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4th ATLAS Machine Learning Workshop

11–15 Nov 2019
CERN
Europe/Zurich timezone

Contribution List

12 out of 12 displayed
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  1. 34. Introduction
    Amir Farbin (University of Texas at Arlington (US)), Dan Guest (University of California Irvine (US))
    11/11/2019, 13:00
  2. 36. Dark Machines
    Bob Stienen
    11/11/2019, 13:27
  3. 30. A deep neural network for simultaneous estimation of b quark energy and resolution for the CMS experiment
    Nadezda Chernyavskaya (Eidgenoessische Tech. Hochschule Zuerich (CH))
    11/11/2019, 13:45

    (note that speaker has to finish before 4pm)

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  4. 33. DNNLikelihood
    Riccardo Torre (CERN)
    11/11/2019, 14:10

    We introduce the DNNLikelihood, a novel framework to easily encode, through Deep Neural Networks (DNN), the full experimental information contained in complicated likelihood functions (LFs). We show how to efficiently parametrize the LF, treated as a multivariate function of parameters and nuisance parameters with high dimensionality, as an interpolating function in the form of a DNN...

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  5. 32. A deep neural network-based tagger to search for new long-lived particle states decaying to jets
    Vilius Cepaitis (Imperial College (GB))
    11/11/2019, 15:05
  6. 26. Fast Machine Learning Inference on FPGAs for Trigger and DAQ with hls4ml
    Sioni Paris Summers (CERN)
    11/11/2019, 15:35
    Public
    Public

    Invited talk

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  7. 44. Dinner at La Potiniere
    13/11/2019, 19:30

    Restaurant: https://www.lapotinieregeneve.com/

    Please register:
    - https://indico.cern.ch/event/844092/registrations/
    - Pay at the ATLAS Secretariat before Wednesday afternoon. Price is 84 CHF per person

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  8. 31. ML Inference in CMSSW
    Huilin Qu (Univ. of California Santa Barbara (US))
    15/11/2019, 14:00
  9. 37. Distributed training and optimization
    Dr Jean-Roch Vlimant (California Institute of Technology (US))
    15/11/2019, 14:35
  10. 2. Adversarial training for ttH(bb) classification
    Paul Glaysher (DESY)
    15/11/2019, 15:35
    Public
    Public

    Event classification trained on Monte Carlo data can lead to a training bias towards the generator of the training sample, typically evaluated as a systematic error by comparing to an alternative generator model.
    For the case of the search for a top-quark pair produced in association with a Higgs boson decaying to bottom-quark at the LHC, we demonstrate how adversarial domain adaptation can...

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  11. 35. Machine Learning for BSM
    Andrea Wulzer (CERN and EPFL)
    15/11/2019, 16:05
  12. 45. GPUs in Reconstruction
    Dr Charles Leggett (Lawrence Berkeley National Lab (US))
    15/11/2019, 16:40