3 papers
cs.LG2025
Graph Diffusion that can Insert and Delete
Matteo Ninniri, Marco Podda, Davide Bacciu
Generative models of graphs based on discrete Denoising Diffusion Probabilistic Models (DDPMs) offer a principled approach to molecular generation by systematically removing struct…
cs.LG2025
ContinualFlow: Learning and Unlearning with Neural Flow Matching
Lorenzo Simone, Davide Bacciu, Shuangge Ma
We introduce ContinualFlow, a principled framework for targeted unlearning in generative models via Flow Matching. Our method leverages an energy-based reweighting loss to softly s…
q-bio.BM2025
Towards Efficient Molecular Property Optimization with Graph Energy Based Models
Luca Miglior, Lorenzo Simone, Marco Podda +1
Optimizing chemical properties is a challenging task due to the vastness and complexity of chemical space. Here, we present a generative energy-based architecture for implicit chem…