Area of research
Artificial Intelligence
Research interest
Research interests include Computer science, Probabilistic logic, Programming language, Artificial intelligence, Semantics (computer science), and Theoretical computer science.
Proceedings 41st International Conference on Logic Programming
Proceedings 41st International Conference on Logic Programming
AIDA4Edge: Twinning for Excellence in Adaptive Edge Artificial Intelligence
Integration between constrained optimization and deep networks: a survey
Integration of Deep Generative Anomaly Detection Algorithm in High-Speed Industrial Line
Exploiting CNN’s visual explanations to drive anomaly detection
GRD‐Net: Generative‐Reconstructive‐Discriminative Anomaly Detection with Region of Interest Attention Module
A Machine Learning Pipeline to Analyse Multispectral and Hyperspectral Images: Full/Regular Research Paper (CSCI-RTHI)
Introduction to Machine Learning
Symbolic DNN-Tuner: A Python and ProbLog-based system for optimizing Deep Neural Networks hyperparameters
Neural-Symbolic Ensemble Learning for early-stage prediction of critical state of Covid-19 patients
A Probabilistic Logic Model of Lightning Network
An Iterative Fixpoint Semantics for MKNF Hybrid Knowledge Bases with Function Symbols
Neural Networks and Deep Learning Fundamentals
Proceedings 38th International Conference on Logic Programming
A semantics for Hybrid Probabilistic Logic programs with function symbols
Learning hierarchical probabilistic logic programs
Nonground Abductive Logic Programming with Probabilistic Integrity Constraints
Proceedings 37th International Conference on Logic Programming (Technical Communications)
Probabilistic inductive constraint logic
Declarative and Mathematical Programming approaches to Decision Support Systems for food recycling
Automatic Setting of DNN Hyper-Parameters by Mixing Bayesian Optimization and Tuning Rules
Proceedings 36th International Conference on Logic Programming (Technical Communications)
Modeling Smart Contracts with Probabilistic Logic Programming
An Analysis of Gibbs Sampling for Probabilistic Logic Programs.
International Conference on Lightning Protection 2020cited by 1position: last
Automatic Setting of DNN Hyper-Parameters by Mixing Bayesian Optimization and Tuning Rules
A Framework for Reasoning on Probabilistic Description Logics
Dischargeable Obligations in the 𝒮CIFF Framework
Proceedings 36th International Conference on Logic Programming (Technical Communications)