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Transformer with Memory Replay. (arXiv:2205.09869v1 [cs.LG])
May 23, 2022, 1:10 a.m. | Rui Liu, Barzan Mozafari
cs.LG updates on arXiv.org arxiv.org
Transformers achieve state-of-the-art performance for natural language
processing tasks by pre-training on large-scale text corpora. They are
extremely compute-intensive and have very high sample complexity. Memory replay
is a mechanism that remembers and reuses past examples by saving to and
replaying from a memory buffer. It has been successfully used in reinforcement
learning and GANs due to better sample efficiency. In this paper, we propose
\emph{Transformer with Memory Replay} (TMR), which integrates memory replay
with transformer, making transformer more sample-efficient. …
More from arxiv.org / cs.LG updates on arXiv.org
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