instead deep learning tends to generalise incredibly well to examples it hasn’t seen already. How and why it does so is, however, still poorly-understood.
In my opinion generalisation is a very interesting point!
Are there any new insights into deep learning generalisation, similar to the ideas of:
1) implicit regularisation through optimisation methods like stochastic gradient descent, 2) the double descent risk curve where more parameters can reduce error again, or 3) margin-based measures to predict generalisation gaps?
Or more generally asked: How do we maybe ensure regular update(s) of this or similar article(s)?
In my opinion generalisation is a very interesting point!
Are there any new insights into deep learning generalisation, similar to the ideas of:
1) implicit regularisation through optimisation methods like stochastic gradient descent,
2) the double descent risk curve where more parameters can reduce error again,
or
3) margin-based measures to predict generalisation gaps?
Or more generally asked:
How do we maybe ensure regular update(s) of this or similar article(s)?