From this answer, stability is attributed to a learning algorithm
A stable learning algorithm is one for which the prediction does not change much when the training data is modified slightly.
At some other places, I read the phrase "stability of neural network model". I am not sure whether the stability of a learning algorithm and the stability of a model are the same or not. If same then
A stable model is one for which the prediction does not change much when the training data is modified slightly.
Is it true? If not, is there anything called stability of a model and is different from the stability of a learning algorithm?
Suppose I am training a neural network model with a gradient descent algorithm. For which one, do I need to attribute stability or instability? Is it to the neural network model or to the gradient descent training algorithm? Or it should be attributed to the combination of both?