2022
DOI: 10.2139/ssrn.4241508
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P-Adic Statistical Field Theory and Deep Belief Networks

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Cited by 2 publications
(5 citation statements)
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“…We draw attention to certain parallels between our approach, which leads to the emergence of quantum theory from DHT, and the neural network model of the universe presented in articles [35][36][37][38][39] . In that sense a machine learning, ML, (or neural network) is naturally identified with a probability density function p(x; θ ) depending on a set of parameters.…”
Section: Discussionmentioning
confidence: 99%
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“…We draw attention to certain parallels between our approach, which leads to the emergence of quantum theory from DHT, and the neural network model of the universe presented in articles [35][36][37][38][39] . In that sense a machine learning, ML, (or neural network) is naturally identified with a probability density function p(x; θ ) depending on a set of parameters.…”
Section: Discussionmentioning
confidence: 99%
“…Then, giving a probability density p(x; θ ) for x ∈ S ⊂ Q p and θ ∈ R k is equivalent to define an ML on S . Recently, p-adic neural networks have been studied in connection with Euclidean quantum field theory 38,39 .…”
Section: Discussionmentioning
confidence: 99%
“…In these studies, the laws of physics are shown to emerge as a learning procedures. Thus, action principles might be referred trivially as a particular learning procedure [34][35][36][37][38]. In recent studies [5,6], we have already drawn attention to intriguing parallels between our approach, which yields the emergence of quantum theory from DHT, and the neural network model of the universe as described in articles [34][35][36][37][38].…”
Section: P-adic Treelike Formalization Of Leibniz's Principlementioning
confidence: 99%
“…Thus, action principles might be referred trivially as a particular learning procedure [34][35][36][37][38]. In recent studies [5,6], we have already drawn attention to intriguing parallels between our approach, which yields the emergence of quantum theory from DHT, and the neural network model of the universe as described in articles [34][35][36][37][38]. Notably, we should highlight that p-adic neural networks have been investigated in conjunction with Euclidean quantum field theory, as detailed in references [37,38], thereby establishing a closer connection between our approach and the domain of ML.…”
Section: P-adic Treelike Formalization Of Leibniz's Principlementioning
confidence: 99%
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