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Simone Lionetti

Lucerne University of Applied Sciences and Arts

17 papers hereh-index 9265 citations31 works total

Matching runs newest-first, so older work may not be attached to this profile yet.

author position
  • first author2
  • middle author14
  • last author1

Across the 17 of 17 papers where every author was matched, so the position is known.

fields
  • cs.CV9
  • hep-ph5
  • cs.AI1
  • cs.DL1
  • cs.SD1
affiliations
  • Lucerne University of Applied Sciences and Arts
ORCID 0000-0001-7305-8957
same name
  • Simone Lionetti — 3 papers, h 1

Either other researchers who publish under this name, or the same person where the external sources have not merged their records.

identity via Semantic Scholar / OpenAlex

activity
20112026
most citedPrecision NNLO determination of alpha_s(M_Z) using an unbiased global parton set

72 citations · 165 across the 17 of their papers we have counts for

collaborators
Showing 2025Show all

4 papers · 1 filter

cs.SD2025

Representation-Based Data Quality Audits for Audio

Alvaro Gonzalez-Jimenez, Fabian Gröger, Linda Wermelinger +4

Data quality issues such as off-topic samples, near duplicates, and label errors often limit the performance of audio-based systems. This paper addresses these issues by adapting S…

cs.AI2025★ 2 cited

Clinical Uncertainty Impacts Machine Learning Evaluations

Simone Lionetti, Fabian Gröger, Philippe Gottfrois +4

Clinical dataset labels are rarely certain as annotators disagree and confidence is not uniform across cases. Typical aggregation procedures, such as majority voting, obscure this…

cs.CV2025

Is Hyperbolic Space All You Need for Medical Anomaly Detection?

Alvaro Gonzalez-Jimenez, Simone Lionetti, Ludovic Amruthalingam +4

Medical anomaly detection has emerged as a promising solution to challenges in data availability and labeling constraints. Traditional methods extract features from different layer…

cs.CV2025

CleanPatrick: A Benchmark for Image Data Cleaning

Fabian Gröger, Simone Lionetti, Philippe Gottfrois +12

Robust machine learning depends on clean data, yet current image data cleaning benchmarks rely on synthetic noise or narrow human studies, limiting comparison and real-world releva…

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Not affiliated with arXiv. Researcher data from Semantic Scholar (ODC-BY) and OpenAlex.