rohan anil @_arohan_
aspiring to understand deep learning Joined December 2017-
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My timing was perfect
rohan anil retweeted
Incredible work - this should immediately become one of the most important metrics for policy makers to track. We’re probably only a few months from crossing the parity line. Huge props to OAI for both doing the hard work of pulling this together and including our scores.…
Incredible work - this should immediately become one of the most important metrics for policy makers to track. We’re probably only a few months from crossing the parity line. Huge props to OAI for both doing the hard work of pulling this together and including our scores.… https://t.co/9JTrYoYN1y
Caught with an old teammate from GDM after N months and they said my skin is glowing since I took the latest job.
rohan anil retweeted
So I understand that was unexpected for a lot of people, @Apple MLR has released a protein folding model! arxiv.org/abs/2509.18480. Here’s a summary of what SimpleFold is and what it represents: - What is SimpleFold? A generative model that essentially treats protein folding…
So I understand that was unexpected for a lot of people, @Apple MLR has released a protein folding model! arxiv.org/abs/2509.18480. Here’s a summary of what SimpleFold is and what it represents: - What is SimpleFold? A generative model that essentially treats protein folding…
Prateek Jain is amazing, exceptional researcher and great collaborator! Had a great time working with him on Gemini. Worth checking out the podcast!
Prateek Jain is amazing, exceptional researcher and great collaborator! Had a great time working with him on Gemini. Worth checking out the podcast! https://t.co/6tzK61vGW4
Hear hear!
rohan anil retweeted
Underrated dynamic in the next ~12-18 months is we should expect models to get as good at kernel writing as they are at competition math/code contests. This is bullish for chip startups, since one of the major obstacles to adoption (learning your software stack), is softened
Underrated dynamic in the next ~12-18 months is we should expect models to get as good at kernel writing as they are at competition math/code contests. This is bullish for chip startups, since one of the major obstacles to adoption (learning your software stack), is softened
Most of my contribution to deep learning: starting from codistillation, sm3, distributed shampoo, locoprop, ngrammers were when I was on h1b.
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