arXiv Machine Learning By Lukas Billera, Hedwig Nora Nordlinder, Jack Collier Ryder, Anton Oresten, Aron St{\aa}lmarck, Theodor Mosetti Bj\"ork, Ben Murrell

Branching Flows: Discrete, Continuous, and Manifold Flow Matching with Splits and Deletions

Read the original on arXiv Machine Learning →

arXiv:2511. 09465v4 Announce Type: replace-cross Abstract: Diffusion and flow matching approaches to generative modeling have shown promise in domains where the state space is continuous, such as image generation or protein folding & design, and discrete, exemplified by diffusion large language models.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

arXiv AI
Sep 15

Branched Optimal Transport Amortization

arXiv:2609.15072v1 Announce Type: cross Abstract: Methods of Branched Optimal Transport (BOT) mimic the economy and efficiency of natural tree-like structures, such as those found in rivers and biolo...

By Semyon Semenov, Viktor Kovalchuk, Meir Roketlishvili, Albert Baichorov, Fakhri Karray, Martin Takac, Arip Asadulaev