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- Dimension 1: per Machine Learning typology (classification, regression, clustering, ...)
- Dimension 2: per Machine Learning algorithms technology (CNN, Auto encoders, LSTM, GraphNet, ...)
- Dimension 23: per scientific field (High Energy Physics, Gravitational Waves, Medical Physics, ...)
- Dimension 34: per type of used tool
and is implemented via Confluence labels.
Table of Use cases
| Name and Link | Type of ML | ML |
|---|
| Algorithms | Scientific Field | ML Tools | Comments | ||
|---|---|---|---|---|---|
| Btagging in CMS (templated version) | Classification | CNN, LSTM | High Energy Physics | Keras + Tensorflow | Realistic application |
| LHCb Masterclass, with Keras |
| Density estimation and classification | MLP | High Energy Physics | ROOT + Keras + TF | Introductory tutorial | |
| MNIST in a C header | Classification | MLP | Keras | Free-styling tutorial | |
Technological | CNN, RNN, GNN | High Energy Physics | PyTorch | Package use examples | |
| INFERNO: Inference-Aware Neural Optimisation | Classification | NN | High Energy Physics | Keras + Tensorflow | Technique application example |
| An introduction to classification with CMS data | Classification | Fisher, BDT, MLP | High Energy Physics | Scikit-learn, TF2 | Tutorials for Master Students |
| Virgo Autoencoder tutorial | Data Compression | Autoencoder | General Relativity | Python Keras | Tutorial for student |
| Distributed training of neural networks with Apache Spark | Technological | DNN | High Energy Physics | Spark + BigDL | Tutorial |
| FTS log analysis with NLP | Clustering | NLP | High Energy Physics, Computing | Word2Vec + Rake + sklearn | |
Image Inpainting tutorial: how to digitally restore damaged images | Inpainting | CNN U-Net | Applied Physics | Keras + Sci-kit image, PIL, OpenCV, matplotlib | Tutorial |
| ANNs, RF | High Energy Physics | Keras , TensorFlow, Scikit-learn | Tutorial | ||
| CNN | Medical Physics | Keras, Tensorflow | Tutorial | ||
| ML/RL | High Energy Physics, Computing, Cache | Keras, Tensorflow, sklearn | Demo, playground | ||
| pNN | High Energy Physics | Keras + TensorFlow 2 | Tutorial | ||
| New Physics Learning Machine | Density Estimation | NN | High Energy Physics | Keras, Tensorflow | package + tutorial |
| MLaaS4HEP for the Higgs boson ML challenge | Technological | DT, MLP | High Energy Physics | XGBoost, Keras + TensorFlow 2, PyTorch | Tutorial |
| Object counting with c-ResUnet | CNN, ResUnet, semantic segmentation | Computer Vision | PyTorch, fastai | Real application, Tutorial | |
| Fast classifier-based goodness of fit test for online data quality monitoring | Logistic Regression, Kernel Methods | High Energy Physics | Pytorch, Falkon library | Tutorial + Realistic application |
How to insert a new use case
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