ML4Jets2020

Material Package

Added Since

Include only attachments uploaded after this date

Include*

Everything

Specific sessions

Specific contributions

Specific days

Sessions*

Workshop Dinner

Registration

Applications

Experimental methods

Machine Learning Inference and Interpretation

Anomaly detection (LHCO)

ML Beyond HEP

Decorrelation and Semi/Unsupervised approaches

Generative Models

Architectures

Introduction

Include materials from selected sessions

Contributions*

A generator cell for LHC event GANs (in session "Generative Models")

A Normalizing Flow Model for Boosted Jets (in session "Decorrelation and Semi/Unsupervised approaches")

Beyond monotonic, autoregressive sequence modeling (in session "ML Beyond HEP")

Bounding high-dimensional uncertainties with adversarial approaches (in session "Machine Learning Inference and Interpretation")

CapsNets Continuing the Convolutional Quest (in session "Architectures")

CLARIANT: Covariant Lorentz Group Architecture for Artificial Neural Networks (in session "Architectures")

Closeout

Comparing weak and unsupervised anomaly detection (in session "Anomaly detection (LHCO)")

Convolutional neural networks with event images for pileup mitigation [cancelled due to illness] (in session "Experimental methods")

Cornering charming Higgs decays (in session "Applications")

Data ex Machina: Machine Learning with Jets in CMS Open Data (in session "Decorrelation and Semi/Unsupervised approaches")

Deep Learning based Energy Reconstruction and Event Generation for the CALICE AHCAL (in session "Experimental methods")

Deep Learning Jet Substructure from Two-Particle Correlation (in session "Architectures")

Deep learning methods to improve Particle Flow reconstruction (in session "Experimental methods")

Deep-Learning Jets with Uncertainties and More (in session "Machine Learning Inference and Interpretation")

DijetGAN: A Generative-Adversarial Network approach for the simulation of QCD Dijet events at the LHC (in session "Generative Models")

Disco Fever (in session "Decorrelation and Semi/Unsupervised approaches")

Experiment Introduction (20'+10') (in session "Introduction")

Fast Calorimeter Simulation in ATLAS with DNNs (in session "Generative Models")

Fast Calorimeter Simulation in ATLAS: FastCaloSimV2 (in session "Generative Models")

GAN based event subtraction for Monte Carlo methods (in session "Generative Models")

GANning away detector effects (in session "Machine Learning Inference and Interpretation")

Generative Models in CALICE (in session "Generative Models")

Guided discussion (in session "ML Beyond HEP")

How to GAN LHC Events (in session "Generative Models")

Introduction (in session "Generative Models")

Jet or Event? - Physics at Future $e^-e^+$ Colliders (in session "Applications")

Jet substructure tagging and pileup mitigation (in session "Applications")

LHC Olympics 2020: Berkeley Cosmology (in session "Anomaly detection (LHCO)")

LHC Olympics 2020: Columbia University (in session "Anomaly detection (LHCO)")

LHC Olympics 2020: MIT (in session "Anomaly detection (LHCO)")

LHCO2020: Outcome of the Challenge (in session "Anomaly detection (LHCO)")

LHCO Introduction and overview (in session "Anomaly detection (LHCO)")

Looking into Jets with Machine Learning (in session "Machine Learning Inference and Interpretation")

Lund jet images from generative and cycle-consistent adversarial networks (in session "Generative Models")

Machine learning approaches to the identification of jets originating from heavy-flavor quarks. (in session "Applications")

Machine Learning Based Jet $p_{T}$ Reconstruction in ALICE (in session "Experimental methods")

Metrics and Machine Learning Algorithms for Collider Space (in session "Decorrelation and Semi/Unsupervised approaches")

Mixture Density Networks for tracking in dense environments on ATLAS (in session "Experimental methods")

ML Introduction (20'+10') (in session "Introduction")

Modeling a top jet classifier with two-point energy correlation and geometry of soft emission (in session "Architectures")

OmniFold: Simultaneously Unfolding All Observables (in session "Machine Learning Inference and Interpretation")

Optimal Transport (in session "ML Beyond HEP")

Quark-gluon discrimination with point clouds (in session "Architectures")

Realigning the goals of machine learning with the goals of physics (in session "Machine Learning Inference and Interpretation")

Representation Learning for Collider Events (in session "Decorrelation and Semi/Unsupervised approaches")

ROB: Reproducible Open Benchmarks for Data Analysis Platform (in session "Machine Learning Inference and Interpretation")

Searching for long lived particles with a neural-network-based displaced jet tagger (in session "Applications")

Secondary Vertex finding in Jets with Graph Neural Networks (in session "Experimental methods")

Tag N’ Train : Combining Autoencoders and CWoLa for Better Unsupervised Searches (in session "Anomaly detection (LHCO)")

Tagger-mass decorrelation: experience within CMS (in session "Decorrelation and Semi/Unsupervised approaches")

Teaching a Computer to Integrate (in session "Generative Models")

The Di-Higgs Photography with Deep Neural Networks (in session "Applications")

Theory Introduction (20'+10') (in session "Introduction")

Unsupervised new physics searches with data-driven inference (in session "Anomaly detection (LHCO)")

Using machine learning to constrain the Higgs total width (in session "Applications")

Variational Autoencoders for Anomalous Jet Tagging (in session "Anomaly detection (LHCO)")

Welcome and Logistics (in session "Introduction")

Include materials from selected contributions

Events scheduled on*

15/01/2020

16/01/2020

17/01/2020

18/01/2020

Include materials from sessions/contributions scheduled on the selected dates