Papers by Dagmar Adamcová
Working Memory Constraints Scaffold Learning in Transformers under Data Scarcity (2026.findings-acl)
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| Challenge: | a recent study has shown that human-like working memory constraints can be integrated into the Transformer architecture . our model incorporates fixed-width windows and temporal decay based attention mechanisms . |
| Approach: | They propose to integrate working memory constraints into the Transformer architecture . they use fixed-width windows and temporal decay-based attention mechanisms . |
| Outcome: | The proposed models show that they can learn better when training data is scarce . the findings suggest that such constraints may serve as a beneficial bias guiding models towards more robust representations . |