Papers

• Gaussian Sheaf Neural Networks (2026)
  Andr\'e Ribeiro et al.
  4.22
• Graphical einops: bridging tensor networks and computation graphs (2026)
  Vincent Wang-Ma\'scianica et al.
  1.83
• Weisfeiler and Lehman Go Categorical (2026)
  Seongjin Choi et al.
  1.67
• Coalgebraic analysis of social systems (2026)
  Nima Motamed et al.
  1.67
• Categorical Belief Propagation: Sheaf-Theoretic Inference via Descent and Holonomy (2026)
  Enrique ter Horst et al.
  1.61
• Bayesian Networks, Markov Networks, Moralisation, Triangulation: a Categorical Perspective (2025)
  Antonio Lorenzin and Fabio Zanasi
  1.56
• Graph Lineages and Skeletal Graph Products (2025)
  Eric Mjolsness and Cory B. Scott
  1.33
• Modeling GRNs with a Probabilistic Categorical Framework (2025)
  Yiyang Jia et al.
  1.33
• The Relativity of Causal Knowledge (2025)
  Gabriele D'Acunto et al.
  1.06
• An Algebraic Approach to Moralisation and Triangulation of Probabilistic Graphical Models (2025)
  Antonio Lorenzin et al.
  1.06
• Graph Neural Networks are Dynamic Programmers (2022)
  Andrew Dudzik et al.
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• Markov categories, causal theories, and the do-calculus (2022)
  Yimu Yin et al.
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• Graph Convolutional Neural Networks as Parametric CoKleisli morphisms (2022)
  Bruno Gavranovi\'c et al.
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• Algebraic and Geometric Models for Space Networking (2023)
  William Bernardoni et al.
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• Causal models in string diagrams (2023)
  Robin Lorenz and Sean Tull
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• Algebraic Dynamical Systems in Machine Learning (2023)
  Iolo Jones et al.
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• The Topos of Transformer Networks (2024)
  Mattia Jacopo Villani and Peter McBurney
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• A classification of overlapping clustering schemes for hypergraphs (2024)
  Vilhelm Agdur
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