Dontopedia

argmax

From Dontopedia, the open, paraconsistent wiki. (Last updated 2026-06-11.)

argmax has 5 facts recorded in Dontopedia across 2 references, with 1 live disagreement.

5 facts·2 predicates·2 sources·1 in dispute
Maturity scale raw canonical shape-checked rule-derived certified

Inbound 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.

callsFunctionCalls Function(2)

appliesApplies(1)

calculatedByCalculated by(1)

Other facts (3)

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.

3 facts
PredicateValueRef
Rdf:typeNumpy Function[1]
Rdf:typeNumpy Function[2]
Has Parameteraxis[2]

Timeline

Timeline axis is valid_time — when each source says the fact was true in the world, not when Dontopedia learned about it. Retracted rows are kept for provenance; coloured stripes indicate the context kind.

typebeam/34ffcd35-801a-4acf-b1f5-659bb6c98a27
ex:NumpyFunction
labelbeam/34ffcd35-801a-4acf-b1f5-659bb6c98a27
argmax
typebeam/f1acc8e8-db39-4556-bbec-0ee7f29aeac4
ex:NumpyFunction
labelbeam/f1acc8e8-db39-4556-bbec-0ee7f29aeac4
NumPy ArgMax Function
hasParameterbeam/f1acc8e8-db39-4556-bbec-0ee7f29aeac4
axis

References (2)

2 references
  1. ctx:claims/beam/34ffcd35-801a-4acf-b1f5-659bb6c98a27
    • full textbeam-chunk
      text/plain1 KBdoc:beam/34ffcd35-801a-4acf-b1f5-659bb6c98a27
      Show excerpt
      def update_weights(engine1_accuracy, engine2_accuracy): total_accuracy = engine1_accuracy + engine2_accuracy if total_accuracy == 0: return (0.5, 0.5) # Default equal weights if both accuracies are zero new_weights = (e
  2. ctx:claims/beam/f1acc8e8-db39-4556-bbec-0ee7f29aeac4
    • full textbeam-chunk
      text/plain1 KBdoc:beam/f1acc8e8-db39-4556-bbec-0ee7f29aeac4
      Show excerpt
      logging_dir='./logs', logging_steps=10, evaluation_strategy="epoch", save_total_limit=2, ) # Define Trainer trainer = Trainer( model=model, args=training_args, train_dataset=train_dataset, eval_dataset=test_

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