Conveners
Submitted contributions: Session 1
- Paul Seyfert (CERN)
- Lorenzo Moneta (CERN)
Machine learning is a powerful tool to reveal and exploit correlations in a multi-dimensional parameter space. Making predictions from such correlations is a highly non-trivial task, in particular when the details of the underlying dynamics of a theoretical model are not fully understood. Uncertainties in the training data add towards the complexity of performing machine learning tasks such as...
A more dedicated study on the information flow in DNNs will help us understand their behaviour and the deep connection between DNN models and the corresponding tasks. Taking into account our well-established physics analysis framework (observable-based), we present a novel way to interpret DNNs results for HEP, which not only gives a clear physics picture but also inspires interfaces with the...
Invariance of learned representations of neural networks against certain sensitive attributes of the input data is a desirable trait in many modern-day applications of machine learning, such as precision measurements in experimental high-energy physics and enforcing algorithmic fairness in the social and financial domain. We present a method for enforcing this invariance through regularization...
Neural networks are so powerful universal approximator of complicated patterns in large-scale data, leading the explosive developments of AI in terms of deep learning. However, in many cases, usual neural networks are trained to possess poor level of abstraction, so that the model's predictability and generalizability can be quite unstable, depending on the quality and amount of the data used...
Physicists want to use modern open source machine learning tools developed by industry for machine learning projects and analyses in high energy physics. The software environment that a physicist prototypes, tests, and runs these projects in is ideally the same regardless of compute site (be it their laptop or on the GRID). However, historically it has been difficult to find compute sites that...
