Researchers have introduced LapMerge, a novel differentiable operator designed to efficiently reduce token sequences in modern vision and language models. This method addresses the trade-off between information loss and computational cost by partitioning sequences into a fixed number of segments using a learned mass distribution based on a Laplace kernel. LapMerge achieves linear time complexity for both forward and backward passes, allowing for dynamic budget adjustments without retraining and providing a token-level allocation map. Tested on vision and vision-language tasks, LapMerge matches or surpasses existing token reducers in performance while being significantly faster, particularly for long sequences where token order is crucial. AI
IMPACT LapMerge offers a significant speedup for processing long sequences in vision and language models, potentially reducing computational costs and enabling new applications.
RANK_REASON The cluster contains a research paper detailing a new method for token merging in AI models. [lever_c_demoted from research: ic=1 ai=1.0]
- alphaXiv
- arXiv
- CatalyzeX
- Connected Papers
- DagsHub
- Gotit.pub
- Hugging Face
- Laplace kernel
- LapMerge
- Litmaps
- Magdalena Trędowicz
- ScienceCast
- scite Smart Citations
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