8 papers
IE2Video: Adapting Pretrained Diffusion Models for Event-Based Video Reconstruction
Dmitrii Torbunov, Onur Okuducu, Yi Huang +4
Continuous video monitoring in surveillance, robotics, and wearable systems faces a fundamental power constraint: conventional RGB cameras consume substantial energy through fixed-…
Dynamical Implicit Neural Representations
Yesom Park, Kelvin Kan, Thomas Flynn +4
Implicit Neural Representations (INRs) provide a powerful continuous framework for modeling complex visual and geometric signals, but spectral bias remains a fundamental challenge,…
Robust and Generalizable Background Subtraction on Images of Calorimeter Jets using Unsupervised Generative Learning
Yeonju Go, Dmitrii Torbunov, Yi Huang +8
Accurate separation of signal from background is one of the main challenges for precision measurements across high-energy and nuclear physics. Conventional supervised learning meth…
TPCpp-10M: Simulated proton-proton collisions in a Time Projection Chamber for AI Foundation Models
Shuhang Li, Yi Huang, David Park +10
Scientific foundation models hold great promise for advancing nuclear and particle physics by improving analysis precision and accelerating discovery. Yet, progress in this field i…
FM4NPP: A Scaling Foundation Model for Nuclear and Particle Physics
David Park, Shuhang Li, Yi Huang +9
Large language models have revolutionized artificial intelligence by enabling large, generalizable models trained through self-supervision. This paradigm has inspired the developme…
Effectiveness of denoising diffusion probabilistic models for fast and high-fidelity whole-event simulation in high-energy heavy-ion experiments
Yeonju Go, Dmitrii Torbunov, Timothy Rinn +6
Artificial intelligence (AI) generative models, such as generative adversarial networks (GANs), variational auto-encoders, and normalizing flows, have been widely used and studied…