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New SCALLOP method speeds up Boltzmann generators by 10x

By PulseAugur Editorial · [2 sources] ·

Researchers have developed SCAlable LikeLihood distillation of flOw maPs (SCALLOP), a new method for training Boltzmann generators. This approach builds upon the F2D2 model but introduces a more scalable, Hutchinson-free likelihood distillation objective that can be vectorized. SCALLOP has demonstrated effectiveness in molecular science and image datasets, significantly reducing training variance and time while improving performance and offering up to a 10x inference speedup over existing methods. AI

IMPACT This new method offers significant speedups for Boltzmann generators, potentially accelerating research in molecular science and other fields.

RANK_REASON The cluster contains a research paper detailing a new method for generative modeling.

Read on arXiv stat.ML →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

New SCALLOP method speeds up Boltzmann generators by 10x

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The cluster contains a research paper detailing a new method for generative modeling.
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COVERAGE [2]

  1. arXiv stat.ML TIER_1 English(EN) · RuiKang OuYang, Hanlin Yu, Xinyue Ai, Yutong He, Nicholas M. Boffi, Pradeep Ravikumar, Jose Miguel Hernandez-Lobato, Max Simchowitz, Benjamin Kurt Miller, Omar Chehab ·

    Few-Step Boltzmann Generators via Scalable Likelihood Flow Maps

    arXiv:2606.29110v1 Announce Type: cross Abstract: Recent progress in flow-based generative modeling has led to models that output high-quality samples while using only a small number of function evaluations. However, at present, there is a lack of similar advances in estimating t…

  2. arXiv stat.ML TIER_1 English(EN) · Omar Chehab ·

    Few-Step Boltzmann Generators via Scalable Likelihood Flow Maps

    Recent progress in flow-based generative modeling has led to models that output high-quality samples while using only a small number of function evaluations. However, at present, there is a lack of similar advances in estimating the model likelihood. In particular, most existing …

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