Deep Learning, Dark Knowledge, and Dark Matter

37 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Peeking Inside the Black-Box: A Survey on Explainable Artificial Intelligence (XAI) (2018, neutral): “Distillation is a model compression to transfer information (dark knowledge) from deep networks (the ‘‘teacher’’) to shallow networks (the ‘‘student’’) [121], [122].”
Born Again Neural Networks (2018, methodology): “One paper applying ML to Higgs Boson and supersymmetry detection, made the perhaps inevitable leap of applying Dark Knowledge to the search for dark matter (Sadowski et al., 2015).”
Detecting Cardiovascular Disease from Mammograms with Deep Learning (2017, unknown): “have been used to successfully tackle a variety of problems in engineering, ranging from computer vision [24]–[27] to speech recognition [28], as well as in the natural sciences, in areas ranging from high energy physics [29], [30], to chemistry [31]–[33], and to biology [34]–[36].”
Jet Substructure Classification in High-Energy Physics with Deep Neural Networks (2016, background): “[18] Peter Sadowski, Julian Collado, Daniel Whiteson, and Pierre Baldi.”