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Pasquale Minervini

47 papers hereh-index 12638 citations52 works total

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

author position
  • middle author33
  • last author12

Across the 45 of 47 papers where every author was matched, so the position is known.

fields
  • cs.CL23
  • cs.LG10
  • cs.CV5
  • cs.AI4
  • cs.IR2
  • cs.PL1
same name
  • Pasquale Minervini — 46 papers, h 24
  • Pasquale Minervini — 12 papers, h 5
  • Pasquale Minervini — 3 papers, h 2
  • Pasquale Minervini — 2 papers
  • Pasquale Minervini — 2 papers, h 2

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
20232026
most citedConditional computation in neural networks: principles and research trends

10 citations · 38 across the 45 of their papers we have counts for

collaborators
Showing 2026Show all

4 papers · 1 filter

math-ph2026

Two routes to the Hadamard condition

Eleanor March

In quantum field theory on curved spacetime, the Hadamard condition is often presented as a necessary condition on 'physically reasonable', 'vacuum-like' states of linear quantum f…

cs.CL2026

Logit-Contribution Scoring Identifies Non-Literal Retrieval Heads

Aryo Pradipta Gema, Beatrice Alex, Pasquale Minervini

In long-context use, large language models frequently synthesize answers from the meaning of a relevant context span rather than literally copy-pasting them. Identifying which atte…

cs.LG2026

Rethinking the Harmonic Loss via Non-Euclidean Distance Layers

Maxwell Miller-Golub, Collin Coil, Kamil Faber +4

Cross-entropy loss has long been the standard choice for training deep neural networks, yet it suffers from interpretability limitations, unbounded weight growth, and inefficiencie…

cs.CV2026

VLM-RobustBench: A Comprehensive Benchmark for Robustness of Vision-Language Models

Rohit Saxena, Alessandro Suglia, Pasquale Minervini

Vision-language models (VLMs) achieve strong performance on standard, high-quality datasets, but we still do not fully understand how they perform under real-world image distortion…

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