13 citations · 13 across the 7 of their papers we have counts for
4 papers · 1 filter
PolyGraph Discrepancy: a classifier-based metric for graph generation
Markus Krimmel, Philip Hartout, Karsten Borgwardt +1
Existing methods for evaluating graph generative models primarily rely on Maximum Mean Discrepancy (MMD) metrics based on graph descriptors. While these metrics can rank generative…
A Comprehensive Benchmark for RNA 3D Structure-Function Modeling
Luis Wyss, Vincent Mallet, Wissam Karroucha +2
The relationship between RNA structure and function has recently attracted interest within the deep learning community, a trend expected to intensify as nucleic acid structure mode…
Fast Graph Generation via Autoregressive Noisy Filtration Modeling
Markus Krimmel, Jenna Wiens, Karsten Borgwardt +1
Existing graph generative models often face a critical trade-off between sample quality and generation speed. We introduce Autoregressive Noisy Filtration Modeling (ANFM), a flexib…
Flatten Graphs as Sequences: Transformers are Scalable Graph Generators
Dexiong Chen, Markus Krimmel, Karsten Borgwardt
We introduce AutoGraph, a scalable autoregressive model for attributed graph generation using decoder-only transformers. By flattening graphs into random sequences of tokens throug…