Perverformer Scat < Direct - 2026 >

| # | Paper | Year | Key Idea | Link | |---|-------|------|----------|------| | 1 | (Choromanski et al. ) | 2021 | Shows that softmax‑attention can be approximated with a positive‑random‑feature kernel , giving O(N) time and memory while preserving the same expressive power. | https://arxiv.org/abs/2009.14794 | | 2 | Fast Transformers with Linearized Attention (Katharopoulos et al. ) | 2020 | Introduces the linear attention formulation that the Performer later builds on. | https://arxiv.org/abs/2006.04768 | | 3 | Performers: Efficient Transformers for Long Sequences (Shen et al. ) – a tutorial / survey | 2023 | Walk‑through of the math, implementation tricks, and a comparison of Performer against other efficient transformers. | https://arxiv.org/abs/2302.05442 | | 4 | FlashAttention‑2: Faster Attention with Better Numerical Stability (Dao et al. ) – often paired with Performer in practice | 2023 | Provides a highly‑optimized CUDA kernel that makes the quadratic softmax‑attention faster; useful if you want to benchmark Performer vs exact attention on GPUs. | https://arxiv.org/abs/2307.08691 |

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Performer scat, also known as scat singing, is a vocal improvisation technique used by musicians, particularly in jazz and musical theater. It involves creating melodic lines or vocalizations using nonsensical syllables, sounds, and phrases. Scat singing allows performers to express themselves freely, adding a unique dimension to their performances. | # | Paper | Year | Key

– If you need to process very long sequences (e.g., DNA, audio, video frames) the Performer gives you the same attention semantics as a vanilla Transformer but with linear cost. The paper also includes a ready‑to‑use PyTorch implementation (see the accompanying performer-pytorch repo). ) | 2020 | Introduces the linear attention

# 2️⃣ SCAT sparse causal mask on top x = self.scat(x) + x