1 citations · 1 across the 2 of their papers we have counts for
3 papers
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…
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…
q-bio.BM2025★ 1 cited
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…