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Damage-Aware Bandit Pruning for Vision and Language Transformers
Author1, Author2, Author3, Author4, Author5
pruningtransformersbandit algorithmsmultimodal
2609.05448
Builder Relevance
1h ago80%
Abstract
This paper presents a method for structured post-training pruning of transformers that minimizes performance degradation by treating the selection of functional units as a damage-aware multi-armed bandit problem.
Reality Card
Core Claim
The proposed bandit pruning method reduces degradation in transformer models compared to traditional budgeted greedy selection methods across multiple datasets.
Method / Result
In 28 comparisons, 23 bootstrap confidence intervals exclude zero, indicating significant improvements.
Limitations
The method's effectiveness may vary across different models and datasets, which could affect reproducibility.
Paper to code
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