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Pedro Zuidberg Dos Martires

4 papers here

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

author position
  • first author1
  • middle author1
  • last author2

Across the 4 of 4 papers where every author was matched, so the position is known.

fields
  • cs.AI3
  • cs.LG1
ORCID 0000-0001-5834-0188
same name
  • Pedro Zuidberg Dos Martires — 17 papers, h 10
  • Pedro Zuidberg Dos Martires — 2 papers, h 1
  • Pedro Zuidberg Dos Martires — 2 papers, h 1
  • Pedro Zuidberg Dos Martires — 1 paper, h 1
  • Pedro Zuidberg Dos Martires — 1 paper

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
20232025
most citedNeural Probabilistic Logic Programming in Discrete-Continuous Domains

8 citations · 8 across the 4 of their papers we have counts for

collaborators

4 papers

cs.AI2025

Two Constraint Compilation Methods for Lifted Planning

Periklis Mantenoglou, Luigi Bonassi, Enrico Scala +1

We study planning in a fragment of PDDL with qualitative state-trajectory constraints, capturing safety requirements, task ordering conditions, and intermediate sub-goals commonly…

cs.LG2024

KLay: Accelerating Arithmetic Circuits for Neurosymbolic AI

Jaron Maene, Vincent Derkinderen, Pedro Zuidberg Dos Martires

A popular approach to neurosymbolic AI involves mapping logic formulas to arithmetic circuits (computation graphs consisting of sums and products) and passing the outputs of a neur…

cs.AI2023★ 8 cited

Neural Probabilistic Logic Programming in Discrete-Continuous Domains

Lennert De Smet, Pedro Zuidberg Dos Martires, Robin Manhaeve +3

Neural-symbolic AI (NeSy) allows neural networks to exploit symbolic background knowledge in the form of logic. It has been shown to aid learning in the limited data regime and to…

cs.AI2023

Declarative Probabilistic Logic Programming in Discrete-Continuous Domains

Pedro Zuidberg Dos Martires, Luc De Raedt, Angelika Kimmig

Over the past three decades, the logic programming paradigm has been successfully expanded to support probabilistic modeling, inference and learning. The resulting paradigm of prob…

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