Randomized ReLU
E565191
Randomized ReLU is a neural network activation function that introduces randomness into the slope of the negative part of the ReLU to improve robustness and generalization.
All labels observed (1)
| Label | Occurrences |
|---|---|
| Randomized ReLU canonical | 1 |
How this entity was disambiguated
This entity first appeared as the object of triple T6042478 — resolving that mention is where its identity was fixed. The disambiguator weighed these candidate entities and picked the highlighted one (or “None”, minting a new entity). This is how homonymy is resolved: the same surface form can point to different entities.
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Randomized ReLU Context triple: [ReLU, relatedFunction, Randomized ReLU]
-
A.
Adam: A Method for Stochastic Optimization
"Adam: A Method for Stochastic Optimization" is a highly influential machine learning paper that introduces the Adam optimizer, a widely used adaptive gradient-based optimization algorithm for training deep neural networks.
-
B.
“Stochastic Gradient Descent Tricks”
“Stochastic Gradient Descent Tricks” is a well-known paper by Léon Bottou that surveys practical techniques and heuristics for effectively applying stochastic gradient descent in machine learning.
-
C.
“A fast learning algorithm for deep belief nets”
“A fast learning algorithm for deep belief nets” is a seminal 2006 paper by Geoffrey Hinton that introduced an efficient unsupervised pretraining method for deep neural networks using stacked restricted Boltzmann machines.
-
D.
Intriguing properties of neural networks
"Intriguing properties of neural networks" is a highly influential research paper that revealed surprising vulnerabilities and behaviors of deep neural networks, particularly their susceptibility to adversarial examples.
-
E.
“Large-Scale Machine Learning with Stochastic Gradient Descent”
“Large-Scale Machine Learning with Stochastic Gradient Descent” is a widely cited work by Léon Bottou that analyzes and advocates stochastic gradient descent as an efficient optimization method for large-scale machine learning problems.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Randomized ReLU Target entity description: Randomized ReLU is a neural network activation function that introduces randomness into the slope of the negative part of the ReLU to improve robustness and generalization.
-
A.
Adam: A Method for Stochastic Optimization
"Adam: A Method for Stochastic Optimization" is a highly influential machine learning paper that introduces the Adam optimizer, a widely used adaptive gradient-based optimization algorithm for training deep neural networks.
-
B.
“Stochastic Gradient Descent Tricks”
“Stochastic Gradient Descent Tricks” is a well-known paper by Léon Bottou that surveys practical techniques and heuristics for effectively applying stochastic gradient descent in machine learning.
-
C.
“A fast learning algorithm for deep belief nets”
“A fast learning algorithm for deep belief nets” is a seminal 2006 paper by Geoffrey Hinton that introduced an efficient unsupervised pretraining method for deep neural networks using stacked restricted Boltzmann machines.
-
D.
Intriguing properties of neural networks
"Intriguing properties of neural networks" is a highly influential research paper that revealed surprising vulnerabilities and behaviors of deep neural networks, particularly their susceptibility to adversarial examples.
-
E.
“Large-Scale Machine Learning with Stochastic Gradient Descent”
“Large-Scale Machine Learning with Stochastic Gradient Descent” is a widely cited work by Léon Bottou that analyzes and advocates stochastic gradient descent as an efficient optimization method for large-scale machine learning problems.
- F. None of above. chosen
Statements (45)
| Predicate | Object |
|---|---|
| instanceOf |
activation function
ⓘ
neural network component ⓘ |
| affects |
model variance
ⓘ
network training dynamics ⓘ |
| aimsTo |
improve generalization
ⓘ
improve robustness ⓘ reduce overfitting ⓘ |
| appliedElementwiseTo | neuron pre-activations ⓘ |
| basedOn | ReLU NERFINISHED ⓘ |
| canBeViewedAs | noise injection method ⓘ |
| category | rectifier activation ⓘ |
| comparedWith |
Leaky ReLU
NERFINISHED
ⓘ
Parametric ReLU NERFINISHED ⓘ |
| goal |
improve test performance
ⓘ
increase robustness to input perturbations ⓘ |
| hasAbbreviation | RReLU NERFINISHED ⓘ |
| hasHyperparameter |
lower bound of negative slope distribution
ⓘ
upper bound of negative slope distribution ⓘ |
| hasInputDomain | real numbers ⓘ |
| hasOutputRange | real numbers ⓘ |
| hasProperty |
non-saturating for positive inputs
ⓘ
nonlinear ⓘ piecewise linear ⓘ random negative slope ⓘ stochastic ⓘ |
| helpsWith |
gradient flow for negative inputs
ⓘ
regularization ⓘ |
| implementedIn | deep learning frameworks ⓘ |
| introducesRandomnessIn | slope of negative region ⓘ |
| isDifferentiableAlmostEverywhere | true ⓘ |
| lessCommonIn | output layers ⓘ |
| modifies | Rectified Linear Unit ⓘ |
| negativeSlopeSampledFrom | uniform distribution ⓘ |
| oftenDeterministicDuring | inference phase ⓘ |
| reduces | dying ReLU problem ⓘ |
| relatedTo |
dropout
ⓘ
stochastic regularization techniques ⓘ |
| requires | random number generation ⓘ |
| usedDuring | training phase ⓘ |
| usedFor |
classification tasks
ⓘ
image recognition tasks ⓘ regression tasks ⓘ |
| usedIn |
convolutional neural networks
NERFINISHED
ⓘ
deep neural networks ⓘ hidden layers ⓘ |
How these facts were elicited
The pipeline generated the facts above by prompting gpt-5.1 with this entity's name + description and the instruction below.
Instruction
You are a knowledge base construction expert. Given a subject entity and a description of it, return factual statements that you know for the subject as a JSON list of dictionaries(triples), where keys must be "subject", "predicate" and "object". The number of facts may be very high, between 25 to 50 or more, for very popular subjects. For less popular subjects, the number of facts can be very low, like 5 or 10. # Requirements - If you don't know the subject at all, return an empty list. - If the subject is not a named entity, return an empty list. - Include at least one triple where predicate is "instanceOf". - Do not get too wordy. - Separate several objects into multiple triples with one object.
Input
Subject: Randomized ReLU Description of subject: Randomized ReLU is a neural network activation function that introduces randomness into the slope of the negative part of the ReLU to improve robustness and generalization.
Referenced by (1)
Full triples — surface form annotated when it differs from this entity's canonical label.