Stochastic and deterministic processes in Asymmetric Tsetlin Machine
Frontiers in Artificial Intelligence
Mohamed-Bachir Belaid
Senior Scientist
+47 453 99 611
Digital Technologies
Kjeller
bbel@nilu.no
About
Dr. Mohamed-Bachir Belaid serves as a researcher at NILU based at Kjeller, Norway.
He earned his master’s degree in computer science and optimization in 2016 at USTO university in Oran, Algeria.
He then continued his studies at the University of Montpellier, south of France, where he earned his PhD in Constraint reasoning and Data mining. His PhD work was implemented as part of a library in the Choco constraint solver.
Before joining NILU, he worked as postdoc at Simula and at OsloMet. At Simula, he worked on constraint acquisition and qualitative constraints reasoning. At OsloMet, he worked on learning automata and Tsetlin Machine.
His expertise involves computer science, optimization, data mining, constraint reasoning, concept learning, qualitative constraints, and learning automata.
He also currently works on implementing AI and ML models on earth observation and various environmental applications.
Publications at NILU
Generalized Convergence Analysis of Tsetlin Automaton Based Algorithms: A Probabilistic Approach to Concept Learning
Proceedings of the Thirty-Ninth AAAI Conference on Artificial Intelligence
Query-driven Qualitative Constraint Acquisition
The journal of artificial intelligence research
Deep Learning-Enhanced Gap Filling in Drosophila Melanogaster Genomic Data
ICMLA 2024- 2024 International Conference on Machine Learning and Applications
Machine Learning Predictions of GDP at higher spatial and sectoral resolution
32nd IIOA & 11th SHAIO Conference in Seville, Spain
Estimating Residential Building Energy Demand at City Scale: Heuristic vs. Machine Learning
SBE25 Trondheim – Cities and Climate Change
Using machine learning to estimate building energy demand
Estimate Rooftop Heights from Orthophotos: a Machine Learning Approach
ESA-NASA International Workshop on AI Foundation Model for EO
Approche générique pour l’acquisition de contraintes qualitatives
Journées Francophones de Programmation par Contraintes (JFPC)
European Topic Centre on Data Integration and Digitalisation (ETC DI)
The European Topic Centre on Data Integration and Digitalisation (ETC DI) multiannual objective is to contribute to the activities of the European Environment Agency’s (EEA) by supporting the digitalisation process […]
AETHER
AETHER is a collaborative European research and innovation project funded by the European Space Agency (ESA) under the EXPRO+ programme, in response to the ITT: “Opening the Black Box: Self-Explainable […]
F.A.I.R. information cube (FAIRiCUBE)
The core objective of FAIRiCUBE is to enable players from beyond classic Earth Observation (EO) domains to provide, access, process, and share gridded data and algorithms in a FAIR and […]
Stochastic and deterministic processes in Asymmetric Tsetlin Machine
Frontiers in Artificial Intelligence
Generalized Convergence Analysis of Tsetlin Automaton Based Algorithms: A Probabilistic Approach to Concept Learning
Proceedings of the Thirty-Ninth AAAI Conference on Artificial Intelligence
Query-driven Qualitative Constraint Acquisition
The journal of artificial intelligence research
Deep Learning-Enhanced Gap Filling in Drosophila Melanogaster Genomic Data
ICMLA 2024- 2024 International Conference on Machine Learning and Applications
Machine Learning Predictions of GDP at higher spatial and sectoral resolution
32nd IIOA & 11th SHAIO Conference in Seville, Spain
Estimating Residential Building Energy Demand at City Scale: Heuristic vs. Machine Learning
SBE25 Trondheim – Cities and Climate Change
Using machine learning to estimate building energy demand
Estimate Rooftop Heights from Orthophotos: a Machine Learning Approach
ESA-NASA International Workshop on AI Foundation Model for EO
Approche générique pour l’acquisition de contraintes qualitatives
Journées Francophones de Programmation par Contraintes (JFPC)
European Topic Centre on Data Integration and Digitalisation (ETC DI)
The European Topic Centre on Data Integration and Digitalisation (ETC DI) multiannual objective is to contribute to the activities of the European Environment Agency’s (EEA) by supporting the digitalisation process […]
AETHER
AETHER is a collaborative European research and innovation project funded by the European Space Agency (ESA) under the EXPRO+ programme, in response to the ITT: “Opening the Black Box: Self-Explainable […]
F.A.I.R. information cube (FAIRiCUBE)
The core objective of FAIRiCUBE is to enable players from beyond classic Earth Observation (EO) domains to provide, access, process, and share gridded data and algorithms in a FAIR and […]