Henry Smith

9 posts

Henry Smith

Henry Smith

@smithhenryd

Statistics PhD Student, Knight-Hennessy Scholar, and NSF Fellow @Stanford | Previously @Yale | he/him

Stanford, CA Katılım Ekim 2025
147 Takip Edilen73 Takipçiler
Henry Smith retweetledi
Alisa Levin
Alisa Levin@alisadlevin·
Excited to share our new work on cross-brain transfer of speech and handwriting BCIs! Some studies have required 10+ days of training data for peak BCI performance. Can pooling neural data from other users speed things up? 1/6 biorxiv.org/content/10.648…
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Scott Linderman
Scott Linderman@scott_linderman·
My lab is presenting several papers at NeurIPS this week. Swing by the posters below to learn about our work! 🧵
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Amber Hu
Amber Hu@amberhu63·
I'm very excited to share our work on SING, a new method for inferring latent dynamical systems from noisy time series data! I had a great time working on this project with @smithhenryd and @scott_linderman. We'll be at @NeurIPSConf this week, swing by our poster to learn more!
Scott Linderman@scott_linderman

New at @NeurIPSConf 2025: We are excited to share SING, our algorithm for unsupervised discovery of low-dimensional latent dynamical systems from high-dimensional, noisy observations. 🎶 Thread🧵

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Mohammed AlQuraishi
Mohammed AlQuraishi@MoAlQuraishi·
OpenFold3-preview (OF3p) is out: a sneak peek of our AF3-based structure prediction model. Our aim for OF3 is full AF3-parity for every modality. We now believe we have a clear path towards this goal and are releasing OF3p to enable building in the OF3 ecosystem. More👇
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Scott Linderman
Scott Linderman@scott_linderman·
📣Announcing 2 @NeurIPSConf papers! "Parallelizing MCMC Across the Sequence Length": uses Newton iterations to parallelize MCMC! 🤯 But can we parallelize any nonlinear state space model? "Predictability Enables Parallelizability": proves what systems we can parallelize. 🧵
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Brian L Trippe
Brian L Trippe@brianltrippe·
🚨New paper! Generative models are often “miscalibrated”. We calibrate diffusion models, LLMs, and more to meet desired distributional properties. E.g. we finetune protein models to better match the diversity of natural proteins. arxiv.org/abs/2510.10020 github.com/smithhenryd/cgm
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