Papers

• Generative adversarial neural networks for simulating neutrino interactions (2025)
  Jose L. Bonilla et al.
  4.19
• Enabling Low-Latency Machine learning on Radiation-Hard FPGAs with hls4ml (2026)
  Katya Govorkova et al.
  1.67
• Amortized Inference of Multi-Modal Posteriors using Likelihood-Weighted Normalizing Flows (2025)
  Rajneil Baruah
  1.56
• 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
• Communicating Likelihoods with Normalising Flows (2025)
  Jack Y. Araz et al.
  1.00
• Improving $\Lambda$ Signal Extraction with Domain Adaptation via Normalizing Flows (2024)
  Rowan Kelleher et al.
  —
• 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.
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• Convolutional L2LFlows: Generating Accurate Showers in Highly Granular Calorimeters Using Convolutional Normalizing Flows (2024)
  Thorsten Buss et al.
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• Generative Diffusion Models for Fast Simulations of Particle Collisions at CERN (2024)
  Miko{\l}aj Kita et al.
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• An information theoretic limit to data amplification (2024)
  S. J. Watts and L. Crow
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