This section of the ML-INFN Confluence Space contains the Knowledge Base of fully implemented use cases. This has been created in order to provide new users getting close to Machine learning with concrete examples, with step by step guides for reproducibility.

The division into categories is multidimensional 

  • Dimension 1: per Machine Learning technology (CNN, Auto encoders, LSTM, GraphNet, ...)
  • Dimension 2: per scientific field (High Energy Physics, Gravitational Waves, Medical Physics, ...) 
  • Dimension 3: per type of used tool

and is implemented via Confluence labels.

Table of Use cases

Name and LinkML TechnologiesScientific FieldML ToolsComments
Btagging in CMS (templated version)CNN, LSTMHigh Energy PhysicsKeras + TensorflowRealistic application
LHCb Masterclass, with KerasDE, MLPHigh Energy PhysicsROOT + Keras + TFIntroductory tutorial
MNIST in a C headerMLP
KerasFree-styling tutorial

LUMIN: Lumin Unifies Many Improvements for Networks

CNN, RNN, GNNHigh Energy PhysicsPyTorchPackage use examples
INFERNO: Inference-Aware Neural OptimisationNNHigh Energy PhysicsKeras + TensorflowTechnique application example
An introduction to classification with CMS dataFisher, BDT, MLPHigh Energy PhysicsScikit-learn, TF2

Tutorials for Master Students

Virgo Autoencoder tutorialAutoencoderGeneral RelativityPython KerasTutorial for student
Distributed training of neural networks with Apache SparkDNNHigh Energy PhysicsSpark + BigDLTutorial
FTS log analysis with NLPNLPHigh Energy Physics, ComputingWord2Vec + Rake + sklearn

Image Inpainting tutorial: how to digitally restore damaged images

CNN U-NetApplied PhysicsKeras + Sci-kit image, PIL, OpenCV, matplotlibTutorial

Signal/background discrimination for the VBF Higgs four lepton decay channel with the CMS experiment using Machine Learning classification techniques

ANNs, RFHigh Energy PhysicsKeras , TensorFlow, Scikit-learnTutorial

Explainability of a CNN classifier for breast density assessment

CNNMedical PhysicsKeras, TensorflowTutorial

ML for smart caching

ML/RLHigh Energy Physics, Computing, CacheKeras, Tensorflow, sklearnDemo, playground

Signal-background Classification with Parametric Neural Networks

pNNHigh Energy PhysicsKeras + TensorFlow 2Tutorial
MLaaS4HEP for the Higgs boson ML challengeDT, MLPHigh Energy PhysicsXGBoost, Keras + TensorFlow 2, PyTorchTutorial

How to insert a new use case

Follow the instructions provided in the How To: Create a KB entry

Once you finish with the creation of the page don't forget to edit the page "Machine Learning Knowledge Base" (this same page!) and add the use case in the "Table of Use cases". 

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