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New tensor network architecture represents discrete maximum entropy distributions

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a new tensor network architecture called Computation-Activation Networks (CompActNets) to represent discrete maximum entropy distributions under expectation constraints. This framework leverages the geometry of convex polytopes to represent distributions and their support, suggesting tensor network ranks as complexity measures for faces. A case study on Boolean statistics demonstrates a direct link between the geometry of 0/1-polytopes and propositional formulas. AI

IMPACT Introduces a novel mathematical framework for representing complex distributions, potentially impacting AI model development and theoretical understanding.

RANK_REASON The cluster contains a single academic paper detailing a new theoretical framework and architecture. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

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

New tensor network architecture represents discrete maximum entropy distributions

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15 / 100
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The cluster contains a single academic paper detailing a new theoretical framework and architecture. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Alex Goessmann, Martin Eigel ·

    Tensor network representations of discrete maximum entropy distributions via mean polytopes

    arXiv:2609.07184v1 Announce Type: cross Abstract: We present tensor network representations for discrete maximum entropy distributions under expectation constraints. To this end, we introduce Computation-Activation Networks (CompActNets), a tensor network architecture that subsum…

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