27 citations · 43 across the 11 of their papers we have counts for
4 papers · 1 filter
LLM-to-Flow Distillation: Teaching Natural Language Goals to Scientific Generators
Riccardo De Santi, Federico Di Gennaro, Angelo Gnazzo +6
Generative discovery in science requires coupling high-level scientific reasoning with the ability to generate samples respecting the structure of a design space. Yet these capabil…
Global Transport Couplings for Classifier-Free Guided Flows
Katarina Petrović, Zander W. Blasingame, Danyal Rehman +5
Optimal-transport couplings have been shown to reduce training variance in unconditional flow models, but their role in conditional generation remains unclear. A natural approach c…
RelAgent: LLM Agents as Data Scientists for Relational Learning
Xingyue Huang, Louis Tichelman, Jinwoo Kim +2
Relational learning is a challenging problem that has motivated a wide range of approaches, including graph-based models (e.g., graph neural networks, graph transformers), tabular…
Categorical Flow Maps
Daan Roos, Oscar Davis, Floor Eijkelboom +5
We introduce Categorical Flow Maps, a flow-matching method for accelerated few-step generation of categorical data via self-distillation. Building on recent variational formulation…