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Heavy Flavour Data Mining workshop

18–20 Feb 2016
University of Zurich, Irchel Campus
Europe/Zurich timezone

Contribution List

25 out of 25 displayed
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  1. 1. Welcome
    18/02/2016, 09:00
  2. 16. Data Science at LHCb
    Tim Head (Ecole Polytechnique Federale de Lausanne (CH))
    18/02/2016, 09:10
  3. 3. Summary of «Flavors of Physics» Challenge
    Andrey Ustyuzhanin (Yandex School of Data Analysis (RU))
    18/02/2016, 09:50
  4. 13. Data Doping solution for "Flavours of Physics" challenge
    Dr Vicens Gaitan
    18/02/2016, 10:50
  5. 14. Transfer Learning solution for "Flavours of Physics" challenge
    Dr Alexander Rakhlin
    18/02/2016, 11:30
  6. 20. Pitfalls of evaluating a classifier's performance in high energy physics applications
    Dr Gilles Louppe (New York University (US))
    18/02/2016, 11:50
  7. 9. An introduction to machine learning with Scikit-Learn
    Dr Gilles Louppe (New York University (US))
    18/02/2016, 15:00
    https://github.com/glouppe/tutorial-scikit-learn
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  8. 7. Boosting applications for HEP
    Aleksei Rogozhnikov (Yandex School of Data Analysis (RU))
    18/02/2016, 17:20
  9. 4. Classifier output calibration to probability
    Tatiana Likhomanenko (National Research Centre Kurchatov Institute (RU))
    19/02/2016, 09:00
  10. 2. Classifiers for centrality determination in proton-nucleus and nucleus-nucleus collisions
    Igor Altsybeev (St. Petersburg State University (RU))
    19/02/2016, 09:40
    Centrality, as a geometrical property of the collision, is crucial for the physical interpretation of proton-nucleus and nucleus-nucleus experimental data. However, it cannot be directly accessed in event-by-event data analysis. Contemporary methods of the centrality estimation in A-A and p-A collisions usually rely on a single detector (either on the signal in zero-degree calorimeters or on...
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  11. 6. Data Fusion Surogate Modeling on Incomplete Factorial Design of Experiments
    Prof. Eugene Burnaev (IITP)
    19/02/2016, 10:00
    This work concerns a construction of surrogate models for a specific aerodynamic data base. This data base is generally available from wind tunnel testing or from CFD aerodynamic simulations and contains aerodynamic coefficients for different flight conditions and configurations (such as Mach number, angle-of-attack, vehicle configuration angle) encountered over different space vehicles...
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  12. 11. Mathematics of Big Data
    Prof. Dmitry Vetrov (Skoltech, Yandex School of Data Analysis, Higher School of Economics)
    19/02/2016, 11:00
  13. 12. Efficient Elastic Net Regularization for Sparse Linear Models in the Multilabel Setting
    Mr Zachary Chase Lipton (University of California, Amazon)
    19/02/2016, 11:40
  14. 22. Optimized Methods to Apply Neural Networks in HEP
    Lev Dudko (M.V. Lomonosov Moscow State University (RU))
    19/02/2016, 12:10
    Different steps of NN application in HEP are considered. Possible optimization methods for each of the steps are discussed. The proposed methods were applied for the single top quark analysis in CMS and corresponding examples are presented in the talk.
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  15. 15. OpenML: Collaborative machine learning
    Dr Joaquin Vanschoren (https://www.tue.nl)
    19/02/2016, 15:00
  16. 26. Calibration curves as tool to test for over- and underfitting
    Dr Artem Vorozhtsov (Yandex)
    19/02/2016, 16:10
    We present a simple approach to test correctness of bias or regularization strength, or other hyperparameters. The main idea is to fit hyperparameters so that test and train calibration curves after applying proper isotonic regression should intersect at diagonal.
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  17. 10. Reproducible Experiment Platform & Everware
    Aleksei Rogozhnikov (Yandex School of Data Analysis (RU)), Andrey Ustyuzhanin (Yandex School of Data Analysis (RU))
    19/02/2016, 16:30
  18. 25. Automatic Tuning of Hyperparameters
    Mr Alexander Fonarev (Skoltech)
    20/02/2016, 09:00
    The training process of a machine learning algorithm includes tuning of hyperparameters, such as the regularization coefficient of a linear model or the depth of a decision tree. Unfortunately, it usually is conducted manually, what is very expensive to be done on a regular basis. Moreover, the growing number of hyperparameters in modern complex machine learning methods additionally...
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  19. 28. Deep Learning for event reconstruction
    Amir Farbin (University of Texas at Arlington (US))
    20/02/2016, 09:40
  20. 27. Fast multimodal clustering: searching for optimal patterns
    Dr Dmitry Ignatov (HSE)
    20/02/2016, 10:40
    In Machine Learning, we usually deal with object-attribute tables. However, underlying objects may have other modalities than attributes only. For instance, an object may have a certain attribute only under specific conditions. The real examples came from gene expression data, where a gene can be active (expressed) in particular situations at a certain moment of time, implying ternary relation...
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  21. 18. Summary of open space discussions
    Andrey Ustyuzhanin (Yandex School of Data Analysis (RU))
    20/02/2016, 11:20
  22. 31. Nvidia tutorial
    20/02/2016, 11:50
  23. 19. TensorFlow introduction & tutorial #1
    Rafal Jozefowicz (Google)
    20/02/2016, 12:00
    Introduction into deep learning, hands-on tutorial and demonstration of TensorFlow using HiggsML challenge dataset.
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  24. 30. TensorFlow introduction & tutorial. Continuation
    Rafal Jozefowicz (Google)
    20/02/2016, 13:30
  25. 17. Closing Remarks
    Andrey Ustyuzhanin (Yandex School of Data Analysis (RU)), Marcin Chrzaszcz (Universitaet Zuerich (CH), Institute of Nuclear Physics (PL))
    20/02/2016, 14:50