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A. Patané

18 papers hereh-index 16952 citations43 works total

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

author position
  • first author1
  • middle author14
  • last author2

Across the 17 of 18 papers where every author was matched, so the position is known.

fields
  • cs.LG15
  • eess.SY1
  • physics.app-ph1
  • stat.ML1
same name
  • A. Patané — 25 papers, h 40
  • A. Patané — 5 papers, h 7

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
20182024
most citedRobustness of Bayesian Neural Networks to Gradient-Based Attacks

27 citations · 74 across the 16 of their papers we have counts for

collaborators
Showing 2021Show all

4 papers · 1 filter

physics.app-ph2021★ 12 cited

Design and characterization of effective solar cells

Varun Ojha, Giorgio Jansen, Andrea Patane +3

We propose a two-stage multi-objective optimization framework for full scheme solar cell structure design and characterization, cost minimization and quantum efficiency maximizatio…

cs.LG2021★ 2 cited

Certification of Iterative Predictions in Bayesian Neural Networks

Matthew Wicker, Luca Laurenti, Andrea Patane +3

We consider the problem of computing reach-avoid probabilities for iterative predictions made with Bayesian neural network (BNN) models. Specifically, we leverage bound propagation…

cs.LG2021★ 4 cited

Adversarial Robustness Guarantees for Gaussian Processes

Andrea Patane, Arno Blaas, Luca Laurenti +3

Gaussian processes (GPs) enable principled computation of model uncertainty, making them attractive for safety-critical applications. Such scenarios demand that GP decisions are no…

cs.LG2021★ 8 cited

Bayesian Inference with Certifiable Adversarial Robustness

Matthew Wicker, Luca Laurenti, Andrea Patane +3

We consider adversarial training of deep neural networks through the lens of Bayesian learning, and present a principled framework for adversarial training of Bayesian Neural Netwo…

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