Papers

• Tensor Network Machine Learning for Wildfire Susceptibility Mapping: from Grokking Dynamics to Quantum Mixedness of Class Representations (2026)
  Domenico Pomarico et al.
  4.95
• Deep learning-based prediction of time-resolved adhesive forces in viscoelastic Hertzian contacts (2026)
  Ali Maghami et al.
  4.33
• The Score Hamiltonian: Mapping Diffusion Models to Adiabatic Transport (2026)
  Peter Halmos et al.
  1.89
• Accelerate Vector Diffusion Maps by Landmarks (2026)
  Sing-Yuan Yeh et al.
  1.72
• Amortized Inference of Multi-Modal Posteriors using Likelihood-Weighted Normalizing Flows (2025)
  Rajneil Baruah
  1.56
• Data-Efficient Learning of Anomalous Diffusion with Wavelet Representations: Enabling Direct Learning from Experimental Trajectories (2025)
  Gongyi Wang et al.
  1.56
• On the continuity of flows (2025)
  Congzhou M Sha
  1.56
• Fisher Information, Training and Bias in Fourier Regression Models (2025)
  Lorenzo Pastori et al.
  1.44
• CaloHadronic: a diffusion model for the generation of hadronic showers (2025)
  Thorsten Buss et al.
  1.22
• Mitigating mode collapse in normalizing flows by annealing with an adaptive schedule: Application to parameter estimation (2025)
  Yihang Wang et al.
  1.17
• Contrastive Normalizing Flows for Uncertainty-Aware Parameter Estimation (2025)
  Ibrahim Elsharkawy et al.
  1.17
• Data augmentation using diffusion models to enhance inverse Ising inference (2025)
  Yechan Lim et al.
  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.
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• Landmark Alternating Diffusion (2024)
  Sing-Yuan Yeh 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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• 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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• An information theoretic limit to data amplification (2024)
  S. J. Watts and L. Crow
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