activity
20232026
most citedDeFoG: Discrete Flow Matching for Graph Generation

1 citations · 1 across the 5 of their papers we have counts for

collaborators

7 papers

stat.ML2026

DiPhon: Diffusion on Graphons for Scalable Graph Generation

Sergio Rozada, Yiming Qin, Manuel Madeira +2

Diffusion models represent a leading paradigm for graph generation, with notable impact in domains such as molecular design. Yet, scaling these models to large graphs remains an op…

cs.LG2026

Balancing Symmetry and Efficiency in Graph Flow Matching

Benjamin Honoré, Alba Carballo-Castro, Yiming Qin +1

Equivariance is central to graph generative models, as it ensures the model respects the permutation symmetry of graphs. However, strict equivariance can increase computational cos…

cs.CV2025

Chain-of-Visual-Thought: Teaching VLMs to See and Think Better with Continuous Visual Tokens

Yiming Qin, Bomin Wei, Jiaxin Ge +4

Vision-Language Models (VLMs) excel at reasoning in linguistic space but struggle with perceptual understanding that requires dense visual perception, e.g., spatial reasoning and g…

cs.LG2025

Generating Directed Graphs with Dual Attention and Asymmetric Encoding

Alba Carballo-Castro, Manuel Madeira, Yiming Qin +2

Directed graphs naturally model systems with asymmetric, ordered relationships, essential to applications in biology, transportation, social networks, and visual understanding. Gen…

cs.CL2025

MEMOIR: Lifelong Model Editing with Minimal Overwrite and Informed Retention for LLMs

Ke Wang, Yiming Qin, Nikolaos Dimitriadis +2

Language models deployed in real-world systems often require post-hoc updates to incorporate new or corrected knowledge. However, editing such models efficiently and reliably-witho…

cs.LG2024★ 1 cited

DeFoG: Discrete Flow Matching for Graph Generation

Yiming Qin, Manuel Madeira, Dorina Thanou +1

Graph generative models are essential across diverse scientific domains by capturing complex distributions over relational data. Among them, graph diffusion models achieve superior…

cs.LG2023

Sparse Training of Discrete Diffusion Models for Graph Generation

Yiming Qin, Clement Vignac, Pascal Frossard

Generative graph models struggle to scale due to the need to predict the existence or type of edges between all node pairs. To address the resulting quadratic complexity, existing…