80 citations · 96 across the 5 of their papers we have counts for
7 papers
Versatile Energy-Based Probabilistic Models for High Energy Physics
Taoli Cheng, Aaron Courville
As a classical generative modeling approach, energy-based models have the natural advantage of flexibility in the form of the energy function. Recently, energy-based models have ac…
Bridging Machine Learning and Sciences: Opportunities and Challenges
Taoli Cheng
The application of machine learning in sciences has seen exciting advances in recent years. As a widely applicable technique, anomaly detection has been long studied in the machine…
Invariant Representation Driven Neural Classifier for Anti-QCD Jet Tagging
Taoli Cheng, Aaron Courville
We leverage representation learning and the inductive bias in neural-net-based Standard Model jet classification tasks, to detect non-QCD signal jets. In establishing the framework…
Variational Autoencoders for Anomalous Jet Tagging
Taoli Cheng, Jean-François Arguin, Julien Leissner-Martin +2
We present a detailed study on Variational Autoencoders (VAEs) for anomalous jet tagging at the Large Hadron Collider. By taking in low-level jet constituents' information, and tra…
Interpretability Study on Deep Learning for Jet Physics at the Large Hadron Collider
Taoli Cheng
Using deep neural networks for identifying physics objects at the Large Hadron Collider (LHC) has become a powerful alternative approach in recent years. After successful training…
Recursive Neural Networks in Quark/Gluon Tagging
Taoli Cheng
Since the machine learning techniques are improving rapidly, it has been shown that the image recognition techniques in deep neural networks can be used to detect jet substructure.…