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Shubhendu Trivedi @_onionesque

Cultivated Abandon. Twitter interests: Machine learning research, applied mathematics, mathematical miscellany, ML for physics/chemistry, books. shubhendu-trivedi.org New York, NY / Cambridge, MA Joined October 2008
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  • Followers
    9K
  • Following
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Shubhendu Trivedi retweeted
itai_gat Profile Picture

Itai Gat @itai_gat

a day ago

Check out our recent work Set Block Decoding! Super simple modeling, combining NTP and discrete diffusion, allowing parallel decoding without any architectural changes and with exact KV cache! Arxiv: arxiv.org/abs/2509.04185

helibenhamu Profile Picture

Heli Ben-Hamu @helibenhamu

a day ago

Check out our recent work Set Block Decoding! Super simple modeling, combining NTP and discrete diffusion, allowing parallel decoding without any architectural changes and with exact KV cache! Arxiv: arxiv.org/abs/2509.04185

_onionesque Profile Picture

Shubhendu Trivedi @_onionesque

2 days ago

Gian-Carlo Rota

NonwayneWayne Profile Picture

Wayne Bradshaw @NonwayneWayne

2 days ago

Gian-Carlo Rota https://t.co/c5hOjMDpPh

Shubhendu Trivedi retweeted
gregeganSF Profile Picture

Greg Egan @gregeganSF

3 days ago

I proved this was sufficient in 2014, but I couldn’t prove that it was necessary. Now Sergei Drozdov has proved that it is both necessary and sufficient: sciencedirect.com/science/articl…

Shubhendu Trivedi retweeted
michaelchchoi Profile Picture

Michael Choi @michaelchchoi

4 days ago

[Group-averaged Markov chains] How to leverage group structure and averaging to accelerate mixing of Markov chains? This paper presents a modifier that can be supplemented to any existing MCMC sampler. Check it out! RG version: dx.doi.org/10.13140/RG.2.… Submitted to arXiv.

_onionesque Profile Picture

Shubhendu Trivedi @_onionesque

5 days ago

Nice paper arxiv.org/abs/2508.21184

_onionesque Profile Picture

Shubhendu Trivedi @_onionesque

5 days ago

Every once in a while, I come across one of the vilest posts around here. Then I visit the profile (also full of such posts), and it’s always the same 5–10 "mutuals" following them.

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