Graph Transformers produce, for each attention head, a dense $n\times n$ matrix of learned pairwise interactions. We ask a fundamental question: do these attention-induced graphs converge to a stable limit object as $n$ grows, or does the learned interaction pattern remain unstructured and size-dependent? We answer thi...
Caio F. Deberaldini Netto, Moshe Eliasof, Luana Ruiz· 0 citations
GeoCFM is introduced, a conditional flow-matching model that generates mineral occurrence point sets conditioned on multi-channel geo-images that improves geometric agreement with observed occurrences over score-map and non-conditional baselines, while representing epistemic uncertainty through conditional sampling.
This work explores image generation using flow matching using flow matching and proposes an iterative process that can be integrated into virtually any generative modeling technique, thereby enhancing the performance and robustness of image synthesis systems.
Eldad Haber, Shadab Ahamed, Md Shahriar Rahim Siddiqui et al.· SIAM Journal on Scientific C...· 3 citations
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