activity
20232026
most citedscDataset: Scalable Data Loading for Deep Learning on Large-Scale Single-Cell Omics

1 citations · 1 across the 4 of their papers we have counts for

collaborators

7 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.LG2026

When Labels Have Structure: Improving Image Classification with Hierarchy-Aware Cross-Entropy

April Chan, Davide D'Ascenzo, Sebastiano Cultrera di Montesano

Standard cross-entropy is the default classification loss across virtually all of machine learning, yet it treats all misclassifications equally, ignoring the semantic distances th…

cs.LG20251 cited

scDataset: Scalable Data Loading for Deep Learning on Large-Scale Single-Cell Omics

Davide D'Ascenzo, Sebastiano Cultrera di Montesano

Training deep learning models on single-cell datasets with hundreds of millions of cells requires loading data from disk, as these datasets exceed available memory. While random sa…

cs.CG2024

Chromatic Topological Data Analysis

Sebastiano Cultrera di Montesano, Ondrej Draganov, Herbert Edelsbrunner +1

Exploring the shape of point configurations has been a key driver in the evolution of TDA (short for topological data analysis) since its infancy. This survey illustrates the recen…

cs.DS2024

Banana Trees for the Persistence in Time Series Experimentally

Lara Ost, Sebastiano Cultrera di Montesano, Herbert Edelsbrunner

In numerous fields, dynamic time series data require continuous updates, necessitating efficient data processing techniques for accurate analysis. This paper examines the banana tr…

cs.CG2024

The Euclidean MST-ratio for Bi-colored Lattices

Sebastiano Cultrera di Montesano, Ondřej Draganov, Herbert Edelsbrunner +1

Given a finite set, , and a subset, , the \emph{MST-ratio} is the combined length of the minimum spanning trees of and