divergence
From Dontopedia, the open, paraconsistent wiki. (Last updated 2026-06-10.)
divergence has 7 facts recorded in Dontopedia across 4 references, with 2 live disagreements.
Mostly:affects parameter(2), rdf:type(2), caused by(1)
Maturity scale
raw canonical shape-checked rule-derived certifiedInbound mentions (5)
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causesCauses(1)
- Learning Rate
ex:learning-rate
exhibitsBehaviorExhibits Behavior(1)
- Fd Training
ex:fd-training
hasEffectHas Effect(1)
- Learning Rate
ex:learning-rate
impliesEndOfImplies End of(1)
- Improvement
ex:improvement
includesPatternIncludes Pattern(1)
- Training Patterns
ex:training-patterns
Other facts (6)
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 |
|---|---|---|
| Affects Parameter | Omega | [2] |
| Affects Parameter | Gamma | [2] |
| Rdf:type | Phenomenon | [3] |
| Rdf:type | Training Pattern | [4] |
| Caused by | Bug R Global 0 59 | [1] |
| Described As | blowing up | [2] |
Timeline
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References (4)
ctx:discord/blah/watt-activation/197- full textwatt-activation-197text/plain2 KB
doc:agent/watt-activation-197/63f2f0e2-9ecd-4515-a9de-2f25b46f6dfcShow excerpt
[2026-03-10 05:54] xenonfun: ⏺ Now I can analyze this properly. --- What we actually lose readout_dim = 2·G·H + n_pairs + 1 = 2·32 + 28 + 1 = 93 features is the concatenation of: - [0..31]: post-sync spectra (normalized to S^{H…
ctx:discord/blah/watt-activation/497- full textwatt-activation-497text/plain2 KB
doc:agent/watt-activation-497/e72fbd50-bc16-4a38-8957-fe8531b9864cShow excerpt
[2026-03-22 17:52] xenonfun: if I am seeing this correct we are using 8 MB of memory. ⏺ The FD training is diverging — omega and gamma blowing up. The Euler ODE integrator is unstable at these parameter scales. This needs: 1. Much lower …
ctx:claims/beam/1714914a-4272-4b7c-91df-6c89df9429f8- full textbeam-chunktext/plain1 KB
doc:beam/1714914a-4272-4b7c-91df-6c89df9429f8Show excerpt
- **Reason**: More epochs can lead to overfitting, but fewer epochs might not be enough for the model to learn the data well. 2. **Batch Size (`per_device_train_batch_size` and `per_device_eval_batch_size`)**: - **Suggested Value**: …
ctx:claims/beam/a72253d1-4d49-4967-ab0e-27d511ab4abb- full textbeam-chunktext/plain1 KB
doc:beam/a72253d1-4d49-4967-ab0e-27d511ab4abbShow excerpt
- **Choose an Appropriate Optimizer**: Different optimizers (e.g., SGD, Adam, RMSprop) have different convergence properties. Experiment with different optimizers to find the one that works best for your model. ### 6. **Learning Rate Sc…
See also
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