flatten
From Dontopedia, the open, paraconsistent wiki. (Last updated 2026-06-09.)
flatten has 11 facts recorded in Dontopedia across 4 references, with 1 live disagreement.
Mostly:rdf:type(2), applied to(1), outputs shape(1)
Maturity scale
raw canonical shape-checked rule-derived certifiedInbound mentions (4)
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.
appliesApplies(1)
- Postprocess Method
ex:postprocess-method
contains-stepContains Step(1)
- Step Sequence
ex:step-sequence
precedesPrecedes(1)
- Context Window Extraction
ex:context-window-extraction
processedByProcessed by(1)
- Gap Output
ex:gap-output
Other facts (10)
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 | Operation | [1] |
| Rdf:type | Operation | [4] |
| Applied to | query_vector initialization | [1] |
| Outputs Shape | [B,64] | [2] |
| Applied to | Kernel Result | [3] |
| Input | Context Window Tensor | [4] |
| Output | Flattened Tensor | [4] |
| Step Number | 4 | [4] |
| Precedes | Lstm Layer Processing | [4] |
| Occurs Before | Lstm Processing | [4] |
Timeline
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References (4)
ctx:claims/beam/a62e0ed1-9011-4f17-b311-aa52982c8569ctx:discord/blah/watt-activation/686- full textwatt-activation-686text/plain3 KB
doc:agent/watt-activation-686/87dc1f6d-de3b-4f99-bdf2-bfce9ede6dd6Show excerpt
[2026-04-24 00:49] xenonfun: have cliffordnet workong on the medical images, does that at 60% but only 9K parms, but CNNs are in the 80s. its really not good at simple stuff does suggest hybrid of our manifoldunit which is great at simple b…
ctx:claims/beam/8036737b-9c5e-4cf6-8fd5-40137132613b- full textbeam-chunktext/plain1 KB
doc:beam/8036737b-9c5e-4cf6-8fd5-40137132613bShow excerpt
Finally, you can combine the results from both sparse and dense retrievals. One common approach is to use a weighted sum of the scores from both methods. Here's a more complete example: ```python import numpy as np from sklearn.feature_ex…
ctx:claims/beam/897b7b85-132e-45ab-a5df-34500775a74a- full textbeam-chunktext/plain1 KB
doc:beam/897b7b85-132e-45ab-a5df-34500775a74aShow excerpt
3. **Extract Context Window**: Define a lambda layer to extract the context window around each token. The context size is calculated dynamically based on the query length. 4. **Flatten Context Window**: Flatten the context window tensor to …
See also
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