2023
Multi-Agent Reinforcement Learning with Epistemic Priors
Walker T, Ide J, Choi M, Guarino M, Alcedo K. Multi-Agent Reinforcement Learning with Epistemic Priors. 2023, 00: 2514-2518. DOI: 10.1109/codit58514.2023.10284342.Peer-Reviewed Original Research
2022
Soft Actor-Critic with Inhibitory Networks for Retraining UAV Controllers Faster
Choi M, Filter M, Alcedo K, Walker T, Rosenbluth D, Ide J. Soft Actor-Critic with Inhibitory Networks for Retraining UAV Controllers Faster. 2022, 00: 1561-1570. DOI: 10.1109/icuas54217.2022.9836052.Peer-Reviewed Original ResearchSoft Actor-CriticDeep reinforcement learningUnmanned aerial vehiclesActor-CriticAutonomous unmanned aerial vehiclesRealistic simulation environmentLow-level controlNon-stationary environmentsDRL agentDRL algorithmCatastrophic forgettingReinforcement learningBaseline methodsSimulation environmentUAV controllerSample efficiencyAerial vehiclesReal worldDifficult taskProportional-IntegralNovel approachNetworkActive researchValue evaluationAlgorithm
2011
AUTOMATIC SUBCORTICAL TISSUE SEGMENTATION OF MR IMAGES USING OPTIMUM-PATH FOREST CLUSTERING
Cappabianco F, Ide J, Falcão A, Li C. AUTOMATIC SUBCORTICAL TISSUE SEGMENTATION OF MR IMAGES USING OPTIMUM-PATH FOREST CLUSTERING. 2011, 2653-2656. DOI: 10.1109/icip.2011.6116212.Peer-Reviewed Original ResearchOptimum-Path Forest ClusteringAutomatic MR image segmentationMR image segmentationLocal image propertiesSegmentation accuracyOptimum connectivityFeature spaceGlobal informationTissue segmentationImage propertiesMR imagesPopular techniqueProbabilistic atlasExtant methodsSegmentationImportant issueImagesProbability valuesWhite matter voxelsAlgorithmVoxelsClusteringNew methodAccuracyConnectivity
2008
Approximate algorithms for credal networks with binary variables
Ide J, Cozman F. Approximate algorithms for credal networks with binary variables. International Journal Of Approximate Reasoning 2008, 48: 275-296. DOI: 10.1016/j.ijar.2007.09.003.Peer-Reviewed Original ResearchCredal networksApproximate inferenceBinary variablesApproximate algorithmLoopy belief propagationFamily of algorithmsOnly binary variablesExact inferenceVariational techniquePolynomial complexityBelief propagationBayesian networkSuch networksBelief functionsInferencePossibilistic measuresAlgorithmVague beliefPolytreesNetworkVariablesPropagationComplexity
2002
Random Generation of Bayesian Networks
Ide J, Cozman F. Random Generation of Bayesian Networks. Lecture Notes In Computer Science 2002, 2507: 366-376. DOI: 10.1007/3-540-36127-8_35.Peer-Reviewed Original ResearchBayesian networkAcyclic graphConditional probability distributionNumber of arcsProbability distributionMarkov chainDirichlet distributionConditional distributionUniform generationNumber of nodesAverage propertiesRandom generationGraphNode degreeSuch networksSuch methodsAlgorithmNew methodNetworkDistributionTheoryInferenceConstraintsGuarantees