The Higgs boson machine learning challenge

By C. Adam-Bourdarios, G. Cowan, Cécile Germain, Isabelle M Guyon, Balázs Kégl, D. Rousseau. 2014

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Review of Artificial Intelligence Adversarial Attack and Defense Technologies (2019, methodology): “Due to high performance, high availability and high intelligence, artificial intelligence technologies have been applied in image classification, object detection, voice control, machine translation and more advanced fields, such as drug composition analysis [1], brain circuit reconstruction [2], pa”
On Last-Layer Algorithms for Classification: Decoupling Representation from Uncertainty Estimation (2020, methodology): “More concretely, deep models are now applied to diverse ﬁelds such as physics [ABCG + 15, RWR + 18, HRH18], biology [AIP + 15], healthcare [LAA + 17, NPAA18, LKN + 18], or autonomous driving [KG17, MKG18] to name a few.”
Learning to Pivot with Adversarial Networks (2016, background): “20 of Adam-Bourdarios et al. (2014)).”
INFERNO: Inference-Aware Neural Optimisation (2018, methodology): “pervised learning classiﬁcation. In many cases, the nature of the generative model (a mixture of different processes) allows the treatment of the problem as signal (S) vs background (B) classiﬁcation [7], when the task becomes one of effectively estimating an approximation of p S(x)/p B(x) which wil”