| Background Estimation with Neural AutoRegressive Flows |
| Deep Regression Techniques for Decoding Dark Matter with Strong Gravitational Lensing |
| Diffusion Models for Fast Detector Simulation |
| End-to-End Deep Learning Reconstruction for CMS Experiment |
| End-to-End Deep Learning Regression for Measurements with the CMS Experiment |
| Equivariant Neural Networks for Dark Matter Morphology with Strong Gravitational Lensing |
| Fast Accurate Symbolic Empirical Representation Of Histograms |
| Finding Exoplanets with Astronomical Observations |
| Graph Neural Networks for End-to-End Particle Identification with the CMS Experiment |
| Graph Neural Networks for Particle Momentum Estimation in the CMS Trigger System |
| Graph Representation Learning for Fast Detector Simulation |
| Graph Transformers for Fast Detector Simulation |
| Gravitational Lens Finding for Dark Matter Substructure Pipeline |
| Implementation of Quantum Generative Adversarial Networks to Perform High Energy Physics Analysis at the LHC |
| Implementation of Quantum Variational Autoencoders to Perform High Energy Physics Analysis at the LHC |
| Machine Learning Model for the Albedo of Mercury |
| Optimal Transport in High Energy Physics |
| Quantum Convolutional Neural Networks for High Energy Physics Analysis at the LHC |
| Quantum Graph Neural Networks for High Energy Physics Analysis at the LHC |
| Revealing the Identities of Thermonuclear Scenarios Using the Ultraviolet, Optical, and Infrared (UVOIR) Light Curves of Type-Ia Supernovae |
| Symbolic empirical representation of squared amplitudes in high-energy physics |
| Thermonuclear Supernova Classification via their Multi-Wavelength Signatures |
| Transformers for Dark Matter Morphology with Strong Gravitational Lensing |
| Updating the DeepLense Pipeline |
| Vision Transformers for End-to-End Particle Reconstruction for the CMS Experiment |