Search results for "variational inference"

showing 2 items of 2 documents

Expanding the Active Inference Landscape: More Intrinsic Motivations in the Perception-Action Loop

2018

Active inference is an ambitious theory that treats perception, inference and action selection of autonomous agents under the heading of a single principle. It suggests biologically plausible explanations for many cognitive phenomena, including consciousness. In active inference, action selection is driven by an objective function that evaluates possible future actions with respect to current, inferred beliefs about the world. Active inference at its core is independent from extrinsic rewards, resulting in a high level of robustness across e.g.\ different environments or agent morphologies. In the literature, paradigms that share this independence have been summarised under the notion of in…

FOS: Computer and information sciencesComputer scienceComputer Science - Artificial Intelligencepredictive informationBiomedical EngineeringInferenceSystems and Control (eess.SY)02 engineering and technologyAction selectionI.2.0; I.2.6; I.5.0; I.5.1lcsh:RC321-57103 medical and health sciences0302 clinical medicineactive inferenceArtificial IntelligenceFOS: Electrical engineering electronic engineering information engineering0202 electrical engineering electronic engineering information engineeringFormal concept analysisMethodsperception-action loopuniversal reinforcement learningintrinsic motivationlcsh:Neurosciences. Biological psychiatry. NeuropsychiatryFree energy principleCognitive scienceRobotics and AII.5.0I.5.1I.2.6Partially observable Markov decision processI.2.0Artificial Intelligence (cs.AI)Action (philosophy)empowermentIndependence (mathematical logic)free energy principleComputer Science - Systems and Control020201 artificial intelligence & image processingBiological plausibility62F15 91B06030217 neurology & neurosurgeryvariational inference
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Discovering human mobility from mobile data : probabilistic models and learning algorithms

2020

Smartphone usage data can be used to study human indoor and outdoor mobility. In our work, we investigate both aspects in proposing machine learning-based algorithms adapted to the different information sources that can be collected.In terms of outdoor mobility, we use the collected GPS coordinate data to discover the daily mobility patterns of the users. To this end, we propose an automatic clustering algorithm using the Dirichlet process Gaussian mixture model (DPGMM) so as to cluster the daily GPS trajectories. This clustering method is based on estimating probability densities of the trajectories, which alleviate the problems caused by the data noise.By contrast, we utilize the collecte…

[INFO.INFO-OH] Computer Science [cs]/Other [cs.OH]Machine LearningDeep LearningDonnées mobiles[INFO.INFO-OH]Computer Science [cs]/Other [cs.OH]Variational InferenceApprentissage machineMobile DataProbabilistic Models
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