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GFlowNets and variational inference algorithms shown to be equivalent in certain cases

By PulseAugur Editorial · [1 sources] ·

This paper explores the connection between Generative Flow Networks (GFlowNets) and variational inference (VI), two families of probabilistic algorithms. The authors demonstrate that VI algorithms can be seen as special cases of GFlowNets, particularly in their expected gradient objectives. The research highlights GFlowNets' advantage in off-policy training and their potential for capturing diversity in multimodal distributions, drawing parallels to reinforcement learning techniques. AI

IMPACT This research could lead to more diverse and efficient AI models by bridging two distinct probabilistic modeling approaches.

RANK_REASON The cluster contains a single academic paper detailing algorithmic research. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

GFlowNets and variational inference algorithms shown to be equivalent in certain cases

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23 / 100
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The cluster contains a single academic paper detailing algorithmic research. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Esmeralda S. Whitammer, Salem Lahlou, Tristan Deleu, Xu Ji, Edward Hu, Katie Everett, Dinghuai Zhang, Yoshua Bengio ·

    GFlowNets and variational inference

    arXiv:2210.00580v4 Announce Type: replace Abstract: This paper builds bridges between two families of probabilistic algorithms: (hierarchical) variational inference (VI), which is typically used to model distributions over continuous spaces, and generative flow networks (GFlowNet…

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