Guan-Horng Liu
@guanhorng_liu
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Research Scientist @MetaAI (FAIR NY) • Schrödinger Bridge, diffusion, flow, stochastic optimal control • prev ML PhD @GeorgiaTech 🚀
New York, USA
Joined September 2017
RT @AlexanderTong7: Thrilled to be co-organizing FPI at #NeurIPS2025! I'm particularly excited about our new 'Call for Open Problems'track.….
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📢 We're organizing a #NeurIPS2025 workshop on generative modeling, learning to sample, and optimal transport / control. Two submission tracks this year! (deadline #Aug22). 📰 Call for 4-page paper on research / dataset .💡 Call for #Open #Question: 2-page proposal on open.
1/ Where do Probabilistic Models, Sampling, Deep Learning, and Natural Sciences meet? 🤔 The workshop we’re organizing at #NeurIPS2025!. 📢 FPI@NeurIPS 2025: Frontiers in Probabilistic Inference – Learning meets Sampling. Learn more and submit →
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RT @k_neklyudov: 1/ Where do Probabilistic Models, Sampling, Deep Learning, and Natural Sciences meet? 🤔 The workshop we’re organizing at #….
fpineurips.framer.website
FPI Workshop
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RT @bose_joey: 🚨 Our workshop on Frontiers of Probabilistic Inference: Learning meets Sampling got accepted to #NeurIPS2025!!. After the in….
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RT @lipmanya: **Transition Matching** is a new iterative generative paradigm using Flow Matching or AR models to transition between generat….
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RT @bkmi13: Cool work led by @guanhorng_liu! Removing the restriction on memoryless SDEs enables a lot of relevant cases in chemistry and m….
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RT @RickyTQChen: This new work generalizes the recent Adjoint Sampling approach from Stochastic Control to Schrodinger Bridges, enabling me….
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RT @itai_gat: Excited to share our recent work on corrector sampling in language models! A new sampling method that mitigates error accumul….
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RT @lipmanya: A new paper: We finetune an LLM to rethink and resample previously generated tokens, allowing to reduce sampling errors and i….
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RT @vdbergrianne: 🚀 After two+ years of intense research, we’re thrilled to introduce Skala — a scalable deep learning density functional t….
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RT @RickyTQChen: Padding in our non-AR sequence models? Yuck. 🙅. 👉 Instead of unmasking, our new work *Edit Flows* perform iterative refine….
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RT @AIatMeta: Announcing the newest releases from Meta FAIR. We’re releasing new groundbreaking models, benchmarks, and datasets that will….
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RT @RickyTQChen: Against conventional wisdom, I will be giving a talk with particular focus on the "how" and the various intricacies of app….
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RT @xiangfu_ml: We have released an eSEN model that is the current SOTA on Matbench-Discovery. Code/checkpoints are available for both non-….
arxiv.org
Machine learning interatomic potentials (MLIPs) have become increasingly effective at approximating quantum mechanical calculations at a fraction of the computational cost. However, lower errors...
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RT @cdomingoenrich: 🚀Excited to open source the code for Adjoint Matching --- as part of a new repo centered around reward fine-tuning via….
github.com
Reward fine-tuning for Stable Diffusion models based on stochastic optimal control, including Adjoint Matching - microsoft/soc-fine-tuning-sd
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📢#Adjoint #Sampling is a new Diffusion Sampler for Boltzmann distribution that . - Grounded on stochastic control.- Enjoy scalable matching objective.- Extremely efficient in energy NFE.- Does NOT require/estimate target data. Checkout @aaronjhavens talk on Monday #FPI workshop!.
New paper out with FAIR(+FAIR-Chemistry):. Adjoint Sampling: Highly Scalable Diffusion Samplers via Adjoint Matching. We present a scalable method for sampling from unnormalized densities beyond classical force fields. 📄:
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RT @YuanqiD: Excited to share React-OT is on the cover of @NatMachIntell, both paper and codes are fully accessible! React-OT significantly….
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RT @bravo_abad: Optimal-transport generative modelling of transition states. Transition-state structures control chemical kinetics, yet loc….
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