%0 Generic %1 He2017DeepLearning %A He, Yang-Hui %D 2017 %K statistics %T Deep-Learning the Landscape %U http://arxiv.org/abs/1706.02714 %X 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 and vector bundles, to quiver representations for gauge theories. We find that even a relatively simple neural network can learn many significant quantities to astounding accuracy in a matter of minutes and can also predict hithertofore unencountered results. This paradigm should prove a valuable tool in various investigations in landscapes in physics as well as pure mathematics.

@misc{He2017DeepLearning, abstract = {{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 and vector bundles, to quiver representations for gauge theories. We find that even a relatively simple neural network can learn many significant quantities to astounding accuracy in a matter of minutes and can also predict hithertofore unencountered results. This paradigm should prove a valuable tool in various investigations in landscapes in physics as well as pure mathematics.}}, added-at = {2019-02-23T22:09:48.000+0100}, archiveprefix = {arXiv}, author = {He, Yang-Hui}, biburl = {https://www.bibsonomy.org/bibtex/20bf5385e1c285272643c26ddf437228e/cmcneile}, citeulike-article-id = {14374368}, citeulike-linkout-0 = {http://arxiv.org/abs/1706.02714}, citeulike-linkout-1 = {http://arxiv.org/pdf/1706.02714}, day = 8, eprint = {1706.02714}, interhash = {de776c1297640cc0c804b7e8de856dc7}, intrahash = {0bf5385e1c285272643c26ddf437228e}, keywords = {statistics}, month = jun, posted-at = {2017-06-12 10:07:18}, priority = {2}, timestamp = {2019-02-23T22:15:27.000+0100}, title = {{Deep-Learning the Landscape}}, url = {http://arxiv.org/abs/1706.02714}, year = 2017 }