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  1. Home
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  3. Regularizing Attention Scores with Bootstrapping
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Regularizing Attention Scores with Bootstrapping

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Viability
0.0/10

Compared to this week’s papers

Evidence fresh

Evidence Receipt

Freshness: 2026-04-03T20:18:17.99417+00:00

Claims: 0

References: 0

Proof: partial

Freshness: fresh

Source paper: Regularizing Attention Scores with Bootstrapping

PDF: https://arxiv.org/pdf/2604.01339v1

Repository: https://github.com/ncchung/AttentionRegularization

Source count: 0

Coverage: 50%

Last proof check: 2026-04-03T20:30:36.195Z

Paper Conversation

Citation-first answers with explicit evidence receipts, disagreement handling, commercialization framing, and next actions.

Paper Mode

Regularizing Attention Scores with Bootstrapping

Overall score: 7/10
Lineage: fdfca8ce30b8…
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Canonical Paper Receipt

Last verification: 2026-04-03T20:30:36.195Z

Freshness: fresh

Proof: partial

Repo: active

References: 0

Sources: 0

Coverage: 50%

Missingness
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Unknowns
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Starting…

Dimensions overall score 7.0

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Last commit
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Keep exploring

Builds On This
ViT-AdaLA: Adapting Vision Transformers with Linear Attention
Score 6.0down
Builds On This
A saccade-inspired approach to image classification using visiontransformer attention maps
Score 6.0down
Prior Work
Concept-Guided Fine-Tuning: Steering ViTs away from Spurious Correlations to Improve Robustness
Score 7.0stable
Prior Work
Attention Retention for Continual Learning with Vision Transformers
Score 7.0stable
Prior Work
Weak-SIGReg: Covariance Regularization for Stable Deep Learning
Score 7.0stable
Higher Viability
Bootstrapping-based Regularisation for Reducing Individual Prediction Instability in Clinical Risk Prediction Models
Score 8.0up
Higher Viability
BinaryAttention: One-Bit QK-Attention for Vision and Diffusion Transformers
Score 8.0up
Competing Approach
HAViT: Historical Attention Vision Transformer
Score 7.0stable

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