Model Merging is Secretly Certifiable: Non-Vacuous Generalisation Bounds for Low-Shot Learning
The episode discusses 'Model Merging is Secretly Certifiable,' detailing how researchers can achieve non-trivial generalization guarantees for large neural networks using very few data points (as few as one hundred). The discussion focuses on moving beyond simply proving models are 'good' to mathematically proving they will be reliable when encountering new data.