2019:groups:tools:mlregression
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2019:groups:tools:mlregression [2019/06/22 18:05] roberto.ruiz_de_austri |
2019:groups:tools:mlregression [2019/06/25 10:38] (current) sascha.caron |
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| == Machine Learning: Regression and classification == | == Machine Learning: Regression and classification == | ||
| - | Aims: | ||
| - | * Library of regression and classification networks | ||
| - | * Keras/Tensorflow and Scikit-learn to start | ||
| - | * Library of Training data, maybe connected to hep-data (database) | ||
| - | Train regression on the following: | + | See protected pages |
| - | * electroweak cross-section in pMSSM19 (DeepXs, ...) | + | |
| - | * cross sections Wprime, Zprime (how many dimensions?) | + | |
| - | * Doublet Higgs model (6 dim) | + | |
| - | + | ||
| - | Train likelihoods: | + | |
| - | * global fits Gambit Xenoda data (train on likehoods on different observables, e.g. only colliderbit) | + | |
| - | * Higgs model with many nuisance parameters (100?) | + | |
| - | + | ||
| - | Train on observables: | + | |
| - | * example relic density | + | |
| - | + | ||
| - | Train on reconstruction efficiencies: | + | |
| - | * LLSps | + | |
| - | + | ||
| - | (want to share code for the networks...) | + | |
| - | + | ||
| - | + | ||
| - | Output: | + | |
| - | * git repositories for networks | + | |
| - | * Zenodo data | + | |
| - | * talk with hepdata | + | |
| - | * Les Houches paper for project | + | |
| - | * prototype code for the network library | + | |
| - | + | ||
| - | + | ||
| - | Other info: | + | |
| - | * How to sample ? Depends on goal, proposed active learning etc. | + | |
| - | + | ||
| - | + | ||
2019/groups/tools/mlregression.1561219526.txt.gz · Last modified: 2019/06/22 18:05 by roberto.ruiz_de_austri
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