ReLU
E134578
ReLU (Rectified Linear Unit) is a widely used activation function in neural networks that outputs zero for negative inputs and the input value itself for positive inputs, enabling efficient and stable training of deep models.
All labels observed (4)
| Label | Occurrences |
|---|---|
| Rectified Linear Unit | 2 |
| Leaky ReLU | 1 |
| Parametric ReLU | 1 |
| ReLU canonical | 1 |
How this entity was disambiguated
This entity first appeared as the object of triple T1180412 — 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: ReLU Context triple: [deep feedforward networks, canUseActivationFunction, ReLU]
-
A.
RBM
RBM is a global partnership initiative dedicated to coordinating and scaling up efforts to prevent, control, and ultimately eliminate malaria worldwide.
-
B.
ResNet
ResNet is a deep convolutional neural network architecture known for its use of residual connections to enable very deep models and achieve state-of-the-art performance in image recognition tasks.
-
C.
LeNet
LeNet is one of the earliest convolutional neural network architectures, pioneering modern deep learning approaches to image recognition and handwritten digit classification.
-
D.
Perceptrons
Perceptrons is a seminal 1969 book by Marvin Minsky and Seymour Papert that critically analyzes the capabilities and limitations of early neural network models, profoundly influencing the development of artificial intelligence and machine learning.
-
E.
.bn
.bn is the country code top-level domain (ccTLD) assigned to Brunei Darussalam for use in its internet addresses.
- 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: ReLU Target entity description: ReLU (Rectified Linear Unit) is a widely used activation function in neural networks that outputs zero for negative inputs and the input value itself for positive inputs, enabling efficient and stable training of deep models.
-
A.
RBM
RBM is a global partnership initiative dedicated to coordinating and scaling up efforts to prevent, control, and ultimately eliminate malaria worldwide.
-
B.
ResNet
ResNet is a deep convolutional neural network architecture known for its use of residual connections to enable very deep models and achieve state-of-the-art performance in image recognition tasks.
-
C.
LeNet
LeNet is one of the earliest convolutional neural network architectures, pioneering modern deep learning approaches to image recognition and handwritten digit classification.
-
D.
Perceptrons
Perceptrons is a seminal 1969 book by Marvin Minsky and Seymour Papert that critically analyzes the capabilities and limitations of early neural network models, profoundly influencing the development of artificial intelligence and machine learning.
-
E.
.bn
.bn is the country code top-level domain (ccTLD) assigned to Brunei Darussalam for use in its internet addresses.
- F. None of above. chosen
Statements (48)
| Predicate | Object |
|---|---|
| instanceOf |
activation function
ⓘ
nonlinear function ⓘ |
| abbreviationOf |
ReLU
self-linksurface differs
ⓘ
surface form:
Rectified Linear Unit
|
| advantageOver |
sigmoid activation
ⓘ
tanh activation ⓘ |
| boundedAbove | false ⓘ |
| boundedBelow | true ⓘ |
| derivativeAtZero | undefined or set-valued ⓘ |
| derivativeForNegativeInput | 0 ⓘ |
| derivativeForPositiveInput | 1 ⓘ |
| differentiableAlmostEverywhere | true ⓘ |
| disadvantage | dying ReLU problem ⓘ |
| field |
artificial neural networks
ⓘ
deep learning ⓘ machine learning ⓘ |
| fullName |
ReLU
self-linksurface differs
ⓘ
surface form:
Rectified Linear Unit
|
| graphShape | horizontal line at 0 for x < 0 and line y = x for x ≥ 0 ⓘ |
| historicalNote | popularized in deep learning around 2010s ⓘ |
| implementation | element-wise operation on tensors ⓘ |
| inputDomain | real numbers ⓘ |
| mathematicalDefinition | f(x) = max(0, x) ⓘ |
| monotonic | true ⓘ |
| nonDifferentiableAt | 0 ⓘ |
| optimizationBenefit |
faster training convergence
ⓘ
simpler gradient computation ⓘ |
| outputForNegativeInput | 0 ⓘ |
| outputForPositiveInput | x ⓘ |
| outputForZeroInput | 0 ⓘ |
| outputRange | nonnegative real numbers ⓘ |
| property |
computationally efficient
ⓘ
helps mitigate vanishing gradient problem ⓘ non-saturating for positive inputs ⓘ piecewise linear ⓘ sparse activation ⓘ |
| relatedFunction |
ELU
ⓘ
GELU ⓘ ReLU self-linksurface differs ⓘ
surface form:
Leaky ReLU
ReLU self-linksurface differs ⓘ
surface form:
Parametric ReLU
Randomized ReLU ⓘ |
| typicalUse |
computer vision models
ⓘ
hidden layers of deep networks ⓘ natural language processing models ⓘ speech recognition models ⓘ |
| usedIn |
convolutional neural networks
ⓘ
deep neural networks ⓘ deep feedforward networks ⓘ
surface form:
feedforward neural networks
deep feedforward networks ⓘ
surface form:
multilayer perceptrons
|
| zeroCentered | false ⓘ |
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: ReLU Description of subject: ReLU (Rectified Linear Unit) is a widely used activation function in neural networks that outputs zero for negative inputs and the input value itself for positive inputs, enabling efficient and stable training of deep models.
Referenced by (5)
Full triples — surface form annotated when it differs from this entity's canonical label.
this entity surface form:
Rectified Linear Unit
this entity surface form:
Leaky ReLU
this entity surface form:
Parametric ReLU