Deep-Learning the Landscape
We propose a paradigm to deep-learn the ever-expanding databases which have emerged in mathematical physics and particle phenomenology, as diverse as the statistics of string vacua or combinatorial and algebraic geometry. As concrete examples, we establish multi-layer neural networks as both classifiers and predictors and train them with a host of available data ranging from Calabi-Yau manifolds a
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[WSS18] Diffusion of Gun Protests - Machine Learning on Social Events - Online Technical Discussion Groups—Wolfram Community
