Luca Pulina
Artificial Intelligence · 2010–2012
A researcher in the Science corpus: 2 papers; trusts 9 researchers and is trusted by 36 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 9 citations into it; #4,512 of 35,345 by trust here, #11,921 by citations
- Computational Theory and Mathematics — 9 citations into it; #718 of 12,182 by trust here, #2,015 by citations
- Model Architectures — 5 citations into it
Whom Luca Pulina trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Christian Igel
- Robert Nieuwenhuis
- Edmund M. Clarke
- Kurt Hornik
- Michael Hüsken
- G. Zhang
- Sibylle D. Müller
- Albert Oliveras
- Cesare Tinelli
- Petros Koumoutsakos
Who trusts Luca Pulina overall
- Souradeep Dutta
- Guy Katz
- Nicholas Carlini
- Chih-Hong Cheng
- Clark W. Barrett
- Susmit Jha
- Weiming Xiang
- Xiaowei Huang
- Sriram Sankaranarayanan
- Jin Xu
Papers from Luca Pulina's perspective
Ordered by how strongly the researchers Luca Pulina trusts endorse each paper. Same corpus, a different order for every researcher.
- INTERPRETATION : ‘A UNIFIED LATTICE MODEL FOR STATIC ANALYSIS OF PROGRAMS BY CONSTRUCTION OR APPROXIMATION
- Reducing the Time Complexity of the Derandomized Evolution Strategy with Covariance Matrix Adaptation (CMA-ES)
- Efficient Solving of Large Non-linear Arithmetic Constraint Systems with Complex Boolean Structure (Journal on Satisfiability, Boolean Modeling and Computation)
- Solving SAT and SAT Modulo Theories: From an abstract Davis--Putnam--Logemann--Loveland procedure to DPLL(T)
- Neural Networks: A Comprehensive Foundation
- Neural networks for classification: a survey (IEEE Trans. Syst. Man Cybern. Part C)
- Empirical evaluation of the improved Rprop learning algorithms
- Neural Networks and Their Applications
- Counterexample-guided abstraction refinement for symbolic model checking (JACM)
- A machine program for theorem-proving (CACM)
Their papers
- An Abstraction-Refinement Approach to Verification of Artificial Neural Networks (2010) — 308 citations
- Challenging SMT solvers to verify neural networks (2012) — 135 citations