A computational shortcut for neural networks

Neural networks (centre) can be used to investigate phase transitions, for instance of magnetic materials (arrows). Credit: Department of Physics, University of Basel

Neural networks are learning algorithms that approximate the solution to a task by training with available data. However, it is usually unclear how exactly they accomplish this. Two young Basel physicists have now derived mathematical expressions that allow one to calculate the optimal solution without training a network. Their results not only give insight into how those learning algorithms work, but could also help to detect unknown phase transitions in physical systems in the future.

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