The diversity of concepts, terminologies, and interpretations in the broader machine learning community make it difficult to narrow down exactly what machine learning is, let alone compare it to more formal notions of learning like Bayesian …
Supervised machine learning will be central in the analysis of upcoming large-scale sky surveys. However, selection bias for astronomical objects yields labelled training data that are not representative of the unlabelled target data distribution. …
We discuss how Deep Learning could be used to help our quest for new physics at the LHC. After introducing the basic ingredients, we review how new search strategies could be designed, exploiting new opportunities offered by Deep Learning. We then …
Large astrophysical surveys provide us with a wealth of data that has important implications for fundamental physics questions. Fully exploiting these datasets requires new techniques and approaches, including machine learning. However, it is …
LHC physics is a unique field in the sense that we compare vast and highly complex data sets with precise first-principles predictions. Generative neural networks can supplement these simulations and come with conceptional advantages. I will show …
In recent years, there has been growing interest and many new ideas for anomaly detection and model-independent new physics searches at the LHC, enabled by powerful advances in deep learning. I will give an overview of some of the recent progress in …