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HEPML workshop at NIPS14

December 13, 2014
Palais des Congrès de Montréal
Europe/Paris timezone

Session

Session 4

Dec 13, 2014, 5:00 PM
Level 5, room 511 c (Palais des Congrès de Montréal)

Level 5, room 511 c

Palais des Congrès de Montréal

Presentation materials

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  1. 10. Ensemble of maximied Weighted AUC models for the maximization of the median discovery significance
    Roberto Diaz Morales (University Carlos III de Madrid)
    12/13/14, 5:00 PM
    From May 12th 2014 to September 15th 2014 took place the Higgs Boson Machine Learning Challenge. Its goal was to explore machine learning methods to improve the discovery significance of the ATLAS experiment. This talk describes the preprocessing, training and results of our model, that finished in 9th position among the solutions of 1785 teams.
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  2. 11. Deep Learning In High-Energy Physics (invited talk)
    Pierre Baldi
    12/13/14, 5:20 PM
    We will provide a brief overview of the challenges and opportunities facing machine learning in the natural sciences, from physics to biology, and then focus on the application of deep learning methods to problems in high-energy physics. In particular we will describe the results obtained on three different problems (Higgs boson detection, Supersymmetry, and Higgs boson decay).
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  3. 12. Panel discussion
    12/13/14, 6:00 PM
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