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- W4288350560 abstract "We propose a novel self-attention mechanism that can learn its optimal attention span. This allows us to extend significantly the maximum context size used in Transformer, while maintaining control over their memory footprint and computational time. We show the effectiveness of our approach on the task of character level language modeling, where we achieve state-of-the-art performances on text8 and enwiki8 by using a maximum context of 8k characters." @default.
- W4288350560 created "2022-07-29" @default.
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- W4288350560 date "2019-05-19" @default.
- W4288350560 modified "2023-10-01" @default.
- W4288350560 title "Adaptive Attention Span in Transformers" @default.
- W4288350560 doi "https://doi.org/10.48550/arxiv.1905.07799" @default.
- W4288350560 hasPublicationYear "2019" @default.
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