It’s an interesting idea, I have two questions.
- Surprise is detected by the norm of the gradients. So, doesn’t this suggest that the model already has a way of adjusting to surprise?
- Is there a danger of model instability when the gradients become larger and the learning rate is also increased?
1. an overly strong surprise is like PTSD in humans - it changes the model's previously learned experience forever, this is what we want to avoid
2. it's bound to happen, and our PILR-S is designed to keep the learning rate within the bell curve and decreasing as the surprise decreases (less new information, less learning).