"cross-attention" meaning in All languages combined

{
  "etymology_templates": [
    {
      "args": {
        "1": "en",
        "2": "cross-",
        "3": "attention"
      },
      "expansion": "cross- + attention",
      "name": "af"
    }
  ],
  "etymology_text": "From cross- + attention.",
  "forms": [
    {
      "form": "cross attention",
      "tags": [
        "alternative"
      ]
    }
  ],
  "head_templates": [
    {
      "args": {
        "1": "-"
      },
      "expansion": "cross-attention (uncountable)",
      "name": "en-noun"
    }
  ],
  "lang": "English",
  "lang_code": "en",
  "pos": "noun",
  "senses": [
    {
      "antonyms": [
        {
          "word": "self-attention"
        }
      ],
      "categories": [
        {
          "kind": "other",
          "name": "English entries with incorrect language header",
          "parents": [],
          "source": "w"
        },
        {
          "kind": "other",
          "name": "English terms prefixed with cross-",
          "parents": [],
          "source": "w"
        },
        {
          "kind": "other",
          "name": "Pages with 1 entry",
          "parents": [],
          "source": "w"
        },
        {
          "kind": "other",
          "name": "Pages with entries",
          "parents": [],
          "source": "w"
        },
        {
          "kind": "other",
          "langcode": "en",
          "name": "Machine learning",
          "orig": "en:Machine learning",
          "parents": [],
          "source": "w"
        }
      ],
      "examples": [
        {
          "bold_text_offsets": [
            [
              4,
              19
            ],
            [
              88,
              103
            ]
          ],
          "ref": "2024, Benoit Liquet, Sarat Moka, Yoni Nazarathy, Mathematical Engineering of Deep Learning, CRC Press, page 286:",
          "text": "The cross attention layer inside each transformer decoder block is in fact a multi-head cross attention layer […]",
          "type": "quote"
        }
      ],
      "glosses": [
        "A form of attention (machine learning method) where two different input sequences are compared, i.e. the keys and queries differ."
      ],
      "id": "en-cross-attention-en-noun-f8FW3H07",
      "links": [
        [
          "machine learning",
          "machine learning"
        ],
        [
          "attention",
          "attention"
        ],
        [
          "input",
          "input"
        ],
        [
          "sequence",
          "sequence"
        ],
        [
          "key",
          "key"
        ],
        [
          "queries",
          "query"
        ]
      ],
      "qualifier": "machine learning",
      "raw_glosses": [
        "(machine learning) A form of attention (machine learning method) where two different input sequences are compared, i.e. the keys and queries differ."
      ],
      "tags": [
        "uncountable"
      ]
    }
  ],
  "word": "cross-attention"
}

{
  "etymology_templates": [
    {
      "args": {
        "1": "en",
        "2": "cross-",
        "3": "attention"
      },
      "expansion": "cross- + attention",
      "name": "af"
    }
  ],
  "etymology_text": "From cross- + attention.",
  "forms": [
    {
      "form": "cross attention",
      "tags": [
        "alternative"
      ]
    }
  ],
  "head_templates": [
    {
      "args": {
        "1": "-"
      },
      "expansion": "cross-attention (uncountable)",
      "name": "en-noun"
    }
  ],
  "lang": "English",
  "lang_code": "en",
  "pos": "noun",
  "senses": [
    {
      "antonyms": [
        {
          "word": "self-attention"
        }
      ],
      "categories": [
        "English entries with incorrect language header",
        "English lemmas",
        "English multiword terms",
        "English nouns",
        "English terms prefixed with cross-",
        "English terms with quotations",
        "English uncountable nouns",
        "Pages with 1 entry",
        "Pages with entries",
        "en:Machine learning"
      ],
      "examples": [
        {
          "bold_text_offsets": [
            [
              4,
              19
            ],
            [
              88,
              103
            ]
          ],
          "ref": "2024, Benoit Liquet, Sarat Moka, Yoni Nazarathy, Mathematical Engineering of Deep Learning, CRC Press, page 286:",
          "text": "The cross attention layer inside each transformer decoder block is in fact a multi-head cross attention layer […]",
          "type": "quote"
        }
      ],
      "glosses": [
        "A form of attention (machine learning method) where two different input sequences are compared, i.e. the keys and queries differ."
      ],
      "links": [
        [
          "machine learning",
          "machine learning"
        ],
        [
          "attention",
          "attention"
        ],
        [
          "input",
          "input"
        ],
        [
          "sequence",
          "sequence"
        ],
        [
          "key",
          "key"
        ],
        [
          "queries",
          "query"
        ]
      ],
      "qualifier": "machine learning",
      "raw_glosses": [
        "(machine learning) A form of attention (machine learning method) where two different input sequences are compared, i.e. the keys and queries differ."
      ],
      "tags": [
        "uncountable"
      ]
    }
  ],
  "word": "cross-attention"
}