Methods › General › Parameter Norm Penalties
Parameter Norm Penalties
The archive attaches this collection's text per method and the copies differ: 2 distinct texts across 2 of the 2 methods here. All are shown, most-carried first (a tie goes to the text carrying Papers with Code's collection boilerplate, then to the longer text); no vote is taken between them.
Text 1, carried by 1 of 2 methods:
Parameter Norm Penalties are regularization methods that apply a penalty to the norm of parameters in the objective function of a neural network. Below you can find a continuously updating list of parameter norm penalties.
Text 2, carried by 1 of 2 methods:
Regularization strategies are designed to reduce the test error of a machine learning algorithm, possibly at the expense of training error. Many different forms of regularization exist in the field of deep learning. Below you can find a constantly updating list of regularization strategies.
Methods
All 2 methods in this collection, most-tagged first. Year is the archive's introduced_year; the archive stores 2000 when it has none, shown here as “–”. Papers counts distinct papers the archive tags with the method. Click a heading to sort.
| L1 Regularization | 1986 | 90 |
| ROME Rank-One Model Editing | – | 23 |