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SUMMARY:James Halverson (Northeastern University)
DTSTART:20210120T180000Z
DTEND:20210120T190000Z
DTSTAMP:20260423T003244Z
UID:MPML/26
DESCRIPTION:Title: <a href="https://researchseminars.org/talk/MPML/26/">Ne
 ural Networks and Quantum Field Theory</a>\nby James Halverson (Northeaste
 rn University) as part of Mathematics\, Physics and Machine Learning (IST\
 , Lisbon)\n\n\nAbstract\nIn this talk I will review essentials of quantum 
 field theory (QFT) and demonstrate how the function-space distribution of 
 many neural networks (NNs) shares similar properties. This allows\, for in
 stance\, computation of correlators of neural network outputs in terms of 
 Feynman diagrams and a direct analogy between non-Gaussian corrections in 
 NN distributions and particle interactions. Some cases yield divergences i
 n perturbation theory\, requiring the introduction of regularization and r
 enormalization. Potential advantages of this perspective will be discussed
 \, including a duality between function-space and parameter-space descript
 ions of neural networks.\n
LOCATION:https://researchseminars.org/talk/MPML/26/
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