Stochastic and deterministic processes in Asymmetric Tsetlin Machine
Frontiers in Artificial Intelligence
Mohamed-Bachir Belaid
Forsker
453 99 611
63 89 82 63
Digitale teknologier
Kjeller
bbel@nilu.no
Om
Dr. Mohamed-Bachir Belaid er forsker ved NILU med base på Kjeller.
Han tok sin mastergrad i informatikk og optimalisering i 2016 ved USTO-universitetet i Oran, Algerie.
Han fortsatte deretter studiene ved University of Montpellier, sør i Frankrike, hvor han tok sin doktorgrad i Constraint reasoning and Data mining. Doktorgradsarbeidet hans ble implementert som en del av et bibliotek i Choco Constraint Solver.
Før han begynte i NILU jobbet han som postdoktor ved Simula og ved OsloMet. På Simula jobbet han med constraint-innhenting og kvalitative constraint-resonnement. Ved OsloMet jobbet han med lærende automata og Tsetlin-maskinen.
Hans ekspertise involverer informatikk, optimalisering, data mining, constraint-resonnement, konseptlæring, kvalitative begrensninger og lærende automata.
Han jobber også for tiden med å implementere AI- og ML-modeller på jordobservasjon og ulike miljøapplikasjoner.
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Generalized Convergence Analysis of Tsetlin Automaton Based Algorithms: A Probabilistic Approach to Concept Learning
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Deep Learning-Enhanced Gap Filling in Drosophila Melanogaster Genomic Data
ICMLA 2024- 2024 International Conference on Machine Learning and Applications
Approche générique pour l’acquisition de contraintes qualitatives
Journées Francophones de Programmation par Contraintes (JFPC)
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
Generalized Convergence Analysis of Tsetlin Automaton Based Algorithms: A Probabilistic Approach to Concept Learning
Proceedings of the Thirty-Ninth AAAI Conference on Artificial Intelligence
Deep Learning-Enhanced Gap Filling in Drosophila Melanogaster Genomic Data
ICMLA 2024- 2024 International Conference on Machine Learning and Applications
Approche générique pour l’acquisition de contraintes qualitatives
Journées Francophones de Programmation par Contraintes (JFPC)