4th ATLAS Machine Learning Workshop

Contribution List

34. Introduction

Amir Farbin (University of Texas at Arlington (US)), Dan Guest (University of California Irvine (US))

11/11/2019, 13:00

Public

36. Dark Machines

Bob Stienen

11/11/2019, 13:27

Public

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

Public

(note that speaker has to finish before 4pm)

33. DNNLikelihood

Riccardo Torre (CERN)

11/11/2019, 14:10

Public

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...

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

Public

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

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

31. ML Inference in CMSSW

Huilin Qu (Univ. of California Santa Barbara (US))

15/11/2019, 14:00

Public

37. Distributed training and optimization

Dr Jean-Roch Vlimant (California Institute of Technology (US))

15/11/2019, 14:35

Public

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...

35. Machine Learning for BSM

Andrea Wulzer (CERN and EPFL)

15/11/2019, 16:05

Public

45. GPUs in Reconstruction

Dr Charles Leggett (Lawrence Berkeley National Lab (US))

15/11/2019, 16:40

Public