2nd IML Machine Learning Workshop

Session

Session 4

10 Apr 2018, 14:00

500/1-001 - Main Auditorium (CERN)

Conveners

Session 4

• Markus Stoye (CERN)
• Lorenzo Moneta (CERN)

33. Tutorial: Keras/TensorFlow

Stefan Wunsch (KIT - Karlsruhe Institute of Technology (DE))

10/04/2018, 14:00

34. Interpreting Deep Neural Networks and their Predictions

Wojciech Samek (Fraunhofer HHI)

10/04/2018, 15:00

Invited talk, http://iphome.hhi.de/samek/

5. What is the machine learning.

Bryan Ostdiek (University of Oregon)

10/04/2018, 16:30

Applications of machine learning tools to problems of physical interest are often criticized for producing sensitivity at the expense of transparency. In this talk, I explore a procedure for identifying combinations of variables -- aided by physical intuition -- that can discriminate signal from background. Weights are introduced to smooth away the features in a given variable(s). New networks...

7. Identifying the relevant dependencies of the neural network response on characteristics of the input space

Stefan Wunsch (KIT - Karlsruhe Institute of Technology (DE))

10/04/2018, 16:55

The use of neural networks in physics analyses poses new challenges for the estimation of systematic uncertainties. Since the key to a proper estimation of uncertainties is the precise understanding of the algorithm, novel methods for the detailed study of the trained neural network are valuable.
This talk presents an approach to identify those characteristics of the neural network inputs that...

23. Studies to mitigate difference between real data and simulation for jet tagging

Markus Stoye (CERN), Mauro Verzetti (CERN), Jan Kieseler (CERN), Arabella Martelli (Imperial College (GB)), Oliver Buchmuller (Imperial College (GB))

10/04/2018, 17:20

The aim of the studies presented is to improve the performance of jet flavour tagging on real data while still exploiting a simulated dataset for the learning of the main classification task. In the presentation we explore “off the shelf” domain adaptation techniques as well as customised additions to them. The latter improves the calibration of the tagger, potentially leading to smaller...

18. Particle identification at LHCb: new calibration techniques and machine learning classification algorithms

Miriam Lucio Martinez (Universidade de Santiago de Compostela (ES))

10/04/2018, 17:45

Particle identification (PID) plays a crucial role in LHCb analyses. Combining information from LHCb subdetectors allows one to distinguish between various species of long-lived charged and neutral particles. PID performance directly affects the sensitivity of most LHCb measurements. Advanced multivariate approaches are used at LHCb to obtain the best PID performance and control systematic...