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 |