4th Inter-experiment Machine Learning Workshop

Session

Plenary

20 Oct 2020, 10:00

Conveners

Plenary: Tuesday morning

• Pietro Vischia (Universite Catholique de Louvain (UCL) (BE))

Plenary: Tuesday afternoon

• David Rousseau (LAL-Orsay, FR)

Plenary: Tuesday Afternoon'

• David Rousseau (LAL-Orsay, FR)

Plenary: Plenary Wednesday Afternoon

• Simon Akar (University of Cincinnati (US))

84. Introduction

Andrea Wulzer (CERN and EPFL), David Rousseau (LAL-Orsay, FR), Gian Michele Innocenti (CERN), Lorenzo Moneta (CERN), Dr Pietro Vischia (Universite Catholique de Louvain (UCL) (BE)), Riccardo Torre (CERN), Simon Akar (University of Cincinnati (US))

20/10/2020, 10:00

86. CERN Knowledge Transfer

Han Hubert Dols (CERN), Nick Ziogas (CERN)

20/10/2020, 10:15

82. Machine Learning in Procter and Gamble

Michele Floris (University of Derby (GB))

20/10/2020, 10:25

(no recording)

Procter & Gamble (P&G) is one of the oldest and largest “consumer goods” companies in the world. It is present in about 180 markets, with operations in 70 countries and almost 100 thousand employes. Machine Learning models created by the P&G Data Scientists support every aspect of this global business, from R&D, to shipment to marketing. The Data Science teams in the company...

83. Using Topological Data Analysis to Disentangle Complex Data Sets

Maurizio Sanarico (SDG Group)

20/10/2020, 10:55

A recent new branch of the, currently called AI, is the Topological Data Analysis (TDA). TDA was born as an extension of algebraic topology to discrete data and, therefore, is a combination of algebraic topology, geometry, statistics and computational methods. According to E. Munch TDA comprises “a collection of powerful tools that can quantify shape and structure in data in order to answer...

81. Zenseact : Deep learning and computer vision for self-driving cars

Christoffer Petersson

20/10/2020, 11:25

(no recording)

The mission of Zenseact is to develop a world-leading software platform for autonomous driving, with the main goal to dramatically reduce the number of traffic accidents in the world. I will discuss how we use deep learning and computer vision to reach this goal, and some of the challenges we face. I will also discuss the ongoing research collaboration between Zenseact and...

74. Solving Inverse Problems with Invertible Neural Networks

Ullrich Koethe (Visual Learning Lab Heidelberg)

20/10/2020, 14:00

Interpretable models are a hot topic in neural network research. My talk will look on interpretability from the perspective of inverse problems, where one wants to infer backwards from observations to the hidden characteristics of a system. I will focus on three aspects: reliable uncertainty quantification, outlier detection, and disentanglement into meaningful features. It turns out that...

76. Deep Learning @ LHC: An ATLAS Perspective

Amir Farbin (University of Texas at Arlington (US))

20/10/2020, 16:00

6 Years after first demonstration of Deep Learning in HEP, the LHC community has explored a broad range of applications aiming for better, cheaper, faster, and easier solutions that ultimately extend the physics reach of the experiments and over come HL-LHC computing challenges. I’ll present a snapshot of where the ATLAS experiment currently stands in adoption of Deep Learning and suggest...

73. End-to-End, Machine Learning-based Data Reconstruction for Particle Imaging Neutrino Detectors

kazuhiro terao (Stanford University)

20/10/2020, 16:45

With firm evidence of neutrino oscillation and measurements of mixing parameters, neutrino experiments are entering the high precision measurement era. The detector is becoming larger and denser to gain high statistics of measurements, and detector technologies evolve toward particle imaging, essentially a hi-resolution "camera", in order to capture every single detail of particles produced in...

75. Neural Network Pruning:
 from over-parametrized to under-parametrized networks

Dr Michela Paganini (Facebook AI Research)

21/10/2020, 17:00

This talk will provide an introduction to the concept of over-parametrization in neural networks and the associated benefits that have been identified from the theoretical and empirical standpoints. It will then present the practice of pruning as both a practical engineering intervention to reduce model size and a scientific tool to investigate the behavior and trainability of compressed...