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A. Bragagnolo

9 papers hereh-index 7148 citations19 works total

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

author position
  • first author1
  • middle author8

Across the 9 of 9 papers where every author was matched, so the position is known.

fields
  • cs.LG5
  • cs.CV4
same name
  • A. Bragagnolo — 284 papers
  • A. Bragagnolo — 23 papers, h 49
  • A. Bragagnolo — 9 papers
  • A. Bragagnolo — 7 papers
  • A. Bragagnolo — 7 papers
  • A. Bragagnolo — 6 papers

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
20202026
most citedLOss-Based SensiTivity rEgulaRization: towards deep sparse neural networks

30 citations · 61 across the 8 of their papers we have counts for

collaborators
Showing cs.CVShow all

4 papers · 1 filter

cs.CV2026

Temporal Modelling for Burn Scars on Sentinel-3

Luca Barco, Edoardo Arnaudo, Andrea Bragagnolo +2

Rapid and accurate burn scar delineation from satellite imagery is essential for post-fire damage assessment. Sentinel-3 OLCI, with daily revisit and 21 spectral bands, suits rapid…

cs.CV2026

GEOID-Flood: A Large-Scale Multi-Modal Benchmark Dataset for Flood Segmentation

Gaetano Chiriaco, Luca Barco, Andrea Bragagnolo +2

Geospatial foundation models aim to learn representations that transfer across regions and sensors, yet evaluating them on specific tasks requires large, high-quality, multi-modal…

cs.CV2026

Cardiac Output Prediction from Echocardiograms: Self-Supervised Learning with Limited Data

Adson Duarte, Davide Vitturini, Emanuele Milillo +9

Cardiac Output (CO) is a key parameter in the diagnosis and management of cardiovascular diseases. However, its accurate measurement requires right-heart catheterization, an invasi…

cs.CV2025

When Does Pruning Benefit Vision Representations?

Enrico Cassano, Riccardo Renzulli, Andrea Bragagnolo +1

Pruning is widely used to reduce the complexity of deep learning models, but its effects on interpretability and representation learning remain poorly understood. This paper invest…

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