing or "ablating" specific features, neurons, or data segments, researchers can see how these changes affect the model's accuracy. This helps them understand which components are critical and can lead to more efficient, interpretable, and optimized models tailored to specific tasks or applications. Essentially, custom ablation is about carefully testing and removing parts to better understand and improve a machine learning system.