6533b821fe1ef96bd127ae02
RESEARCH PRODUCT
Symmetry meets AI
Johannes HirnVeronica SanzVeronica SanzGabriela Barenboimsubject
FOS: Computer and information sciencesComputer Science - Machine Learning0303 health sciencesTheoretical computer scienceArtificial neural networkComputer Vision and Pattern Recognition (cs.CV)PhysicsQC1-999Computer Science - Computer Vision and Pattern RecognitionFOS: Physical sciencesGeneral Physics and Astronomy01 natural sciencesMachine Learning (cs.LG)Task (project management)High Energy Physics - Phenomenology03 medical and health sciencesHigh Energy Physics - Phenomenology (hep-ph)0103 physical sciencesHomogeneous spacePICASSOHidden layerSymmetry (geometry)010306 general physics030304 developmental biologydescription
We explore whether Neural Networks (NNs) can {\it discover} the presence of symmetries as they learn to perform a task. For this, we train hundreds of NNs on a {\it decoy task} based on well-controlled Physics templates, where no information on symmetry is provided. We use the output from the last hidden layer of all these NNs, projected to fewer dimensions, as the input for a symmetry classification task, and show that information on symmetry had indeed been identified by the original NN without guidance. As an interdisciplinary application of this procedure, we identify the presence and level of symmetry in artistic paintings from different styles such as those of Picasso, Pollock and Van Gogh.
| year | journal | country | edition | language |
|---|---|---|---|---|
| 2021-03-10 | SciPost Physics |