通过进一步微调这些预训练模型,我们可以有效地转移这些知识以有益于下游任务。 现有的 long-range transformers 研究通常需要从头开始对所提出的模型进行预训练,以适应新的架构和长输入。 然而,巨大的训练开销为这些方法在不同语言模型中的广泛使用设置了障碍。受此启发,我们探索利用现有预训练模型并通过持续训练使其适应...
Efficient Long-Range Transformers: You Need to Attend More, but Not Necessarily at Every Layer. EMNLP 2023. 作者的Rebuttal: Our emphasis on the importance of pretraining is underscored by recent works such as [Amos], which indicate that Transformers are competitive with state-of-the-art SSM ...
(Long Range Arena)[1]2020.11 Long Range Arena: A Benchmark for Efficient Transformers: https://arxiv.org/abs/2011.04006 2020.11 LRA 是专门为测试模型的长序列语境建模/依赖能力的基准测试,其引入了五种任务 (1) ListOps, 旨在测试模型在长上下文情境中处理具有分层结构数据的能力,通过对ListOps序列进行十...
但是Transformer中的自注意力计算量代价过于昂贵,同时某些操作对于超分而言可能是冗余的,这就限制了自注意力的计算范围,进而限制了超分性能。 本文提出了一种用于图像超分的高效长程距离网络ELAN(Efficient Long-range Attention Network),该模型架构如下所示,核心是 ELAB 模块,下面对这个模块进行详细介绍。 2、ELAB模...
Long Range Arena : A Benchmark for Efficient TransformersYi TayMostafa DehghaniSamira AbnarYikang ShenDara BahriPhilip PhamJinfeng RaoLiu YangSebastian RuderDonald MetzlerInternational Conference on Learning Representations
FlashAttention 将 Transformers 扩展到更长的序列,从而提高它们的质量并启用新功能。我们观察到 GPT-2 的困惑度提高了 0.7,在长文档分类上对较长序列进行建模得到了 6.4 个提升点。 FlashAttention 使第一个 Transformer 能够在 Path-X挑战中实现优于机会的性能,仅通过使用更长的序列长度 (16K)。块稀疏 FlashAtten...
Large Transformer models routinely achieve state-of-the-art results on a number of tasks but training these models can be prohibitively costly, especially on long sequences. We introduce two techniques to improve the efficiency of Transformers. For one, we replace dot-product attention by one that...
DeiT翻译【Training data-efficient image transformers & distillation through attention】,程序员大本营,技术文章内容聚合第一站。
The Vision Transformers [17, 76] decompose an image into a sequence of patches (local windows) and learn their mutual relationships. The distinguishing feature of these models is the strong ca- pability to learn long-range dependencies between image patch sequences...
Long-range arena also implements different variants of Transformer models inJAX, usingFlax. This first initial release includes the benchmarks for the paper "Long Range Arena: A benchmark for Efficient Transformers. Currently we have released all the necessary code to get started and run our benc...