A new field/branch of Deep Learning in Artificial Intelligence: Supersymmetric Gradient Descent (Supersymmetric Artificial Neural Network)

Source: “Thought Curvature” or “Supersymmetric Artificial Neural Network” paper: https://github.com/JordanMicahBennett/Supersymmetric-artificial-neural-network
  1. “Thought Curvature” or the “Supersymmetric Artificial Neural Network (2016)” is reasonably observable as a new branch or field of Deep Learning in Artificial Intelligence, called Supersymmetric Deep Learning, by Bennett. Supersymmetric Artificial Intelligence (though not Deep Gradient Descent-like machine learning) can be traced back to work by Czachor et al, concerning a “Supersymmetric Latent Semantic Analysis (2004)” based thought experiment. (See item 8 from my repository’s source). Biological science/Neuroscience saw application of supersymmetry, as far back as 2007 by Perez et al.
  2. “Thought Curvature” is similar to “Thought vectors”, with the distinction that supermanifold/curvatures are used to describe the “Supersymmetric Artificial Neural Network”(SANN) model. (See manifold/curvature work in geometric deep learning by Michael Bronstein et al.)
  3. See also, review of (1) by a physics person, namely Mitchell Porter, as I cited here.
  4. See also this layman explanation from this part of the github repository.
  5. See a quick snippet below, taken from main paper cited throughout:
Source: “Thought Curvature” or “Supersymmetric Artificial Neural Network” paper: https://github.com/JordanMicahBennett/Supersymmetric-artificial-neural-network

Some extra snippets:

Source: My Wikiversity “Supersymmetric Artificial Neural Network” page: https://en.wikiversity.org/wiki/Supersymmetric_Artificial_Neural_Network
“Supersymmetric Artificial Neural Network” published on DeepAi.

Signature:

~God Bennett (Why I an atheist, legally changed my name to God Bennett):

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God Bennett

God Bennett

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Lecturer of Artificial Intelligence, and inventor of “Supersymmetric Deep Learning” → Github/Supersymmetric-artificial-neural-network