mlsanityData to 7 Oct 2026

Fermions and Supersymmetry in Neural Network Field Theories

arXiv 2511.16741 ↗First posted 20 Nov 2025hep-th cs.LGcited by 5

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Abstract (arXiv)

We introduce fermionic neural network field theories via Grassmann-valued neural networks. Free theories are obtained by a generalization of the Central Limit Theorem to Grassmann variables. This enables the realization of the free Dirac spinor at infinite width and a four fermion interaction at finite width. Yukawa couplings are introduced by breaking the statistical independence of the output weights for the fermionic and bosonic fields. A large class of interacting supersymmetric quantum mechanics and field theory models are introduced by super-affine transformations on the input that realize a superspace formalism.

Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). Builds on 28 of its 51 references that are in the map. First posted on arXiv 20 Nov 2025.

Builds on

28 of 51 references.

References · 28

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  2. 2018Gaussian Process Behaviour in Wide Deep Neural Networkscited by 314
  3. 2021The Principles of Deep Learning Theorycited by 169
  4. 2019Tensor Programs I: Wide Feedforward or Recurrent Neural Networks of Any Architecture are Gaussian Processescited by 148
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16 more
  1. 2021Building Quantum Field Theories Out of Neuronscited by 27
  2. 2021Nonperturbative renormalization for the neural network-QFT correspondencecited by 24
  3. 2019Finite size corrections for neural network Gaussian processescited by 22
  4. 2023Effective Theory of Transformers at Initializationcited by 21
  5. 2023Neural Network Field Theories: Non-Gaussianity, Actions, and Localitycited by 21
  6. 2017A Correspondence Between Random Neural Networks and Statistical Field Theorycited by 13
  7. 2023Structures of Neural Network Effective Theoriescited by 13
  8. 2021Towards quantifying information flows: relative entropy in deep neural networks and the renormalization groupcited by 12
  9. 2022The Inverse of Exact Renormalization Group Flows as Statistical Inferencecited by 11
  10. 2024Conformal Fields from Neural Networkscited by 10
  11. 2024Bayesian RG Flow in Neural Network Field Theoriescited by 9
  12. 2024Wilsonian Renormalization of Neural Network Gaussian Processescited by 8
  13. 2025Applications of Statistical Field Theory in Deep Learningcited by 8
  14. 2024NCoder -- A Quantum Field Theory approach to encoding datacited by 7
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  16. 2024TASI Lectures on Physics for Machine Learningcited by 4

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 2; 2026: 3.

5 papers.

  1. 2025Conformal Defects in Neural Network Field Theoriescited by 2
  2. 2025Virasoro Symmetry in Neural Network Field Theoriescited by 2
  3. 2026Topological Effects in Neural Network Field Theorycited by 1
  4. 2026Deep neural networks as lattice gauge theoriescited by 0
  5. 2026Machine learning for the LHC physics program: a 2025-2026 stocktakecited by 0