Johannes Brandstetter

Machine learning researcher working on physics-informed and geometric deep learning, with senior-author standing on work spanning associative memory and physical-systems simulation.

He is the one person this vault has found at the connection point between two of its otherwise unrelated reading threads: he is a senior/corresponding author of Ramsauer et al.'s 2020 "Hopfield Networks is All You Need" — the paper establishing that a modern Hopfield network's update rule is equivalent to transformer attention, co-authored with fellow Hopfield-paper author Markus Holzleitner — and he is also a senior/corresponding author of Bi-FORK (Zimmel, Hendriks, Holzleitner, Sestak, Weichselbaumer, Menkovski & Brandstetter, arXiv:2610.12449, 2026), the bifurcating-generative-modeling preprint this vault is tracking under the single-source concentration cap. Markus Holzleitner is a co-author on both papers as well. No independent standing beyond these two co-authorships has been established for him in this vault yet — the hub is earned by bridging two separate clusters, not by either paper alone.

References

• claim-modern-hopfield-update-rule-equals-transformer-attention
• claim-bi-fork-whole-trajectory-latent-flow-matching-for-global-branch-consistency
• claim-bi-fork-260k-point-scale-is-first-demonstration-of-one-to-many-bifurcation-recovery
• claim-bi-fork-particle-guidance-sampler-is-parallelized-deflation-analog
• claim-bi-fork-stflow-baseline-shares-author-reduces-independence