Papers by Sayeed Chowdhury

1 papers
Segmented Recurrent Transformer: An Efficient Sequence-to-Sequence Model (2023.findings-emnlp)

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Challenge: Transformers have shown dominant performance across a range of domains including language and vision, but their computational cost grows quadratically with the sequence length, making their usage prohibitive for resource-constrained applications.
Approach: They propose a segmented recurrent transformer that combines segmente recursion with recursive attention to reduce the computational cost.
Outcome: The proposed model achieves higher ROUGE1 scores and lower computational complexity than current approaches.

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