% Key papers for Flow matching and flow sampling (scime atlas, https://atlas.scime.ai). Authors are not included: add them from the paper pages.

@inproceedings{iclr2023_flow_32546,
  title = {{Flow Matching for Generative Modeling}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2023},
  url = {https://iclr.cc/virtual/2023/poster/11309},
}

@inproceedings{neurips2024_discrete_71347,
  title = {{Discrete Flow Matching}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/95902},
}

@inproceedings{icml2020_acflow_37891,
  title = {{ACFlow: Flow Models for Arbitrary Conditional Likelihoods}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2020},
  url = {https://icml.cc/virtual/2020/poster/6265},
}

@inproceedings{neurips2022_poisson_66322,
  title = {{Poisson Flow Generative Models}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2022},
  url = {https://nips.cc/virtual/2022/poster/54000},
}

@inproceedings{neurips2024_optimal_73624,
  title = {{Optimal Flow Matching: Learning Straight Trajectories in Just One Step}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/93864},
}

@inproceedings{icml2019_sliced_37745,
  title = {{Sliced-Wasserstein Flows: Nonparametric Generative Modeling via Optimal Transport and Diffusions}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2019},
  url = {https://icml.cc/virtual/2019/poster/3561},
}

@inproceedings{neurips2024_fisher_70617,
  title = {{Fisher Flow Matching for Generative Modeling over Discrete Data}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/96502},
}

@inproceedings{icml2022_generative_40626,
  title = {{Generative Flow Networks for Discrete Probabilistic Modeling}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2022},
  url = {https://icml.cc/virtual/2022/poster/17135},
}

@inproceedings{icml2023_a_41813,
  title = {{A theory of continuous generative flow networks}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2023},
  url = {https://icml.cc/virtual/2023/poster/25220},
}

@inproceedings{icml2023_minimizing_42543,
  title = {{Minimizing Trajectory Curvature of ODE-based Generative Models}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2023},
  url = {https://icml.cc/virtual/2023/poster/24336},
}

@inproceedings{neurips2024_categorical_70536,
  title = {{Categorical Flow Matching on Statistical Manifolds}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/96577},
}

@inproceedings{iclr2024_analysis_35224,
  title = {{Analysis of Learning a Flow-based Generative Model from Limited Sample Complexity}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2024},
  url = {https://iclr.cc/virtual/2024/poster/17849},
}

@inproceedings{neurips2023_joint_69709,
  title = {{Joint Bayesian Inference of Graphical Structure and Parameters with a Single Generative Flow Network}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2023},
  url = {https://nips.cc/virtual/2023/poster/70228},
}

@inproceedings{neurips2024_variational_72397,
  title = {{Variational Flow Matching for Graph Generation}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/94958},
}

@inproceedings{cvpr2025_tensoflow_27840,
  title = {{TensoFlow: Tensorial Flow-based Sampler for Inverse Rendering}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2025},
  url = {https://cvpr.thecvf.com/virtual/2025/poster/34327},
}

@inproceedings{icml2023_multisample_42892,
  title = {{Multisample Flow Matching: Straightening Flows with Minibatch Couplings}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2023},
  url = {https://icml.cc/virtual/2023/poster/23709},
}

@inproceedings{iclr2024_phylogfn_34967,
  title = {{PhyloGFN: Phylogenetic inference with generative flow networks}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2024},
  url = {https://iclr.cc/virtual/2024/poster/18107},
}

@inproceedings{neurips2024_learning_72103,
  title = {{Learning Distributions on Manifolds with Free-Form Flows}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/95225},
}

@inproceedings{neurips2024_theoretical_73476,
  title = {{Theoretical guarantees in KL for Diffusion Flow Matching}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/93996},
}

@inproceedings{neurips2024_dynamic_74239,
  title = {{Dynamic Conditional Optimal Transport through Simulation-Free Flows}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/93315},
}

@inproceedings{aaai2024_learning_6503,
  title = {{Learning to Build Solutions in Stochastic Matching Problems Using Flows (Student Abstract)}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2024},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/30515},
}

@inproceedings{neurips2025_energy_77688,
  title = {{Energy Matching: Unifying Flow Matching and Energy-Based Models for Generative Modeling}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2025},
  url = {https://openreview.net/forum?id=WYSCCw7mCe},
}

@inproceedings{neurips2025_gradient_79444,
  title = {{Gradient Variance Reveals Failure Modes in Flow-Based Generative Models}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2025},
  url = {https://openreview.net/forum?id=pVaqdFlUAO},
}

@inproceedings{neurips2025_on_79991,
  title = {{On the Relation between Rectified Flows and Optimal Transport}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2025},
  url = {https://openreview.net/forum?id=v04csnvCfd},
}

@inproceedings{iclr2025_deep_82046,
  title = {{Deep MMD Gradient Flow without adversarial training}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2025},
  url = {https://iclr.cc/virtual/2025/poster/29740},
}

@inproceedings{iclr2026_alphaflow_87354,
  title = {{AlphaFlow: Understanding and Improving MeanFlow Models}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2026},
  url = {https://openreview.net/forum?id=adacb4JTIv},
}

@inproceedings{aaai2024_a_5071,
  title = {{A Theory of Non-acyclic Generative Flow Networks}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2024},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/28989},
}

@inproceedings{iclr2024_diffusion_34329,
  title = {{Diffusion Generative Flow Samplers: Improving learning signals through partial trajectory optimization}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2024},
  url = {https://iclr.cc/virtual/2024/poster/18746},
}

@inproceedings{iclr2024_expected_35492,
  title = {{Expected flow networks in stochastic environments and two-player zero-sum games}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2024},
  url = {https://iclr.cc/virtual/2024/poster/17581},
}