Dynamic Composition of Functions for Modular Learning

Compositionality is useful to reduce the complexity of machine learning models and increase their generalization capabilities, because new problems can be linked to the composition of existing solutions. Recent work has shown that compositional approaches can offer substantial benefits over a wide variety of tasks, from multi-task learning over visual question-answering to natural language inference, among others. A key variant is functional compositionality, where a meta-learner composes different (trainable) functions into complex machine learning models. In this thesis, I generalize existing approaches to functional compositionality under the umbrella of the routing paradigm, where trainable arbitrary functions are \u27stacked\u27 to form complex machine learning models

A study of the material in the ATLAS inner detector using secondary hadronic interactions

"The ATLAS inner detector is used to reconstruct secondary vertices due to hadronic interactions of primary collision products, so probing the location and amount of material in the inner region of ATLAS. Data collected in 7 TeV pp collisions at the LHC, with a minimum bias trigger, are used for comparisons with simulated events. The reconstructed secondary vertices have spatial resolutions ranging from similar to 200 mu m to 1 mm. The overall material description in the simulation is validated to within an experimental uncertainty of about 7%. This will lead to a better understanding of the reconstruction of various objects such as tracks, leptons, jets, and missing transverse momentum.

A study of the material in the ATLAS inner detector using secondary hadronic interactions

"The ATLAS inner detector is used to reconstruct secondary vertices due to hadronic interactions of primary collision products, so probing the location and amount of material in the inner region of ATLAS. Data collected in 7 TeV pp collisions at the LHC, with a minimum bias trigger, are used for comparisons with simulated events. The reconstructed secondary vertices have spatial resolutions ranging from similar to 200 mu m to 1 mm. The overall material description in the simulation is validated to within an experimental uncertainty of about 7%. This will lead to a better understanding of the reconstruction of various objects such as tracks, leptons, jets, and missing transverse momentum.