The Emergence Machine

Attention Mechanism

abstract · Computing · Level 9 · E10

E10Institutions

Each concept here is mapped to its prerequisites — the ideas you'd need first to understand it — all the way down to four foundations: Space, Time, Energy, Pattern. Click any prerequisite to drill down, or scroll for the chain graph.

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Emergence definition

attention-mechanism requires neural-network, computation.

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Wiktionary senses

External reference — all senses of the word “attention mechanism” on Wiktionary. This atlas concept maps to only the slice of meaning relevant to the prerequisite graph.

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Source: Wiktionary — “attention mechanism”. Content available under CC BY-SA 4.0.

Historical origin

Origin word
attention mechanism
Origin language
English

Prerequisite chain

Possible path of this concept down to the fundamental substrate.

thisfoundationsL9L8L7L6L5L3L2L1L0Attention Mechan…Neural NetworkAttentionMachine LearningArtificial Intel…CognitionAlgorithmComputationPerceptionCausalityCellMechanismSystem… intermediate l…ForceFormLifeOperationActionChangeMatterMotionEnergyPatternSpaceTimeE1 concrete → E14 abstract

Neighborhood

Direct prerequisites above, concepts that depend on this one below.

thisprerequisitesAttention Mechan…L9MechanismL3ComputationL5AttentionL7Neural NetworkL8E1 concrete → E14 abstract

In other languages

Prerequisites

What you need to understand first.

  • Mechanism L3 (requires)
    An attention mechanism is a computational process that selectively filters and weights input data, allowing models to focus on relevant information while ignoring irrelevant parts, thereby enhancing the accuracy and efficiency of neural network-based computations.
  • Computation L5 (requires)
    An attention mechanism is a computational process that selectively filters and weights input data, allowing models to focus on relevant information while ignoring irrelevant parts, thereby enhancing the accuracy and efficiency of neural network-based computations.
  • Attention L7 (requires)
    An attention mechanism is a computational process that selectively filters and weights input data, allowing models to focus on relevant information while ignoring irrelevant parts, thereby enhancing the accuracy and efficiency of neural network-based computations.
  • Neural Network L8 (requires)
    An attention mechanism is a neural network technique that allows models to focus on relevant parts of input, inspired by human attention.