4 papers
You Only Train Once: Differentiable Subset Selection for Omics Data
Daphné Chopard, Jorge da Silva Gonçalves, Irene Cannistraci +2
Selecting compact and informative gene subsets from single-cell transcriptomic data is essential for biomarker discovery, improving interpretability, and cost-effective profiling.…
Rethinking Machine Unlearning: Models Designed to Forget via Key Deletion
Sonia Laguna, Jorge da Silva Goncalves, Moritz Vandenhirtz +3
Machine unlearning is rapidly becoming a practical requirement, driven by privacy regulations, data errors, and the need to remove harmful or corrupted training samples. Despite th…
TreeDiffusion: Hierarchical Generative Clustering for Conditional Diffusion
Jorge da Silva Gonçalves, Laura Manduchi, Moritz Vandenhirtz +1
Generative modeling and clustering are conventionally distinct tasks in machine learning. Variational Autoencoders (VAEs) have been widely explored for their ability to integrate b…
Structured Generations: Using Hierarchical Clusters to guide Diffusion Models
Jorge da Silva Goncalves, Laura Manduchi, Moritz Vandenhirtz +1
This paper introduces Diffuse-TreeVAE, a deep generative model that integrates hierarchical clustering into the framework of Denoising Diffusion Probabilistic Models (DDPMs). The p…