Papers

• Generative adversarial neural networks for simulating neutrino interactions (2025)
  Jose L. Bonilla et al.
  4.19
• Generative models on phase space (2026)
  Zachary Bogorad et al.
  1.78
• Amortized Inference of Multi-Modal Posteriors using Likelihood-Weighted Normalizing Flows (2025)
  Rajneil Baruah
  1.56
• Jet Image Generation in High Energy Physics Using Diffusion Models (2025)
  Victor D. Martinez et al.
  1.33
• Transfer Learning for Neutrino Scattering: Domain Adaptation with GANs (2025)
  Jose L. Bonilla et al.
  1.33
• CaloHadronic: a diffusion model for the generation of hadronic showers (2025)
  Thorsten Buss et al.
  1.22
• Contrastive Normalizing Flows for Uncertainty-Aware Parameter Estimation (2025)
  Ibrahim Elsharkawy et al.
  1.17
• Diffusion-model approach to flavor models: A case study for $S_4^\prime$ modular flavor model (2025)
  Satsuki Nishimura et al.
  1.11
• Exploring the flavor structure of leptons via diffusion models (2025)
  Satsuki Nishimura et al.
  1.06
• Learnable cut flow for high energy physics (2025)
  Jing Li and Hao Sun
  1.06
• Communicating Likelihoods with Normalising Flows (2025)
  Jack Y. Araz et al.
  1.00
• One flow to correct them all: improving simulations in high-energy physics with a single normalising flow and a switch (2024)
  Caio Cesar Daumann et al.
  —
• Convolutional L2LFlows: Generating Accurate Showers in Highly Granular Calorimeters Using Convolutional Normalizing Flows (2024)
  Thorsten Buss et al.
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• Variational inference for pile-up removal at hadron colliders with diffusion models (2024)
  Malte Algren et al.
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• Flow Annealed Importance Sampling Bootstrap meets Differentiable Particle Physics (2024)
  Annalena Kofler and Vincent Stimper and Mikhail Mikhasenko and Michael Kagan and Lukas Heinrich
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• Quantum Diffusion Model for Quark and Gluon Jet Generation (2024)
  Mariia Baidachna et al.
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