Papers › Sparse and Continuous Attention Mechanisms

Sparse and Continuous Attention Mechanisms

12 Jun 2020NeurIPS 2020 12arXiv:2006.07214archive 2025-07-28

André F. T. Martins, António Farinhas, Marcos Treviso, Vlad Niculae, Pedro M. Q. Aguiar, Mário A. T. Figueiredo

Exponential families are widely used in machine learning; they include many distributions in continuous and discrete domains (e.g., Gaussian, Dirichlet, Poisson, and categorical distributions via the softmax transformation). Distributions in each of these families have fixed support. In contrast, for finite domains, there has been recent work on sparse alternatives to softmax (e.g. sparsemax and alpha-entmax), which have varying support, being able to assign zero probability to irrelevant categories. This paper expands that work in two directions: first, we extend alpha-entmax to continuous domains, revealing a link with Tsallis statistics and deformed exponential families. Second, we introduce continuous-domain attention mechanisms, deriving efficient gradient backpropagation algorithms for alpha in {1,2}. Experiments on attention-based text classification, machine translation, and visual question answering illustrate the use of continuous attention in 1D and 2D, showing that it allows attending to time intervals and compact regions.

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ContinuousSparsemax deep-spin/mcan-vqa-continuous-attention/core/model/continuous_sparsemax.py official repository unverified no licence file found · pointer only · 4d564215130f97e9 · report
ContinuousSparsemaxFunction deep-spin/mcan-vqa-continuous-attention/core/model/continuous_sparsemax.py official repository unverified no licence file found · pointer only · dc6bd3d88b59cbda · report
BasisFunctions deep-spin/quati/quati/modules/continuous_attention.py community (archive-listed) ran MIT (permissive) · 544ace0143134f21 · report
ContinuousSoftmax deep-spin/quati/quati/modules/continuous_attention.py community (archive-listed) ran · metamorphic tier: deterministic fingerprinted MIT (permissive) · 88d5d46a3e9f268f · report
ContinuousSoftmaxFunction deep-spin/quati/quati/modules/continuous_attention.py community (archive-listed) ran MIT (permissive) · 2abd421a2409332c · report
ContinuousSparsemax deep-spin/quati/quati/modules/continuous_attention.py community (archive-listed) ran · metamorphic tier: deterministic MIT (permissive) · 91ec632f03a8f06b · report
ContinuousSparsemaxFunction deep-spin/quati/quati/modules/continuous_attention.py community (archive-listed) ran MIT (permissive) · 45b9c83a5ff5f057 · report
CosineBasisFunctions deep-spin/quati/quati/modules/continuous_attention.py community (archive-listed) ran · metamorphic tier: deterministic MIT (permissive) · 0513653665d957f6 · report
GaussianBasisFunctions deep-spin/quati/quati/modules/continuous_attention.py community (archive-listed) ran · metamorphic tier: deterministic MIT (permissive) · d4a4993b0f77f70a · report
PowerBasisFunctions deep-spin/quati/quati/modules/continuous_attention.py community (archive-listed) ran MIT (permissive) · af4a48bd715bb239 · report
SineBasisFunctions deep-spin/quati/quati/modules/continuous_attention.py community (archive-listed) ran · metamorphic tier: deterministic MIT (permissive) · a041cde2266ca5ff · report
add_gaussian_basis_functions deep-spin/quati/quati/modules/continuous_attention.py community (archive-listed) ran · our draft was wrong MIT (permissive) · b2b0552bda1b4c2d · report
add_power_basis_functions deep-spin/quati/quati/modules/continuous_attention.py community (archive-listed) ran · our draft was wrong MIT (permissive) · 4500f4cfd4afb4ee · report
add_wave_basis_functions deep-spin/quati/quati/modules/continuous_attention.py community (archive-listed) ran · our draft was wrong fingerprinted MIT (permissive) · 7811322c90f19d30 · report
ContinuousAttention deep-spin/quati/quati/modules/continuous_attention.py community (archive-listed) unverified MIT (permissive) · 7bc1d8632d6b41c5 · report

Tasks

Machine TranslationQuestion AnsweringText ClassificationTranslationVisual Question AnsweringVisual Question Answering (VQA)text-classification

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
Visual Question Answering (VQA) VQA v2 test-dev 2D continuous softmax Accuracy 65.96 #41 of 56 Archive leaderboard report
Visual Question Answering (VQA) VQA v2 test-std 2D continuous softmax overall 66.27 #35 of 38 Archive leaderboard report

Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.

Methods

SoftmaxSparsemax

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