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Found 4 papers, 0 papers with code
Transitive inference as probabilistic preference learning
We propose a novel framework that casts TI as a probabilistic preference learning task, using one-parameter Mallows models.
Neural representation in active inference: using generative models to interact with -- and understand -- the lived world
This paper considers neural representation through the lens of active inference, a normative framework for understanding brain function.
Modeling motor control in continuous-time Active Inference: a survey
This article provides a technical illustration of Active Inference models in continuous time and a brief survey of Active Inference models that solve four kinds of control problems; namely, the control of goal-directed reaching movements, active sensing, the resolution of multisensory conflict during movement and the integration of decision-making and motor control.
Supervised Topological Maps
Controlling the internal representation space of a neural network is a desirable feature because it allows to generate new data in a supervised manner.
