References#

  1. Yaron Lipman, Ricky T. Q. Chen, Heli Ben-Hamu, Maximilian Nickel, and Matt Le. Flow matching for generative modeling. 2023. URL: https://arxiv.org/abs/2210.02747, arXiv:2210.02747.

  2. Itai Gat, Tal Remez, Neta Shaul, Felix Kreuk, Ricky T. Q. Chen, Gabriel Synnaeve, Yossi Adi, and Yaron Lipman. Discrete flow matching. 2024. URL: https://arxiv.org/abs/2407.15595, arXiv:2407.15595.

  3. Ricky T. Q. Chen and Yaron Lipman. Flow matching on general geometries. 2024. URL: https://arxiv.org/abs/2302.03660, arXiv:2302.03660.

  4. Peter Holderrieth, Marton Havasi, Jason Yim, Neta Shaul, Itai Gat, Tommi Jaakkola, Brian Karrer, Ricky TQ Chen, and Yaron Lipman. Generator matching: generative modeling with arbitrary markov processes. 2024. URL: https://arxiv.org/abs/2410.20587, arXiv:2410.20587.

  5. Neta Shaul, Itai Gat, Marton Havasi, Daniel Severo, Anuroop Sriram, Peter Holderrieth, Brian Karrer, Yaron Lipman, and Ricky T. Q. Chen. Flow matching with general discrete paths: a kinetic-optimal perspective. 2024. URL: https://arxiv.org/abs/2412.03487, arXiv:2412.03487.

  6. Michael S Albergo and Eric Vanden-Eijnden. Building normalizing flows with stochastic interpolants. arXiv preprint arXiv:2209.15571, 2022.

  7. Xingchao Liu, Chengyue Gong, and Qiang Liu. Flow straight and fast: learning to generate and transfer data with rectified flow. arXiv preprint arXiv:2209.03003, 2022.

  8. Alexander Tong, Nikolay Malkin, Guillaume Huguet, Yanlei Zhang, Jarrid Rector-Brooks, Kilian Fatras, Guy Wolf, and Yoshua Bengio. Improving and generalizing flow-based generative models with minibatch optimal transport. arXiv preprint arXiv:2302.00482, 2023.

  9. Heli Ben-Hamu, Samuel Cohen, Joey Bose, Brandon Amos, Maximillian Nickel, Aditya Grover, Ricky T. Q. Chen, and Yaron Lipman. Matching normalizing flows and probability paths on manifolds. Proceedings of the 39th International Conference on Machine Learning, 2022.

  10. Andrew Campbell, Jason Yim, Regina Barzilay, Tom Rainforth, and Tommi Jaakkola. Generative flows on discrete state-spaces: enabling multimodal flows with applications to protein co-design. arXiv preprint arXiv:2402.04997, 2024.

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