Attention

General • 125 methods

Attention is a technique for attending to different parts of an input vector to capture long-term dependencies. Within the context of NLP, traditional sequence-to-sequence models compressed the input sequence to a fixed-length context vector, which hindered their ability to remember long inputs such as sentences. In contrast, attention creates shortcuts between the context vector and the entire source input. Below you will find a continuously updating list of attention based building blocks used in deep learning.

Subcategories

Method Year Papers
2017 17526
2017 17434
2019 1362
2019 1361
2017 261
2015 218
2017 217
2014 196
2018 177
2017 166
2021 162
2018 135
2022 113
2014 105
2020 84
2021 81
2020 78
2020 77
2020 71
2020 70
2018 68
2020 66
2019 66
2019 52
2019 48
2021 45
2020 43
2018 39
2020 39
2019 37
2017 37
2015 33
2014 32
2021 32
2020 32
2015 31
2015 24
2018 24
2020 24
2021 23
2019 22
2022 22
2021 21
2019 20
2018 20
2015 19
2020 19
2020 17
2020 17
2018 16
2020 14
2022 13
2020 11
2021 11
2019 11
2017 9
2018 9
2019 9
2023 8
2017 8
2020 7
2019 7
2021 7
2019 7
2019 7
2020 7
2015 6
2019 6
2019 6
2018 6
2019 5
2021 5
2021 4
2018 4
2020 4
2020 4
2021 3
2018 3
2016 3
2020 3
2020 3
2021 3
2021 3
2020 3
2015 3
2018 3
2020 2
2018 2
2
2021 2
2020 2
2021 2
2017 2
2021 2
2017 2
2016 2
2021 2
2019 2
2020 2
2021 2
2021 2
2019 2
2020 1
2020 1
2018 1
2018 1
2020 1
2022 1
2016 1
2022 1
2020 1
2021 1
2021 1
2023 1
2020 1
2021 1
2022 1
2020 1
2019 1
2021 1
2020 1
2019 1
2020 1
2021 1
2020 1
2020 1
2000 0