RMSProp
E134579
RMSProp is an adaptive gradient-based optimization algorithm commonly used to efficiently train deep neural networks by adjusting learning rates for individual parameters.
All labels observed (1)
| Label | Occurrences |
|---|---|
| RMSProp canonical | 5 |
How this entity was disambiguated
This entity first appeared as the object of triple T1180424 — 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: RMSProp Context triple: [deep feedforward networks, canUseOptimizer, RMSProp]
-
A.
Automatic Adam
Automatic Adam is the nickname of Adam Vinatieri, a legendary NFL placekicker renowned for his clutch, game-winning field goals in high-pressure situations.
-
B.
TRPO
TRPO (Trust Region Policy Optimization) is a reinforcement learning algorithm that optimizes policies with guaranteed monotonic improvement by constraining each update within a trust region to maintain stability.
-
C.
SGD
SGD is the official currency code for the Singapore dollar, the national currency of Singapore used in domestic and international transactions.
-
D.
RBM
RBM is a global partnership initiative dedicated to coordinating and scaling up efforts to prevent, control, and ultimately eliminate malaria worldwide.
-
E.
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.
- 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: RMSProp Target entity description: RMSProp is an adaptive gradient-based optimization algorithm commonly used to efficiently train deep neural networks by adjusting learning rates for individual parameters.
-
A.
Automatic Adam
Automatic Adam is the nickname of Adam Vinatieri, a legendary NFL placekicker renowned for his clutch, game-winning field goals in high-pressure situations.
-
B.
TRPO
TRPO (Trust Region Policy Optimization) is a reinforcement learning algorithm that optimizes policies with guaranteed monotonic improvement by constraining each update within a trust region to maintain stability.
-
C.
SGD
SGD is the official currency code for the Singapore dollar, the national currency of Singapore used in domestic and international transactions.
-
D.
RBM
RBM is a global partnership initiative dedicated to coordinating and scaling up efforts to prevent, control, and ultimately eliminate malaria worldwide.
-
E.
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.
- F. None of above. chosen
Statements (45)
| Predicate | Object |
|---|---|
| instanceOf |
adaptive learning rate method
ⓘ
gradient-based optimization method ⓘ optimization algorithm ⓘ |
| addresses | rapidly decaying learning rate problem of AdaGrad ⓘ |
| adjusts | per-parameter learning rates ⓘ |
| aimsTo |
mitigate vanishing and exploding gradients in practice
ⓘ
stabilize the magnitude of parameter updates ⓘ |
| appliedIn |
reinforcement learning
ⓘ
supervised learning ⓘ unsupervised deep learning ⓘ |
| assumes | stochastic gradient estimates ⓘ |
| basedOn | gradient descent ⓘ |
| belongsTo | family of adaptive gradient methods ⓘ |
| category | first-order optimization method ⓘ |
| commonlyUsedIn |
computer vision models
ⓘ
deep learning frameworks ⓘ recurrent neural networks ⓘ |
| commonlyUsedWith | mini-batch gradient descent ⓘ |
| designedFor | non-stationary objectives ⓘ |
| goal | maintain a roughly constant step size for each parameter ⓘ |
| handles | sparse gradients better than vanilla SGD ⓘ |
| hasHyperparameter |
decay rate
ⓘ
epsilon ⓘ learning rate ⓘ |
| helpsWith | faster convergence in deep learning ⓘ |
| implementedIn |
Keras
ⓘ
PyTorch ⓘ TensorFlow ⓘ |
| improvesOn | AdaGrad ⓘ |
| introducedBy | Geoffrey Hinton ⓘ |
| introducedIn | 2012 ⓘ |
| introducedInContext |
Coursera
ⓘ
surface form:
Coursera Neural Networks for Machine Learning lecture
|
| oftenComparedWith |
Adam optimizer
ⓘ
SGD with momentum ⓘ |
| optimizes | neural network parameters ⓘ |
| relatedTo |
AdaDelta
ⓘ
AdaGrad ⓘ Adam ⓘ |
| requires | gradient information ⓘ |
| typicalDefaultLearningRate | 0.001 ⓘ |
| updateRuleIncludes | division by root mean square of recent squared gradients ⓘ |
| usedFor |
stochastic optimization
ⓘ
training deep neural networks ⓘ |
| uses |
element-wise scaling of gradients
ⓘ
exponentially weighted moving average of squared gradients ⓘ |
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: RMSProp Description of subject: RMSProp is an adaptive gradient-based optimization algorithm commonly used to efficiently train deep neural networks by adjusting learning rates for individual parameters.
Referenced by (5)
Full triples — surface form annotated when it differs from this entity's canonical label.