arXiv Machine Learning By Giovanni di Sarra, Yasser Roudi

Activation Functions, Statistics and Learning of Higher-Order Interactions in Restricted Boltzmann Machines

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arXiv:2605. 19178v2 Announce Type: replace-cross Abstract: The great success of neural networks primarily arises from the presence of the large number of weight parameters combined with nonlinearities in the input-output relationship of single neurons.

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arXiv Machine Learning
Sep 16

Formalized Hopfield Networks and Boltzmann Machines

arXiv:2512.07766v2 Announce Type: replace Abstract: Neural networks are widely used, yet their analysis and verification remain challenging. We present a Lean~4 formalization covering both determinis...

By Matteo Cipollina, Michail Karatarakis, Freek Wiedijk
arXiv Machine Learning
Aug 10

Weak Adversarial Neural Pushforward Method for Boltzmann Equation

arXiv:2608. 06823v1 Announce Type: cross Abstract: In this paper, we extend a weak adversary neural network pushforward method for solving time dependent Boltzmann equation and a weak formulation of the collision operator is proposed where an invertible neural pushforward mapping is used to generating samples given by the distribution governed by the Boltzmann equation.

By Jenia Fardousi Koly, Andrew Qing He, Wei Cai