3 papers
cs.LG2026
Score Distributions, Not Cells: Evaluating Single-Cell Perturbations Under Class Overlap
Youssef Marrakchi, Davide D'Ascenzo, Sebastiano Cultrera di Montesano
Most classification problems assume the classes are roughly separable, so that an individual sample can usually be assigned to one class. Single-cell perturbation data violates thi…
cs.SE2026
Efficient and Scalable Provenance Tracking for LLM-Generated Code Snippets
Andrea Gurioli, Davide D'Ascenzo, Federico Pennino +2
Large language models (LLMs) for code completion and generation are increasingly used in software development, yet they may reproduce training examples verbatim and without authors…
cs.LG2025
Position: A Theory of Deep Learning Must Include Compositional Sparsity
David A. Danhofer, Davide D'Ascenzo, Rafael Dubach +1
Overparametrized Deep Neural Networks (DNNs) have demonstrated remarkable success in a wide variety of domains too high-dimensional for classical shallow networks subject to the cu…