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 uses locality-sensitive hashing, changing its complexity from O(L 2 ) to O(L log L), where L is the length of the sequence. Furthermore, we use reversible residual layers instead of the standard residuals, which allows storing activations only once in the training process instead of N times, where N is the number of layers. The resulting model, the Reformer, performs on par with Transformer models while being much more memory-efficient and much faster on long sequences. * Equal Contribution 1 https://hackingsemantics.xyz/2019/leaderboards/ arXiv:2001.04451v1 [cs. LG] 13 Jan 2020 Published as a conference paper at ICLR 2020 We introduce the Reformer model which solves these problems using the following techniques:
Reformer REF uses locality-sensitive hashing to approximate attention in time O(n log n).
Reformer: The Efficient Transformer
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Mathematics Computer Science