regularization
P16020
predicate
Indicates the application of a constraint or penalty to a model or function to prevent overfitting and encourage simpler, more generalizable behavior.
All labels observed (3)
| Label | Occurrences |
|---|---|
| usesRegularization | 8 |
| regularizedBy | 6 |
| regularization canonical | 3 |
Description generation (PDg)
The one-sentence description above was generated by prompting gpt-5.1 with the predicate name and this instruction.
Instruction
Given a predicate that represents a relationship or action between entities, generate a one-sentence description explaining its meaning. # Instructions Focus on describing the relationship, not the entities themselves. # Response Format Begin the description with \' Indicates...\'
Input
Predicate: regularization
Generated description
Indicates the application of a constraint or penalty to a model or function to prevent overfitting and encourage simpler, more generalizable behavior.
Sample triples (17)
| Subject | Object |
|---|---|
| LeNet | weight decay ⓘ |
| deep feedforward networks | weight decay via predicate surface "regularizedBy" ⓘ |
| deep feedforward networks | dropout via predicate surface "regularizedBy" ⓘ |
| deep feedforward networks | early stopping via predicate surface "regularizedBy" ⓘ |
| AlexNet | dropout via predicate surface "usesRegularization" ⓘ |
| AlexNet | data augmentation via predicate surface "usesRegularization" ⓘ |
| GoogLeNet | dropout ⓘ |
| GoogLeNet | data augmentation ⓘ |
| Network-in-Network architecture | dropout via predicate surface "usesRegularization" ⓘ |
| Network-in-Network architecture | weight decay via predicate surface "usesRegularization" ⓘ |
| ImageNet Classification with Deep Convolutional Neural Networks | weight decay via predicate surface "usesRegularization" ⓘ |
| ImageNet Classification with Deep Convolutional Neural Networks | dropout via predicate surface "usesRegularization" ⓘ |
| NASNet | dropout via predicate surface "usesRegularization" ⓘ |
| NASNet | batch normalization via predicate surface "usesRegularization" ⓘ |
| recurrent neural networks | dropout via predicate surface "regularizedBy" ⓘ |
| recurrent neural networks | L2 weight decay via predicate surface "regularizedBy" ⓘ |
| recurrent neural networks | early stopping via predicate surface "regularizedBy" ⓘ |