RMSprop
From Dontopedia, the open, paraconsistent wiki. (Last updated 2026-06-09.)
RMSprop has 17 facts recorded in Dontopedia across 2 references, with 3 live disagreements.
Mostly:rdf:type(2), is sensitive to(2), is variant of(1)
Maturity scale
raw canonical shape-checked rule-derived certifiedInbound mentions (10)
Other subjects in dontopedia point AT this entity as a value. These are inverse relationships — e.g. "X motherOf this subject" — and answer questions the forward facts can't. Grouped by predicate.
hasMemberHas Member(2)
- All Optimizers
ex:all-optimizers - Optimizer List
ex:optimizer-list
isUsedInIs Used in(2)
- Moving Average
ex:moving-average - Squared Gradient
ex:squared-gradient
relatedOptimizerRelated Optimizer(2)
- Adamw
ex:adamw - Sgd With Momentum
ex:sgd-with-momentum
hasSequentialOrderHas Sequential Order(1)
- Optimizer List
ex:optimizer-list
isAdaptedByIs Adapted by(1)
- Learning Rate
ex:learning-rate
isHandledByIs Handled by(1)
- Sparse Gradients
ex:sparse-gradients
recommendsRecommends(1)
- Try Different Optimizers
ex:try-different-optimizers
Other facts (14)
The long tail: predicates that appear too rarely to warrant their own section. Filter or scroll to find a specific one. Each row links to its source.
| Predicate | Value | Ref |
|---|---|---|
| Rdf:type | Optimizer | [1] |
| Rdf:type | Optimizer | [2] |
| Is Sensitive to | Decay Rate | [2] |
| Is Sensitive to | Initial Learning Rate | [2] |
| Is Variant of | Optimizer | [1] |
| Has Description | RMSProp adapts the learning rate by dividing the gradient by a moving average of the squared gradient. | [2] |
| Has Pros | Effective in handling sparse gradients and can converge faster than SGD. | [2] |
| Has Cons | May require tuning of the decay rate and initial learning rate. | [2] |
| Adapts Learning Rate by | Dividing Gradient by Moving Average of Squared Gradient | [2] |
| Handles | Sparse Gradients | [2] |
| Convergence Speed Comparison | Sgd | [2] |
| Is Part of | Optimizer List | [2] |
| Has Number | 3 | [2] |
| Has Performance Metric | Convergence Speed | [2] |
Timeline
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References (2)
ctx:claims/beam/0bad15fa-6517-4657-9af4-7dd611969d1a- full textbeam-chunktext/plain1 KB
doc:beam/0bad15fa-6517-4657-9af4-7dd611969d1aShow excerpt
- **Batch Size**: Larger batch sizes can sometimes lead to better convergence, but they require more memory. Smaller batch sizes can introduce more noise, which can help escape local minima. - **Optimizer**: Try different optimizers l…
ctx:claims/beam/8b665ecf-2e25-4fa0-956a-5aa3e3d09673- full textbeam-chunktext/plain1 KB
doc:beam/8b665ecf-2e25-4fa0-956a-5aa3e3d09673Show excerpt
- **Cons**: Can sometimes converge to suboptimal solutions if the learning rate is not decreased over time. ### 2. **SGD (Stochastic Gradient Descent)** - **Description**: A classic optimizer that updates model parameters based on th…
See also
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