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Unified field theoretical approach to deep and recurrent neuronal networks

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Publication:5043121
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DOI10.1088/1742-5468/ac8e57OpenAlexW4303984627MaRDI QIDQ5043121

Kai Segadlo, Moritz Helias, Alexander van Meegen, Michael Krämer, David Dahmen, Bastian Epping

Publication date: 20 October 2022

Published in: Journal of Statistical Mechanics: Theory and Experiment (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/2112.05589

zbMATH Keywords

statistical inferencenetwork dynamicsmachine learninginformation processing


Mathematics Subject Classification ID

Statistical mechanics, structure of matter (82-XX)




Cites Work

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  • Path integral methods for stochastic differential equations
  • Statistical field theory for neural networks
  • Quantum field theory in the large \(N\) limit: a review
  • Bayesian learning for neural networks
  • Path integral methods for the dynamics of stochastic and disordered systems
  • The Principles of Deep Learning Theory
  • Learning representations by back-propagating errors
  • The space of interactions in neural network models
  • A Fast Learning Algorithm for Deep Belief Nets
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