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🤫 celebrates

Jason Ramapuram

Apple researcher, lead author of sigmoid self-attention

EngineeringmacOSiOS
The work

What we are celebrating, specifically.

  • Theory, Analysis, and Best Practices for Sigmoid Self-Attention (ICLR 2025)
  • ml-sigmoid-attention
  • FlashSigmoid kernels

Apple

Machine Learning Researcher · current

Why

Why we celebrate them.

Jason led the sigmoid attention work that made transformer attention measurably faster and then open-sourced the hardware-aware kernels to prove it. Shipping the code alongside the theory is what makes a paper a contribution.

Sources — their own pages, so you can check us

  • GitHub
  • ml-sigmoid-attention
This page is yours

Jason Ramapuram, this belongs to you.

We built this from your public work because we think it deserves celebrating. You did not ask us to, so the only fair thing is that you decide what happens to it. Claim it and it is yours to edit. Ask us to change something and we will. Ask us to take it down and it is gone within 72 hours — free, no account needed, and nobody will try to talk you out of it.

Claim or remove this pageEveryone we celebrate