The Oracle of DLphi

Alfke, Dominik, Baines, Weston, Blechschmidt, Jan, Sarmina, Mauricio J. del Razo, Drory, Amnon, Elbrächter, Dennis, Farchmin, Nando, Gambara, Matteo, Glas, Silke, Grohs, Philipp, Hinz, Peter, Kivaranovic, Danijel, Kümmerle, Christian, Kutyniok, Gitta, Lunz, Sebastian, Macdonald, Jan, Malthaner, Ryan, Naisat, Gregory, Neufeld, Ariel, Petersen, Philipp Christian, Reisenhofer, Rafael, Sheng, Jun-Da, Thesing, Laura, Trunschke, Philipp, von Lindheim, Johannes, Weber, David, Weber, Melanie

arXiv.org Machine Learning 

This paper takes aim at achieving nothing less than the impossible. To be more precise, we seek to predict labels of unknown data from entirely uncorrelated labelled training data. This will be accomplished by an application of an algorithm based on deep learning, as well as, by invoking one of the most fundamental concepts of set theory. Estimating the behaviour of a system in unknown situations is one of the central problems of humanity. Indeed, we are constantly trying to produce predictions for future events to be able to prepare ourselves.

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