Guolin Ke

@guolin_ke

Machine Learning & AI for Science. Created #LightGBM, #Graphormer, #UniFold, Uni-Mol, Uni-3DAR. Former Senior Researcher @MSFTResearch. Opinions are my own.

Beijing

guolinke.github.io

Joined February 2017

377 Following

1,155 Followers

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Guolin Ke
@guolin_ke
Sep 30, 2025
A simple yet effective idea: enforcing *hypersphere (manifold) constraint* unlock the potential of continuous-token AR generative models. Our next-token, raster-order AR model hits FID 1.34 on ImageNet-256, setting a new SOTA for AR image generation. check huggingface.co/papers/2509.24…
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Guolin Ke
@guolin_ke
Apr 9, 2025
🚀 Uni-3DAR’s crystal generation code and models are now publicly available. ✅ github.com/dptech-corp/Un… Crystal generation has become a hot topic in AI4S, with most prior work leveraging diffusion models that typically employ fractional coordinates to scale atomic positions
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Guolin Ke
@guolin_ke
Mar 29, 2025
Uni-3DAR generates 3D structures in a coarse-to-fine autoregressive manner. By dynamically expanding subcells with known positional priors, it captures 3D context better than traditional set-based AR models (e.g. a set of atoms) —boosting performance significantly. Home Page:
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Guolin Ke
@guolin_ke
Mar 22, 2025
The model, training scripts, and dataset for small molecule generation (QM9) with Uni-3DAR are now released! Check it out on GitHub: github.com/dptech-corp/Un…
Guolin Ke
@guolin_ke
Mar 21, 2025
The world is 3D—and many scientific laws are rooted in 3D space. If we want a world model, 3D structure modeling is essential. Yet today, there’s no universal 3D representation—not across scales, not even within microscopic systems. We present Uni-3DAR, a unified 3D framework
Guolin Ke
@guolin_ke
Mar 21, 2025
The world is 3D—and many scientific laws are rooted in 3D space. If we want a world model, 3D structure modeling is essential. Yet today, there’s no universal 3D representation—not across scales, not even within microscopic systems. We present Uni-3DAR, a unified 3D framework

Guolin Ke

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