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Found 6 papers, 2 papers with code
Versatile Energy-Based Probabilistic Models for High Energy Physics
As a classical generative modeling approach, energy-based models have the natural advantage of flexibility in the form of the energy function.
Bridging Machine Learning and Sciences: Opportunities and Challenges
The application of machine learning in sciences has seen exciting advances in recent years.
Invariant Representation Driven Neural Classifier for Anti-QCD Jet Tagging
We leverage representation learning and the inductive bias in neural-net-based Standard Model jet classification tasks, to detect non-QCD signal jets.
Variational Autoencoders for Anomalous Jet Tagging
To build a performant mass-decorrelated anomalous jet tagger, we propose the Outlier Exposed VAE (OE-VAE), for which some outlier samples are introduced in the training process to guide the learned information.
Interpretability Study on Deep Learning for Jet Physics at the Large Hadron Collider
Using deep neural networks for identifying physics objects at the Large Hadron Collider (LHC) has become a powerful alternative approach in recent years.
Recursive Neural Networks in Quark/Gluon Tagging
It shows that even taking only particle flow identification as input feature without any extra information on momentum or angular position is already giving a fairly good result, which indicates that the most of the information for quark/gluon discrimination is already included in the tree-structure itself.
