Body: <p>From <a href="https://ai.stackexchange.com/a/28024/18758">this</a> answer, stability is attributed to a learning algorithm</p>
  <blockquote>
  <p>A <strong>stable learning algorithm</strong> is one for which the prediction does not
  change much when the training data is modified slightly.</p>
  </blockquote>
  <p>At some other places, I read the phrase &quot;stability of neural network model&quot;. 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</p>
  <blockquote>
  <p>A <strong>stable model</strong> is one for which the prediction does not
  change much when the training data is modified slightly.</p>
  </blockquote>
  <p>Is it true? If not, is there anything called stability of a model and is different from the stability of a learning algorithm?</p>
  <p>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?</p>
  
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2021-08-09T00:18:29.647Z
Comments.Text: is one for which the prediction does not change much when the training data is modified slightly. Is example back to do it again
Comments.UserDisplayName: Rachrachben
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2021-08-15T23:50:55.340Z
Comments.Text: @Rachrachben Do you mean it applies to both model and training algorithm?
Comments.UserDisplayName: hanugm
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2021-10-09T12:13:37.277Z
Comments.Text: Can you provide a source for the second part(stable model) as well?  The answer you refer to cites a Wikipedia article which gives a list of [stable learning algorithms](https://en.wikipedia.org/wiki/Stability_(learning_theory)#Algorithms_that_are_stable). Neither gradient descent nor neural networks are on the list.
Comments.UserDisplayName: serali
Tags: <neural-networks><training><stability>
Title: Is stability an attribute of model or training algorithm used or combination of both?
UserDisplayName: hanugm
ThreadText: # Is stability an attribute of model or training algorithm used or combination of both?
  
  (Asked by: username_0 on 2021-08-07)
  
  From [this](https://ai.stackexchange.com/a/28024/18758) 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?
  
  **Tags:** neural-networks, training, stability
  
  > **Comments:**
  > **username_1**: is one for which the prediction does not change much when the training data is modified slightly. Is example back to do it again  
  > **username_0**: @username_1 Do you mean it applies to both model and training algorithm?  
  > **username_2**: Can you provide a source for the second part(stable model) as well?  The answer you refer to cites a Wikipedia article which gives a list of [stable learning algorithms](https://en.wikipedia.org/wiki/Stability_(learning_theory)#Algorithms_that_are_stable). Neither gradient descent nor neural networks are on the list.  
  
  ---
  
  ## 0 answers
  
  