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@inproceedings{schmitt2023detecting,
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  organization={Springer}
}

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@misc{radev2023bayesflow,
  title = {BayesFlow: Amortized Bayesian Workflows With Neural Networks},
  author = {Stefan T Radev and Marvin Schmitt and Lukas Schumacher and Lasse Elsem\"{u}ller and Valentin Pratz and Yannik Sch\"{a}lte and Ullrich K\"{o}the and Paul-Christian B\"{u}rkner},
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  publisher= {arXiv},
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}

@inproceedings{radev2023jana,
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  author={Stefan T. Radev and Marvin Schmitt and Valentin Pratz and Umberto Picchini and Ullrich Koethe and Paul-Christian B{\"u}rkner},
  booktitle={The 39th Conference on Uncertainty in Artificial Intelligence},
  year={2023},
  url={https://openreview.net/forum?id=dS3wVICQrU0}
}

@article{radev2020bayesflow,
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@article{radev2021amortized,
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  year={2024}
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@book{chollet2021deep,
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  title={Auto-encoding variational bayes},
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@article{song2023consistency,
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@article{papamakarios2021normalizing,
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@article{lipman2022flow,
  title={Flow matching for generative modeling},
  author={Lipman, Yaron and Chen, Ricky TQ and Ben-Hamu, Heli and Nickel, Maximilian and Le, Matt},
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  year={2022}
}

@article{kobyzev2020normalizing,
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  pages={3964--3979},
  year={2020},
  publisher={IEEE}
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@article{durkan2019neural,
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@article{muller2019neural,
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  number={5},
  pages={1--19},
  year={2019},
  publisher={ACM New York, NY, USA}
}

@article{dinh2016density,
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@misc{lipman2024flowmatchingguidecode,
      title={Flow Matching Guide and Code}, 
      author={Yaron Lipman and Marton Havasi and Peter Holderrieth and Neta Shaul and Matt Le and Brian Karrer and Ricky T. Q. Chen and David Lopez-Paz and Heli Ben-Hamu and Itai Gat},
      year={2024},
      eprint={2412.06264},
      archivePrefix={arXiv},
      primaryClass={cs.LG},
      url={https://arxiv.org/abs/2412.06264}, 
}


@misc{fjelde2024introduction,
	title = {An {Introduction} to {Flow} {Matching}},
	url = {https://mlg.eng.cam.ac.uk/blog/2024/01/20/flow-matching.html},
	author = {Fjelde, Tor and Mathieu, Emile and Dutordoir, Vincent},
  year={2024}
}

@article{pooladian2023multisample,
  title={Multisample flow matching: Straightening flows with minibatch couplings},
  author={Pooladian, Aram-Alexandre and Ben-Hamu, Heli and Domingo-Enrich, Carles and Amos, Brandon and Lipman, Yaron and Chen, Ricky TQ},
  journal={arXiv preprint arXiv:2304.14772},
  year={2023}
}

@article{vaswani2017attention,
  title={Attention is all you need},
  author={Vaswani, Ashish and Shazeer, Noam and Parmar, Niki and Uszkoreit, Jakob and Jones, Llion and Gomez, Aidan N and Kaiser, {\L}ukasz and Polosukhin, Illia},
  journal={Advances in neural information processing systems},
  volume={30},
  year={2017}
}


@inproceedings{zaheer_deep_2017,
	title = {Deep {Sets}},
	volume = {30},
	abstract = {We study the problem of designing models for machine learning tasks defined on sets. In contrast to the traditional approach of operating on fixed dimensional vectors, we consider objective functions defined on sets and are invariant to permutations. Such problems are widespread, ranging from the estimation of population statistics, to anomaly detection in piezometer data of embankment dams, to cosmology. Our main theorem characterizes the permutation invariant objective functions and provides a family of functions to which any permutation invariant objective function must belong. This family of functions has a special structure which enables us to design a deep network architecture that can operate on sets and which can be deployed on a variety of scenarios including both unsupervised and supervised learning tasks. We demonstrate the applicability of our method on population statistic estimation, point cloud classification, set expansion, and outlier detection.},
	booktitle = {Advances in {Neural} {Information} {Processing} {Systems}},
	author = {Zaheer, Manzil and Kottur, Satwik and Ravanbakhsh, Siamak and Poczos, Barnabas and Salakhutdinov, Russ R and Smola, Alexander J},
	year = {2017},
	file = {Full Text PDF:/Users/simonkucharsky/Zotero/storage/LPWEVIE9/Zaheer et al. - 2017 - Deep Sets.pdf:application/pdf},
}