| CEBRA-Based Data Processing Pipeline for Mapping Time-Locked EEG Paired Sets in Interacting Participants |
| Continuous learning for high-energy physics data quality monitoring |
| Data Processing Pipeline for the LSST |
| Deep Graph anomaly detection with contrastive learning for new physics searches |
| Deep Learning Inference for mass regression |
| Denoising Astronomical Observations of Protoplanetary Disks |
| Diffusion Models for Fast Detector Simulation |
| Diffusion Models for Gravitational Lensing Simulation |
| Diffusion models for fast and accurate simulations of low level CMS experiment data. |
| Discovery of hidden symmetries and conservation laws |
| End-to-End event classification with sparse autoencoders |
| End-to-End particle collision track reconstruction |
| Equivariant Vision Networks for Predicting Planetary Systems’ Architectures |
| Equivariant quantum neural networks for High Energy Physics Analysis at the LHC |
| Event Classification With Masked Transformer Autoencoders |
| Evolutionary and Transformer Models for Symbolic Regression |
| Exoplanet Atmosphere Characterization |
| Fast Accurate Symbolic Empirical Representation Of Histograms |
| Foundation Model for Gravitational Lensing |
| Foundation Models for Exoplanet Characterization |
| Foundation models for End-to-End event reconstruction |
| Foundation models for symbolic regression tasks |
| Graph Representation Learning for Fast Detector Simulation |
| Graph Transformers for Fast Detector Simulation |
| Gravitational Lens Finding |
| Implementation of Quantum Generative Adversarial Networks to Perform High Energy Physics Analysis at the LHC |
| Learning Parametrization with Implicit Neural Representations |
| Learning quantum representations of classical high energy physics data with contrastive learning |
| Learning the Latent Structure with Diffusion Models |
| Next generation vision transformers for end to end mass regression and classification |
| Next-Generation Transformer Models for Symbolic Calculations of Squared Amplitudes in HEP |
| Non-local GNNs for Jet Classification |
| Optimal Transport in High Energy Physics |
| Physics Guided Machine Learning on Real Lensing Images |
| Physics-Informed Neural Network Diffusion Equation (PINNDE) |
| Q-MAML - Quantum Model-Agnostic Meta-Learning for Variational Quantum Algorithms for High Energy Physics Analysis at the LHC |
| Quantum Diffusion Model for High Energy Physics |
| Quantum Foundation Model for High Energy Physics |
| Quantum Graph Neural Networks for High Energy Physics Analysis at the LHC |
| Quantum Kolmogorov-Arnold Networks for High Energy Physics Analysis at the LHC |
| Quantum Machine Learning for Exoplanet Characterization |
| Quantum Machine Learning for Exoplanet Characterization |
| Quantum Particle transformer for High Energy Physics Analysis at the LHC |
| Quantum transformer for High Energy Physics Analysis at the LHC |
| Semi-supervised Symmetry Discovery |
| State-space models for squared amplitude calculation in high-energy physics |
| Super resolution at the CMS detector |
| Symbolic empirical representation of squared amplitudes in high-energy physics |
| Titans for squared amplitude calculation |
| Transformer Models for Symbolic Regression |
| Unsupervised Super-Resolution and Analysis of Real Lensing Images |