Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Scatterbrain: Unifying Sparse and Low-rank Attention Approximation   Recent advances in efficient Transformers have exploited either the sparsity
or low-rank properties of attention matrices to reduce the computational and
memory bottlenecks of modeling long sequences. However, it is still challenging
to balance the trade-off between model quality and efficiency to perform a
one-size-fits-all approximation for different tasks. To better understand this
trade-off, we observe that sparse and low-rank approximations excel in
different regimes, determined by the softmax temperature in attention, and
sparse + low-rank can outperform each individually. Inspired by the classical
robust-PCA algorithm for sparse and low-rank decomposition, we propose
Scatterbrain, a novel way to unify sparse (via locality sensitive hashing) and
low-rank (via kernel feature map) attention for accurate and efficient
approximation. The estimation is unbiased with provably low error. We
empirically show that Scatterbrain can achieve 2.1x lower error than baselines
when serving as a drop-in replacement in BigGAN image generation and
pre-trained T2T-ViT. On a pre-trained T2T Vision transformer, even without
fine-tuning, Scatterbrain can reduce 98% of attention memory at the cost of
only 1% drop in accuracy. We demonstrate Scatterbrain for end-to-end training
with up to 4 points better perplexity and 5 points better average accuracy than
sparse or low-rank efficient transformers on language modeling and
long-range-arena tasks.

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The categories of this paper are: patt-sol
33
The categories of this paper are: plasm-ph
37
The categories of this paper are: supr-con
35
The categories of this paper are: mtrl-th
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The categories of this paper are: nucl-ex
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The categories of this paper are: funct-an
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The categories of this paper are: alg-geom
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The categories of this paper are: cmp-lg
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The categories of this paper are: econ
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The categories of this paper are: eess
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The categories of this paper are: nucl-th
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The categories of this paper are: quant-ph
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The categories of this paper are: gr-qc
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The categories of this paper are: hep-th
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The categories of this paper are: hep-ph
34
The categories of this paper are: chem-ph