5 papers
Interpreting machine learning functions as physical observables
Gert Aarts, Dimitrios Bachtis, Biagio Lucini
We propose to interpret machine learning functions as physical observables, opening up the possibility to apply "standard" statistical-mechanical methods to outputs from neural net…
Quantum field-theoretic machine learning
Dimitrios Bachtis, Gert Aarts, Biagio Lucini
We derive machine learning algorithms from discretized Euclidean field theories, making inference and learning possible within dynamics described by quantum field theory. Specifica…
Adding machine learning within Hamiltonians: Renormalization group transformations, symmetry breaking and restoration
Dimitrios Bachtis, Gert Aarts, Biagio Lucini
We present a physical interpretation of machine learning functions, opening up the possibility to control properties of statistical systems via the inclusion of these functions in…
Mapping distinct phase transitions to a neural network
Dimitrios Bachtis, Gert Aarts, Biagio Lucini
We demonstrate, by means of a convolutional neural network, that the features learned in the two-dimensional Ising model are sufficiently universal to predict the structure of symm…
Extending machine learning classification capabilities with histogram reweighting
Dimitrios Bachtis, Gert Aarts, Biagio Lucini
We propose the use of Monte Carlo histogram reweighting to extrapolate predictions of machine learning methods. In our approach, we treat the output from a convolutional neural net…